def make_movie(
field="uu1",
datadir="data/",
proc=-1,
extension="xz",
format="native",
tmin=0.0,
tmax=1.0e38,
amin=0.0,
amax=1.0,
transform="",
oldfile=False,
norm=None,
save=None,
figsize=(16, 4),
fps=12,
):
"""
read 2D slice files and assemble an animation in a mpg movie.
Quickly written from the example at http://matplotlib.sourceforge.net/faq/howto_faq.html
Options:
field --- which variable to slice
datadir--- path to data directory
proc --- an integer giving the processor to read a slice from
extension --- which plane of xy,xz,yz,Xz. for 2D this should be overwritten.
format --- endian. one of little, big, or native (default)
tmin --- start time
tmax --- end time
amin --- minimum value for image scaling
amax --- maximum value for image scaling
transform --- insert arbitrary numerical code to modify the slice
norm --- scales calar data
save --- directory to save file
figsize --- tuple containing the size of the figure
fps --- Frames per seconds for the video
"""
import os
from pencil.io import npfile
from pencil import read
import numpy as np
import pylab as plt
from matplotlib import colors
# Global configuration:
# lines
plt.rcParams["lines.linewidth"] = 2
plt.rcParams["lines.color"] = "k"
# font
plt.rcParams["font.size"] = 30
plt.rcParams["font.family"] = "serif"
# legend
plt.rcParams["legend.fontsize"] = 20
plt.rcParams["legend.fancybox"] = False
plt.rcParams["legend.numpoints"] = 2
plt.rcParams["legend.shadow"] = False
plt.rcParams["legend.frameon"] = False
# latex
plt.rc("text", usetex=True)
plt.rcParams["text.latex.preamble"] = [r"\usepackage{amsmath}"]
datadir = os.path.expanduser(datadir)
if proc < 0:
filename = os.path.join(datadir, "slice_" + field + "." + extension)
else:
filename = os.path.join(
datadir, "proc" + str(proc) + "/slice_" + field + "." + extension)
# global dim
# param = read.param(datadir)
dim = read.dim(datadir, proc)
if dim.precision == "D":
precision = "d"
else:
precision = "f"
grid = read.grid(datadir=datadir, trim=True)
# set up slice plane
if extension == "xy" or extension == "Xy":
hsize = dim.nx
vsize = dim.ny
xlabel = "x"
ylabel = "y"
x = grid.x
y = grid.y
if extension == "xz":
hsize = dim.nx
vsize = dim.nz
xlabel = "x"
ylabel = "z"
x = grid.x
y = grid.z
if extension == "yz":
hsize = dim.ny
vsize = dim.nz
xlabel = "y"
ylabel = "z"
x = grid.y
y = grid.z
plane = np.zeros((vsize, hsize), dtype=precision)
infile = npfile(filename, endian=format)
files = []
fig = plt.figure(figsize=figsize)
fig.subplots_adjust(left=0.12,
bottom=0.1,
right=0.98,
top=0.96,
wspace=0.23,
hspace=0.2)
ax = fig.add_subplot(111)
ifirst = True
islice = 0
while 1:
try:
raw_data = infile.fort_read(precision)
except ValueError:
break
except TypeError:
break
if oldfile:
t = raw_data[-1]
plane = raw_data[:-1].reshape(vsize, hsize)
else:
slice_z2pos = raw_data[-1]
t = raw_data[-2]
plane = raw_data[:-2].reshape(vsize, hsize)
if transform:
exec("plane = plane" + transform)
if t > tmin and t < tmax:
ax.cla()
title = "t = %11.3e" % t
ax.set_title(title)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.imshow(
plane,
origin="lower",
vmin=amin,
vmax=amax,
norm=norm,
cmap="hot",
extent=[x[0], x[-1], y[0], y[-1]],
aspect=1,
)
fname = "_tmp%03d.png" % islice
print("Saving frame", fname)
fig.savefig(fname)
files.append(fname)
if ifirst:
print(
"----islice----------t---------min-------max-------delta")
print("%10i %10.3e %10.3e %10.3e %10.3e" %
(islice, t, plane.min(), plane.max(),
plane.max() - plane.min()))
ifirst = False
islice += 1
if t > tmax:
break
print("Making movie animation.mpg - this make take a while")
os.system(
f"mencoder 'mf://_tmp*.png' -mf type=png:fps={fps} -ovc lavc -lavcopts vcodec=wmv2 -oac copy -o animation.mpg"
)
if save:
os.system(f"mv _tmp*.png {save}")
print(f"Moving files to {save}")
else:
os.system("rm _tmp*.png")
print("Removing all files")
infile.close()
def calc(
self,
aver=[],
datatopdir=".",
lskip_zeros=False,
proc=0,
rank=0,
rmfzeros=1,
rmbzeros=1,
iy=None,
l_correction=False,
t_correction=0.0,
dim=None,
timereducer=None,
trargs=[],
tindex=(0, None),
imask=None,
):
"""object returns time dependent meridional tensors
from Averages object aver.z. u, acoef and bcoef and aver.t
For long DNS runs the 'zaverages.dat' file can be very large
so MPI may be required and the data is loaded by processor
as default.
lskip_zeros=True identifies the resetting of the testfield
and rmbzeros and rmfzeros number to exclude before and following
By default none are removed.
iy is the index array that is computed in this MPI process, which
may be a subset of the array on this processor
l_correction=True permits the pencil coefficients computed
prior to the Pencil Code correction implemented after
time=t_correction to be rescaled accordingly to match the new
formulation.
trargs contain optional arguments for the time treatments: mean,
smoothing, etc.
tindex is set to limit the range of the iterations loaded from
Averages in zaverages.dat
The index imask, excluding the resets, can be specified to
ensure all processes use the same mask
"""
import numpy as np
import os
from pencil import read
os.chdir(datatopdir) # return to working directory
grid = read.grid(proc=proc, trim=True, quiet=True)
# if iy None or scalar create numpy array
try:
iy.size > 0
except:
print("exception")
if iy == None:
print("exception None")
iy = np.arange(grid.y.size)
else:
print("exception int")
iy = np.array(iy)
if rank == 0:
print("iy size is {0}".format(iy.shape))
r, theta = np.meshgrid(grid.x, grid.y[iy], indexing="ij")
del (grid, theta) # conserve memory
print("rank {0} calculating tensors for proc {1}".format(rank, proc))
# string containers for zaverages.z keys
uformat = "u{0}mxy"
alpformat = "alp{0}{1}xy"
etaformat = "eta{0}{1}{2}xy"
# imask calculated once for MPI/processor consistency
if rank == 0:
print("Removing zeros")
old_size = aver.t.shape
# if imask is not provided either exclude the zeros or use the full time series
try:
imask.size > 0
print("imask shape is {}".format(imask.shape))
except:
if lskip_zeros:
index = alpformat.format(1, 1)
izero = np.array(
np.where(
aver.z.__getattribute__(index)
[:,
int(aver.z.__getattribute__(index).shape[-2] / 2),
int(aver.z.__getattribute__(index).shape[-1] /
2), ] == 0))[0]
rmfrange = np.arange(0, rmfzeros - 1)
rmbrange = np.arange(0, rmbzeros - 1)
rmpoints = np.array([], dtype=int)
for zero in izero:
rmpoints = np.append(rmpoints, rmfrange + zero)
rmpoints = np.append(rmpoints, zero - rmbrange)
if izero.size > 0:
imask = np.delete(np.where(aver.t), rmpoints)
if rank == 0:
print("Removed {0} zeros from {1} resets".format(
len(rmpoints), len(izero)))
print(
"Resets occured at save points {0}".format(izero))
else:
imask = np.where(aver.t)[0]
del (rmpoints, rmbrange, rmfrange)
else:
imask = np.arange(aver.t.size)
if rank == 0:
print("Skipped zero removals.")
# update the time of the snapshots included
self.t = aver.t[imask]
# Correction to Pencil Code error may be required on old data
if l_correction:
if dim == None:
dim = read.dim(quiet=True)
itcorr = np.where(aver.t[imask] < t_correction)[0]
index = alpformat.format(1, 3)
aver.z.__getattribute__(
index)[itcorr] *= -dim.nprocz / (dim.nprocz - 2.0)
for j in range(0, 3):
index = alpformat.format(3, j + 1)
aver.z.__getattribute__(
index)[itcorr] *= -dim.nprocz / (dim.nprocz - 2.0)
index = etaformat.format(1, 1, 1)
aver.z.__getattribute__(
index)[itcorr] *= -dim.nprocz / (dim.nprocz - 2.0)
for j in range(0, 3):
index = etaformat.format(j + 1, 2, 1)
aver.z.__getattribute__(
index)[itcorr] *= -dim.nprocz / (dim.nprocz - 2.0)
index = etaformat.format(1, 1, 2)
aver.z.__getattribute__(
index)[itcorr] *= -dim.nprocz / (dim.nprocz - 2.0)
for j in range(0, 3):
index = etaformat.format(j + 1, 2, 2)
aver.z.__getattribute__(
index)[itcorr] *= -dim.nprocz / (dim.nprocz - 2.0)
# set up place holders for the Pencil Code tensor coefficients
index = alpformat.format(1, 1)
u = np.zeros(
[3,
len(imask),
aver.z.__getattribute__(index).shape[-2], iy.size])
alp = np.zeros([
3, 3,
len(imask),
aver.z.__getattribute__(index).shape[-2], iy.size
])
eta = np.zeros([
3, 3, 3,
len(imask),
aver.z.__getattribute__(index).shape[-2], iy.size
])
if rank == 0:
print(u.shape, aver.z.__getattribute__(index)[imask, :, :].shape)
# store the individual components in the z-averages as tensors
for i, coord in zip(range(0, 3), ("x", "y", "z")):
try:
index = uformat.format(coord)
if iy.size > 1:
tmp = aver.z.__getattribute__(index)[:, :, iy]
u[i, :, :, :] = tmp[imask]
else:
u[i, :, :, 0] = aver.z.__getattribute__(index)[imask, :,
iy]
except KeyError:
pass
for i in range(0, 3):
for j in range(0, 3):
index = alpformat.format(i + 1, j + 1)
if iy.size > 1:
tmp = aver.z.__getattribute__(index)[:, :, iy]
alp[j, i, :, :, :] = tmp[imask]
else:
alp[j, i, :, :,
0] = aver.z.__getattribute__(index)[imask, :, iy]
for i in range(0, 3):
for j in range(0, 3):
index1 = etaformat.format(i + 1, j + 1, 1)
index2 = etaformat.format(i + 1, j + 1, 2)
# Sign difference with Schrinner + r correction
if iy.size > 1:
tmp = aver.z.__getattribute__(index1)[:, :, iy]
# eta[0,j,i,:,:,:] = -tmp[imask] # JOERN, no sign correction
eta[0, j, i, :, :, :] = tmp[imask]
tmp = aver.z.__getattribute__(index2)[:, :, iy]
# eta[1,j,i,:,:,:] = -tmp[imask]*r # JOERN, no sign correction
eta[1, j, i, :, :, :] = tmp[imask] * r
del tmp
else:
# eta[0,j,i,:,:,0] = -aver.z.__getattribute__(index1)[imask,:,iy] # JOERN, no sign correction
# eta[1,j,i,:,:,0] = -aver.z.__getattribute__(index2)[imask,:,iy]*r[:,0] # JOERN, no sign correction
eta[0, j, i, :, :,
0] = aver.z.__getattribute__(index1)[imask, :, iy]
eta[1, j, i, :, :,
0] = (aver.z.__getattribute__(index2)[imask, :, iy] *
r[:, 0])
# apply the specified averaging or smoothing: 'None' returns unprocessed arrays
if callable(timereducer):
u = timereducer(u, trargs)
alp = timereducer(alp, trargs)
eta = timereducer(eta, trargs)
if rank == 0:
print("Old time dimension has length: {0}".format(old_size))
print("New time dimension has length: {0}".format(alp.shape[-3]))
# Create output tensors
datatype = alp.dtype
datashape = [alp.shape[-3], alp.shape[-2], alp.shape[-1], 1]
setattr(self, "utensor", np.zeros([3] + datashape, dtype=datatype))
setattr(self, "alpha", np.zeros([3, 3] + datashape, dtype=datatype))
setattr(self, "beta", np.zeros([3, 3] + datashape, dtype=datatype))
setattr(self, "gamma", np.zeros([3] + datashape, dtype=datatype))
setattr(self, "delta", np.zeros([3] + datashape, dtype=datatype))
setattr(self, "kappa", np.zeros([3, 3, 3] + datashape, dtype=datatype))
setattr(self, "acoef", np.zeros([3, 3] + datashape, dtype=datatype))
setattr(self, "bcoef", np.zeros([3, 3, 3] + datashape, dtype=datatype))
"""
All tensors need to be reordered nz,ny,nx,nt for efficient writing to disk
"""
# Calculating a and b matrices
self.acoef[:, :, :, :, :, 0] = np.copy(alp)
self.acoef = np.swapaxes(self.acoef, -4, -1)
self.acoef = np.swapaxes(self.acoef, -3, -2)
self.bcoef[:, :, :, :, :, :, 0] = np.copy(eta)
self.bcoef = np.swapaxes(self.bcoef, -4, -1)
self.bcoef = np.swapaxes(self.bcoef, -3, -2)
irr, ith, iph = 0, 1, 2
# u-tensor
print("Calculating utensor on rank {}".format(rank))
# utensor[:,:,:,:,0] = u[:,:,:,:] - np.mean(u[:,:,:,:],axis=1,keepdims=True)
self.utensor[:, :, :, :, 0] = u[:, :, :, :]
self.utensor = np.swapaxes(self.utensor, -4, -1)
self.utensor = np.swapaxes(self.utensor, -3, -2)
# Alpha tensor
print("Calculating alpha on rank {}".format(rank))
self.alpha[irr, irr, :, :, :, 0] = (alp[irr, irr, :, :, :] -
eta[ith, ith, irr, :, :, :] / r)
self.alpha[irr, ith, :, :, :, 0] = 0.5 * (
alp[ith, irr, :, :, :] + eta[ith, irr, irr, :, :, :] / r +
alp[irr, ith, :, :, :] - eta[ith, ith, ith, :, :, :] / r)
self.alpha[irr, iph, :, :, :,
0] = 0.5 * (alp[iph, irr, :, :, :] + alp[irr, iph, :, :, :]
- eta[ith, ith, iph, :, :, :] / r)
self.alpha[ith, irr, :, :, :, 0] = self.alpha[irr, ith, :, :, :, 0]
self.alpha[ith, ith, :, :, :, 0] = (alp[ith, ith, :, :, :] +
eta[ith, irr, ith, :, :, :] / r)
self.alpha[ith, iph, :, :, :,
0] = 0.5 * (alp[iph, ith, :, :, :] + alp[ith, iph, :, :, :]
+ eta[ith, irr, iph, :, :, :] / r)
self.alpha[iph, irr, :, :, :, 0] = self.alpha[irr, iph, :, :, :, 0]
self.alpha[iph, ith, :, :, :, 0] = self.alpha[ith, iph, :, :, :, 0]
self.alpha[iph, iph, :, :, :, 0] = alp[iph, iph, :, :, :]
self.alpha = np.swapaxes(self.alpha, -4, -1)
self.alpha = np.swapaxes(self.alpha, -3, -2)
# Gamma vector
print("Calculating gamma on rank {}".format(rank))
self.gamma[irr, :, :, :,
0] = -0.5 * (alp[iph, ith, :, :, :] - alp[ith, iph, :, :, :]
- eta[ith, irr, iph, :, :, :] / r)
self.gamma[ith, :, :, :,
0] = -0.5 * (alp[irr, iph, :, :, :] - alp[iph, irr, :, :, :]
- eta[ith, ith, iph, :, :, :] / r)
self.gamma[iph, :, :, :, 0] = -0.5 * (
alp[ith, irr, :, :, :] - alp[irr, ith, :, :, :] +
eta[ith, irr, irr, :, :, :] / r + eta[ith, ith, ith, :, :, :] / r)
self.gamma = np.swapaxes(self.gamma, -4, -1)
self.gamma = np.swapaxes(self.gamma, -3, -2)
# Beta tensor
print("Calculating beta on rank {}".format(rank))
self.beta[irr, irr, :, :, :, 0] = -0.5 * eta[ith, iph, irr, :, :, :]
self.beta[irr, ith, :, :, :, 0] = 0.25 * (eta[irr, iph, irr, :, :, :] -
eta[ith, iph, ith, :, :, :])
self.beta[irr, iph, :, :, :, 0] = 0.25 * (eta[ith, irr, irr, :, :, :] -
eta[ith, iph, iph, :, :, :] -
eta[irr, ith, irr, :, :, :])
self.beta[ith, irr, :, :, :, 0] = self.beta[irr, ith, :, :, :, 0]
self.beta[ith, ith, :, :, :, 0] = 0.5 * eta[irr, iph, ith, :, :, :]
self.beta[ith, iph, :, :, :, 0] = 0.25 * (eta[ith, irr, ith, :, :, :] +
eta[irr, iph, iph, :, :, :] -
eta[irr, ith, ith, :, :, :])
self.beta[iph, irr, :, :, :, 0] = self.beta[irr, iph, :, :, :, 0]
self.beta[iph, ith, :, :, :, 0] = self.beta[ith, iph, :, :, :, 0]
self.beta[iph, iph, :, :, :, 0] = 0.5 * (eta[ith, irr, iph, :, :, :] -
eta[irr, ith, iph, :, :, :])
# Sign convention to match with meanfield_e_tensor
# self.beta = -self.beta #JOERN, not needed
self.beta = np.swapaxes(self.beta, -4, -1)
self.beta = np.swapaxes(self.beta, -3, -2)
# Delta vector
print("Calculating delta on rank {}".format(rank))
self.delta[irr, :, :, :, 0] = 0.25 * (eta[irr, ith, ith, :, :, :] -
eta[ith, irr, ith, :, :, :] +
eta[irr, iph, iph, :, :, :])
self.delta[ith, :, :, :, 0] = 0.25 * (eta[ith, irr, irr, :, :, :] -
eta[irr, ith, irr, :, :, :] +
eta[ith, iph, iph, :, :, :])
self.delta[iph, :, :, :, 0] = -0.25 * (eta[irr, iph, irr, :, :, :] +
eta[ith, iph, ith, :, :, :])
# Sign convention to match with meanfield_e_tensor
# self.delta = -self.delta #JOERN, not needed
self.delta = np.swapaxes(self.delta, -4, -1)
self.delta = np.swapaxes(self.delta, -3, -2)
# Kappa tensor
print("Calculating kappa on rank {}".format(rank))
for i in range(0, 3):
self.kappa[irr, irr, i, :, :, :, 0] = -eta[irr, irr, i, :, :, :]
self.kappa[ith, irr, i, :, :, :,
0] = -0.5 * (eta[ith, irr, i, :, :, :] +
eta[irr, ith, i, :, :, :])
self.kappa[iph, irr, i, :, :, :,
0] = -0.5 * eta[irr, iph, i, :, :, :]
self.kappa[irr, ith, i, :, :, :, 0] = self.kappa[ith, irr,
i, :, :, :, 0]
self.kappa[ith, ith, i, :, :, :, 0] = -eta[ith, ith, i, :, :, :]
self.kappa[iph, ith, i, :, :, :,
0] = -0.5 * eta[ith, iph, i, :, :, :]
self.kappa[irr, iph, i, :, :, :, 0] = self.kappa[iph, irr,
i, :, :, :, 0]
self.kappa[ith, iph, i, :, :, :, 0] = self.kappa[iph, ith,
i, :, :, :, 0]
self.kappa[iph, iph, i, :, :, :, 0] = 1e-91
# Sign convention to match with meanfield_e_tensor
# self.kappa = -self.kappa #JOERN, not needed
self.kappa = np.swapaxes(self.kappa, -4, -1)
self.kappa = np.swapaxes(self.kappa, -3, -2)
setattr(self, "imask", imask)
def zav2h5(
folder='.',
dataset='',
filename='emftensors.h5',
timereducer='mean',
hdf5dir='data/',
l_correction=True,
t_correction=8972.,
rmfzeros=4,
rmbzeros=2,
dgroup='emftensor',
):
"""
If large dataset MPI may be required.
Loads Averages object and applies tensors calculation and reforms for
efficient writing to hdf5 for mean field module simulations.
MPI call needs to be improved to avoid MemoryError for large files
with read.aver(plane_list=['z'])
timereducers needs to be expanded to include various smoothing options
"""
import numpy as np
from pencil import read
from pencil.read import aver
from pencil.export import create_h5, fvars, create_aver_sph
# from pencil.export import create_h5.fvars as fvars
# from pencil.export import create_aver_sph
from pencil.calc import tensors_sph
import h5py
import copy
timereducers = {
'mean':
lambda x, args: np.mean(x, axis=-3, keepdims=True),
#np.std(x,axis=-3)),
'mean_last':
lambda x, args: np.mean(np.take(
x, np.arange(-int(args[0]), 0, 1), axis=-3),
axis=-3,
keepdims=True),
'none':
lambda x, args: x
}
if not timereducer in timereducers:
raise ValueError(
'timereducer "{}" undefined in timereducers'.format(timereducer) +
' options: {}'.format(timereducers.keys()))
if len(dataset) == 0:
dataset = timereducer
with open('zaver.in', 'r') as f:
zavers = f.read().splitlines()
""" Find out if the calculation is parallel and distribute the arrays
according to y-index and ipz=0 processor layout
"""
try:
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank() # rank of processor on which this script runs
size = comm.Get_size() # number of ~ ~ ~ ~
l_mpi = True
l_mpi = l_mpi and (size != 1)
except ImportError:
l_mpi = False
rank = 0
size = 1
comm = None
dim = read.dim()
nx, nny = dim.nx, dim.ny
ayindex = np.arange(nny)
if l_mpi:
y_chunks = np.array_split(ayindex, size, axis=0)
yindex = y_chunks[rank]
ny = yindex.size
else:
yindex = ayindex # vector 0 ... nygrid-1
ny = nny
ncpus = dim.nprocx * dim.nprocy
aprocs = np.arange(ncpus) # vector 0 ... nprocx*nprocy-1
if np.mod(ncpus, size) > 0:
print('number of processes must divide {} cpus'.format(ncpus))
quit()
if l_mpi:
if size > aprocs.size:
nsz = size / aprocs.size
for ii in range(1, nsz):
tmproc = np.append(aprocs, aprocs)
aprocs = np.sort(tmproc)
proc_chunks = np.array_split(aprocs, size, axis=0)
proc = proc_chunks[rank]
else:
proc = aprocs
"""Set up hdf5 file and create datasets in which to save the tensors
"""
lskip_zeros = rmfzeros + rmbzeros > 0
if rank == 0: # if root processor
grid = read.grid(trim=True, quiet=True) # read grid
zav = read.aver(proc=0,
plane_list=['z'
]) # read zaverages of root proc of PC run
tensor = tensors_sph( # decompose into individual effect tensors
zav,
proc=proc[0],
rank=0,
lskip_zeros=lskip_zeros,
iy=[
int(ny / 2 / dim.nprocy),
],
timereducer=timereducers[timereducer],
#trargs=trargs,
rmfzeros=rmfzeros,
rmbzeros=rmbzeros,
l_correction=l_correction,
t_correction=t_correction,
dim=dim,
#tindex=tindex
)
if 'mean' in dataset:
nt = 1
else:
nt = tensor.t.size
create_aver_sph(hdf5dir + filename,
dataset,
fvars, (1, ny, nx, nt), (0, grid.y, grid.x, tensor.t),
hdf5dir=hdf5dir,
dgroup=dgroup)
if l_mpi:
imask = comm.bcast(tensor.imask, root=0)
else:
imask = tensor.imask
import os
if os.path.exists(os.path.join(folder, 'averages/z.h5')):
zav = aver(plane_list=['z']) # read all averages
tensor_buf = tensors_sph( # calculate tensors
aver=zav,
rank=rank,
lskip_zeros=lskip_zeros,
timereducer=timereducers[timereducer],
#trargs=trargs,
rmfzeros=rmfzeros,
rmbzeros=rmbzeros,
l_correction=l_correction,
t_correction=t_correction,
dim=dim,
#tindex=tindex,
imask=imask)
else:
yndx_tmp = np.array_split(yindex, dim.nprocy)
# list of vectors ipy*ny/nprocy ... (ipy+1)*ny/nprocy - 1, ipy=0,nprocy-1
for ipy in range(dim.nprocy): # over all y processors of the PC run
for ipx in range(
dim.nprocx): # over all x processors of the PC run
iproc = dim.nprocx * ipy + ipx # proc rank of the PC run (0 ... nprocx*nprocy-1)
yndx = yndx_tmp[ipy] - ipy * int(dim.nygrid / dim.nprocy)
zav = aver(proc=iproc,
plane_list=['z']) # read averages from proc iproc
print('calculating tensors on proc {0} rank {1}'.format(
iproc, rank))
"""
if iproc==1: # as there is corrupted data on proc 1
with open('zaver.in', 'r') as f:
zavers = f.read().splitlines()
for zaver in zavers:
zav.z.__setattr__(zaver,np.insert(
zav.z.__getattribute__(zaver),3766,
0.5*(zav.z.__getattribute__(zaver)[3766]+
zav.z.__getattribute__(zaver)[3767]),axis=0))
zav.t=np.insert(zav.t,3766,0.5*(zav.t[3766]+zav.t[3767]),axis=0)
"""
tensor_buf = tensors_sph( # calculate tensors
aver=zav,
proc=iproc,
rank=rank,
lskip_zeros=lskip_zeros,
iy=yndx,
timereducer=timereducers[timereducer],
#trargs=trargs,
rmfzeros=rmfzeros,
rmbzeros=rmbzeros,
l_correction=l_correction,
t_correction=t_correction,
dim=dim,
#tindex=tindex,
imask=imask)
if ipx == 0:
tensor = copy.deepcopy(tensor_buf)
else:
for field, comp in fvars:
setattr(
tensor, field,
np.concatenate(
(tensor.__getattribute__(field),
tensor_buf.__getattribute__(field)),
axis=len(comp) + 2))
if l_mpi:
comm.barrier()
ds = h5py.File(hdf5dir + filename, 'a', driver='mpio', comm=comm)
else:
ds = h5py.File(hdf5dir + filename, 'a') # open HDF5 file
for field, comp in fvars:
print('writing {0} from rank {1} for proc {2}'.format(
field, rank, iproc))
dsname = '{0}/{1}/{2}'.format(dgroup, field, dataset)
if len(comp) == 1:
ds[dsname][:, :,
yndx_tmp[ipy], :] = tensor.__getattribute__(field)
elif len(comp) == 2:
ds[dsname][:, :, :,
yndx_tmp[ipy], :] = tensor.__getattribute__(field)
else:
ds[dsname][:, :, :, :,
yndx_tmp[ipy], :] = tensor.__getattribute__(field)
ds.close()
def find_fixed(
self,
datadir="data",
var_file="VAR0",
trace_field="bb",
ti=-1,
tf=-1,
tracer_file_name=None,
):
"""
Find the fixed points to a snapshot or existing tracer file.
call signature::
find_fixed(datadir='data', var_file='VAR0', trace_field='bb',
ti=-1, tf=-1, tracer_file_name=None):
Keyword arguments:
*datadir*:
Data directory.
*var_file*:
Varfile to be read.
*trace_field*:
Vector field used for the streamline tracing.
*ti*:
Initial VAR file index for tracer time sequences. Overrides 'var_file'.
*tf*:
Final VAR file index for tracer time sequences. Overrides 'var_file'.
*tracer_file_name*
Name of the tracer file to be read.
If 'None' compute the tracers.
"""
import numpy as np
import multiprocessing as mp
from pencil import read
from pencil import math
from pencil.diag.tracers import Tracers
from pencil.calc.streamlines import Stream
from pencil.math.interpolation import vec_int
if self.params.int_q == "curly_A":
self.curly_A = []
if self.params.int_q == "ee":
self.ee = []
# Multi core setup.
if not (np.isscalar(self.params.n_proc)) or (self.params.n_proc % 1 != 0):
print("Error: invalid processor number")
return -1
queue = mp.Queue()
# Make sure to read the var files with the correct magic.
magic = []
if trace_field == "bb":
magic.append("bb")
if trace_field == "jj":
magic.append("jj")
if trace_field == "vort":
magic.append("vort")
if self.params.int_q == "ee":
magic.append("bb")
magic.append("jj")
dim = read.dim(datadir=datadir)
# Check if user wants a tracer time series.
if (ti % 1 == 0) and (tf % 1 == 0) and (ti >= 0) and (tf >= ti):
series = True
var_file = "VAR{0}".format(ti)
n_times = tf - ti + 1
else:
series = False
n_times = 1
self.t = np.zeros(n_times)
# Read the initial field.
var = read.var(
var_file=var_file, datadir=datadir, magic=magic, quiet=True, trimall=True
)
self.t[0] = var.t
grid = read.grid(datadir=datadir, quiet=True, trim=True)
field = getattr(var, trace_field)
param2 = read.param(datadir=datadir, quiet=True)
if self.params.int_q == "ee":
ee = var.jj * param2.eta - math.cross(var.uu, var.bb)
self.params.datadir = datadir
self.params.var_file = var_file
self.params.trace_field = trace_field
# Get the simulation parameters.
self.params.dx = var.dx
self.params.dy = var.dy
self.params.dz = var.dz
self.params.Ox = var.x[0]
self.params.Oy = var.y[0]
self.params.Oz = var.z[0]
self.params.Lx = grid.Lx
self.params.Ly = grid.Ly
self.params.Lz = grid.Lz
self.params.nx = dim.nx
self.params.ny = dim.ny
self.params.nz = dim.nz
tracers = Tracers()
tracers.params = self.params
# Create the mapping for all times.
if not tracer_file_name:
tracers.find_tracers(
var_file=var_file,
datadir=datadir,
trace_field=trace_field,
ti=ti,
tf=tf,
)
else:
tracers.read(datadir=datadir, file_name=tracer_file_name)
self.tracers = tracers
# Set some default values.
self.t = np.zeros((tf - ti + 1) * series + (1 - series))
self.fixed_index = np.zeros((tf - ti + 1) * series + (1 - series))
self.poincare = np.zeros(
[
int(self.params.trace_sub * dim.nx),
int(self.params.trace_sub * dim.ny),
n_times,
]
)
ix0 = range(0, int(self.params.nx * self.params.trace_sub) - 1)
iy0 = range(0, int(self.params.ny * self.params.trace_sub) - 1)
self.fixed_points = []
self.fixed_sign = []
self.fixed_tracers = []
# Start the parallelized fixed point finding.
for tidx in range(n_times):
if tidx > 0:
var = read.var(
var_file="VAR{0}".format(tidx + ti),
datadir=datadir,
magic=magic,
quiet=True,
trimall=True,
)
field = getattr(var, trace_field)
self.t[tidx] = var.t
proc = []
sub_data = []
fixed = []
fixed_sign = []
fixed_tracers = []
for i_proc in range(self.params.n_proc):
proc.append(
mp.Process(
target=self.__sub_fixed,
args=(queue, ix0, iy0, field, self.tracers, tidx, var, i_proc),
)
)
for i_proc in range(self.params.n_proc):
proc[i_proc].start()
for i_proc in range(self.params.n_proc):
sub_data.append(queue.get())
for i_proc in range(self.params.n_proc):
proc[i_proc].join()
for i_proc in range(self.params.n_proc):
# Extract the data from the single cores. Mind the order.
sub_proc = sub_data[i_proc][0]
fixed.extend(sub_data[i_proc][1])
fixed_tracers.extend(sub_data[i_proc][2])
fixed_sign.extend(sub_data[i_proc][3])
self.fixed_index[tidx] += sub_data[i_proc][4]
self.poincare[sub_proc :: self.params.n_proc, :, tidx] = sub_data[
i_proc
][5]
for i_proc in range(self.params.n_proc):
proc[i_proc].terminate()
# Discard fixed points which lie too close to each other.
fixed, fixed_tracers, fixed_sign = self.__discard_close_fixed_points(
np.array(fixed), np.array(fixed_sign), np.array(fixed_tracers), var
)
if self.fixed_points is None:
self.fixed_points = []
self.fixed_sign = []
self.fixed_tracers = []
self.fixed_points.append(np.array(fixed))
self.fixed_sign.append(np.array(fixed_sign))
self.fixed_tracers.append(fixed_tracers)
# Compute the traced quantities along the fixed point streamlines.
if (self.params.int_q == "curly_A") or (self.params.int_q == "ee"):
for t_idx in range(0, n_times):
if self.params.int_q == "curly_A":
self.curly_A.append([])
if self.params.int_q == "ee":
self.ee.append([])
for fixed in self.fixed_points[t_idx]:
# Trace the stream line.
xx = np.array([fixed[0], fixed[1], self.params.Oz])
# time = np.linspace(0, self.params.Lz/np.max(abs(field[2])), 10)
field_strength_z0 = vec_int(
xx,
field,
[var.dx, var.dy, var.dz],
[var.x[0], var.y[0], var.z[0]],
[len(var.x), len(var.y), len(var.z)],
interpolation=self.params.interpolation,
)
field_strength_z0 = np.sqrt(np.sum(field_strength_z0 ** 2))
time = np.linspace(0, 4 * self.params.Lz / field_strength_z0, 500)
stream = Stream(field, self.params, xx=xx, time=time)
# Do the field line integration.
if self.params.int_q == "curly_A":
curly_A = 0
for l in range(stream.iterations - 1):
aaInt = vec_int(
(stream.tracers[l + 1] + stream.tracers[l]) / 2,
var.aa,
[var.dx, var.dy, var.dz],
[var.x[0], var.y[0], var.z[0]],
[len(var.x), len(var.y), len(var.z)],
interpolation=self.params.interpolation,
)
curly_A += np.dot(
aaInt, (stream.tracers[l + 1] - stream.tracers[l])
)
self.curly_A[-1].append(curly_A)
if self.params.int_q == "ee":
ee_p = 0
for l in range(stream.iterations - 1):
eeInt = vec_int(
(stream.tracers[l + 1] + stream.tracers[l]) / 2,
ee,
[var.dx, var.dy, var.dz],
[var.x[0], var.y[0], var.z[0]],
[len(var.x), len(var.y), len(var.z)],
interpolation=self.params.interpolation,
)
ee_p += np.dot(
eeInt, (stream.tracers[l + 1] - stream.tracers[l])
)
self.ee[-1].append(ee_p)
if self.params.int_q == "curly_A":
self.curly_A[-1] = np.array(self.curly_A[-1])
if self.params.int_q == "ee":
self.ee[-1] = np.array(self.ee[-1])
return 0
def update(self, hard=False, quiet=True):
"""Update simulation object:
if not read in:
- read param.nml
- read grid and ghost grid
Set hard=True to force update.
"""
from os.path import exists
from os.path import join
from pencil.read import param, grid, dim
REEXPORT = False
if hard == True:
self.param = False
self.grid = False
self.ghost_grid = False
self.dim = False
REEXPORT = True
if self.param == False:
try:
if exists(join(self.datadir, 'param.nml')):
print('~ Reading param.nml.. ')
param = param(quiet=quiet, datadir=self.datadir)
self.param = {}
# read params into Simulation object
for key in dir(param):
if key.startswith('_') or key == 'read': continue
if type(getattr(param,
key)) in [bool, list, float, int, str]:
self.param[key] = getattr(param, key)
else:
try:
# allow for nested param objects
self.param[key] = {}
for subkey in dir(getattr(param, key)):
if subkey.startswith(
'_') or subkey == 'read':
continue
if type(
getattr(getattr(param, key),
subkey)) in [
bool, list, float, int,
str
]:
self.param[key][subkey] = getattr(
getattr(param, key), subkey)
except:
# not nested param objects
continue
REEXPORT = True
else:
if not quiet:
print('? WARNING: for ' + self.path +
'\n? Simulation has ' +
'not run yet! Meaning: No param.nml found!')
REEXPORT = True
except:
print('! ERROR: while reading param.nml for ' + self.path)
self.param = False
REEXPORT = True
if self.param != False and (self.grid == False
or self.ghost_grid == False):
# read grid only if param is not False
try:
print('~ Reading grid.. ')
self.grid = grid(datadir=self.datadir, trim=True, quiet=True)
print('~ Reading ghost_grid.. ')
self.ghost_grid = grid(datadir=self.datadir,
trim=False,
quiet=True)
print('~ Reading dim.. ')
self.dim = dim(datadir=self.datadir)
if not quiet:
print('# Updating grid and ghost_grid succesfull')
REEXPORT = True
# adding lx, dx etc to params
self.param['Lx'] = self.grid.Lx
self.param['Ly'] = self.grid.Ly
self.param['Lz'] = self.grid.Lz
self.param['lx'] = self.grid.Lx
self.param['ly'] = self.grid.Ly
self.param['lz'] = self.grid.Lz
self.param['dx'] = self.grid.dx
self.param['dy'] = self.grid.dy
self.param['dz'] = self.grid.dz
except:
if not quiet:
print(
'? WARNING: Updating grid and ghost_grid ' +
'was not successfull, since run has not yet started.')
if self.started() or (not quiet):
print('? WARNING: Couldnt load grid for ' + self.path)
self.grid = False
self.ghost_grid = False
self.dim = False
REEXPORT = True
elif self.param == False:
if not quiet:
print('? WARNING: Updating grid and ghost_grid ' +
'was not successfull, since run has not yet started.')
self.grid = False
self.ghost_grid = False
self.dim = False
REEXPORT = True
if REEXPORT == True: self.export()
return self
def kernel_smooth(
sim_path,
src,
dst,
magic=["meanuu"],
par=[],
comm=None,
gd=[],
grp_overwrite=False,
overwrite=False,
rank=0,
size=1,
nghost=3,
kernel=1.,
status="a",
chunksize=1000.0,
dtype=np.float64,
quiet=True,
nmin=32,
typ='piecewise',
mode=list(),
):
if comm:
overwrite = False
if isinstance(par, list):
os.chdir(sim_path)
par = read.param(quiet=True, conflicts_quiet=True)
if isinstance(gd, list):
os.chdir(sim_path)
gd = read.grid(quiet=True)
# get data dimensions
nx, ny, nz = (
src["settings"]["nx"][0],
src["settings"]["ny"][0],
src["settings"]["nz"][0],
)
mx, my, mz = (
src["settings"]["mx"][0],
src["settings"]["my"][0],
src["settings"]["mz"][0],
)
# extend gost zones to include up to 1.5 * kernel length)
dx = max(src['grid/dx'][()], src['grid/dy'][()], src['grid/dz'][()])
nkernel = np.int(2.5 * kernel / dx)
sigma = kernel / dx
print('sigma {:.2f}, kernel {:.2f}, dx {:.2f}'.format(sigma, kernel, dx))
# split data into manageable memory chunks
dstchunksize = 8 * nx * ny * nz / 1024 * 1024
if dstchunksize > chunksize:
nchunks = cpu_optimal(
nx,
ny,
nz,
quiet=quiet,
mvar=src["settings/mvar"][0],
maux=src["settings/maux"][0],
MBmin=chunksize,
nmin=nmin,
size=size,
)[1]
else:
nchunks = [1, 1, 1]
print("nchunks {}".format(nchunks))
# for mpi split chunks across processes
if size > 1:
locindx = np.array_split(np.arange(nx) + nghost, nchunks[0])
locindy = np.array_split(np.arange(ny) + nghost, nchunks[1])
locindz = np.array_split(np.arange(nz) + nghost, nchunks[2])
indx = [
locindx[np.mod(
rank + int(rank / nchunks[2]) + int(rank / nchunks[1]),
nchunks[0])]
]
indy = [locindy[np.mod(rank + int(rank / nchunks[2]), nchunks[1])]]
indz = [locindz[np.mod(rank, nchunks[2])]]
allchunks = 1
else:
locindx = np.array_split(np.arange(nx) + nghost, nchunks[0])
locindy = np.array_split(np.arange(ny) + nghost, nchunks[1])
locindz = np.array_split(np.arange(nz) + nghost, nchunks[2])
indx = np.array_split(np.arange(nx) + nghost, nchunks[0])
indy = np.array_split(np.arange(ny) + nghost, nchunks[1])
indz = np.array_split(np.arange(nz) + nghost, nchunks[2])
allchunks = nchunks[0] * nchunks[1] * nchunks[2]
if 1 in nchunks:
mode = ["reflect", "reflect", "reflect"]
for ich in range(3):
if nchunks[ich] == 1:
mode[2 - ich] = "wrap"
if mode[2 - ich] == "reflect":
typ = "piecewise"
else:
typ = "all"
print('mode:', mode, 'typ:', typ)
# save time
dataset_h5(
dst,
"time",
status=status,
data=src["time"][()],
comm=comm,
size=size,
rank=rank,
overwrite=overwrite,
dtype=dtype,
)
# ensure derived variables are in a list
if isinstance(magic, list):
magic = magic
else:
magic = [magic]
# initialise group
group = group_h5(
dst,
"data",
status="a",
overwrite=grp_overwrite,
comm=comm,
rank=rank,
size=size,
)
for key in magic:
if is_vector(key):
dataset_h5(
group,
key + str(nkernel),
status=status,
shape=[3, mz, my, mx],
comm=comm,
size=size,
rank=rank,
overwrite=overwrite,
dtype=dtype,
)
print("writing " + key + " shape {}".format([3, mz, my, mx]))
else:
dataset_h5(
group,
key + str(nkernel),
status=status,
shape=[mz, my, mx],
comm=comm,
size=size,
rank=rank,
overwrite=overwrite,
dtype=dtype,
)
print("writing " + key + " shape {}".format([mz, my, mx]))
for ichunk in range(allchunks):
for iz in [indz[np.mod(ichunk, nchunks[2])]]:
if nchunks[2] == 1:
zextra = nghost
else:
zextra = nkernel + nghost
n1, n2 = iz[0] - zextra, iz[-1] + zextra + 1
lindz = np.arange(n1, n2)
n1out = n1 + zextra
n2out = n2 - zextra
varn1 = zextra
varn2 = -zextra
if iz[0] == locindz[0][0]:
n1out = 0
varn1 = zextra - nghost
if iz[-1] == locindz[-1][-1]:
n2out = n2 - zextra + nghost
varn2 = n2 - n1 - zextra + nghost
if n1 < 0:
lindz[np.where(lindz < nghost)[0]] += nz
if n2 > mz - 1:
lindz[np.where(lindz > mz - 1 - nghost)[0]] -= nz
print('n1out {},n2out {},varn1 {},varn2 {},zextra {}'.format(
n1out, n2out, varn1, varn2, zextra))
for iy in [
indy[np.mod(ichunk + int(ichunk / nchunks[2]),
nchunks[1])]
]:
if nchunks[1] == 1:
yextra = nghost
else:
yextra = nkernel + nghost
m1, m2 = iy[0] - yextra, iy[-1] + yextra + 1
lindy = np.arange(m1, m2)
m1out = m1 + yextra
m2out = m2 + 1 - yextra
varm1 = yextra
varm2 = -yextra
if iy[0] == locindy[0][0]:
m1out = 0
varm1 = yextra - nghost
if iy[-1] == locindy[-1][-1]:
m2out = m2 - yextra + nghost
varm2 = m2 - m1 - yextra + nghost
if m1 < 0:
lindy[np.where(lindy < 0)[0]] += ny
if m2 > my - 1:
lindy[np.where(lindy > my - 1)[0]] -= ny
print(
'm1out {},m2out {},varm1 {},varm2 {},yextra {}'.format(
m1out, m2out, varm1, varm2, yextra))
for iy in [
indy[np.mod(ichunk + int(ichunk / nchunks[2]),
nchunks[1])]
]:
for ix in [
indx[np.mod(
ichunk + int(ichunk / nchunks[2]) +
int(ichunk / nchunks[1]),
nchunks[0],
)]
]:
if nchunks[1] == 1:
xextra = nghost
else:
xextra = nkernel + nghost
l1, l2 = ix[0] - xextra, ix[-1] + xextra + 1
lindx = np.arange(l1, l2)
l1out = l1 + xextra
l2out = l2 + 1 - xextra
varl1 = xextra
varl2 = -xextra
if ix[0] == locindx[0][0]:
l1out = 0
varl1 = xextra - nghost
if ix[-1] == locindx[-1][-1]:
l2out = l2 - xextra + nghost
varl2 = l2 - l1 - xextra + nghost
if l1 < 0:
lindx[np.where(lindx < 0)[0]] += nx
if l2 > mx - 1:
lindx[np.where(lindx > mx - 1)[0]] -= nx
print('l1out {},l2out {},varl1 {},varl2 {},xextra {}'.
format(l1out, l2out, varl1, varl2, xextra))
if not quiet:
print("remeshing " + key +
" chunk {}".format([iz, iy, ix]))
print('sending ichunk {} with index ranges {}'.format(
ichunk, [n1, n2, m1, m2, l1, l2]))
var = smoothed_data(src["data"], dst["data"], key, par,
gd, lindx, lindy, lindz, nghost,
sigma, typ, mode)
print(
'ichunk {}, var min {:.1e}, var max {:.1e}'.format(
ichunk, var.min(), var.max()))
# print('var shape {}'.format(var.shape))
# if not quiet:
# print('writing '+key+
# ' shape {} chunk {}'.format(
# var.shape, [iz,iy,ix]))
print('ichunk: out indices {}'.format(
[n1out, n2out, m1out, m2out, l1out, l2out]))
if is_vector(key):
dst["data"][key +
str(nkernel)][:, n1out:n2out,
m1out:m2out,
l1out:l2out] = dtype(
var[:, varn1:varn2,
varm1:varm2,
varl1:varl2])
else:
dst["data"][key +
str(nkernel)][n1out:n2out, m1out:m2out,
l1out:l2out] = dtype(
var[varn1:varn2,
varm1:varm2,
varl1:varl2])
def rhs_data(sim_path, src, dst, magic=["uxb","etadel2a"], par=[], comm=None,
gd=[], grp_overwrite=False, overwrite=False,
rank=0, size=1, nghost=3,status="a",
chunksize = 1000.0, dtype=np.float64, quiet=True, nmin=32,
Reynolds_shock=False, lmix=False
):
if comm:
overwrite = False
if isinstance(par, list):
os.chdir(sim_path)
par = read.param(quiet=True,conflicts_quiet=True)
if isinstance(gd, list):
os.chdir(sim_path)
gd = read.grid(quiet=True)
#get data dimensions
nx, ny, nz = src["settings"]["nx"][0],\
src["settings"]["ny"][0],\
src["settings"]["nz"][0]
mx, my, mz = src["settings"]["mx"][0],\
src["settings"]["my"][0],\
src["settings"]["mz"][0]
#split data into manageable memory chunks
dstchunksize = 8*nx*ny*nz/1024*1024
if dstchunksize > chunksize:
nchunks = cpu_optimal(nx,ny,nz,quiet=quiet,
mvar=src["settings/mvar"][0],
maux=src["settings/maux"][0],
MBmin=chunksize,nmin=nmin,size=size)[1]
else:
nchunks = [1,1,1]
print("nchunks {}".format(nchunks))
# for mpi split chunks across processes
if size > 1:
locindx = np.array_split(np.arange(nx)+nghost,nchunks[0])
locindy = np.array_split(np.arange(ny)+nghost,nchunks[1])
locindz = np.array_split(np.arange(nz)+nghost,nchunks[2])
indx = [locindx[np.mod(rank+int(rank/nchunks[2])
+int(rank/nchunks[1]),nchunks[0])]]
indy = [locindy[np.mod(rank+int(rank/nchunks[2]),nchunks[1])]]
indz = [locindz[np.mod(rank,nchunks[2])]]
allchunks = 1
else:
locindx = np.array_split(np.arange(nx)+nghost,nchunks[0])
locindy = np.array_split(np.arange(ny)+nghost,nchunks[1])
locindz = np.array_split(np.arange(nz)+nghost,nchunks[2])
indx = np.array_split(np.arange(nx)+nghost,nchunks[0])
indy = np.array_split(np.arange(ny)+nghost,nchunks[1])
indz = np.array_split(np.arange(nz)+nghost,nchunks[2])
allchunks = nchunks[0]*nchunks[1]*nchunks[2]
# save time
dataset_h5(dst, "time", status=status, data=src["time"][()],
comm=comm, size=size, rank=rank,
overwrite=overwrite, dtype=dtype)
# ensure derived variables are in a list
if isinstance(magic, list):
magic = magic
else:
magic = [magic]
# confirm exists group
group_h5(dst, "data", status="a", overwrite=grp_overwrite,
comm=comm, rank=rank, size=size)
# initialise group
group = group_h5(dst, "calc", status="a", overwrite=grp_overwrite,
comm=comm, rank=rank, size=size)
for key in magic:
if is_vector(key):
dataset_h5(group, key, status=status, shape=[3,mz,my,mx],
comm=comm, size=size, rank=rank,
overwrite=overwrite, dtype=dtype)
print("writing "+key+" shape {}".format([3,mz,my,mx]))
else:
dataset_h5(group, key, status=status, shape=[mz,my,mx],
comm=comm, size=size, rank=rank,
overwrite=overwrite, dtype=dtype)
print("writing "+key+" shape {}".format([mz,my,mx]))
for ichunk in range(allchunks):
for iz in [indz[np.mod(ichunk,nchunks[2])]]:
n1, n2 = iz[ 0]-nghost,\
iz[-1]+nghost+1
n1out = n1+nghost
n2out = n2-nghost
varn1 = nghost
varn2 = -nghost
if iz[0] == locindz[0][0]:
n1out = 0
varn1 = 0
if iz[-1] == locindz[-1][-1]:
n2out = n2
varn2 = n2
for iy in [indy[np.mod(ichunk+
int(ichunk/nchunks[2]),nchunks[1])]]:
m1, m2 = iy[ 0]-nghost,\
iy[-1]+nghost+1
m1out = m1+nghost
m2out = m2-nghost
varm1 = nghost
varm2 = -nghost
if iy[0] == locindy[0][0]:
m1out = 0
varm1 = 0
if iy[-1] == locindy[-1][-1]:
m2out = m2
varm2 = m2
for ix in [indx[np.mod(ichunk+int(ichunk/nchunks[2])
+int(ichunk/nchunks[1]),nchunks[0])]]:
l1, l2 = ix[ 0]-nghost,\
ix[-1]+nghost+1
l1out = l1+nghost
l2out = l2-nghost
varl1 = nghost
varl2 = -nghost
if ix[0] == locindx[0][0]:
l1out = 0
varl1 = 0
if ix[-1] == locindx[-1][-1]:
l2out = l2
varl2 = l2
if not quiet:
print("remeshing "+key+" chunk {}".format(
[iz,iy,ix]))
var = calc_rhs_data(src, dst,
key, par, gd, l1, l2, m1, m2, n1, n2,
nghost=nghost, Reynolds_shock=Reynolds_shock,
lmix=lmix)
if is_vector(key):
dst["calc"][key][:,n1out:n2out,
m1out:m2out,
l1out:l2out] = dtype(var[:,
varn1:varn2,
varm1:varm2,
varl1:varl2])
else:
dst["calc"][key][n1out:n2out,
m1out:m2out,
l1out:l2out] = dtype(var[
varn1:varn2,
varm1:varm2,
varl1:varl2])
def write_h5_snapshot(
snapshot,
file_name="VAR0",
datadir="data/allprocs",
precision="d",
nghost=3,
persist=None,
settings=None,
param=None,
grid=None,
lghosts=False,
indx=None,
proc=None,
ipx=None,
ipy=None,
ipz=None,
procdim=None,
unit=None,
t=None,
x=None,
y=None,
z=None,
state="a",
quiet=True,
lshear=False,
driver=None,
comm=None,
overwrite=False,
rank=0,
size=1,
):
"""
Write a snapshot given as numpy array.
We assume by default that a run simulation directory has already been
constructed and start completed successfully in h5 format so that
files dim, grid and param files are already present.
If not the contents of these will need to be supplied as dictionaries
along with persist if included.
call signature:
write_h5_snapshot(snapshot, file_name='VAR0', datadir='data/allprocs',
precision='d', nghost=3, persist=None, settings=None,
param=None, grid=None, lghosts=False, indx=None,
unit=None, t=None, x=None, y=None, z=None, procdim=None,
quiet=True, lshear=False, driver=None, comm=None)
Keyword arguments:
*snapshot*:
Numpy array containing the snapshot.
Must be of shape [nvar, nz, ny, nx] without boundaries or.
Must be of shape [nvar, mz, my, mx] with boundaries for lghosts=True.
*file_name*:
Name of the snapshot file to be written, e.g. VAR0 or var.
*datadir*:
Directory where the data is stored.
*precision*:
Single 'f' or double 'd' precision.
*persist*:
optional dictionary of persistent variable.
*settings*:
optional dictionary of persistent variable.
*param*:
optional Param object.
*grid*:
optional Pencil Grid object of grid parameters.
*nghost*:
Number of ghost zones.
*lghosts*:
If True the snapshot contains the ghost zones.
*indx*
Index object of index for each variable in f-array
*unit*:
Optional dictionary of simulation units.
*quiet*:
Option to print output.
*t*:
Time of the snapshot.
*xyz*:
xyz arrays of the domain with ghost zones.
This will normally be obtained from Grid object, but facility to
redefine an alternative grid value.
*lshear*:
Flag for the shear.
*driver*
File driver for hdf5 io for use in serial or MPI parallel.
*comm*
MPI objects supplied if driver is 'mpio'.
*overwrite*
flag to replace existing h5 snapshot file.
*rank*
rank of process with root=0.
"""
import numpy as np
from os.path import join
from pencil import read
from pencil.io import open_h5, group_h5, dataset_h5
from pencil import is_sim_dir
# test if simulation directory
if not is_sim_dir():
print("ERROR: Directory needs to be a simulation")
sys.stdout.flush()
if indx == None:
indx = read.index()
#
if settings == None:
settings = {}
skeys = [
"l1",
"l2",
"m1",
"m2",
"n1",
"n2",
"nx",
"ny",
"nz",
"mx",
"my",
"mz",
"nprocx",
"nprocy",
"nprocz",
"maux",
"mglobal",
"mvar",
"precision",
]
dim = read.dim()
for key in skeys:
settings[key] = dim.__getattribute__(key)
settings["precision"] = precision.encode()
settings["nghost"] = nghost
settings["version"] = np.int32(0)
nprocs = settings["nprocx"] * settings["nprocy"] * settings["nprocz"]
gkeys = [
"x",
"y",
"z",
"Lx",
"Ly",
"Lz",
"dx",
"dy",
"dz",
"dx_1",
"dy_1",
"dz_1",
"dx_tilde",
"dy_tilde",
"dz_tilde",
]
if grid == None:
grid = read.grid(quiet=True)
else:
gd_err = False
for key in gkeys:
if not key in grid.__dict__.keys():
print("ERROR: key " + key + " missing from grid")
sys.stdout.flush()
gd_err = True
if gd_err:
print("ERROR: grid incomplete")
sys.stdout.flush()
ukeys = [
"length",
"velocity",
"density",
"magnetic",
"time",
"temperature",
"flux",
"energy",
"mass",
"system",
]
if param == None:
param = read.param(quiet=True)
param.__setattr__("unit_mass",
param.unit_density * param.unit_length**3)
param.__setattr__("unit_energy",
param.unit_mass * param.unit_velocity**2)
param.__setattr__("unit_time", param.unit_length / param.unit_velocity)
param.__setattr__("unit_flux", param.unit_mass / param.unit_time**3)
param.unit_system = param.unit_system.encode()
# check whether the snapshot matches the simulation shape
if lghosts:
try:
snapshot.shape[0] == settings["mvar"]
snapshot.shape[1] == settings["mx"]
snapshot.shape[2] == settings["my"]
snapshot.shape[3] == settings["mz"]
except ValueError:
print("ERROR: snapshot shape {} ".format(snapshot.shape) +
"does not match simulation dimensions with ghosts.")
sys.stdout.flush()
else:
try:
snapshot.shape[0] == settings["mvar"]
snapshot.shape[1] == settings["nx"]
snapshot.shape[2] == settings["ny"]
snapshot.shape[3] == settings["nz"]
except ValueError:
print("ERROR: snapshot shape {} ".format(snapshot.shape) +
"does not match simulation dimensions without ghosts.")
sys.stdout.flush()
# Determine the precision used and ensure snapshot has correct data_type.
if precision == "f":
data_type = np.float32
snapshot = np.float32(snapshot)
elif precision == "d":
data_type = np.float64
snapshot = np.float64(snapshot)
else:
print("ERROR: Precision {0} not understood.".format(precision) +
" Must be either 'f' or 'd'")
sys.stdout.flush()
return -1
# Check that the shape does not conflict with the proc numbers.
if ((settings["nx"] % settings["nprocx"] > 0)
or (settings["ny"] % settings["nprocy"] > 0)
or (settings["nz"] % settings["nprocz"] > 0)):
print("ERROR: Shape of the input array is not compatible with the " +
"cpu layout. Make sure that nproci devides ni.")
sys.stdout.flush()
return -1
# Check the shape of the xyz arrays if specified and overwrite grid values.
if x != None:
if len(x) != settings["mx"]:
print("ERROR: x array is incompatible with the shape of snapshot.")
sys.stdout.flush()
return -1
grid.x = data_type(x)
if y != None:
if len(y) != settings["my"]:
print("ERROR: y array is incompatible with the shape of snapshot.")
sys.stdout.flush()
return -1
grid.y = data_type(y)
if z != None:
if len(z) != settings["mz"]:
print("ERROR: z array is incompatible with the shape of snapshot.")
sys.stdout.flush()
return -1
grid.z = data_type(z)
# Define a time.
if t is None:
t = data_type(0.0)
# making use of pc_hdf5 functionality:
if not proc == None:
state = "a"
else:
state = "w"
filename = join(datadir, file_name)
print("write_h5_snapshot: filename =", filename)
with open_h5(
filename,
state,
driver=driver,
comm=comm,
overwrite=overwrite,
rank=rank,
size=size,
) as ds:
data_grp = group_h5(
ds,
"data",
status=state,
delete=False,
overwrite=overwrite,
rank=rank,
size=size,
)
if not procdim:
for key in indx.__dict__.keys():
if key in ["uu", "keys", "aa", "KR_Frad", "uun", "gg", "bb"]:
continue
#create ghost zones if required
if not lghosts:
tmp_arr = np.zeros([
snapshot.shape[1] + 2 * nghost,
snapshot.shape[2] + 2 * nghost,
snapshot.shape[3] + 2 * nghost,
])
tmp_arr[dim.n1:dim.n2 + 1, dim.m1:dim.m2 + 1,
dim.l1:dim.l2 + 1] = np.array(
snapshot[indx.__getattribute__(key) - 1])
dataset_h5(
data_grp,
key,
status=state,
data=tmp_arr,
dtype=data_type,
overwrite=overwrite,
rank=rank,
comm=comm,
size=size,
)
else:
dataset_h5(
data_grp,
key,
status=state,
data=np.array(snapshot[indx.__getattribute__(key) -
1]),
dtype=data_type,
overwrite=overwrite,
rank=rank,
comm=comm,
size=size,
)
else:
for key in indx.__dict__.keys():
if key in ["uu", "keys", "aa", "KR_Frad", "uun", "gg", "bb"]:
continue
dataset_h5(
data_grp,
key,
status=state,
shape=(settings["mz"], settings["my"], settings["mx"]),
dtype=data_type,
rank=rank,
comm=comm,
size=size,
)
# adjust indices to include ghost zones at boundaries
l1, m1, n1 = procdim.l1, procdim.m1, procdim.n1
if procdim.ipx == 0:
l1 = 0
if procdim.ipy == 0:
m1 = 0
if procdim.ipz == 0:
n1 = 0
l2, m2, n2 = procdim.l2, procdim.m2, procdim.n2
if procdim.ipx == settings["nprocx"] - 1:
l2 = procdim.l2 + settings["nghost"]
if procdim.ipy == settings["nprocy"] - 1:
m2 = procdim.m2 + settings["nghost"]
if procdim.ipz == settings["nprocz"] - 1:
n2 = procdim.n2 + settings["nghost"]
nx, ny, nz = procdim.nx, procdim.ny, procdim.nz
ipx, ipy, ipz = procdim.ipx, procdim.ipy, procdim.ipz
for key in indx.__dict__.keys():
if key in ["uu", "keys", "aa", "KR_Frad", "uun", "gg", "bb"]:
continue
tmp_arr = np.array(snapshot[indx.__getattribute__(key) - 1])
data_grp[key][n1 + ipz * nz:n2 + ipz * nz + 1,
m1 + ipy * ny:m2 + ipy * ny + 1,
l1 + ipx * nx:l2 + ipx * nx +
1, ] = tmp_arr[n1:n2 + 1, m1:m2 + 1, l1:l2 + 1]
dataset_h5(
ds,
"time",
status=state,
data=np.array(t),
size=size,
dtype=data_type,
rank=rank,
comm=comm,
overwrite=overwrite,
)
# add settings
sets_grp = group_h5(
ds,
"settings",
status=state,
delete=False,
overwrite=overwrite,
rank=rank,
size=size,
)
for key in settings.keys():
if "precision" in key:
dataset_h5(
sets_grp,
key,
status=state,
data=(settings[key], ),
dtype=None,
rank=rank,
comm=comm,
size=size,
overwrite=overwrite,
)
else:
dataset_h5(
sets_grp,
key,
status=state,
data=(settings[key], ),
dtype=data_type,
rank=rank,
comm=comm,
size=size,
overwrite=overwrite,
)
# add grid
grid_grp = group_h5(
ds,
"grid",
status=state,
delete=False,
overwrite=overwrite,
rank=rank,
size=size,
)
for key in gkeys:
dataset_h5(
grid_grp,
key,
status=state,
data=(grid.__getattribute__(key)),
dtype=data_type,
rank=rank,
comm=comm,
size=size,
overwrite=overwrite,
)
dataset_h5(
grid_grp,
"Ox",
status=state,
data=(param.__getattribute__("xyz0")[0], ),
dtype=data_type,
rank=rank,
comm=comm,
size=size,
overwrite=overwrite,
)
dataset_h5(
grid_grp,
"Oy",
status=state,
data=(param.__getattribute__("xyz0")[1], ),
dtype=data_type,
rank=rank,
comm=comm,
size=size,
overwrite=overwrite,
)
dataset_h5(
grid_grp,
"Oz",
status=state,
data=(param.__getattribute__("xyz0")[2], ),
dtype=data_type,
rank=rank,
comm=comm,
size=size,
overwrite=overwrite,
)
# add physical units
unit_grp = group_h5(
ds,
"unit",
status=state,
delete=False,
overwrite=overwrite,
rank=rank,
size=size,
)
for key in ukeys:
if "system" in key:
dataset_h5(
unit_grp,
key,
status=state,
data=(param.__getattribute__("unit_" + key), ),
rank=rank,
comm=comm,
size=size,
overwrite=overwrite,
)
else:
dataset_h5(
unit_grp,
key,
status=state,
data=param.__getattribute__("unit_" + key),
rank=rank,
comm=comm,
size=size,
overwrite=overwrite,
)
# add optional persistent data
if persist != None:
pers_grp = group_h5(
ds,
"persist",
status=state,
size=size,
delete=False,
overwrite=overwrite,
rank=rank,
)
for key in persist.keys():
if not quiet:
print(key, type(persist[key][()]))
sys.stdout.flush()
arr = np.empty(nprocs, dtype=type(persist[key][()]))
arr[:] = persist[key][()]
dataset_h5(
pers_grp,
key,
status=state,
data=(arr),
size=size,
dtype=data_type,
rank=rank,
comm=comm,
overwrite=overwrite,
)
def write_h5_grid(
file_name="grid",
datadir="data",
precision="d",
nghost=3,
settings=None,
param=None,
grid=None,
unit=None,
quiet=True,
driver=None,
comm=None,
overwrite=False,
rank=0,
):
"""
Write the grid information as hdf5.
We assume by default that a run simulation directory has already been
constructed, but start has not been executed in h5 format so that
binary sim files dim, grid and param files are already present in the sim
directory, or provided from an old binary sim source directory as inputs.
call signature:
write_h5_grid(file_name='grid', datadir='data', precision='d', nghost=3,
settings=None, param=None, grid=None, unit=None, quiet=True,
driver=None, comm=None)
Keyword arguments:
*file_name*:
Prefix of the file name to be written, 'grid'.
*datadir*:
Directory where 'grid.h5' is stored.
*precision*:
Single 'f' or double 'd' precision.
*nghost*:
Number of ghost zones.
*settings*:
Optional dictionary of persistent variable.
*param*:
Optional Param object.
*grid*:
Optional Pencil Grid object of grid parameters.
*unit*:
Optional dictionary of simulation units.
*quiet*:
Option to print output.
"""
from os.path import join
import numpy as np
from pencil import read
from pencil.io import open_h5, group_h5, dataset_h5
from pencil import is_sim_dir
# test if simulation directory
if not is_sim_dir():
print("ERROR: Directory needs to be a simulation")
sys.stdout.flush()
#
if settings == None:
settings = {}
skeys = [
"l1",
"l2",
"m1",
"m2",
"n1",
"n2",
"nx",
"ny",
"nz",
"mx",
"my",
"mz",
"nprocx",
"nprocy",
"nprocz",
"maux",
"mglobal",
"mvar",
"precision",
]
dim = read.dim()
for key in skeys:
settings[key] = dim.__getattribute__(key)
settings["precision"] = precision.encode()
settings["nghost"] = nghost
settings["version"] = np.int32(0)
gkeys = [
"x",
"y",
"z",
"Lx",
"Ly",
"Lz",
"dx",
"dy",
"dz",
"dx_1",
"dy_1",
"dz_1",
"dx_tilde",
"dy_tilde",
"dz_tilde",
]
if grid == None:
grid = read.grid(quiet=True)
else:
gd_err = False
for key in gkeys:
if not key in grid.__dict__.keys():
print("ERROR: key " + key + " missing from grid")
sys.stdout.flush()
gd_err = True
if gd_err:
print("ERROR: grid incomplete")
sys.stdout.flush()
ukeys = [
"length",
"velocity",
"density",
"magnetic",
"time",
"temperature",
"flux",
"energy",
"mass",
"system",
]
if param == None:
param = read.param(quiet=True)
param.__setattr__("unit_mass",
param.unit_density * param.unit_length**3)
param.__setattr__("unit_energy",
param.unit_mass * param.unit_velocity**2)
param.__setattr__("unit_time", param.unit_length / param.unit_velocity)
param.__setattr__("unit_flux", param.unit_mass / param.unit_time**3)
param.unit_system = param.unit_system.encode()
# open file for writing data
filename = join(datadir, file_name + ".h5")
with open_h5(filename,
"w",
driver=driver,
comm=comm,
overwrite=overwrite,
rank=rank) as ds:
# add settings
sets_grp = group_h5(ds, "settings", status="w")
for key in settings.keys():
if "precision" in key:
dataset_h5(sets_grp, key, status="w", data=(settings[key], ))
else:
dataset_h5(sets_grp, key, status="w", data=(settings[key], ))
# add grid
grid_grp = group_h5(ds, "grid", status="w")
for key in gkeys:
dataset_h5(grid_grp,
key,
status="w",
data=(grid.__getattribute__(key)))
dataset_h5(grid_grp,
"Ox",
status="w",
data=(param.__getattribute__("xyz0")[0], ))
dataset_h5(grid_grp,
"Oy",
status="w",
data=(param.__getattribute__("xyz0")[1], ))
dataset_h5(grid_grp,
"Oz",
status="w",
data=(param.__getattribute__("xyz0")[2], ))
# add physical units
unit_grp = group_h5(ds, "unit", status="w")
for key in ukeys:
if "system" in key:
dataset_h5(
unit_grp,
key,
status="w",
data=(param.__getattribute__("unit_" + key), ),
)
else:
dataset_h5(
unit_grp,
key,
status="w",
data=param.__getattribute__("unit_" + key),
)
def sim2h5(
newdir=".",
olddir=".",
varfile_names=None,
todatadir="data/allprocs",
fromdatadir="data",
precision="d",
nghost=3,
lpersist=True,
x=None,
y=None,
z=None,
lshear=False,
snap_by_proc=False,
aver_by_proc=False,
lremove_old_snapshots=False,
lremove_old_slices=False,
lread_all_videoslices=False,
vlarge=100000000,
lremove_old_averages=False,
execute=False,
quiet=True,
l2D=True,
lvars=True,
lvids=True,
laver=True,
laver2D=False,
lremove_deprecated_vids=False,
lsplit_slices=False,
):
"""
Copy a simulation object written in Fortran binary to hdf5.
The default is to copy all snapshots from/to the current simulation
directory. Optionally the old files can be removed to
call signature:
sim2h5(newdir='.', olddir='.', varfile_names=None,
todatadir='data/allprocs', fromdatadir='data',
precision='d', nghost=3, lpersist=False,
x=None, y=None, z=None, lshear=False,
snap_by_proc=False, aver_by_proc=False,
lremove_old_snapshots=False,
lremove_old_slices=False, lread_all_videoslices=True,
lremove_old_averages=False, execute=False, quiet=True,
l2D=True, lvars=True, lvids=True, laver=True)
Keyword arguments:
*olddir*:
String path to simulation source directory.
Path may be relative or absolute.
*newdir*:
String path to simulation destination directory.
Path may be relative or absolute.
*varfile_names*:
A list of names of the snapshot files to be written, e.g. VAR0
If None all varfiles in olddir+'/data/proc0/' will be converted
*todatadir*:
Directory to which the data is stored.
*fromdatadir*:
Directory from which the data is collected.
*precision*:
Single 'f' or double 'd' precision for new data.
*nghost*:
Number of ghost zones.
TODO: handle switching size of ghost zones.
*lpersist*:
option to include persistent variables from snapshots.
*xyz*:
xyz arrays of the domain with ghost zones.
This will normally be obtained from Grid object, but facility to
redefine an alternative grid value.
*lshear*:
Flag for the shear.
*execute*:
optional confirmation required if lremove_old.
*lremove_old_snapshots*:
If True the old snapshot data will be deleted once the new h5 data
has been saved.
*lremove_old_slices*:
If True the old video slice data will be deleted once the new h5 data
has been saved.
*lremove_old_averages*:
If True the old averages data will be deleted once the new h5 data
has been saved.
*aver_by_proc*
Option to read old binary files by processor and write in
parallel
*laver2D*
If True apply to each plane_list 'y', 'z' and load each variable
sequentially
*l_mpi*:
Applying MPI parallel process
*driver*:
HDF5 file io driver either None or mpio
*comm*:
MPI library calls
*rank*:
Integer ID of processor
*size*:
Number of MPI processes
"""
import glob
import numpy as np
import os
from os.path import exists, join
import subprocess as sub
import sys
from .. import read
from .. import sim
from . import write_h5_grid
from pencil.util import is_sim_dir
try:
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
driver = "mpio"
l_mpi = True
l_mpi = l_mpi and (size != 1)
except ImportError:
comm = None
driver = None
rank = 0
size = 1
l_mpi = False
if not l_mpi:
comm = None
driver = None
print("rank {} and size {}".format(rank, size))
sys.stdout.flush()
if rank == size - 1:
print("l_mpi", l_mpi)
sys.stdout.flush()
# test if simulation directories
if newdir == ".":
newdir = os.getcwd()
if olddir == ".":
olddir = os.getcwd()
os.chdir(olddir)
if not is_sim_dir():
if rank == 0:
print("ERROR: Directory (" + olddir + ") needs to be a simulation")
sys.stdout.flush()
return -1
if newdir != olddir:
if not exists(newdir):
cmd = "pc_newrun -s " + newdir
if rank == size - 1:
process = sub.Popen(cmd.split(), stdout=sub.PIPE)
output, error = process.communicate()
print(cmd, output, error)
# os.system(cmd)
if comm:
comm.Barrier()
os.chdir(newdir)
if not is_sim_dir():
if rank == 0:
print("ERROR: Directory (" + newdir +
") needs to be a simulation")
sys.stdout.flush()
return -1
#
lremove_old = lremove_old_snapshots or lremove_old_slices or lremove_old_averages
if lremove_old:
if not execute:
os.chdir(olddir)
if rank == 0:
print("WARNING: Are you sure you wish to remove the Fortran" +
" binary files from \n" + os.getcwd() + ".\n" +
"Set execute=True to proceed.")
sys.stdout.flush()
return -1
os.chdir(olddir)
if lvars:
if varfile_names == None:
os.chdir(fromdatadir + "/proc0")
lVARd = False
varfiled_names = natural_sort(glob.glob("VARd*"))
if len(varfiled_names) > 0:
varfile_names = natural_sort(glob.glob("VAR*"))
for iv in range(len(varfile_names) - 1, -1, -1):
if "VARd" in varfile_names[iv]:
varfile_names.remove(varfile_names[iv])
lVARd = True
else:
varfile_names = natural_sort(glob.glob("VAR*"))
os.chdir(olddir)
else:
lVARd = False
if isinstance(varfile_names, list):
varfile_names = varfile_names
else:
varfile_names = [varfile_names]
varfiled_names = []
tmp_names = []
for varfile_name in varfile_names:
if "VARd" in varfile_names:
varfiled_names.append(varfile_name)
lVARd = True
else:
tmp_names.append(varfile_name)
varfile_names = tmp_names
gkeys = [
"x",
"y",
"z",
"Lx",
"Ly",
"Lz",
"dx",
"dy",
"dz",
"dx_1",
"dy_1",
"dz_1",
"dx_tilde",
"dy_tilde",
"dz_tilde",
]
grid = None
if rank == size - 1:
grid = read.grid(quiet=True)
if l_mpi:
grid = comm.bcast(grid, root=size - 1)
if not quiet:
print(rank, grid)
sys.stdout.flush()
for key in gkeys:
if not key in grid.__dict__.keys():
if rank == 0:
print("ERROR: key " + key + " missing from grid")
sys.stdout.flush()
return -1
# obtain the settings from the old simulation
settings = {}
skeys = [
"l1",
"l2",
"m1",
"m2",
"n1",
"n2",
"nx",
"ny",
"nz",
"mx",
"my",
"mz",
"nprocx",
"nprocy",
"nprocz",
"maux",
"mglobal",
"mvar",
"precision",
]
if rank == 0:
olddim = read.dim()
for key in skeys:
settings[key] = np.array(olddim.__getattribute__(key))
olddim = None
settings["nghost"] = np.array(nghost)
settings["precision"] = precision.encode()
if l_mpi:
settings = comm.bcast(settings, root=0)
if snap_by_proc:
nprocs = settings["nprocx"] * settings["nprocy"] * settings["nprocz"]
if np.mod(nprocs, size) != 0:
print("WARNING: efficiency requires cpus to divide ncpus")
sys.stdout.flush()
if not quiet:
print(rank, grid)
sys.stdout.flush()
# obtain physical units from old simulation
ukeys = [
"length",
"velocity",
"density",
"magnetic",
"time",
"temperature",
"flux",
"energy",
"mass",
"system",
]
param = read.param(quiet=True)
param.__setattr__("unit_mass", param.unit_density * param.unit_length**3)
param.__setattr__("unit_energy", param.unit_mass * param.unit_velocity**2)
param.__setattr__("unit_time", param.unit_length / param.unit_velocity)
param.__setattr__("unit_flux", param.unit_mass / param.unit_time**3)
param.unit_system = param.unit_system.encode()
# index list for variables in f-array
if not quiet:
print(rank, param)
sys.stdout.flush()
indx = None
if rank == 0:
indx = read.index()
if l_mpi:
indx = comm.bcast(indx, root=0)
# check consistency between Fortran binary and h5 data
os.chdir(newdir)
dim = None
if is_sim_dir():
if rank == size - 1:
if exists(join(newdir, "data", "dim.dat")):
try:
dim = read.dim()
except ValueError:
pass
if l_mpi:
dim = comm.bcast(dim, root=size - 1)
if dim:
if not quiet:
print(rank, dim)
sys.stdout.flush()
try:
dim.mvar == settings["mvar"]
dim.mx == settings["mx"]
dim.my == settings["my"]
dim.mz == settings["mz"]
except ValueError:
if rank == size - 1:
print("ERROR: new simulation dimensions do not match.")
sys.stdout.flush()
return -1
dim = None
os.chdir(olddir)
if rank == size - 1:
print("precision is ", precision)
sys.stdout.flush()
if laver2D:
aver2h5(
newdir,
olddir,
todatadir="data/averages",
fromdatadir="data",
l2D=False,
precision=precision,
quiet=quiet,
laver2D=laver2D,
lremove_old_averages=False,
aver_by_proc=aver_by_proc,
l_mpi=l_mpi,
driver=driver,
comm=comm,
rank=rank,
size=size,
)
l2D = False
# copy snapshots
if lvars and len(varfile_names) > 0:
var2h5(
newdir,
olddir,
varfile_names,
todatadir,
fromdatadir,
snap_by_proc,
precision,
lpersist,
quiet,
nghost,
settings,
param,
grid,
x,
y,
z,
lshear,
lremove_old_snapshots,
indx,
l_mpi=l_mpi,
driver=driver,
comm=comm,
rank=rank,
size=size,
)
# copy downsampled snapshots if present
if lvars and lVARd:
var2h5(
newdir,
olddir,
varfiled_names,
todatadir,
fromdatadir,
False,
precision,
lpersist,
quiet,
nghost,
settings,
param,
grid,
x,
y,
z,
lshear,
lremove_old_snapshots,
indx,
trimall=True,
l_mpi=l_mpi,
driver=driver,
comm=comm,
rank=rank,
size=size,
)
if lvars:
var2h5(
newdir,
olddir,
[
"var.dat",
],
todatadir,
fromdatadir,
snap_by_proc,
precision,
lpersist,
quiet,
nghost,
settings,
param,
grid,
x,
y,
z,
lshear,
lremove_old_snapshots,
indx,
l_mpi=l_mpi,
driver=driver,
comm=comm,
rank=rank,
size=size,
)
# copy old video slices to new h5 sim
if lvids:
if lremove_deprecated_vids:
for ext in [
"bb.", "uu.", "ux.", "uy.", "uz.", "bx.", "by.", "bz."
]:
cmd = "rm -f " + join(olddir, fromdatadir, "proc*",
"slice_" + ext + "*")
if rank == 0:
process = sub.Popen(cmd.split(), stdout=sub.PIPE)
output, error = process.communicate()
print(cmd, output, error)
cmd = "rm -f " + join(fromdatadir, "slice_" + ext + "*")
if rank == 0:
process = sub.Popen(cmd.split(), stdout=sub.PIPE)
output, error = process.communicate()
print(cmd, output, error)
if comm:
comm.Barrier()
cmd = "src/read_all_videofiles.x"
if rank == size - 1 and lread_all_videoslices:
process = sub.Popen(cmd.split(), stdout=sub.PIPE)
output, error = process.communicate()
print(cmd, output, error)
if comm:
comm.Barrier()
slices2h5(
newdir,
olddir,
grid,
todatadir="data/slices",
fromdatadir=fromdatadir,
precision=precision,
quiet=quiet,
vlarge=vlarge,
lsplit_slices=lsplit_slices,
lremove_old_slices=lremove_old_slices,
l_mpi=l_mpi,
driver=driver,
comm=comm,
rank=rank,
size=size,
)
# copy old averages data to new h5 sim
if laver:
aver2h5(
newdir,
olddir,
todatadir="data/averages",
fromdatadir=fromdatadir,
l2D=l2D,
precision=precision,
quiet=quiet,
aver_by_proc=False,
lremove_old_averages=lremove_old_averages,
l_mpi=l_mpi,
driver=driver,
comm=comm,
rank=rank,
size=size,
)
# check some critical sim files are present for new sim without start
# construct grid.h5 sim information if requied for new h5 sim
os.chdir(newdir)
if l_mpi:
comm.Barrier()
if rank == 0:
write_h5_grid(
file_name="grid",
datadir="data",
precision=precision,
nghost=nghost,
settings=settings,
param=param,
grid=grid,
unit=None,
quiet=quiet,
)
source_file = join(olddir, fromdatadir, "proc0/varN.list")
target_file = join(newdir, todatadir, "varN.list")
if exists(source_file):
cmd = "cp " + source_file + " " + target_file
process = sub.Popen(cmd.split(), stdout=sub.PIPE)
output, error = process.communicate()
print(cmd, output, error)
items = [
"def_var.pro",
"index.pro",
"jobid.dat",
"param.nml",
"particle_index.pro",
"pc_constants.pro",
"pointmass_index.pro",
"pt_positions.dat",
"sn_series.dat",
"svnid.dat",
"time_series.dat",
"tsnap.dat",
"tspec.dat",
"tvid.dat",
"t2davg.dat",
"var.general",
"variables.pro",
"varname.dat",
]
for item in items:
source_file = join(olddir, fromdatadir, item)
target_file = join(newdir, fromdatadir, item)
if exists(source_file):
if not exists(target_file):
cmd = "cp " + source_file + " " + target_file
process = sub.Popen(cmd.split(), stdout=sub.PIPE)
output, error = process.communicate()
print(cmd, output, error)
print("Simulation Fortran to h5 completed on rank {}.".format(rank))
sys.stdout.flush()
def find_tracers(self, var_file='VAR0', datadir='data', trace_field='bb',
ti=-1, tf=-1):
"""
Trace streamlines of the vectofield 'field' from z = z0 to z = z1
and integrate quantities 'int_q' along the lines. Creates a 2d
mapping as in 'streamlines.f90'.
call signature:
find_tracers(var_file='VAR0', datadir='data', trace_field='bb',
ti=-1, tf=-1)
Keyword arguments:
*var_file*:
Varfile to be read.
*datadir*:
Directory where the data is stored.
*trace_field*:
Vector field used for the streamline tracing.
*ti*:
Initial VAR file index for tracer time sequences. Overrides 'var_file'.
*tf*:
Final VAR file index for tracer time sequences. Overrides 'var_file'.
"""
import numpy as np
import multiprocessing as mp
from pencil import read
from pencil import math
# Write the tracing parameters.
self.params.trace_field = trace_field
self.params.datadir = datadir
# Multi core setup.
if not(np.isscalar(self.params.n_proc)) or (self.params.n_proc%1 != 0):
print("error: invalid processor number")
return -1
queue = mp.Queue()
# Read the data.
magic = []
if trace_field == 'bb':
magic.append('bb')
if trace_field == 'jj':
magic.append('jj')
if trace_field == 'vort':
magic.append('vort')
if self.params.int_q == 'ee':
magic.append('bb')
magic.append('jj')
dim = read.dim(datadir=datadir)
self.params.var_file = var_file
# Check if user wants a tracer time series.
if (ti%1 == 0) and (tf%1 == 0) and (ti >= 0) and (tf >= ti):
series = True
nTimes = tf-ti+1
else:
series = False
nTimes = 1
# Initialize the arrays.
self.x0 = np.zeros([int(self.params.trace_sub*dim.nx),
int(self.params.trace_sub*dim.ny), nTimes])
self.y0 = np.zeros([int(self.params.trace_sub*dim.nx),
int(self.params.trace_sub*dim.ny), nTimes])
self.x1 = np.zeros([int(self.params.trace_sub*dim.nx),
int(self.params.trace_sub*dim.ny), nTimes])
self.y1 = np.zeros([int(self.params.trace_sub*dim.nx),
int(self.params.trace_sub*dim.ny), nTimes])
self.z1 = np.zeros([int(self.params.trace_sub*dim.nx),
int(self.params.trace_sub*dim.ny), nTimes])
self.l = np.zeros([int(self.params.trace_sub*dim.nx),
int(self.params.trace_sub*dim.ny), nTimes])
if self.params.int_q == 'curly_A':
self.curly_A = np.zeros([int(self.params.trace_sub*dim.nx),
int(self.params.trace_sub*dim.ny), nTimes])
if self.params.int_q == 'ee':
self.ee = np.zeros([int(self.params.trace_sub*dim.nx),
int(self.params.trace_sub*dim.ny), nTimes])
self.mapping = np.zeros([int(self.params.trace_sub*dim.nx),
int(self.params.trace_sub*dim.ny),
nTimes, 3])
self.t = np.zeros(nTimes)
for t_idx in range(ti, tf+1):
if series:
var_file = 'VAR' + str(t_idx)
# Read the data.
var = read.var(var_file=var_file, datadir=datadir, magic=magic,
quiet=True, trimall=True)
grid = read.grid(datadir=datadir, quiet=True, trim=True)
param2 = read.param(datadir=datadir, quiet=True)
self.t[t_idx] = var.t
# Extract the requested vector trace_field.
field = getattr(var, trace_field)
if self.params.int_q == 'curly_A':
self.aa = var.aa
if self.params.int_q == 'ee':
self.ee = var.jj*param2.eta - math.cross(var.uu, var.bb)
# Get the simulation parameters.
self.params.dx = var.dx
self.params.dy = var.dy
self.params.dz = var.dz
self.params.Ox = var.x[0]
self.params.Oy = var.y[0]
self.params.Oz = var.z[0]
self.params.Lx = grid.Lx
self.params.Ly = grid.Ly
self.params.Lz = grid.Lz
self.params.nx = dim.nx
self.params.ny = dim.ny
self.params.nz = dim.nz
# Initialize the tracers.
for ix in range(int(self.params.trace_sub*dim.nx)):
for iy in range(int(self.params.trace_sub*dim.ny)):
self.x0[ix, iy, t_idx] = grid.x[0] + grid.dx/self.params.trace_sub*ix
self.x1[ix, iy, t_idx] = self.x0[ix, iy, t_idx].copy()
self.y0[ix, iy, t_idx] = grid.y[0] + grid.dy/self.params.trace_sub*iy
self.y1[ix, iy, t_idx] = self.y0[ix, iy, t_idx].copy()
self.z1[ix, iy, t_idx] = grid.z[0]
proc = []
sub_data = []
for i_proc in range(self.params.n_proc):
proc.append(mp.Process(target=self.__sub_tracers,
args=(queue, field, t_idx, i_proc, self.params.n_proc)))
for i_proc in range(self.params.n_proc):
proc[i_proc].start()
for i_proc in range(self.params.n_proc):
sub_data.append(queue.get())
for i_proc in range(self.params.n_proc):
proc[i_proc].join()
for i_proc in range(self.params.n_proc):
# Extract the data from the single cores. Mind the order.
sub_proc = sub_data[i_proc][0]
self.x1[sub_proc::self.params.n_proc, :, t_idx] = sub_data[i_proc][1]
self.y1[sub_proc::self.params.n_proc, :, t_idx] = sub_data[i_proc][2]
self.z1[sub_proc::self.params.n_proc, :, t_idx] = sub_data[i_proc][3]
self.l[sub_proc::self.params.n_proc, :, t_idx] = sub_data[i_proc][4]
self.mapping[sub_proc::self.params.n_proc, :, t_idx, :] = sub_data[i_proc][5]
if self.params.int_q == 'curly_A':
self.curly_A[sub_proc::self.params.n_proc, :, t_idx] = sub_data[i_proc][6]
if self.params.int_q == 'ee':
self.ee[sub_proc::self.params.n_proc, :, t_idx] = sub_data[i_proc][7]
for i_proc in range(self.params.n_proc):
proc[i_proc].terminate()
return 0
def slices2vtk(field='',
extension='',
datadir='data',
destination='slices',
proc=-1):
"""
Convert slices from PencilCode format to vtk.
call signature::
slices2vtk(field='', extension='', datadir='data', destination='slices', proc=-1)
Read slice files specified by *variables* and convert
them into vtk format for the specified extensions.
Write the result in *destination*.
NB: You need to have called src/read_videofiles.x before using this script.
Keyword arguments:
*field*:
All allowed fields which can be written as slice files, e.g. b2, uu1, lnrho, ...
See the pencil code manual for more (chapter: "List of parameters for `video.in'").
*extension*:
List of slice positions.
*datadir*:
Directory where the data is stored.
*destination*:
Destination files.
*proc*:
Processor which should be read. Set to -1 for all processors.
"""
import sys
import numpy as np
from pencil import read
# Convert single variable string into length 1 list of arrays.
if (len(field) > 0):
if (len(field[0]) == 1):
field = [field]
if (len(extension) > 0):
if (len(extension[0]) == 1):
extension = [extension]
# Read the grid dimensions.
grid = read.grid(datadir=datadir, proc=proc, trim=True, quiet=True)
# Read the dimensions.
dim = read.dim(datadir=datadir, proc=proc)
# Read the user given parameters for the slice positions.
params = read.param(quiet=True)
# Read the slice file for all specified variables and extensions.
slices = read.slices(field=field,
extension=extension,
datadir=datadir,
proc=proc)
# Determine the position of the slices.
if params.ix != -1:
x0 = grid.x[params.ix]
elif params.slice_position == 'm':
x0 = grid.x[int(len(grid.x) / 2)]
if params.iy != -1:
y0 = grid.y[params.iy]
elif params.slice_position == 'm':
y0 = grid.y[int(len(grid.y) / 2)]
if params.iz != -1:
z0 = grid.z[params.iz]
elif params.slice_position == 'm':
z0 = grid.z[int(len(grid.z) / 2)]
if params.iz2 != -1:
z02 = grid.z[params.iz]
elif params.slice_position == 'm':
z02 = grid.z[int(len(grid.z) / 2)]
for t_idx, t in enumerate(slices.t):
for ext in extension:
# Open the destination file for writing.
fd = open(destination + '_' + ext + '_' + str(t_idx) + '.vtk',
'wb')
# Write the header.
fd.write('# vtk DataFile Version 2.0\n'.encode('utf-8'))
fd.write('slices {0}\n'.format(ext).encode('utf-8'))
fd.write('BINARY\n'.encode('utf-8'))
fd.write('DATASET STRUCTURED_POINTS\n'.encode('utf-8'))
if ext == 'xy':
fd.write('DIMENSIONS {0:9} {1:9} {2:9}\n'.format(
dim.nx, dim.ny, 1).encode('utf-8'))
fd.write('ORIGIN {0:8.12} {1:8.12} {2:8.12}\n'.format(
grid.x[0], grid.y[0], z0).encode('utf-8'))
fd.write('SPACING {0:8.12} {1:8.12} {2:8.12}\n'.format(
grid.dx, grid.dy, 1.).encode('utf-8'))
dim_p = dim.nx
dim_q = dim.ny
if ext == 'xy2':
fd.write('DIMENSIONS {0:9} {1:9} {2:9}\n'.format(
dim.nx, dim.ny, 1).encode('utf-8'))
fd.write('ORIGIN {0:8.12} {1:8.12} {2:8.12}\n'.format(
grid.x[0], grid.y[0], z02).encode('utf-8'))
fd.write('SPACING {0:8.12} {1:8.12} {2:8.12}\n'.format(
grid.dx, grid.dy, 1.).encode('utf-8'))
dim_p = dim.nx
dim_q = dim.ny
if ext == 'xz':
fd.write('DIMENSIONS {0:9} {1:9} {2:9}\n'.format(
dim.nx, 1, dim.nz).encode('utf-8'))
fd.write('ORIGIN {0:8.12} {1:8.12} {2:8.12}\n'.format(
grid.x[0], y0, grid.z[0]).encode('utf-8'))
fd.write('SPACING {0:8.12} {1:8.12} {2:8.12}\n'.format(
grid.dx, 1., grid.dz).encode('utf-8'))
dim_p = dim.nx
dim_q = dim.nz
if ext == 'yz':
fd.write('DIMENSIONS {0:9} {1:9} {2:9}\n'.format(
1, dim.ny, dim.nz).encode('utf-8'))
fd.write('ORIGIN {0:8.12} {1:8.12} {2:8.12}\n'.format(
x0, grid.y[0], grid.z[0]).encode('utf-8'))
fd.write('SPACING {0:8.12} {1:8.12} {2:8.12}\n'.format(
1., grid.dy, grid.dz).encode('utf-8'))
dim_p = dim.ny
dim_q = dim.nz
fd.write('POINT_DATA {0:9}\n'.format(dim_p *
dim_q).encode('utf-8'))
# Write the data.
for fi in field:
data = getattr(getattr(slices, ext), fi)
fd.write(
('SCALARS ' + ext + '_' + fi + ' float\n').encode('utf-8'))
fd.write('LOOKUP_TABLE default\n'.encode('utf-8'))
if sys.byteorder == 'little':
data = data.astype(np.float32).byteswap()
else:
data = data.astype(np.float32)
fd.write(data[t_idx].tobytes())
fd.close()
def var2vtk(var_file='var.dat',
datadir='data',
proc=-1,
variables=None,
b_ext=False,
magic=[],
destination='work',
quiet=True,
trimall=True,
ti=-1,
tf=-1):
"""
Convert data from PencilCode format to vtk.
call signature::
var2vtk(var_file='', datadir='data', proc=-1,
variables='', b_ext=False,
destination='work', quiet=True, trimall=True, ti=-1, tf=-1)
Read *var_file* and convert its content into vtk format. Write the result
in *destination*.
Keyword arguments:
*var_file*:
The original var_file.
*datadir*:
Directory where the data is stored.
*proc*:
Processor which should be read. Set to -1 for all processors.
*variables*:
List of variables which should be written. If None all.
*b_ext*:
Add the external magnetic field.
*destination*:
Destination file.
*quiet*:
Keep quiet when reading the var files.
*trimall*:
Trim the data cube to exclude ghost zones.
*ti, tf*:
Start and end index for animation. Leave negative for no animation.
Overwrites variable var_file.
"""
import numpy as np
import sys
from pencil import read
from pencil import math
# Determine of we want an animation.
if ti < 0 or tf < 0:
animation = False
else:
animation = True
# If no variables specified collect all by default
if not variables:
variables = []
indx = read.index()
for key in indx.__dict__.keys():
if 'keys' not in key:
variables.append(key)
if 'uu' in variables:
magic.append('vort')
variables.append('vort')
if 'rho' in variables or 'lnrho' in variables:
if 'ss' in variables:
magic.append('tt')
variables.append('tt')
magic.append('pp')
variables.append('pp')
if 'aa' in variables:
magic.append('bb')
variables.append('bb')
magic.append('jj')
variables.append('jj')
variables.append('ab')
variables.append('b_mag')
variables.append('j_mag')
else:
# Convert single variable string into length 1 list of arrays.
if (len(variables) > 0):
if (len(variables[0]) == 1):
variables = [variables]
if 'tt' in variables:
magic.append('tt')
if 'pp' in variables:
magic.append('pp')
if 'bb' in variables:
magic.append('bb')
if 'jj' in variables:
magic.append('jj')
if 'vort' in variables:
magic.append('vort')
if 'b_mag' in variables and not 'bb' in magic:
magic.append('bb')
if 'j_mag' in variables and not 'jj' in magic:
magic.append('jj')
if 'ab' in variables and not 'bb' in magic:
magic.append('bb')
for t_idx in range(ti, tf + 1):
if animation:
var_file = 'VAR' + str(t_idx)
# Read the PencilCode variables and set the dimensions.
var = read.var(var_file=var_file,
datadir=datadir,
proc=proc,
magic=magic,
trimall=True,
quiet=quiet)
grid = read.grid(datadir=datadir, proc=proc, trim=trimall, quiet=True)
params = read.param(quiet=True)
# Add external magnetic field.
if (b_ext == True):
B_ext = np.array(params.b_ext)
var.bb[0, ...] += B_ext[0]
var.bb[1, ...] += B_ext[1]
var.bb[2, ...] += B_ext[2]
dimx = len(grid.x)
dimy = len(grid.y)
dimz = len(grid.z)
dim = dimx * dimy * dimz
dx = (np.max(grid.x) - np.min(grid.x)) / (dimx - 1)
dy = (np.max(grid.y) - np.min(grid.y)) / (dimy - 1)
dz = (np.max(grid.z) - np.min(grid.z)) / (dimz - 1)
# Write the vtk header.
if animation:
fd = open(destination + str(t_idx) + '.vtk', 'wb')
else:
fd = open(destination + '.vtk', 'wb')
fd.write('# vtk DataFile Version 2.0\n'.encode('utf-8'))
fd.write('VAR files\n'.encode('utf-8'))
fd.write('BINARY\n'.encode('utf-8'))
fd.write('DATASET STRUCTURED_POINTS\n'.encode('utf-8'))
fd.write('DIMENSIONS {0:9} {1:9} {2:9}\n'.format(dimx, dimy,
dimz).encode('utf-8'))
fd.write('ORIGIN {0:8.12} {1:8.12} {2:8.12}\n'.format(
grid.x[0], grid.y[0], grid.z[0]).encode('utf-8'))
fd.write('SPACING {0:8.12} {1:8.12} {2:8.12}\n'.format(
dx, dy, dz).encode('utf-8'))
fd.write('POINT_DATA {0:9}\n'.format(dim).encode('utf-8'))
# Write the data.
for v in variables:
print('Writing {0}.'.format(v))
# Prepare the data to the correct format.
if v == 'ab':
data = math.dot(var.aa, var.bb)
elif v == 'b_mag':
data = np.sqrt(math.dot2(var.bb))
elif v == 'j_mag':
data = np.sqrt(math.dot2(var.jj))
else:
data = getattr(var, v)
if sys.byteorder == 'little':
data = data.astype(np.float32).byteswap()
else:
data = data.astype(np.float32)
# Check if we have vectors or scalars.
if data.ndim == 4:
data = np.moveaxis(data, 0, 3)
fd.write('VECTORS {0} float\n'.format(v).encode('utf-8'))
else:
fd.write('SCALARS {0} float\n'.format(v).encode('utf-8'))
fd.write('LOOKUP_TABLE default\n'.encode('utf-8'))
fd.write(data.tobytes())
del (var)
fd.close()
def derive_data(sim_path,
src,
dst,
magic=['pp', 'tt'],
par=[],
comm=None,
gd=[],
overwrite=False,
rank=0,
size=1,
nghost=3,
status='a',
chunksize=1000.0,
dtype=np.float64,
quiet=True,
nmin=32):
if comm:
overwrite = False
if isinstance(par, list):
os.chdir(sim_path)
par = read.param(quiet=True, conflicts_quiet=True)
if isinstance(gd, list):
os.chdir(sim_path)
gd = read.grid(quiet=True)
#get data dimensions
nx, ny, nz = src['settings']['nx'][0],\
src['settings']['ny'][0],\
src['settings']['nz'][0]
mx, my, mz = src['settings']['mx'][0],\
src['settings']['my'][0],\
src['settings']['mz'][0]
#split data into manageable memory chunks
dstchunksize = 8 * nx * ny * nz / 1024 * 1024
if dstchunksize > chunksize:
nchunks = cpu_optimal(nx,
ny,
nz,
quiet=quiet,
mvar=src['settings/mvar'][0],
maux=src['settings/maux'][0],
MBmin=chunksize,
nmin=nmin,
size=size)[1]
else:
nchunks = [1, 1, 1]
print('nchunks {}'.format(nchunks))
# for mpi split chunks across processes
if size > 1:
locindx = np.array_split(np.arange(nx) + nghost, nchunks[0])
locindy = np.array_split(np.arange(ny) + nghost, nchunks[1])
locindz = np.array_split(np.arange(nz) + nghost, nchunks[2])
indx = [
locindx[np.mod(
rank + int(rank / nchunks[2]) + int(rank / nchunks[1]),
nchunks[0])]
]
indy = [locindy[np.mod(rank + int(rank / nchunks[2]), nchunks[1])]]
indz = [locindz[np.mod(rank, nchunks[2])]]
allchunks = 1
else:
locindx = np.array_split(np.arange(nx) + nghost, nchunks[0])
locindy = np.array_split(np.arange(ny) + nghost, nchunks[1])
locindz = np.array_split(np.arange(nz) + nghost, nchunks[2])
indx = np.array_split(np.arange(nx) + nghost, nchunks[0])
indy = np.array_split(np.arange(ny) + nghost, nchunks[1])
indz = np.array_split(np.arange(nz) + nghost, nchunks[2])
allchunks = nchunks[0] * nchunks[1] * nchunks[2]
# save time
dataset_h5(dst,
'time',
status=status,
data=src['time'][()],
comm=comm,
size=size,
rank=rank,
overwrite=overwrite,
dtype=dtype)
# ensure derived variables are in a list
if isinstance(magic, list):
magic = magic
else:
magic = [magic]
# initialise group
group = group_h5(dst,
'data',
status='a',
overwrite=overwrite,
comm=comm,
rank=rank,
size=size)
for key in magic:
if is_vector(key):
dataset_h5(group,
key,
status=status,
shape=[3, mz, my, mx],
comm=comm,
size=size,
rank=rank,
overwrite=overwrite,
dtype=dtype)
print('writing ' + key + ' shape {}'.format([3, mz, my, mx]))
else:
dataset_h5(group,
key,
status=status,
shape=[mz, my, mx],
comm=comm,
size=size,
rank=rank,
overwrite=overwrite,
dtype=dtype)
print('writing ' + key + ' shape {}'.format([mz, my, mx]))
for ichunk in range(allchunks):
for iz in [indz[np.mod(ichunk, nchunks[2])]]:
n1, n2 = iz[ 0]-nghost,\
iz[-1]+nghost+1
n1out = n1 + nghost
n2out = n2 - nghost
varn1 = nghost
varn2 = -nghost
if iz[0] == locindz[0][0]:
n1out = 0
varn1 = 0
if iz[-1] == locindz[-1][-1]:
n2out = n2
varn2 = n2
for iy in [
indy[np.mod(ichunk + int(ichunk / nchunks[2]),
nchunks[1])]
]:
m1, m2 = iy[ 0]-nghost,\
iy[-1]+nghost+1
m1out = m1 + nghost
m2out = m2 - nghost
varm1 = nghost
varm2 = -nghost
if iy[0] == locindy[0][0]:
m1out = 0
varm1 = 0
if iy[-1] == locindy[-1][-1]:
m2out = m2
varm2 = m2
for ix in [
indx[np.mod(
ichunk + int(ichunk / nchunks[2]) +
int(ichunk / nchunks[1]), nchunks[0])]
]:
l1, l2 = ix[ 0]-nghost,\
ix[-1]+nghost+1
l1out = l1 + nghost
l2out = l2 - nghost
varl1 = nghost
varl2 = -nghost
if ix[0] == locindx[0][0]:
l1out = 0
varl1 = 0
if ix[-1] == locindx[-1][-1]:
l2out = l2
varl2 = l2
if not quiet:
print('remeshing ' + key +
' chunk {}'.format([iz, iy, ix]))
var = calc_derived_data(src['data'],
dst['data'],
key,
par,
gd,
l1,
l2,
m1,
m2,
n1,
n2,
nghost=nghost)
#print('var shape {}'.format(var.shape))
#if not quiet:
# print('writing '+key+
# ' shape {} chunk {}'.format(
# var.shape, [iz,iy,ix]))
if is_vector(key):
dst['data'][key][:, n1out:n2out, m1out:m2out,
l1out:l2out] = dtype(
var[:, varn1:varn2,
varm1:varm2, varl1:varl2])
else:
dst['data'][key][n1out:n2out, m1out:m2out,
l1out:l2out] = dtype(
var[varn1:varn2, varm1:varm2,
varl1:varl2])
