def dispersion_and_drift(sim=False,
OVERWRITE=False,
GLOBAL=True,
LOCAL=True,
use_IDL=False,
recalculate_gas_velo_at_particle_pos=False):
"""This calculates the dispersion (sigma) and drift (zeta) locally and globally
by using the gas_velo_at_particle_pos script and dataset for all particles.
With sigma = sqrt( 1/N_par * sum_i^N_par( (v_par(i) - <v_par>)^2 ) ) and
zeta = sqrt( 1/N_par * sum_i^N_par( (v_par(i) - u(xp_i))^2 ) )
Arg:
OVERWRITE: set to True to overwrite already calculated results
GLOBAL: Calculate drift and dispersion globally, i.e. whole simulation domain
LOCAL: Calculate drift and dispersion locally, i.e. grid cell wise
recalculate_gas_velo_at_particle_pos:
if the dataset shall be recalcualted
use_IDL: use backup solution of IDL script and sav files
returns True if successfull
"""
from pencil import get_sim
from pencil import io
from pencil import read
from pencil.diag.particle import gas_velo_at_particle_pos
from scipy.io import readsav
from os import listdir
from os.path import exists, join, dirname
import numpy as np
if sim == False:
sim = get_sim()
if sim == False:
print('! ERROR: Specify simulation object!')
return False
SIM = sim
## calculate gas speed at particle position dataset
gas_velo_at_particle_pos(OVERWRITE=recalculate_gas_velo_at_particle_pos,
use_IDL=use_IDL,
sim=sim)
print(
'\n##################### Starting the whole calculation process of DISPERSON and DRIFT for '
+ SIM.name + ' #####################')
## default and setup
GASVELO_DESTINATION = 'gas_velo_at_particle_pos'
GASVELO_DIR = join(SIM.pc_datadir, GASVELO_DESTINATION)
## get list of available files
if use_IDL:
file_filetype = '.sav'
else:
file_filetype = '.pkl'
files = []
if exists(GASVELO_DIR):
files = [
i for i in listdir(GASVELO_DIR)
if i.startswith(GASVELO_DESTINATION) and i.endswith(file_filetype)
]
if files == []:
print(
'!! ERROR: No calc_gas_speed_at_particle_position-files found for '
+ SIM.name + '! Use idl script to produce them first!')
if use_IDL:
USE_PKL_FILES = False
files = [i.split('_')[-1].split(file_filetype)[0] for i in files]
scheme = ''
else:
USE_PKL_FILES = True
files = [i.split('_')[-1].split(file_filetype)[0] for i in files]
scheme = '_tsc'
## calculate global dispersion for all snapshot for which gas_velo_at_particle_pos files are found
for file_no in files:
print(
'## Starting the calculation for DISPERSON and DRIFT for ### VAR'
+ str(file_no) + ' ###')
# check if files already exist
if (not OVERWRITE) and io.exists(
'sigma_' + file_no, folder=SIM.pc_datadir) and io.exists(
'zeta_' + file_no, folder=SIM.pc_datadir) and io.exists(
'sigma_l_' + file_no,
folder=join(SIM.pc_datadir, 'sigma_l')) and io.exists(
'zeta_l_' + file_no,
folder=join(SIM.pc_datadir, 'zeta_l')):
print('## Skipping calculations')
continue
## read sav and var file
print('## reading gas_velo_at_particle_pos file and VAR')
if USE_PKL_FILES:
sav_file = io.load(
join(GASVELO_DIR, GASVELO_DESTINATION + scheme + '_' +
file_no + '.pkl'))[GASVELO_DESTINATION]
else:
sav_file = readsav(
join(GASVELO_DIR, GASVELO_DESTINATION + scheme + '_' +
file_no + '.sav'))[GASVELO_DESTINATION]
var_file = read.var(varfile='VAR' + file_no,
quiet=True,
trimall=True,
datadir=SIM.datadir)
## get everything ready
dim = SIM.dim
pdim = read.pdim(sim=SIM)
npar = pdim.npar
npar1 = 1. / npar
## get first quantities and setup the DATA_SET
if use_IDL:
time = sav_file['time'][0]
DATA_SET = np.core.records.fromarrays(
[
range(0, npar), sav_file['par_idx'][0][0].astype('int'),
sav_file['par_idx'][0][1].astype('int'),
sav_file['par_idx'][0][2].astype('int'),
sav_file['par_pos'][0][0], sav_file['par_pos'][0][1],
sav_file['par_pos'][0][2], sav_file['par_velo'][0][0],
sav_file['par_velo'][0][1], sav_file['par_velo'][0][2],
sav_file['npar'][0], sav_file['gas_velo'][0][0],
sav_file['gas_velo'][0][1], sav_file['gas_velo'][0][2],
var_file.rho[sav_file['par_idx'][0][2].astype('int'),
sav_file['par_idx'][0][1].astype('int'),
sav_file['par_idx'][0][0].astype('int')],
var_file.rhop[sav_file['par_idx'][0][2].astype('int'),
sav_file['par_idx'][0][1].astype('int'),
sav_file['par_idx'][0][0].astype('int')]
],
names=
'parid, idx, idy, idz, posx, posy, posz, vx, vy, vz, npar, gasv_x, gasv_y, gasv_z, rho, rhop',
formats=
'int, int,int,int, float,float,float, float,float,float, int, float,float,float, float, float'
)
else:
time = sav_file['time']
DATA_SET = np.core.records.fromarrays(
[
range(0, npar), sav_file['par_idx'][0].astype('int'),
sav_file['par_idx'][1].astype('int'),
sav_file['par_idx'][2].astype('int'),
sav_file['par_pos'][0], sav_file['par_pos'][1],
sav_file['par_pos'][2], sav_file['par_velo'][0],
sav_file['par_velo'][1], sav_file['par_velo'][2],
sav_file['npar'], sav_file['gas_velo'][0],
sav_file['gas_velo'][1], sav_file['gas_velo'][2],
var_file.rho[sav_file['par_idx'][2].astype('int'),
sav_file['par_idx'][1].astype('int'),
sav_file['par_idx'][0].astype('int')],
var_file.rhop[sav_file['par_idx'][2].astype('int'),
sav_file['par_idx'][1].astype('int'),
sav_file['par_idx'][0].astype('int')]
],
names=
'parid, idx, idy, idz, posx, posy, posz, vx, vy, vz, npar, gasv_x, gasv_y, gasv_z, rho, rhop',
formats=
'int, int,int,int, float,float,float, float,float,float, int, float,float,float, float, float'
)
DATA_SET = np.sort(DATA_SET, order=['idx', 'idy', 'idz'])
# calculate GLOBAL DISPERSION in x, y and z direction, also the absolute magnitude
if GLOBAL:
print(
'## Calculating GLOBAL DISPERSION values in x,y,z direction and abs value'
)
mean_vx = np.mean(DATA_SET['vx'])
mean_vy = np.mean(DATA_SET['vy'])
mean_vz = np.mean(DATA_SET['vz'])
SIGMA = {
'SIGMA_o_x':
np.sqrt(npar1 * np.sum((DATA_SET['vx'] - mean_vx)**2)),
'SIGMA_o_y':
np.sqrt(npar1 * np.sum((DATA_SET['vy'] - mean_vy)**2)),
'SIGMA_o_z':
np.sqrt(npar1 * np.sum((DATA_SET['vz'] - mean_vz)**2)),
'SIGMA_o':
np.sqrt(npar1 * np.sum((DATA_SET['vx'] - mean_vx)**2 +
(DATA_SET['vy'] - mean_vy)**2 +
(DATA_SET['vz'] - mean_vz)**2))
}
# calculate GLOBAL DRIFT in x, y and z direction, also the absolute magnitude
print(
'## Calculating GLOBAL DRIFT values in x,y,z direction and abs value'
)
ZETA = {
'ZETA_o_x':
np.sqrt(npar1 * np.sum(
(DATA_SET['vx'] - DATA_SET['gasv_x'])**2)),
'ZETA_o_y':
np.sqrt(npar1 * np.sum(
(DATA_SET['vy'] - DATA_SET['gasv_y'])**2)),
'ZETA_o_z':
np.sqrt(npar1 * np.sum(
(DATA_SET['vz'] - DATA_SET['gasv_z'])**2)),
'ZETA_o':
np.sqrt(npar1 *
np.sum((DATA_SET['vx'] - DATA_SET['gasv_x'])**2 +
(DATA_SET['vy'] - DATA_SET['gasv_y'])**2 +
(DATA_SET['vz'] - DATA_SET['gasv_z'])**2))
}
print('## saving calculated GLOBAL DISPERSION and DRIFT')
io.save(SIGMA, 'sigma_' + file_no, folder=SIM.pc_datadir)
io.save(ZETA, 'zeta_' + file_no, folder=SIM.pc_datadir)
# calculate LOCAL DISPERSION and DRIFT
if LOCAL:
print('## Calculating LOCAL DISPERSION and DRIFT values')
tmp_id = [
DATA_SET[0]['idx'], DATA_SET[0]['idy'], DATA_SET[0]['idz']
]
sigma_l = np.zeros((dim.nx, dim.ny, dim.nz))
zeta_l = np.zeros((dim.nx, dim.ny, dim.nz))
particles_l = []
for particle in DATA_SET:
if [particle['idx'], particle['idy'],
particle['idz']] == tmp_id:
particles_l.append(particle)
else:
np_l = np.size(particles_l)
if np_l != 0:
np1_l = 1. / np_l
mean_vx_l = 0
mean_vy_l = 0
mean_vz_l = 0
sum_s_l = 0
sum_z_l = 0
for entry in particles_l:
mean_vx_l = mean_vx_l + np1_l * entry['vx']
mean_vy_l = mean_vy_l + np1_l * entry['vy']
mean_vz_l = mean_vz_l + np1_l * entry['vz']
for entry in particles_l:
sum_s_l = sum_s_l + (entry['vx'] - mean_vx_l)**2 + (
entry['vy'] - mean_vy_l)**2 + (entry['vz'] -
mean_vz_l)**2
sum_z_l = sum_z_l + (
entry['vx'] - entry['gasv_x'])**2 + (
entry['vy'] - entry['gasv_y'])**2 + (
entry['vz'] - entry['gasv_z'])**2
sigma_l[tmp_id[0]][tmp_id[1]][tmp_id[2]] = np.sqrt(np1_l *
sum_s_l)
zeta_l[tmp_id[0]][tmp_id[1]][tmp_id[2]] = np.sqrt(np1_l *
sum_z_l)
# reset all local variables and lists to the new state (ie towards the newst particle)
tmp_id = [
particle['idx'], particle['idy'], particle['idz']
]
particles_l = []
particles_l.append(particle)
# do this local calculations a last time for the last grid cell
np_l = np.size(particles_l)
np1_l = 1. / np_l
mean_vx_l = 0
mean_vy_l = 0
mean_vz_l = 0
sum_s_l = 0
sum_z_l = 0
for entry in particles_l:
mean_vx_l = mean_vx_l + np1_l * entry['vx']
mean_vy_l = mean_vy_l + np1_l * entry['vy']
mean_vz_l = mean_vz_l + np1_l * entry['vz']
for entry in particles_l:
sum_s_l = sum_s_l + (entry['vx'] - mean_vx_l)**2 + (
entry['vy'] - mean_vy_l)**2 + (entry['vz'] - mean_vz_l)**2
sum_z_l = sum_z_l + (entry['vx'] - entry['gasv_x'])**2 + (
entry['vy'] - entry['gasv_y'])**2 + (entry['vz'] -
entry['gasv_z'])**2
sigma_l[tmp_id[0]][tmp_id[1]][tmp_id[2]] = np.sqrt(np1_l * sum_s_l)
zeta_l[tmp_id[0]][tmp_id[1]][tmp_id[2]] = np.sqrt(np1_l * sum_z_l)
# save sigma, zeta locally and globally to SIM.pc_datadir
print('## saving calculated LOCAL DISPERSION and DRIFT')
io.save(sigma_l,
'sigma_l_' + file_no,
folder=join(SIM.pc_datadir, 'sigma_l'))
io.save(zeta_l,
'zeta_l_' + file_no,
folder=join(SIM.pc_datadir, 'zeta_l'))
## Please keep this lines as a reminder on how to add columns to an record array!
# add colums to DATA_SET fror local zeta and sigma for the individuel particle
#DATA_SET = add_column_to_record_array(DATA_SET, 'zeta_l', zeta_l[DATA_SET['idx'],DATA_SET['idy'],DATA_SET['idz']], dtypes='float', usemask=False, asrecarray=True)
#DATA_SET = add_column_to_record_array(DATA_SET, 'sigma_l', sigma_l[DATA_SET['idx'],DATA_SET['idy'],DATA_SET['idz']], dtypes='float', usemask=False, asrecarray=True)
return True
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 gas_velo_at_particle_pos(
varfiles="last4", sim=False, scheme="tsc", use_IDL=False, OVERWRITE=False
):
"""This script calulates the gas velocity at the particle position and stores this together
with particle position, containing grid cell idicies, particle velocities, and particle index
in a gas_velo_at_particle_pos file.
Args:
varfiles: specifiy varfiles for calculation, e.g. 'last', 'first',
'all', 'VAR###', 'last4', 'first3'
scheme: possible are:
- ngp: nearest grid point
- cic: cloud in cell
- tsc: triangular shaped cloud
OVERWRITE: set to True to overwrite already calculated results
"""
from pencil import get_sim
from pencil import read
from pencil import diag
from pencil.io import mkdir
from os import listdir
from os.path import exists, join, dirname
import numpy as np
GAS_VELO_TAG = "gas_velo_at_particle_pos"
if sim == False:
sim = get_sim()
if sim == False:
print("! ERROR: Specify simulation object!")
return False
SIM = sim
if use_IDL:
print(
"? WARNING: IDL VERSION OF THIS SCRIPT BY JOHANSEN, not recommended for 2D data"
)
from ...backpack import pidly
print("## starting IDL engine..")
IDL = pidly.IDL(long_delay=0.05) # start IDL engine
## skip if nothing is new
if (
(not OVERWRITE)
and (exists(join(SIM.pc_datadir, "sigma.pkl")))
and (exists(join(SIM.pc_datadir, "zeta.pkl")))
):
print(
"~ "
+ SIM.name
+ " is already calculated and up-to-date! -> skipping it!"
)
else:
## start calculations
print(
'~ Calculating gas_velo_at_particle_pos for "'
+ SIM.name
+ '" in "'
+ SIM.path
+ '"'
)
IDL(
".COMPILE "
+ str(
join(
dirname(diag.particle.__file__), "gas_velo_at_particle_pos.pro"
)
)
)
IDL.pro(
"gas_velo_at_particle_pos",
datadir=SIM.datadir,
destination=GAS_VELO_TAG,
doforthelastNvar=varfiles[4:],
)
files = [
i.split("_")[-1].split(".sav")[0]
for i in listdir(join(SIM.pc_datadir, GAS_VELO_TAG))
if i.startswith(GAS_VELO_TAG)
and i.endswith(".sav")
or i.endswith(".pkl")
]
if files == []:
print(
"!! ERROR: No calc_gas_speed_at_particle_position-files found for "
+ SIM.name
+ "! Use idl script to produce them first!"
)
IDL.close()
return True
else:
print(
'~ Calculating gas_velo_at_particle_pos for "'
+ SIM.name
+ '" in "'
+ SIM.path
+ '"'
)
save_destination = join(SIM.pc_datadir, GAS_VELO_TAG)
mkdir(save_destination)
varlist = SIM.get_varlist(pos=varfiles, particle=False)
pvarlist = SIM.get_varlist(pos=varfiles, particle=True)
for f, p in zip(varlist, pvarlist):
save_filename = GAS_VELO_TAG + "_" + scheme + "_" + f[3:]
if not OVERWRITE and exists(save_filename, folder=save_destination):
continue
print("## Reading " + f + " ...")
ff = read.var(datadir=SIM.datadir, varfile=f, quiet=True, trimall=False)
pp = read.pvar(datadir=SIM.datadir, varfile=p)
## remove ghost zones from grid, call the reduced grid the "real grid"
realgridx = ff.x[ff.l1 : ff.l2]
realgridy = ff.y[ff.m1 : ff.m2]
realgridz = ff.z[ff.n1 : ff.n2]
nx = ff.l2 - ff.l1
ny = ff.m2 - ff.m1
nz = ff.n2 - ff.n1
## prepare list for all quantities
l_ipars = pp.ipars # particle number KNOWN
l_px = pp.xp
l_py = pp.yp
l_pz = pp.zp # particle absolut position KNOWN
l_vx = pp.vpx
l_vy = pp.vpy
l_vz = pp.vpz # particle velocity KNOWN
l_rix = []
l_riy = []
l_riz = (
[]
) # particle untrimmed realgrid index (grid index = l/m/n + readgrid index ???)
l_ix = []
l_iy = []
l_iz = [] # particle grid index (in untrimmed grid)
l_ux = []
l_uy = []
l_uz = [] # underlying gas velocity at position of particle
## get index of realgrid cell for each particle
for i in range(len(l_ipars)):
l_rix.append(np.abs(realgridx - l_px[i]).argmin())
l_riy.append(np.abs(realgridy - l_py[i]).argmin())
l_riz.append(np.abs(realgridz - l_pz[i]).argmin())
## convert into untrimmed grid
l_ix = np.array(l_rix) + ff.l1
l_iy = np.array(l_riy) + ff.m1
l_iz = np.array(l_riz) + ff.n1
## NGP
if scheme == "ngp" or scheme == "NGP":
print("## Calculating gas velocities via " + scheme)
l_ux = ff.ux[l_iz, l_iy, l_ix]
l_uy = ff.uy[l_iz, l_iy, l_ix]
l_uz = ff.uz[l_iz, l_iy, l_ix]
## CIC
if scheme == "cic" or scheme == "CIC":
print("## Calculating gas velocities via " + scheme)
for ix0, iy0, iz0, px, py, pz in zip(
l_ix, l_iy, l_iz, l_px, l_py, l_pz
): # for each particle
if ff.x[ix0] > px:
ix0 = ix0 - 1 # ix0 must be left to particle
if ff.y[iy0] > py:
iy0 = iy0 - 1 # iy0 must be below the particle
if ff.z[iz0] > pz:
iz0 = iz0 - 1 # iz0 must be under particle
ix1 = ix0
iy1 = iy0
iz1 = iz0 # if a dim. is zero, this is default, else:
if nx > 1:
ix1 = ix0 + 1
dx_1 = (
1.0 / ff.dx
) # if a dim is non-zero, ajust ix1 to right cell
if ny > 1:
iy1 = iy0 + 1
dy_1 = (
1.0 / ff.dy
) # if a dim is non-zero, ajust iy1 to above cell
if nz > 1:
iz1 = iz0 + 1
dz_1 = (
1.0 / ff.dz
) # if a dim is non-zero, ajust iz1 to above cell
ux = 0.0
uy = 0.0
uz = 0.0
for ix in [ix0, ix1]:
for iy in [iy0, iy1]:
for iz in [iz0, iz1]:
weight = 1.0
if nx > 1:
weight = weight * (1.0 - abs(px - ff.x[ix]) * dx_1)
if ny > 1:
weight = weight * (1.0 - abs(py - ff.y[iy]) * dy_1)
if nz > 1:
weight = weight * (1.0 - abs(pz - ff.z[iz]) * dz_1)
ux = ux + weight * ff.ux[iz, iy, ix]
uy = uy + weight * ff.uy[iz, iy, ix]
uz = uz + weight * ff.uz[iz, iy, ix]
if iz0 == iz1:
break # beware of degeneracy:
if iy0 == iy1:
break # beware of degeneracy:
if ix0 == ix1:
break # beware of degeneracy:
l_ux.append(ux)
l_uy.append(uy)
l_uz.append(uz)
## TSC
if scheme == "tsc" or scheme == "TSC":
for ix0, iy0, iz0, px, py, pz in zip(
l_ix, l_iy, l_iz, l_px, l_py, l_pz
): # for each particle
ixx0 = ix0
ixx1 = ix0 # beware of degeneracy
iyy0 = iy0
iyy1 = iy0
izz0 = iz0
izz1 = iz0
if nx > 1:
ixx0 = ix0 - 1
ixx1 = ix0 + 1
dx_1 = 1.0 / ff.dx
dx_2 = 1.0 / ff.dx ** 2
if ny > 1:
iyy0 = iy0 - 1
iyy1 = iy0 + 1
dy_1 = 1.0 / ff.dy
dy_2 = 1.0 / ff.dy ** 2
if nz > 1:
izz0 = iz0 - 1
izz1 = iz0 + 1
dz_1 = 1.0 / ff.dz
dz_2 = 1.0 / ff.dz ** 2
ux = 0.0
uy = 0.0
uz = 0.0
for ix in [ix0, ixx0, ixx1]:
weight_x = 0.0
if ix - ix0 == -1 or ix - ix0 == 1:
weight_x = (
1.125
- 1.5 * abs(px - ff.x[ix]) * dx_1
+ 0.5 * abs(px - ff.x[ix]) ** 2 * dx_2
)
elif nx != 1:
weight_x = 0.75 - (px - ff.x[ix]) ** 2 * dx_2
for iy in [iy0, iyy0, iyy1]:
weight_y = 0.0
if iy - iy0 == -1 or iy - iy0 == 1:
weight_y = (
1.125
- 1.5 * abs(py - ff.y[iy]) * dy_1
+ 0.5 * abs(py - ff.y[iy]) ** 2 * dy_2
)
elif ny != 1:
weight_y = 0.75 - (py - ff.y[iy]) ** 2 * dy_2
for iz in [iz0, izz0, izz1]:
weight_z = 0.0
if iz - iz0 == -1 or iz - iz0 == 1:
weight_z = (
1.125
- 1.5 * abs(pz - ff.z[iz]) * dz_1
+ 0.5 * abs(pz - ff.z[iz]) ** 2 * dz_2
)
elif nz != 1:
weight_z = 0.75 - (pz - ff.z[iz]) ** 2 * dz_2
weight = 1.0
if nx > 1:
weight = weight * weight_x
if ny > 1:
weight = weight * weight_y
if nz > 1:
weight = weight * weight_z
ux = ux + weight * ff.ux[iz, iy, ix]
uy = uy + weight * ff.uy[iz, iy, ix]
uz = uz + weight * ff.uz[iz, iy, ix]
if izz0 == izz1:
break # beware of degeneracy:
if iyy0 == iyy1:
break # beware of degeneracy:
if ixx0 == ixx1:
break # beware of degeneracy:
l_ux.append(ux)
l_uy.append(uy)
l_uz.append(uz)
## Convert all information into a single record array
data_set = np.core.records.fromarrays(
[
l_ipars.astype("int"),
l_px,
l_py,
l_pz,
l_vx,
l_vy,
l_vz,
l_rix,
l_riy,
l_riz,
l_ix,
l_iy,
l_iz,
l_ux,
l_uy,
l_uz,
],
names="ipar, ipx, ipy, ipz, vx, vy, vz, rix, riy, riz, ix, iy, iz, ux, uy, uz",
formats="int, float, float, float, float, float, float, int, int, int, int, int, int, float, float, float",
)
gas_velo_at_particle_pos = np.sort(data_set, order=["ix", "iy", "iz"])
Nix = int(gas_velo_at_particle_pos["rix"].max() + 1)
Niy = int(gas_velo_at_particle_pos["riy"].max() + 1)
Niz = int(gas_velo_at_particle_pos["riz"].max() + 1)
Npar_arr = np.array(
[
gas_velo_at_particle_pos["rix"],
gas_velo_at_particle_pos["riy"],
gas_velo_at_particle_pos["riz"],
]
)
# rgrid_edges = (grid.x[1:]-(grid.x[1:]-grid.x[:-1])/2)[2:-2]
xrange = np.arange(0, float(gas_velo_at_particle_pos["rix"].max()) + 2)
xrange = xrange - 0.5
yrange = np.arange(0, float(gas_velo_at_particle_pos["riy"].max()) + 2)
zrange = np.arange(0, float(gas_velo_at_particle_pos["riz"].max()) + 2)
Npar_hist, edges = np.histogramdd(Npar_arr.T, bins=(xrange, yrange, zrange))
Npar_hist, edges = np.histogramdd(Npar_arr.T, bins=(Nix, Niy, Niz))
gas_velo_at_particle_pos = {
"time": ff.t,
"par_pos": np.array(
[
gas_velo_at_particle_pos["ipx"],
gas_velo_at_particle_pos["ipy"],
gas_velo_at_particle_pos["ipz"],
]
),
"par_velo": np.array(
[
gas_velo_at_particle_pos["vx"],
gas_velo_at_particle_pos["vy"],
gas_velo_at_particle_pos["vz"],
]
),
"par_idx": np.array(
[
gas_velo_at_particle_pos["rix"],
gas_velo_at_particle_pos["riy"],
gas_velo_at_particle_pos["riz"],
]
),
"npar": np.array(
Npar_hist[
gas_velo_at_particle_pos["rix"],
gas_velo_at_particle_pos["riy"],
gas_velo_at_particle_pos["riz"],
]
),
"gas_velo": np.array(
[
gas_velo_at_particle_pos["ux"],
gas_velo_at_particle_pos["uy"],
gas_velo_at_particle_pos["uz"],
]
),
}
print("## Saving dataset into " + save_destination + "...")
pkl_save(
{"gas_velo_at_particle_pos": gas_velo_at_particle_pos, "t": ff.t},
save_filename,
folder=save_destination,
)
print("## Done!")
def gas_velo_at_particle_pos(varfiles='last4',
sim=False,
scheme='tsc',
use_IDL=False,
OVERWRITE=False):
"""This script calulates the gas velocity at the particle position and stores this together
with particle position, containing grid cell idicies, particle velocities, and particle index
in a gas_velo_at_particle_pos file.
Args:
varfiles: specifiy varfiles for calculation, e.g. 'last', 'first',
'all', 'VAR###', 'last4', 'first3'
scheme: possible are:
- ngp: nearest grid point
- cic: cloud in cell
- tsc: triangular shaped cloud
OVERWRITE: set to True to overwrite already calculated results
"""
import os
from pencil import io
from pencil import read
from pencil import get_sim
from os.path import exists
import numpy as np
GAS_VELO_TAG = 'gas_velo_at_particle_pos'
if sim == False:
sim = get_sim()
if sim == False:
print('! ERROR: Specify simulation object!')
return False
SIM = sim
if use_IDL:
print(
'? WARNING: IDL VERSION OF THIS SCRIPT BY JOHANSEN, not recommended for 2D data'
)
from pencil.backpack import pidly
print('## starting IDL engine..')
IDL = pidly.IDL(long_delay=0.05) # start IDL engine
## skip if nothing is new
if (not OVERWRITE) and (exists(
os.path.join(SIM.pc_datadir, 'sigma.pkl'))) and (exists(
os.path.join(SIM.pc_datadir, 'zeta.pkl'))):
print('~ ' + SIM.name +
' is already calculated and up-to-date! -> skipping it!')
else:
## start calculations
print('~ Calculating gas_velo_at_particle_pos for "' + SIM.name +
'" in "' + SIM.path + '"')
IDL.pro('gas_velo_at_particle_pos',
datadir=SIM.datadir,
destination=GAS_VELO_TAG,
doforthelastNvar=varfiles[4:])
files = [
i.split('_')[-1].split('.sav')[0]
for i in os.listdir(os.path.join(SIM.pendatadir, GAS_VELO_TAG))
if i.startswith(GAS_VELO_TAG) and i.endswith('.sav')
or i.endswith('.pkl')
]
if files == []:
print(
'!! ERROR: No calc_gas_speed_at_particle_position-files found for '
+ SIM.name + '! Use idl script to produce them first!')
IDL.close()
return True
else:
print('~ Calculating gas_velo_at_particle_pos for "' + SIM.name +
'" in "' + SIM.path + '"')
save_destination = os.path.join(SIM.pc_datadir, GAS_VELO_TAG)
io.mkdir(save_destination)
varlist = SIM.get_varlist(pos=varfiles, particle=False)
pvarlist = SIM.get_varlist(pos=varfiles, particle=True)
for f, p in zip(varlist, pvarlist):
save_filename = GAS_VELO_TAG + '_' + scheme + '_' + f[3:]
if not OVERWRITE and exists(save_filename,
folder=save_destination):
continue
print('## Reading ' + f + ' ...')
ff = read.var(datadir=SIM.datadir,
varfile=f,
quiet=True,
trimall=False)
pp = read.pvar(datadir=SIM.datadir, varfile=p)
## remove ghost zones from grid, call the reduced grid the "real grid"
realgridx = ff.x[ff.l1:ff.l2]
realgridy = ff.y[ff.m1:ff.m2]
realgridz = ff.z[ff.n1:ff.n2]
nx = ff.l2 - ff.l1
ny = ff.m2 - ff.m1
nz = ff.n2 - ff.n1
## prepare list for all quantities
l_ipars = pp.ipars # particle number KNOWN
l_px = pp.xp
l_py = pp.yp
l_pz = pp.zp # particle absolut position KNOWN
l_vx = pp.vpx
l_vy = pp.vpy
l_vz = pp.vpz # particle velocity KNOWN
l_rix = []
l_riy = []
l_riz = [
] # particle untrimmed realgrid index (grid index = l/m/n + readgrid index ???)
l_ix = []
l_iy = []
l_iz = [] # particle grid index (in untrimmed grid)
l_ux = []
l_uy = []
l_uz = [] # underlying gas velocity at position of particle
## get index of realgrid cell for each particle
for i in range(len(l_ipars)):
l_rix.append(np.abs(realgridx - l_px[i]).argmin())
l_riy.append(np.abs(realgridy - l_py[i]).argmin())
l_riz.append(np.abs(realgridz - l_pz[i]).argmin())
## convert into untrimmed grid
l_ix = np.array(l_rix) + ff.l1
l_iy = np.array(l_riy) + ff.m1
l_iz = np.array(l_riz) + ff.n1
## NGP
if scheme == 'ngp' or scheme == 'NGP':
print('## Calculating gas velocities via ' + scheme)
l_ux = ff.ux[l_iz, l_iy, l_ix]
l_uy = ff.uy[l_iz, l_iy, l_ix]
l_uz = ff.uz[l_iz, l_iy, l_ix]
## CIC
if scheme == 'cic' or scheme == 'CIC':
print('## Calculating gas velocities via ' + scheme)
for ix0, iy0, iz0, px, py, pz in zip(
l_ix, l_iy, l_iz, l_px, l_py,
l_pz): # for each particle
if ff.x[ix0] > px:
ix0 = ix0 - 1 # ix0 must be left to particle
if ff.y[iy0] > py:
iy0 = iy0 - 1 # iy0 must be below the particle
if ff.z[iz0] > pz:
iz0 = iz0 - 1 # iz0 must be under particle
ix1 = ix0
iy1 = iy0
iz1 = iz0 # if a dim. is zero, this is default, else:
if nx > 1:
ix1 = ix0 + 1
dx_1 = 1. / ff.dx # if a dim is non-zero, ajust ix1 to right cell
if ny > 1:
iy1 = iy0 + 1
dy_1 = 1. / ff.dy # if a dim is non-zero, ajust iy1 to above cell
if nz > 1:
iz1 = iz0 + 1
dz_1 = 1. / ff.dz # if a dim is non-zero, ajust iz1 to above cell
ux = 0.
uy = 0.
uz = 0.
for ix in [ix0, ix1]:
for iy in [iy0, iy1]:
for iz in [iz0, iz1]:
weight = 1.
if nx > 1:
weight = weight * (
1. - abs(px - ff.x[ix]) * dx_1)
if ny > 1:
weight = weight * (
1. - abs(py - ff.y[iy]) * dy_1)
if nz > 1:
weight = weight * (
1. - abs(pz - ff.z[iz]) * dz_1)
ux = ux + weight * ff.ux[iz, iy, ix]
uy = uy + weight * ff.uy[iz, iy, ix]
uz = uz + weight * ff.uz[iz, iy, ix]
if iz0 == iz1: break # beware of degeneracy:
if iy0 == iy1: break # beware of degeneracy:
if ix0 == ix1: break # beware of degeneracy:
l_ux.append(ux)
l_uy.append(uy)
l_uz.append(uz)
## TSC
if scheme == 'tsc' or scheme == 'TSC':
for ix0, iy0, iz0, px, py, pz in zip(
l_ix, l_iy, l_iz, l_px, l_py,
l_pz): # for each particle
ixx0 = ix0
ixx1 = ix0 # beware of degeneracy
iyy0 = iy0
iyy1 = iy0
izz0 = iz0
izz1 = iz0
if nx > 1:
ixx0 = ix0 - 1
ixx1 = ix0 + 1
dx_1 = 1. / ff.dx
dx_2 = 1. / ff.dx**2
if ny > 1:
iyy0 = iy0 - 1
iyy1 = iy0 + 1
dy_1 = 1. / ff.dy
dy_2 = 1. / ff.dy**2
if nz > 1:
izz0 = iz0 - 1
izz1 = iz0 + 1
dz_1 = 1. / ff.dz
dz_2 = 1. / ff.dz**2
ux = 0.
uy = 0.
uz = 0.
for ix in [ix0, ixx0, ixx1]:
weight_x = 0.
if ix - ix0 == -1 or ix - ix0 == 1:
weight_x = 1.125 - 1.5 * abs(
px - ff.x[ix]) * dx_1 + 0.5 * abs(
px - ff.x[ix])**2 * dx_2
elif nx != 1:
weight_x = 0.75 - (px - ff.x[ix])**2 * dx_2
for iy in [iy0, iyy0, iyy1]:
weight_y = 0.
if iy - iy0 == -1 or iy - iy0 == 1:
weight_y = 1.125 - 1.5 * abs(
py - ff.y[iy]) * dy_1 + 0.5 * abs(
py - ff.y[iy])**2 * dy_2
elif ny != 1:
weight_y = 0.75 - (py - ff.y[iy])**2 * dy_2
for iz in [iz0, izz0, izz1]:
weight_z = 0.
if iz - iz0 == -1 or iz - iz0 == 1:
weight_z = 1.125 - 1.5 * abs(
pz - ff.z[iz]) * dz_1 + 0.5 * abs(
pz - ff.z[iz])**2 * dz_2
elif nz != 1:
weight_z = 0.75 - (pz - ff.z[iz])**2 * dz_2
weight = 1.
if nx > 1: weight = weight * weight_x
if ny > 1: weight = weight * weight_y
if nz > 1: weight = weight * weight_z
ux = ux + weight * ff.ux[iz, iy, ix]
uy = uy + weight * ff.uy[iz, iy, ix]
uz = uz + weight * ff.uz[iz, iy, ix]
if izz0 == izz1: break # beware of degeneracy:
if iyy0 == iyy1: break # beware of degeneracy:
if ixx0 == ixx1: break # beware of degeneracy:
l_ux.append(ux)
l_uy.append(uy)
l_uz.append(uz)
## Convert all information into a single record array
data_set = np.core.records.fromarrays(
[
l_ipars.astype('int'), l_px, l_py, l_pz, l_vx, l_vy, l_vz,
l_rix, l_riy, l_riz, l_ix, l_iy, l_iz, l_ux, l_uy, l_uz
],
names=
'ipar, ipx, ipy, ipz, vx, vy, vz, rix, riy, riz, ix, iy, iz, ux, uy, uz',
formats=
'int, float, float, float, float, float, float, int, int, int, int, int, int, float, float, float'
)
gas_velo_at_particle_pos = np.sort(data_set,
order=['ix', 'iy', 'iz'])
Nix = int(gas_velo_at_particle_pos['rix'].max() + 1)
Niy = int(gas_velo_at_particle_pos['riy'].max() + 1)
Niz = int(gas_velo_at_particle_pos['riz'].max() + 1)
Npar_arr = np.array([
gas_velo_at_particle_pos['rix'],
gas_velo_at_particle_pos['riy'],
gas_velo_at_particle_pos['riz']
])
#rgrid_edges = (grid.x[1:]-(grid.x[1:]-grid.x[:-1])/2)[2:-2]
xrange = np.arange(
0,
float(gas_velo_at_particle_pos['rix'].max()) + 2)
xrange = xrange - 0.5
yrange = np.arange(
0,
float(gas_velo_at_particle_pos['riy'].max()) + 2)
zrange = np.arange(
0,
float(gas_velo_at_particle_pos['riz'].max()) + 2)
Npar_hist, edges = np.histogramdd(Npar_arr.T,
bins=(xrange, yrange, zrange))
Npar_hist, edges = np.histogramdd(Npar_arr.T, bins=(Nix, Niy, Niz))
gas_velo_at_particle_pos = {
'time':
ff.t,
'par_pos':
np.array([
gas_velo_at_particle_pos['ipx'],
gas_velo_at_particle_pos['ipy'],
gas_velo_at_particle_pos['ipz']
]),
'par_velo':
np.array([
gas_velo_at_particle_pos['vx'],
gas_velo_at_particle_pos['vy'],
gas_velo_at_particle_pos['vz']
]),
'par_idx':
np.array([
gas_velo_at_particle_pos['rix'],
gas_velo_at_particle_pos['riy'],
gas_velo_at_particle_pos['riz']
]),
'npar':
np.array(Npar_hist[gas_velo_at_particle_pos['rix'],
gas_velo_at_particle_pos['riy'],
gas_velo_at_particle_pos['riz']]),
'gas_velo':
np.array([
gas_velo_at_particle_pos['ux'],
gas_velo_at_particle_pos['uy'],
gas_velo_at_particle_pos['uz']
])
}
print('## Saving dataset into ' + save_destination + '...')
io.pkl_save(
{
'gas_velo_at_particle_pos': gas_velo_at_particle_pos,
't': ff.t
},
save_filename,
folder=save_destination)
print('## Done!')
def var2h5(
newdir,
olddir,
allfile_names,
todatadir,
fromdatadir,
snap_by_proc,
precision,
lpersist,
quiet,
nghost,
settings,
param,
grid,
x,
y,
z,
lshear,
lremove_old_snapshots,
indx,
trimall=False,
l_mpi=False,
driver=None,
comm=None,
rank=0,
size=1,
):
"""
Copy a simulation snapshot set written in Fortran binary to hdf5.
call signature:
var2h5(newdir, olddir, allfile_names, todatadir, fromdatadir, snap_by_proc,
precision, lpersist, quiet, nghost, settings, param, grid,
x, y, z, lshear, lremove_old_snapshots, indx,
trimall=False, l_mpi=False, driver=None, comm=None, rank=0, size=1
)
Keyword arguments:
*newdir*:
String path to simulation destination directory.
*olddir*:
String path to simulation destination directory.
*allfile_names*:
A list of names of the snapshot files to be written, e.g. VAR0.
*todatadir*:
Directory to which the data is stored.
*fromdatadir*:
Directory from which the data is collected.
*snap_by_proc*:
Read and write snapshots by procdir of the fortran binary tree
*precision*:
Single 'f' or double 'd' precision for new data.
*lpersist*:
option to include persistent variables from snapshots.
*quiet*
Option not to print output.
*nghost*:
Number of ghost zones.
*settings*
simulation properties.
*param*
simulation Param object.
*grid*
simulation Grid object.
*xyz*:
xyz arrays of the domain with ghost zones.
*lshear*:
Flag for the shear.
*lremove_old_snapshots*:
If True the old snapshots will be deleted once the new snapshot has
been saved.
*indx*:
List of variable indices in the f-array.
*trimall*:
Strip ghost zones from snapshots
*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 os
from os.path import exists, join
import numpy as np
import glob
from pencil import read
from pencil import sim
from pencil.io import write_h5_snapshot
import sys
import time
import subprocess as sub
if isinstance(allfile_names, list):
allfile_names = allfile_names
else:
allfile_names = [allfile_names]
# proceed to copy each snapshot in varfile_names
nprocs = settings["nprocx"] * settings["nprocy"] * settings["nprocz"]
if l_mpi:
if not snap_by_proc:
file_names = np.array_split(allfile_names, size)
if "VARd1" in allfile_names:
varfile_names = file_names[size - rank - 1]
else:
varfile_names = file_names[rank]
else:
os.chdir(olddir)
if size > nprocs:
nnames = len(allfile_names)
if size > nnames * nprocs:
file_names = np.array_split(allfile_names, nnames)
varfile_names = file_names[np.mod(rank, nnames)]
nprocsplit = int(size / nnames)
iprocs = np.array_split(np.arange(nprocs), nprocs)
procs = iprocs[np.mod(rank, nprocs)]
else:
file_names = np.array_split(allfile_names, nnames)
varfile_names = file_names[np.mod(rank, nnames)]
if nnames > size:
procs = np.arange(nprocs)
else:
nproc_per_fname = int(size / nnames)
isize = np.int(np.mod(rank, nnames) / nproc_per_fname)
if np.mod(isize, nproc_per_fname + 1) == 0:
npf = nproc_per_fname + 1
iprocs = np.array(
np.array_split(np.arange(nprocs), npf)).T
else:
npf = nproc_per_fname
iprocs = np.array(
np.array_split(np.arange(nprocs), npf)).T
procs = iprocs[np.mod(int((rank * nnames) / size), npf)]
else:
if np.mod(nprocs, size) > 0:
procs = np.arange(nprocs + size - np.mod(nprocs, size))
procs[-size + np.mod(nprocs, size):] = np.arange(
size - np.mod(nprocs, size))
else:
procs = np.arange(nprocs)
iprocs = np.array_split(procs, size)
procs = iprocs[rank]
varfile_names = allfile_names
print("rank {} procs:".format(rank), procs)
sys.stdout.flush()
else:
varfile_names = allfile_names
procs = np.arange(nprocs)
if len(varfile_names) > 0:
for file_name in varfile_names:
# load Fortran binary snapshot
if not quiet:
print("rank {}:".format(rank) + "saving " + file_name)
sys.stdout.flush()
if snap_by_proc:
if len(procs) > 0:
proctime = time.time()
for proc in procs:
os.chdir(olddir)
if np.mod(proc, size) == size - 1:
print(
"rank {}:".format(rank) + "saving " +
file_name + " on proc{}\n".format(proc),
time.ctime(),
)
sys.stdout.flush()
procdim = read.dim(proc=proc)
var = read.var(
file_name,
datadir=fromdatadir,
quiet=quiet,
lpersist=lpersist,
trimall=trimall,
proc=proc,
)
try:
var.deltay
lshear = True
except:
lshear = False
if lpersist:
persist = {}
for key in read.record_types.keys():
try:
persist[key] = var.__getattribute__(key)[(
)]
if type(persist[key][0]) == str:
persist[key][0] = var.__getattribute__(
key)[0].encode()
except:
pass
else:
persist = None
if np.mod(proc, size) == size - 1:
print("rank {}:".format(rank) + "loaded " +
file_name +
" on proc{} in {} seconds".format(
proc,
time.time() - proctime))
sys.stdout.flush()
# write data to h5
os.chdir(newdir)
write_h5_snapshot(
var.f,
file_name=file_name,
state="a",
datadir=todatadir,
precision=precision,
nghost=nghost,
persist=persist,
proc=proc,
procdim=procdim,
settings=settings,
param=param,
grid=grid,
lghosts=True,
indx=indx,
t=var.t,
x=x,
y=y,
z=z,
quiet=quiet,
rank=rank,
size=size,
lshear=lshear,
driver=driver,
comm=comm,
)
if np.mod(proc, size) == size - 1:
print("rank {}:".format(rank) + "written " +
file_name +
" on proc{} in {} seconds".format(
proc,
time.time() - proctime))
sys.stdout.flush()
proctime = time.time()
else:
var = read.var(
file_name,
datadir=fromdatadir,
quiet=quiet,
lpersist=lpersist,
trimall=trimall,
)
try:
var.deltay
lshear = True
except:
lshear = False
if lpersist:
persist = {}
for key in read.record_types.keys():
try:
persist[key] = var.__getattribute__(key)[()]
if type(persist[key][0]) == str:
persist[key][0] = var.__getattribute__(
key)[0].encode()
except:
pass
else:
persist = None
# write data to h5
os.chdir(newdir)
write_h5_snapshot(
var.f,
file_name=file_name,
datadir=todatadir,
precision=precision,
nghost=nghost,
persist=persist,
settings=settings,
param=param,
grid=grid,
lghosts=True,
indx=indx,
t=var.t,
x=x,
y=y,
z=z,
lshear=lshear,
driver=None,
comm=None,
)
if lremove_old_snapshots:
os.chdir(olddir)
cmd = "rm -f " + join(olddir, fromdatadir, "proc*", file_name)
os.system(cmd)
del var
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 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()
