def get_sims(path='.', depth=1, nuhide=False):
"""Returns all found simulations as object list from all subdirs, not
following symbolic links.
Args:
depth depth of searching for simulations, default is only one level
higher directories will be searched
unhide unhides all simulation found if True, if False hidden sim will
stay hidden
"""
import os
import numpy as np
from pencilnew.io import load
from pencilnew.io import save
from pencilnew.sim import Simulation
#from pen.intern.class_simdict import Simdict
#from intern import get_simdict
from pencilnew.io import walklevel
#import intern.debug_breakpoint as debug_breakpoint
new_sim_list = []
old_sim_list = False
print '~ A list of pencil code simulations is generated from this dir downwards, this may take some time..'
print '~ Symbolic links will not be followed, since this can lead to infinit recursion'
# get overview of simulations in all lower dirs
sim_paths = []
for path, dirs, files in walklevel('.', depth):
if 'start.in' in files and 'run.in' in files:
print '# Found Simulation in ' + path
sim_paths.append(path)
# take care for each simulation found
# generate new simulation object for all found simulations and append on new_sim_list
for path in sim_paths:
sim = Simulation(path)
# check if sim.name is already existing as name for a different simulation
for s in new_sim_list: # check for double names
if sim.name == s.name:
sim.name = sim.name + '#' # add # to dublicate
print "?? Warning: Found two simulatoins with the same name: " + sim.path + ' and ' + s.path
print "?? Changed name of " + sim.path + ' to ' + sim.name
if old_sim_list:
if not unhide: # take over hidden status from previouse simDICT if existing and unhide = False (which is default!)
for old_sim in old_sim_list:
if (sim.path == old_sim.path) and (old_sim.hidden == True):
sim.hide()
## ADD: auto hide simulations which are not runned yet, i.e. without param.nml
new_sim_list.append(sim) # add new sim object to new_sim_list
sim.export()
# is new_sim_list empty?
if new_sim_list == []: print '?? WARNING: no simulations found!'
save(new_sim_list, 'sim_list', folder='.pen')
return new_sim_list
def export(self):
"""Export simulation object to its root/.pc-dir"""
from pencilnew.io import save
if self == False: print('! ERROR: Simulation object is bool object and False!')
# clean self.tmp_dict
tmp_dict = self.tmp_dict; self.tmp_dict = {}
save(self, name='sim', folder=self.pc_dir)
# restore self.tmp_dict
self.tmp_dict = tmp_dict
def export(self):
"""Export simulation object to its root/.pc-dir"""
from pencilnew.io import save
if self == False:
print('! ERROR: Simulation object is bool object and False!')
save(self, name='sim', folder=self.pc_dir)
def __init__(self,
direction='x',
trange=[0, -1],
sim='.',
OVERWRITE=False,
quiet=True,
jump_distance=0.5):
from os.path import join
from os.path import exists as path_exists
import numpy as np
from scipy.stats import linregress
from pencilnew.io import load, save, exists
from pencilnew.read import pstalk as read_pstalk
from pencilnew.math.derivatives import simple_centered as derivatives_centered
from pencilnew.backpack import printProgressBar
# .. no pstalk file is found
if not path_exists(join(sim.datadir, 'proc0',
'particles_stalker.dat')):
print(
'?? WARNING: No particles_stalker.dat found for simulation ' +
sim.name + '!! Skipping this run!')
return False
# check if already existing. if, then load it
out_path = join(sim.pc_datadir, 'particle', 'diffusion')
out_name = direction + '_' + str(trange[0]) + '_' + str(trange[1])
# skip if diffusion already exists
if not OVERWRITE and exists(name=out_name, folder=out_path):
self_tmp = load(out_name, folder=out_path)
for key in [a for a in dir(self_tmp) if not a.startswith('__')]:
setattr(self, key, getattr(self_tmp, key))
else:
#### start calculations ####
print('##')
print('## Calculating particle diffusion for "' + sim.name +
'" in "' + sim.path + '"')
print('## reading particle stalker file..')
pstalk = read_pstalk(sim=sim)
grid = sim.grid
dim = sim.dim
# get time range as index for pstalk dataset
argmin = np.abs(pstalk.t - trange[0]).argmin()
if trange[1] < 0.:
argmax = pstalk.t.argmax()
else:
argmax = np.abs(pstalk.t - trange[1]).argmin()
time_range = pstalk.t[argmin:argmax + 1]
# modify time range with time_range[0] == 0 by substracting first time entry from time list
#time_offset = pstalk.t[argmin:argmax][0]
#time_range = pstalk.t[argmin:argmax]-time_offset
print('\n## doing the calculation for ' + direction)
L = getattr(grid, 'L' + direction) # domain size in direction
N = getattr(dim, 'n' + direction) # number grid cells in direction
pos_series = getattr(pstalk, direction + 'p').T[
argmin:argmax +
1] # get position timeseries series for direction for all particles
N_dt = pos_series.shape[0] # get number of time steps available
N_par = pos_series.shape[1] # get number of stalked particles
travel_distance = 0. * pos_series # prepare 'travel distance' array for all particles
mean_travel_dist = np.array(0)
# prepare mean, var and sigma arrays
variance = np.array(0)
sigma = np.array(0)
## calulate travel distances for each particle with corrections for jumps at the boundary
for i_t, pos in enumerate(pos_series):
if i_t == 0: continue # skip first time_step
printProgressBar(i_t, N_dt)
# calculate the distance dx made in dt for all particles at once
dx = pos - pos_series[i_t - 1]
travel_distance[i_t] = travel_distance[i_t - 1] + dx
# correct for negative jumps
jumps = np.where(dx > jump_distance * L)
travel_distance[i_t][jumps] = travel_distance[i_t][jumps] - L
# correct for positive jumps
jumps = np.where(dx < -jump_distance * L)
travel_distance[i_t][jumps] = travel_distance[i_t][jumps] + L
# calculate mean, variance and sigma for at 'time' i
mean_travel_dist = np.mean(travel_distance, axis=1)
# 1. estimate variance as time series
variance = np.var(travel_distance, axis=1)
sigma = np.sqrt(variance)
# 2. estimate diffusion by using centered derivatives method !!!
diff_dvar = 0.5 * derivatives_centered(time_range, variance)
diffusion_mean = np.mean(diff_dvar)
diffusion_std = np.std(diff_dvar)
# create diffusion object
self.diffusion = diffusion_mean
self.diffusion_error = diffusion_std
self.travel_distance = travel_distance
self.mean_travel_distance = mean_travel_dist
self.timerange = time_range
self.variance = variance
self.sigma = sigma
self.direction = direction
print('~ diffusion = ' + str(self.diffusion))
print('~ diffusion std = ' + str(self.diffusion_error))
print('~ direction = ' + str(self.direction))
try:
print('## saving results in' + join(out_path, out_name))
save(obj=self, name=out_name, folder=out_path)
except:
print("!! Unexpected error:", sys.exc_info()[0])
print("!! Check if you have writing rights.")
raise
def __init__(self, direction='x', trange=[0, -1], sim='.',
OVERWRITE=False, quiet=True, jump_distance=0.5, use_existing_pstalk_sav=False):
from os.path import join
from os.path import exists as path_exists
import numpy as np
from scipy.stats import linregress
from pencilnew.io import load, save, exists
from pencilnew.read import pstalk as read_pstalk
from pencilnew.math.derivatives import simple_centered as derivatives_centered
from pencilnew.backpack import printProgressBar
# .. no pstalk file is found
if not path_exists(join(sim.datadir, 'proc0', 'particles_stalker.dat')):
print('?? WARNING: No particles_stalker.dat found for simulation '+sim.name+'!! Skipping this run!')
return False
# check if already existing. if, then load it
out_path = join(sim.pc_datadir, 'particle', 'diffusion')
out_name = direction+'_'+str(trange[0])+'_'+str(trange[1])
# skip if diffusion already exists
if not OVERWRITE and exists(name=out_name, folder=out_path):
self_tmp = load(out_name, folder=out_path)
for key in [a for a in dir(self_tmp) if not a.startswith('__')]:
setattr(self, key, getattr(self_tmp, key))
else:
#### start calculations ####
print('##')
print('## Calculating particle diffusion for "'+sim.name+'" in "'+sim.path+'"')
print('## reading particle stalker file..')
pstalk = read_pstalk(sim=sim, use_existing_pstalk_sav=use_existing_pstalk_sav, tmin=trange[0], tmax=trange[1])
grid = sim.grid
dim = sim.dim
# get time range as index for pstalk dataset
argmin = np.abs(pstalk.t-trange[0]).argmin()
if trange[1] < 0.:
argmax = pstalk.t.argmax()
else:
argmax = np.abs(pstalk.t-trange[1]).argmin()
time_range = pstalk.t[argmin:argmax+1]
# modify time range with time_range[0] == 0 by substracting first time entry from time list
#time_offset = pstalk.t[argmin:argmax][0]
#time_range = pstalk.t[argmin:argmax]-time_offset
print('\n## doing the calculation for '+direction)
L = getattr(grid, 'L'+direction) # domain size in direction
N = getattr(dim, 'n'+direction) # number grid cells in direction
pos_series = getattr(pstalk,direction+'p').T[argmin:argmax+1] # get position timeseries series for direction for all particles
N_dt = pos_series.shape[0] # get number of time steps available
N_par = pos_series.shape[1] # get number of stalked particles
travel_distance = 0. * pos_series # prepare 'travel distance' array for all particles
mean_travel_dist = np.array(0); # prepare mean, var and sigma arrays
variance = np.array(0);
sigma = np.array(0)
## calulate travel distances for each particle with corrections for jumps at the boundary
pbar = False; Nt = np.size(pos_series)
for i_t,pos in enumerate(pos_series):
if i_t == 0: continue # skip first time_step
pbar = printProgressBar(i_t, Nt, pbar=pbar)
# calculate the distance dx made in dt for all particles at once
dx = pos - pos_series[i_t-1]
travel_distance[i_t] = travel_distance[i_t-1] + dx
# correct for negative jumps
jumps = np.where(dx > jump_distance*L)
travel_distance[i_t][jumps] = travel_distance[i_t][jumps] - L
# correct for positive jumps
jumps = np.where(dx < -jump_distance*L)
travel_distance[i_t][jumps] = travel_distance[i_t][jumps] + L
# calculate mean, variance and sigma for at 'time' i
mean_travel_dist = np.mean(travel_distance, axis=1)
# 1. estimate variance as time series
variance = np.var(travel_distance, axis=1)
sigma = np.sqrt(variance)
# 2. estimate diffusion by using centered derivatives method !!!
diff_dvar = 0.5*derivatives_centered(time_range, variance)
diffusion_mean = np.mean(diff_dvar)
diffusion_std = np.std(diff_dvar)
# create diffusion object
self.diffusion = diffusion_mean
self.diffusion_error = diffusion_std
self.travel_distance = travel_distance
self.mean_travel_distance = mean_travel_dist
self.timerange = time_range
self.variance = variance
self.sigma = sigma
self.direction = direction
print('~ diffusion = '+str(self.diffusion))
print('~ diffusion std = '+str(self.diffusion_error))
print('~ direction = '+str(self.direction))
try:
print('## saving results in' + join(out_path,out_name))
save(obj=self, name=out_name, folder=out_path)
except:
print("!! Unexpected error:", sys.exc_info()[0])
print("!! Check if you have writing rights.")
raise