pickle_l = pickle.load(handle)
dummyx = pickle.loads(pickle_l)
particles = pickle.loads(dummyx[0])
[leftover, drained, t, TSstore, ix] = pickle.loads(dummyx[1])
ix += 1
print('resuming into stored run at t=' + str(t) + '...')
except:
print('starting new run...')
#loop through plot cycles
for i in np.arange(dummy.astype(int))[ix:]:
plotparticles_weier(particles,
mc,
pdyn,
vG,
runname,
t,
i,
saving=True,
relative=False,
wdir=wdir)
[particles, npart, thS, leftover, drained,
t] = rE.CAOSpy_rundx1(i * output, (i + 1) * output,
mc,
pdyn,
cinf,
precTS,
particles,
leftover,
drained,
6.,
splitfac=4,
def echoRD_job(mcinif='mcini',
mcpick='mc.pickle3',
runname='test',
wdir='./',
pathdir='../echoRD/',
saveDT=True,
aref='Shipitalo',
LTEdef='instant',
infilM='MDA',
exfilM='Ediss',
parallel=False,
largepick=False):
'''
This is a wrapper for running echoRD easily.
Be warned that some definitions are implicit in this wrapper
mcini -- echoRD ini file of run
mcpick -- pickled mode setup
runname -- name of model run
wdir -- working directory path
pathdir -- path to echoRD model
Model parameters
aref -- Advection reference [Shipitalo | Weiler | Zehe | geogene | geogene2 | obs]
infilM -- Infiltration handling [MDA | MED | rand]
LTEdef -- Infiltration assumption [instant | ks | random]
exfilM -- Exfiltration from macropores [Ediss | RWdiff]
saveDT -- optional modified time steps [True | int (factor) | double (static step)]
parallel-- flag for parallel initialisation of particles
largepick- flag for pickling many particles
'''
import numpy as np
import pandas as pd
import scipy as sp
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os, sys
try:
import cPickle as pickle
except:
import pickle
#connect echoRD Tools
lib_path = os.path.abspath(pathdir)
sys.path.append(lib_path)
import vG_conv as vG
from hydro_tools import plotparticles_t, hydroprofile, plotparticles_weier
#connect to echoRD
import run_echoRD as rE
#connect and load project
[dr, mc, mcp, pdyn, cinf, vG] = rE.loadconnect(pathdir='../',
mcinif=mcinif,
experimental=True)
mc = mcp.mcpick_out(mc, mcpick)
mc.advectref = aref
precTS = pd.read_csv(mc.precf, sep=',', skiprows=3)
mc.prects = False
#check for previous runs to hook into
try:
#unpickle:
if largepick:
with open(''.join([wdir, '/results/L', runname, '_Mstat.pick']),
'rb') as handle:
pickle_l = pickle.load(handle)
dummyx = pickle.loads(pickle_l)
particles = pickle.loads(dummyx[0])
[t, ix] = pickle.loads(dummyx[1])
ix += 1
else:
with open(''.join([wdir, '/results/Z', runname, '_Mstat.pick']),
'rb') as handle:
pickle_l = pickle.load(handle)
dummyx = pickle.loads(pickle_l)
particles = pickle.loads(dummyx[0])
[leftover, drained, t, TSstore, thetastore, npart,
ix] = pickle.loads(dummyx[1])
ix += 1
print('resuming into stored run at t=' + str(t) + '...')
# define particle size
# WARNING: as in any model so far, we have a volume problem here.
# we consider all parts of the domain as static in volume at this stage.
# however, we will work on a revision of this soon.
mc.gridcellA = mc.mgrid.vertfac * mc.mgrid.latfac
mc.particleA = abs(mc.gridcellA.values) / (
2 * mc.part_sizefac
) #assume average ks at about 0.5 as reference of particle size
mc.particleD = 2. * np.sqrt(mc.particleA / np.pi)
mc.particleV = 3. / 4. * np.pi * (mc.particleD / 2.)**3.
mc.particleD /= np.sqrt(abs(mc.gridcellA.values))
mc.particleV /= np.sqrt(abs(
mc.gridcellA.values)) #assume grid size as 3rd dimension
mc.particlemass = dr.waterdensity(np.array(20), np.array(
-9999)) * mc.particleV #assume 20C as reference for particle mass
#DEBUG: a) we assume 2D=3D; b) change 20C to annual mean T?
#initialise bins and slopes
mc = dr.ini_bins(mc)
mc = dr.mc_diffs(mc, np.max(np.max(mc.mxbin)))
# estimate macropore capacity as relative share of space (as particle volume is not well-defined for the 1D-2D-3D references)
mc.maccap = np.ceil(
(2. * mc.md_area /
(-mc.gridcellA.values * mc.mgrid.latgrid.values)) *
mc.part_sizefac)
mc.mactopfill = np.ceil(
(2. * mc.md_area /
(-mc.gridcellA.values * mc.mgrid.latgrid.values)) *
mc.part_sizefac)[:, 0] * 0. #all empty
# assumption: the pore space is converted into particles through mc.part_sizefac. this is now reprojected to the macropore by using the areal share of the macropores
# DEBUG: there is still some inconcistency about the areas and volumes, but it may be a valid estimate with rather few assumptions
if largepick:
[thS, npart] = pdyn.gridupdate_thS(particles.lat, particles.z, mc)
mc.mgrid['cells'] = len(npart.ravel())
except:
#initialise particles
[mc, particles, npart] = dr.particle_setup(mc, paral=parallel)
t = 0.
ix = 0
leftover = 0
dummy = np.floor(mc.t_end / mc.t_out)
TSstore = np.zeros((int(dummy), mc.mgrid.cells[0], 2))
thetastore = np.zeros(
(int(dummy), mc.mgrid.vertgrid[0], mc.mgrid.latgrid[0]))
print('starting new run...')
#define bin assignment mode for infiltration particles
mc.LTEdef = LTEdef #'instant'#'ks' #'instant' #'random'
mc.LTEmemory = mc.soilgrid.ravel() * 0.
#macropore reference
mc.maccon = np.where(
mc.macconnect.ravel() > 0)[0] #index of all connected cells
mc.md_macdepth = np.abs(mc.md_macdepth)
#############
# Run Model #
#############
mc.LTEpercentile = 70 #new parameter
t_end = mc.t_end
infiltmeth = infilM
exfiltmeth = exfilM
film = True
macscale = 1. #scale the macropore coating
clogswitch = False
infiltscale = False
drained = pd.DataFrame(np.array([]))
output = mc.t_out #mind to set also in TXstore.index definition
dummy = np.floor(t_end / output)
#loop through plot cycles
for i in np.arange(dummy.astype(int))[ix:]:
plotparticles_weier(particles,
mc,
pdyn,
vG,
runname,
t,
i,
saving=True,
relative=False,
wdir=wdir)
[particles, npart, thS, leftover, drained,
t] = rE.CAOSpy_rundx1(i * output, (i + 1) * output,
mc,
pdyn,
cinf,
precTS,
particles,
leftover,
drained,
6.,
splitfac=4,
prec_2D=False,
maccoat=macscale,
saveDT=saveDT,
clogswitch=clogswitch,
infilt_method=infiltmeth,
exfilt_method=exfiltmeth,
film=film,
infiltscale=infiltscale)
TSstore[i, :, :] = rE.part_store(particles, mc)
thetastore[i, :, :] = np.reshape(
(mc.soilmatrix.loc[mc.soilgrid.ravel() - 1, 'tr'] +
(mc.soilmatrix.ts - mc.soilmatrix.tr)[mc.soilgrid.ravel() - 1] *
thS.ravel() * 0.01).values, np.shape(thS))
if largepick:
with open(''.join([wdir, '/results/L', runname, '_Mstat.pick']),
'wb') as handle:
pickle.dump(pickle.dumps(
[pickle.dumps(particles),
pickle.dumps([t, i])]),
handle,
protocol=2)
else:
with open(''.join([wdir, '/results/Z', runname, '_Mstat.pick']),
'wb') as handle:
pickle.dump(pickle.dumps([
pickle.dumps(particles),
pickle.dumps(
[leftover, drained, t, TSstore, thetastore, npart, i])
]),
handle,
protocol=2)