/
run_echoRD.py
394 lines (344 loc) · 19.1 KB
/
run_echoRD.py
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import numpy as np
import pandas as pd
import scipy as sp
import matplotlib.pyplot as plt
import scipy.constants as const
import os, sys
def run_from_ipython():
try:
__IPYTHON__
return True
except NameError:
return False
def loadconnect(pathdir='./', mcinif='mcini', oldvers=False):
lib_path = os.path.abspath(pathdir)
sys.path.append(lib_path)
import dataread as dr
if oldvers:
import mcpickle as mcp
else:
import mcpickle2 as mcp
import infilt as cinf
import partdyn_d2 as pdyn
mc = __import__(mcinif)
import vG_conv as vG
return(dr,mc,mcp,pdyn,cinf,vG)
def preproc_echoRD(mc, dr, mcp, pickfile='test.pickle'):
mc=dr.dataread_caos(mc)
mcp.mcpick_in(mc,pickfile)
return mc
def pickup_echoRD(mc, mcp, dr, pickfile='test.pickle'):
mcp.mcpick_out(mc,pickfile)
[mc,particles,npart]=dr.particle_setup(mc)
precTS=pd.read_csv(mc.precf, sep=',',skiprows=3)
return(mc,particles,npart,precTS)
def particle_setup_obs(theta_obs,mc,vG,dr,pdyn):
moistdomain=np.tile(theta_obs,int(mc.mgrid.latgrid)).reshape((mc.mgrid.latgrid,mc.mgrid.vertgrid)).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.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?
# define macropore capacity based on particle size
# we introduce a scale factor for converting macropore space and particle size
mc.maccap=np.round(mc.md_area/((mc.particleD**2)*np.pi*mc.macscalefac)).astype(int)
# convert theta to particles
# npart=moistdomain*(2*mc.part_sizefac)
npart=np.floor(mc.part_sizefac*vG.thst_theta(moistdomain,mc.soilmatrix.ts[mc.soilgrid.ravel()-1].reshape(np.shape(mc.soilgrid)), mc.soilmatrix.tr[mc.soilgrid.ravel()-1].reshape(np.shape(mc.soilgrid)))).astype(int)
# setup particle domain
particles=pd.DataFrame(np.zeros(int(np.sum(npart))*8).reshape(int(np.sum(npart)),8),columns=['lat', 'z', 'conc', 'temp', 'age', 'flag', 'fastlane', 'advect'])
particles['cell']=pd.Series(np.zeros(int(np.sum(npart)),dtype=int), index=particles.index)
# distribute particles
k=0
npartr=npart.ravel()
cells=len(npartr)
for i in np.arange(cells):
j=int(npartr[i])
particles.cell[k:(k+j)]=i
rw,cl=np.unravel_index(i,(mc.mgrid.vertgrid,mc.mgrid.latgrid))
particles.lat[k:(k+j)]=(cl+np.random.rand(j))*mc.mgrid.latfac.values
particles.z[k:(k+j)]=(rw+np.random.rand(j))*mc.mgrid.vertfac.values
k+=j
particles.fastlane=np.random.randint(len(mc.t_cdf_fast.T), size=len(particles))
particles.advect=pdyn.assignadvect(int(np.sum(npart)),mc,particles.fastlane.values,True)
mc.mgrid['cells']=cells
return [mc,particles.iloc[0:k,:],npart]
def start_echoRDdx(mc,particles,npart,precTS,pdyn,cinf,runname='echoRD',t_end=3600.,output=60.,start_offset=0.,splitfac=10):
[thS,npart]=pdyn.gridupdate_thS(particles.lat,particles.z,mc)
drained=pd.DataFrame(np.array([]))
leftover=0
plotparticles_t(runname,0.,0,particles,(thS/100.).reshape(np.shape(npart)),mc)
#loop through plot cycles
dummy=np.floor(t_end/output)
for i in np.arange(dummy.astype(int)):
[particles,npart,thS,leftover,drained,t]=CAOSpy_rundx(i*output+start_offset,(i+1)*output+start_offset,mc,pdyn,cinf,precTS,particles,leftover,drained,splitfac=splitfac)
plotparticles_t(runname,t,i+1,particles,(thS/100.).reshape(np.shape(npart)),mc)
def start_echoRDxstore(mc,particles,npart,precTS,pdyn,cinf,runname='echoRD',t_end=3600.,output=60.,start_offset=0.,splitfac=10,maccoat=10.,exfilt_method='Ediss'):
[thS,npart]=pdyn.gridupdate_thS(particles.lat,particles.z,mc)
drained=pd.DataFrame(np.array([]))
leftover=0
plotparticles_t(runname,0.,0,particles,(thS/100.).reshape(np.shape(npart)),mc)
#loop through plot cycles
dummy=np.floor(t_end/output)
TSstore=np.zeros((int(dummy),np.shape(thS)[0],np.shape(thS)[1]))
for i in np.arange(dummy.astype(int)):
[particles,npart,thS,leftover,drained,t]=CAOSpy_rundx(i*output+start_offset,(i+1)*output+start_offset,mc,pdyn,cinf,precTS,particles,leftover,drained,splitfac=splitfac,prec_2D=True,maccoat=maccoat,exfilt_method=exfilt_method)
plotparticles_t(runname,t,i+1,particles,(thS/100.).reshape(np.shape(npart)),mc)
TSstore[i,:,:]=thS
return TSstore
def CAOSpy_rundx(tstart,tstop,mc,pdyn,cinf,precTS,particles,leftover,drained,dt_max=1.,splitfac=10,prec_2D=False,maccoat=10.,exfilt_method='Ediss',saveDT=True,vertcalfac=1.,latcalfac=1.,clogswitch=False,infilt_method='MDA',film=True,infiltscale=False):
if run_from_ipython():
from IPython import display
timenow=tstart
prec_part=0. #precipitation which is less than one particle to accumulate
acc_mxinf=0. #matrix infiltration may become very small - this shall handle that some particles accumulate to infiltrate
exfilt_p=0. #exfiltration from the macropores
s_red=0.
#loop through time
while timenow < tstop:
[thS,npart]=pdyn.gridupdate_thS(particles.lat,particles.z,mc)
if saveDT==True:
#define dt as Courant/Neumann criterion
dt_D=(mc.mgrid.vertfac.values[0])**2 / (6*np.nanmax(mc.D[np.amax(thS),:]))
dt_ku=-mc.mgrid.vertfac.values[0]/np.nanmax(mc.ku[np.amax(thS),:])
dt=np.amin([dt_D,dt_ku,dt_max,tstop-timenow])
else:
if type(saveDT)==float:
#define dt as pre-defined
dt=np.amin([saveDT,tstop-timenow])
elif type(saveDT)==int:
#define dt as modified Corant/Neumann criterion
dt_D=(mc.mgrid.vertfac.values[0])**2 / (6*np.nanmax(mc.D[np.amax(thS),:]))*saveDT
dt_ku=-mc.mgrid.vertfac.values[0]/np.nanmax(mc.ku[np.amax(thS),:])*saveDT
dt=np.amin([dt_D,dt_ku,dt_max,tstop-timenow])
#INFILTRATION
[p_inf,prec_part,acc_mxinf]=cinf.pmx_infilt(timenow,precTS,prec_part,acc_mxinf,thS,mc,pdyn,dt,0.,prec_2D,particles.index[-1],infilt_method,infiltscale) #drain all ponding // leftover <-> 0.
particles=pd.concat([particles,p_inf])
#DIFFUSION
[particles,thS,npart,phi_mx]=pdyn.part_diffusion_split(particles,npart,thS,mc,dt,False,splitfac,vertcalfac,latcalfac)
#ADVECTION
if not particles.loc[(particles.flag>0) & (particles.flag<len(mc.maccols)+1)].empty:
[particles,s_red,exfilt_p]=pdyn.mac_advection(particles,mc,thS,dt,clogswitch,maccoat,exfilt_method,film=film)
#INTERACT
particles=pdyn.mx_mp_interact_nobulk(particles,npart,thS,mc,dt)
if run_from_ipython():
display.clear_output()
display.display_pretty(''.join(['time: ',str(timenow),'s | precip: ',str(len(p_inf)),' particles | mean v(adv): ',str(particles.loc[particles.flag>0,'advect'].mean()),' m/s | exfilt: ',str(int(exfilt_p)),' particles']))
else:
print 'time: ',timenow,'s'
#CLEAN UP DATAFRAME
drained=drained.append(particles[particles.flag==len(mc.maccols)+1])
particles=particles[particles.flag!=len(mc.maccols)+1]
pondparts=(particles.z<0.)
leftover=np.count_nonzero(-pondparts)
particles.cell[particles.cell<0]=mc.mgrid.cells.values
particles=particles[pondparts]
timenow=timenow+dt
return(particles,npart,thS,leftover,drained,timenow)
def CAOSpy_rundx_noise(tstart,tstop,mc,pdyn,cinf,precTS,particles,leftover,drained,dt_max=1.,splitfac=10,prec_2D=False,maccoat=10.,exfilt_method='Ediss',saveDT=True,vertcalfac=1.,latcalfac=1.,clogswitch=False,infilt_method='MDA',film=True,dynamic_pedo=True,ksnoise=1.):
if run_from_ipython():
from IPython import display
timenow=tstart
prec_part=0. #precipitation which is less than one particle to accumulate
acc_mxinf=0. #matrix infiltration may become very small - this shall handle that some particles accumulate to infiltrate
exfilt_p=0. #exfiltration from the macropores
s_red=0.
#loop through time
while timenow < tstop:
[thS,npart]=pdyn.gridupdate_thS(particles.lat,particles.z,mc)
if saveDT==True:
#define dt as Courant/Neumann criterion
dt_D=(mc.mgrid.vertfac.values[0])**2 / (6*np.nanmax(mc.D[np.amax(thS),:]))
dt_ku=-mc.mgrid.vertfac.values[0]/np.nanmax(mc.ku[np.amax(thS),:])
dt=np.amin([dt_D,dt_ku,dt_max,tstop-timenow])
else:
if type(saveDT)==float:
#define dt as pre-defined
dt=np.amin([saveDT,tstop-timenow])
elif type(saveDT)==int:
#define dt as modified Corant/Neumann criterion
dt_D=(mc.mgrid.vertfac.values[0])**2 / (6*np.nanmax(mc.D[np.amax(thS),:]))*saveDT
dt_ku=-mc.mgrid.vertfac.values[0]/np.nanmax(mc.ku[np.amax(thS),:])*saveDT
dt=np.amin([dt_D,dt_ku,dt_max,tstop-timenow])
#INFILTRATION
[p_inf,prec_part,acc_mxinf]=cinf.pmx_infilt(timenow,precTS,prec_part,acc_mxinf,thS,mc,pdyn,dt,0.,prec_2D,particles.index[-1],infilt_method) #drain all ponding // leftover <-> 0.
particles=pd.concat([particles,p_inf])
#DIFFUSION
[particles,thS,npart,phi_mx]=pdyn.part_diffusion_split(particles,npart,thS,mc,dt,False,splitfac,vertcalfac,latcalfac,dynamic_pedo=True,ksnoise=ksnoise)
#ADVECTION
if not particles.loc[(particles.flag>0) & (particles.flag<len(mc.maccols)+1)].empty:
[particles,s_red,exfilt_p]=pdyn.mac_advection(particles,mc,thS,dt,clogswitch,maccoat,exfilt_method,film=film,dynamic_pedo=True,ksnoise=ksnoise)
#INTERACT
particles=pdyn.mx_mp_interact_nobulk(particles,npart,thS,mc,dt,dynamic_pedo=True,ksnoise=ksnoise)
if run_from_ipython():
display.clear_output()
display.display_pretty(''.join(['time: ',str(timenow),'s | precip: ',str(len(p_inf)),' particles | mean v(adv): ',str(particles.loc[particles.flag>0,'advect'].mean()),' m/s | exfilt: ',str(int(exfilt_p)),' particles']))
else:
print 'time: ',timenow,'s'
#CLEAN UP DATAFRAME
drained=drained.append(particles[particles.flag==len(mc.maccols)+1])
particles=particles[particles.flag!=len(mc.maccols)+1]
pondparts=(particles.z<0.)
leftover=np.count_nonzero(-pondparts)
particles.cell[particles.cell<0]=mc.mgrid.cells.values
particles=particles[pondparts]
timenow=timenow+dt
return(particles,npart,thS,leftover,drained,timenow)
def CAOSpy_rund_diffonly(tstart,tstop,mc,pdyn,cinf,precTS,particles,leftover,drained,dt_max=1.,splitfac=10,prec_2D=False,saveDT=True,vertcalfac=1.,latcalfac=1.):
if run_from_ipython():
from IPython import display
timenow=tstart
prec_part=0. #precipitation which is less than one particle to accumulate
acc_mxinf=0. #matrix infiltration may become very small - this shall handle that some particles accumulate to infiltrate
exfilt_p=0. #exfiltration from the macropores
s_red=0.
#loop through time
while timenow < tstop:
[thS,npart]=pdyn.gridupdate_thS(particles.lat,particles.z,mc)
if saveDT==True:
#define dt as Courant/Neumann criterion
dt_D=(mc.mgrid.vertfac.values[0])**2 / (6*np.nanmax(mc.D[np.amax(thS),:]))
dt_ku=-mc.mgrid.vertfac.values[0]/np.nanmax(mc.ku[np.amax(thS),:])
dt=np.amin([dt_D,dt_ku,dt_max,tstop-timenow])
else:
if type(saveDT)==float:
#define dt as pre-defined
dt=np.amin([saveDT,tstop-timenow])
elif type(saveDT)==int:
#define dt as modified Corant/Neumann criterion
dt_D=(mc.mgrid.vertfac.values[0])**2 / (6*np.nanmax(mc.D[np.amax(thS),:]))*saveDT
dt_ku=-mc.mgrid.vertfac.values[0]/np.nanmax(mc.ku[np.amax(thS),:])*saveDT
dt=np.amin([dt_D,dt_ku,tstop-timenow])
#INFILTRATION
[p_inf,prec_part,acc_mxinf]=cinf.pmx_infilt(timenow,precTS,prec_part,acc_mxinf,thS,mc,pdyn,dt,0.,prec_2D,particles.index[-1]) #drain all ponding // leftover <-> 0.
p_inf.flag=0
particles=pd.concat([particles,p_inf])
#DIFFUSION
[particles,thS,npart,phi_mx]=pdyn.part_diffusion_split(particles,npart,thS,mc,dt,False,splitfac,vertcalfac,latcalfac)
if run_from_ipython():
display.clear_output()
display.display_pretty(''.join(['time: ',str(timenow),'s | precip: ',str(len(p_inf)),' particles']))
else:
print 'time: ',timenow,'s'
#CLEAN UP DATAFRAME
drained=drained.append(particles[particles.flag==len(mc.maccols)+1])
particles=particles[particles.flag!=len(mc.maccols)+1]
pondparts=(particles.z<0.)
leftover=np.count_nonzero(-pondparts)
particles.cell[particles.cell<0]=mc.mgrid.cells.values
particles=particles[pondparts]
timenow=timenow+dt
return(particles,npart,thS,leftover,drained,timenow)
def plotparticles2(runname,t,ix,particles,npart,mc):
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.gridspec as gridspec
fig=plt.figure(figsize=(8, 8))
gs = gridspec.GridSpec(2, 2, width_ratios=[1,2], height_ratios=[1,5])
ax1 = plt.subplot(gs[0])
ax11 = ax1.twinx()
advect_dummy=np.bincount(np.round(100.0*particles.loc[((particles.age>0.)),'lat'].values).astype(np.int))
old_dummy=np.bincount(np.round(100.0*particles.loc[((particles.age<=0.)),'lat'].values).astype(np.int))
ax1.plot((np.arange(0,len(advect_dummy))/100.)[1:],advect_dummy[1:],'b-')
ax11.plot((np.arange(0,len(old_dummy))/100.)[1:],old_dummy[1:],'g-')
ax11.set_ylabel('Particle Count', color='g')
ax11.set_xlim([0.,mc.mgrid.width.values])
ax1.set_xlim([0.,mc.mgrid.width.values])
ax1.set_ylabel('New Particle Count', color='b')
ax1.set_xlabel('Lat [m]')
ax1.set_title('Lateral Particles Concentration')
ax2 = plt.subplot(gs[1])
ax2.axis('off')
ax2.text(0.1, 0.8, 'Particles @ t='+str(np.round(t/60.))+'min', fontsize=20)
ax3 = plt.subplot(gs[2])
plt.imshow(sp.ndimage.filters.median_filter(npart,size=mc.smooth),vmin=1, vmax=mc.part_sizefac, cmap='jet')
#plt.imshow(npart)
plt.colorbar()
plt.xlabel(''.join(['Width [cells a ',str(np.round(1000*mc.mgrid.latfac.values[0],decimals=1)),' mm]']))
plt.ylabel(''.join(['Depth [cells a ',str(np.round(1000*mc.mgrid.vertfac.values[0],decimals=1)),' mm]']))
plt.title('Particle Density')
plt.tight_layout()
ax4 = plt.subplot(gs[3])
#ax41 = ax4.twiny()
z1=np.append(particles.loc[((particles.age>0.)),'z'].values,mc.onepartpercell[1][:mc.mgrid.vertgrid.values.astype(int)])
advect_dummy=np.bincount(np.round(-100.0*z1).astype(np.int))-1
old_dummy=np.bincount(np.round(-100.0*particles.loc[((particles.age<=0.)),'z'].values).astype(np.int))
ax4.plot(advect_dummy,(np.arange(0,len(advect_dummy))/-100.),'r-',label='new particles')
ax4.plot(advect_dummy+old_dummy,(np.arange(0,len(old_dummy))/-100.),'b-',label='all particles')
ax4.plot(old_dummy,(np.arange(0,len(old_dummy))/-100.),'g-',label='old particles')
ax4.set_xlabel('Particle Count')
#ax4.set_xlabel('New Particle Count', color='r')
ax4.set_ylabel('Depth [m]')
#ax4.set_title('Number of Particles')
ax4.set_ylim([mc.mgrid.depth.values,0.])
ax4.set_xlim([0.,np.max(old_dummy+advect_dummy)])
#ax41.set_xlim([0.,np.max(old_dummy[1:])])
#ax41.set_ylim([mc.mgrid.depth.values,0.])
handles1, labels1 = ax4.get_legend_handles_labels()
#handles2, labels2 = ax41.get_legend_handles_labels()
ax4.legend(handles1, labels1, loc=4)
# ax41.legend(loc=4)
plt.savefig(''.join(['./results/',runname,str(ix).zfill(3),'.png']))
#savefig('runname %(i)03d .png'.translate(None, ' '))
plt.close(fig)
def plotparticles_t(runname,t,ix,particles,thS,mc):
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.gridspec as gridspec
fig=plt.figure(figsize=(8, 8))
gs = gridspec.GridSpec(2, 2, width_ratios=[2,1], height_ratios=[1,5])
ax1 = plt.subplot(gs[0])
ax11 = ax1.twinx()
advect_dummy=np.bincount(np.round(100.0*particles.loc[((particles.age>0.)),'lat'].values).astype(np.int))
old_dummy=np.bincount(np.round(100.0*particles.loc[((particles.age<=0.)),'lat'].values).astype(np.int))
ax1.plot((np.arange(0,len(advect_dummy))/100.)[1:],advect_dummy[1:],'b-')
ax11.plot((np.arange(0,len(old_dummy))/100.)[1:],old_dummy[1:],'g-')
ax11.set_ylabel('Particle Count', color='g')
ax11.set_xlim([0.,mc.mgrid.width.values])
ax1.set_xlim([0.,mc.mgrid.width.values])
ax1.set_ylabel('New Particle Count', color='b')
ax1.set_xlabel('Lat [m]')
ax1.set_title('Lateral Particles Concentration')
ax2 = plt.subplot(gs[1])
ax2.axis('off')
ax2.text(0.1, 0.8, 't='+str(np.round(t/60.,1))+'min', fontsize=20)
ax3 = plt.subplot(gs[2])
plt.imshow(sp.ndimage.filters.median_filter(thS,size=mc.smooth),vmin=0., vmax=1., cmap='Blues')
#plt.imshow(npart)
plt.colorbar()
plt.xlabel('Width [cells a 5 mm]')
plt.ylabel('Depth [cells a 5 mm]')
plt.title('Particle Density')
plt.tight_layout()
ax4 = plt.subplot(gs[3])
#ax41 = ax4.twiny()
onez=np.arange(0.,mc.mgrid.depth-0.004,-0.01)-0.004
z1=np.append(particles.loc[((particles.age>0.)),'z'].values,onez)
advect_dummy=np.bincount(np.round(-100.0*z1).astype(np.int))-1
z2=np.append(particles.loc[((particles.age<=0.)),'z'].values,onez)
old_dummy=np.bincount(np.round(-100.0*z2).astype(np.int))-1
ax4.plot(advect_dummy,(np.arange(0,len(advect_dummy))/-100.),'r-',label='new particles')
ax4.plot(advect_dummy+old_dummy,(np.arange(0,len(old_dummy))/-100.),'b-',label='all particles')
ax4.plot(old_dummy,(np.arange(0,len(old_dummy))/-100.),'g-',label='old particles')
ax4.set_xlabel('Particle Count')
#ax4.set_xlabel('New Particle Count', color='r')
ax4.set_ylabel('Depth [m]')
#ax4.set_title('Number of Particles')
ax4.set_ylim([mc.mgrid.depth.values,0.])
ax4.set_xlim([0.,np.max(old_dummy+advect_dummy)])
#ax41.set_xlim([0.,np.max(old_dummy[1:])])
#ax41.set_ylim([mc.mgrid.depth.values,0.])
handles1, labels1 = ax4.get_legend_handles_labels()
#handles2, labels2 = ax41.get_legend_handles_labels()
ax4.legend(handles1, labels1, loc=4)
# ax41.legend(loc=4)
plt.savefig(''.join(['./results/',runname,'t_',str(ix).zfill(3),'.png']))
#savefig('runname %(i)03d .png'.translate(None, ' '))
plt.close(fig)