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_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 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 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 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_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)
