def RunSpytfire ( grid_id, year, theta, Datos ):
(pftpar, sla, tree, evergreen, summergreen, raingreen,\
needle, boreal, lm_sapl, \
sm_sapl, hm_sapl, rm_sapl, latosa, allom1, allom2,\
allom3, allom4, wooddens, reinickerp) =\
spytfire.pftparameters()
#s = SpitfireDB()
datos = deepcopy(Datos)
a_nd = theta[0]
#pdb.set_trace()
cnt = 1
for ipft in xrange(9):
if (ipft+1) in datos['present']:
pftpar[ ipft,36 ] = theta [ cnt ]
cnt += 1
####pftpar[0,36] = theta[1] # Fuel Bulk Density
####pftpar[1,36] = theta[2]
####pftpar[7,36] = theta[3]
####pftpar[8,36] = theta[4]
sigma_1hr = theta[cnt] ; sigma_10hr = theta[cnt+1] ; sigma_100hr=theta[cnt+2]
#moistfactor_livegrass = theta[13] ; moistfactor_1hr = theta[14]
####moistfactor_10hr = theta[15]
####moistfactor_100hr = theta[16]
####moistfactor_1000hr = theta[17]
moistfactor_livegrass = 0.2 ; moistfactor_1hr = 0.404
moistfactor_10hr = 0.487
moistfactor_100hr = 0.525
moistfactor_1000hr = 0.544
fbd_a = 1.2 ; fbd_b = 1.4
fbd_C3_livegrass = 4. ; fbd_C4_livegrass = 4.
#fbd_a = theta[1] ; fbd_b = theta[2] #1.2, 1.4
#fbd_C3_livegrass = theta[3] ; fbd_C4_livegrass = theta[4] # 4.0, 4.0
#datos = s.GetSpinUpVars ( grid_id, year )
#modis_ba = s.GetMODISBA ( grid_id, year )
datos['a_nd'] = a_nd*numpy.ones((1,12),dtype=numpy.float32)
#datos['lightn'] =numpy.array( datos['lightn'])*kappa
#pdb.set_trace()
( d_fdi, acflux_fire, mcflux_fire, afire_frac, num_fire, annum_fire, \
area_burnt, an_areafires, mfdi,an_fdi,\
an_fseason, mcflux_trace, acflux_trace, m_fc_crown, an_fc_crown, m_i_surface,\
an_i_surface, dlm_lg, dlm_1hr, dlm_10hr, nesterov, fire_durat, d_area_burnt, \
d_numfire, ros_f, ros_b,lb,d_fuel_consumed, d_i_surface, cf) = \
spytfire.fire ( year, pftpar, datos['dtemp'], datos['dtemp_min'], datos['dtemp_max'],\
datos['dprec'], datos['dwindsp'], datos['lightn'], \
datos['dphen'], datos['litter_ag'], datos['litter_bg'], \
datos['fuel_1hr'], datos['fuel_10hr'], datos['fuel_100hr'], datos['fuel_1000hr'],\
datos['lm_ind'], datos['rm_ind'], datos['sm_ind'], datos['hm_ind'], datos['nind'],\
datos['dw1'],datos['present'],\
tree, datos['lat'], datos['mw1'], datos['fpc_grid'], datos['popden'],\
datos['a_nd'], datos['height'], datos['height_class'],\
datos['dbh'],datos['tau_c'],datos['cl_t'], fbd_a, fbd_b, fbd_C3_livegrass,fbd_C4_livegrass,\
sigma_1hr,sigma_10hr,sigma_100hr,\
moistfactor_livegrass,moistfactor_1hr, moistfactor_10hr,\
moistfactor_100hr,moistfactor_1000hr )
return (area_burnt)
def RunSpytfire(
grid_id,
year,
theta,
Datos,
theta0=numpy.array([0.39, 14.0, 12.0, 23.0, 28.0, 22.0, 18.0, 16.0, 4.0, 2.0, 66.0, 3.58, 0.98]),
):
# Obtain the parameters from subroutine...
(
pftpar,
sla,
tree,
evergreen,
summergreen,
raingreen,
needle,
boreal,
lm_sapl,
sm_sapl,
hm_sapl,
rm_sapl,
latosa,
allom1,
allom2,
allom3,
allom4,
wooddens,
reinickerp,
) = spytfire.pftparameters()
# theta is defined as [a_nd, fuel_bulk_density[pfts_present], sigma_1hr, sigma_10hr, sigma_100hr, alpha, moisture_extinction, moisture_conversion, duration_exponential]
datos = deepcopy(Datos)
a_nd = theta[0]
cnt = 1
for ipft in xrange(9):
if datos["present"][ipft] == 1:
pftpar[ipft, 36] = theta[cnt]
cnt += 1
else:
pftpar[ipft, 36] = theta0[ipft + 1]
sigma_1hr = theta[cnt]
sigma_10hr = theta[cnt + 1]
sigma_100hr = theta[cnt + 2]
fbd_C3_livegrass = 4.0
fbd_C4_livegrass = 4.0
datos["a_nd"] = a_nd * numpy.ones((1, 12), dtype=numpy.float32)
(
d_fdi,
acflux_fire,
mcflux_fire,
afire_frac,
num_fire,
annum_fire,
area_burnt,
an_areafires,
mfdi,
an_fdi,
an_fseason,
mcflux_trace,
acflux_trace,
m_fc_crown,
an_fc_crown,
m_i_surface,
an_i_surface,
dlm_lg,
dlm_1hr,
dlm_10hr,
nesterov,
fire_durat,
d_area_burnt,
d_numfire,
ros_f,
ros_b,
lb,
d_fuel_consumed,
d_i_surface,
cf,
) = spytfire.fire(
year,
pftpar,
datos["dtemp"],
datos["dtemp_min"],
datos["dtemp_max"],
datos["dprec"],
datos["dwindsp"],
datos["lightn"],
datos["dphen"],
datos["litter_ag"],
datos["litter_bg"],
datos["fuel_1hr"],
datos["fuel_10hr"],
datos["fuel_100hr"],
datos["fuel_1000hr"],
datos["lm_ind"],
datos["rm_ind"],
datos["sm_ind"],
datos["hm_ind"],
datos["nind"],
datos["dw1"],
datos["present"],
tree,
datos["lat"],
datos["mw1"],
datos["fpc_grid"],
datos["popden"],
datos["a_nd"],
datos["height"],
datos["height_class"],
datos["dbh"],
datos["tau_c"],
datos["cl_t"],
fbd_C3_livegrass,
fbd_C4_livegrass,
sigma_1hr,
sigma_10hr,
sigma_100hr,
alpha,
moisture_extinction,
moisture_conversion,
duration_exponential,
)
return (area_burnt, dlm_lg, num_fire, ros_f, ros_b, dlm_10hr, fire_durat)