Example #1
0
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)
Example #2
0
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)