def init_caete_dyn(self, input_fpath, stime_i, co2, pls_table):
"""
input_fpath: Files with climate and soil data
co2: (list) a alist (association list) with yearly cCO2 ATM data
pls_table: np.ndarray with functional traits of a set of PLant life strategies
name: str a name for the gridcell group"""
assert self.filled == False, "already done"
self.input_fpath = Path(os.path.join(input_fpath, self.input_fname))
assert self.input_fpath.exists()
with bz2.BZ2File(self.input_fpath, mode='r') as fh:
self.data = pkl.load(fh)
os.makedirs(self.out_dir, exist_ok=True)
self.flush_data = 0
self.pr = self.data['pr']
self.ps = self.data['ps']
self.rsds = self.data['rsds']
self.tas = self.data['tas']
self.rhs = self.data['hurs']
# SOIL AND NUTRIENTS
self.input_nut = []
self.nutlist = ['tn', 'tp', 'ap', 'ip', 'op']
for nut in self.nutlist:
self.input_nut.append(self.data[nut])
self.soil_dict = dict(zip(self.nutlist, self.input_nut))
self.data = None
# TIME
self.stime = copy.deepcopy(stime_i)
self.calendar = self.stime['calendar']
self.time_index = self.stime['time_index']
self.time_unit = self.stime['units']
self.ssize = self.time_index.size
self.sind = int(self.time_index[0])
self.eind = int(self.time_index[-1])
self.start_date = cftime.num2date(self.time_index[0],
self.time_unit,
calendar=self.calendar)
self.end_date = cftime.num2date(self.time_index[-1],
self.time_unit,
calendar=self.calendar)
# OTHER INPUTS
self.pls_table = pls_table.copy()
self.neighbours = neighbours_index(self.pos, mask)
self.soil_temp = st.soil_temp_sub(self.tas[:1095] - 273.15)
# Prepare co2 inputs (we have annually means)
self.co2_data = copy.deepcopy(co2)
# STATE
self.wfim = None
self.gfim = None
self.sfim = None
self.wp_water = 0.01
self.wp_ice = 0.0
self.wp_snow = 0.0
self.tsoil = []
self.emaxm = []
# start biomass
self.vp_cleaf, self.vp_croot, self.vp_cwood = m.spinup2(
1.0, self.pls_table)
a, b, c, d = m.pft_area_frac(self.vp_cleaf, self.vp_croot,
self.vp_cwood, self.pls_table[6, :])
self.vp_lsid = np.where(a > 0.0)[0]
del a, b, c, d
self.vp_csap = np.zeros(shape=(npls, ), order='F')
self.vp_cheart = np.zeros(shape=(npls, ), order='F')
self.vp_dcl = np.zeros(shape=(npls, ), order='F')
self.vp_dca = np.zeros(shape=(npls, ), order='F')
self.vp_dcf = np.zeros(shape=(npls, ), order='F')
self.vp_ocp = np.zeros(shape=(npls, ), order='F')
self.vp_sto = np.zeros(shape=(3, npls), order='F')
# # # SOIL START
self.sp_csoil = np.zeros(shape=(4, ), order='F') + 1.0
self.sp_snc = np.zeros(shape=(8, ), order='F')
self.sp_available_p = self.soil_dict['ap']
self.sp_available_n = 0.2 * self.soil_dict['tn']
self.sp_in_n = 0.5 * self.soil_dict['tn']
self.sp_so_n = 0.3 * self.soil_dict['tn']
self.sp_so_p = self.soil_dict['tp'] - sum(self.input_nut[2:])
self.sp_in_p = self.soil_dict['ip']
self.sp_uptk_costs = np.zeros(npls, order='F')
self.sp_organic_n = 0.01
self.sp_sorganic_n = 0.01
self.sp_organic_p = 0.01
self.sp_sorganic_p = 0.01
self.outputs = dict()
self.filled = True
return None
def init_caete_dyn(self, input_fpath, stime_i, co2, pls_table, tsoil,
ssoil):
""" PREPARE A GRIDCELL TO RUN
TODO adapt to change climatic data/ maybe another method
input_fpath:(str or pathlib.Path) path to Files with climate and soil data
co2: (list) a alist (association list) with yearly cCO2 ATM data(yyyy\t[CO2]\n)
pls_table: np.ndarray with functional traits of a set of PLant life strategies
"""
assert self.filled == False, "already done"
self.input_fpath = Path(os.path.join(input_fpath, self.input_fname))
assert self.input_fpath.exists()
with bz2.BZ2File(self.input_fpath, mode='r') as fh:
self.data = pkl.load(fh)
os.makedirs(self.out_dir, exist_ok=True)
self.flush_data = 0
self.pr = self.data['pr']
self.ps = self.data['ps']
self.rsds = self.data['rsds']
self.tas = self.data['tas']
self.rhs = self.data['hurs']
# SOIL AND NUTRIENTS
self.input_nut = []
self.nutlist = ['tn', 'tp', 'ap', 'ip', 'op']
for nut in self.nutlist:
self.input_nut.append(self.data[nut])
self.soil_dict = dict(zip(self.nutlist, self.input_nut))
self.data = None
# TIME
self.stime = copy.deepcopy(stime_i)
self.calendar = self.stime['calendar']
self.time_index = self.stime['time_index']
self.time_unit = self.stime['units']
self.ssize = self.time_index.size
self.sind = int(self.time_index[0])
self.eind = int(self.time_index[-1])
self.start_date = cftime.num2date(self.time_index[0],
self.time_unit,
calendar=self.calendar)
self.end_date = cftime.num2date(self.time_index[-1],
self.time_unit,
calendar=self.calendar)
# OTHER INPUTS
self.pls_table = pls_table.copy()
self.neighbours = neighbours_index(self.pos, mask)
self.soil_temp = st.soil_temp_sub(self.tas[:1095] - 273.15)
# Prepare co2 inputs (we have annually means)
self.co2_data = copy.deepcopy(co2)
self.tsoil = []
self.emaxm = []
# STATE
self.wp_water_upper = np.float64(0.1) # vol/vol
self.wp_water_lower = np.float64(0.1) # vol/vol
self.wp_water_upper_mm = self.wp_water_upper * 300.0
self.wp_water_lower_mm = self.wp_water_lower * 700.0
self.wp_sat_water_upper_ratio = tsoil[0][self.y, self.x].copy()
self.wp_field_capacity_upper = tsoil[1][self.y, self.x].copy()
self.wp_wilting_point_upper = tsoil[2][self.y, self.x].copy()
self.wp_sat_water_lower_ratio = ssoil[0][self.y, self.x].copy()
self.wp_field_capacity_lower = ssoil[1][self.y, self.x].copy()
self.wp_wilting_point_lower = ssoil[2][self.y, self.x].copy()
self.wp_sat_water_upper_mm = self.wp_sat_water_upper_ratio * 300.0
self.wp_sat_water_lower_mm = self.wp_sat_water_lower_ratio * 700.0
self.wmax_mm = np.float64(self.wp_sat_water_upper_mm +
self.wp_sat_water_lower_mm)
# start biomass
self.vp_cleaf, self.vp_croot, self.vp_cwood = m.spinup2(
1.0, self.pls_table)
a, b, c, d = m.pft_area_frac(self.vp_cleaf, self.vp_croot,
self.vp_cwood, self.pls_table[6, :])
self.vp_lsid = np.where(a > 0.0)[0]
del a, b, c, d
self.vp_dcl = np.zeros(shape=(npls, ), order='F')
self.vp_dca = np.zeros(shape=(npls, ), order='F')
self.vp_dcf = np.zeros(shape=(npls, ), order='F')
self.vp_ocp = np.zeros(shape=(npls, ), order='F')
self.vp_sto = np.zeros(shape=(3, npls), order='F')
# # # SOIL START
self.sp_csoil = np.zeros(shape=(4, ), order='F') + 1.0
self.sp_snc = np.zeros(shape=(8, ), order='F')
self.sp_available_p = self.soil_dict['ap']
self.sp_available_n = 0.2 * self.soil_dict['tn']
self.sp_in_n = 0.5 * self.soil_dict['tn']
self.sp_so_n = 0.3 * self.soil_dict['tn']
self.sp_so_p = self.soil_dict['tp'] - sum(self.input_nut[2:])
self.sp_in_p = self.soil_dict['ip']
self.sp_uptk_costs = np.zeros(npls, order='F')
self.sp_organic_n = 0.01
self.sp_sorganic_n = 0.01
self.sp_organic_p = 0.01
self.sp_sorganic_p = 0.01
self.outputs = dict()
self.filled = True
return None
def run_dyn(grd, at=np.copy(d_at)):
"""apply CAETÊ in dynamical mode in gridcell grd for nt1 days"""
# starting variables
# execute the model according to grd time variables
RUN = 0
w0 = np.zeros(shape=npls,) + 0.01
g0 = np.zeros(shape=npls,)
s0 = np.zeros(shape=npls,)
dcl = np.zeros(npls,)
dca = np.zeros(npls,)
dcf = np.zeros(npls,)
if grd.run_id == 'spinup':
grd.clin, grd.cfin, grd.cwin = model_funcs.spinup2(0.5, at)
# ndays,x,y,run,dt,w0,g0,s0,dcl,dca,dcf,prec,temp,p0,par,rhs&
# &,cleaf_ini,cawood_ini,cfroot_ini
outputs = model.caete_dyn(grd.x, grd.y, RUN, at, w0, g0, s0, dcl, dca, dcf, grd.pr,
grd.tas, grd.ps, grd.rsds,
grd.rhs, grd.clin, grd.cwin, grd.cfin)
# TODO
# atualizar attributos de tempo de grd
# Cut off no_data tails
# Include the cut of the time variables
# def cut_tail(in_array):
# return in_array[0:ndays-1]
grd.emaxm = outputs[0]
grd.tsoil = outputs[1]
grd.photo = outputs[2]
grd.aresp = outputs[3]
grd.npp = outputs[4]
grd.lai = outputs[5]
grd.c**t = outputs[6] # 2D
grd.csoil = outputs[7] # 3D
grd.hresp = outputs[8]
grd.rcm = outputs[9]
grd.f5 = outputs[10]
grd.runom = outputs[11]
grd.evapm = outputs[12]
grd.wsoil = outputs[13]
grd.rm = outputs[14]
grd.rg = outputs[15]
grd.cleaf = outputs[16]
grd.cawood = outputs[17]
grd.cfroot = outputs[18]
grd.area = outputs[19]
grd.wue = outputs[20]
grd.cue = outputs[21]
grd.cdef = outputs[22]
grd.wfim = outputs[23]
grd.gfim = outputs[24]
grd.sfim = outputs[25]
grd.dl_final = outputs[26]
grd.dw_final = outputs[27]
grd.dr_final = outputs[28]
grd.clf = outputs[29]
grd.caf = outputs[30]
grd.cff = outputs[31]
grd.nitro_min = outputs[32]
grd.phop_lab = outputs[33]
grd.vcmax = outputs[34]
grd.specific_la = outputs[35]
grd.nupt = outputs[36]
grd.pupt = outputs[37]
grd.litter_l = outputs[38]
grd.cwd = outputs[39]
grd.litter_fr = outputs[40]
grd.lnr = outputs[41]
grd.storage_pool = outputs[42]
def spin(grd1, r_number):
""" Continuation Runs """
outputs = model.caete_dyn(grd.x, grd.y, r_number, at, grd1.wfim, grd1.gfim, grd1.sfim,
grd1.dl_final, grd1.dw_final, grd1.dr_final, grd1.pr,
grd1.tas, grd1.ps, grd1.rsds, grd1.rhs,
grd1.clf, grd1.caf, grd1.cff)
grd1.emaxm = np.hstack((grd1.emaxm, outputs[0]))
grd1.tsoil = np.hstack((grd1.tsoil, outputs[1]))
grd1.photo = np.hstack((grd1.photo, outputs[2]))
grd1.aresp = np.hstack((grd1.aresp, outputs[3]))
grd1.npp = np.hstack((grd1.npp, outputs[4]))
grd1.lai = np.hstack((grd1.lai, outputs[5]))
grd1.c**t = np.hstack((grd1.c**t, outputs[6]))
grd1.csoil = np.hstack((grd1.csoil, outputs[7]))
grd1.hresp = np.hstack((grd1.hresp, outputs[8]))
grd1.rcm = np.hstack((grd1.rcm, outputs[9]))
grd1.f5 = np.hstack((grd1.f5, outputs[10]))
grd1.runom = np.hstack((grd1.runom, outputs[11]))
grd1.evapm = np.hstack((grd1.evapm, outputs[12]))
grd1.wsoil = np.hstack((grd1.wsoil, outputs[13]))
grd1.rm = np.hstack((grd1.rm, outputs[14]))
grd1.rg = np.hstack((grd1.rg, outputs[15]))
grd1.cleaf = np.hstack((grd1.cleaf, outputs[16]))
grd1.cawood = np.hstack((grd1.cawood, outputs[17]))
grd1.cfroot = np.hstack((grd1.cfroot, outputs[18]))
grd1.area = np.vstack((grd1.area, outputs[19]))
grd1.wue = np.hstack((grd1.wue, outputs[20]))
grd1.cue = np.hstack((grd1.cue, outputs[21]))
grd1.cdef = np.hstack((grd1.cdef, outputs[22]))
grd1.wfim = outputs[23]
grd1.gfim = outputs[24]
grd1.sfim = outputs[25]
grd1.dl_final = outputs[26]
grd1.dw_final = outputs[27]
grd1.dr_final = outputs[28]
grd1.clf = outputs[29]
grd1.caf = outputs[30]
grd1.cff = outputs[31]
grd1.nitro_min = np.hstack((grd1.nitro_min, outputs[32]))
grd1.phop_lab = np.hstack((grd1.phop_lab, outputs[33]))
grd1.vcmax = np.hstack((grd1.vcmax, outputs[34]))
grd1.specific_la = np.hstack((grd1.specific_la, outputs[35]))
grd1.nupt = np.hstack((grd1.nupt, outputs[36]))
grd1.pupt = np.hstack((grd1.pupt, outputs[37]))
grd1.litter_l = np.hstack((grd1.litter_l, outputs[38]))
grd1.cwd = np.hstack((grd1.cwd, outputs[39]))
grd1.litter_fr = np.hstack((grd1.litter_fr, outputs[40]))
grd1.lnr = np.hstack((grd1.lnr, outputs[41]))
grd1.storage_pool = np.hstack((grd1.storage_pool, outputs[42]))
# # GAMBIARRA NERVOSA
print('\n--run 1\n')
RUN += int(gp.nt1)
spin(grd, RUN)
print('\n--run 2\n')
RUN += int(gp.nt1)
spin(grd, RUN)
print('\n--run 3\n')
RUN += int(gp.nt1)
spin(grd, RUN)
print('\n--run 4\n')
RUN += int(gp.nt1)
spin(grd, RUN)
print('\n--run 5\n')
RUN += int(gp.nt1)
spin(grd, RUN)