clim_metadata = Path(
os.path.join(s_data, clim_and_soil_data,
"ISIMIP_HISTORICAL_METADATA.pbz2"))
with bz2.BZ2File(clim_metadata, mode='r') as fh:
clim_metadata = pkl.load(fh)
stime = copy.deepcopy(clim_metadata[0])
del clim_metadata
# open co2 data
with open(os.path.join(s_data,
"co2/historical_CO2_annual_1765_2018.txt")) as fh:
co2_data = fh.readlines()
# FUNCTIONAL TRAITS DATA
pls_table = pls.table_gen(npls)
# # Create the gridcell objects
if sombrero:
# Running in all gridcells of mask
grid_mn = []
for Y in range(360):
for X in range(720):
if not mask[Y, X]:
grid_mn.append(grd(X, Y))
else:
grid_mn = []
for Y in range(168, 171):
for X in range(225, 228):
if not mask[Y, X]:
def pls_generator():
print("running table gen from pls_generator")
return table_gen(npls)
# Function to test productivity
import sys
sys.path.insert(0, '../src/')
import numpy as np
import matplotlib.pyplot as plt
import caete_module as m
import plsgen as pls
dt = pls.table_gen(m.global_par.npls)
def test_prod(n):
# real(kind=r_4), intent(in) :: temp !Mean monthly temperature (oC)
temp = 22.0
# real(kind=r_4), intent(in) :: p0 !Mean surface pressure (hPa)
p0 = 1010.0
# real(kind=r_4), intent(in) :: w
w = 50
# real(kind=r_4), intent(in) :: ipar !Incident photosynthetic active radiation (w/m2)
ipar = 160 / 2.18e5
# real(kind=r_4), intent(in) :: rh,emax !Relative humidity/MAXIMUM EVAPOTRANSPIRATION
rh = 0.60; emax = 1.5 # kg m-2 day-1
# real(kind=r_8), intent(in) :: cl1, cf1, ca1 !Carbon in plant tissues (kg/m2)
cl1 = 0.5; cf1 =0.5; ca1 = 6
# real(kind=r_8), intent(in) :: beta_leaf !npp allocation to carbon pools (kg/m2/day)
beta_leaf = 1e-2; beta_awood = 1e-2; beta_froot = 1e-2
# real(kind=r_8), intent(in) :: beta_awood
# create_plsbin.py import caete_module import plsgen as pls pls.table_gen(caete_module.global_par.npls)
from netCDF4 import num2date, MFDataset
import numpy as np
import plsgen as pls
from caete_input import data_in
from caete_module import global_par as gp
from caete_module import caete as model
from caete_module import photo as model_funcs
npls = gp.npls
# Mask for model execution
#mask = np.load('../input/random_amazon_mask_315.npy')
# Create the semi-random table of Plant Life Strategies
d_at = pls.table_gen(npls)
class gridcell_dyn:
"""
Defines the gridcell object - This object stores all the input data,
the data comming from model runs for each grid point and all the metadata
describing the life cycle of the gridcell
"""
def __init__(self, x, y):
"""Construct the gridcell object"""
# CELL Identifiers
