def test_monthly_to_yearly(self):
ex_monthly = np.ones(shape=(3840, 181, 360))
ref = np.ones(shape=(320, 181, 360))
res = monthly_to_yearly(ex_monthly)
self.assertTrue((res == ref).all())
ex_monthly = np.ones(shape=(3840, ))
ref = np.ones(shape=(320, ))
res = monthly_to_yearly(ex_monthly)
self.assertTrue((res == ref).all())
def setUp(self):
self.dataPath = dataPath
npp, rh, ra, csoil, cveg = get_example_site_vars(
Path(conf_dict['dataPath']))
print(list(map(lambda a: a.shape, (npp, rh, ra, csoil, cveg))))
#nyears = 320
nyears = 2
obs_tup = Observables(c_veg=cveg,
c_soil=csoil,
a_respiration=monthly_to_yearly(ra),
h_respiration=monthly_to_yearly(rh))
self.obs = np.stack(obs_tup, axis=1)[0:nyears, :]
self.epa0 = EstimatedParameters(
beta_leaf=0.15, # 0
beta_root=0.2, # 1
k_leaf=1 / 365, # 2
k_root=1 / (365 * 5), # 3
k_wood=1 / (365 * 40), # 4
k_cwd=1 / (365 * 5), # 5
k_samet=0.5 / (365 * 0.1), # 6
k_sastr=0.5 / (365 * 0.1), # 7
k_samic=0.3 / (365 * 0.137), # 8
k_slmet=0.3 / (365), # 9
k_slstr=0.3 / (365), # 10
k_slmic=0.3 / (365), # 11
k_slow=0.3 / (365 * 5), # 12
k_arm=0.3 / (222 * 365), # 13
f_samet_leaf=0.3, # 14
f_slmet_root=0.3, # 15
f_samic_cwd=0.3, # 16
C_leaf_0=cveg[0] / 5, # 17
C_root_0=cveg[0] / 5, # 18
C_cwd_0=cveg[0] / 50, # 19
C_samet_0=cveg[0] / 300, # 20
C_sastr_0=cveg[0] / 300, # 21
C_samic_0=cveg[0] / 500, # 22
C_slmet_0=csoil[0] / 10, # 23
C_slstr_0=csoil[0] / 10, # 24
C_slmic_0=csoil[0] / 10, # 25
C_slow_0=csoil[0] / 10 # 26
)
self.cpa = UnEstimatedParameters(C_soil_0=csoil[0],
C_veg_0=cveg[0],
rh_0=rh[0],
ra_0=ra[0],
npp=npp,
clay=0.2028,
silt=0.2808,
nyears=nyears)
self.pa = Parameters.from_EstimatedParametersAndUnEstimatedParameters(
self.epa0, self.cpa)
self.mpa = ModelParameters(
**{
k: v
for k, v in self.pa._asdict().items()
if k in ModelParameters._fields
})
self.epa0 = self.epa0
#import general functions
from general_helpers import (make_uniform_proposer,
make_multivariate_normal_proposer, mcmc,
autostep_mcmc, make_feng_cost_func,
make_jon_cost_func, plot_solutions, autostep_mcmc)
#load file path from json file
with Path('config.json').open(mode='r') as f:
conf_dict = json.load(f)
dataPath = Path(conf_dict['dataPath'])
#get data streams
npp, rh, ra, cveg, csoil = get_example_site_vars(Path(conf_dict['dataPath']))
nyears = 320
obs_tup = Observables(h_respiration=monthly_to_yearly(rh),
a_respiration=monthly_to_yearly(ra),
c_veg=cveg,
c_soil=csoil)
obs = np.stack(obs_tup, axis=1)[0:nyears, :]
#set estimated parameters
epa0 = EstimatedParameters(
beta_leaf=0.15, # 0
beta_root=0.2, # 1
k_leaf=1 / 365, # 2
k_root=1 / (365 * 5), # 3
k_wood=1 / (365 * 40), # 4
k_cwd=1 / (365 * 5), # 5
k_samet=0.5 / (365 * 0.1), # 6
k_sastr=0.5 / (365 * 0.1), # 7
#import general functions
from general_helpers import (make_uniform_proposer,
make_multivariate_normal_proposer, mcmc,
autostep_mcmc, make_feng_cost_func,
make_jon_cost_func, plot_solutions, autostep_mcmc)
#load file path from json file
with Path('config.json').open(mode='r') as f:
conf_dict = json.load(f)
dataPath = Path(conf_dict['dataPath'])
#get data streams
npp, rh, ra, cveg, csoil = get_example_site_vars(Path(conf_dict['dataPath']))
nyears = 320
obs_tup = Observables(
h_respiration=monthly_to_yearly(rh),
#a_respiration=monthly_to_yearly(ra),
c_veg=cveg,
c_soil=csoil)
obs = np.stack(obs_tup, axis=1)[0:nyears, :]
#set estimated parameters
epa0 = EstimatedParameters(beta_leaf=0.21,
beta_root=0.27,
k_leaf=0.014,
k_root=0.022,
k_wood=0.003,
k_cwd=0.005,
k_samet=0.01,
k_sastr=0.001,
k_samic=0.05,
def test_forward_simulation(self):
# compare stored monthly timesteps (although the computation happens in daily steps)
t0 = time()
npp, rh, ra, csoil, cveg = get_example_site_vars(
Path(conf_dict['dataPath']))
print("data_loaded after", time() - t0)
print(list(map(lambda a: a.shape, (npp, rh, ra, csoil, cveg))))
nyears = 320
#nyears = 2
obs_tup = Observables(c_veg=cveg,
c_soil=csoil,
a_respiration=monthly_to_yearly(ra),
h_respiration=monthly_to_yearly(rh))
obs = np.stack(obs_tup, axis=1)[0:nyears, :]
epa0 = EstimatedParameters(
beta_leaf=0.15, # 0
beta_root=0.2, # 1
k_leaf=1 / 365, # 2
k_root=1 / (365 * 5), # 3
k_wood=1 / (365 * 40), # 4
k_cwd=1 / (365 * 5), # 5
k_samet=0.5 / (365 * 0.1), # 6
k_sastr=0.5 / (365 * 0.1), # 7
k_samic=0.3 / (365 * 0.137), # 8
k_slmet=0.3 / (365), # 9
k_slstr=0.3 / (365), # 10
k_slmic=0.3 / (365), # 11
k_slow=0.3 / (365 * 5), # 12
k_arm=0.3 / (222 * 365), # 13
f_samet_leaf=0.3, # 14
f_slmet_root=0.3, # 15
f_samic_cwd=0.3, # 16
C_leaf_0=cveg[0] / 5, # 17
C_root_0=cveg[0] / 5, # 18
C_cwd_0=cveg[0] / 50, # 19
C_samet_0=cveg[0] / 300, # 20
C_sastr_0=cveg[0] / 300, # 21
C_samic_0=cveg[0] / 500, # 22
C_slmet_0=csoil[0] / 10, # 23
C_slstr_0=csoil[0] / 10, # 24
C_slmic_0=csoil[0] / 10, # 25
C_slow_0=csoil[0] / 10 # 26
)
cpa = UnEstimatedParameters(C_soil_0=csoil[0],
C_veg_0=cveg[0],
rh_0=rh[0],
ra_0=ra[0],
npp=npp,
clay=0.2028,
silt=0.2808,
nyears=320)
param2res = make_param2res(cpa)
t1 = time()
res = param2res(epa0)
print(time() - t1)
#from IPython import embed; embed()
# the times have to be computed in days
fig = plt.figure()
plot_solutions(fig,
times=np.array(range(nyears)),
var_names=Observables._fields,
tup=(res, obs),
names=["solution with initial params", "observations"])
fig.savefig('solutions.pdf')