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DAA_EnKF_batchOSS.py
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DAA_EnKF_batchOSS.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Jul 18 17:32:06 2021
@author: Qi Yang
OSS Experiment for only assimilating Canopy Cover (CC)
"""
import numpy as np
import matlab.engine
from KFs import EnsembleKalmanFilter as EnKF
from DAA_UncertainPara import uncertain_para
import matplotlib.pyplot as plt
import matplotlib
import os
matplotlib.rcParams['font.family'] = 'Times New Roman'
matplotlib.rcParams['figure.dpi'] = 300
class Aquacrop_env():
def __init__(self, inputpath, start_date, ensemble_n, init_para, state_case = 1, initUpdate = False):
"""
Parameters
----------
inputpath : str
output : str
start_date : list
ensemble_n : int
init_para : list >> the list of n (sample number) * N (parameter number) parameters
"""
self.eng = matlab.engine.start_matlab()
self.eng.addpath('AquaCropOS_v60a')
self.inputpath = inputpath
self.start_date = start_date
self.ensemble_n = ensemble_n
self.init_para = init_para
if len(init_para) != ensemble_n:
raise ValueError('ensemble samples number error!')
self.init_state = []
self.state_case = state_case
self.updateNextStep = initUpdate
if self.state_case == 1:
self.stateList = ['CC']
elif self.state_case == 2:
self.stateList = ['CC', 'biomass']
elif self.state_case == 3:
self.stateList = ['CC', 'biomass', 'Para_CDC', 'Para_CGC','Para_CCx']
elif self.state_case == 4:
self.stateList = ['CC', 'biomass', 'Para_CDC', 'Para_CGC','Para_CCx', 'Para_WP','Para_Zmax','Para_p_up2','Para_p_up4']
elif self.state_case == 5:
self.stateList = ['CC', 'biomass', 'Para_CDC', 'Para_CGC','Para_CCx', 'Para_WP','Para_Zmax','Para_p_up2','Para_p_up4'
,'GDDcum','Para_HIstart','Para_YldForm', 'Para_Senescence']
elif self.state_case == 6:
self.stateList = ['CC', 'biomass', 'Para_CDC', 'Para_CGC','Para_CCx', 'Para_WP','Para_Zmax','Para_p_up2','Para_p_up4'
, 'Para_Tbase','Para_HIstart','Para_YldForm', 'Para_Senescence']
elif self.state_case == 7: # update the planting date and other revelated paras based on GDD
self.stateList = ['CC', 'biomass', 'Para_CDC', 'Para_CGC','Para_CCx', 'Para_WP','Para_Zmax','Para_p_up2','Para_p_up4'
, 'GDDcum','Para_HIstart','Para_YldForm', 'Para_Senescence']
elif self.state_case == 8:
self.stateList = ['CC', 'biomass', 'Para_CDC', 'Para_CGC','Para_CCx','Para_WP','Para_Zmax','Para_p_up2','Para_p_up4',
'GDDcum','Para_Tbase','Para_HIstart','Para_YldForm', 'Para_Senescence']
self.reset()
def reset(self):
print ('Initializing the ensemble models...')
for n in range(self.ensemble_n):
# engine start and initialaze
if self.init_para != None:
self.eng.DA_pyaqua_init(inputpath, start_date,
matlab.double(self.init_para[n]), int(n + 1), nargout= 0) # inside matlab.double should be a list
else:
self.eng.DA_pyaqua_init(inputpath, start_date,
[], int(n + 1), nargout= 0)
def steprun(self, state_in, dt, sample_n):
out = self.eng.step_run_DA_OSS(matlab.double(list(state_in)), int(dt), int(sample_n + 1), int(self.state_case), self.updateNextStep) # sample_n start from 1
state_out = [out[t] for t in self.stateList]
return state_out, out
def hx(x):
return np.array([x[0]])
def plotState(xs_enkf, color = 'r', DA_state_loc = 0, P_enkf = None, label = ' '):
y = np.asarray([x[DA_state_loc] for x in xs_enkf])
plt.plot(range(len(y)),y,color = color, label = label)
ps = [p[DA_state_loc,DA_state_loc]**.5 for p in P_enkf]
ps = np.asarray(ps)
plt.fill_between(range(len(y)), y + ps ,
y - ps,color = color,alpha=.25,linewidth=0.5,linestyle='--')
def plotState_extent(All_state, state_key, color = 'g', color2 = 'darkgreen', label = ' '):
y = []
std = []
Yall=[]
for state in All_state:
tmp = []
for s in state:
tmp.append(s[state_key])
Yall.append(tmp)
std.append(np.std(tmp,ddof = 1)) # ddof = 1 mean no-bias std, divide (n-1)
y.append(np.mean(tmp))
y = np.asarray(y)
Yall = np.array(Yall)
for i in range(Yall.shape[1]):
plt.plot(range(len(Yall[:,i])),Yall[:,i],color = color,alpha=.10,linewidth=0.5)
plt.plot(range(len(y)),y,color = color2, label = label)
# plt.plot(range(len(y)),y,color = color, label = label)
# plt.fill_between(range(len(y)), y + std ,
# y - std,color = color,alpha=.25,linewidth=0.5,linestyle='--')
def DA_plot(state_key, y_trueth, ylabel, title, plotPara = False):
# fig = plt.figure()
fig, ax = plt.subplots(1, 1,figsize = (6,5))
plotState_extent(All_state, state_key = state_key, color = 'b', color2 = 'darkblue',label = 'Open-loop')
plotState_extent(All_state2, state_key = state_key, color = 'g', color2 = 'darkgreen', label = 'EnKF')
if plotPara:
plt.axhline(y_trueth, color = 'k', linestyle = '--', label = 'Reference')
else:
plt.plot(range(len(y_trueth)),y_trueth, color = 'k', linestyle = '--', label = 'Reference')
plt.xlim(0,100)
plt.legend()
plt.xlabel('Day after plant',fontsize=16)
plt.ylabel(ylabel,fontsize=16)
# plt.title(title)
plt.text(0.45, 0.9, title, transform=ax.transAxes,fontsize=16)
return fig
def DA_plot_state(DA_state_loc, y_trueth, ylabel, title):
fig = plt.figure()
plotState(xs_enkf = xs_enkf, color = 'r', DA_state_loc = DA_state_loc, P_enkf = P_enkf, label = 'Open-loop')
plotState(xs_enkf = xs_enkf2, color = 'g', DA_state_loc = DA_state_loc, P_enkf = P_enkf2, label = 'EnKF')
# plotState_extent(All_state2, state_key = 'CC', color = 'b', DA_state_loc = 0, P_enkf = P_enkf2, label = 'EnKF-modelstate')
plt.plot(range(len(y_trueth)),y_trueth, color = 'k', label = 'Reference')
plt.xlim(0,100)
plt.legend()
plt.xlabel('Day after plant')
plt.ylabel(ylabel)
plt.title(title)
return fig
if __name__ == '__main__':
## model settings
inputpath = './AquaCropOS_v60a/Input/rice_NN/' # Nanning station
start_date = ['2039','7','15'] # normal year
ensemble_n = 300
np.random.seed(0)
OSS_path='OSSE_truth'
outPath = 'OSSresult_1obs'#'OSSresult_1obs'
saveResult = False#True
forceOpenloop = True#False
## Ensemble samples sampling
UP = uncertain_para()
u_para_default = UP.u_para_default
CV = [0.5, 0.05, 0.05, 0.08, 0.1,
0.1, 0.1, 0.05, 0.05, 0.1,
0.05, 0.15, 0.1, 0.1]
std2_0 = list((np.asarray(u_para_default) * np.asarray(CV))**2)
P_u_para = np.diag(std2_0)
init_paras = list(np.random.multivariate_normal(mean=u_para_default, cov=P_u_para, size=ensemble_n))
for i,init_para in enumerate(init_paras):
extent_para = UP.replace_uncertain(init_para) # padding the uncertain parameters to full size
valid_para = UP.validation_check(extent_para) # correct the init parameters
init_paras[i] = valid_para
## batch Run EnKF
# state_case_list = [6,7]
state_case_list = [8]
# load the "ground truth" of OSS experiment which was generated by one random run
z_origin = np.loadtxt('%s/sim_z.in'%OSS_path)
z_para = np.loadtxt('%s/sim_para.in'%OSS_path)
for case_n, state_case in enumerate(state_case_list):
print ('Runing case %d ...'%state_case)
## initialized the ensembled Aquacrop models
Aquacrop = Aquacrop_env(inputpath, start_date, ensemble_n, init_para = init_paras, state_case = state_case, initUpdate = False)
## EnKF settings
x_paraLoc = None # If the first step needs to update and discrete the states, should give the paralocation and set initUpdate = True
R = np.diag([0.03**2]) # measurement cov matrix, for Canopy Cover
P0 = np.diag([0.])
if case_n == 0:
if (not os.path.exists('openLoopResult.npy')) | forceOpenloop:
# ENKF - open loop
DAA_enkf = EnKF(x=np.zeros(len(Aquacrop.stateList)), P=P0, dim_z=1, N=ensemble_n, hx = hx, fx = Aquacrop.steprun, x_paraLoc = x_paraLoc)
DAA_enkf.R = R
dt = 1
xs_enkf = []
sigmas_enkf = []
P_enkf = []
K_enkf = []
All_state = []
count = 0
while not DAA_enkf.allModelDone:
# predict
DAA_enkf.predict(dt = dt)
xs_enkf.append(DAA_enkf.x.copy())
P_enkf.append(DAA_enkf.P.copy())
K_enkf.append(DAA_enkf.K.copy())
sigmas_enkf.append(DAA_enkf.sigmas.copy())
All_state.append(DAA_enkf.allState.copy())
count += 1
if count%10 == 0:
print ('Simulating %d days...'%count)
Aquacrop.reset()
np.save('openLoopResult.npy',[xs_enkf,P_enkf,K_enkf,sigmas_enkf,All_state])
else:
xs_enkf,P_enkf,K_enkf,sigmas_enkf,All_state = np.load('openLoopResult.npy',allow_pickle=True)
# ENKF - assimilation
DAA_enkf = EnKF(x=np.zeros(len(Aquacrop.stateList)), P=P0, dim_z=1, N=ensemble_n, hx = hx, fx = Aquacrop.steprun, x_paraLoc = x_paraLoc)
DAA_enkf.R = R.copy()
dt = 1
xs_enkf2 = []
sigmas_enkf2 = []
P_enkf2 = []
K_enkf2 = []
All_state2 = []
z_DAP = list(np.arange(1,len(z_origin),5))
z = [t for i,t in enumerate(z_origin) if (i+1) in z_DAP]
DAP = 1
ass_n = 0
while not DAA_enkf.allModelDone:
# predict
DAA_enkf.predict(dt = dt)
Aquacrop.updateNextStep = False
# update
if DAP in z_DAP:
DAA_enkf.update(np.asarray([z[ass_n]]))
Aquacrop.updateNextStep = True
ass_n += 1
if DAP%10 == 0:
print ('Simulating %d days...'%DAP)
# record
xs_enkf2.append(DAA_enkf.x.copy())
P_enkf2.append(DAA_enkf.P.copy())
K_enkf2.append(DAA_enkf.K.copy())
sigmas_enkf2.append(DAA_enkf.sigmas.copy())
All_state2.append(DAA_enkf.allState.copy())
DAP += 1
print ('Simulation of case %d is finished...'%state_case)
if saveResult:
if not os.path.exists(outPath):
os.mkdir(outPath)
np.save('%s/case%d.npy'%(outPath,state_case),[All_state,All_state2])
# plot CC
# fig = DA_plot_state(DA_state_loc = 0, y_trueth = z_origin, ylabel = 'Canoy cover %', title = 'Case %d'%state_case)
fig = DA_plot(state_key = 'CC', y_trueth = z_origin, ylabel = 'Canoy cover %', title = 'Case %d'%state_case)
if saveResult:
fig.savefig(outPath + '/CC_case%d.jpg'%state_case, bbox_inches='tight')
# plot yield
y_trueth = np.loadtxt('%s/sim_yield.in'%OSS_path)
fig = DA_plot(state_key = 'yield',y_trueth = y_trueth, ylabel = 'Yield t/ha', title = 'Case %d'%state_case)
plt.ylim(-0.2,8)
if saveResult:
fig.savefig(outPath + '/Yield_case%d.jpg'%state_case, bbox_inches='tight')
# plot Biomass
y_trueth = np.loadtxt('%s/sim_bio.in'%OSS_path)
fig = DA_plot(state_key = 'biomass',y_trueth = y_trueth, ylabel = 'Biomass g/m2', title = 'Case %d'%state_case)
plt.ylim(-50,2000)
if saveResult:
fig.savefig(outPath + '/Bio_case%d.jpg'%state_case, bbox_inches='tight')
if state_case == 6:
# plot Tbase
y_trueth = z_para[21]
fig = DA_plot(state_key = 'Para_Tbase',y_trueth = y_trueth, ylabel = 'Tbase C', plotPara = True, title = 'Case %d'%state_case)
plt.ylim(7,11)
if saveResult:
fig.savefig(outPath + '/Tbase_case%d.jpg'%state_case, bbox_inches='tight')
if state_case in [5,6]:
# plot phenology parameters
y_trueth = z_para[11]
fig = DA_plot(state_key = 'Para_HIstart',y_trueth = y_trueth, ylabel = 'HIstart', plotPara = True, title = 'Case %d'%state_case)
plt.ylim(1000,1300)
if saveResult:
fig.savefig(outPath + '/HIstart_case%d.jpg'%state_case, bbox_inches='tight')
y_trueth = z_para[14]
fig = DA_plot(state_key = 'Para_YldForm',y_trueth = y_trueth, ylabel = 'YldForm', plotPara = True, title = 'Case %d'%state_case)
plt.ylim(600,850)
if saveResult:
fig.savefig(outPath + '/YldForm_case%d.jpg'%state_case, bbox_inches='tight')
y_trueth = z_para[12]
fig = DA_plot(state_key = 'Para_Senescence',y_trueth = y_trueth, ylabel = 'Senescence', plotPara = True, title = 'Case %d'%state_case)
plt.ylim(1000,1400)
if saveResult:
fig.savefig(outPath + '/Sen_case%d.jpg'%state_case, bbox_inches='tight')
if state_case >= 3:
# plot CDC
y_trueth = z_para[20]
fig = DA_plot(state_key = 'Para_CDC',y_trueth = y_trueth, ylabel = 'CDC', plotPara = True, title = 'Case %d'%state_case)
plt.ylim(0.003,0.006)
if saveResult:
fig.savefig(outPath + '/CDC_case%d.jpg'%state_case, bbox_inches='tight')
# plot CGC
y_trueth = z_para[18]
fig = DA_plot(state_key = 'Para_CGC',y_trueth = y_trueth, ylabel = 'CGC', plotPara = True, title = 'Case %d'%state_case)
plt.ylim(0.003,0.009)
if saveResult:
fig.savefig(outPath + '/CGC_case%d.jpg'%state_case, bbox_inches='tight')
# plot CCx
y_trueth = z_para[19]
fig = DA_plot(state_key = 'Para_CCx',y_trueth = y_trueth, ylabel = 'CCx', plotPara = True, title = 'Case %d'%state_case)
plt.ylim(0.6,1)
if saveResult:
fig.savefig(outPath + '/CCx_case%d.jpg'%state_case, bbox_inches='tight')
if state_case >= 4:
# plot WP
y_trueth = z_para[32]
fig = DA_plot(state_key = 'Para_WP',y_trueth = y_trueth, ylabel = 'WP', plotPara = True, title = 'Case %d'%state_case)
plt.ylim(14,21)
if saveResult:
fig.savefig(outPath + '/WP_case%d.jpg'%state_case, bbox_inches='tight')
# plot Zmax
y_trueth = z_para[24]
fig = DA_plot(state_key = 'Para_Zmax',y_trueth = y_trueth, ylabel = 'Zmax', plotPara = True, title = 'Case %d'%state_case)
plt.ylim(0.3, 0.6)
if saveResult:
fig.savefig(outPath + '/Zmax_case%d.jpg'%state_case, bbox_inches='tight')
# plot p_up2
y_trueth = z_para[39]
fig = DA_plot(state_key = 'Para_p_up2',y_trueth = y_trueth, ylabel = 'p_up2', plotPara = True, title = 'Case %d'%state_case)
plt.ylim(0.3, 0.6)
if saveResult:
fig.savefig(outPath + '/p_up2_case%d.jpg'%state_case, bbox_inches='tight')
# plot p_up4
y_trueth = z_para[43]
fig = DA_plot(state_key = 'Para_p_up4',y_trueth = y_trueth, ylabel = 'p_up4', plotPara = True, title = 'Case %d'%state_case)
plt.ylim(0.4, 0.9)
if saveResult:
fig.savefig(outPath + '/p_up4_case%d.jpg'%state_case, bbox_inches='tight')
if state_case >= 5:
# plot GDD
y_trueth = np.loadtxt('%s/sim_GDD.in'%OSS_path)
fig = DA_plot(state_key = 'GDDcum', y_trueth = y_trueth, ylabel = 'GDD g/m2', title = 'Case %d'%state_case)
plt.ylim(-50,2200)
if saveResult:
fig.savefig(outPath + '/GDD_case%d.jpg'%state_case, bbox_inches='tight')