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groimp_v3.py
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#!/home/renato/anaconda2/bin/python
import numpy as np
import matplotlib.pyplot as plt
import os, sys
from scipy.interpolate import interp2d
from pylab import *
import pandas as pd
for k in range(1,95):
if(k == 1):
beta = np.ones(94)
data = np.array([beta]).T
df4 = pd.DataFrame(data)
df4.to_excel("/home/renato/groimp_efficient/beta_1.xls", index=False, header=False)
print ("----------------------------------------",
"----------------------------------")
print ("--------------- Writing feddes.soi for Min3pArchiSImple",
" ------------------")
df2 = pd.read_csv('/home/renato/groimp_efficient/input/feddes.soi',sep='\t',names=['time','ET','canopy_int','solar_ratio','scale_tree_growth','rub1','rub2','rub3'])
ET = np.empty(94)
ET.fill(2.0E-8)
i = len(ET)
rootbiom = np.empty(94)
rootbiom.fill(3.)
print ("----------------------------------",
"----------------------------------------" )
print ("------------------------ Start GroIMP 1.5 -----",
"---------------------------")
os.system("java -Xmx2000m -jar /home/renato/Downloads/GroIMP-1.5/core.jar --headless /home/renato/Downloads/FSPM_BASIC-master-transpired-efficient/project.gs")
print ("---------------------------------",
"-----------------------------------------")
print ("------------------------ Reading PET from GroIMP 1.5",
"---------------------")
df1 = pd.read_csv('/home/renato/groimp_efficient/transp.txt',header=0, names=['transpiration'])
PET = df1.transpiration.values
PET = np.array(PET)
for count in range(1,k):
ET[count-1] = PET[count-1]
print ("-------------------------------------",
"-------------------------------------")
print ("------------------------ Writing update of feddes.soi ",
"--------------------")
data = np.array([df2.time[:i].values,
ET[:i],
df2.canopy_int[:i].values,
df2.solar_ratio[:i].values,
df2.scale_tree_growth[:i].values]).T
df3 = pd.DataFrame(data)
df3.to_csv('/home/renato/groimp_efficient/input/feddes.soi',sep='\t', index=False, header=None )
df3.to_csv('/home/renato/groimp_efficient/output_94steps_biom_feedback/feddes_original_%s.soi' %k,sep='\t', index=False, header=None)
print ("--------------------------------------",
"------------------------------------")
print ("------------------- Reading RootBiom from GroIMP 1.5 ",
"---------------------")
df1 = pd.read_csv('/home/renato/groimp_efficient/root.txt',header=0, names=['rootbiom'])
root = df1.rootbiom.values
# Divided by 1000. because GroIMP values in mg
# ArchiSimple values in g
root = np.array(root)/1000.
for count in range(1,k):
rootbiom[count-1] = root[count-1]
print ("----------------------------------",
"----------------------------------------")
print ("---------------------- Writing update of biomrac.txt",
"---------------------")
data = np.array([rootbiom[:i]]).T
df4 = pd.DataFrame(data)
df4.to_csv('/home/renato/groimp_efficient/input/biomrac.txt',sep='\t', index=False, header=None)
df4.to_csv('/home/renato/groimp_efficient/output_94steps_biom_feedback/biomrac_%s.txt' %k ,sep='\t', index=False, header=None)
print ("-----------------------------------",
"---------------------------------------")
print ("------------------------- Start Min3pArchiSimple",
"-------------------------")
print ("-------------------------------------",
"-------------------------------------")
os.system("/home/renato/Desktop/Min3pArchi91_reconstruc/bin/main.exe")
os.system("cp /home/renato/groimp_efficient/input/RSD_2D_MIN3P94.txt /home/renato/groimp/output_94steps_biom_feedback/RSD_2D_MIN3P94_%s.txt" %k)
print ("--------------------------------------",
"------------------------------------")
print ("------------------------- Calculating root water",
"-------------------------")
print ("------------------------- uptake from Min3pArchi",
"-------------------------")
df0 = pd.read_csv('feddes_%s.gsp' %k,
delim_whitespace=True,skiprows=3,header=None,
names=["x", "y", "z", "h_w", "p_w", "s_w", "theta_w", "transp", "evap"])
transp = df0['transp'].values
transp_sum = np.sum(np.array(transp).astype(np.float))
# water uptake value
print (transp_sum)
ET[k-1] = transp_sum
data = np.array([ET]).T
df8 = pd.DataFrame(data)
df8.to_csv('/home/renato/groimp_efficient/output_94steps_biom_feedback/RWU_%s.txt' %k,sep='\t', index=False, header=None )
# calculate
if(PET[k-1] != 0.):
beta[k-1] = ET[k-1]/PET[k-1]
else:
beta[k-1] = 1.0
#try:
# np.divide(ET[:i],PET,beta)
#except ZeroDivisionError:
# pass
beta[beta == 0.] = 1.0
beta[beta > 1.] = 1.0
#beta[beta < 0.3] = 0.3
os.system("rm /home/renato/groimp_efficient/beta_1.xls")
data = np.array([beta[:i]]).T
df4 = pd.DataFrame(data)
df4.to_excel("/home/renato/groimp_efficient/beta_1.xls", index=False, header=False)
df4.to_csv('/home/renato/groimp_efficient/output_94steps_biom_feedback/beta_1_%s.txt' %k,sep='\t', index=False, header=None)
print "beta_1.xls updated for time = ", k
print "--------------------------------------------------------------------------"
print "------------------------ Start GroIMP 1.5 --------------------------------"
#os.system("java -Xmx2000m -jar /home/renato/Downloads/GroIMP-1.5/core.jar --headless /home/renato/Downloads/FSPM_BASIC-master-transpired-efficient/project.gs")
df0 = pd.read_csv('/home/renato/groimp_efficient/field.txt',
delim_whitespace=True,skiprows=1,header=None,
names=["time", "species", "LAI", "nrShoots", "fAbs", "assCO2", "biomAbove", "yield", "harvestIndex","leafArea","fieldRFR"])
df0.to_csv('/home/renato/groimp_efficient/output_94steps_biom_feedback/field_%s.txt' %k ,sep='\t', index=False, header=None)
df0 = pd.read_csv('/home/renato/groimp_efficient/plant.txt',
delim_whitespace=True,skiprows=1,header=None,
names=["time", "tt", "plant", "strip", "row", "pos", "species", "weed",
"age","nrbranches","leafArea","fpar","rfr","biom","yield","leafmass",
"stemmass", "rootmass","shootrootratio","abovebiom","transpiration"])
df0.to_csv('/home/renato/groimp_efficient/output_94steps_biom_feedback/plant_%s.txt' %k ,sep='\t', index=False, header=None)