# Decentralised water storage model: master file - Neal Hughes
# Model testing
from __future__ import division
import numpy as np
from para import Para
import model
import time
from econlearn.samplegrid import test
import pylab
if __name__ == '__main__':
para = Para(rebuild=True)
para.set_property_rights(scenario='RS')
para.ch7['inflow_share'] = 0
para.ch7['capacity_share'] = 0
mod = model.Model(para, ch7=True, turn_off_env=True)
#mod.plannerQV_ch7(T=125000, stage2=True, d=0.2, simulate=True, envoff=True)
#print mod.sim.ITEROLD
#stats_envoff = mod.sim.stats
#series_envoff = mod.sim.series
#del mod
#mod = model.Model(para, ch7=True, turn_off_env=False)
#mod.plannerQV_ch7(T=125000, stage2=True, d=0.2, simulate=True, envoff=False)
#print mod.sim.ITEROLD
#stats = mod.sim.stats
NCI = '/short/fr3/ndh401/chapter6/'
out = '/Dropbox/Thesis/IMG/chapter6/'
para = Para()
scenarios = ['CS'] #['RS-HL-O', 'RS-HL', 'RS-O', 'RS', 'CS', 'CS-O', 'CS-HL', 'CS-HL-O', 'CS-U']
results = {scen: 0 for scen in scenarios}
Lambda = {scen: 0 for scen in scenarios}
LambdaK = {scen: 0 for scen in scenarios}
#==========================================
# Central case (with trade)
#==========================================
for scen in scenarios:
para.set_property_rights(scenario=scen)
para.aproximate_shares(nonoise=True)
mod = Model(para)
results[scen], Lambda[scen], LambdaK[scen] = mod.chapter6(sens=True)
del mod
#chapter6.tables(results, scenarios, Lambda, LambdaK, label='central')
#with open(home + folder + 'central_result.pkl', 'wb') as f:
# pickle.dump(results, f)
# f.close()
# Decentralised water storage model: master file - Neal Hughes
# Chapter 5 model runs
from __future__ import division
import numpy as np
from para import Para
import model
import pickle
para = Para()
para.central_case(N=100, printp=False)
para.set_property_rights(scenario='OA')
para.solve_para()
home = '/home/nealbob'
folder = '/Dropbox/Model/results/chapter5/'
scenarios = ['CS', 'SWA', 'OA', 'NS', 'CS-SL', 'SWA-SL', 'CS-SWA']
results = {scen: 0 for scen in scenarios}
policies = {scen: 0 for scen in scenarios}
for i in range(1):
#try:
para.central_case(N = 100)
para.t_cost = 100000000000
para.aproximate_shares(nonoise=True)
#if i > 0:
# para.randomize(N = 100)
# para.aproximate_shares()
NCIhome = '/short/fr3/ndh401'
NCIfolder = '/chapter7/'
#==========================================
# NCI general case - initialisation
#==========================================
try:
arg1 = sys.argv[1]
except IndexError:
print "Provide arguments <runnum> <numofjobs> <scenario>"
para = Para()
para.central_case(N = 100)
para.randomize()
para.set_property_rights(scenario='CS')
run_no = int(arg1)
print '============================================================'
print 'Initialisation for run no: ' + str(run_no)
print '============================================================'
mod = Model(para, ch7=True, turn_off_env=True)
E_lambda = mod.chapter7_initialise()
print '============================================================'
print 'E_lambda: ' + str(E_lambda)
print '============================================================'
para.E_lambda_hat = E_lambda
