# Decentralised water storage model: master file - Neal Hughes

# Chapter 6 model runs

from __future__ import division

import numpy as np

from para import Para

from model import Model

from results import chapter6

from results.chartbuilder import *

import multiprocessing

from multiprocessing.queues import Queue

import sys

home = '/home/nealbob'

folder = '/Dropbox/Model/results/chapter6/'

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()

"""

#==========================================

# No trade case

#==========================================

para.t_cost = 10000000000

for scen in scenarios:

para.set_property_rights(scenario=scen)

mod = Model(para)

results[scen], Lambda[scen], LambdaK[scen] = mod.chapter6()

del mod

chapter6.tables(results, scenarios, Lambda, LambdaK, label='notrade')

with open(home + folder + 'notrade_result.pkl', 'wb') as f:

pickle.dump(results, f)

f.close()

"""

"""

#==========================================

# Risk aversion

#==========================================

para.central_case(utility=True, risk=3)

for scen in scenarios:

para.set_property_rights(scenario=scen)

mod = Model(para)

results[scen], Lambda[scen], LambdaK[scen] = mod.chapter6()

del mod

chapter6.tables(results, scenarios, Lambda, LambdaK, label='risk1', risk=True)

with open(home + folder + 'risk1_result.pkl', 'wb') as f:

pickle.dump(results, f)

f.close()

"""

"""

#==========================================

# Inflow share search

#==========================================

try:

arg1 = sys.argv[1]

arg2 = sys.argv[2]

except IndexError:

print "Provide arguments <runnum> <numofjobs>"

para.central_case(utility=True)

N = int(arg2)

paralist = []

resultlist = []

def solve_model(para, scen, que):

para.set_property_rights(scenario=scen)

para.aproximate_shares()

mod = Model(para)

stats, _, _ = mod.chapter6(sens=True)

Lambda = [mod.RSLambda, mod.CSLambda]

del mod

que.put([stats, Lambda])

def retry_on_eintr(function, *args, **kw):

while True:

try:

return function(*args, **kw)

except IOError, e:

if e.errno == errno.EINTR:

continue

else:

raise

class RetryQueue(Queue):

def get(self, block=True, timeout=None):

return retry_on_eintr(Queue.get, self, block, timeout)

for i in range(N):

#try:

res = []

para.randomize()

para.CPU_CORES = 2

paralist.append(para.para_list)

ques = [RetryQueue(), RetryQueue()]

args = [(para, 'CS', ques[0]), (para, 'CS-HL', ques[1])]

jobs = [multiprocessing.Process(target=solve_model, args=(a)) for a in args]

for j in jobs: j.start()

for q in ques: res.append(q.get())

for j in jobs: j.join()

resultlist.append(res)

#with open(NCI + 'sens_para_' + arg1 + '.pkl', 'wb') as f:

# pickle.dump(paralist, f)

# f.close()

#with open(NCI + 'sens_result_' + arg1 +'.pkl', 'wb') as f:

# pickle.dump(resultlist, f)

# f.close()

#except KeyboardInterrupt:

# raise

#except:

# pass

"""

"""

#==========================================

# Risk aversion plot

#==========================================

Y = np.zeros([3, 200])

X = np.zeros([3, 200])

risk = [0.25, 1.5, 3]

names = ['0', '1.5', '3.0']

for i in range(3):

para.central_case(N=100, printp=False, utility=True, risk=risk[i])

mod = model.Model(para)

X[i,:], Y[i,:] = mod.users.SW_f.plot(['x', 1], returndata=True)

Y[i,:] = Y[i,:] / Y[i , 199]

df = dataframe(200, 3, names, X[0,:], Y.T)

chart = {'OUTFILE': home + out + 'Risk.pdf',

'XLABEL': 'Total water use, $Q_t$',

'YLABEL': 'Social welfare (utility)',

'YMIN' : 0.2,

'YMAX' : 1.1}

build_chart(chart, df, chart_type='date', ylim=True)

para.central_case(N=100, printp=False)

para.set_property_rights(scenario='CS')

Y = np.zeros([3, 200])

X = np.linspace(0, 1, 200)

risk = [0.001, 1.5, 3]

names = ['0', '1.5', '3.0']

for i in range(3):

Y[i,:] = 1 - np.exp(-risk[i]* X)

Y[i,:] = Y[i,:] / Y[i , 199]

df = dataframe(200, 3, names, X, Y.T)

chart = {'OUTFILE': home + out + 'Risk0.pdf',

'XLABEL': 'Scaled user profit, $u_{it}/{\\bar \\pi_h}$',

'YLABEL': 'Scaled utility',

'YMIN' : 0,

'YMAX' : 1.1}

build_chart(chart, df, chart_type='date', ylim=True)

para.central_case(N=100, printp=False)

para.set_property_rights(scenario='CS')

"""