out = '/Dropbox/Thesis/IMG/chapter7/'
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
out = '/Dropbox/Thesis/IMG/chapter7/'
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
mod.simulate_myopic(500000)
result['stats'].append(mod.sim.stats)
result['paras'].append(para.para_list)
#result['series'].append(mod.sim.series)
#mod2 = Model(para)
#mod2.simulate_myopic(500000)
#temp[1] = mod2.sim.series
#result['series'].append(temp)
if i == 0:
W_f = mod.sdp.W_f
V_f = mod.sdp.V_f
SW_f = mod.users.SW_f
para.SDP_GRID = 35
para.randomize(N = 100)
mod = Model(para)
except KeyboardInterrupt:
raise
except:
raise
with open(NCI + 'result.pkl', 'wb') as f:
pickle.dump(result, f)
f.close()
#chapter3.build(results)