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 #Truncated normal E_lambda = truncnorm((0.01 - E_lambda) / 0.05, (0.99 - E_lambda) / 0.05, loc=E_lambda, scale=0.05).rvs() print '============================================================' print 'E_lambda: ' + str(E_lambda) print '============================================================' para.ch7['inflow_share'] = E_lambda para.ch7['capacity_share'] = E_lambda para.t_cost = para.t_cost / 2.0 para.aproximate_shares_ch7() print '============================================================' print 'Lambda high: ' + str(para.Lambda_high) print 'Lambda high HL: ' + str(para.Lambda_high_HL) print '============================================================' with open(NCIhome + NCIfolder + str(run_no) + '_para.pkl', 'wb') as f: pickle.dump(para, f) f.close()
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() for scen in scenarios: para.set_property_rights(scenario=scen) res = {'paras' : [], 'stats' : [], 'VE': [], 'PE' : []} pol = [] mod = model.Model(para) VE, PE, stats, policy = mod.chapter5() res['stats'].append(stats) res['paras'].append(para.para_list)
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 #Truncated normal E_lambda = truncnorm((0.01 - E_lambda) / 0.05, (0.99 - E_lambda) / 0.05, loc=E_lambda, scale=0.05).rvs() print '============================================================' print 'E_lambda: ' + str(E_lambda) print '============================================================' para.ch7['inflow_share'] = E_lambda para.ch7['capacity_share'] = E_lambda para.t_cost = para.t_cost/2.0 para.aproximate_shares_ch7() print '============================================================' print 'Lambda high: ' + str(para.Lambda_high) print 'Lambda high HL: ' + str(para.Lambda_high_HL) print '============================================================' with open(NCIhome + NCIfolder + str(run_no) + '_para.pkl', 'wb') as f: pickle.dump(para, f) f.close()