def perform_loop_knockout(): unique_edges = [['In Goods', 'lost'], ['loss unprofitable extraction capacity', 'decommissioning extraction capacity'], ['production', 'In Goods'], ['production', 'lost'], ['production', 'Supply'], ['Real Annual Demand', 'substitution losses'], ['Real Annual Demand', 'price elasticity of demand losses'], ['Real Annual Demand', 'desired extraction capacity'], ['Real Annual Demand', 'economic demand growth'], ['average recycling cost', 'relative market price'], ['recycling fraction', 'lost'], ['commissioning recycling capacity', 'Recycling Capacity Under Construction'], ['maximum amount recyclable', 'recycling fraction'], ['profitability recycling', 'planned recycling capacity'], ['relative market price', 'price elasticity of demand losses'], ['constrained desired recycling capacity', 'gap between desired and constrained recycling capacity'], ['profitability extraction', 'planned extraction capacity'], ['commissioning extraction capacity', 'Extraction Capacity Under Construction'], ['desired recycling', 'gap between desired and constrained recycling capacity'], ['Installed Recycling Capacity', 'decommissioning recycling capacity'], ['Installed Recycling Capacity', 'loss unprofitable recycling capacity'], ['average extraction costs', 'profitability extraction'], ['average extraction costs', 'relative attractiveness recycling']] unique_cons_edges = [['recycling', 'recycling'], ['recycling', 'supply demand ratio'], ['decommissioning recycling capacity', 'recycling fraction'], ['returns to scale', 'relative attractiveness recycling'], ['shortage price effect', 'relative price last year'], ['shortage price effect', 'profitability extraction'], ['loss unprofitable extraction capacity', 'loss unprofitable extraction capacity'], ['production', 'recycling fraction'], ['production', 'constrained desired recycling capacity'], ['production', 'new cumulatively recycled'], ['production', 'effective fraction recycled of supplied'], ['loss unprofitable recycling capacity', 'recycling fraction'], ['average recycling cost', 'loss unprofitable recycling capacity'], ['recycling fraction', 'new cumulatively recycled'], ['substitution losses', 'supply demand ratio'], ['Installed Extraction Capacity', 'Extraction Capacity Under Construction'], ['Installed Extraction Capacity', 'commissioning extraction capacity'], ['Installed Recycling Capacity', 'Recycling Capacity Under Construction'], ['Installed Recycling Capacity', 'commissioning recycling capacity'], ['average extraction costs', 'profitability extraction']] # CONSTRUCTING THE ENSEMBLE AND SAVING THE RESULTS EMAlogging.log_to_stderr(EMAlogging.INFO) results = load_results(r'base.cPickle') # GETTING OUT THOSE BEHAVIOURS AND EXPERIMENT SETTINGS # Indices of a number of examples, these will be looked at. runs = [526,781,911,988,10,780,740,943,573,991] VOI = 'relative market price' results_of_interest = experiment_settings(results,runs,VOI) cases_of_interest = experiments_to_cases(results_of_interest[0]) behaviour_int = results_of_interest[1][VOI] # CONSTRUCTING INTERVALS OF ATOMIC BEHAVIOUR PATTERNS ints = intervals(behaviour_int,False) # GETTING OUT ONLY THOSE OF MAXIMUM LENGTH PER BEHAVIOUR max_intervals = intervals_interest(ints) # THIS HAS TO DO WITH THE MODEL FORMULATION OF THE SWITCHES/VALUES double_list = [6,9,11,17,19] indCons = len(unique_edges) # for elem in unique_cons_edges: # unique_edges.append(elem) current = os.getcwd() for beh_no in range(0,10): # beh_no = 0 # Varies between 0 and 9, index style. interval = max_intervals[beh_no] rmp = behaviour_int[beh_no] # rmp = rmp[interval[0]:interval[1]] x = range(0,len(rmp)) fig = plt.figure() ax = fig.add_subplot(111) vensim.be_quiet() # for loop_index in range(7,8): for loop_index in range(1,len(unique_edges)+1): if loop_index-indCons > 0: model_location = current + r'\Models\Consecutive\Metals EMA.vpm' elif loop_index == 0: model_location = current + r'\Models\Base\Metals EMA.vpm' else: model_location = current + r'\Models\Switches\Metals EMA.vpm' serie = run_interval(model_location,loop_index, interval,'relative market price', unique_edges,indCons,double_list, cases_of_interest[beh_no]) if serie.shape != rmp.shape: EMAlogging.info('Loop %s created a floating point error' % (loop_index)) EMAlogging.info('Caused by trying to switch %s' % (unique_edges[loop_index-1])) if serie.shape == rmp.shape: ax.plot(x,serie,'b') # data = np.zeros(rmp.shape[0]) # data[0:serie.shape[0]] = serie # ax.plot(x,data,'b') ax.plot(x,rmp,'r') ax.axvspan(interval[0]-1,interval[1], facecolor='lightgrey', alpha=0.5) f_name = 'switched unique edges only'+str(beh_no) plt.savefig(f_name)