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)
