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
ema_logging.log_to_stderr(ema_logging.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:
ema_logging.info('Loop %s created a floating point error' % (loop_index))
ema_logging.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)
def run_interval(model, loop_index, interval, VOI, edges, ind_cons, double_list, uncertain_names, uncertain_values):
# Load the model.
vensim.load_model(model)
# We don't want any screens.
vensim.be_quiet()
# We run the model in game mode.
step = vensim.get_val(r"TIME STEP")
start_interval = str(interval[0] * step)
venDLL.command("GAME>GAMEINTERVAL|" + start_interval)
# Initiate the model to be run in game mode.
venDLL.command("MENU>GAME")
while True:
if vensim.get_val(r"TIME") == 2000:
# Initiate the experiment of interest.
# In other words set the uncertainties to the same value as in
# those experiments.
for i, value in enumerate(uncertain_values):
name = uncertain_names[i]
value = uncertain_values[i]
vensim.set_value(name, value)
print vensim.get_val(r"TIME")
try:
# Run the model for the length specified in game on-interval.
venDLL.command("GAME>GAMEON")
step = vensim.get_val(r"TIME STEP")
if vensim.get_val(r"TIME") == (2000 + step * interval[0]):
if loop_index != 0:
# If loop elimination method is based on unique edge.
if loop_index - 1 < ind_cons:
constant_value = vensim.get_val(edges[int(loop_index - 1)][0])
vensim.set_value("value loop " + str(loop_index), constant_value)
vensim.set_value("switch loop " + str(loop_index), 0)
# Else it is based on unique consecutive edges.
else:
constant_value = vensim.get_val(edges[int(loop_index - 1)][0])
print constant_value
# Name of constant value used does not fit loop index minus 'start of cons'-index.
if loop_index - ind_cons in double_list:
vensim.set_value("value cons loop " + str(loop_index - ind_cons - 1), constant_value)
vensim.set_value("switch cons loop " + str(loop_index - ind_cons - 1), 0)
else:
vensim.set_value("value cons loop " + str(loop_index - ind_cons), constant_value)
vensim.set_value("switch cons loop " + str(loop_index - ind_cons), 0)
except venDLL.VensimWarning:
# The game on command will continue to the end of the simulation and
# than raise a warning.
print "The end of simulation."
break
venDLL.finish_simulation()
interval_series = vensim.get_data("Base.vdf", VOI)
interval_series = interval_series[interval[0] : interval[1]]
return interval_series
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)
def run_interval(model, loop_index, interval, VOI, edges, ind_cons,
double_list, uncertain_names, uncertain_values):
# Load the model.
vensim.load_model(model)
# We don't want any screens.
vensim.be_quiet()
# We run the model in game mode.
step = vensim.get_val(r'TIME STEP')
start_interval = str(interval[0] * step)
venDLL.command('GAME>GAMEINTERVAL|' + start_interval)
# Initiate the model to be run in game mode.
venDLL.command("MENU>GAME")
while True:
if vensim.get_val(r'TIME') == 2000:
# Initiate the experiment of interest.
# In other words set the uncertainties to the same value as in
# those experiments.
for i, value in enumerate(uncertain_values):
name = uncertain_names[i]
value = uncertain_values[i]
vensim.set_value(name, value)
print vensim.get_val(r'TIME')
try:
# Run the model for the length specified in game on-interval.
venDLL.command('GAME>GAMEON')
step = vensim.get_val(r'TIME STEP')
if vensim.get_val(r'TIME') == (2000 + step * interval[0]):
if loop_index != 0:
# If loop elimination method is based on unique edge.
if loop_index - 1 < ind_cons:
constant_value = vensim.get_val(edges[int(loop_index -
1)][0])
vensim.set_value('value loop ' + str(loop_index),
constant_value)
vensim.set_value('switch loop ' + str(loop_index), 0)
# Else it is based on unique consecutive edges.
else:
constant_value = vensim.get_val(edges[int(loop_index -
1)][0])
print constant_value
# Name of constant value used does not fit loop index minus 'start of cons'-index.
if loop_index - ind_cons in double_list:
vensim.set_value(
'value cons loop ' +
str(loop_index - ind_cons - 1), constant_value)
vensim.set_value(
'switch cons loop ' +
str(loop_index - ind_cons - 1), 0)
else:
vensim.set_value(
'value cons loop ' +
str(loop_index - ind_cons), constant_value)
vensim.set_value(
'switch cons loop ' +
str(loop_index - ind_cons), 0)
except venDLL.VensimWarning:
# The game on command will continue to the end of the simulation and
# than raise a warning.
print "The end of simulation."
break
venDLL.finish_simulation()
interval_series = vensim.get_data('Base.vdf', VOI)
interval_series = interval_series[interval[0]:interval[1]]
return interval_series
'''
Created on Jun 26, 2012
@author: localadmin
'''
from expWorkbench import vensim
from expWorkbench import vensimDLLwrapper as venDLL
#load model
vensim.load_model(r'C:\workspace\EMA-workbench\src\sandbox\sils\MODEL.vpm')
# we don't want any screens
vensim.be_quiet()
# we run the model in game mode, with 10 timesteps at a time
# the interval can be modified even during the game, thus allowing for dynamic
# interaction
venDLL.command('GAME>GAMEINTERVAL|10')
# initiate the model to be run in game mode
venDLL.command("MENU>GAME")
while True:
print vensim.get_val(r'FRAC EP FOR TRAINING')
print vensim.get_val(r'TIME')
try:
#run the model for the length specified via the game interval command
venDLL.command('GAME>GAMEON')
except venDLL.VensimWarning:
# the game on command will continue to the end of the simulation and
'''
Created on Jun 26, 2012
@author: localadmin
'''
from expWorkbench import vensim
from expWorkbench import vensimDLLwrapper as venDLL
#load model
vensim.load_model(r'C:\workspace\EMA-workbench\src\sandbox\sils\MODEL.vpm')
# we don't want any screens
vensim.be_quiet()
# we run the model in game mode, with 10 timesteps at a time
# the interval can be modified even during the game, thus allowing for dynamic
# interaction
venDLL.command('GAME>GAMEINTERVAL|10')
# initiate the model to be run in game mode
venDLL.command("MENU>GAME")
while True:
print vensim.get_val(r'FRAC EP FOR TRAINING')
print vensim.get_val(r'TIME')
try:
#run the model for the length specified via the game interval command
venDLL.command('GAME>GAMEON')
except venDLL.VensimWarning:
# the game on command will continue to the end of the simulation and
