def run_model(self, case):
for key, value in self.policy.items():
vensim.set_value(key, value)
switches = case.pop("preference switches")
case["SWITCH preference for MIC"] = switches[0]
case["SWITCH preference for expected cost per MWe"]= switches[1]
case["SWITCH preference against unknown"]= switches[2]
case["SWITCH preference for expected progress"]= switches[3]
case["SWITCH preference against specific CO2 emissions"]= switches[4]
for key, value in case.items():
vensim.set_value(key, value)
ema_logging.debug("model parameters set successfully")
ema_logging.debug("run simulation, results stored in " + self.workingDirectory+self.resultFile)
try:
vensim.run_simulation(self.workingDirectory+self.resultFile)
except VensimError:
raise
results = {}
error = False
for output in self.outcomes:
ema_logging.debug("getting data for %s" %output.name)
result = vensim.get_data(self.workingDirectory+self.resultFile,
output.name
)
ema_logging.debug("successfully retrieved data for %s" %output.name)
if not result == []:
if result.shape[0] != self.runLength:
a = np.zeros((self.runLength))
a[0:result.shape[0]] = result
result = a
error = True
else:
result = result[0::self.step]
try:
results[output.name] = result
except ValueError:
print "what"
a = results.keys()
for output in self.activation:
value = results[output.name]
time = results["TIME"]
activationTimeStep = time[value>0]
if activationTimeStep.shape[0] > 0:
activationTimeStep = activationTimeStep[0]
else:
activationTimeStep = np.array([0])
results[output.name] = activationTimeStep
self.output = results
if error:
raise CaseError("run not completed", case)
def model_init(self, policy, kwargs):
super(FluModel, self).model_init(policy, kwargs)
#pop name
policy = copy.copy(policy)
policy.pop('name')
for key, value in policy.items():
vensim.set_value(key, value)
def run_interval(model,loop_index,interval,VOI,edges,ind_cons,
double_list,case):
# Load the model.
vensim.load_model(model)
case = copy.deepcopy(case)
set_lookups(case)
for key,value in case.items():
vensim.set_value(key,repr(value))
def model_init(self, policy, kwargs):
# try:
# self.modelFile = policy['file']
# except:
# EMAlogging.debug("no policy specified")
super(EnergyTrans, self).model_init(policy, kwargs)
#pop name
policy = copy.copy(policy)
policy.pop('name')
for key, value in policy.items():
vensim.set_value(key, value)
def run_model(self, case):
switches = case.pop("preference switches")
case["SWITCH preference for MIC"] = switches[0]
case["SWITCH preference for expected cost per MWe"] = switches[1]
case["SWITCH preference against unknown"] = switches[2]
case["SWITCH preference for expected progress"] = switches[3]
case["SWITCH preference against specific CO2 emissions"] = switches[4]
if np.sum(switches) == 0:
print "sifir olan cikti haci!"
for key, value in case.items():
vensim.set_value(key, value)
def run_model(self, case):
for key, value in case.items():
vensim.set_value(key, value)
ema_logging.debug("model parameters set successfully")
ema_logging.debug("run simulation, results stored in " + self.workingDirectory+self.resultFile)
try:
vensim.run_simulation(self.workingDirectory+self.resultFile)
except VensimError:
raise
results = {}
error = False
for output in self.outcomes:
ema_logging.debug("getting data for %s" %output.name)
result = vensim.get_data(self.workingDirectory+self.resultFile,
output.name
)
ema_logging.debug("successfully retrieved data for %s" %output.name)
if not result == []:
if result.shape[0] != self.runLength:
a = np.zeros((self.runLength))
a[0:result.shape[0]] = result
result = a
error = True
else:
result = result[0::self.step]
try:
results[output.name] = result
except ValueError:
print "what"
self.output = results
if error:
raise CaseError("run not completed", case)
def set_lookups(case):
kwargs = case
def returnsToScale(x, speed, scale):
return (x*1000, scale*1/(1+exp(-1* speed * (x-50))))
def approxLearning(x, speed, scale, start):
x = x-start
loc = 1 - scale
a = (x*10000, scale*1/(1+exp(speed * x))+loc)
return a
def f(x, speed, loc):
return (x/10, loc*1/(1+exp(speed * x)))
def priceSubstite(x, speed, begin, end):
scale = 2 * end
start = begin - scale/2
return (x+2000, scale*1/(1+exp(-1* speed * x)) +start)
loc = kwargs.pop("lookup shortage loc")
speed = kwargs.pop("lookup shortage speed")
lookup = [f(x/10, speed, loc) for x in range(0,100)]
vensim.set_value('shortage price effect lookup', lookup )
# print venDLL.get_varattrib('shortage price effect lookup', attribute=3)
# print lookup
speed = kwargs.pop("lookup price substitute speed")
begin = kwargs.pop("lookup price substitute begin")
end = kwargs.pop("lookup price substitute end")
vensim.set_value('relative price substitute lookup', [priceSubstite(x, speed, begin, end) for x in range(0,100, 10)])
scale = kwargs.pop("lookup returns to scale speed")
speed = kwargs.pop("lookup returns to scale scale")
vensim.set_value('returns to scale lookup', [returnsToScale(x, speed, scale) for x in range(0, 101, 10)])
scale = kwargs.pop("lookup approximated learning speed")
speed = kwargs.pop("lookup approximated learning scale")
start = kwargs.pop("lookup approximated learning start")
vensim.set_value('approximated learning effect lookup', [approxLearning(x, speed, scale, start) for x in range(0, 101, 10)])
def set_lookups(case):
kwargs = case
def returnsToScale(x, speed, scale):
return (x*1000, scale*1/(1+exp(-1* speed * (x-50))))
def approxLearning(x, speed, scale, start):
x = x-start
loc = 1 - scale
a = (x*10000, scale*1/(1+exp(speed * x))+loc)
return a
def f(x, speed, loc):
return (x/10, loc*1/(1+exp(speed * x)))
def priceSubstite(x, speed, begin, end):
scale = 2 * end
start = begin - scale/2
return (x+2000, scale*1/(1+exp(-1* speed * x)) +start)
loc = kwargs.pop("lookup shortage loc")
speed = kwargs.pop("lookup shortage speed")
lookup = [f(x/10, speed, loc) for x in range(0,100)]
vensim.set_value('shortage price effect lookup', lookup )
# print venDLL.get_varattrib('shortage price effect lookup', attribute=3)
# print lookup
speed = kwargs.pop("lookup price substitute speed")
begin = kwargs.pop("lookup price substitute begin")
end = kwargs.pop("lookup price substitute end")
vensim.set_value('relative price substitute lookup', [priceSubstite(x, speed, begin, end) for x in range(0,100, 10)])
scale = kwargs.pop("lookup returns to scale speed")
speed = kwargs.pop("lookup returns to scale scale")
vensim.set_value('returns to scale lookup', [returnsToScale(x, speed, scale) for x in range(0, 101, 10)])
scale = kwargs.pop("lookup approximated learning speed")
speed = kwargs.pop("lookup approximated learning scale")
start = kwargs.pop("lookup approximated learning start")
vensim.set_value('approximated learning effect lookup', [approxLearning(x, speed, scale, start) for x in range(0, 101, 10)])
def run_interval(model,loop_index,interval,VOI,edges,ind_cons,
double_list,case):
# Load the model.
vensim.load_model(model)
case = copy.deepcopy(case)
set_lookups(case)
for key,value in case.items():
vensim.set_value(key,repr(value))
# print key, repr(value), vensim.get_val(key), value-vensim.get_val(key)
# We run the model in game mode.
step = vensim.get_val(r'TIME STEP')
start_interval = interval[0]*step
end_interval = interval[1]*step
venDLL.command('GAME>GAMEINTERVAL|'+str(start_interval))
# Initiate the model to be run in game mode.
venDLL.command("MENU>GAME")
if start_interval > 0:
venDLL.command('GAME>GAMEON')
loop_on = 1
loop_off = 0
loop_turned_off = False
while True:
# Initiate the experiment of interest.
# In other words set the uncertainties to the same value as in
# those experiments.
time = vensim.get_val(r'TIME')
ema_logging.debug(time)
if time ==(2000+step*interval[0]) and not loop_turned_off:
loop_turned_off = True
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])
if loop_off==1:
constant_value = 0
vensim.set_value('value loop '+str(loop_index),
constant_value)
vensim.set_value('switch loop '+str(loop_index),
loop_off)
# Else it is based on unique consecutive edges.
else:
constant_value = vensim.get_val(edges[int(loop_index-1)][0])
if loop_off==1:
constant_value = 0
# 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),
loop_off)
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),
loop_off)
venDLL.command('GAME>GAMEINTERVAL|'+str(end_interval-start_interval))
elif time ==(2000+step*interval[1]) and loop_turned_off:
loop_turned_off = False
if loop_index != 0:
# If loop elimination method is based on unique edge.
if loop_index-1 < ind_cons:
constant_value = 0
vensim.set_value('value loop '+str(loop_index),
constant_value)
vensim.set_value('switch loop '+str(loop_index),
loop_on)
# Else it is based on unique consecutive edges.
else:
constant_value = 0
# 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),
loop_on)
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),
loop_on)
finalT = vensim.get_val('FINAL TIME')
currentT = vensim.get_val('TIME')
venDLL.command('GAME>GAMEINTERVAL|'+str(finalT - currentT))
else:
break
finalT = vensim.get_val('FINAL TIME')
currentT = vensim.get_val('TIME')
if finalT != currentT:
venDLL.command('GAME>GAMEON')
venDLL.command('GAME>ENDGAME')
interval_series = vensim.get_data('Base.vdf',VOI)
return interval_series
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
time = vensim.get_val(r'TIME')
EMAlogging.debug(time)
if time ==(2000+step*interval[0]) and not loop_turned_off:
loop_turned_off = True
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])
if loop_off==1:
constant_value = 0
vensim.set_value('value loop '+str(loop_index),
constant_value)
vensim.set_value('switch loop '+str(loop_index),
loop_off)
# Else it is based on unique consecutive edges.
else:
constant_value = vensim.get_val(edges[int(loop_index-1)][0])
if loop_off==1:
constant_value = 0
# 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),
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
#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
# than raise a warning
print "blaat"
break
vensim.set_value(r'FRAC EP FOR TRAINING',0.04)
#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
# than raise a warning
print "blaat"
break
vensim.set_value(r'FRAC EP FOR TRAINING',0.04)