def multiprocessingSim(params):
# Create Sim instance
folderRun = params[0]['rootFolder'] + '/Rep_' + str(params[0]['repeatIndex'])
s = Sim(params[0]['scenarioIndex'], params[0], folderRun)
print''
print params[1]['policyIndex']
print''
s.run(params[1]['policyIndex'], params[1], params[1]['randomSeed'])
def gemRun(reps):
taxMeans = []
taxSEs = []
p['verboseDebugging'] = False
p['singleRunGraphs'] = False
p['interactiveGraphics'] = False
dataFile = open('GEMSA data new.txt', 'a')
meansFile = open('GEMSA means new.txt', 'a')
outFile = open('GEMSA outputs new.txt', 'a')
# agingParentList = [ 0.0, 0.1, 0.2, 0.4 ]
# careProbList = [ 0.0004, 0.0008, 0.0012, 0.0016 ]
# retiredHoursList = [ 20.0, 30.0, 40.0, 60.0 ]
# retiredAgeList = [ 60.0 ]
# ageingParentList = [ 0.0, 0.1 ]
# careProbList = [ 0.0004 ]
# retiredHoursList = [ 20.0 ]
# retiredAgeList = [ 60.0 ]
for variableCare in p['ageingParentList']:
for variableProb in p['careProbList']:
for variableRetired in p['retiredHoursList']:
for variableAge in p['retiredAgeList']:
p['agingParentsMoveInWithKids'] = variableCare
p['personCareProb'] = variableProb
p['retiredHours'] = variableRetired
p['ageOfRetirement'] = variableAge
print "Trying parents-moving-in probability: ", variableCare
print "Trying person care probability: ", variableProb
print "Trying retired hours: ", variableRetired
print "Trying retirement age: ", variableAge
taxList = []
taxSum = 0.0
meansFile.write(
str(variableCare) + "\t" + str(variableProb) + "\t" +
str(variableRetired) + "\t" + str(variableAge) + "\n")
for i in range(0, reps):
print i,
s = Sim(p)
tax, seed = s.run()
taxList.append(tax)
taxSum += tax
print tax
dataFile.write(
str(seed) + "\t" + str(variableCare) + "\t" +
str(variableProb) + "\t" + str(variableRetired) +
"\t" + str(variableAge) + "\t" + str(tax) + "\n")
taxMeans.append(pylab.mean(taxList))
outFile.write(str(taxSum / reps) + "\n")
taxSEs.append(pylab.std(taxList) / math.sqrt(reps))
dataFile.close()
meansFile.close()
outFile.close()
def sensitivityRun(runtype, ageingList, careList, retiredHList, retiredAList,
reps):
taxMeans = []
taxSEs = []
p['verboseDebugging'] = False
p['singleRunGraphs'] = False
p['interactiveGraphics'] = False
dataFile = open(runtype + ' GEMSA data.txt', 'a')
meansFile = open(runtype + ' GEMSA means.txt', 'a')
outFile = open(runtype + ' GEMSA outputs.txt', 'a')
# agingParentList = [ 0.0, 0.1, 0.2, 0.4 ]
# careProbList = [ 0.0004, 0.0008, 0.0012, 0.0016 ]
# retiredHoursList = [ 20.0, 30.0, 40.0, 60.0 ]
# retiredAgeList = [ 60.0 ]
# ageingParentList = [ 0.0, 0.1 ]
# careProbList = [ 0.0004 ]
# retiredHoursList = [ 20.0 ]
# retiredAgeList = [ 60.0 ]
for run in xrange(len(ageingList)):
p['agingParentsMoveInWithKids'] = ageingList[run]
p['personCareProb'] = careList[run]
p['retiredHours'] = retiredHList[run]
p['ageOfRetirement'] = retiredAList[run]
print "Trying parents-moving-in probability: ", ageingList[run]
print "Trying person care probability: ", careList[run]
print "Trying retired hours: ", retiredHList[run]
print "Trying retirement age: ", retiredAList[run]
taxList = []
taxSum = 0.0
meansFile.write(
str(ageingList[run]) + "\t" + str(careList[run]) + "\t" +
str(retiredHList[run]) + "\t" + str(retiredAList[run]) + "\n")
for i in range(0, reps):
print i,
s = Sim(p)
tax, seed = s.run()
taxList.append(tax)
taxSum += tax
print tax
dataFile.write(
str(seed) + "\t" + str(ageingList[run]) + "\t" +
str(careList[run]) + "\t" + str(retiredHList[run]) + "\t" +
str(retiredAList[run]) + "\t" + str(tax) + "\n")
taxMeans.append(pylab.mean(taxList))
outFile.write(str(taxSum / reps) + "\n")
taxSEs.append(pylab.std(taxList) / math.sqrt(reps))
dataFile.close()
meansFile.close()
outFile.close()
def batchRun(num):
p['interactiveGraphics'] = False
dataFile = open('batchRunData.txt', 'w')
for i in range(0, num):
print "Doing batch run: ", i
taxList = []
s = Sim(p)
tax = s.run()
taxList.append(tax)
print "Social care cost per taxpayer: ", tax
dataFile.write(str(i) + "\t" + str(tax) + "\n")
dataFile.close()
def sensitivityLarge(runtype, input_list, reps):
taxMeans = []
taxSEs = []
p['verboseDebugging'] = False
p['singleRunGraphs'] = False
p['interactiveGraphics'] = False
outFile = open(runtype + ' GEMSA outputs large.txt', 'a')
for run in xrange(len(input_list[0])):
print("Running simulation number {}...".format(run))
print("Number of reps: {}".format(reps))
sim_list = np.array(input_list)
print(sim_list)
p['agingParentsMoveInWithKids'] = sim_list[0, run]
print(p['agingParentsMoveInWithKids'])
p['personCareProb'] = sim_list[1, run]
p['retiredHours'] = sim_list[2, run]
p['ageOfRetirement'] = sim_list[3, run]
p['baseDieProb'] = sim_list[4, run]
p['babyDieProb'] = sim_list[5, run]
p['personCareProb'] = sim_list[6, run]
p['maleAgeCareScaling'] = sim_list[7, run]
p['femaleAgeCareScaling'] = sim_list[8, run]
p['childHours'] = sim_list[9, run]
p['homeAdultHours'] = sim_list[10, run]
p['workingAdultHours'] = sim_list[11, run]
p['lowCareHandicap'] = sim_list[12, run]
p['growingPopBirthProb'] = sim_list[13, run]
p['basicDivorceRate'] = sim_list[14, run]
p['variableDivorce'] = sim_list[15, run]
p['basicMaleMarriageProb'] = sim_list[16, run]
p['basicFemaleMarriageProb'] = sim_list[17, run]
p['probApartWillMoveTogether'] = sim_list[18, run]
p['coupleMovesToExistingHousehold'] = sim_list[19, run]
p['basicProbAdultMoveOut'] = sim_list[20, run]
p['variableMoveBack'] = sim_list[21, run]
taxList = []
taxSum = 0.0
for i in range(0, reps):
print i,
s = Sim(p)
tax, seed = s.run()
taxList.append(tax)
taxSum += tax
print tax
taxMeans.append(pylab.mean(taxList))
outFile.write(str(taxSum / reps) + "\n" + str(seed) + "\n")
taxSEs.append(pylab.std(taxList) / math.sqrt(reps))
outFile.close()
def retireRun(reps):
taxMeans = []
taxSEs = []
p['verboseDebugging'] = False
p['singleRunGraphs'] = False
p['interactiveGraphics'] = False
dataFile = open('retirementAgeData2.txt', 'w')
#p['ageingParentList'] = [50, 55, 65, 70, 75, 80]
for variableCare in p['ageingParentList']:
p['ageOfRetirement'] = variableCare
print "Trying retirement age: ", variableCare
taxList = []
for i in range(0, reps):
print i,
s = Sim(p)
tax = s.run()
taxList.append(tax)
print tax
dataFile.write(
str(variableCare) + "\t" + str(i) + "\t" + str(tax) + "\n")
taxMeans.append(pylab.mean(taxList))
taxSEs.append(pylab.std(taxList) / math.sqrt(reps))
dataFile.close()
indices1 = pylab.arange(len(p['ageingParentList']))
taxFig = pylab.figure()
taxBar = taxFig.add_subplot(1, 1, 1)
taxBar.bar(indices1,
taxMeans,
facecolor='red',
align='center',
yerr=taxSEs,
ecolor='black')
taxBar.set_ylabel('Mean social care cost per taxpayer')
taxBar.set_xlabel('Age of retirement')
taxBar.set_xticks(indices1)
taxBar.set_xticklabels(p['ageingParentList'])
pylab.savefig('retirementAgeRunSet1.pdf')
pylab.show()
def sensitivityTenParams(runtype, input_list, reps):
taxMeans = []
taxSEs = []
p['verboseDebugging'] = False
p['singleRunGraphs'] = False
p['interactiveGraphics'] = False
outFile = open(runtype + ' GEMSA outputs.txt', 'a')
for run in xrange(len(input_list[0])):
print("Running simulation number {}...".format(run))
print("Number of reps: {}".format(reps))
sim_list = np.array(input_list)
print(sim_list)
p['agingParentsMoveInWithKids'] = sim_list[0, run]
p['baseCareProb'] = sim_list[1, run]
p['retiredHours'] = sim_list[2, run]
p['ageOfRetirement'] = sim_list[3, run]
p['personCareProb'] = sim_list[4, run]
p['maleAgeCareScaling'] = sim_list[5, run]
p['femaleAgeCareScaling'] = sim_list[6, run]
p['childHours'] = sim_list[7, run]
p['homeAdultHours'] = sim_list[8, run]
p['workingAdultHours'] = sim_list[9, run]
taxList = []
taxSum = 0.0
for i in range(0, reps):
print i,
s = Sim(p)
tax, seed = s.run()
taxList.append(tax)
taxSum += tax
print tax
taxMeans.append(pylab.mean(taxList))
outFile.write(str(taxSum / reps) + "\t" + str(seed) + "\n")
taxSEs.append(pylab.std(taxList) / math.sqrt(reps))
outFile.close()
for r in range(numRepeats):
# Create Run folders
folderRun = folder + '/Rep_' + str(r)
if not os.path.exists(folderRun):
os.makedirs(folderRun)
# Set seed
seed = fSeed
if r > 0:
seed = int(time.time() / (r + 1))
for i in range(len(scenariosParams)):
n = OrderedDict(scenariosParams[i])
s = Sim(i, n, folderRun)
for j in range(len(policiesParams[i])):
p = OrderedDict(policiesParams[i][j])
s.run(j, p, seed) # Add policy paramters later
else:
processors = int(metaParams['numberProcessors'])
if processors > multiprocessing.cpu_count():
processors = multiprocessing.cpu_count()
pool = multiprocessing.Pool(processors)
# Create a list of dictionaries (number repetitions times number of scenarios), adding repeat index for folders' creation
params = multiprocessParams(scenariosParams, policiesParams,
metaParams['numRepeats'], fSeed, folder, 0)
pool.map(multiprocessingSim, params)
pool.close()
pool.join()
if numberPolicies > 1:
p['pixelsInPopPyramid'] = 2000
p['careLevelColour'] = ['blue', 'green', 'yellow', 'orange', 'red']
p['houseSizeColour'] = ['brown', 'purple', 'yellow']
p['pixelsPerTown'] = 56
p['maxTextUpdateList'] = 22
return p
p = init_params()
#######################################################
## A basic single run
s = Sim(p)
tax = s.run()
##runs for sensitivity analysis using GEM-SA
##taxMeans = []
##taxSEs = []
##
##p['verboseDebugging'] = False
##p['singleRunGraphs'] = False
##p['interactiveGraphics'] = False
##
##dataFile = open('GEMSA data new.txt','a')
##meansFile = open('GEMSA means new.txt', 'a')
##
##agingParentList = [ 0.0, 0.1, 0.2, 0.4 ]
##careProbList = [ 0.0004, 0.0008, 0.0012, 0.0016 ]
##retiredHoursList = [ 20.0, 30.0, 40.0, 60.0 ]
def basicRun(p):
s = Sim(p)
tax = s.run()