"""Set up the simulation parameters."""

p = {}

## The basics: starting population and year, etc.

p['initialPop'] = 750

p['startYear'] = 1860

p['endYear'] = 2050

p['thePresent'] = 2012

p['statsCollectFrom'] = 1960

p['minStartAge'] = 20

p['maxStartAge'] = 40

p['verboseDebugging'] = False

p['singleRunGraphs'] = True

p['favouriteSeed'] = None

p['numRepeats'] = 1

p['loadFromFile'] = False

## Mortality statistics

p['baseDieProb'] = 0.0001

p['babyDieProb'] = 0.005

p['maleAgeScaling'] = 14.0

p['maleAgeDieProb'] = 0.00021

p['femaleAgeScaling'] = 15.5

p['femaleAgeDieProb'] = 0.00019

p['num5YearAgeClasses'] = 28

## Transitions to care statistics

p['baseCareProb'] = 0.0002

p['personCareProb'] = 0.0008

##p['maleAgeCareProb'] = 0.0008

p['maleAgeCareScaling'] = 18.0

##p['femaleAgeCareProb'] = 0.0008

p['femaleAgeCareScaling'] = 19.0

p['numCareLevels'] = 5

p['cdfCareTransition'] = [ 0.7, 0.9, 0.95, 1.0 ]

p['careLevelNames'] = ['none','low','moderate','substantial','critical']

p['careDemandInHours'] = [ 0.0, 8.0, 16.0, 30.0, 80.0 ]

## Availability of care statistics

p['childHours'] = 5.0

p['homeAdultHours'] = 30.0

p['workingAdultHours'] = 25.0

p['retiredHours'] = 60.0

p['lowCareHandicap'] = 0.5

p['hourlyCostOfCare'] = 20.0

## Fertility statistics

p['growingPopBirthProb'] = 0.215

p['steadyPopBirthProb'] = 0.13

p['transitionYear'] = 1965

p['minPregnancyAge'] = 17

p['maxPregnancyAge'] = 42

## Class and employment statistics

p['numOccupationClasses'] = 3

p['occupationClasses'] = ['lower','intermediate','higher']

p['cdfOccupationClasses'] = [ 0.6, 0.9, 1.0 ]

## Age transition statistics

p['ageOfAdulthood'] = 17

p['ageOfRetirement'] = 65

## Marriage and divorce statistics (partnerships really)

p['basicFemaleMarriageProb'] = 0.25

p['femaleMarriageModifierByDecade'] = [ 0.0, 0.5, 1.0, 1.0, 1.0, 0.6, 0.5, 0.4, 0.1, 0.01, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0 ]

p['basicMaleMarriageProb'] = 0.3

p['maleMarriageModifierByDecade'] = [ 0.0, 0.16, 0.5, 1.0, 0.8, 0.7, 0.66, 0.5, 0.4, 0.2, 0.1, 0.05, 0.01, 0.0, 0.0, 0.0 ]

p['basicDivorceRate'] = 0.06

p['variableDivorce'] = 0.06

p['divorceModifierByDecade'] = [ 0.0, 1.0, 0.9, 0.5, 0.4, 0.2, 0.1, 0.03, 0.01, 0.001, 0.001, 0.001, 0.0, 0.0, 0.0, 0.0 ]

## Leaving home and moving around statistics

p['probApartWillMoveTogether'] = 0.3

p['coupleMovesToExistingHousehold'] = 0.3

p['basicProbAdultMoveOut'] = 0.22

p['probAdultMoveOutModifierByDecade'] = [ 0.0, 0.2, 1.0, 0.6, 0.3, 0.15, 0.03, 0.03, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]

p['basicProbSingleMove'] = 0.05

p['probSingleMoveModifierByDecade'] = [ 0.0, 1.0, 1.0, 0.8, 0.4, 0.06, 0.04, 0.02, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]

p['basicProbFamilyMove'] = 0.03

p['probFamilyMoveModifierByDecade'] = [ 0.0, 0.5, 0.8, 0.5, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1 ]

p['agingParentsMoveInWithKids'] = 0.1

p['variableMoveBack'] = 0.1

## Description of the map, towns, and houses

p['mapGridXDimension'] = 8

p['mapGridYDimension'] = 12

p['townGridDimension'] = 40

p['numHouseClasses'] = 3

p['houseClasses'] = ['small','medium','large']

p['cdfHouseClasses'] = [ 0.6, 0.9, 5.0 ]

p['ukMap'] = [ [ 0.0, 0.1, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0 ],

[ 0.1, 0.1, 0.2, 0.2, 0.3, 0.0, 0.0, 0.0 ],

[ 0.0, 0.2, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0 ],

[ 0.0, 0.2, 1.0, 0.5, 0.0, 0.0, 0.0, 0.0 ],

[ 0.4, 0.0, 0.2, 0.2, 0.4, 0.0, 0.0, 0.0 ],

[ 0.6, 0.0, 0.0, 0.3, 0.8, 0.2, 0.0, 0.0 ],

[ 0.0, 0.0, 0.0, 0.6, 0.8, 0.4, 0.0, 0.0 ],

[ 0.0, 0.0, 0.2, 1.0, 0.8, 0.6, 0.1, 0.0 ],

[ 0.0, 0.0, 0.1, 0.2, 1.0, 0.6, 0.3, 0.4 ],

[ 0.0, 0.0, 0.5, 0.7, 0.5, 1.0, 1.0, 0.0 ],

[ 0.0, 0.0, 0.2, 0.4, 0.6, 1.0, 1.0, 0.0 ],

[ 0.0, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0 ] ]

p['mapDensityModifier'] = 0.6

p['ukClassBias'] = [

[ 0.0, -0.05, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],

[ -0.05, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ],

[ 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0 ],

[ 0.0, -0.05, -0.05, 0.05, 0.0, 0.0, 0.0, 0.0 ],

[ -0.05, 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],

[ -0.05, 0.0, 0.0, -0.05, -0.05, -0.05, 0.0, 0.0 ],

[ 0.0, 0.0, 0.0, -0.05, -0.05, -0.05, 0.0, 0.0 ],

[ 0.0, 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],

[ 0.0, 0.0, -0.05, 0.0, -0.05, 0.0, 0.0, 0.0 ],

[ 0.0, 0.0, 0.0, -0.05, 0.0, 0.2, 0.15, 0.0 ],

[ 0.0, 0.0, 0.0, 0.0, 0.1, 0.2, 0.15, 0.0 ],

[ 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0 ] ]

## Graphical interface details

p['interactiveGraphics'] = True

p['delayTime'] = 0.0

p['screenWidth'] = 1300

p['screenHeight'] = 700

p['bgColour'] = 'black'

p['mainFont'] = 'Helvetica 18'

p['fontColour'] = 'white'

p['dateX'] = 70

p['dateY'] = 20

p['popX'] = 70

p['popY'] = 50

p['pixelsInPopPyramid'] = 2000

p['careLevelColour'] = ['blue','green','yellow','orange','red']

p['houseSizeColour'] = ['brown','purple','yellow']

p['pixelsPerTown'] = 56

p['maxTextUpdateList'] = 22

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")

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')

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()

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()

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))

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))

"""

Given a JSON filename and a dictionary, return the dictionary with

the file's fields merged into it.

Example: if the initial dictionary is

dict['bobAge'] = 90 and dict['samAge']=20 and the JSON data is

{'age':{'bob':40, 'fred':35}}

the returned dictionary contains the following data values:

dict['bobAge'] = 40, dict['fredAge'] = 35, dict['samAge'] = 20

"""

json_data = open(file).read()

data = json.loads(json_data)

for group in data:

fields = data.get(group)

if type({}) == type(fields):

# Group of fields - create name from item and group

for item in fields:

name = item + group[:1].upper() + group[1:]

value = data [group][item]

dict [name] = value

else:

# Single data value - naming is assumed to be correct case

dict [group] = fields

"""Process the command line, loading params file (if required). The dict

argument will be augmented with data from the user-specified parameters

file (if required), otherwise will return the dict argument unchanged"""

parser = argparse.ArgumentParser(

description='lives v1.0: complex social behaviour simulation.',

epilog='Example: "maintest.py -f test.json -n 3" --- run 3 sims with test.json\'s params',

formatter_class=argparse.RawTextHelpFormatter,

prog='lives',

usage='use "%(prog)s -h" for more information')

group = parser.add_mutually_exclusive_group()

parser.add_argument(

'-f', '--file',

help='parameters file in JSON format e.g. soylent.json')

group.add_argument(

'-n', '--num', metavar='N', type=int, default=0,

help='number of runs to carry out.')

group.add_argument('-r', '--retire', metavar='R', type=int, default=0,

help='retirement batch, number of iterations.')

group.add_argument('-g', '--gem', metavar='G', type=int, default=0,

help='GEM-SA batch for sensitivity analysis, number of iterations.')

group.add_argument('-l', '--lptau', metavar='L', type=int, default=0,

help='sensitivity analysis batch with LPtau sampling.')

group.add_argument('-m', '--maximin', metavar='M', type=int, default=0,

help='sensitivity analysis batch with maximin latin hypercube sampling.')

group.add_argument('-b', '--bigly', metavar='B', type=int, default=0,

help='bigly sensitivity analysis batch with maximin latin hypercube sampling.')

group.add_argument('-t', '--tenparams', metavar='T', type=int, default=0,

help='10 parameter sensitivity analysis batch with maximin latin hypercube sampling.')

args = parser.parse_args()

print("~ Filename: {}".format(args.file))

print("~ Number: {}".format(args.num))

print("~ Retire: {}".format(args.retire))

print("~ GEM-SA: {}".format(args.gem))

print("~ LPtau: {}".format(args.lptau))

print("~ Maximin: {}".format(args.maximin))

print("~ Big SA: {}".format(args.bigly))

print("~Ten Params: {}".format(args.tenparams))

if args.file:

#agingParentList = json.load(retireList, parse_float=decimal.Decimal)

res = loadParamFile (args.file, dict)

print ("p = {}".format(dict))

basicRun(dict)

elif args.num >= 1:

batchRun(args.num)

elif args.retire:

p['ageingParentList'] = []

res = loadParamFile('retire.json', dict)

print("List = {}".format(dict))

retireRun(args.retire)

elif args.gem:

p['ageingParentList'] = []

p['careProbList'] = []

p['retiredHoursList'] = []

p['retiredAgeList'] = []

res = loadParamFile('gem.json', dict)

print("List = {}".format(dict))

gemRun(args.gem)

elif args.lptau:

sim_array = np.genfromtxt('lptau-4params.txt', delimiter=' ')

sim_list = list(sim_array.T)

# print(sim_list)

ageingParentSettings = sim_list[0]

careProbSettings = sim_list[1]

retiredHoursSettings = sim_list[2]

retiredAgeSettings = sim_list[3]

# print(ageingParentSettings)

# print(careProbSettings)

# print(retiredHoursSettings)

# print(retiredAgeSettings)

sensitivityRun('LPtau', ageingParentSettings, careProbSettings, retiredHoursSettings, retiredAgeSettings, args.lptau)

elif args.maximin:

sim_array = np.genfromtxt('latinhypercube-4params.txt', delimiter=' ')

sim_list = list(sim_array.T)

# print(sim_list)

ageingParentSettings = sim_list[0]

careProbSettings = sim_list[1]

retiredHoursSettings = sim_list[2]

retiredAgeSettings = sim_list[3]

# print(ageingParentSettings)

# print(careProbSettings)

# print(retiredHoursSettings)

# print(retiredAgeSettings)

sensitivityRun('Maximin', ageingParentSettings, careProbSettings, retiredHoursSettings, retiredAgeSettings, args.maximin)

elif args.bigly:

sim_array = np.genfromtxt('latinhypercube-22params.txt', delimiter=' ')

sim_list = list(sim_array.T)

#print(sim_list)

np.savetxt('hypercube22_GEMSA_inputs.txt', sim_array, fmt='%1.8f', delimiter='\t', newline='\n')

sensitivityLarge('hypercube22', sim_list, args.bigly)

elif args.tenparams:

sim_array = np.genfromtxt('lptau10-1600runsfull.txt', delimiter='\t')

sim_list = list(sim_array.T)

#print(sim_list)

np.savetxt('LPtau1600runs_GEMSA_inputs.txt', sim_array, fmt='%1.8f', delimiter='\t', newline='\n')

sensitivityTenParams('LPtau1600runs', sim_list, args.tenparams)

else:

basicRun(p)