modelX.moveOneTimeStep()
modelXList.append(dcp(modelX))
plotData.append({})
plotData[run]["modelList"] = modelXList
plotData[run]["tag"] = nametag
plotData[run]["color"] = plotcolor
run += 1
#------------------------------------------------------------------------
filenamePrefix = '%s_%s_addInvest' % (country, version)
output.getCombinedPlots(run,
plotData,
startYear=startYear - 1,
filenamePrefix=filenamePrefix,
save=True)
output.getCompareDeathsAverted(run,
plotData,
scalePercent=0.1,
filenamePrefix=filenamePrefix,
title=title,
save=True)
output.getU5StuntingCasesAverted(run,
plotData,
scalePercent=0.5,
filenamePrefix=filenamePrefix,
title=title,
save=True)
output.getA5StuntingCasesAverted(run,
try:
modelList.append(pickle.load(infile))
except (EOFError):
break
infile.close()
plotData.append({})
plotData[run]["modelList"] = modelList
plotData[run]["tag"] = nametag
plotData[run]["color"] = plotcolor
run += 1
# ------------------------------------------------------------------------
numRuns = run
output.getCombinedPlots(run, plotData, startYear=startYear, filenamePrefix=filenamePrefix, save=True)
# ------------------------------------------------------------------------
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.ticker import FuncFormatter
from matplotlib import rcParams
# set up
modelList = plotData[0]["modelList"]
ageList = modelList[0].ages
numAges = len(ageList)
numMonths = len(modelList)
# numYears = int(len(modelList)/12)
totalPopU5 = {}
for t in range(numsteps-2):
modelZ.moveOneTimeStep()
pickle.dump(modelZ, outfile)
outfile.close()
# collect output, make graphs etc.
infile = open(pickleFilename, 'rb')
newModelList = []
while 1:
try:
newModelList.append(pickle.load(infile))
except (EOFError):
break
infile.close()
plotData.append({})
plotData[run]["modelList"] = newModelList
plotData[run]["tag"] = nametag
plotData[run]["color"] = plotcolor
run += 1
#------------------------------------------------------------------------
output.getCombinedPlots(run, plotData)
output.getDeathsAverted(modelList, newModelList, 'test')
pickleFilename = '%s_Intervention%i_P%i.pkl' % (country, ichoose,
percentageIncrease)
nametag = chosenIntervention
print "\n" + nametag
fileX = open(pickleFilename, 'rb')
# read the model output with simple intervention
modelXList = []
while 1:
try:
modelXList.append(pickle.load(fileX))
except (EOFError):
break
fileX.close()
plotData.append({})
plotData[run]["modelList"] = modelXList
plotData[run]["tag"] = nametag
plotData[run]["color"] = (1.0 - colorStep * run, 1.0 - 0.23 * abs(run - 4),
0.0 + colorStep * run)
run += 1
output.getCombinedPlots(run,
plotData,
filenamePrefix=filenamePrefix,
save=True)
output.getCompareDeathsAverted(run,
plotData,
filenamePrefix=filenamePrefix,
title=title,
save=True)
inputData.coverage[intervention])
newCoverages[intervention] = max(newCoverages[intervention], 0.0)
modelZ.updateCoverages(newCoverages)
# Run model
for t in range(numsteps - 2):
modelZ.moveOneTimeStep()
pickle.dump(modelZ, outfile)
outfile.close()
# collect output, make graphs etc.
infile = open(pickleFilename, 'rb')
newModelList = []
while 1:
try:
newModelList.append(pickle.load(infile))
except (EOFError):
break
infile.close()
plotData.append({})
plotData[run]["modelList"] = newModelList
plotData[run]["tag"] = nametag
plotData[run]["color"] = plotcolor
run += 1
#------------------------------------------------------------------------
output.getCombinedPlots(run, plotData)
output.getDeathsAverted(modelList, newModelList, 'test')
