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')