def main(subArea, runName, chartTitlePre):
outDir = "C:\\Users\\olsenk\\Dropbox\\FPF\\Envision Runs\\keith_production\\"
varList = [' Total Merch Harvest (m3)']
chartTitle = chartTitlePre
# PMG345Ha = reporterFunc.getPMG345Ha(subArea)
PMG12345Ha = reporterFunc.getPMG12345Ha(subArea)
# list of ownerships to graphs
reporterName = r'WoodProducts_by_OWNER_DETL_pivot.csv'
ownerLabelField = ' OWNER_DETL_label'
pdfFile = PdfPages(outDir + 'report5_Volume_byOwner_noFire.pdf')
for plotPage in [1,2]:
# setup plot for all scenarios
fig = pl.figure(1, figsize=(8.5,11))
for splot in [1, 2]:
ax = fig.add_subplot(2,1,splot)
inDir = outDir + runName + "_noFireCP\\"
if plotPage == 1:
if splot == 1:
ownersToGraph = ['USFS','State','Corporate Forest']
elif splot == 2:
ownersToGraph = ['Chemult Ranger District (Fremont-Winema NF)','Chiloquin Ranger District (Fremont-Winema NF)','Klamath Ranger District (Fremont-Winema NF)']
elif plotPage == 2:
if splot == 1:
ownersToGraph = ['ODF Sun Pass','ODF Gilchrist']
elif splot == 2:
ownersToGraph = ['JWTR Timber Holdings','Cascade Timberlands (Mazama Forest)','Jeld-Wen Inc','J-Spear Ranch']
if os.path.isdir(inDir):
yearList = list(set(pd.io.parsers.read_csv(inDir + reporterName)[' Year']))
repList = list(set(pd.io.parsers.read_csv(inDir + reporterName)[' Run']))
totalArea = pd.io.parsers.read_csv(inDir + reporterName)
# sum output over selected ownerships
for ownership in ownersToGraph:
print ownership
# get stats from multiple reps
statsList = []
for year in range(1,max(yearList) + 1):
yearArea = totalArea[totalArea[' Year'] == year]
dataList = []
for rep in repList:
repArea = yearArea[yearArea[' Run'] == rep]
if ownership == 'USFS':
ownerArea = pd.DataFrame(repArea[repArea[ownerLabelField] == 'Chemult Ranger District (Fremont-Winema NF)'])
fireProneArea = PMG12345Ha['Chemult Ranger District (Fremont-Winema NF)']
for subOwner in ['Chiloquin Ranger District (Fremont-Winema NF)','Klamath Ranger District (Fremont-Winema NF)']:
tempArea = repArea[repArea[ownerLabelField] == subOwner]
fireProneArea += PMG12345Ha[subOwner]
for varName in varList:
ownerArea[varName].iloc[0] += tempArea[varName].iloc[0]
elif ownership == 'State':
ownerArea = pd.DataFrame(repArea[repArea[ownerLabelField] == 'ODF Sun Pass'])
fireProneArea = PMG12345Ha['ODF Sun Pass']
for subOwner in ['ODF Gilchrist']:
tempArea = repArea[repArea[ownerLabelField] == subOwner]
fireProneArea += PMG12345Ha[subOwner]
for varName in varList:
ownerArea[varName].iloc[0] += tempArea[varName].iloc[0]
elif ownership == 'Corporate Forest':
ownerArea = pd.DataFrame(repArea[repArea[ownerLabelField] == 'JWTR Timber Holdings'])
fireProneArea = PMG12345Ha['JWTR Timber Holdings']
for subOwner in ['Cascade Timberlands (Mazama Forest)','Jeld-Wen Inc','J-Spear Ranch']:
tempArea = repArea[repArea[ownerLabelField] == subOwner]
fireProneArea += PMG12345Ha[subOwner]
for varName in varList:
ownerArea[varName].iloc[0] += tempArea[varName].iloc[0]
else:
ownerArea = pd.DataFrame(repArea[repArea[ownerLabelField] == ownership])
fireProneArea = PMG12345Ha[ownership]
if fireProneArea > 0:
dataList.append(ownerArea[' Total Merch Harvest (m3)'].iloc[0])
else:
dataList.append(0.0)
# convert to numpy array
numpyList = np.array(dataList)
lower95th = np.mean(numpyList, axis=0) - ((1.96 * np.std(numpyList, axis=0)) / np.sqrt(len(repList)))
upper95th = np.mean(numpyList, axis=0) + ((1.96 * np.std(numpyList, axis=0)) / np.sqrt(len(repList)))
if lower95th < 0:
lower95th = 0.0
# add year data to dictionary
dataDict = {'timeStep': year, 'mean': np.mean(numpyList, axis=0), 'std': np.std(numpyList, axis=0), 'lower': lower95th, 'upper': upper95th}
# convert to list for DataFrame
statsList.append(dataDict)
# convert to DataFrame
dataTable = pd.DataFrame(statsList)
if splot == 1:
labelXaxis = False
plotLegend = True
yLabelText = 'Total Merchantable Harvest (m3) - No Fire'
elif splot == 2:
labelXaxis = True
plotLegend = True
yLabelText = 'Total Merchantable Harvest (m3) - No Fire'
reporterFunc.plotReporter5(fig, ax, '', pdfFile, dataTable, ['mean','lower','upper'], yLabelText, ownership, labelXaxis, plotLegend)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(subArea, runName, chartTitlePre):
outDir = "C:\\Users\\olsenk\\Dropbox\\FPF\\Envision Runs\\keith_production\\"
varList = [' Potential HighSev Fire (ha) PMG1', ' Potential HighSev Fire (ha) PMG2', ' Potential HighSev Fire (ha) PMG3', ' Potential HighSev Fire (ha) PMG4', ' Potential HighSev Fire (ha) PMG5']
chartTitle = chartTitlePre
forestedHa = reporterFunc.getPMG12345Ha(subArea)
# PMG12345Ha = reporterFunc.getPMG12345Ha(subArea)
# list of ownerships to graphs
reporterName = r'PotentialDisturbance_by_OWNER_DETL_pivot.csv'
ownerLabelField = ' OWNER_DETL_label'
pdfFile = PdfPages(outDir + 'report5_PMG12345_HS_Hazard_byOwner.pdf')
# for plotPage in [1,2]:
# setup plot for all scenarios
fig = pl.figure(1, figsize=(6.5,6.5))
for splot in [1, 2, 3, 4]:
ax = fig.add_subplot(2,2,splot)
inDir = outDir + runName + "_CurrentPolicy\\"
# if plotPage == 1:
if splot == 1:
ownersToGraph = ['USFS','State','Corporate Forest']
elif splot == 2:
ownersToGraph = ['Chemult Ranger District (Fremont-Winema NF)','Chiloquin Ranger District (Fremont-Winema NF)','Klamath Ranger District (Fremont-Winema NF)']
# elif plotPage == 2:
if splot == 3:
ownersToGraph = ['ODF Sun Pass','ODF Gilchrist']
elif splot == 4:
ownersToGraph = ['JWTR Timber Holdings','Cascade Timberlands (Mazama Forest)','Jeld-Wen Inc','J-Spear Ranch']
if os.path.isdir(inDir):
yearList = list(set(pd.io.parsers.read_csv(inDir + reporterName)[' Year']))
repList = list(set(pd.io.parsers.read_csv(inDir + reporterName)[' Run']))
totalArea = pd.io.parsers.read_csv(inDir + reporterName)
# sum output over selected ownerships
for ownership in ownersToGraph:
print ownership
# get stats from multiple reps
statsList = []
for year in range(1,max(yearList) + 1):
yearArea = totalArea[totalArea[' Year'] == year]
dataList = []
for rep in repList:
repArea = yearArea[yearArea[' Run'] == rep]
if ownership == 'USFS':
ownerArea = pd.DataFrame(repArea[repArea[ownerLabelField] == 'Chemult Ranger District (Fremont-Winema NF)'])
fireProneArea = forestedHa['Chemult Ranger District (Fremont-Winema NF)']
for subOwner in ['Chiloquin Ranger District (Fremont-Winema NF)','Klamath Ranger District (Fremont-Winema NF)']:
tempArea = repArea[repArea[ownerLabelField] == subOwner]
fireProneArea += forestedHa[subOwner]
for varName in varList:
ownerArea[varName].iloc[0] += tempArea[varName].iloc[0]
elif ownership == 'State':
ownerArea = pd.DataFrame(repArea[repArea[ownerLabelField] == 'ODF Sun Pass'])
fireProneArea = forestedHa['ODF Sun Pass']
for subOwner in ['ODF Gilchrist','ODF Gilchrist (The Conservation Fund)']:
tempArea = repArea[repArea[ownerLabelField] == subOwner]
fireProneArea += forestedHa[subOwner]
for varName in varList:
ownerArea[varName].iloc[0] += tempArea[varName].iloc[0]
elif ownership == 'ODF Gilchrist':
ownerArea = pd.DataFrame(repArea[repArea[ownerLabelField] == 'ODF Gilchrist'])
fireProneArea = forestedHa['ODF Gilchrist']
for subOwner in ['ODF Gilchrist (The Conservation Fund)']:
tempArea = repArea[repArea[ownerLabelField] == subOwner]
fireProneArea += forestedHa[subOwner]
for varName in varList:
ownerArea[varName].iloc[0] += tempArea[varName].iloc[0]
elif ownership == 'Corporate Forest':
ownerArea = pd.DataFrame(repArea[repArea[ownerLabelField] == 'JWTR Timber Holdings'])
fireProneArea = forestedHa['JWTR Timber Holdings']
for subOwner in ['Cascade Timberlands (Mazama Forest)','Jeld-Wen Inc','J-Spear Ranch']:
tempArea = repArea[repArea[ownerLabelField] == subOwner]
fireProneArea += forestedHa[subOwner]
for varName in varList:
ownerArea[varName].iloc[0] += tempArea[varName].iloc[0]
else:
ownerArea = pd.DataFrame(repArea[repArea[ownerLabelField] == ownership])
fireProneArea = forestedHa[ownership]
if fireProneArea > 0:
dataList.append((ownerArea[' Potential HighSev Fire (ha) PMG1'].iloc[0] + ownerArea[' Potential HighSev Fire (ha) PMG2'].iloc[0] + ownerArea[' Potential HighSev Fire (ha) PMG3'].iloc[0] + ownerArea[' Potential HighSev Fire (ha) PMG4'].iloc[0] + ownerArea[' Potential HighSev Fire (ha) PMG5'].iloc[0]) / fireProneArea * 100)
else:
dataList.append(0.0)
# convert to numpy array
numpyList = np.array(dataList)
lower95th = np.mean(numpyList, axis=0) - ((1.96 * np.std(numpyList, axis=0)) / np.sqrt(len(repList)))
upper95th = np.mean(numpyList, axis=0) + ((1.96 * np.std(numpyList, axis=0)) / np.sqrt(len(repList)))
if lower95th < 0:
lower95th = 0.0
# add year data to dictionary
dataDict = {'timeStep': year, 'mean': np.mean(numpyList, axis=0), 'std': np.std(numpyList, axis=0), 'lower': lower95th, 'upper': upper95th}
# convert to list for DataFrame
statsList.append(dataDict)
# convert to DataFrame
dataTable = pd.DataFrame(statsList)
plotLegend = (0.99,0.99)
labelYtick = True
if splot == 1:
labelXtick = False
figText = 'a'
plotLegend = (0.99,0.65)
elif splot == 2:
labelXtick = False
figText = 'b'
elif splot == 3:
labelXtick = True
figText = 'c'
plotLegend = (0.68,0.99)
elif splot == 4:
labelXtick = True
figText = 'd'
plotLegend = (0.9,0.65)
xLabelText = yLabelText = ''
reporterFunc.plotReporter5(fig, ax, '', pdfFile, dataTable, ['mean','lower','upper'], xLabelText, yLabelText, ownership, labelXtick, labelYtick, plotLegend, figText)
reporterFunc.plotFigureText(fig, 'Simulation year', 'Potential fire hazard (%)')
pl.savefig(outDir + 'report5_PMG12345_HS_Hazard_byOwner.png', bbox_inches='tight', dpi=300)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."