def main(subArea, runName, chartTitlePre, ownership):
outDir = "C:\\Users\\olsenk\\Dropbox\\FPF\\Envision Runs\\keith_production\\"
varList = [' Stand Replacing FirePMG3', ' Stand Replacing FirePMG4', ' Stand Replacing FirePMG5']
yLabelText = 'Percent of PMG 3, 4, and 5 in Stand Replacing Fire (top 10%)'
chartTitle = chartTitlePre
PMG345Ha = reporterFunc.getPMG345Ha(subArea)
# list of ownerships to graphs
if ownership == 'All':
ownersToGraph = ['Federal','State','Private Non-Industrial','Private Industrial','Tribal','Homeowner']
pdfFile = PdfPages(outDir + 'report3_PMG345_in_HS_Fire_top10pct_All.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + 'report3_PMG345_in_HS_Fire_top10pct_' + ownersToGraph[0] + '.pdf')
# setup plot for all scenarios
fig = pl.figure(1, figsize=(11,8.5))
ax = fig.add_subplot(1,1,1)
for scenario in ['CurrentPolicy','NoFedTreat','Restoration']:
inDir = outDir + runName + "_" + scenario + "\\"
yearList = list(set(pd.io.parsers.read_csv(inDir + r'FireOccurance_by_OWNER_pivot.csv')[' Year']))
repList = list(set(pd.io.parsers.read_csv(inDir + r'FireOccurance_by_OWNER_pivot.csv')[' Run']))
totalArea = pd.io.parsers.read_csv(inDir + r'FireOccurance_by_OWNER_pivot.csv')
dataTable = pd.DataFrame(totalArea[totalArea[' Year'] > 0])
dataTable['key'] = dataTable[' OWNER_label'] + '_' + dataTable[' Run'].astype(str) + '_' + dataTable[' Year'].astype(str)
if scenario == 'CurrentPolicy':
scenarioTable = dataTable
else:
scenarioTable = pd.merge(scenarioTable, dataTable, how='left', on='key')
# sum over owners
scenarioTable.index = scenarioTable['key']
fireProneArea = PMG345Ha[ownersToGraph[0]]
if ownership == 'All':
for rep in range(len(repList)):
for year in range(1, len(yearList)):
for owner in ownersToGraph:
key = owner + '_' + str(rep) + '_' + str(year)
print(key)
if owner != ownersToGraph[0]:
for varName in varList:
scenarioTable.loc[ownersToGraph[0] + '_' + str(rep) + '_' + str(year),varName] += scenarioTable.loc[key,varName]
scenarioTable.loc[ownersToGraph[0] + '_' + str(rep) + '_' + str(year),varName + '_x'] += scenarioTable.loc[key,varName + '_x']
scenarioTable.loc[ownersToGraph[0] + '_' + str(rep) + '_' + str(year),varName + '_y'] += scenarioTable.loc[key,varName + '_y']
for owner in ownersToGraph:
fireProneArea += PMG345Ha[owner]
ownerArea = pd.DataFrame(scenarioTable[scenarioTable[' OWNER_label'] == ownersToGraph[0]])
# calculate percent of PMG345 area for each row
ownerArea['CP_HS_pct'] = (ownerArea[varList[0] + '_x'] + ownerArea[varList[1] + '_x'] + ownerArea[varList[2] + '_x']) / fireProneArea * 100
ownerArea['NFT_HS_pct'] = (ownerArea[varList[0] + '_y'] + ownerArea[varList[1] + '_y'] + ownerArea[varList[2] + '_y']) / fireProneArea * 100
ownerArea['Res_HS_pct'] = (ownerArea[varList[0]] + ownerArea[varList[1]] + ownerArea[varList[2]]) / fireProneArea * 100
#Sort dataTable on percent PMG345 in HS fire
dataTable = ownerArea.sort('NFT_HS_pct', ascending=False)
statsList1 = []
statsList2 = []
statsList3 = []
valueSet = []
maxValueSet = []
for i in range(int(round(len(dataTable) * 0.1))):
statsList1.append(dataTable['CP_HS_pct'].iloc[i])
statsList2.append(dataTable['NFT_HS_pct'].iloc[i])
statsList3.append(dataTable['Res_HS_pct'].iloc[i])
valueSet.append(statsList1)
valueSet.append(statsList2)
valueSet.append(statsList3)
maxValueSet.append(max(statsList1))
maxValueSet.append(max(statsList2))
maxValueSet.append(max(statsList3))
labelList = ['Current Policy','No Fed Treatment','Restoration']
reporterFunc.plotReporter3(fig, ax, ownership + ' - ' + str(len(repList)) + ' reps', pdfFile, valueSet, labelList, subArea, chartTitle, yLabelText, scenario, maxValueSet)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(outDir, subArea, runList, chartTitlePre, ownership):
varList = [
' Stand Replacing FirePMG3', ' Stand Replacing FirePMG4',
' Stand Replacing FirePMG5'
]
chartTitle = chartTitlePre
PMG345Ha = reporterFunc.getPMG345Ha(subArea)
# list of ownerships to graphs
if ownership == 'All':
ownersToGraph = [
'Federal', 'State', 'Private Non-Industrial', 'Private Industrial',
'Tribal', 'Homeowner'
]
pdfFile = PdfPages(outDir + 'report6bw_PMG345_in_HS_Fire_All.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + 'report6bw_PMG345_in_HS_Fire_' +
ownersToGraph[0] + '.pdf')
# Current Policy run required!
for scenario in ['Restoration', 'CurrentPolicy']:
inDir = outDir + scenario + "\\"
if scenario == 'Restoration':
yearList = list(
set(
pd.io.parsers.read_csv(
inDir + r'FireOccurance_by_OWNER_pivot.csv')[' Year']))
repList = list(
set(
pd.io.parsers.read_csv(
inDir + r'FireOccurance_by_OWNER_pivot.csv')[' Run']))
totalArea = pd.io.parsers.read_csv(inDir +
r'FireOccurance_by_OWNER_pivot.csv')
dataTable = pd.DataFrame(totalArea[totalArea[' Year'] > 0])
dataTable['key'] = dataTable[' OWNER_label'] + '_' + dataTable[
' Run'].astype(str) + '_' + dataTable[' Year'].astype(str)
if scenario == 'Restoration':
scenarioTable = dataTable
else:
scenarioTable = pd.merge(scenarioTable,
dataTable,
how='left',
on='key')
# sum over owners
scenarioTable.index = scenarioTable['key']
fireProneArea = PMG345Ha[ownersToGraph[0]]
if ownership == 'All':
for rep in range(len(repList)):
for year in range(1, len(yearList)):
for owner in ownersToGraph:
key = owner + '_' + str(rep) + '_' + str(year)
print(key)
if owner != ownersToGraph[0]:
for varName in varList:
scenarioTable.loc[
ownersToGraph[0] + '_' + str(rep) + '_' +
str(year), varName +
'_x'] += scenarioTable.loc[key, varName + '_x']
scenarioTable.loc[
ownersToGraph[0] + '_' + str(rep) + '_' +
str(year), varName +
'_y'] += scenarioTable.loc[key, varName + '_y']
for owner in ownersToGraph:
fireProneArea += PMG345Ha[owner]
ownerArea = pd.DataFrame(
scenarioTable[scenarioTable[' OWNER_label_y'] == ownersToGraph[0]])
# calculate percent of PMG345 area for each row
ownerArea['CP_HS_pct'] = (
ownerArea[varList[0] + '_y'] + ownerArea[varList[1] + '_y'] +
ownerArea[varList[2] + '_y']) / fireProneArea * 100
ownerArea['Res_HS_pct'] = (
ownerArea[varList[0] + '_x'] + ownerArea[varList[1] + '_x'] +
ownerArea[varList[2] + '_x']) / fireProneArea * 100
#Sort dataTable on percent PMG345 in HS fire
dataTable = ownerArea.sort('CP_HS_pct', ascending=False)
for splot in [1, 2]:
# setup plot for all scenarios
fig = pl.figure(1, figsize=(4, 4))
ax = fig.add_subplot(1, 1, 1)
# label plots and set percent of top values
if splot == 1:
yLabelText = 'Area of high severity fire in fire-frequent landscape (%)'
labelXaxis = True
figText = 'a'
topPercent = 1.0
elif splot == 2:
yLabelText = 'Area of top 10% stand-replacing fires in fire-frequent landscape (%)'
labelXaxis = True
figText = 'b'
topPercent = 0.1
statsList1 = []
statsList3 = []
valueSet = []
maxValueSet = []
for i in range(int(round(len(dataTable) * topPercent))):
statsList1.append(dataTable['CP_HS_pct'].iloc[i])
statsList3.append(dataTable['Res_HS_pct'].iloc[i])
valueSet.append(statsList1)
valueSet.append(statsList3)
maxValueSet.append(max(statsList1))
maxValueSet.append(max(statsList3))
labelList = ['Current\nManagement', 'Accelerated\nRestoration']
print yLabelText
print 'median for Current Management ' + str(np.median(valueSet[0]))
print 'median for Restoration ' + str(np.median(valueSet[1]))
print
reporterFunc.plotReporter4bw(fig, ax, ownership, pdfFile, valueSet,
labelList, subArea, chartTitle,
yLabelText, maxValueSet, labelXaxis,
figText)
## yLabelText = 'Area of high-severity fire (%)'
## reporterFunc.plotFigureText(fig, '', yLabelText)
##
## pl.savefig(outDir + 'report4bw_PMG345_in_HS_Fire_All.png', bbox_inches='tight', dpi=300)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(subArea, runName, chartTitlePre):
outDir = "C:\\Users\\olsenk\\Dropbox\\FPF\\Envision Runs\\keith_production\\"
varList = [
' Stand Replacing FirePMG3', ' Stand Replacing FirePMG4',
' Stand Replacing FirePMG5'
]
chartTitle = chartTitlePre
PMG345Ha = reporterFunc.getPMG345Ha(subArea)
# PMG12345Ha = reporterFunc.getPMG12345Ha(subArea)
# list of ownerships to graphs
reporterName = r'FireOccurance_by_OWNER_DETL_pivot.csv'
ownerLabelField = ' OWNER_DETL_label'
pdfFile = PdfPages(outDir + 'report5_HS_Fire_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:
elif 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 = PMG345Ha[
'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 += PMG345Ha[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 = PMG345Ha['ODF Sun Pass']
for subOwner in [
'ODF Gilchrist',
'ODF Gilchrist (The Conservation Fund)'
]:
tempArea = repArea[repArea[ownerLabelField] ==
subOwner]
fireProneArea += PMG345Ha[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 = PMG345Ha['ODF Gilchrist']
for subOwner in [
'ODF Gilchrist (The Conservation Fund)'
]:
tempArea = repArea[repArea[ownerLabelField] ==
subOwner]
fireProneArea += PMG345Ha[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 = PMG345Ha['JWTR Timber Holdings']
for subOwner in [
'Cascade Timberlands (Mazama Forest)',
'Jeld-Wen Inc', 'J-Spear Ranch'
]:
tempArea = repArea[repArea[ownerLabelField] ==
subOwner]
fireProneArea += PMG345Ha[subOwner]
for varName in varList:
ownerArea[varName].iloc[0] += tempArea[
varName].iloc[0]
else:
ownerArea = pd.DataFrame(
repArea[repArea[ownerLabelField] == ownership])
fireProneArea = PMG345Ha[ownership]
if fireProneArea > 0:
dataList.append(
(ownerArea[' Stand Replacing FirePMG3'].iloc[0]
+
ownerArea[' Stand Replacing FirePMG4'].iloc[0]
+
ownerArea[' Stand Replacing FirePMG5'].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'
elif splot == 2:
labelXtick = False
figText = 'b'
elif splot == 3:
labelXtick = True
figText = 'c'
elif splot == 4:
labelXtick = True
figText = 'd'
xLabelText = yLabelText = ''
reporterFunc.plotReporter5(fig, ax, '', pdfFile, dataTable,
['mean', 'lower', 'upper'],
xLabelText, yLabelText, ownership,
labelXtick, labelYtick, plotLegend,
figText)
reporterFunc.plotFigureText(
fig, 'Simulation year',
'Fire prone landscape in high severity fire (%)')
pl.savefig(outDir + 'report5_HS_Fire_byOwner.png',
bbox_inches='tight',
dpi=300)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(subArea, runName, chartTitlePre):
outDir = "C:\\Users\\olsenk\\Dropbox\\FPF\\Envision Runs\\keith_production\\"
varList = [' Stand Replacing FirePMG1',' Stand Replacing FirePMG2',' Stand Replacing FirePMG3',' Stand Replacing FirePMG4',' Stand Replacing FirePMG5']
chartTitle = chartTitlePre
PMG345Ha = reporterFunc.getPMG345Ha(subArea)
forestedHa = reporterFunc.getPMG12345Ha(subArea)
# list of ownerships to graphs
reporterName = r'FireOccurance_by_OWNER_DETL_pivot.csv'
ownerLabelField = ' OWNER_DETL_label'
pdfFile = PdfPages(outDir + 'report5bw_HS_Fire_byOwner.pdf')
# for plotPage in [1,2,3,4,5,6,7,8]:
# 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 % 2 == 1:
if splot == 1:
ownersToGraph = ['USFS','State','Corporate Forest']
ownerLabel = ['USFS','State','Corporate']
elif splot == 2:
ownersToGraph = ['Chemult Ranger District (Fremont-Winema NF)','Chiloquin Ranger District (Fremont-Winema NF)','Klamath Ranger District (Fremont-Winema NF)']
ownerLabel = ['Chemult RD','Chiloquin RD','Klamath RD']
# else:
elif splot == 3:
ownersToGraph = ['ODF Sun Pass','ODF Gilchrist']
ownerLabel = ownersToGraph
elif splot == 4:
ownersToGraph = ['JWTR Timber Holdings','Cascade Timberlands (Mazama Forest)','Jeld-Wen Inc','J-Spear Ranch']
ownerLabel = ['PC1','PC2','PC3','PC4']
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
valueSet = []
maxValueSet = []
for ownership in ownersToGraph:
print ownership
# get stats from multiple reps
dataList = []
for year in range(1,max(yearList) + 1):
yearArea = totalArea[totalArea[' Year'] == year]
for rep in repList:
repArea = yearArea[yearArea[' Run'] == rep]
if ownership == 'USFS':
ownerArea = pd.DataFrame(repArea[repArea[ownerLabelField] == 'Chemult Ranger District (Fremont-Winema NF)'])
fireProneArea345 = PMG345Ha['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]
fireProneArea345 += PMG345Ha[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'])
fireProneArea345 = PMG345Ha['ODF Sun Pass']
fireProneArea = forestedHa['ODF Sun Pass']
for subOwner in ['ODF Gilchrist','ODF Gilchrist (The Conservation Fund)']:
tempArea = repArea[repArea[ownerLabelField] == subOwner]
fireProneArea345 += PMG345Ha[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'])
fireProneArea345 = PMG345Ha['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]
fireProneArea345 += PMG345Ha[subOwner]
fireProneArea += forestedHa[subOwner]
for varName in varList:
ownerArea[varName].iloc[0] += tempArea[varName].iloc[0]
else:
ownerArea = pd.DataFrame(repArea[repArea[ownerLabelField] == ownership])
fireProneArea345 = PMG345Ha[ownership]
fireProneArea = forestedHa[ownership]
## if plotPage in [1,2,5,6]:
## if fireProneArea345 > 0:
## dataList.append((ownerArea[' Stand Replacing FirePMG3'].iloc[0] + ownerArea[' Stand Replacing FirePMG4'].iloc[0] + ownerArea[' Stand Replacing FirePMG5'].iloc[0]) / fireProneArea345 * 100)
## else:
## dataList.append(0.0)
## else:
if fireProneArea > 0:
dataList.append((ownerArea[' Stand Replacing FirePMG1'].iloc[0] + ownerArea[' Stand Replacing FirePMG2'].iloc[0] + ownerArea[' Stand Replacing FirePMG3'].iloc[0] + ownerArea[' Stand Replacing FirePMG4'].iloc[0] + ownerArea[' Stand Replacing FirePMG5'].iloc[0]) / fireProneArea * 100)
else:
dataList.append(0.0)
## if plotPage < 5:
## topPercent = 1.0
## else:
topPercent = 0.1
dataList.sort(reverse=True)
subDataList = []
for i in range(int(round(len(dataList) * topPercent))):
subDataList.append(dataList[i])
valueSet.append(subDataList)
maxValueSet.append(max(subDataList))
if splot == 1:
labelXaxis = True
figText = 'a'
elif splot == 2:
labelXaxis = True
figText = 'b'
elif splot == 3:
labelXaxis = True
figText = 'c'
elif splot == 4:
labelXaxis = True
figText = 'd'
## if plotPage in [1,2]:
## yLabelText = 'Fire frequent area (%)'
## elif plotPage in [3,4]:
## yLabelText = 'Forested area (%)'
## elif plotPage in [5,6]:
## yLabelText = 'Top 10% Fire frequent area (%)'
## elif plotPage in [7,8]:
## yLabelText = 'Top 10% Forested area (%)'
#reporterFunc.plotReporter5bw(fig, ax, '', pdfFile, valueSet, ownersToGraph, subArea, chartTitle, yLabelText, maxValueSet)
yLabelText = ''
reporterFunc.plotReporter5bw(fig, ax, '', pdfFile, valueSet, ownerLabel, subArea, chartTitle, yLabelText, maxValueSet, labelXaxis, figText)
yLabelText = 'Forested area burned (%)'
reporterFunc.plotFigureText(fig, '', yLabelText)
pl.savefig(outDir + 'report5bw_HS_Fire_byOwner.png', bbox_inches='tight', dpi=300)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(subArea, runName, chartTitlePre, ownership):
outDir = "C:\\Users\\olsenk\\Dropbox\\FPF\\Envision Runs\\keith_production\\"
# varList = [' Resilient (ha) PMG345', ' Semi-Resilient (ha) PMG345', ' Low Resilience (ha) PMG345']
varList = [' Resilient (ha) PMG345', ' Low Resilience (ha) PMG345']
yLabelText = 'Area (%)'
chartTitle = chartTitlePre
PMG345Ha = reporterFunc.getPMG345Ha(subArea)
forestedHa = reporterFunc.getOwnerForestedHa(subArea)
# figTextList = ['High resilience','Moderate resilience','Low resilience']
figTextList = ['High resilience', 'Low resilience']
# list of ownerships to graphs
ownersToGraph = [
'Federal', 'State', 'Private Non-Industrial', 'Private Industrial',
'Tribal', 'Homeowner'
]
reporterName = r'ForestStructure2_by_OWNER_pivot.csv'
ownerLabelField = ' OWNER_label'
if ownership == 'All':
pdfFile = PdfPages(outDir + 'report4_Resilience_landscape.pdf')
elif ownership in ownersToGraph:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + 'report4_Resilience_' + ownersToGraph[0] +
'.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + 'report4_Resilience_' + ownersToGraph[0] +
'.pdf')
ownerLabelField = ' OWNER_DETL_label'
reporterName = r'ForestStructure2_by_OWNER_DETL_pivot.csv'
fig = pl.figure(1, figsize=(6.5, 4))
for varStruct in varList:
# setup plot for all scenarios
ax = fig.add_subplot(1, 2, varList.index(varStruct) + 1)
for scenario in [
'CurrentPolicy', 'No_Treatment_Fed', 'Restoration',
'noFireNoTreatFed'
]:
inDir = outDir + runName + "_" + scenario + "\\"
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)
# 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]
# sum output over selected ownerships
for ownerToGraph in ownersToGraph:
if ownerToGraph == ownersToGraph[0]:
ownerArea = pd.DataFrame(repArea[
repArea[ownerLabelField] == ownerToGraph])
fireProneArea345 = PMG345Ha[ownerToGraph]
fireProneArea = forestedHa[ownerToGraph]
else:
tempArea = repArea[repArea[ownerLabelField] ==
ownerToGraph]
for varName in varList:
# totalArea.loc[list(ownerArea.index)[0],varName] += tempArea[varName].iloc[0]
ownerArea[varName].iloc[0] += tempArea[
varName].iloc[0]
fireProneArea345 += PMG345Ha[ownerToGraph]
fireProneArea += forestedHa[ownerToGraph]
ownerToGraph = 'All'
if varStruct in [
' Resilient (ha) PMG345',
' Semi-Resilient (ha) PMG345',
' Low Resilience (ha) PMG345'
]:
if fireProneArea345 > 0:
dataList.append(ownerArea[varStruct].iloc[0] /
fireProneArea345 * 100)
else:
dataList.append(0.0)
else:
if fireProneArea > 0:
dataList.append(ownerArea[varStruct].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 = (-99, -99)
if varStruct == ' Pole and Small (ha) Forest' or varStruct == ' Low Resilience (ha) PMG345':
plotLegend = (0.99, 0.25)
if varList.index(varStruct) >= (len(varList) - 3):
labelXtick = True
else:
labelXtick = False
if varList.index(varStruct) == 0:
labelYtick = True
else:
labelYtick = False
xLabelText = yLabelText = ''
reporterFunc.plotReporter4(
fig, ax, '', pdfFile, dataTable,
['mean', 'lower', 'upper'], xLabelText, yLabelText,
scenario, labelXtick, labelYtick, plotLegend,
figTextList[varList.index(varStruct)])
reporterFunc.plotFigureText(fig, 'Simulation Year', 'Area (%)')
pl.savefig(outDir + 'report4_Resilience_landscape.png',
bbox_inches='tight',
dpi=300)
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) PMG3',
' Potential HighSev Fire (ha) PMG4',
' Potential HighSev Fire (ha) PMG5'
]
chartTitle = chartTitlePre
PMG345Ha = reporterFunc.getPMG345Ha(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_PMG345_HS_Hazard_byOwner_NoTreatment.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 + "_NoTreatment\\"
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 = PMG345Ha[
'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 += PMG345Ha[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 = PMG345Ha['ODF Sun Pass']
for subOwner in ['ODF Gilchrist']:
tempArea = repArea[repArea[ownerLabelField]
== subOwner]
fireProneArea += PMG345Ha[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 = PMG345Ha[
'JWTR Timber Holdings']
for subOwner in [
'Cascade Timberlands (Mazama Forest)',
'Jeld-Wen Inc', 'J-Spear Ranch'
]:
tempArea = repArea[repArea[ownerLabelField]
== subOwner]
fireProneArea += PMG345Ha[subOwner]
for varName in varList:
ownerArea[varName].iloc[0] += tempArea[
varName].iloc[0]
else:
ownerArea = pd.DataFrame(repArea[
repArea[ownerLabelField] == ownership])
fireProneArea = PMG345Ha[ownership]
if fireProneArea > 0:
dataList.append(
(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)
if splot == 1:
labelXaxis = False
plotLegend = True
yLabelText = 'Percent of Fire Prone Landscape in High Severity Fire Hazard'
elif splot == 2:
labelXaxis = True
plotLegend = True
yLabelText = 'Percent of Fire Prone Landscape in High Severity Fire Hazard'
reporterFunc.plotReporter5(fig, ax, '', pdfFile, dataTable,
['mean', 'lower', 'upper'],
yLabelText, ownership,
labelXaxis, plotLegend)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(outDir, subArea, runList, chartTitle, ownership):
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']
PMG345Ha = reporterFunc.getPMG345Ha(subArea)
PMG12345Ha = reporterFunc.getPMG12345Ha(subArea)
# list of ownerships to graphs
if ownership == 'All':
ownersToGraph = ['Federal','State','Private Non-Industrial','Private Industrial','Tribal','Homeowner']
pdfFile = PdfPages(outDir + chartTitle + '_Potential_HS_Fire_Landscape_PMG345.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + chartTitle + '_Potential_HS_Fire_' + ownersToGraph[0] + '.pdf')
# setup plot for all scenarios
fig = pl.figure(1, figsize=(4,4))
for splot in [2]:
ax = fig.add_subplot(1,1,1)
for scenario in runList:
inDir = outDir + scenario + "\\"
if os.path.isdir(inDir):
yearList = list(set(pd.io.parsers.read_csv(inDir + r'PotentialDisturbance_by_OWNER_pivot.csv')[' Year']))
repList = list(set(pd.io.parsers.read_csv(inDir + r'PotentialDisturbance_by_OWNER_pivot.csv')[' Run']))
totalArea = pd.io.parsers.read_csv(inDir + r'PotentialDisturbance_by_OWNER_pivot.csv')
# 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]
# sum output over selected ownerships
for ownerToGraph in ownersToGraph:
if ownerToGraph == ownersToGraph[0]:
ownerArea = pd.DataFrame(repArea[repArea[' OWNER_label'] == ownerToGraph])
fireProneArea = PMG345Ha[ownerToGraph]
forestedArea = PMG12345Ha[ownerToGraph]
else:
tempArea = repArea[repArea[' OWNER_label'] == ownerToGraph]
for varName in varList:
# totalArea.loc[list(ownerArea.index)[0],varName] += tempArea[varName].iloc[0]
ownerArea[varName].iloc[0] += tempArea[varName].iloc[0]
fireProneArea += PMG345Ha[ownerToGraph]
forestedArea += PMG12345Ha[ownerToGraph]
ownerToGraph = 'All'
if splot == 1:
if forestedArea > 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]) / forestedArea * 100)
else:
dataList.append(0.0)
elif splot == 2:
if fireProneArea > 0:
dataList.append((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)
labelXtick = True
labelYtick = True
plotLegend = (0.6,0.99)
xLabelText = 'Simulation Year'
if splot == 1:
yLabelText = 'Area of high-severity fire hazard (%)'
elif splot == 2:
yLabelText = 'Area of fire prone landscape in high-severity hazard (%)'
reporterFunc.plotReporter4(fig, ax, '', pdfFile, dataTable, ['mean','lower','upper'], xLabelText, yLabelText, scenario, labelXtick, labelYtick, plotLegend, '')
pl.savefig(outDir + 'report4_Potential_HS_Fire_Landscape.png', bbox_inches='tight', dpi=300)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(outDir, subArea, runName, chartTitlePre, ownership):
varList = [' NumberDwellings1kmFromStandRep']
chartTitle = chartTitlePre
PMG345Ha = reporterFunc.getPMG345Ha(subArea)
# list of ownerships to graphs
# if ownership == 'All':
# ownersToGraph = ['Federal','State','Private Non-Industrial','Private Industrial','Tribal','Homeowner']
pdfFile = PdfPages(outDir + 'report4bw_Homes_Exposed_to_HS_Fire_All.pdf')
# else:
# ownersToGraph = [ownership]
# pdfFile = PdfPages(outDir + 'report4bw_Homes_Exposed_to_HS_Fire_' + ownersToGraph[0] + '.pdf')
# setup plot for all scenarios
fig = pl.figure(1, figsize=(4,6.5))
# Current Policy run required!
for scenario in ['No_Treatment_Fed','Restoration','CurrentPolicy']:
inDir = outDir + runName + "_" + scenario + "\\"
if scenario == 'No_Treatment_Fed':
yearList = list(set(pd.io.parsers.read_csv(inDir + r'FireExperience_pivot.csv')[' Year']))
repList = list(set(pd.io.parsers.read_csv(inDir + r'FireExperience_pivot.csv')[' Run']))
totalArea = pd.io.parsers.read_csv(inDir + r'FireExperience_pivot.csv')
dataTable = pd.DataFrame(totalArea[totalArea[' Year'] > 0])
dataTable['key'] = 'run' + dataTable[' Run'].astype(str) + '_' + dataTable[' Year'].astype(str)
if scenario == 'No_Treatment_Fed':
scenarioTable = dataTable
else:
scenarioTable = pd.merge(scenarioTable, dataTable, how='left', on='key')
# sum over owners
scenarioTable.index = scenarioTable['key']
#Sort dataTable on number of homes exposed to fire
scenarioTable = scenarioTable.rename(columns={' NumberDwellings1kmFromStandRep': 'CP_Dwellings1kmHSFire',' NumberDwellings1kmFromStandRep_x': 'NFT_Dwellings1kmHSFire',' NumberDwellings1kmFromStandRep_y': 'Res_Dwellings1kmHSFire'})
dataTable = scenarioTable.sort('NFT_Dwellings1kmHSFire', ascending=False)
for splot in [1, 2]:
ax = fig.add_subplot(2,1,splot)
# label plots and set percent of top values
if splot == 1:
yLabelText = 'Number of dwellings 1km from stand-replacing fire'
labelXaxis = False
figText = 'A'
topPercent = 1.0
elif splot == 2:
yLabelText = 'Number of dwellings 1km from top 10% of stand-replacing fires'
labelXaxis = True
figText = 'B'
topPercent = 0.1
statsList1 = []
statsList2 = []
statsList3 = []
valueSet = []
maxValueSet = []
for i in range(int(round(len(dataTable) * topPercent))):
statsList1.append(dataTable['CP_Dwellings1kmHSFire'].iloc[i])
statsList2.append(dataTable['NFT_Dwellings1kmHSFire'].iloc[i])
statsList3.append(dataTable['Res_Dwellings1kmHSFire'].iloc[i])
valueSet.append(statsList1)
valueSet.append(statsList2)
valueSet.append(statsList3)
maxValueSet.append(max(statsList1))
maxValueSet.append(max(statsList2))
maxValueSet.append(max(statsList3))
yLabelText = ''
labelList = ['Current\nManagement','No Federal\nTreatment','Accelerated\nRestoration']
reporterFunc.plotReporter4bw(fig, ax, ownership, pdfFile, valueSet, labelList, subArea, chartTitle, yLabelText, maxValueSet, labelXaxis, figText)
yLabelText = 'Number of dwellings 1km from high-severity fire'
reporterFunc.plotFigureText(fig, '', yLabelText)
pl.savefig(outDir + 'report4bw_Homes_Exposed_to_HS_Fire_All.png', bbox_inches='tight', dpi=300)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(subArea, runName, chartTitlePre):
outDir = "C:\\Users\\olsenk\\Dropbox\\FPF\\Envision Runs\\keith_production\\"
varList = [
' Resilient (ha) PMG345', ' Early Successional (ha) Forest',
' Pole and Small (ha) Forest', ' Medium (ha) Forest',
' Large and Giant (ha) Forest', ' Open Canopy (ha) Forest',
' Moderate Canopy (ha) Forest', ' Closed Canopy (ha) Forest'
]
chartTitle = chartTitlePre
PMG345Ha = reporterFunc.getPMG345Ha(subArea)
forestedHa = reporterFunc.getOwnerForestedHa(subArea)
# list of ownerships to graphs
reporterName = r'ForestStructure2_by_OWNER_DETL_pivot.csv'
ownerLabelField = ' OWNER_DETL_label'
pdfFile = PdfPages(outDir + 'report5bar_ForestStructre2_byOwner.pdf')
# setup plot for all owners
fig = pl.figure(1, figsize=(11, 8.5))
inDir = outDir + runName + "_CurrentPolicy\\"
ownersToGraph = [
'USFS', 'State', 'Corporate Forest',
'Chemult Ranger District (Fremont-Winema NF)',
'Chiloquin Ranger District (Fremont-Winema NF)',
'Klamath Ranger District (Fremont-Winema NF)', 'ODF Sun Pass',
'ODF Gilchrist', '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:
ax = fig.add_subplot(2, 6, ownersToGraph.index(ownership) + 1)
print ownership
# get stats from multiple reps
statsList = []
for varStruct in varList:
for year in [1, max(yearList)]:
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)'])
fireProneArea345 = PMG345Ha[
'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]
fireProneArea345 += PMG345Ha[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'])
fireProneArea345 = PMG345Ha['ODF Sun Pass']
fireProneArea = forestedHa['ODF Sun Pass']
for subOwner in [
'ODF Gilchrist',
'ODF Gilchrist (The Conservation Fund)'
]:
tempArea = repArea[repArea[ownerLabelField] ==
subOwner]
fireProneArea345 += PMG345Ha[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'])
fireProneArea345 = PMG345Ha['ODF Gilchrist']
fireProneArea = forestedHa['ODF Gilchrist']
for subOwner in [
'ODF Gilchrist (The Conservation Fund)'
]:
tempArea = repArea[repArea[ownerLabelField] ==
subOwner]
fireProneArea345 += PMG345Ha[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'])
fireProneArea345 = PMG345Ha['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]
fireProneArea345 += PMG345Ha[subOwner]
fireProneArea += forestedHa[subOwner]
for varName in varList:
ownerArea[varName].iloc[0] += tempArea[
varName].iloc[0]
else:
ownerArea = pd.DataFrame(
repArea[repArea[ownerLabelField] == ownership])
fireProneArea345 = PMG345Ha[ownership]
fireProneArea = forestedHa[ownership]
if varStruct in [
' Resilient (ha) PMG345',
' Semi-Resilient (ha) PMG345',
' Low Resilience (ha) PMG345'
]:
if fireProneArea345 > 0:
dataList.append((ownerArea[varStruct].iloc[0]))
else:
dataList.append(0.0)
else:
if fireProneArea > 0:
dataList.append((ownerArea[varStruct].iloc[0]))
else:
dataList.append(0.0)
# calculate percent change
numpyList = np.array(dataList)
if year == 1:
startYrMean = np.mean(numpyList, axis=0)
else:
endYrMean = np.mean(numpyList, axis=0)
# convert to list for DataFrame
statsList.append((endYrMean - startYrMean) / startYrMean * 100)
if 'USFS' in ownership or 'Sun Pass' in ownership:
labelYaxis = True
else:
labelYaxis = False
yLabelText = 'Percent change'
varList2 = []
for varName in varList:
if '(ha)' in varName:
varList2.append(varName[:-12])
statsList.reverse()
varList2.reverse()
statsDF = pd.DataFrame(statsList)
statsDF['positives'] = statsDF[0] > 0
reporterFunc.plotReporter5bar(fig, ax, '', pdfFile, statsDF,
varList2, yLabelText, ownership,
labelYaxis)
reporterFunc.plotFigureText(fig, 'Change in % of ownership', '')
pl.savefig(outDir + 'report5bar_ForestStructre2_byOwner.png',
bbox_inches='tight',
dpi=300)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(subArea, runName, chartTitlePre, ownership):
outDir = "C:\\Users\\olsenk\\Dropbox\\FPF\\Envision Runs\\keith_production\\"
varList = [' Black-backed Woodpecker Good (ha)']
yLabelText = 'Black-backed Woodpecker Good Habitat (ha)'
chartTitle = chartTitlePre
PMG345Ha = reporterFunc.getPMG345Ha(subArea)
# list of ownerships to graphs
if ownership == 'All':
ownersToGraph = [
'Federal', 'State', 'Private Non-Industrial', 'Private Industrial',
'Tribal', 'Homeowner'
]
pdfFile = PdfPages(outDir + 'report2_BBWO_iLAP_All.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + 'report2_BBWO_iLAP_' + ownersToGraph[0] +
'.pdf')
# setup plot for all scenarios
fig = pl.figure(1, figsize=(11, 8.5))
ax = fig.add_subplot(1, 1, 1)
for scenario in [
'CurrentPolicy', 'NoFedTreat', 'Restoration', 'NoFireNoMgmt'
]:
inDir = outDir + runName + "_" + scenario + "\\"
if os.path.isdir(inDir):
yearList = list(
set(
pd.io.parsers.read_csv(
inDir + r'ILAP_wildlife_models_pivot.csv')[' Year']))
repList = list(
set(
pd.io.parsers.read_csv(
inDir + r'ILAP_wildlife_models_pivot.csv')[' Run']))
totalArea = pd.io.parsers.read_csv(
inDir + r'ILAP_wildlife_models_pivot.csv')
# 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]
## # sum output over selected ownerships
## for ownerToGraph in ownersToGraph:
## if ownerToGraph == ownersToGraph[0]:
## ownerArea = pd.DataFrame(repArea[repArea[' OWNER_label'] == ownerToGraph])
## fireProneArea = PMG345Ha[ownerToGraph]
## else:
## tempArea = repArea[repArea[' OWNER_label'] == ownerToGraph]
## for varName in varList:
### totalArea.loc[list(ownerArea.index)[0],varName] += tempArea[varName].iloc[0]
## ownerArea[varName].iloc[0] += tempArea[varName].iloc[0]
##
## fireProneArea += PMG345Ha[ownerToGraph]
## ownerToGraph = 'All'
ownerToGraph = 'All'
ownerArea = repArea
dataList.append(ownerArea[varList[0]].iloc[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)
reporterFunc.plotReporter2(
fig, ax, ownerToGraph + ' - ' + str(len(repList)) + ' reps',
pdfFile, dataTable, ['mean', 'lower', 'upper'], subArea,
chartTitle, yLabelText, scenario)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(subArea, runName, chartTitlePre, ownership):
outDir = "C:\\Users\\olsenk\\Dropbox\\FPF\\Envision Runs\\keith_production\\"
varList = [
' Stand Replacing FirePMG1', ' Stand Replacing FirePMG2',
' Stand Replacing FirePMG3', ' Stand Replacing FirePMG4',
' Stand Replacing FirePMG5'
]
chartTitle = chartTitlePre
PMG345Ha = reporterFunc.getPMG345Ha(subArea)
PMG12345Ha = reporterFunc.getPMG12345Ha(subArea)
# list of ownerships to graphs
ownersToGraph = [
'Federal', 'State', 'Private Non-Industrial', 'Private Industrial',
'Tribal', 'Homeowner'
]
reporterName = r'FireOccurance_by_OWNER_pivot.csv'
ownerLabelField = ' OWNER_label'
if ownership == 'All':
pdfFile = PdfPages(outDir + 'report4_HS_Fire_landscape.pdf')
elif ownership in ownersToGraph:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + 'report4_HS_Fire_' + ownersToGraph[0] +
'.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + 'report4_HS_Fire_' + ownersToGraph[0] +
'.pdf')
ownerLabelField = ' OWNER_DETL_label'
reporterName = r'FireOccurance_by_OWNER_DETL_pivot.csv'
# setup plot for all scenarios
fig = pl.figure(1, figsize=(4, 4))
for splot in [1]:
ax = fig.add_subplot(1, 1, splot)
for scenario in ['CurrentPolicy', 'No_Treatment_Fed', 'Restoration']:
inDir = outDir + runName + "_" + scenario + "\\"
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)
# 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]
# sum output over selected ownerships
for ownerToGraph in ownersToGraph:
if ownerToGraph == ownersToGraph[0]:
ownerArea = pd.DataFrame(repArea[
repArea[ownerLabelField] == ownerToGraph])
fireProneArea = PMG345Ha[ownerToGraph]
forestedArea = PMG12345Ha[ownerToGraph]
else:
tempArea = repArea[repArea[ownerLabelField] ==
ownerToGraph]
for varName in varList:
# totalArea.loc[list(ownerArea.index)[0],varName] += tempArea[varName].iloc[0]
ownerArea[varName].iloc[0] += tempArea[
varName].iloc[0]
fireProneArea += PMG345Ha[ownerToGraph]
forestedArea += PMG12345Ha[ownerToGraph]
ownerToGraph = 'All'
if splot == 1:
if forestedArea > 0:
dataList.append(
(ownerArea[' Stand Replacing FirePMG1'].
iloc[0] + ownerArea[
' Stand Replacing FirePMG2'].iloc[0] +
ownerArea[' Stand Replacing FirePMG3'].
iloc[0] + ownerArea[
' Stand Replacing FirePMG4'].iloc[0] +
ownerArea[' Stand Replacing FirePMG5'].
iloc[0]) / forestedArea * 100)
else:
dataList.append(0.0)
elif splot == 2:
if fireProneArea > 0:
dataList.append(
(ownerArea[' Stand Replacing FirePMG3'].
iloc[0] + ownerArea[
' Stand Replacing FirePMG4'].iloc[0] +
ownerArea[' Stand Replacing FirePMG5'].
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)
labelXtick = True
labelYtick = True
plotLegend = (0.99, 0.99)
xLabelText = 'Simulation Year'
if splot == 1:
yLabelText = 'Area of high-severity fire (%)'
elif splot == 2:
yLabelText = 'Percent of Fire Prone Landscape in high-severity Fire'
reporterFunc.plotReporter4(fig, ax, '', pdfFile, dataTable,
['mean', 'lower', 'upper'],
xLabelText, yLabelText, scenario,
labelXtick, labelYtick, plotLegend,
'')
pl.savefig(outDir + 'report4_HS_Fire_landscape.png',
bbox_inches='tight',
dpi=300)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(subArea, runName, chartTitlePre, ownership):
outDir = "C:\\Users\\olsenk\\Dropbox\\FPF\\Envision Runs\\keith_production\\"
varList = [
' Stand Replacing FirePMG3', ' Stand Replacing FirePMG4',
' Stand Replacing FirePMG5'
]
yLabelText = 'Percent of PMG 3, 4, and 5 in Stand Replacing Fire'
chartTitle = chartTitlePre
PMG345Ha = reporterFunc.getPMG345Ha(subArea)
# list of ownerships to graphs
if ownership == 'All':
ownersToGraph = [
'Federal', 'State', 'Private Non-Industrial', 'Private Industrial',
'Tribal', 'Homeowner'
]
pdfFile = PdfPages(outDir + 'report3_PMG345_in_HS_Fire_All.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + 'report3_PMG345_in_HS_Fire_' +
ownersToGraph[0] + '.pdf')
# setup plot for all scenarios
fig = pl.figure(1, figsize=(11, 8.5))
ax = fig.add_subplot(1, 1, 1)
valueSet1 = []
valueSet2 = []
maxValueSet1 = []
maxValueSet2 = []
for scenario in ['CurrentPolicy', 'NoFedTreat', 'Restoration']:
inDir = outDir + runName + "_" + scenario + "\\"
yearList = list(
set(
pd.io.parsers.read_csv(
inDir + r'FireOccurance_by_OWNER_pivot.csv')[' Year']))
repList = list(
set(
pd.io.parsers.read_csv(
inDir + r'FireOccurance_by_OWNER_pivot.csv')[' Run']))
totalArea = pd.io.parsers.read_csv(inDir +
r'FireOccurance_by_OWNER_pivot.csv')
# get stats from multiple reps
statsList1 = []
statsList2 = []
for year in range(1, max(yearList) + 1):
yearArea = totalArea[totalArea[' Year'] == year]
dataList = []
for rep in repList:
repArea = yearArea[yearArea[' Run'] == rep]
# sum output over selected ownerships
for ownerToGraph in ownersToGraph:
if ownerToGraph == ownersToGraph[0]:
ownerArea = pd.DataFrame(
repArea[repArea[' OWNER_label'] == ownerToGraph])
fireProneArea = PMG345Ha[ownerToGraph]
else:
tempArea = repArea[repArea[' OWNER_label'] ==
ownerToGraph]
for varName in varList:
# totalArea.loc[list(ownerArea.index)[0],varName] += tempArea[varName].iloc[0]
ownerArea[varName].iloc[0] += tempArea[
varName].iloc[0]
fireProneArea += PMG345Ha[ownerToGraph]
ownerToGraph = 'All'
if fireProneArea > 0:
dataList.append(
(ownerArea[varList[0]].iloc[0] +
ownerArea[varList[1]].iloc[0] +
ownerArea[varList[2]].iloc[0]) / fireProneArea * 100)
else:
dataList.append(0.0)
# add year data to dictionary
if year < 25:
statsList1 += dataList
else:
statsList2 += dataList
valueSet1.append(statsList1)
valueSet2.append(statsList2)
maxValueSet1.append(max(statsList1))
maxValueSet2.append(max(statsList2))
labelList = [
'CP 0-24', 'NFT 0-24', 'Res 0-24', 'CP 25-50', 'NFT 25-50', 'Res 25-50'
]
reporterFunc.plotReporter3(
fig, ax, ownerToGraph + ' - ' + str(len(repList)) + ' reps', pdfFile,
valueSet1 + valueSet2, labelList, subArea, chartTitle, yLabelText,
scenario, maxValueSet1 + maxValueSet2)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(subArea, runName, chartTitlePre, ownership):
outDir = "C:\\Users\\olsenk\\Dropbox\\FPF\\Envision Runs\\keith_production\\"
varList = [
' Resilient (ha) PMG345', ' Semi-Resilient (ha) PMG345',
' Low Resilience (ha) PMG345', ' Early Successional (ha) Forest',
' Plantations (ha) Forest', ' Pole and Small (ha) Forest',
' Medium (ha) Forest', ' Large and Giant (ha) Forest'
]
yLabelText = 'Percent of Landscape in'
chartTitle = chartTitlePre
PMG345Ha = reporterFunc.getPMG345Ha(subArea)
forestedHa = reporterFunc.getOwnerForestedHa(subArea)
# list of ownerships to graphs
if ownership == 'All':
ownersToGraph = [
'Federal', 'State', 'Private Non-Industrial', 'Private Industrial',
'Tribal', 'Homeowner'
]
pdfFile = PdfPages(outDir + 'report2_ForestStructure2_All.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + 'report2_ForestStructure2_' +
ownersToGraph[0] + '.pdf')
for varStruct in varList:
# setup plot for all scenarios
fig = pl.figure(1, figsize=(11, 8.5))
ax = fig.add_subplot(1, 1, 1)
for scenario in [
'CurrentPolicy', 'NoFedTreat', 'Restoration', 'NoFireNoMgmt'
]:
inDir = outDir + runName + "_" + scenario + "\\"
if os.path.isdir(inDir):
yearList = list(
set(
pd.io.parsers.read_csv(
inDir +
r'ForestStructure2_by_OWNER_pivot.csv')[' Year']))
repList = list(
set(
pd.io.parsers.read_csv(
inDir +
r'ForestStructure2_by_OWNER_pivot.csv')[' Run']))
totalArea = pd.io.parsers.read_csv(
inDir + r'ForestStructure2_by_OWNER_pivot.csv')
# 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]
# sum output over selected ownerships
for ownerToGraph in ownersToGraph:
if ownerToGraph == ownersToGraph[0]:
ownerArea = pd.DataFrame(repArea[
repArea[' OWNER_label'] == ownerToGraph])
fireProneArea345 = PMG345Ha[ownerToGraph]
fireProneArea = forestedHa[ownerToGraph]
else:
tempArea = repArea[repArea[' OWNER_label'] ==
ownerToGraph]
for varName in varList:
# totalArea.loc[list(ownerArea.index)[0],varName] += tempArea[varName].iloc[0]
ownerArea[varName].iloc[0] += tempArea[
varName].iloc[0]
fireProneArea345 += PMG345Ha[ownerToGraph]
fireProneArea += forestedHa[ownerToGraph]
ownerToGraph = 'All'
if varStruct in [
' Resilient (ha) PMG345',
' Semi-Resilient (ha) PMG345',
' Low Resilience (ha) PMG345'
]:
if fireProneArea345 > 0:
dataList.append(ownerArea[varStruct].iloc[0] /
fireProneArea345 * 100)
else:
dataList.append(0.0)
else:
if fireProneArea > 0:
dataList.append(ownerArea[varStruct].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)
reporterFunc.plotReporter2(
fig, ax,
ownerToGraph + ' - ' + str(len(repList)) + ' reps',
pdfFile, dataTable, ['mean', 'lower', 'upper'], subArea,
chartTitle, yLabelText + varStruct, scenario)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
