def main(outDir, subArea, runList, chartTitle, ownership):
varList = [' Early Successional (ha) Forest', ' Pole and Small (ha) Forest', ' Medium (ha) Forest', ' Large and Giant (ha) Forest',' Open Canopy (ha) Sm+ Forest',' Closed Canopy (ha) Sm+ Forest']
yLabelText = 'Area (%)'
figTextList = ['Early successional','Pole and small','Medium','Large and giant','Open canopy','Closed canopy']
# list of ownerships to graphs
ownersToGraph = ['Federal','State','Private Non-Industrial','Private Industrial','Homeowner']
reporterName = r'ForestStructure2_by_OWNER_pivot.csv'
reporterNameSYU = r'ForestStructure2_SYU_by_OWNER_pivot.csv'
ownerLabelField = ' OWNER_label'
if ownership == 'All':
pdfFile = PdfPages(outDir + chartTitle + '_syu_ForestStructure2_landscape.pdf')
forestedSYUArea = 222072
elif ownership in ownersToGraph:
ownersToGraph = [ownership]
forestedSYUArea = 172556
pdfFile = PdfPages(outDir + chartTitle + '_syu_ForestStructure2_' + ownersToGraph[0] + '.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + chartTitle + '_syu_ForestStructure2_' + ownersToGraph[0] + '.pdf')
ownerLabelField = ' OWNER_DETL_label'
reporterName = r'ForestStructure2_by_OWNER_DETL_pivot.csv'
# load area stats by owner
areaStats = pd.io.parsers.read_csv(outDir + runList[0] + "\\AreaStats_by_OWNER_pivot.csv")
areaStats = areaStats[areaStats[' Year'] == 1]
# sum total areas over selected ownerships
for ownerToGraph in ownersToGraph:
ownerStats = areaStats[areaStats[ownerLabelField] == ownerToGraph]
if ownerToGraph == ownersToGraph[0]:
forestedArea = ownerStats[' Forested (ha)'].iloc[0]
else:
forestedArea += ownerStats[' Forested (ha)'].iloc[0]
fig = pl.figure(1, figsize=(8,6.5))
for varStruct in varList:
# setup plot for all scenarios
ax = fig.add_subplot(2,3,varList.index(varStruct) + 1)
print ('Running ' + varStruct)
for scenario in runList:
inDir = outDir + scenario + "\\"
if os.path.isdir(inDir):
for reporter in [reporterName, reporterNameSYU]:
totalArea = pd.io.parsers.read_csv(inDir + reporter)
yearList = list(set(totalArea[' Year']))
repList = list(set(totalArea[' Run']))
# 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])
else:
ownerArea[varStruct].iloc[0] += repArea[repArea[ownerLabelField] == ownerToGraph][varStruct].iloc[0]
dataList.append(ownerArea[varStruct].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)
if '_SYU_' in reporter:
scenario += '_SYU_'
dataTable['mean'] = dataTable['mean'] / forestedSYUArea * 100
dataTable['lower'] = dataTable['lower'] / forestedSYUArea * 100
dataTable['upper'] = dataTable['upper'] / forestedSYUArea * 100
else:
dataTable['mean'] = dataTable['mean'] / forestedArea * 100
dataTable['lower'] = dataTable['lower'] / forestedArea * 100
dataTable['upper'] = dataTable['upper'] / forestedArea * 100
plotLegend = (-99,-99)
if varStruct == ' Pole and Small (ha) Forest':
plotLegend = (0.8,0.45)
if varList.index(varStruct) >= (len(varList) - 3):
labelXtick = True
else:
labelXtick = False
# if varList.index(varStruct) == 0 or varList.index(varStruct) == 3:
labelYtick = True
xLabelText = yLabelText = ''
mnAxis, mxAxis = reporterFunc.plotReporter4(fig, ax, '', pdfFile, dataTable, ['mean'], xLabelText, yLabelText, scenario, labelXtick, labelYtick, plotLegend, figTextList[varList.index(varStruct)])
reporterFunc.plotFigureText(fig, 'Simulation Year', 'Area (%)')
# pl.savefig(outDir + 'report4_ForestStructure2_landscape.png', bbox_inches='tight', dpi=300)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done with forest structure 2."
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(outDir, subArea, runList, chartTitle, ownership):
varList = [' NumberDwellings1kmFromStandRep']
# list of ownerships to graphs
reporterName = r'FireExperience_pivot.csv'
pdfFile = PdfPages(outDir + chartTitle + '_HS_Fire_Homes_Exposed_cumulative.pdf')
for splot in [1]:
# setup plot for all scenarios
fig = pl.figure(1, figsize=(4,4))
ax = fig.add_subplot(1,1,1)
for scenario in runList:
inDir = outDir + scenario + "\\"
if os.path.isdir(inDir):
totalArea = pd.io.parsers.read_csv(inDir + reporterName)
yearList = list(set(totalArea[' Year']))
repList = list(set(totalArea[' Run']))
# 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]
dataList.append(repArea[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)
labelXtick = True
labelYtick = True
plotLegend = (0.5,0.99)
xLabelText = 'Simulation Year'
dataTable['mean'] = dataTable['mean'] / 1000
dataTable['lower'] = dataTable['lower'] / 1000
dataTable['upper'] = dataTable['upper'] / 1000
if splot == 1:
yLabelText = 'Cumulative Homes Exposed to HS fire (1000 dwellings)'
## mnAxis, mxAxis = reporterFunc.plotReporter6_cumulative(fig, ax, '', pdfFile, dataTable, ['mean'], xLabelText, yLabelText, scenario, labelXtick, labelYtick, plotLegend, '')
dataTable['cum_mean'] = dataTable['mean'].cumsum()
mnAxis, mxAxis = reporterFunc.plotReporter4(fig, ax, '', pdfFile, dataTable, ['cum_mean'], xLabelText, yLabelText, scenario, labelXtick, labelYtick, plotLegend, '')
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done with cumulative HS fire Homes Exposed."
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, runList, chartTitle, ownership):
varList = [
' Smoke_surfaceFire (Mg)', ' Smoke_mixSevFire (Mg)',
' Smoke_standRepFire (Mg)', ' Smoke_prescribedFire (Mg)'
]
# list of ownerships to graphs
## ownersToGraph = ['Federal','State','Private Non-Industrial','Private Industrial','Tribal','Homeowner']
# remove Tribal from owners to graph
ownersToGraph = [
'Federal', 'State', 'Private Non-Industrial', 'Private Industrial',
'Homeowner'
]
reporterName = r'Smoke_by_OWNER_pivot.csv'
ownerLabelField = ' OWNER_label'
# set list of owners to graph
if ownership == 'All':
pdfFile = PdfPages(outDir + chartTitle + '_smoke_landscape.pdf')
elif ownership in ownersToGraph:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + chartTitle + '_smoke_' + ownersToGraph[0] +
'.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + chartTitle + '_smoke_' + ownersToGraph[0] +
'.pdf')
ownerLabelField = ' OWNER_DETL_label'
reporterName = r'Smoke_by_OWNER_DETL_pivot.csv'
for splot in [1, 2, 3, 4]:
# setup plot for all scenarios
fig = pl.figure(1, figsize=(4, 4))
ax = fig.add_subplot(1, 1, 1)
for scenario in runList:
inDir = outDir + scenario + "\\"
if os.path.isdir(inDir):
totalArea = pd.io.parsers.read_csv(inDir + reporterName)
yearList = list(set(totalArea[' Year']))
repList = list(set(totalArea[' Run']))
# 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])
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]
ownerToGraph = 'All'
if splot == 1 or splot == 2:
dataList.append(
ownerArea[' Smoke_surfaceFire (Mg)'].iloc[0] +
ownerArea[' Smoke_mixSevFire (Mg)'].iloc[0] +
ownerArea[' Smoke_standRepFire (Mg)'].iloc[0])
elif splot == 3 or splot == 4:
dataList.append(
ownerArea[' Smoke_prescribedFire (Mg)'].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)
labelXtick = True
labelYtick = True
plotLegend = (0.99, 0.99)
xLabelText = 'Simulation Year'
outVar = 'mean'
if splot == 1:
yLabelText = 'Smoke from wildfire (Mg)'
elif splot == 2:
yLabelText = 'Cumulative smoke from wildfire (1000 Mg)'
dataTable['cum_mean'] = dataTable['mean'].cumsum()
dataTable['cum_mean'] = dataTable['cum_mean'] / 1000
outVar = 'cum_mean'
plotLegend = (0.5, 0.99)
elif splot == 3:
yLabelText = 'Smoke from prescribed fire (Mg)'
elif splot == 4:
yLabelText = 'Cumulative smoke from prescribed fire (Mg)'
dataTable['cum_mean'] = dataTable['mean'].cumsum()
outVar = 'cum_mean'
plotLegend = (0.5, 0.99)
mnAxis, mxAxis = reporterFunc.plotReporter4(
fig, ax, '', pdfFile, dataTable, [outVar], xLabelText,
yLabelText, scenario, labelXtick, labelYtick, plotLegend,
'')
if mxAxis > 1000:
pl.subplots_adjust(left=0.14)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done with smoke."
def main(outDir, subArea, runList, chartTitle, ownership):
varList = [' Stand Replacing FirePMG1', ' Stand Replacing FirePMG2', ' Stand Replacing FirePMG3', ' Stand Replacing FirePMG4', ' Stand Replacing FirePMG5']
# list of ownerships to graphs
ownersToGraph = ['Federal','State','Private Non-Industrial','Private Industrial','Homeowner']
reporterName = r'FireOccurance_by_OWNER_pivot.csv'
ownerLabelField = ' OWNER_label'
# reset list of owners to graph
if ownership == 'All':
pdfFile = PdfPages(outDir + chartTitle + '_HS_Fire_landscape_cumulative.pdf')
elif ownership in ownersToGraph:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + chartTitle + '_HS_Fire_' + ownersToGraph[0] + 'cumulative.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + chartTitle + '_HS_Fire_' + ownersToGraph[0] + 'cumulative.pdf')
ownerLabelField = ' OWNER_DETL_label'
reporterName = r'FireOccurance_by_OWNER_DETL_pivot.csv'
# load area stats by owner
areaStats = pd.io.parsers.read_csv(outDir + runList[0] + "\\AreaStats_by_OWNER_pivot.csv")
areaStats = areaStats[areaStats[' Year'] == 1]
# sum total areas over selected ownerships
for ownerToGraph in ownersToGraph:
ownerStats = areaStats[areaStats[ownerLabelField] == ownerToGraph]
if ownerToGraph == ownersToGraph[0]:
PMG345Area = ownerStats[' PVT MNG GR 3 (ha)'].iloc[0] + ownerStats[' PVT MNG GR 4 (ha)'].iloc[0] + ownerStats[' PVT MNG GR 5 (ha)'].iloc[0]
PMG12345Area = ownerStats[' PVT MNG GR 1 (ha)'].iloc[0] + ownerStats[' PVT MNG GR 2 (ha)'].iloc[0] + ownerStats[' PVT MNG GR 3 (ha)'].iloc[0] + ownerStats[' PVT MNG GR 4 (ha)'].iloc[0] + ownerStats[' PVT MNG GR 5 (ha)'].iloc[0]
PMG1Area = ownerStats[' PVT MNG GR 1 (ha)'].iloc[0]
PMG2Area = ownerStats[' PVT MNG GR 2 (ha)'].iloc[0]
else:
PMG345Area += ownerStats[' PVT MNG GR 3 (ha)'].iloc[0] + ownerStats[' PVT MNG GR 4 (ha)'].iloc[0] + ownerStats[' PVT MNG GR 5 (ha)'].iloc[0]
PMG12345Area += ownerStats[' PVT MNG GR 1 (ha)'].iloc[0] + ownerStats[' PVT MNG GR 2 (ha)'].iloc[0] + ownerStats[' PVT MNG GR 3 (ha)'].iloc[0] + ownerStats[' PVT MNG GR 4 (ha)'].iloc[0] + ownerStats[' PVT MNG GR 5 (ha)'].iloc[0]
PMG1Area += ownerStats[' PVT MNG GR 1 (ha)'].iloc[0]
PMG2Area += ownerStats[' PVT MNG GR 2 (ha)'].iloc[0]
for splot in [1,3,5,7]:
# setup plot for all scenarios
fig = pl.figure(1, figsize=(4,4))
ax = fig.add_subplot(1,1,1)
for scenario in runList:
print "HS Fire Cumulative Running scenario " + scenario
inDir = outDir + scenario + "\\"
if os.path.isdir(inDir):
totalArea = pd.io.parsers.read_csv(inDir + reporterName)
yearList = list(set(totalArea[' Year']))
repList = list(set(totalArea[' Run']))
# get stats from multiple reps
statsList = []
for year in range(1,max(yearList) + 1):
yearArea = totalArea[totalArea[' Year'] == year]
print 'running year ' + str(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])
else:
tempArea = pd.DataFrame(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]
if splot == 1 or splot == 2:
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])
elif splot == 3 or splot == 4:
dataList.append(ownerArea[' Stand Replacing FirePMG3'].iloc[0] + ownerArea[' Stand Replacing FirePMG4'].iloc[0] + ownerArea[' Stand Replacing FirePMG5'].iloc[0])
elif splot == 5 or splot == 6:
dataList.append(ownerArea[' Stand Replacing FirePMG2'].iloc[0])
elif splot == 7 or splot == 8:
dataList.append(ownerArea[' Stand Replacing FirePMG1'].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)
labelXtick = True
labelYtick = True
plotLegend = (0.5,0.99)
xLabelText = 'Simulation Year'
dataTable['mean'] = dataTable['mean'] / 1000
dataTable['lower'] = dataTable['lower'] / 1000
dataTable['upper'] = dataTable['upper'] / 1000
if splot == 1:
yLabelText = 'Cumulative area of HS fire (1000 ha)'
elif splot == 2:
yLabelText = 'Cumulative area of HS fire (1000 ac)'
dataTable['mean'] = dataTable['mean'] * 2.471
dataTable['lower'] = dataTable['lower'] * 2.471
dataTable['upper'] = dataTable['upper'] * 2.471
elif splot == 3:
yLabelText = 'Cumulative area of fire frequent PMG in HS fire (1000 ha)'
elif splot == 4:
yLabelText = 'Cumulative area of fire frequent PMG in HS fire (1000 ac)'
dataTable['mean'] = dataTable['mean'] * 2.471
dataTable['lower'] = dataTable['lower'] * 2.471
dataTable['upper'] = dataTable['upper'] * 2.471
elif splot == 5:
yLabelText = 'Cumulative area of lodgepole PMG in HS fire (1000 ha)'
elif splot == 7:
yLabelText = 'Cumulative area of high elev PMG in HS fire (1000 ha)'
## mnAxis, mxAxis = reporterFunc.plotReporter6_cumulative(fig, ax, '', pdfFile, dataTable, ['mean'], xLabelText, yLabelText, scenario, labelXtick, labelYtick, plotLegend, '')
dataTable['cum_mean'] = dataTable['mean'].cumsum()
mnAxis, mxAxis = reporterFunc.plotReporter4(fig, ax, '', pdfFile, dataTable, ['cum_mean'], xLabelText, yLabelText, scenario, labelXtick, labelYtick, plotLegend, '')
## # get max y value
## if runList.index(scenario) == 0:
## mxAxis = dataTable['mean'].cumsum().max()
## else:
## if dataTable['mean'].cumsum().max() > mxAxis:
## mxAxis = dataTable['mean'].cumsum().max()
# repeat y axis on right with percent
ax.set_ylim(mnAxis,mxAxis)
print "Max y axis value found: " + str(mxAxis) + " and min y axis value: " + str(mnAxis)
if splot == 1:
yLabelText = 'Cumulative area of HS fire (%)'
mnAxis = mnAxis / PMG12345Area * 100000
mxAxis = mxAxis / PMG12345Area * 100000
elif splot == 2:
yLabelText = 'Cumulative area of HS fire (%)'
mnAxis = mnAxis / PMG12345Area * 100000 / 2.471
mxAxis = mxAxis / PMG12345Area * 100000 / 2.471
elif splot == 3:
yLabelText = 'Cumulative area of fire frequent PMG in HS fire (%)'
mnAxis = mnAxis / PMG345Area * 100000
mxAxis = mxAxis / PMG345Area * 100000
elif splot == 4:
yLabelText = 'Cumulative area of fire frequent PMG in HS fire (%)'
mnAxis = mnAxis / PMG345Area * 100000 / 2.471
mxAxis = mxAxis / PMG345Area * 100000 / 2.471
elif splot == 5:
yLabelText = 'Cumulative area of lodgepole PMG in HS fire (%)'
mnAxis = mnAxis / PMG2Area * 100000
mxAxis = mxAxis / PMG2Area * 100000
elif splot == 7:
yLabelText = 'Cumulative area of high elev PMG in HS fire (%)'
mnAxis = mnAxis / PMG1Area * 100000
mxAxis = mxAxis / PMG1Area * 100000
reporterFunc.plotSecondYAxis(ax.twinx(), yLabelText, mnAxis, mxAxis)
pl.subplots_adjust(right=0.89)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done with cumulative HS fire."
def main(outDir, subArea, runList, chartTitle, ownership):
varStruct = ' Total Merch Harvest (m3)'
yLabelText = 'Merchantable large tree vol ( $\mathregular{1x10^3}$ $\mathregular{m^3}$)'
yLabelText2 = 'Cumulative merchantable large tree vol ( $\mathregular{1x10^6}$ $\mathregular{m^3}$)'
figTextList = [
'Federal', 'Tribal', 'Corporate Forest', 'Family Forest', 'Homeowner'
]
# list of ownerships to graphs
ownersToGraph = [
'Federal', 'Tribal', 'Private Non-Industrial', 'Private Industrial',
'Homeowner'
]
pdfFile = PdfPages(outDir + chartTitle + '_Vol_LgTree_owners.pdf')
pdfFile2 = PdfPages(outDir + chartTitle +
'_Vol_LgTree_owners_cumulative.pdf')
for owner in ownersToGraph:
fig = pl.figure(1, figsize=(4, 4))
fig2 = pl.figure(2, figsize=(4, 4))
# setup plot for all scenarios
ax = fig.add_subplot(1, 1, 1)
ax2 = fig2.add_subplot(1, 1, 1)
for scenario in runList:
inDir = outDir + scenario + "\\"
if os.path.isdir(inDir):
totalArea = pd.io.parsers.read_csv(
inDir +
r'LgTreeVol_MerchHarvLive_D3_D55_by_OWNER_pivot.csv')
totalArea2 = pd.io.parsers.read_csv(
inDir + r'LgTreeVol_SalHarvest_by_OWNER_pivot.csv')
yearList = list(set(totalArea[' Year']))
repList = list(set(totalArea[' Run']))
# get stats from multiple reps
statsList = []
for year in range(1, max(yearList) + 1):
yearArea = totalArea[totalArea[' Year'] == year]
yearArea2 = totalArea2[totalArea2[' Year'] == year]
dataList = []
for rep in repList:
repArea = yearArea[yearArea[' Run'] == rep]
repArea2 = yearArea2[yearArea2[' Run'] == rep]
ownerArea = pd.DataFrame(
repArea[repArea[' OWNER_label'] == owner])
ownerArea2 = pd.DataFrame(
repArea2[repArea2[' OWNER_label'] == owner])
dataList.append(
ownerArea[' LgTreeVol MerchHarvLive D55_1 (m3)'].
iloc[0] +
ownerArea[' LgTreeVol MerchHarvLive D55_2 (m3)'].
iloc[0] +
ownerArea[' LgTreeVol MerchHarvLive D55_3 (m3)'].
iloc[0] +
ownerArea[' LgTreeVol MerchHarvLive D55_4 (m3)'].
iloc[0] +
ownerArea[' LgTreeVol MerchHarvLive D55_5 (m3)'].
iloc[0] +
ownerArea[' LgTreeVol MerchHarvLive D3_1 (m3)'].
iloc[0] +
ownerArea[' LgTreeVol MerchHarvLive D3_2 (m3)'].
iloc[0] +
ownerArea[' LgTreeVol MerchHarvLive D3_3 (m3)'].
iloc[0] +
ownerArea[' LgTreeVol MerchHarvLive D3_4 (m3)'].
iloc[0] +
ownerArea2[' LgTreeVol SalHarvest Giant (m3)'].
iloc[0] +
ownerArea2[' LgTreeVol SalHarvest Large (m3)'].
iloc[0] +
ownerArea2[' LgTreeVol SalHarvest Medium (m3)'].
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)
labelXtick = True
labelYtick = True
plotLegend = (0.99, 0.99)
xLabelText = 'Simulation Year'
dataTable['mean'] = dataTable['mean'] / 1000
dataTable['lower'] = dataTable['lower'] / 1000
dataTable['upper'] = dataTable['upper'] / 1000
dataTable['cum_mean'] = dataTable['mean'].cumsum()
dataTable['cum_mean'] = dataTable['cum_mean'] / 1000
if owner == 'Tribal':
plotLegend = (0.84, 0.33)
pl.figure(1)
mnAxis, mxAxis = reporterFunc.plotReporter4(
fig, ax, '', pdfFile, dataTable, ['mean'], xLabelText,
yLabelText, scenario, labelXtick, labelYtick, plotLegend,
figTextList[ownersToGraph.index(owner)])
pl.figure(2)
mnAxis, mxAxis = reporterFunc.plotReporter4(
fig2, ax2, '', pdfFile2, dataTable, ['cum_mean'],
xLabelText, yLabelText2, scenario, labelXtick, labelYtick,
plotLegend, figTextList[ownersToGraph.index(owner)])
pdfFile.savefig(fig)
pdfFile2.savefig(fig2)
pl.close(fig)
pl.close(fig2)
##reporterFunc.plotFigureText(fig, 'Simulation Year', 'Merchantable volume ( $\mathregular{1x10^3}$ $\mathregular{m^3}$)')
## pl.savefig(outDir + 'report4_Vol_Owners.png', bbox_inches='tight', dpi=300)
pdfFile.close()
pdfFile2.close()
print "Done with harvest vol by owner."
def main(outDir, subArea, runList, chartTitle, ownership):
varStruct = ' Total Merch Harvest (m3)'
yLabelText = 'Merchantable volume ( $\mathregular{1x10^3}$ $\mathregular{m^3}$)'
yLabelText2 = 'Cumulative Merchantable volume ( $\mathregular{1x10^6}$ $\mathregular{m^3}$)'
figTextList = ['Federal', 'Tribal', 'Corporate Forest', 'Family Forest']
# list of ownerships to graphs
owner = 'Federal'
pdfFile = PdfPages(outDir + chartTitle + '_Vol_PP_MC_Fed.pdf')
pdfFile2 = PdfPages(outDir + chartTitle + '_Vol_PP_MC_Fed_cumulative.pdf')
for scenario in runList:
fig = pl.figure(1, figsize=(4, 4))
fig2 = pl.figure(2, figsize=(4, 4))
# setup plot for all scenarios
ax = fig.add_subplot(1, 1, 1)
ax2 = fig2.add_subplot(1, 1, 1)
for volSpp in ['', '_PP', '_MC']:
inDir = outDir + scenario + "\\"
if os.path.isdir(inDir):
totalArea = pd.io.parsers.read_csv(inDir + r'WoodProducts' +
volSpp +
'_by_OWNER_pivot.csv')
yearList = list(set(totalArea[' Year']))
repList = list(set(totalArea[' Run']))
# 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]
ownerArea = pd.DataFrame(
repArea[repArea[' OWNER_label'] == owner])
if volSpp == '_PP':
dataList.append(ownerArea[' PP' +
varStruct].iloc[0])
elif volSpp == '_MC':
dataList.append(ownerArea[' MC' +
varStruct].iloc[0])
else:
dataList.append(ownerArea[varStruct].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)
labelXtick = True
labelYtick = True
plotLegend = (0.99, 0.99)
xLabelText = 'Simulation Year'
dataTable['mean'] = dataTable['mean'] / 1000
dataTable['lower'] = dataTable['lower'] / 1000
dataTable['upper'] = dataTable['upper'] / 1000
dataTable['cum_mean'] = dataTable['mean'].cumsum()
dataTable['cum_mean'] = dataTable['cum_mean'] / 1000
if owner == 'Tribal':
plotLegend = (0.84, 0.33)
pl.figure(1)
mnAxis, mxAxis = reporterFunc.plotReporter4(
fig, ax, '', pdfFile, dataTable,
['mean', 'lower', 'upper'], xLabelText, yLabelText,
'HarvVol' + volSpp, labelXtick, labelYtick, plotLegend,
scenario)
pl.figure(2)
mnAxis, mxAxis = reporterFunc.plotReporter4(
fig2, ax2, '', pdfFile2, dataTable, ['cum_mean'],
xLabelText, yLabelText2, 'HarvVol' + volSpp, labelXtick,
labelYtick, plotLegend, scenario)
pdfFile.savefig(fig)
pdfFile2.savefig(fig2)
pl.close(fig)
pl.close(fig2)
##reporterFunc.plotFigureText(fig, 'Simulation Year', 'Merchantable volume ( $\mathregular{1x10^3}$ $\mathregular{m^3}$)')
## pl.savefig(outDir + 'report4_Vol_Owners.png', bbox_inches='tight', dpi=300)
pdfFile.close()
pdfFile2.close()
print "Done with harvest vol by owner."
def main(subArea, runName, chartTitlePre, ownership):
outDir = "C:\\Users\\olsenk\\Dropbox\\FPF\\Envision Runs\\keith_production\\"
varStruct = ' Total Merch Harvest (m3)'
# yLabelText = 'Percent of Landscape in'
chartTitle = chartTitlePre
forestedHa = reporterFunc.getOwnerForestedHa(subArea)
ownerAllHa = reporterFunc.getOwnerHa(subArea)
# list of ownerships to graphs
ownersToGraph = ['BLM Lands','Chemult Ranger District (Fremont-Winema NF)','Chiloquin Ranger District (Fremont-Winema NF)','Klamath Ranger District (Fremont-Winema NF)','ODF Sun Pass','JWTR Timber Holdings','Cascade Timberlands (Mazama Forest)','Jeld-Wen Inc','J-Spear Ranch']
pdfFile = PdfPages(outDir + 'report4_Vol_Owners_Detl.pdf')
fig = pl.figure(1, figsize=(8.5,11))
subPlotNum = 0
for owner in ownersToGraph:
# setup plot for all scenarios
subPlotNum += 1
if subPlotNum == 5:
subPlotNum = 1
pdfFile.savefig()
pl.close()
fig = pl.figure(1, figsize=(8.5,11))
ax = fig.add_subplot(2,2,subPlotNum)
for scenario in ['CurrentPolicy','NoTreatment','NoFedTreat','Restoration','NoFireNoMgmt']:
inDir = outDir + runName + "_" + scenario + "\\"
if os.path.isdir(inDir):
yearList = list(set(pd.io.parsers.read_csv(inDir + r'WoodProducts_by_OWNER_DETL_pivot.csv')[' Year']))
repList = list(set(pd.io.parsers.read_csv(inDir + r'WoodProducts_by_OWNER_DETL_pivot.csv')[' Run']))
totalArea = pd.io.parsers.read_csv(inDir + r'WoodProducts_by_OWNER_DETL_pivot.csv')
yLabelText = 'Merchantable Volume Harvested (m3)'
# 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]
ownerArea = pd.DataFrame(repArea[repArea[' OWNER_DETL_label'] == owner])
dataList.append(ownerArea[varStruct].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)
plotLegend = False
if subPlotNum == 3 or subPlotNum == 4:
labelXaxis = True
else:
labelXaxis = False
yLabelTextOwner = owner + ' ' + yLabelText
reporterFunc.plotReporter4(fig, ax, owner + ' - ' + str(len(repList)) + ' reps', pdfFile, dataTable, ['mean','lower','upper'], yLabelTextOwner, scenario, labelXaxis, plotLegend)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(outDir, subArea, runList, chartTitle, ownership):
varList = [' Seedling-Sapling', ' Pole',' Small tree',' Medium tree',' Large tree',' Giant tree',' Open Canopy',' Moderate Canopy',' Closed Canopy',' Single-story',' Multi-story']
# list of ownerships to graphs
ownersToGraph = ['Federal','State','Private Non-Industrial','Private Industrial','Homeowner']
reporterNameVol = r'ForestStructure3_by_OWNER_pivot.csv'
ownerLabelField = ' OWNER_label'
if ownership == 'All':
pdfFile = PdfPages(outDir + chartTitle + '_ForestStructure3_landscape.pdf')
elif ownership in ownersToGraph:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + chartTitle + '_ForestStructure3_' + ownersToGraph[0] + '.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + chartTitle + '_ForestStructure3_' + ownersToGraph[0] + '.pdf')
ownerLabelField = ' OWNER_DETL_label'
# load area stats by owner
areaStats = pd.io.parsers.read_csv(outDir + runList[0] + "\\AreaStats_by_OWNER_pivot.csv")
areaStats = areaStats[areaStats[' Year'] == 1]
# sum total areas over selected ownerships
for ownerToGraph in ownersToGraph:
ownerStats = areaStats[areaStats[ownerLabelField] == ownerToGraph]
if ownerToGraph == ownersToGraph[0]:
PMG345Area = ownerStats[' PVT MNG GR 3 (ha)'].iloc[0] + ownerStats[' PVT MNG GR 4 (ha)'].iloc[0] + ownerStats[' PVT MNG GR 5 (ha)'].iloc[0]
analysisModeledArea = ownerStats[' Forested (ha)'].iloc[0] + ownerStats[' Arid (ha)'].iloc[0]
analysisForestedArea = ownerStats[' Forested (ha)'].iloc[0]
else:
PMG345Area += ownerStats[' PVT MNG GR 3 (ha)'].iloc[0] + ownerStats[' PVT MNG GR 4 (ha)'].iloc[0] + ownerStats[' PVT MNG GR 5 (ha)'].iloc[0]
analysisModeledArea += ownerStats[' Forested (ha)'].iloc[0] + ownerStats[' Arid (ha)'].iloc[0]
analysisForestedArea += ownerStats[' Forested (ha)'].iloc[0]
for landscape in [' (ha) PMG345',' (ha) Forest']:
for varStruct in varList:
print 'Forest structure 3 - ' + varStruct + landscape
fig = pl.figure(1, figsize=(4,4))
# setup plot for all scenarios
ax = fig.add_subplot(1,1,1)
varStruct += landscape
if landscape == ' (ha) PMG345':
yLabelText = 'Area of FF landscape in ' + varStruct[1:-12] + ' (1000 ha)'
analysisArea = PMG345Area
elif landscape == ' (ha) Forest':
yLabelText = 'Area of ' + varStruct[1:-12] + ' (1000 ha)'
analysisArea = analysisForestedArea
else:
yLabelText = 'Error!! Missing landscape'
analysisArea = 1
for scenario in runList:
inDir = outDir + scenario + "\\"
if os.path.isdir(inDir):
totalArea = pd.io.parsers.read_csv(inDir + reporterNameVol)
yearList = list(set(totalArea[' Year']))
repList = list(set(totalArea[' Run']))
# 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])
else:
ownerArea[varStruct].iloc[0] += repArea[repArea[ownerLabelField] == ownerToGraph][varStruct].iloc[0]
dataList.append(ownerArea[varStruct].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)
labelYtick = True
# unitText = ' ( $\mathregular{1x10^6}$ $\mathregular{m^3}$ )'
dataTable['mean'] = dataTable['mean'] / 1000
dataTable['lower'] = dataTable['lower'] / 1000
dataTable['upper'] = dataTable['upper'] / 1000
labelXtick = True
xLabelText = 'Simulation Year'
plotLegend = (0.5,0.99)
figText = ''
mnAxis, mxAxis = reporterFunc.plotReporter4(fig, ax, '', pdfFile, dataTable, ['mean'], xLabelText, yLabelText, scenario, labelXtick, labelYtick, plotLegend, figText)
# repeat y axis on right with percent
ax.set_ylim(mnAxis,mxAxis)
print "Max y axis value found: " + str(mxAxis) + " and min y axis value: " + str(mnAxis)
yLabelText = yLabelText[:-8] + '%)'
mnAxis = mnAxis / analysisArea * 100000
mxAxis = mxAxis / analysisArea * 100000
reporterFunc.plotSecondYAxis(ax.twinx(), yLabelText, mnAxis, mxAxis)
if mxAxis - mnAxis <= 4.5:
pl.subplots_adjust(right=0.87)
elif (mxAxis * analysisArea / 100000) > 1000:
pl.subplots_adjust(left=0.14)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done with ForestStructure3."
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 = [' Moderate Suitability', ' American Marten', ' Black-backed Woodpecker', ' White-headed Woodpecker', ' Northern Goshawk', ' Western Bluebird', ' Pileated Woodpecker', 'MD Moderate Suitability', ' DownyBrome0.33-0.66 (ha)']
yLabelText = 'Percent of Landscape in'
chartTitle = chartTitlePre
forestedHa = reporterFunc.getOwnerForestedHa(subArea)
ownerAllHa = reporterFunc.getOwnerHa(subArea)
# list of ownerships to graphs
if ownership == 'All':
ownersToGraph = ['Federal','State','Private Non-Industrial','Private Industrial','Tribal','Homeowner']
pdfFile = PdfPages(outDir + 'report4_Wildlife23_landscape.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + 'report4_Wildlife23_' + ownersToGraph[0] + '.pdf')
fig = pl.figure(1, figsize=(8.5,11))
for varStruct in varList:
# setup plot for all scenarios
ax = fig.add_subplot(3,3,varList.index(varStruct) + 1)
if varStruct == ' Moderate Suitability':
varStructAdd = ' High Suitability'
yLabelTextStruct = yLabelText + ' VFO Northern Spotted Owl Mod+High'
modelGroup = 'vfoNSO'
elif varStruct == 'MD Moderate Suitability':
varStructAdd = ' High Suitability'
yLabelTextStruct = yLabelText + ' Mule Deer Mod+High'
varStruct = ' Moderate Suitability'
modelGroup = 'muleDeer'
elif varStruct == ' DownyBrome0.33-0.66 (ha)':
varStructAdd = ' DownyBrome0.66-1 (ha)'
yLabelTextStruct = yLabelText + ' Downy Brome Mod+High'
modelGroup = 'downyBrome'
else:
yLabelTextStruct = yLabelText + varStruct + ' Fair+Good'
varStructAdd = varStruct + ' Good (ha)'
varStruct = varStruct + ' Fair (ha)'
modelGroup = 'iLAP'
for scenario in ['CurrentPolicy','NoTreatment','NoFedTreat','Restoration','NoFireNoMgmt']:
inDir = outDir + runName + "_" + scenario + "\\"
if os.path.isdir(inDir):
if modelGroup == 'vfoNSO':
yearList = list(set(pd.io.parsers.read_csv(inDir + r'VFO_Spotted_Owl_by_OWNER_pivot.csv')[' Year']))
repList = list(set(pd.io.parsers.read_csv(inDir + r'VFO_Spotted_Owl_by_OWNER_pivot.csv')[' Run']))
totalArea = pd.io.parsers.read_csv(inDir + r'VFO_Spotted_Owl_by_OWNER_pivot.csv')
elif modelGroup == 'muleDeer':
yearList = list(set(pd.io.parsers.read_csv(inDir + r'Mule_Deer_by_OWNER_pivot.csv')[' Year']))
repList = list(set(pd.io.parsers.read_csv(inDir + r'Mule_Deer_by_OWNER_pivot.csv')[' Run']))
totalArea = pd.io.parsers.read_csv(inDir + r'Mule_Deer_by_OWNER_pivot.csv')
elif modelGroup == 'downyBrome':
yearList = list(set(pd.io.parsers.read_csv(inDir + r'Downy_Brome_by_OWNER_pivot.csv')[' Year']))
repList = list(set(pd.io.parsers.read_csv(inDir + r'Downy_Brome_by_OWNER_pivot.csv')[' Run']))
totalArea = pd.io.parsers.read_csv(inDir + r'Downy_Brome_by_OWNER_pivot.csv')
else:
yearList = list(set(pd.io.parsers.read_csv(inDir + r'ILAP_wildlife_models_by_OWNER_pivot.csv')[' Year']))
repList = list(set(pd.io.parsers.read_csv(inDir + r'ILAP_wildlife_models_by_OWNER_pivot.csv')[' Run']))
totalArea = pd.io.parsers.read_csv(inDir + r'ILAP_wildlife_models_by_OWNER_pivot.csv')
# sum fair and good habitat into fair column
totalArea[varStruct] = totalArea[varStruct] + totalArea[varStructAdd]
# 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])
analysisTotalArea = ownerAllHa[ownerToGraph]
analysisForestedArea = 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[varStruct].iloc[0] += tempArea[varStruct].iloc[0]
analysisTotalArea += ownerAllHa[ownerToGraph]
analysisForestedArea += forestedHa[ownerToGraph]
ownerToGraph = 'All'
if varStruct == ' DownyBrome0.66-1 (ha)' or varStruct == ' DownyBrome0.33-0.66 (ha)':
if analysisTotalArea > 0:
dataList.append(ownerArea[varStruct].iloc[0] / analysisTotalArea * 100)
else:
dataList.append(0.0)
else:
if analysisForestedArea > 0:
dataList.append(ownerArea[varStruct].iloc[0] / analysisForestedArea * 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 yLabelTextStruct == (yLabelText + ' Pileated Woodpecker Fair+Good') or yLabelTextStruct == (yLabelText + ' Mule Deer Mod+High') or yLabelTextStruct == (yLabelText + ' Downy Brome Mod+High'):
labelXaxis = True
else:
labelXaxis = False
plotLegend = False
## if varList.index(varStruct) + 1 == 8:
## plotLegend = True
reporterFunc.plotReporter4(fig, ax, ownerToGraph + ' - ' + str(len(repList)) + ' reps', pdfFile, dataTable, ['mean','lower','upper'], yLabelTextStruct, scenario, labelXaxis, plotLegend)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(outDir, subArea, runList, chartTitle, ownership):
varList = [' Stand Replacing FirePMG1',' Stand Replacing FirePMG2',' Stand Replacing FirePMG3',' Stand Replacing FirePMG4',' Stand Replacing FirePMG5',' Surface FirePMG1',' Surface FirePMG2',' Surface FirePMG3',' Surface FirePMG4',' Surface FirePMG5',' Mixed Severity FirePMG1',' Mixed Severity FirePMG2',' Mixed Severity FirePMG3',' Mixed Severity FirePMG4',' Mixed Severity FirePMG5']
# list of ownerships to graphs
## ownersToGraph = ['Federal','State','Private Non-Industrial','Private Industrial','Tribal','Homeowner']
# remove Tribal from owners to graph
ownersToGraph = ['Federal','State','Private Non-Industrial','Private Industrial','Homeowner']
reporterName = r'FireOccurance_by_OWNER_pivot.csv'
ownerLabelField = ' OWNER_label'
# set list of owners to graph
if ownership == 'All':
pdfFile = PdfPages(outDir + chartTitle + '_HS_Fire_proportion.pdf')
elif ownership in ownersToGraph:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + chartTitle + '_HS_Fire_prop_' + ownersToGraph[0] + '.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + chartTitle + '_HS_Fire_prop_' + ownersToGraph[0] + '.pdf')
ownerLabelField = ' OWNER_DETL_label'
reporterName = r'FireOccurance_by_OWNER_DETL_pivot.csv'
for splot in [1,3,5,7]:
# setup plot for all scenarios
fig = pl.figure(1, figsize=(4,4))
ax = fig.add_subplot(1,1,1)
for scenario in runList:
inDir = outDir + scenario + "\\"
print ('Running scenario ' + scenario)
if os.path.isdir(inDir):
totalArea = pd.io.parsers.read_csv(inDir + reporterName)
yearList = list(set(totalArea[' Year']))
repList = list(set(totalArea[' Run']))
# 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])
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]
ownerToGraph = 'All'
if splot == 1 or splot == 2:
HSarea = 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]
MSarea = ownerArea[' Mixed Severity FirePMG1'].iloc[0] + ownerArea[' Mixed Severity FirePMG2'].iloc[0] + ownerArea[' Mixed Severity FirePMG3'].iloc[0] + ownerArea[' Mixed Severity FirePMG4'].iloc[0] + ownerArea[' Mixed Severity FirePMG5'].iloc[0]
SUarea = ownerArea[' Surface FirePMG1'].iloc[0] + ownerArea[' Surface FirePMG2'].iloc[0] + ownerArea[' Surface FirePMG3'].iloc[0] + ownerArea[' Surface FirePMG4'].iloc[0] + ownerArea[' Surface FirePMG5'].iloc[0]
elif splot == 3 or splot == 4:
HSarea = ownerArea[' Stand Replacing FirePMG3'].iloc[0] + ownerArea[' Stand Replacing FirePMG4'].iloc[0] + ownerArea[' Stand Replacing FirePMG5'].iloc[0]
MSarea = ownerArea[' Mixed Severity FirePMG3'].iloc[0] + ownerArea[' Mixed Severity FirePMG4'].iloc[0] + ownerArea[' Mixed Severity FirePMG5'].iloc[0]
SUarea = ownerArea[' Surface FirePMG3'].iloc[0] + ownerArea[' Surface FirePMG4'].iloc[0] + ownerArea[' Surface FirePMG5'].iloc[0]
elif splot == 5 or splot == 6:
HSarea = ownerArea[' Stand Replacing FirePMG2'].iloc[0]
MSarea = ownerArea[' Mixed Severity FirePMG2'].iloc[0]
SUarea = ownerArea[' Surface FirePMG2'].iloc[0]
elif splot == 7 or splot == 8:
HSarea = ownerArea[' Stand Replacing FirePMG1'].iloc[0]
MSarea = ownerArea[' Mixed Severity FirePMG1'].iloc[0]
SUarea = ownerArea[' Surface FirePMG1'].iloc[0]
# if fire occured in that rep then add to dataList
if (HSarea + MSarea + SUarea) > 0:
dataList.append(HSarea / (HSarea + MSarea + SUarea) * 100)
# 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
if len(numpyList) > 0:
dataDict = {'timeStep': year, 'mean': np.mean(numpyList, axis=0), 'std': np.std(numpyList, axis=0), 'lower': lower95th, 'upper': upper95th}
else:
dataDict = {'timeStep': year, 'mean': 0.0, 'std': 0.0, 'lower': 0.0, 'upper': 0.0}
# convert to list for DataFrame
statsList.append(dataDict)
# convert to DataFrame
dataTable = pd.DataFrame(statsList)
labelXtick = True
labelYtick = True
plotLegend = (0.5,0.99)
xLabelText = 'Simulation Year'
if splot == 1:
yLabelText = 'Proportion of wildfire in HS (%)'
elif splot == 3:
yLabelText = 'Proportion of wildfire in HS for FF landscape (%)'
if splot == 5:
yLabelText = 'Proportion of wildfire in HS for lodgepole landscape (%)'
if splot == 7:
yLabelText = 'Proportion of wildfire in HS for high elev landscape (%)'
mnAxis, mxAxis = reporterFunc.plotReporter4(fig, ax, '', pdfFile, dataTable, ['mean'], xLabelText, yLabelText, scenario, labelXtick, labelYtick, plotLegend, '')
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done with PMG in HS fire proportion."
def main(outDir, subArea, runList, chartTitle, ownership):
varList = [
' Small Tree Saw Timber Total', ' Medium Tree Saw Timber Total',
' Large Tree Saw Timber Total', ' Giant Tree Saw Timber Total'
]
yLabelText = ''
figTextList = ['Small trees', 'Medium trees', 'Large trees', 'Giant trees']
# list of ownerships to graphs
ownersToGraph = [
'Federal', 'State', 'Private Non-Industrial', 'Private Industrial',
'Homeowner'
]
reporterName = r'StandingVol_bySize_by_OWNER_pivot.csv'
ownerLabelField = ' OWNER_label'
if ownership == 'All':
pdfFile = PdfPages(outDir + chartTitle +
'_syu_StandingVol_bySize_landscape.pdf')
elif ownership in ownersToGraph:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + chartTitle + '_syu_StandingVol_bySize_' +
ownersToGraph[0] + '.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + chartTitle + '_syu_StandingVol_bySize_' +
ownersToGraph[0] + '.pdf')
ownerLabelField = ' OWNER_DETL_label'
reporterName = r'StandingVol_bySize_by_OWNER_DETL_pivot.csv'
fig = pl.figure(1, figsize=(11, 8.5))
for varStruct in varList:
# setup plot for all scenarios
ax = fig.add_subplot(2, 2, varList.index(varStruct) + 1)
print('Running ' + varStruct)
for scenario in runList:
inDir = outDir + scenario + "\\"
if os.path.isdir(inDir):
totalArea = pd.io.parsers.read_csv(inDir + reporterName)
yearList = list(set(totalArea[' Year']))
repList = list(set(totalArea[' Run']))
# get stats from multiple reps
statsList = []
statsList2 = []
for year in range(1, max(yearList) + 1):
yearArea = totalArea[totalArea[' Year'] == year]
dataList = []
dataList2 = []
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])
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]
ownerToGraph = 'All'
dataList.append(ownerArea[varStruct + ' (m3)'].iloc[0])
dataList2.append(ownerArea[varStruct +
' SYU (m3)'].iloc[0])
# convert to numpy array
numpyList = np.array(dataList)
numpyList2 = np.array(dataList2)
# add year data to dictionary
dataDict = {
'timeStep': year,
'mean': np.mean(numpyList, axis=0)
}
dataDict2 = {
'timeStep': year,
'mean': np.mean(numpyList2, axis=0)
}
# convert to list for DataFrame
statsList.append(dataDict)
statsList2.append(dataDict2)
# convert to DataFrame
dataTable = pd.DataFrame(statsList)
dataTable2 = pd.DataFrame(statsList2)
if (varList.index(varStruct)) < (len(varList) / 2):
labelXtick = False
else:
labelXtick = True
labelYtick = True
yLabelText = ''
labelYtick = True
plotLegend = (0.99, 0.99)
xLabelText = ''
dataTable['mean'] = dataTable['mean'] / 1000000
dataTable2['mean'] = dataTable2['mean'] / 1000000
mnAxis, mxAxis = reporterFunc.plotReporter4(
fig, ax, '', pdfFile, dataTable, ['mean'], xLabelText,
yLabelText, scenario, labelXtick, labelYtick, plotLegend,
figTextList[varList.index(varStruct)])
mnAxis, mxAxis = reporterFunc.plotReporter4(
fig, ax, '', pdfFile, dataTable2, ['mean'], xLabelText,
yLabelText, scenario + '_SYU_', labelXtick, labelYtick,
plotLegend, figTextList[varList.index(varStruct)])
reporterFunc.plotFigureText(
fig, 'Simulation Year',
'Standing saw timber volume ( $\mathregular{1x10^6}$ $\mathregular{m^3}$)'
)
# pl.savefig(outDir + 'report4_ForestStructure2_landscape.png', bbox_inches='tight', dpi=300)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done with standing volume."
def main(subArea, runName, chartTitlePre, ownership):
outDir = "C:\\Users\\olsenk\\Dropbox\\FPF\\Envision Runs\\keith_production\\"
varList = [
' High Suitability', ' American Marten Good (ha)',
' Black-backed Woodpecker Good (ha)',
' White-headed Woodpecker Good (ha)', ' Northern Goshawk Good (ha)',
' Western Bluebird Good (ha)', ' Pileated Woodpecker Good (ha)',
'MD High Suitability', ' DownyBrome0.66-1 (ha)'
]
figTextList = [
'Northern Spotted Owl\n(nest)', 'Pacific Marten', 'Black-backed WP',
'White-headed WP', 'Northern Goshawk\n(nest)', 'Western Bluebird',
'Pileated WP', 'Mule Deer', 'Cheatgrass'
]
yLabelText = 'Area (%)'
chartTitle = chartTitlePre
forestedHa = reporterFunc.getOwnerForestedHa(subArea)
ownerAllHa = reporterFunc.getOwnerHa(subArea)
# list of ownerships to graphs
if ownership == 'All':
ownersToGraph = [
'Federal', 'State', 'Private Non-Industrial', 'Private Industrial',
'Tribal', 'Homeowner'
]
pdfFile = PdfPages(outDir + 'report4_Wildlife_landscape.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + 'report4_Wildlife_' + ownersToGraph[0] +
'.pdf')
fig = pl.figure(1, figsize=(6.5, 8))
for varStruct in varList:
# setup plot for all scenarios
ax = fig.add_subplot(3, 3, varList.index(varStruct) + 1)
if varStruct == ' High Suitability':
yLabelTextStruct = yLabelText + ' VFO Northern Spotted Owl High Suitability'
modelGroup = 'vfoNSO'
elif varStruct == 'MD High Suitability':
yLabelTextStruct = yLabelText + ' Mule Deer High Suitability'
varStruct = ' High Suitability'
modelGroup = 'muleDeer'
elif varStruct == ' DownyBrome0.66-1 (ha)':
yLabelTextStruct = yLabelText + ' Downy Brome High Suitability'
modelGroup = 'downyBrome'
else:
yLabelTextStruct = yLabelText + varStruct
yLabelTextStruct = yLabelTextStruct.replace(" (ha)", "")
modelGroup = 'iLAP'
for scenario in [
'CurrentPolicy', 'No_Treatment_Fed', 'Restoration',
'noFireNoTreatFed'
]:
inDir = outDir + runName + "_" + scenario + "\\"
if os.path.isdir(inDir):
if modelGroup == 'vfoNSO':
yearList = list(
set(
pd.io.parsers.read_csv(
inDir + r'VFO_Spotted_Owl_by_OWNER_pivot.csv')
[' Year']))
repList = list(
set(
pd.io.parsers.read_csv(
inDir + r'VFO_Spotted_Owl_by_OWNER_pivot.csv')
[' Run']))
totalArea = pd.io.parsers.read_csv(
inDir + r'VFO_Spotted_Owl_by_OWNER_pivot.csv')
elif modelGroup == 'muleDeer':
yearList = list(
set(
pd.io.parsers.read_csv(
inDir +
r'Mule_Deer_by_OWNER_pivot.csv')[' Year']))
repList = list(
set(
pd.io.parsers.read_csv(
inDir +
r'Mule_Deer_by_OWNER_pivot.csv')[' Run']))
totalArea = pd.io.parsers.read_csv(
inDir + r'Mule_Deer_by_OWNER_pivot.csv')
elif modelGroup == 'downyBrome':
yearList = list(
set(
pd.io.parsers.read_csv(
inDir +
r'Downy_Brome_by_OWNER_pivot.csv')[' Year']))
repList = list(
set(
pd.io.parsers.read_csv(
inDir +
r'Downy_Brome_by_OWNER_pivot.csv')[' Run']))
totalArea = pd.io.parsers.read_csv(
inDir + r'Downy_Brome_by_OWNER_pivot.csv')
else:
yearList = list(
set(
pd.io.parsers.read_csv(
inDir +
r'ILAP_wildlife_models_by_OWNER_pivot.csv')
[' Year']))
repList = list(
set(
pd.io.parsers.read_csv(
inDir +
r'ILAP_wildlife_models_by_OWNER_pivot.csv')
[' Run']))
totalArea = pd.io.parsers.read_csv(
inDir + r'ILAP_wildlife_models_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])
analysisTotalArea = ownerAllHa[ownerToGraph]
analysisForestedArea = 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[varStruct].iloc[0] += tempArea[
varStruct].iloc[0]
analysisTotalArea += ownerAllHa[ownerToGraph]
analysisForestedArea += forestedHa[
ownerToGraph]
ownerToGraph = 'All'
if varStruct == ' DownyBrome0.66-1 (ha)' or varStruct == ' DownyBrome0.33-0.66 (ha)':
if analysisTotalArea > 0:
dataList.append(ownerArea[varStruct].iloc[0] /
analysisTotalArea * 100)
else:
dataList.append(0.0)
else:
if analysisForestedArea > 0:
dataList.append(ownerArea[varStruct].iloc[0] /
analysisForestedArea * 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)
figText = figTextList[varList.index(varStruct)]
if modelGroup == 'muleDeer':
figText = 'Mule Deer'
if varList.index(varStruct) + 1 > 6 or modelGroup == 'muleDeer':
labelXtick = True
else:
labelXtick = False
if figText == 'Black-backed WP':
plotLegend = (1.01, 0.87)
else:
plotLegend = (-99, -99)
if figText == 'Northern Spotted Owl\n(nest)' or figText == 'White-headed WP' or figText == 'Pileated WP':
labelYtick = True
else:
labelYtick = False
labelYtick = True
xLabelText = yLabelText = ''
reporterFunc.plotReporter4(fig, ax, '', pdfFile, dataTable,
['mean', 'lower', 'upper'],
xLabelText, yLabelText, scenario,
labelXtick, labelYtick, plotLegend,
figText)
reporterFunc.plotFigureText(fig, 'Simulation Year', 'Area (%)')
pl.savefig(outDir + 'report4_Wildlife_landscape.png',
bbox_inches='tight',
dpi=300)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(outDir, subArea, runList, chartTitle, ownership):
varList = [' High Suitability', ' American Marten Good (ha)', ' Black-backed Woodpecker Good (ha)', ' White-headed Woodpecker Good (ha)', ' Northern Goshawk Good (ha)', ' Western Bluebird Good (ha)', ' Pileated Woodpecker Good (ha)', 'MD High Suitability', ' DownyBrome0.66-1 (ha)']
figTextList = ['Northern Spotted Owl\n(nest)','Pacific Marten','Black-backed WP','White-headed WP','Northern Goshawk\n(nest)','Western Bluebird','Pileated WP','Mule Deer','Cheatgrass']
yLabelText = 'Area (%)'
ownerLabelField = ' OWNER_label'
# list of ownerships to graphs
if ownership == 'All':
ownersToGraph = ['Federal','State','Private Non-Industrial','Private Industrial','Homeowner']
pdfFile = PdfPages(outDir + chartTitle + '_Wildlife_landscape_BBWO23.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + chartTitle + '_Wildlife_' + ownersToGraph[0] + '_BBWO23.pdf')
# load area stats by owner
areaStats = pd.io.parsers.read_csv(outDir + runList[0] + "\\AreaStats_by_OWNER_pivot.csv")
areaStats = areaStats[areaStats[' Year'] == 1]
# sum total areas over selected ownerships
for ownerToGraph in ownersToGraph:
ownerStats = areaStats[areaStats[ownerLabelField] == ownerToGraph]
if ownerToGraph == ownersToGraph[0]:
analysisModeledArea = ownerStats[' Forested (ha)'].iloc[0] + ownerStats[' Arid (ha)'].iloc[0]
analysisForestedArea = ownerStats[' Forested (ha)'].iloc[0]
else:
analysisModeledArea += ownerStats[' Forested (ha)'].iloc[0] + ownerStats[' Arid (ha)'].iloc[0]
analysisForestedArea += ownerStats[' Forested (ha)'].iloc[0]
for varStruct in varList:
# setup plot for all scenarios
fig = pl.figure(1, figsize=(4,4))
ax = fig.add_subplot(1,1,1)
if varStruct == ' High Suitability':
yLabelTextStruct = yLabelText + ' VFO Northern Spotted Owl High Suitability'
modelGroup = 'vfoNSO'
elif varStruct == 'MD High Suitability':
yLabelTextStruct = yLabelText + ' Mule Deer High Suitability'
varStruct = ' High Suitability'
modelGroup = 'muleDeer'
elif varStruct == ' DownyBrome0.66-1 (ha)':
yLabelTextStruct = yLabelText + ' Downy Brome High Suitability'
modelGroup = 'downyBrome'
else:
yLabelTextStruct = yLabelText + varStruct
yLabelTextStruct = yLabelTextStruct.replace(" (ha)", "")
modelGroup = 'iLAP'
for scenario in runList:
print 'Wildlife Running scenario ' + scenario + ' and variable ' + varStruct
inDir = outDir + scenario + "\\"
if os.path.isdir(inDir):
if modelGroup == 'vfoNSO':
totalArea = pd.io.parsers.read_csv(inDir + r'VFO_Spotted_Owl_by_OWNER_pivot.csv')
elif modelGroup == 'muleDeer':
totalArea = pd.io.parsers.read_csv(inDir + r'Mule_Deer_by_OWNER_pivot.csv')
elif modelGroup == 'downyBrome':
totalArea = pd.io.parsers.read_csv(inDir + r'Downy_Brome_by_OWNER_pivot.csv')
else:
totalArea = pd.io.parsers.read_csv(inDir + r'ILAP_wildlife_models_by_OWNER_pivot.csv')
# if species is Black-backed wookpecker do fair + good
if varStruct == ' Black-backed Woodpecker Good (ha)':
totalArea[' Black-backed Woodpecker Good (ha)'] = totalArea[' Black-backed Woodpecker Good (ha)'] + totalArea[' Black-backed Woodpecker Fair (ha)']
yearList = list(set(totalArea[' Year']))
repList = list(set(totalArea[' Run']))
# 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])
else:
tempArea = repArea[repArea[' OWNER_label'] == ownerToGraph]
# for varName in varList:
# totalArea.loc[list(ownerArea.index)[0],varName] += tempArea[varName].iloc[0]
ownerArea[varStruct].iloc[0] += tempArea[varStruct].iloc[0]
ownerToGraph = 'All'
dataList.append(ownerArea[varStruct].iloc[0])
## if varStruct == ' DownyBrome0.66-1 (ha)' or varStruct == ' DownyBrome0.33-0.66 (ha)':
## if analysisTotalArea > 0:
## dataList.append(ownerArea[varStruct].iloc[0] / analysisTotalArea * 100)
## else:
## dataList.append(0.0)
## else:
## if analysisForestedArea > 0:
## dataList.append(ownerArea[varStruct].iloc[0] / analysisForestedArea * 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)
dataTable['mean'] = dataTable['mean'] / 1000 * 2.471
dataTable['lower'] = dataTable['lower'] / 1000 * 2.471
dataTable['upper'] = dataTable['upper'] / 1000 * 2.471
figText = figTextList[varList.index(varStruct)]
if modelGroup == 'muleDeer':
figText = 'Mule Deer'
labelXtick = True
labelYtick = True
yLabelText = 'Area (1000 ac)'
xLabelText = 'Simulation Year'
if figText == 'Western Bluebird':
plotLegend = (0.99,0.5)
elif figText == 'Northern Goshawk\n(nest)':
plotLegend = (0.99,0.6)
elif figText == 'Pacific Marten' or figText == 'Black-backed WP' or figText == 'White-headed WP' or figText == 'Mule Deer' or figText == 'Pileated WP':
plotLegend = (0.35,0.3)
elif figText == 'Northern Spotted Owl\n(nest)':
plotLegend = (0.35,0.99)
else:
plotLegend = (0.99,0.99)
mnAxis, mxAxis = reporterFunc.plotReporter4(fig, ax, '', pdfFile, dataTable, ['mean'], xLabelText, yLabelText, scenario, labelXtick, labelYtick, plotLegend, figText)
# repeat y axis on right with percent
ax.set_ylim(mnAxis,mxAxis)
print "Max y axis value found: " + str(mxAxis) + " and min y axis value: " + str(mnAxis)
yLabelText = 'Area (%)'
if varStruct == ' DownyBrome0.66-1 (ha)' or varStruct == ' DownyBrome0.33-0.66 (ha)':
mnAxis = mnAxis / analysisModeledArea * 100000 / 2.471
mxAxis = mxAxis / analysisModeledArea * 100000 / 2.471
else:
mnAxis = mnAxis / analysisForestedArea * 100000 / 2.471
mxAxis = mxAxis / analysisForestedArea * 100000 / 2.471
reporterFunc.plotSecondYAxis(ax.twinx(), yLabelText, mnAxis, mxAxis)
if mxAxis - mnAxis <= 4.5:
pl.subplots_adjust(right=0.87)
elif (mxAxis * analysisForestedArea / 100000 * 2.471) > 1000:
pl.subplots_adjust(left=0.14)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done with wildlife BBWO23."
def main(subArea, runName, chartTitlePre, ownership):
outDir = "C:\\Users\\olsenk\\Dropbox\\FPF\\Envision Runs\\keith_production\\"
varList = [' NumberDwellingsFirewise', ' NumberDwellings']
yLabelText = 'Firewised dwellings (%)'
chartTitle = chartTitlePre
# list of ownerships to graphs
reporterName = r'FireWiseWUI_pivot.csv'
pdfFile = PdfPages(outDir + 'report4_Firewise_landscape.pdf')
fig = pl.figure(1, figsize=(8.5, 11))
# setup plot for all scenarios
ax = fig.add_subplot(2, 1, 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]
if repArea[' NumberDwellings'].iloc[0] > 0:
dataList.append(
repArea[' NumberDwellingsFirewise'].iloc[0] /
repArea[' NumberDwellings'].iloc[0] * 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 = (0.3, 0.99)
labelXtick = labelYtick = True
reporterFunc.plotReporter4(fig, ax, '', pdfFile, dataTable,
['mean', 'lower', 'upper'],
'Simulation Year', yLabelText, scenario,
labelXtick, labelYtick, plotLegend, '')
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(outDir, subArea, runName, chartTitlePre, ownership):
varList = [' Total Merch Harvest (m3)', ' Total Carbon (live+dead) (Mg)']
# yLabelText = 'Percent of Landscape in'
chartTitle = chartTitlePre
forestedHa = reporterFunc.getOwnerForestedHa(subArea)
ownerAllHa = reporterFunc.getOwnerHa(subArea)
# list of ownerships to graphs
ownersToGraph = ['Federal','State','Private Non-Industrial','Private Industrial','Tribal','Homeowner']
reporterNameWood = r'WoodProducts_by_OWNER_pivot.csv'
reporterNameVol = r'Carbon_by_OWNER_pivot.csv'
ownerLabelField = ' OWNER_label'
if ownership == 'All':
pdfFile = PdfPages(outDir + 'report4_Vol_Carbon_landscape.pdf')
elif ownership in ownersToGraph:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + 'report4_Vol_Carbon_' + ownersToGraph[0] + '.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + 'report4_Vol_Carbon_' + ownersToGraph[0] + '.pdf')
ownerLabelField = ' OWNER_DETL_label'
reporterNameWood = r'WoodProducts_by_OWNER_DETL_pivot.csv'
reporterNameVol = r'Carbon_by_OWNER_DETL_pivot.csv'
fig = pl.figure(1, figsize=(4,6.5))
for varStruct in varList:
# setup plot for all scenarios
ax = fig.add_subplot(2,1,varList.index(varStruct) + 1)
# for scenario in ['CurrentPolicy','No_Treatment_Fed','Restoration','noFireNoTreatFed','noFireCurrentPolicy']:
for scenario in ['CurrentPolicy','No_Treatment_Fed','Restoration','noFireNoTreatFed']:
inDir = outDir + runName + "_" + scenario + "\\"
if os.path.isdir(inDir):
if varStruct == ' Total Merch Harvest (m3)':
yearList = list(set(pd.io.parsers.read_csv(inDir + reporterNameWood)[' Year']))
repList = list(set(pd.io.parsers.read_csv(inDir + reporterNameWood)[' Run']))
totalArea = pd.io.parsers.read_csv(inDir + reporterNameWood)
figText = 'A'
elif varStruct == ' Total Carbon (live+dead) (Mg)':
yearList = list(set(pd.io.parsers.read_csv(inDir + reporterNameVol)[' Year']))
repList = list(set(pd.io.parsers.read_csv(inDir + reporterNameVol)[' Run']))
totalArea = pd.io.parsers.read_csv(inDir + reporterNameVol)
figText = 'B'
# 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])
analysisTotalArea = ownerAllHa[ownerToGraph]
analysisForestedArea = forestedHa[ownerToGraph]
else:
tempArea = repArea[repArea[ownerLabelField] == ownerToGraph]
# for varName in varList:
# totalArea.loc[list(ownerArea.index)[0],varName] += tempArea[varName].iloc[0]
ownerArea[varStruct].iloc[0] += tempArea[varStruct].iloc[0]
analysisTotalArea += ownerAllHa[ownerToGraph]
analysisForestedArea += forestedHa[ownerToGraph]
ownerToGraph = 'All'
dataList.append(ownerArea[varStruct].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)
labelYtick = True
if varStruct == ' Total Merch Harvest (m3)':
yLabelText = 'Merchantable volume ( $\mathregular{1x10^3}$ $\mathregular{m^3}$)'
dataTable['mean'] = dataTable['mean'] / 1000
dataTable['lower'] = dataTable['lower'] / 1000
dataTable['upper'] = dataTable['upper'] / 1000
labelXtick = False
xLabelText = ''
plotLegend = (0.99,0.99)
elif varStruct == ' Total Carbon (live+dead) (Mg)':
yLabelText = 'Aboveground carbon ( $\mathregular{1x10^6}$ Mg )'
dataTable['mean'] = dataTable['mean'] / 1000000
dataTable['lower'] = dataTable['lower'] / 1000000
dataTable['upper'] = dataTable['upper'] / 1000000
labelXtick = True
xLabelText = 'Simulation Year'
plotLegend = (-99,-99)
reporterFunc.plotReporter4(fig, ax, '', pdfFile, dataTable, ['mean','lower','upper'], xLabelText, yLabelText, scenario, labelXtick, labelYtick, plotLegend, figText)
pl.savefig(outDir + 'report4_Vol_Carbon_landscape.png', bbox_inches='tight', dpi=300)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(outDir, subArea, runList, chartTitle, ownership):
varList = [
' Harvest (ha)', ' Prescribed Fire (ha)', ' Mowing and Grinding (ha)',
' Salvage Logging (ha)', ' Thinning (ha)', ' Partial Harvest (ha)',
' Partial Harvest - Light (ha)', ' Partial Harvest - Heavy (ha)',
' Clear-cut (ha)'
]
## forestedHa = reporterFunc.getOwnerForestedHa(subArea)
## ownerAllHa = reporterFunc.getOwnerHa(subArea)
figTextList = ['Federal', 'Tribal', 'Corporate Forest', 'Family Forest']
# list of ownerships to graphs
# ownersToGraph = ['Federal','Tribal','Private Industrial','Private Non-Industrial']
ownersToGraph = ['Federal']
if ownership == 'SYU':
pdfFile = PdfPages(outDir + chartTitle + '_syu_Management_area.pdf')
varList = [
' Harvest (ha) SYU', ' Prescribed Fire (ha) SYU',
' Mowing and Grinding (ha) SYU', ' Salvage Logging (ha) SYU',
' Thinning (ha) SYU', ' Partial Harvest (ha) SYU',
' Partial Harvest - Light (ha) SYU',
' Partial Harvest - Heavy (ha) SYU', ' Clear-cut (ha) SYU'
]
else:
pdfFile = PdfPages(outDir + chartTitle + '_Management_area.pdf')
for varStruct in varList:
for owner in ownersToGraph:
fig = pl.figure(1, figsize=(4, 4))
# setup plot for all scenarios
ax = fig.add_subplot(1, 1, 1)
yLabelText = 'Area of' + varStruct[:-3] + "1000 ha)"
for scenario in runList:
inDir = outDir + scenario + "\\"
if os.path.isdir(inDir):
# load area stats by owner
areaStats = pd.io.parsers.read_csv(
inDir + "AreaStats_by_OWNER_pivot.csv")
areaStats = areaStats[areaStats[' Year'] == 1]
totalArea = pd.io.parsers.read_csv(
inDir + r'ManagementDisturb_by_OWNER_pivot.csv')
if ownership == 'SYU':
totalArea[' Harvest (ha) SYU'] = totalArea[
' Thinning (ha) SYU'] + totalArea[
' Partial Harvest (ha) SYU'] + totalArea[
' Partial Harvest - Light (ha) SYU'] + totalArea[
' Partial Harvest - Heavy (ha) SYU'] + totalArea[
' Clear-cut (ha) SYU']
else:
totalArea[' Harvest (ha)'] = totalArea[
' Thinning (ha)'] + totalArea[
' Partial Harvest (ha)'] + totalArea[
' Partial Harvest - Light (ha)'] + totalArea[
' Partial Harvest - Heavy (ha)'] + totalArea[
' Clear-cut (ha)']
yearList = list(set(totalArea[' Year']))
repList = list(set(totalArea[' Run']))
# 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]
ownerArea = pd.DataFrame(
repArea[repArea[' OWNER_label'] == owner])
dataList.append(ownerArea[varStruct].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)
labelXtick = True
labelYtick = True
plotLegend = (0.99, 0.99)
xLabelText = 'Simulation Year'
dataTable['mean'] = dataTable['mean'] / 1000
dataTable['lower'] = dataTable['lower'] / 1000
dataTable['upper'] = dataTable['upper'] / 1000
if owner == 'Tribal':
plotLegend = (0.84, 0.33)
mnAxis, mxAxis = reporterFunc.plotReporter4(
fig, ax, '', pdfFile, dataTable,
['mean', 'lower', 'upper'], xLabelText, yLabelText,
scenario, labelXtick, labelYtick, plotLegend,
figTextList[ownersToGraph.index(owner)])
if mxAxis > 7:
mxAxis += 1
ax.set_ylim(mnAxis, mxAxis)
pdfFile.savefig()
pl.close()
##reporterFunc.plotFigureText(fig, 'Simulation Year', 'Merchantable volume ( $\mathregular{1x10^3}$ $\mathregular{m^3}$)')
## pl.savefig(outDir + 'report4_Vol_Owners.png', bbox_inches='tight', dpi=300)
pdfFile.close()
print "Done with Management area."
def main(outDir, subArea, runName, chartTitlePre, ownership):
varStruct = ' Total Merch Harvest (m3)'
# yLabelText = 'Percent of Landscape in'
chartTitle = chartTitlePre
forestedHa = reporterFunc.getOwnerForestedHa(subArea)
ownerAllHa = reporterFunc.getOwnerHa(subArea)
figTextList = ['A', 'B', 'C', 'D']
# list of ownerships to graphs
ownersToGraph = [
'Federal', 'Tribal', 'Private Industrial', 'Private Non-Industrial'
]
pdfFile = PdfPages(outDir + 'report4_Vol_Owners.pdf')
fig = pl.figure(1, figsize=(6.5, 6.5))
for owner in ownersToGraph:
# setup plot for all scenarios
ax = fig.add_subplot(2, 2, ownersToGraph.index(owner) + 1)
# for scenario in ['CurrentPolicy','No_Treatment_Fed','Restoration','noFireNoTreatFed','noFireCurrentPolicy']:
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 +
r'WoodProducts_by_OWNER_pivot.csv')[' Year']))
repList = list(
set(
pd.io.parsers.read_csv(
inDir +
r'WoodProducts_by_OWNER_pivot.csv')[' Run']))
totalArea = pd.io.parsers.read_csv(
inDir + r'WoodProducts_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]
ownerArea = pd.DataFrame(
repArea[repArea[' OWNER_label'] == owner])
dataList.append(ownerArea[varStruct].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)
labelXtick = True
labelYtick = True
plotLegend = (-99, -99)
xLabelText = 'Simulation Year'
dataTable['mean'] = dataTable['mean'] / 1000
dataTable['lower'] = dataTable['lower'] / 1000
dataTable['upper'] = dataTable['upper'] / 1000
if ownersToGraph.index(owner) < 2:
labelXtick = False
xLabelText = ''
if ownersToGraph.index(owner) % 2 == 1:
yLabelText = ''
if owner == 'Tribal':
plotLegend = (0.84, 0.33)
xLabelText = yLabelText = ''
reporterFunc.plotReporter4(
fig, ax, '', pdfFile, dataTable,
['mean', 'lower', 'upper'], xLabelText, yLabelText,
scenario, labelXtick, labelYtick, plotLegend,
figTextList[ownersToGraph.index(owner)])
reporterFunc.plotFigureText(
fig, 'Simulation Year',
'Merchantable volume ( $\mathregular{1x10^3}$ $\mathregular{m^3}$)')
pl.savefig(outDir + 'report4_Vol_Owners.png', bbox_inches='tight', dpi=300)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."