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, 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, 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(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\\"
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(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 + 'report5_ForestStructre2_byOwner.pdf')
for varStruct in varList:
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 + "_CurrentPolicy\\"
if plotPage == 1:
if splot == 1:
ownersToGraph = ['USFS', 'State', 'Corporate Forest']
elif splot == 2:
ownersToGraph = [
'Chemult Ranger District (Fremont-Winema NF)',
'Chiloquin Ranger District (Fremont-Winema NF)',
'Klamath Ranger District (Fremont-Winema NF)'
]
elif plotPage == 2:
if splot == 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)']
)
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']:
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]) /
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)
if splot == 1:
labelXaxis = False
plotLegend = True
yLabelText = 'Area (%) in' + varStruct
elif splot == 2:
labelXaxis = True
plotLegend = True
yLabelText = 'Area (%) in' + varStruct
reporterFunc.plotReporter5(fig, ax, '', pdfFile,
dataTable,
['mean', 'lower', 'upper'],
yLabelText, ownership,
labelXaxis, plotLegend)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(subArea, runName, chartTitlePre, 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(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."
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."