def main(subArea, runName, chartTitlePre, ownership):
outDir = "C:\\Users\\olsenk\\Dropbox\\FPF\\Envision Runs\\keith_production\\"
varList = [' Black-backed Woodpecker Good (ha)']
yLabelText = 'Black-backed Woodpecker Good Habitat (ha)'
chartTitle = chartTitlePre
PMG345Ha = reporterFunc.getPMG345Ha(subArea)
# list of ownerships to graphs
if ownership == 'All':
ownersToGraph = [
'Federal', 'State', 'Private Non-Industrial', 'Private Industrial',
'Tribal', 'Homeowner'
]
pdfFile = PdfPages(outDir + 'report2_BBWO_iLAP_All.pdf')
else:
ownersToGraph = [ownership]
pdfFile = PdfPages(outDir + 'report2_BBWO_iLAP_' + ownersToGraph[0] +
'.pdf')
# setup plot for all scenarios
fig = pl.figure(1, figsize=(11, 8.5))
ax = fig.add_subplot(1, 1, 1)
for scenario in [
'CurrentPolicy', 'NoFedTreat', 'Restoration', 'NoFireNoMgmt'
]:
inDir = outDir + runName + "_" + scenario + "\\"
if os.path.isdir(inDir):
yearList = list(
set(
pd.io.parsers.read_csv(
inDir + r'ILAP_wildlife_models_pivot.csv')[' Year']))
repList = list(
set(
pd.io.parsers.read_csv(
inDir + r'ILAP_wildlife_models_pivot.csv')[' Run']))
totalArea = pd.io.parsers.read_csv(
inDir + r'ILAP_wildlife_models_pivot.csv')
# get stats from multiple reps
statsList = []
for year in range(1, max(yearList) + 1):
yearArea = totalArea[totalArea[' Year'] == year]
dataList = []
for rep in repList:
repArea = yearArea[yearArea[' Run'] == rep]
## # sum output over selected ownerships
## for ownerToGraph in ownersToGraph:
## if ownerToGraph == ownersToGraph[0]:
## ownerArea = pd.DataFrame(repArea[repArea[' OWNER_label'] == ownerToGraph])
## fireProneArea = PMG345Ha[ownerToGraph]
## else:
## tempArea = repArea[repArea[' OWNER_label'] == ownerToGraph]
## for varName in varList:
### totalArea.loc[list(ownerArea.index)[0],varName] += tempArea[varName].iloc[0]
## ownerArea[varName].iloc[0] += tempArea[varName].iloc[0]
##
## fireProneArea += PMG345Ha[ownerToGraph]
## ownerToGraph = 'All'
ownerToGraph = 'All'
ownerArea = repArea
dataList.append(ownerArea[varList[0]].iloc[0])
# convert to numpy array
numpyList = np.array(dataList)
lower95th = np.mean(numpyList, axis=0) - (
(1.96 * np.std(numpyList, axis=0)) / np.sqrt(len(repList)))
upper95th = np.mean(numpyList, axis=0) + (
(1.96 * np.std(numpyList, axis=0)) / np.sqrt(len(repList)))
if lower95th < 0:
lower95th = 0.0
# add year data to dictionary
dataDict = {
'timeStep': year,
'mean': np.mean(numpyList, axis=0),
'std': np.std(numpyList, axis=0),
'lower': lower95th,
'upper': upper95th
}
# convert to list for DataFrame
statsList.append(dataDict)
# convert to DataFrame
dataTable = pd.DataFrame(statsList)
reporterFunc.plotReporter2(
fig, ax, ownerToGraph + ' - ' + str(len(repList)) + ' reps',
pdfFile, dataTable, ['mean', 'lower', 'upper'], subArea,
chartTitle, yLabelText, scenario)
pdfFile.savefig()
pl.close()
pdfFile.close()
print "Done."
def main(subArea, runName, chartTitlePre, ownership):
outDir = "C:\\Users\\olsenk\\Dropbox\\FPF\\Envision Runs\\keith_production\\"
varList = [
' 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."