def validateParameters(self):
'Warn if parameters are missing or unknown'
# Initialize
scenarioInput = self.input
pushWarning = lambda x: store.pushWarning(self.id, x)
configurationPacks = [
('Metric', metric.getModel(scenarioInput['metric model name']), scenarioInput['metric configuration']),
('Network', network.getModel(scenarioInput['network model name']), scenarioInput['network configuration']),
]
# For each configurationPack,
for configurationName, configurationModel, configuration in configurationPacks:
# Load default
valueByOptionBySection = configurationModel.VariableStore().getValueByOptionBySection()
# Make sure that the configuration has the same key hierarchy as the default
for section, valueByOption in valueByOptionBySection.iteritems():
if section not in configuration:
pushWarning('%s section missing: %s' % (configurationName, section))
continue
for option in valueByOption:
if option not in configuration[section]:
pushWarning('%s option missing: %s > %s' % (configurationName, section, option))
# Make sure that the input does not have unnecessary parameters
for section, valueByOption in configuration.iteritems():
if section not in valueByOptionBySection:
pushWarning('%s section unknown: %s' % (configurationName, section))
continue
for option in valueByOption:
if option not in valueByOptionBySection[section]:
pushWarning('%s option unknown: %s > %s' % (configurationName, section, option))
def run(self):
# Prepare
scenarioInput = self.input
scenarioFolder = self.getFolder()
expandPath = lambda x: os.path.join(scenarioFolder, x)
# Register demographics
print 'Registering demographics'
nodesPath = expandPath('nodes')
targetPath = self.getDatasetPath()
sourcePath = expandPath(scenarioInput['demographic file name'])
datasetStore = dataset_store.create(targetPath, sourcePath)
datasetStore.saveNodesSHP(nodesPath)
datasetStore.saveNodesCSV(nodesPath)
# Apply metric
print 'Applying metric'
metricModel = metric.getModel(scenarioInput['metric model name'])
metricConfiguration = scenarioInput['metric configuration']
metricValueByOptionBySection = datasetStore.applyMetric(metricModel, metricConfiguration)
# Build network
print 'Building network'
networkModel = network.getModel(scenarioInput['network model name'])
networkConfiguration = scenarioInput['network configuration']
networkValueByOptionBySection = datasetStore.buildNetwork(networkModel, networkConfiguration)
# Update metric
print 'Updating metric'
metricValueByOptionBySection = datasetStore.updateMetric(metricModel, metricValueByOptionBySection)
# Save output
print 'Saving output'
metric.saveMetricsCSV(expandPath('metrics-global'), metricModel, metricValueByOptionBySection)
datasetStore.saveMetricsCSV(expandPath('metrics-local'), metricModel)
datasetStore.saveSegmentsSHP(expandPath('networks-existing'), is_existing=True)
datasetStore.saveSegmentsSHP(expandPath('networks-proposed'), is_existing=False)
# Bundle
store.zipFolder(scenarioFolder + '.zip', scenarioFolder)
# Validate
self.validateParameters()
# Save output
self.output = {
'variables': {
'node': dict((str(x.id), dict(input=x.input, output=x.output)) for x in datasetStore.cycleNodes()),
'metric': metricValueByOptionBySection,
'network': networkValueByOptionBySection,
},
'statistics': {
'node': datasetStore.getNodeStatistics(),
'metric': datasetStore.getMetricStatistics(),
'network': datasetStore.getNetworkStatistics(),
},
'warnings': store.popWarnings(self.id),
}
# Commit
Session.commit()
def test_scenarioRun(self):
'for now, just make sure it runs'
sourcePath = os.path.join(inputDataPath, "sample_demand_nodes.csv")
# make output dir if not exists
if not os.path.exists(outputDataPath):
os.makedirs(outputDataPath)
targetPath = os.path.join(outputDataPath, "dataset.db")
datasetStore = dataset_store.create(targetPath, sourcePath)
"""
// Sample Model Parameter JSON
metricValueByOptionBySection = {
'demand (household)':
{'household unit demand per household per year': 50}
}
"""
metricConfigPath = os.path.join(baseDirPath, "sample_metric_params.json")
metricConfiguration = json.load(open(metricConfigPath, 'r'))
"""
// Sample Model Parameter JSON
networkValueByOptionBySection = {
'algorithm':
{'minimum node count per subnetwork': 2}
}
"""
networkConfigPath = os.path.join(baseDirPath, "network_params.json")
networkConfiguration = json.load(open(networkConfigPath, 'r'))
# Run metric model
metricModel = metric.getModel("mvMax5")
metricValueByOptionBySection = datasetStore.applyMetric(metricModel, metricConfiguration)
# Now that metrics (mvMax in particular) have been calculated
# we can build the network
networkModel = network.getModel("modKruskal")
networkValueByOptionBySection = datasetStore.buildNetwork(networkModel, networkConfiguration)
# Now that the network's been built (and the electrification option
# is chosen) run the aggregate calculations
metricValueByOptionBySection = datasetStore.updateMetric(metricModel, metricValueByOptionBySection)
metric.saveMetricsConfigurationCSV(os.path.join(outputDataPath, 'metrics-job-input'), metricConfiguration)
metric.saveMetricsCSV(os.path.join(outputDataPath, 'metrics-global'), metricModel, metricValueByOptionBySection)
datasetStore.saveMetricsCSV(os.path.join(outputDataPath, 'metrics-local'), metricModel)
datasetStore.saveSegmentsSHP(os.path.join(outputDataPath, 'networks-proposed'), is_existing=False)
def clone(self, scenarioID):
'Show form to create a new item based on datasets and parameters from existing scenario'
# Make sure the user is logged in
personID = h.getPersonID()
if not personID:
return redirect(url('person_login', targetURL=h.encodeURL(request.path)))
# Make sure the user has access to the scenario
scenario = Session.query(model.Scenario).filter(model.getScopeFilter(personID)).filter(model.Scenario.id==scenarioID).first()
if not scenario:
return redirect(url('new_scenario'))
# Load
scenarioInput = scenario.input
# Prepare
c.scenario = scenario
c.metricModel = metric.getModel(request.GET.get('metricModel', scenarioInput['metric model name']))
c.metricConfiguration = scenarioInput['metric configuration']
c.networkModel = network.getModel(request.GET.get('networkModel', scenarioInput['network model name']))
c.networkConfiguration = scenarioInput['network configuration']
# Return
return render('/scenarios/new.mako')
def new(self, format='html'):
'GET /scenarios/new: Show form to create a new item'
# If the user is not logged in,
if not h.isPerson():
# Redirect to login
return redirect(url('person_login', targetURL=h.encodeURL(h.url('new_scenario'))))
# Make sure that the requested metric model exists
metricModelNames = metric.getModelNames()
metricModelName = request.GET.get('metricModel')
if metricModelName not in metricModelNames:
metricModelName = metricModelNames[0]
c.metricModel = metric.getModel(metricModelName)
c.metricConfiguration = {}
# Make sure that the requested network model exists
networkModelNames = network.getModelNames()
networkModelName = request.GET.get('networkModel')
if networkModelName not in networkModelNames:
networkModelName = networkModelNames[0]
c.networkModel = network.getModel(networkModelName)
c.networkConfiguration = {}
# Render form
c.scenario = None
return render('/scenarios/new.mako')
def run(self):
# Prepare
scenarioInput = self.input
scenarioFolder = self.getFolder()
expandPath = lambda x: os.path.join(scenarioFolder, x)
# Setup status reporting
from time import localtime, strftime
time_format = "%Y-%m-%d %H:%M:%S"
# Register demographics
Job.log("Registering demographics")
print "%s Registering demographics" % strftime(time_format, localtime())
nodesPath = expandPath('nodes')
targetPath = self.getDatasetPath()
sourcePath = expandPath(scenarioInput['demographic file name'])
datasetStore = dataset_store.create(targetPath, sourcePath)
datasetStore.saveNodesSHP(nodesPath)
datasetStore.saveNodesCSV(nodesPath)
# Apply metric
Job.log("Applying metric")
print "%s Applying metric" % strftime(time_format, localtime())
metricModel = metric.getModel(scenarioInput['metric model name'])
metricConfiguration = scenarioInput['metric configuration']
metricValueByOptionBySection = datasetStore.applyMetric(metricModel, metricConfiguration)
# Build network
Job.log("Building network")
print "%s Building network" % strftime(time_format, localtime())
networkModel = network.getModel(scenarioInput['network model name'])
networkConfiguration = scenarioInput['network configuration']
networkValueByOptionBySection = datasetStore.buildNetwork(networkModel, networkConfiguration, jobLogger=Job)
# Update metric
Job.log("Updating metric")
print "%s Updating metric" % strftime(time_format, localtime())
metricValueByOptionBySection = datasetStore.updateMetric(metricModel, metricValueByOptionBySection)
# Save output
Job.log("Saving output")
print "%s Saving output" % strftime(time_format, localtime())
metric.saveMetricsConfigurationCSV(expandPath('metrics-job-input'), metricConfiguration)
metric.saveMetricsCSV(expandPath('metrics-global'), metricModel, metricValueByOptionBySection)
datasetStore.saveMetricsCSV(expandPath('metrics-local'), metricModel)
datasetStore.saveSegmentsSHP(expandPath('networks-existing'), is_existing=True)
datasetStore.saveSegmentsSHP(expandPath('networks-proposed'), is_existing=False)
# Bundle
store.zipFolder(scenarioFolder + '.zip', scenarioFolder)
# Validate
self.validateParameters()
# Save output
self.output = {
'variables': {
'node': dict((str(x.id), dict(input=x.input, output=x.output)) for x in datasetStore.cycleNodes()),
'metric': metricValueByOptionBySection,
'network': networkValueByOptionBySection,
},
'statistics': {
'node': datasetStore.getNodeStatistics(),
'metric': datasetStore.getMetricStatistics(),
'network': datasetStore.getNetworkStatistics(),
},
'warnings': store.popWarnings(self.id),
}
# Commit
Session.commit()
# If we are running the script from the command-line,
if __name__ == '__main__':
# For each metric model,
for metricModelName in metric.getModelNames():
# Load metric model
metricModel = metric.getModel(metricModelName)
metricRoots, metricLines, metricRows = generateDocumentation(metricModel)
# Save and close
referenceFile = open(os.path.join(script_process.basePath, 'docs/metric-%s.rst' % metricModelName), 'wt')
referenceFile.write(formatHeader('Metric Model %s' % metricModelName, '='))
for root in sorted(metricRoots, key=lambda x: metricModel.roots.index(x)):
referenceFile.write('- :ref:`%s`\n' % variable_store.formatLabel(root))
referenceFile.write('\n\n' + 'You can override the value of any variable in the model on a node-by-node basis. To perform a node-level override, use the aliases in the following table as additional columns in your spreadsheet or fields in your shapefile. Both long and short aliases are recognized.')
referenceFile.write('\n\n' + formatTable(['Long alias', 'Short alias', 'Units'], metricRows))
referenceFile.write('\n\n' + '\n'.join(metricLines))
referenceFile.close()
# For each network model,
for networkModelName in network.getModelNames():
# Load network model
networkModel = network.getModel(networkModelName)
networkRoots, networkLines, networkRows = generateDocumentation(networkModel)
# Save and close
referenceFile = open(os.path.join(script_process.basePath, 'docs/network-%s.rst' % networkModelName), 'wt')
referenceFile.write(formatHeader('Network Model %s' % networkModelName, '='))
for root in sorted(networkRoots, key=lambda x: networkModel.roots.index(x)):
referenceFile.write('- :ref:`%s`\n' % variable_store.formatLabel(root))
referenceFile.write('\n\n' + '\n'.join(networkLines))
referenceFile.close()
args = parser.parse_args()
# make output dir if not exists
outputDataPath = args.output_path
if not os.path.exists(outputDataPath):
os.makedirs(outputDataPath)
targetPath = os.path.join(outputDataPath, "dataset.db")
datasetStore = dataset_store.create(targetPath, args.input_nodes_file)
# setup models
metricModel = metric.getModel(args.metric_model_name)
metricConfiguration = json.load(args.metric_model_params)
networkModel = network.getModel(args.network_model_name)
networkConfiguration = json.load(args.network_model_params)
# Run metric model
metricValueByOptionBySection = datasetStore.applyMetric(metricModel, metricConfiguration)
# Now that metrics (mvMax in particular) have been calculated
# we can build the network
networkValueByOptionBySection = datasetStore.buildNetwork(networkModel, networkConfiguration)
# Now that the network's been built (and the electrification option
# is chosen) run the aggregate calculations
metricValueByOptionBySection = datasetStore.updateMetric(metricModel, metricValueByOptionBySection)
metric.saveMetricsConfigurationCSV(os.path.join(outputDataPath, 'metrics-job-input'), metricConfiguration)
metric.saveMetricsCSV(os.path.join(outputDataPath, 'metrics-global'), metricModel, metricValueByOptionBySection)
