def postprocess():
"""Postprocess the results and save."""
print('saving the calibration results\n')
# open the calibrated model
with open(calibrated, 'rb') as f:
hunting = pickle.load(f)
# external targets for calibration
targets = [
'reach_outvolume', 'evaporation', 'groundwater', 'runoff',
'water_state'
]
# build the HSPF input files
hunting.build_wdminfile()
hunting.build_uci(targets, start, end, hydrology=True)
# run it
hunting.run(verbose=True)
# create a postprocessor to analyze the results
postprocessor = Postprocessor(hunting, (start, end))
# make a report of the calibration
report = '{}/calibration_report.csv'.format(hspf)
postprocessor.get_hspexp_parameters(verbose=False)
postprocessor.get_calibration()
postprocessor.calculate_errors(output=report, verbose=False)
# plot the daily hydrograph
hydrograph = '{}/dailyhydrograph'.format(hspf)
postprocessor.plot_hydrograph(tstep='daily', output=hydrograph, show=False)
# plot the monthly hydrograph
hydrograph = '{}/monthlyhydrograph'.format(hspf)
postprocessor.plot_hydrograph(tstep='monthly',
output=hydrograph,
show=False)
# plot the calibration
calibration_graph = '{}/calibration'.format(hspf)
postprocessor.plot_calibration(output=calibration_graph, show=False)
def get_postprocessor(self, hspfmodel, dates, snowdata=None, verbose=True):
"""Postprocesses the data."""
if verbose: print('postprocessing simulation results\n')
start, tstep, data = hspfmodel.flowgages[self.gageid]
end = start + datetime.timedelta(minutes=tstep) * len(data)
if self.process_dates[0] < start or end < self.process_dates[1]:
print(('warning: missing data; processing only period of ' +
'available gage data\n'))
process_dates = start, end
else:
process_dates = self.process_dates
postprocessor = Postprocessor(hspfmodel,
process_dates,
comid=self.gagecomid,
upcomids=self.upcomids)
return postprocessor
def validate(directory, HUC8, NWISgage, start, end, temp = True,
snow = True, hydrology = True, warmup = 1, upcomids = []):
run_dates = (datetime.datetime(start, 1, 1),
datetime.datetime(end, 1, 1))
process_dates = (datetime.datetime(start + warmup, 1, 1),
datetime.datetime(end, 1, 1))
f = '{}/{}/calibrations/{}/hspfmodel'.format(directory, HUC8, NWISgage)
folder = '{}/{}/validations/{}'.format(directory, HUC8, NWISgage)
with open(f, 'rb') as m: hspfmodel = pickle.load(m)
# figure out the external targets needed
targets = ['reach_outvolume', 'groundwater', 'water_state',
'evaporation', 'runoff', 'snow_state', 'snowpack',
'snowfall']
# build the input wdmfile
hspfmodel.build_wdminfile()
# create the UCI file and the output WDM file
hspfmodel.build_uci(targets, run_dates[0], run_dates[1], temp = temp,
snow = snow, hydrology = hydrology)
hspfmodel.messagepath = None
# run it
hspfmodel.run(verbose = True)
# add the simulation parameters to the calibrator
postprocessor = Postprocessor(hspfmodel, process_dates)
# calculate the errors and make a calibration report
postprocessor.get_hspexp_parameters(verbose = False)
postprocessor.get_calibration(verbose = False, vverbose = False)
# plot everything and save to file
if not os.path.isdir('{}/{}/validations'.format(directory, HUC8)):
os.mkdir('{}/{}/validations'.format(directory, HUC8))
if not os.path.isdir(folder): os.mkdir(folder)
output = folder + '/calibration_report.csv'
postprocessor.calculate_errors(verbose = False, output = output)
output = folder + '/snow'
postprocessor.plot_snow(output = output, show = False)
output = folder + '/snowcalibration'
postprocessor.plot_snowcalibration(output = output, show = False)
output = folder + '/monthlyhydrograph'
postprocessor.plot_hydrograph(output = output, show = False)
output = folder + '/dayofyear'
postprocessor.plot_dayofyear(output = output, show = False)
output = folder + '/averagewaterbudget'
postprocessor.plot_waterbudget(output = output, show = False)
output = folder + '/waterbudgets'
postprocessor.plot_allwaterbudgets(output = output, show = False)
output = folder + '/runoff'
postprocessor.plot_runoff(output = output, show = False)
output = folder + '/storms'
postprocessor.plot_storms(output = output, show = False)
output = folder + '/calibration'
postprocessor.plot_calibration(output = output, show = False)
postprocessor.close()
def postprocess():
# open the calibrated model
with open(calibrated, 'rb') as f: hunting = pickle.load(f)
# external targets for calibration
targets = ['reach_outvolume',
'evaporation',
'groundwater',
'runoff',
'water_state'
]
# build the HSPF input files
hunting.build_wdminfile()
hunting.build_uci(targets, start, end, hydrology = True)
# run it
hunting.run(verbose = True)
# create a postprocessor to analyze the results
postprocessor = Postprocessor(hunting, (start, end))
# make a report of the calibration
report = '{}/calibration_report.csv'.format(hspf)
postprocessor.get_hspexp_parameters(verbose = False)
postprocessor.get_calibration()
postprocessor.calculate_errors(output = report, verbose = False)
# plot the daily hydrograph
hydrograph = '{}/dailyhydrograph'.format(hspf)
postprocessor.plot_hydrograph(tstep = 'daily', output = hydrograph,
show = False)
# plot the monthly hydrograph
hydrograph = '{}/monthlyhydrograph'.format(hspf)
postprocessor.plot_hydrograph(tstep = 'monthly', output = hydrograph,
show = False)
# plot the calibration
calibration_graph = '{}/calibration'.format(hspf)
postprocessor.plot_calibration(output = calibration_graph, show = False)
# storm events from HSPExp file (for plotting)
storms = [[datetime.datetime(1989, 2, 20), datetime.datetime(1989, 2, 26)],
[datetime.datetime(1989, 5, 1), datetime.datetime(1989, 5, 5)],
[datetime.datetime(1989, 6, 6), datetime.datetime(1989, 6, 11)],
[datetime.datetime(1989, 9, 25), datetime.datetime(1989, 9, 29)],
[datetime.datetime(1989,10, 1), datetime.datetime(1989,10, 5)],
[datetime.datetime(1989,12, 31), datetime.datetime(1990, 1, 5)],
[datetime.datetime(1990, 1, 25), datetime.datetime(1990, 1, 30)],
[datetime.datetime(1990, 5, 4), datetime.datetime(1990, 5, 8)],
[datetime.datetime(1990, 5, 28), datetime.datetime(1990, 6, 1)],
[datetime.datetime(1990, 6, 15), datetime.datetime(1990, 6, 18)],
]
# plot the storm events
storm_plots = '{}/storms'.format(hspf)
postprocessor.plot_storms(stormdates = storms, output = storm_plots,
tstep = 'hourly', show = False)
