postprocessor = Postprocessor(hspfmodel, process_dates, comid = comid)
# make the figure canvas
fig = pyplot.figure(figsize = (15,9))
# font sizes
titlesize = 14
axissize = 12
ticksize = 11
# get the monthly flows
otimes, m_oflow = postprocessor.get_obs_flow(tstep = 'monthly')
stimes, m_sflow = postprocessor.get_sim_flow(comid, tstep = 'monthly')
# plot the monthly simulated versus the observed
ax3 = pyplot.subplot2grid((2,3), (0,1), aspect = 'equal')
ax3.set_title('Monthly Flow Parity Plot', size = titlesize)
ax3.set_xlabel('Observed Monthly Flows (m\u00B3/s)', size = axissize)
ax3.set_ylabel('Simulated Monthly Flows (m\u00B3/s)', size = axissize)
# get the max value for the limits
maxflow = max(max(m_oflow), max(m_sflow))
# add plot for the data
# dates for the calibration
start = datetime.datetime(y, 1, 1)
end = datetime.datetime(y + 2, 1, 1)
ds = start, end
# use the postprocessor to get the time series
postprocessor = Postprocessor(hspfmodel, ds, comid=comid)
# get the flows and other timeseries from the two-year calibration
stimes, sflow = postprocessor.get_sim_flow(comid)
otimes, oflow = postprocessor.get_obs_flow()
ptimes, precip = postprocessor.get_precipitation(comid)
etimes, evap = postprocessor.get_evaporation(comid)
times, pet = postprocessor.get_pet(comid=comid)
# close this postprocessor
postprocessor.close()
# get the flows for the thirty-year model
i = ttimes.index(start)
if end in ttimes:
j = ttimes.index(end)
thirty = tflow[i:j]
# dates for the calibration
start = datetime.datetime(y, 1, 1)
end = datetime.datetime(y + 2, 1, 1)
ds = start, end
# use the postprocessor to get the time series
postprocessor = Postprocessor(hspfmodel, ds, comid = comid)
# get the flows and other timeseries from the two-year calibration
stimes, sflow = postprocessor.get_sim_flow(comid)
otimes, oflow = postprocessor.get_obs_flow()
ptimes, precip = postprocessor.get_precipitation(comid)
etimes, evap = postprocessor.get_evaporation(comid)
times, pet = postprocessor.get_pet(comid = comid)
# close this postprocessor
postprocessor.close()
# get the flows for the thirty-year model
i = ttimes.index(start)
if end in ttimes:
j = ttimes.index(end)
thirty = tflow[i:j]
interflow = ifwo / area / (end - start).days * 365.25
baseflow = agwo / area / (end - start).days * 365.25
evapotranspiration = evap / area / (end - start).days * 365.25
# use the postprocessor to get the simulation stats
dates = start,end
postprocessor = Postprocessor(hspfmodel, dates, comid = comid)
# get the NSE during the calibration period
d = datetime.datetime(y, 1, 1), datetime.datetime(y + 2 , 1, 1)
stimes, sflow = postprocessor.get_sim_flow(comid, tstep = 'daily',
dates = d)
otimes, oflow = postprocessor.get_obs_flow(tstep = 'daily',
dates = d)
NSEcalibration = (1 - sum((numpy.array(sflow)-numpy.array(oflow))**2) /
sum((numpy.array(oflow) - numpy.mean(oflow))**2))
# get the total precipitation during the calibration period
ptimes, precip = postprocessor.get_precipitation(comid, dates = d)
precipitation = sum(precip) / (d[1] - d[0]).days * 365.25
# get the validation NSE
stimes, sflow = postprocessor.get_sim_flow(comid, tstep = 'daily')
otimes, oflow = postprocessor.get_obs_flow(tstep = 'daily')
postprocessor = Postprocessor(hspfmodel, process_dates, comid=comid)
# make the figure canvas
fig = pyplot.figure(figsize=(15, 9))
# font sizes
titlesize = 14
axissize = 12
ticksize = 11
# get the monthly flows
otimes, m_oflow = postprocessor.get_obs_flow(tstep='monthly')
stimes, m_sflow = postprocessor.get_sim_flow(comid, tstep='monthly')
# plot the monthly simulated versus the observed
ax3 = pyplot.subplot2grid((2, 3), (0, 1), aspect='equal')
ax3.set_title('Monthly Flow Parity Plot', size=titlesize)
ax3.set_xlabel('Observed Monthly Flows (m\u00B3/s)', size=axissize)
ax3.set_ylabel('Simulated Monthly Flows (m\u00B3/s)', size=axissize)
# get the max value for the limits
maxflow = max(max(m_oflow), max(m_sflow))
# add plot for the data
