# evapotranspiration time series; let's make an instance to use
calculator = ETCalculator()
# the ETCalculator makes use of hourly and daily time series that must be
# supplied externally as:
#
# 1. time series type (temperature, dewpoint, humidity, wind, solar, etc)
# 2. time step step ("daily", "hourly", or size in minutes, e.g., 60, 1440)
# 3. start time
# 4. list of data values
#
# note these are the same formats used by the PyHSPF HSPFModel class
# so now we can add the daily timeseries from above
calculator.add_timeseries('tmin', 'daily', start, tmin)
calculator.add_timeseries('tmax', 'daily', start, tmax)
calculator.add_timeseries('dewpoint', 'daily', start, dewt)
calculator.add_timeseries('wind', 'daily', start, wind)
calculator.add_timeseries('solar', 'daily', start, dsolar)
# the temperature and dewpoint are assumed to be in C, wind speed in m/s, and
# solar radiation in W/m2; these are the units supplied by the other classes
# in PyHSPF already so no manipulation is needed
# some of the parameters in the Penman-Monteith Equation depend on the
# geographic location so let's use the information in the shapefile to
# provide the average longitude, latitude, and elevation
sf = Reader(filename)
ys = [r[fields.index('CenY') - 1] for r in sf.records()]
zs = [r[fields.index('AvgElevM') - 1] for r in sf.records()]
# get the areal-weighted averages
lon = sum([a * x for a, x in zip(areas, xs)]) / sum(areas)
lat = sum([a * y for a, y in zip(areas, ys)]) / sum(areas)
elev = sum([a * z for a, z in zip(areas, zs)]) / sum(areas)
# add the information to the calculator
calculator.add_location(lon, lat, elev)
# add the daily time series to the calculator
calculator.add_timeseries('tmin', 'daily', start, tmin)
calculator.add_timeseries('tmax', 'daily', start, tmax)
calculator.add_timeseries('dewpoint', 'daily', start, dewt)
calculator.add_timeseries('wind', 'daily', start, wind)
calculator.add_timeseries('solar', 'daily', start, solar)
# calculate the reference evapotranspiration (RET) time series from the daily
# Penman-Monteith Equation
calculator.penman_daily(start, end)
# save the time series for later (i.e., to add to an HSPF Model)
RET = [e for e in calculator.daily['RET'][1]]
data = start, 1440, RET
ys = [r[fields.index("CenY") - 1] for r in sf.records()]
zs = [r[fields.index("AvgElevM") - 1] for r in sf.records()]
# get the areal-weighted averages
lon = sum([a * x for a, x in zip(areas, xs)]) / sum(areas)
lat = sum([a * y for a, y in zip(areas, ys)]) / sum(areas)
elev = sum([a * z for a, z in zip(areas, zs)]) / sum(areas)
# add the information to the calculator
calculator.add_location(lon, lat, elev)
# use the daily tmin and tmax time series to the calculator to get hourly temps
calculator.add_timeseries("tmin", "daily", start, tmin)
calculator.add_timeseries("tmax", "daily", start, tmax)
# calculate the hourly temperature time series
hourlytemps = calculator.interpolate_temperatures(start, end)
# assume the values for wind speed and dewpoint are constant throughout the day
hdewt = [v for v in dewt for i in range(24)]
hwind = [v for v in wind for i in range(24)]
# now add the hourly time series to the calculator
calculator.add_timeseries("temperature", "hourly", start, hourlytemps)
calculator.add_timeseries("dewpoint", "hourly", start, hdewt)
# evapotranspiration time series; let's make an instance to use
calculator = ETCalculator()
# the ETCalculator makes use of hourly and daily time series that must be
# supplied externally as:
#
# 1. time series type (temperature, dewpoint, humidity, wind, solar, etc)
# 2. time step step ("daily", "hourly", or size in minutes, e.g., 60, 1440)
# 3. start time
# 4. list of data values
#
# note these are the same formats used by the PyHSPF HSPFModel class
# so now we can add the daily timeseries from above
calculator.add_timeseries('tmin', 'daily', start, tmin)
calculator.add_timeseries('tmax', 'daily', start, tmax)
calculator.add_timeseries('dewpoint', 'daily', start, dewt)
calculator.add_timeseries('wind', 'daily', start, wind)
calculator.add_timeseries('solar', 'daily', start, solar)
# the temperature and dewpoint are assumed to be in C, wind speed in m/s, and
# solar radiation in W/m2; these are the units supplied by the other classes
# in PyHSPF already so no manipulation is needed
# some of the parameters in the Penman-Monteith Equation depend on the
# geographic location so let's use the information in the shapefile to
# provide the average longitude, latitude, and elevation
sf = Reader(filename)
ys = [r[fields.index('CenY') - 1] for r in sf.records()]
zs = [r[fields.index('AvgElevM') - 1] for r in sf.records()]
# get the areal-weighted averages
lon = sum([a * x for a, x in zip(areas, xs)]) / sum(areas)
lat = sum([a * y for a, y in zip(areas, ys)]) / sum(areas)
elev = sum([a * z for a, z in zip(areas, zs)]) / sum(areas)
# add the information to the calculator
calculator.add_location(lon, lat, elev)
# add the daily time series to the calculator for plotting
calculator.add_timeseries('tmin', 'daily', start, tmin)
calculator.add_timeseries('tmax', 'daily', start, tmax)
calculator.add_timeseries('dewpoint', 'daily', start, dewt)
calculator.add_timeseries('wind', 'daily', start, wind)
calculator.add_timeseries('solar', 'daily', start, dsolar)
# calculate the hourly temperature time series
hourlytemps = calculator.interpolate_temperatures(start, end)
# assume the values for wind speed and dewpoint are constant throughout the day
hdewt = [v for v in dewt for i in range(24)]
hwind = [v for v in wind for i in range(24)]
# now add the hourly time series to the calculator
