# are provided. the file also contains the observed flow at the outlet.
# this is set up to find the dsns, time steps etc, though if they were
# known they could be provided directly.
# open the wdm for read access
wdm.open(f, 'r')
# find all the dsns
dsns = wdm.get_datasets(f)
# find all the time series types
# (this is how they are identified in the exp file)
tstypes = [wdm.get_attribute(f, n, 'TSTYPE') for n in dsns]
# find the precip and evap timeseries (we could also just look at the exp files
# to figure this out, but this illustrates some of the flexibility of PyHSPF)
precip_dsn = dsns[tstypes.index('HPCP')]
evap_dsn = dsns[tstypes.index('EVAP')]
# get the time series and start and end dates
precip = wdm.get_data(f, precip_dsn)
start, end = wdm.get_dates(f, precip_dsn)
evap = wdm.get_data(f, evap_dsn, start = start, end = end)
wdm = WDMUtil()
# open the file for read access
wdm.open(wdmoutfile, 'r')
# pull up the flow at the outlet and plot it along with the precipitation
# and evapotranspiration. the attributes that identify the data are "IDCONS"
# (constituent ID) and "STAID " (station ID). these were assigned by the
# build_wdminfile and build_uci routines automatically; they can be modified
# as needed. the attributes always have six characters so make sure to add
# trailing spaces.
dsns = wdm.get_datasets(wdmoutfile)
idconss = [wdm.get_attribute(wdmoutfile, n, 'IDCONS') for n in dsns]
staids = [wdm.get_attribute(wdmoutfile, n, 'STAID ') for n in dsns]
# uncomment this line to see what's here in the output file
# print(dsns, idconss, staids)
# one HSPF parameter we saved is ROVOL (PyHSPF has a Postprocessor that can
# be used to simplify this, but WDMUtil can also be used more directly).
# The following statement finds the dataset number for the ROVOL timeseries
# for the reach for subbasin 101.
n = [dsn for dsn, idcons, staid in zip(dsns, idconss, staids)
if idcons == 'ROVOL' and staid == '101'][0]
# get the data for the reach volume flux dataset
hspfmodel.build_uci(targets, start, end, hydrology=True, verbose=False)
hspfmodel.run(verbose=True)
# retrieve results using WDMUtil
wdm = WDMUtil()
# open the file for read access
wdm.open(wdmoutfile, 'r')
# pull up the flow at the outlet and plot it along with the precipitation
# and evapotranspiration
dsns = wdm.get_datasets(wdmoutfile)
idconss = [wdm.get_attribute(wdmoutfile, n, 'IDCONS') for n in dsns]
staids = [wdm.get_attribute(wdmoutfile, n, 'STAID ') for n in dsns]
# find the dsn
n = [
dsn for dsn, idcons, staid in zip(dsns, idconss, staids)
if idcons == 'ROVOL' and staid == '101'
][0]
rovol = wdm.get_data(wdmoutfile, n)
# close up the fortran files.
wdm.close(wdmoutfile)
print(('error: required data in {} do not exist!\n'.format(d)))
raise
# use WDMUtil to read the BASINS data
wdm = WDMUtil(verbose=verbose)
# open the precipitation file and the other climate data file
wdm.open(f1, 'r')
wdm.open(f2, 'r')
# make a list of the datasets and numbers
dsns = wdm.get_datasets(f2)
tstypes = [wdm.get_attribute(f2, n, 'TSTYPE') for n in dsns]
# start date for the BASINS data (after the warmup period)
bstart = start + datetime.timedelta(days=warmup)
# get the precipitation data
i = tstypes.index('PREC')
prec = wdm.get_data(f2, dsns[i], start=bstart, end=end)
# get the potential evapotranspiration and other climate data
dsns = wdm.get_datasets(f1)
tstypes = [wdm.get_attribute(f1, n, 'TSTYPE') for n in dsns]
# calculate the runoff components in each land segment and store
# the results in a structure as [subbasin][landuse][runoff/area]
results = {}
# use WDMUtil to read the data
output = hspfmodel.filename + '_out.wdm'
wdmutil = WDMUtil()
wdmutil.open(output, 'r')
# read the metadata for each timeseries in the WDM file
dsns = wdmutil.get_datasets(output)
idconss = [wdmutil.get_attribute(output, n, 'IDCONS') for n in dsns]
descrps = [wdmutil.get_attribute(output, n, 'DESCRP') for n in dsns]
staids = [wdmutil.get_attribute(output, n, 'STAID ') for n in dsns]
# go through the impervious land segments to get the surface runoff
for o in hspfmodel.implnds:
c = o.subbasin
# make a data dictionary for each subbasin
results[c] = {}
# make a data dictionary for the land use and add the area
# are provided. the file also contains the observed flow at the outlet.
# this is set up to find the dsns, time steps etc, though if they were
# known they could be provided directly.
# open the wdm for read access
wdm.open(f, 'r')
# find all the dsns
dsns = wdm.get_datasets(f)
# find all the time series types
# (this is how they are identified in the exp file)
tstypes = [wdm.get_attribute(f, n, 'TSTYPE') for n in dsns]
# find the precip and evap timeseries (we could also just look at the exp files
# to figure this out, but this illustrates some of the flexibility of PyHSPF)
precip_dsn = dsns[tstypes.index('HPCP')]
evap_dsn = dsns[tstypes.index('EVAP')]
# get the time series and start and end dates
precip = wdm.get_data(f, precip_dsn)
start, end = wdm.get_dates(f, precip_dsn)
evap = wdm.get_data(f, evap_dsn, start=start, end=end)
print('error: required data in {} do not exist!\n'.format(d))
raise
# use WDMUtil to read the BASINS data
wdm = WDMUtil(verbose = verbose)
# open the precipitation file and the other climate data file
wdm.open(f1, 'r')
wdm.open(f2, 'r')
# make a list of the datasets and numbers
dsns = wdm.get_datasets(f2)
tstypes = [wdm.get_attribute(f2, n, 'TSTYPE') for n in dsns]
# start date for the BASINS data (after the warmup period)
bstart = start + datetime.timedelta(days = warmup)
# get the precipitation data
i = tstypes.index('PREC')
prec = wdm.get_data(f2, dsns[i], start = bstart, end = end)
# get the potential evapotranspiration and other climate data
dsns = wdm.get_datasets(f1)
tstypes = [wdm.get_attribute(f1, n, 'TSTYPE') for n in dsns]
import pandas as pd
import csv, os, datetime, numpy
from matplotlib import pyplot as plt
from pyhspf import HSPFModel, WDMUtil, Postprocessor
messagepath = 'hspfmsg.wdm'
wdm = WDMUtil(verbose=True, messagepath=messagepath)
# EXTRACT MODELED FLOWS FROM WDM OUTPUT FILE AND COMPARE TO
wdmFile = 'siletz_river_out.wdm'
wdm.open(wdmFile, 'r')
dsns = wdm.get_datasets(wdmFile)
idcons = [wdm.get_attribute(wdmFile, n, 'IDCONS') for n in dsns]
staids = [wdm.get_attribute(wdmFile, n, 'STAID ') for n in dsns]
pars = [dsns, idcons, staids]
dsnBas1 = [
dsns for dsns, idcons, staid in zip(dsns, idconss, staids) if staid == '1'
] # These are the dsns for all Basin 1 outputs
indBas1 = [dsn - 1
for dsn in dsnBas1] # These are element indeces for Basin 1 dsns
start = datetime.datetime(2012, 1, 1)
end = datetime.datetime(2013, 1, 1)
dttm = [
