# 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)
flows = [r * 10**6 / 3600 / 4 for r in rovol]
# plot it
from matplotlib import pyplot
# need a list of the dates/times for the plot
times = [
start + i * datetime.timedelta(hours=4)
# 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
rovol = wdm.get_data(wdmoutfile, n)
# need to close up the files opened by Fortran
wdm.close(wdmoutfile)
# rovol is the total volume (in Mm3) at each time step. so we need to convert
# it m3/s. we could have had HSPF do this, but it's nice to keep track of all
# the fluxes for looking at mass balance checks.
flows = [r * 10**6 / 3600 / 4 for r in rovol]
# plot it up right quick with matplotlib using the plotdate method.
from matplotlib import pyplot
wdm.close('test.wdm')
# Run example1.uci--this simulation just inputs data from TEST01DT.91 to the
# test.wdm file we just made
hspf.hsppy('test01.uci', messagepath)
# let's go back into the test.wdm and pull out the data and graph it (you may
# get a warning about the file being open--i've worked around it but needs
# a better fix)
wdm.open('test.wdm', 'r')
# get the datasets
wtemps = wdm.get_data('test.wdm', 134)
evaps = wdm.get_data('test.wdm', 41)
winds = wdm.get_data('test.wdm', 42)
flow1 = wdm.get_data('test.wdm', 113)
flow2 = wdm.get_data('test.wdm', 119)
dewp1 = wdm.get_data('test.wdm', 124)
dewp2 = wdm.get_data('test.wdm', 125)
dewp3 = wdm.get_data('test.wdm', 126)
sedm = wdm.get_data('test.wdm', 127)
flow3 = wdm.get_data('test.wdm', 136)
# start and end dates (these are all the same so I will only do this once)
start, end = wdm.get_dates('test.wdm', 134)
times = [start + i * (end - start) / len(wtemps) for i in range(len(wtemps))]
# See if you can read the data from the WDM file
wdm = WDMUtil(verbose=True, messagepath=mssgpath)
# ADD BASIN TIMESERIES FROM THE WDM TO HSPFMODEL
# open the wdm for read access
wdm.open(wdmFile, 'r')
start, end = wdm.get_dates(wdmFile, 101)
x = 1
# Add specific basin met data
for basin in range(0, len(basinRecords)):
# The DSNs are known from the exp file so just use those this time
prcp = wdm.get_data(wdmFile, 100 + x)
evap = wdm.get_data(wdmFile, 200 + x)
# Add and assign timeseries data
hspfmodel.add_timeseries('precipitation', ('prcp_' + str(x)),
start,
prcp,
tstep=tstep)
hspfmodel.add_timeseries('evaporation', ('evap_' + str(x)),
start,
evap,
tstep=tstep)
# Assign to specific basin
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]
dates = [wdm.get_dates(f1, n) for n in dsns]
tssteps = [wdm.get_attribute(f1, n, 'TSSTEP') for n in dsns]
tcodes = [wdm.get_attribute(f1, n, 'TCODE ') for n in dsns]
print(('Time series available in WDM file {}:\n'.format(f1)))
for n, t, d, tstep, tcode in zip(dsns, tstypes, dates, tssteps, tcodes):
s, e = d
print(('{:02d}'.format(n), t, s))
# get the PyHSPF evapotranspiration data
dttm = [
start + t * datetime.timedelta(hours=1)
for t in range(int((end - start).total_seconds() / 3600))
]
datOut = [dttm]
datNms = ['Date']
for i in dsnBas1:
tmpNme = idcons[i - 1] + '_' + staids[i - 1]
datNms.append(tmpNme)
tmpDat = wdm.get_data(wdmFile, i)
datOut.append(tmpDat)
volMod = wdm.get_data(wdmFile, 11)
wdm.close(wdmFile)
datDFOut = pd.DataFrame.from_items(zip(datNms, datOut))
datDFOut.to_csv('basin_1_output.csv', index=False)
qMod = [q * 10**4 * 35.314666721 / (60 * 60) for q in volMod]
# Read flow data
flwData = pd.read_csv(
hspf.hsppy(ucifile, messagepath)
wdm = WDMUtil()
wdm.open(wdmfile, 'r')
dsns = wdm.get_datasets(wdmfile)
# see the datasets in the WDM file
#for n in dsns: print(n, wdm.get_attribute(wdmfile, n, 'TSTYPE'))
# look at the UCI file to get more info on the datasets
precip = wdm.get_data(wdmfile, 106)
evap = wdm.get_data(wdmfile, 426)
pet = wdm.get_data(wdmfile, 425)
rovol = wdm.get_data(wdmfile, 420) # acre-ft
oflow = wdm.get_data(wdmfile, 281) # cfs
start, end = wdm.get_dates(wdmfile, 420)
# calculate the watershed area from the SCHEMATIC block in the UCI file
area = (32 + 6 + 1318 + 193 + 231 + 84 + 3078 + 449 + 540 + 35)
#print('watershed area: {} acres'.format(area))
# convert ROVOL and observed flows to m3/s
# first pass: subbasin
if staid == o.subbasin:
# second: land use category
if descrp == o.landtype:
# third: constituent id
if idcons == v:
# get the time series data
data = wdmutil.get_data(output, n,
start=start, end=end)
# add the total to the database
results[c][o.landtype][v] = data.sum()
# go through the pervious land segments and get the area and runoff
for o in hspfmodel.perlnds:
c = o.subbasin
# add the area
results[c][o.landtype] = {'area': o.area}
f = 'hunting.wdm'
# import from exp to wdm
wdm.import_exp(hunthour, f)
# copy the data to the hspfmodel using WDMUtil
# open the wdm for read access
wdm.open(f, 'r')
# the dsns are known from the exp file so just use those this time
precip = wdm.get_data(f, 106)
evap = wdm.get_data(f, 111)
oflow = wdm.get_data(f, 281)
start, end = wdm.get_dates(f, 106)
# close up the wdm file (forgetting this WILL cause trouble)
wdm.close('hunting.wdm')
# the evaporation data is daily so it needs to be disaggregated to hourly for
# an hourly simulation (see how easy this is with Python)
# the time series in the WDM file starts at 1 am so had to add one extra
# value to the beginning of the time series for consistency
evap = [0] + [e / 24 for e in evap for i in range(24)]
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)
# the observed flow is dsn 281
oflow = wdm.get_data(f, 281, start=start, end=end)
# close up the wdm file (forgetting this WILL cause trouble)
wdm.close('hunting.wdm')
# make a list of the times in the daily time series using datetime "timedelta"
hspf.hsppy(ucifile, messagepath)
wdm = WDMUtil()
wdm.open(wdmfile, 'r')
dsns = wdm.get_datasets(wdmfile)
# see the datasets in the WDM file
#for n in dsns: print(n, wdm.get_attribute(wdmfile, n, 'TSTYPE'))
# look at the UCI file to get more info on the datasets
precip = wdm.get_data(wdmfile, 106)
evap = wdm.get_data(wdmfile, 426)
pet = wdm.get_data(wdmfile, 425)
rovol = wdm.get_data(wdmfile, 420) # acre-ft
oflow = wdm.get_data(wdmfile, 281) # cfs
start, end = wdm.get_dates(wdmfile, 420)
# calculate the watershed area from the SCHEMATIC block in the UCI file
area = (32 +
6 +
1318 +
193 +
231 +
84 +
f = 'hunting.wdm'
# import from exp to wdm
wdm.import_exp(hunthour, f)
# copy the data to the hspfmodel using WDMUtil
# open the wdm for read access
wdm.open(f, 'r')
# the dsns are known from the exp file so just use those this time
precip = wdm.get_data(f, 106)
evap = wdm.get_data(f, 111)
oflow = wdm.get_data(f, 281)
start, end = wdm.get_dates(f, 106)
# close up the wdm file (forgetting this WILL cause trouble)
wdm.close('hunting.wdm')
# the evaporation data is daily so it needs to be disaggregated to hourly for
# an hourly simulation (see how easy this is with Python)
# the time series in the WDM file starts at 1 am so had to add one extra
# value to the beginning of the time series for consistency
evap = [0] + [e / 24 for e in evap for i in range(24)]
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]
dates = [wdm.get_dates(f1, n) for n in dsns]
tssteps = [wdm.get_attribute(f1, n, 'TSSTEP') for n in dsns]
tcodes = [wdm.get_attribute(f1, n, 'TCODE ') for n in dsns]
print('Time series available in WDM file {}:\n'.format(f1))
for n, t, d, tstep, tcode in zip(dsns, tstypes, dates, tssteps, tcodes):
s, e = d
print('{:02d}'.format(n), t, s)
# get the PyHSPF evapotranspiration data
f = 'hunting.wdm'
# import from exp to wdm
wdm.import_exp(hunthour, f)
# copy the data to the hspfmodel using WDMUtil
# open the wdm for read access
wdm.open(f, 'r')
# the dsns are known from the exp file so just use those this time
precip = wdm.get_data(f, 106)
evap = wdm.get_data(f, 111)
oflow = wdm.get_data(f, 281)
start, end = wdm.get_dates(f, 106)
# close up the wdm file (forgetting this WILL cause trouble)
wdm.close('hunting.wdm')
# the evaporation data is daily so it needs to be disaggregated to hourly for
# an hourly simulation (see how easy this is with Python)
# the time series in the WDM file starts at 1 am so had to add one extra
# value to the beginning of the time series for consistency
evap = [0] + [e / 24 for e in evap for i in range(24)]
wdm.close('test.wdm')
# Run example1.uci--this simulation just inputs data from TEST01DT.91 to the
# test.wdm file we just made
hspf.hsppy('test01.uci', messagepath)
# let's go back into the test.wdm and pull out the data and graph it (you may
# get a warning about the file being open--i've worked around it but needs
# a better fix)
wdm.open('test.wdm', 'r')
# get the datasets
wtemps = wdm.get_data('test.wdm', 134)
evaps = wdm.get_data('test.wdm', 41)
winds = wdm.get_data('test.wdm', 42)
flow1 = wdm.get_data('test.wdm', 113)
flow2 = wdm.get_data('test.wdm', 119)
dewp1 = wdm.get_data('test.wdm', 124)
dewp2 = wdm.get_data('test.wdm', 125)
dewp3 = wdm.get_data('test.wdm', 126)
sedm = wdm.get_data('test.wdm', 127)
flow3 = wdm.get_data('test.wdm', 136)
# start and end dates (these are all the same so I will only do this once)
start, end = wdm.get_dates('test.wdm', 134)
times = [start + i * (end - start) / len(wtemps) for i in range(len(wtemps))]
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)
flows = [r * 10**6 / 3600 / 4 for r in rovol]
# plot it
from matplotlib import pyplot
# need a list of the dates/times for the plot
times = [start + i * datetime.timedelta(hours = 4)
for i in range(int((end - start).total_seconds() / 3600 / 4))]
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)
# the observed flow is dsn 281
oflow = wdm.get_data(f, 281, start = start, end = end)
# close up the wdm file (forgetting this WILL cause trouble)
wdm.close('hunting.wdm')
# make a list of the times in the daily time series using datetime "timedelta"
# 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
rovol = wdm.get_data(wdmoutfile, n)
# need to close up the files opened by Fortran
wdm.close(wdmoutfile)
# rovol is the total volume (in Mm3) at each time step. so we need to convert
# it m3/s. we could have had HSPF do this, but it's nice to keep track of all
# the fluxes for looking at mass balance checks.
flows = [r * 10**6 / 3600 / 4 for r in rovol]
# plot it up right quick with matplotlib using the plotdate method.
from matplotlib import pyplot
