# 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))]
# use matplotlib to make the chart
from matplotlib import pyplot, dates
fig, subs = pyplot.subplots(nrows=3, ncols=2, sharex=True, figsize=(12, 9))
fig.suptitle('1976 Iowa River Water Quality Data', size=14)
subs[0, 0].plot_date(times, dewp1, 'r-')
subs[0, 0].plot_date(times, dewp2, 'g-')
subs[0, 0].plot_date(times, dewp3, 'b-')
subs[0, 0].set_ylabel('Dew point\n(\u00B0F)', multialignment='center')
# 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"
delta = datetime.timedelta(days = 1)
times = [start + i * delta for i in range(len(precip))]
# 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
i = tstypes.index('PEVT')
evap = wdm.get_data(f1, dsns[i], start=bstart, end=end)
# get the PyHSPF wind speed data
# 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))]
# use matplotlib to make the chart
from matplotlib import pyplot, dates
fig, subs = pyplot.subplots(nrows = 3, ncols = 2, sharex = True,
figsize = (12,9))
fig.suptitle('1976 Iowa River Water Quality Data', size = 14)
subs[0,0].plot_date(times, dewp1, 'r-')
subs[0,0].plot_date(times, dewp2, 'g-')
subs[0,0].plot_date(times, dewp3, 'b-')
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
ft3m3 = 35.314
# conversion from uci ext targets
c = 0.0020107
flwData = pd.read_csv(
os.path.abspath(os.path.curdir) + '\\siletz_HSPF_flw.csv')
ts_to_wdmFile(wdmFile=wdmFile,
pcpData=pcpData,
petData=petData,
flwData=flwData)
# 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,
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)]
precip = [0] + [p for p in precip]
oflow = [0] + [o for o in oflow]
# list of times
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
)
# 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"
delta = datetime.timedelta(days=1)
times = [start + i * delta for i in range(len(precip))]
# 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
i = tstypes.index('PEVT')
evap = wdm.get_data(f1, dsns[i], start = bstart, end = end)
# get the PyHSPF wind speed data
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)]
precip = [0] + [p for p in precip]
oflow = [0] + [o for o in oflow]
# list of times