wdm.create_dataset('test.wdm', 119, attributes)
attributes['TSTYPE'] = 'DEWP'
wdm.create_dataset('test.wdm', 124, attributes)
wdm.create_dataset('test.wdm', 125, attributes)
wdm.create_dataset('test.wdm', 126, attributes)
attributes['TSTYPE'] = 'SEDM'
wdm.create_dataset('test.wdm', 127, attributes)
attributes['TSTYPE'] = 'FLOW'
wdm.create_dataset('test.wdm', 136, attributes)
# then we have to manually close the WDM file
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)
# 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))]
# build the model (file will all be called example02)
hspfmodel.build_from_watershed(watershed, 'example02', ifraction = ifraction,
tstep = tstep)
# now add the time series to the model
hspfmodel.add_timeseries('precipitation', 'hunting_prec', start, precip,
tstep = tstep)
wdm.create_dataset('test.wdm', 119, attributes)
attributes['TSTYPE'] = 'DEWP'
wdm.create_dataset('test.wdm', 124, attributes)
wdm.create_dataset('test.wdm', 125, attributes)
wdm.create_dataset('test.wdm', 126, attributes)
attributes['TSTYPE'] = 'SEDM'
wdm.create_dataset('test.wdm', 127, attributes)
attributes['TSTYPE'] = 'FLOW'
wdm.create_dataset('test.wdm', 136, attributes)
# then we have to manually close the WDM file
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)
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))
]
fig = pyplot.figure(figsize=(8, 10))
i = tstypes.index('DEWP')
dewt = wdm.get_data(f1, dsns[i], start=bstart, end=end)
# get the PyHSPF solar radiation data
i = tstypes.index('SOLR')
solar = wdm.get_data(f1, dsns[i], start=bstart, end=end)
# get the PyHSPF air temperature data
i = tstypes.index('ATEM')
temp = wdm.get_data(f1, dsns[i], start=bstart, end=end)
# close the WDM files
wdm.close(f1)
wdm.close(f2)
# number of years
years = (end - bstart).days / 365.25
# compare BASINS with PyHSPF generated timeseries for 07080106
pfiles = [f for f in os.listdir(pdata)]
pseries = []
for pfile in pfiles:
with open('{}/{}'.format(pdata, pfile), 'rb') as f:
# 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
# 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))]
hspfmodel.add_timeseries('evaporation', ('evap_' + str(x)),
start,
evap,
tstep=tstep)
# Assign to specific basin
hspfmodel.assign_subbasin_timeseries('precipitation', str(basin + 1),
('prcp_' + str(x)))
hspfmodel.assign_subbasin_timeseries('evaporation', str(basin + 1),
('evap_' + str(x)))
x += 1
# Add flow data to the gaged basin
flow = wdm.get_data(wdmFile, 301)
hspfmodel.add_timeseries('flowgage', 'flow', start, flow, tstep=tstep)
hspfmodel.assign_subbasin_timeseries('flowgage', '11', 'flow')
wdm.close(wdmFile)
# COMPLETE AND EXECUTE MODEL
hspfmodel.add_hydrology()
with open('siletz_river', 'wb') as f:
pickle.dump(hspfmodel, f)
print('\nsuccessfully created new model "siletz_river."\n')
# 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
times = [start + (end - start) / len(precip) * i for i in range(len(precip))]
# make the HSPFModel instance
# 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()
# close up the WDM file
wdmutil.close(output)
wdmutil = None
# free up memory
gc.collect()
# find the part of the watershed contributing to the gage
d = {v:k for k, v in hspfmodel.subbasin_timeseries['flowgage'].items()}
comid = d[NWISgage]
# find the comids of the all the subbasins upstream of the gage
comids = [comid]
n = 0
i = tstypes.index('DEWP')
dewt = wdm.get_data(f1, dsns[i], start = bstart, end = end)
# get the PyHSPF solar radiation data
i = tstypes.index('SOLR')
solar = wdm.get_data(f1, dsns[i], start = bstart, end = end)
# get the PyHSPF air temperature data
i = tstypes.index('ATEM')
temp = wdm.get_data(f1, dsns[i], start = bstart, end = end)
# close the WDM files
wdm.close(f1)
wdm.close(f2)
# number of years
years = (end - bstart).days / 365.25
# compare BASINS with PyHSPF generated timeseries for 07080106
pfiles = [f for f in os.listdir(pdata)]
pseries = []
for pfile in pfiles:
with open('{}/{}'.format(pdata, pfile), 'rb') as f:
# 10 m3 per hour. Given that context, HSPF groups all variables into one of
# three categories (the examples reference heat transfer concepts):
# TSFORM = 1 -- The mean value of a state variable (such as temperature)
# TSFORM = 2 -- The total flux across a time step (such as heat flux energy)
# TSFORM = 3 -- The value at the end of the time step (such as temperature)
# for precip and pet, the TSFORM value would be 2 becuase it would be the total precip that occured over the time-step
attributes['TSFORM'] = 3
attributes['TSTYPE'] = 'PREC'
wdm.create_dataset(str_wdm_new, 11, attributes)
attributes['TSTYPE'] = 'PET'
wdm.create_dataset(str_wdm_new, 12, attributes)
# add precip data for sub-basin 1
start_date = df_prec['DATE'][0]
date_start = datetime.datetime(int(start_date[5:9]), int(start_date[0:2]),
int(start_date[3:4]))
prec_add = [float(x) for x in list(df_prec.iloc[:, 1])]
wdm.add_data(str_wdm_new, 11, prec_add, date_start)
# add pet data for sub-basin 1
start_date = df_prec['DATE'][0]
date_start = datetime.datetime(int(start_date[5:9]), int(start_date[0:2]),
int(start_date[3:4]))
pet_add = [float(x) for x in list(df_pet.iloc[:, 1])]
wdm.add_data(str_wdm_new, 12, pet_add, date_start)
# close wdm file
wdm.close(str_wdm_new)
