else:
print('you appear to be missing the data files in the data/tests')
print('directory that are needed for this simulation')
raise
# this is the path to the message file in PyHSPF (hspfmsg.wdm)
# the HSPF main routine needs the location of this file and the UCI file
pyhspfdirectory = os.path.dirname(hspf.__file__)
messagepath = '{}/pyhspf/core/hspfmsg.wdm'.format(pyhspfdirectory)
# before running the examples, we have to create the WDM files used by the
# test runs, which we will do with the WDMUtil class
wdm = WDMUtil(verbose=True)
# the name of the WDM file used in the test UCIs is test.wdm
# open it up for write access
wdm.open('test.wdm', 'w')
# the first few test runs write data to the WDM files, but they assume the
# datasets already exist so we need to create them. have a look at the test
# UCI files if you are curious
attributes = {
'TCODE ': 4,
'TSSTEP': 1,
'TSTYPE': 'WTMP',
'TSFORM': 3,
# build the wdm input file using the timeseries hspfmodel.build_wdminfile() # external targets targets = ['reach_outvolume', 'evaporation', 'reach_volume', 'runoff'] # build the input files and run 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 = [
print(('error: required file {} does not exist!\n'.format(f)))
raise
# make sure the PyHSPF data have been generated
for d in (climatedata, pdata, edata, cdata):
if not os.path.isdir(d):
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)
pcpData = pd.read_csv(
os.path.abspath(os.path.curdir) + '\\siletz_HSPF_precip.csv')
petData = pd.read_csv(
os.path.abspath(os.path.curdir) + '\\siletz_HSPF_pet.csv')
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)
print('reading year', y)
p = '{}/{}_{}/{}'.format(directory, y, y + 2, NWISgage)
with open(p, 'rb') as f: hspfmodel = pickle.load(f)
# 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
str_precip = 'siletz_HSPF_precip.csv'
str_pet = 'siletz_HSPF_PET.csv'
str_wdm = 'bigelk_in.wdm'
str_wdm_new = 'siletz.wdm'
# need to set variable for HSPF message file. WDMItil uses this file
messagepath = os.path.abspath(os.path.curdir) + '\\hspfmsg.wdm'
df_prec = pd.read_csv(str_precip)
df_prec.head()
df_pet = pd.read_csv(str_pet)
df_pet.head()
# create an instance of WDMUtil class
wdm = WDMUtil(verbose=True, messagepath='hspfmsg.wdm')
# create a new wdm file
wdm.open(str_wdm_new, 'w')
# take from pyHSPF test01.py example
#
# the first few test runs write data to the WDM files, but they assume the
# datasets already exist so we need to create them. have a look at the test
# UCI files if you are curious
attributes = {
'TCODE ': 4,
'TSSTEP': 1,
'TSTYPE': 'WTMP',
'TSFORM': 3,
