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)
raise
if not os.path.isfile('test.wdm'):
print('warning, this simulation assumes the test.wdm file from test01')
print('and exists; please run this example first')
raise
# this is the path to the message file in PyHSPF (hspfmsg.wdm)
pyhspfdirectory = os.path.dirname(hspf.__file__)
messagepath = '{}/pyhspf/core/hspfmsg.wdm'.format(pyhspfdirectory)
# the 2nd simulation adds more data to "test.wdm," so we need to create the
# datasets as before.
wdm = WDMUtil(verbose=True)
wdm.open('test.wdm', 'rw')
attributes = {
'TCODE ': 3, # hourly units
'TSSTEP': 1, # one unit (hourly) time step
'TSTYPE': 'PREC', # precipitation type
'TSFORM': 2 # precip is a total amount across the step
}
wdm.create_dataset('test.wdm', 39, attributes)
wdm.create_dataset('test.wdm', 131, attributes)
wdm.create_dataset('test.wdm', 132, attributes)
# solar radiation -- the file aggregates the hourly to bi-hourly so TSSTEP = 2
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)
raise
if not os.path.isfile('test.wdm'):
print('warning, this simulation assumes the test.wdm file from test01')
print('and test02 exists; please run these examples first')
raise
# this is the path to the message file in PyHSPF (hspfmsg.wdm)
pyhspfdirectory = os.path.dirname(hspf.__file__)
messagepath = '{}/pyhspf/core/hspfmsg.wdm'.format(pyhspfdirectory)
# the 3rd simulation adds more data to "test.wdm," so we need to create the
# datasets as before.
wdm = WDMUtil(verbose = True)
wdm.open('test.wdm', 'rw')
attributes = {'TCODE ': 4, # hourly units
'TSSTEP': 1, # one unit (hourly) time step
'TSFORM': 1 # cloud cover is an average across the step
}
attributes['TSTYPE'] = 'CLND'
wdm.create_dataset('test.wdm', 140, attributes)
attributes['TSTYPE'] = 'CLDC'
wdm.create_dataset('test.wdm', 135, attributes)
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,
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,
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
else:
print('you appear to be missing the data files in the "data"')
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,
}
watershed.add_outlet(sname) # make the HSPFModel instance (the data for this example use the non-default # option of English instead of metric units) from pyhspf import HSPFModel hspfmodel = HSPFModel(units='English') # since the climate data are provided with hspexp in an export file called # "huntobs.exp." WDMUtil has a method to automatically import the data to a # WDM file. from pyhspf import WDMUtil wdm = WDMUtil() # path to hspexp2.4 data files (make sure the path is correct) # the data from the export file (*.exp) provided with hspexp need to be # imported into a wdm file; the WDMUtil class has a method for this huntday = 'huntday/huntobs.exp' f = 'hunting.wdm' # import from exp to wdm wdm.import_exp(huntday, f) # copy the data to the hspfmodel using WDMUtil. in general climate # data would need to come from some other place (not a wdm file);
# check that the file exists
if not os.path.isfile(ucifile):
print('file does not exist')
raise
# find the path to the HSPF message file
pyhspfdirectory = os.path.dirname(hspf.__file__)
messagepath = '{}/pyhspf/core/hspfmsg.wdm'.format(pyhspfdirectory)
# run the simulation
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
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)
subbasins['32'] = subbasin
# create an instance of the watershed class from the subbasin information
watershed = Watershed(description, subbasins)
# add the network and the outlet subbasin
watershed.add_mass_linkage(updown)
watershed.add_outlet('30')
# since the climate data are provided with hspexp in an export file called
# "huntobs.exp." WDMUtil has a method to automatically import the data to a
# WDM file.
wdm = WDMUtil()
# the data from the export file (*.exp) provided with hspexp need to be
# imported into a wdm file. WDMUtil has a method for this.
hunthour = 'hunthour/huntobs.exp'
# this is just a check to see the file is there
if not os.path.isfile(hunthour):
print('error: file {} seems to be missing'.format(hunthour))
print('please update the path and re-run\n')
raise
# the path to the wdm file to create
raise
if not os.path.isfile('test.wdm'):
print('warning, this simulation assumes the test.wdm file from test01')
print('and exists; please run this example first')
raise
# this is the path to the message file in PyHSPF (hspfmsg.wdm)
pyhspfdirectory = os.path.dirname(hspf.__file__)
messagepath = '{}/pyhspf/core/hspfmsg.wdm'.format(pyhspfdirectory)
# the 2nd simulation adds more data to "test.wdm," so we need to create the
# datasets as before.
wdm = WDMUtil(verbose = True)
wdm.open('test.wdm', 'rw')
attributes = {'TCODE ': 3, # hourly units
'TSSTEP': 1, # one unit (hourly) time step
'TSTYPE': 'PREC', # precipitation type
'TSFORM': 2 # precip is a total amount across the step
}
wdm.create_dataset('test.wdm', 39, attributes)
wdm.create_dataset('test.wdm', 131, attributes)
wdm.create_dataset('test.wdm', 132, attributes)
# solar radiation -- the file aggregates the hourly to bi-hourly so TSSTEP = 2
# always there if you want to see it, but changing the parameters is much
# easier in Python, and can even be scripted. The "with" statement just closes
# the file where the HSPFModel is stored.
import pickle
with open('hspfmodel', 'wb') as f: pickle.dump(hspfmodel, f)
# assuming that went ok (look at the HSPF-generated .ech and .out files),
# the results can be retrieved using WDMUtil
from pyhspf import WDMUtil
# create an instance of 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. 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]
subbasins['32'] = subbasin
# create an instance of the watershed class from the subbasin information
watershed = Watershed(description, subbasins)
# add the network and the outlet subbasin
watershed.add_mass_linkage(updown)
watershed.add_outlet('30')
# since the climate data are provided with hspexp in an export file called
# "huntobs.exp." WDMUtil has a method to automatically import the data to a
# WDM file.
wdm = WDMUtil()
# the data from the export file (*.exp) provided with hspexp need to be
# imported into a wdm file. WDMUtil has a method for this.
hunthour = 'hunthour/huntobs.exp'
# this is just a check to see the file is there
if not os.path.isfile(hunthour):
print('error: file {} seems to be missing'.format(hunthour))
print('please update the path and re-run\n')
raise
# the path to the wdm file to create
raise
if not os.path.isfile('test.wdm'):
print('warning, this simulation assumes the test.wdm file from test01')
print('and test02 exists; please run these examples first')
raise
# this is the path to the message file in PyHSPF (hspfmsg.wdm)
pyhspfdirectory = os.path.dirname(hspf.__file__)
messagepath = '{}/pyhspf/core/hspfmsg.wdm'.format(pyhspfdirectory)
# the 3rd simulation adds more data to "test.wdm," so we need to create the
# datasets as before.
wdm = WDMUtil(verbose=True)
wdm.open('test.wdm', 'rw')
attributes = {
'TCODE ': 4, # hourly units
'TSSTEP': 1, # one unit (hourly) time step
'TSFORM': 1 # cloud cover is an average across the step
}
attributes['TSTYPE'] = 'CLND'
wdm.create_dataset('test.wdm', 140, attributes)
attributes['TSTYPE'] = 'CLDC'
# 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 = [dsn for dsn, idcons, staid in zip(dsns, idconss, staids)
# 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 = [
# check that the file exists
if not os.path.isfile(ucifile):
print('file does not exist')
raise
# find the path to the HSPF message file
pyhspfdirectory = os.path.dirname(hspf.__file__)
messagepath = '{}/pyhspf/core/hspfmsg.wdm'.format(pyhspfdirectory)
# run the simulation
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
watershed.add_outlet(sname) # make the HSPFModel instance (the data for this example use the non-default # option of English instead of metric units) from pyhspf import HSPFModel hspfmodel = HSPFModel(units = 'English') # since the climate data are provided with hspexp in an export file called # "huntobs.exp." WDMUtil has a method to automatically import the data to a # WDM file. from pyhspf import WDMUtil wdm = WDMUtil() # path to hspexp2.4 data files (make sure the path is correct) # the data from the export file (*.exp) provided with hspexp need to be # imported into a wdm file; the WDMUtil class has a method for this huntday = 'huntday/huntobs.exp' f = 'hunting.wdm' # import from exp to wdm wdm.import_exp(huntday, f) # copy the data to the hspfmodel using WDMUtil. in general climate # data would need to come from some other place (not a wdm file);
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 = [
