def calibrate():
with open(calibrated, "rb") as f:
hunting = pickle.load(f)
# find the comid of the gage
flowgages = {v: k for k, v in hunting.subbasin_timeseries["flowgage"].items()}
comid = flowgages["Hunting"]
# create an instance of the autocalibrator and provide it with the
# HSPFModel, the start and end dates, the working directory for the
# calibration testing, the comid for the gage, and turn on the
# hydrology (and other) modules
calibrator = AutoCalibrator(hunting, start, end, hspf, comid=comid, hydrology=True)
# provide the output location for the calibrated model, the variables
# to use in the calibration, the optimization parameter, the perturbation
# levels, and the parallel flag
calibrator.autocalibrate(
calibrated, variables=variables, optimization=optimization, perturbations=perturbations, parallel=parallel
)
for variable, value in zip(calibrator.variables, calibrator.values):
print("{:6s} {:5.3f}".format(variable, value))
print("\nsaving the calibration results\n")
def calibrate():
with open(calibrated, 'rb') as f: hunting = pickle.load(f)
# find the comid of the gage
flowgages = {v:k
for k, v in list(hunting.subbasin_timeseries['flowgage'].items())}
comid = flowgages['Hunting']
# create an instance of the autocalibrator and provide it with the
# HSPFModel, the start and end dates, the working directory for the
# calibration testing, the comid for the gage, and turn on the
# hydrology (and other) modules
calibrator = AutoCalibrator(hunting, start, end, hspf, comid = comid,
hydrology = True)
# provide the output location for the calibrated model, the variables
# to use in the calibration, the optimization parameter, the perturbation
# levels, and the parallel flag
calibrator.autocalibrate(calibrated,
variables = variables,
optimization = optimization,
perturbations = perturbations,
parallel = parallel,
)
for variable, value in zip(calibrator.variables, calibrator.values):
print(('{:6s} {:5.3f}'.format(variable, value)))
print('\nsaving the calibration results\n')
def calibrate():
with open(calibrated, 'rb') as f: hunting = pickle.load(f)
calibrator = AutoCalibrator(hunting, start, end, hspf)
calibrator.autocalibrate(calibrated,
variables = variables,
optimization = optimization,
perturbations = perturbations,
parallel = parallel
)
for variable, value in zip(calibrator.variables, calibrator.values):
print('{:6s} {:5.3f}'.format(variable, value))
print('\nsaving the calibration results\n')
calibrated = '{}/{}'.format(calibration, gageid)
# make the directory for the calibration simulations
if not os.path.isdir(calibration): os.mkdir(calibration)
# make an instance of the autocalibrator and give it the path to the model,
# the start and end dates for the calibration period, the working directory
# location to use for simulation input and output files, the NWIS id
# (or comid) of the gage, and the HSPF modules to use (SNOW, PWATER, etc.)
calibrator = AutoCalibrator(filename,
start,
end,
directory,
warmup=warmup,
gageid=gageid,
atemp=True,
snow=True,
hydrology=True)
# calibrate the model and save it to the "calibrated" location
calibrator.autocalibrate(
calibrated,
variables=variables,
optimization=optimization,
perturbations=perturbations,
parallel=parallel,
nprocessors=nprocessors,
)
# file path to place the calibrated model and results
calibration = '{}/{}/calibration'.format(destination, HUC8)
# path where the calibrated model will be saved/located
calibrated = '{}/{}'.format(calibration, gageid)
# make the directory for the calibration simulations
if not os.path.isdir(calibration): os.mkdir(calibration)
# make an instance of the autocalibrator and give it the path to the model,
# the start and end dates for the calibration period, the working directory
# location to use for simulation input and output files, the NWIS id
# (or comid) of the gage, and the HSPF modules to use (SNOW, PWATER, etc.)
calibrator = AutoCalibrator(filename, start, end, directory,
warmup = warmup, gageid = gageid, atemp = True,
snow = True, hydrology = True)
# calibrate the model and save it to the "calibrated" location
calibrator.autocalibrate(calibrated,
variables = variables,
optimization = optimization,
perturbations = perturbations,
parallel = parallel,
nprocessors = nprocessors,
)
def main():
# create an instance of the NWIS extractor
nwisextractor = NWISExtractor(NWIS)
# download and decompress the source metadata files
nwisextractor.download_metadata()
# extract all the gage stations and metadata into a shapefile for the HUC8
nwisextractor.extract_HUC8(HUC8, output)
# tell the extractor to use the metadata file above to find gage data
nwisextractor.set_metadata(gagefile)
# create an instance of the NHDPlus extractor
nhdplusextractor = NHDPlusExtractor(drainid, VPU, NHDPlus)
# download and decompress the source data for the Mid Atlantic Region
nhdplusextractor.download_data()
# extract the HUC8 data for the Patuxent watershed
nhdplusextractor.extract_HUC8(HUC8, output)
# create an instance of the NHDPlusDelineator to use to build the Watershed
delineator = NHDPlusDelineator(VAAfile, flowfile, catchfile, elevfile,
gagefile = gagefile)
# delineate the watershed (extract the flowlines, catchments and other data)
delineator.delineate_gage_watershed(gageid, output = gagepath)
# add land use data from 1988 to the delineator
delineator.add_basin_landuse(1988, landuse)
# build the watershed
delineator.build_gage_watershed(gageid, watershed, masslinkplot = masslink)
# make the working directory for HSPF simulation files
if not os.path.isdir(hspf): os.mkdir(hspf)
# import old data for Hunting Creek
wdm = WDMUtil()
# path to hspexp2.4 data files (modify as needed)
directory = os.path.abspath(os.path.dirname(__file__)) + '/data'
# 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'.format(directory)
f = 'temp.wdm'
# import from exp to wdm
wdm.import_exp(hunthour, f)
# close the file and re-open the wdm for read access
wdm.close(f)
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)
flow = wdm.get_data(f, 281)
s, e = wdm.get_dates(f, 106)
# add the time series to deal with HSPF looking backward stepping
precip = [0] + [p * 25.4 for p in precip]
evap = [e * 25.4 / 24 for e in evap for i in range(24)]
wdm.close(f)
# create an HSPF model instance
hunting = HSPFModel()
# open the watershed built above
with open(watershed, 'rb') as f: w = pickle.load(f)
# use the data to build an HSPFModel
hunting.build_from_watershed(w, model, ifraction = 1., verbose = True)
# turn on the hydrology modules to the HSPF model
hunting.add_hydrology()
# add precip timeseries with label BWI and provided start date to the model
hunting.add_timeseries('precipitation', 'BWI', s, precip)
# add evap timeseries with label Beltsville and provided start date
hunting.add_timeseries('evaporation', 'Beltsville', s, evap)
# add flow timeseries with label Hunting, start date, tstep (days)
hunting.add_timeseries('flowgage', 'Hunting', s, flow, tstep = 60)
# assign the evaporation and precipiation timeseries to the whole watershed
hunting.assign_watershed_timeseries('precipitation', 'BWI')
hunting.assign_watershed_timeseries('evaporation', 'Beltsville')
# find the subbasin indentfier for the watershed outlet
subbasin = [up for up, down in w.updown.items() if down == 0][0]
# assign the flowgage to the outlet subbasin
hunting.assign_subbasin_timeseries('flowgage', subbasin, 'Hunting')
# using pan evaporation data, so need a pan coefficient < 1
hunting.evap_multiplier = 0.75
calibrator = AutoCalibrator(hunting, start, end, hspf)
calibrator.autocalibrate(calibrated,
variables = variables,
optimization = optimization,
perturbations = perturbations,
parallel = parallel
)
for variable, value in zip(calibrator.variables, calibrator.values):
print('{:6s} {:5.3f}'.format(variable, value))
print('\nsaving the calibration results\n')
def calibrate():
"""Builds and calibrates the model."""
# make the working directory for HSPF
if not os.path.isdir(hspf): os.mkdir(hspf)
# import old data for Hunting Creek
wdm = WDMUtil()
# path to hspexp2.4 data files (modify as needed)
directory = os.path.abspath(os.path.dirname(__file__)) + '/data'
# 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'.format(directory)
f = 'temp.wdm'
# import from exp to wdm
wdm.import_exp(hunthour, f)
# close the file and re-open the wdm for read access
wdm.close(f)
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)
flow = wdm.get_data(f, 281)
s, e = wdm.get_dates(f, 106)
# add the time series to deal with HSPF looking backward stepping
precip = [0] + [p * 25.4 for p in precip]
evap = [e * 25.4 / 24 for e in evap for i in range(24)]
wdm.close(f)
# create an HSPF model instance
hunting = HSPFModel()
# open the watershed built above
with open(watershed, 'rb') as f:
w = pickle.load(f)
# use the data to build an HSPFModel
hunting.build_from_watershed(w, model, verbose=True)
# turn on the hydrology modules to the HSPF model
hunting.add_hydrology()
# add precip timeseries with label BWI and provided start date to the model
hunting.add_timeseries('precipitation', 'BWI', s, precip)
# add evap timeseries with label Beltsville and provided start date
hunting.add_timeseries('evaporation', 'Beltsville', s, evap)
# add flow timeseries with label Hunting, start date, tstep (days)
#hunting.add_timeseries('flowgage', 'Hunting', start, flow, tstep = 1440)
hunting.add_timeseries('flowgage', 'Hunting', s, flow, tstep=60)
# assign the evaporation and precipiation timeseries to the whole watershed
hunting.assign_watershed_timeseries('precipitation', 'BWI')
hunting.assign_watershed_timeseries('evaporation', 'Beltsville')
# find the subbasin indentfier for the watershed outlet
subbasin = [up for up, down in w.updown.items() if down == 0][0]
# assign the flowgage to the outlet subbasin
hunting.assign_subbasin_timeseries('flowgage', subbasin, 'Hunting')
# using pan evaporation data, so need a pan coefficient < 1
hunting.evap_multiplier = 0.75
calibrator = AutoCalibrator(hunting, start, end, hspf)
calibrator.autocalibrate(calibrated,
variables=variables,
optimization=optimization,
perturbations=perturbations,
parallel=parallel)
for variable, value in zip(calibrator.variables, calibrator.values):
print('{:6s} {:5.3f}'.format(variable, value))
print('')
