from pyhspf.preprocessing import Preprocessor
# 8-digit hydrologic unit code of interest; the lists here of states, years,
# and RPUs are just used to point to location of the data files below
HUC8 = '02040101'
state = 'Delaware'
start = datetime.datetime(1980, 1, 1)
end = datetime.datetime(2011, 1, 1)
drainmax = 400
aggregation = 'cdlaggregation.csv'
landuse = 'lucs.csv'
if __name__ == '__main__':
processor = Preprocessor()
processor.set_network(source)
processor.set_output(destination)
processor.set_parameters(HUC8 = HUC8,
start = start,
end = end,
state = state,
cdlaggregate = aggregation,
landuse = landuse)
processor.preprocess(drainmax = drainmax, parallel = False)
# this took about 40 minutes to run on my 3 year old laptop not counting the
# time to download the raw data from the NHDPlus and CDL
# file path to place the calibrated model and results
calibration = '{}/{}/calibrations'.format(destination, HUC8)
# path where the calibrated model will be saved/located
calibrated = '{}/{}'.format(calibration, gageid)
# Because parallel processing is (optionally) used, the process method has
# to be called at runtime as shown below
if __name__ == '__main__':
# make an instance of the Preprocessor
processor = Preprocessor()
# set up the directory locations
processor.set_network(network)
processor.set_output(destination)
# set the simulation-specific parameters
processor.set_parameters(HUC8=HUC8,
start=start,
end=end,
cdlaggregate=aggregation,
landuse=landuse)
# preprocess the HUC8
source = 'Z:'
destination = 'C:/HSPF_data'
from pyhspf.preprocessing import Preprocessor
# 8-digit hydrologic unit code of interest; the lists here of states, years,
# and RPUs are just used to point to location of the data files below
HUC8 = '02040101'
start = datetime.datetime(1980, 1, 1)
end = datetime.datetime(2011, 1, 1)
drainmax = 400
aggregation = 'cdlaggregation.csv'
landuse = 'lucs.csv'
if __name__ == '__main__':
processor = Preprocessor()
processor.set_network(source)
processor.set_output(destination)
processor.set_parameters(HUC8=HUC8,
start=start,
end=end,
cdlaggregate=aggregation,
landuse=landuse)
processor.preprocess(drainmax=drainmax, parallel=False)
# this took about 40 minutes to run on my 3 year old laptop not counting the
# time to download the raw data from the NHDPlus and CDL
# file path to place the calibrated model and results
calibration = '{}/{}/calibrations'.format(destination, HUC8)
# path where the calibrated model will be saved/located
calibrated = '{}/{}'.format(calibration, gageid)
# Because parallel processing is (optionally) used, the process method has
# to be called at runtime as shown below
if __name__ == '__main__':
# make an instance of the Preprocessor
processor = Preprocessor()
# set up the directory locations
processor.set_network(network)
processor.set_output(destination)
# set the simulation-specific parameters
processor.set_parameters(HUC8 = HUC8,
start = start,
end = end,
state = state,
cdlaggregate = aggregation,
landuse = landuse)
# if the watershed is in more than one state, this will probably not work
# (this is a feature that should be added in the future).
# start and end dates (2001 to 2010)
start = datetime.datetime(2001, 1, 1)
end = datetime.datetime(2011, 1, 1)
# comma separated value file linking land use codes from the cropland data
# layer to RGB colors and HSPF land segments
landcodes = 'aggregate.csv'
# because parallel processing is (optionally) used, the process method has
# to be called at runtime as shown below
if __name__ == '__main__':
# make an instance of the preprocessor
processor = Preprocessor(network, destination, landcodes = landcodes)
# preprocess the HUC8
processor.preprocess(HUC8, state, start, end)
# so using the preprocessor in other watersheds *should* be as simple as
# supplying the state and 8-digit HUC; if you try and get errors please
# report them!
aggregation = 'cdlaggregation.csv'
# Comma separated value file of parameters for the HSPF land use categories
# including RGB values for plots and evapotranspiration crop coefficients
landuse = 'lucs.csv'
# Because parallel processing is (optionally) used, the process method has
# to be called at runtime as shown below
if __name__ == '__main__':
# make an instance of the preprocessor
processor = Preprocessor()
# set the paths to the source network files and the working directory for
# the preprocessing, etc.
processor.set_network(network)
processor.set_output(destination)
# set the parameters for the processing
processor.set_parameters(HUC8 = HUC8,
start = start,
end = end,
cdlaggregate = aggregation,
landuse = landuse)
