# David J. Lampert ([email protected]) # # last updated: 02/21/2015 # # illustrates how to use the ClimateProcessor class to aggregate climate time # series from the GSOD database (very similar to the last example) import os, datetime, pickle # import the ClimateProcessor from pyhspf.preprocessing import ClimateProcessor # create an instance of the ClimateProcessor class processor = ClimateProcessor() # working directory location for all the data files output = 'HSPF_data' if not os.path.isdir(output): os.mkdir(output) # this is the bounding box of interest from the last example bbox = -77.2056, 38.2666, -76.4008, 39.3539 # start and end dates (just look at a few months to highlight the concept) start = datetime.datetime(1980, 1, 1) end = datetime.datetime(1990, 1, 2)
# David J. Lampert ([email protected]) # # last updated: 02/21/2015 # # illustrates how to use the ClimateProcessor class to aggregate climate time # series from the hourly precipitation database (similar to the last example) import os, datetime, pickle # import the ClimateProcessor from pyhspf.preprocessing import ClimateProcessor # create an instance of the ClimateProcessor class processor = ClimateProcessor() # working directory location for all the data files output = 'HSPF_data' if not os.path.isdir(output): os.mkdir(output) # this is the bounding box of interest from the last example bbox = -77.2056, 38.2666, -76.4008, 39.3539 # start and end dates (aggregate the whole 31 years) start = datetime.datetime(1980, 1, 1) end = datetime.datetime(2011, 1, 1)
# last updated: 02/15/2015 # # illustrates how to use the ClimateProcessor class to download data from # various online databases and then aggregate the time series using some # numpy features. The first part is the essentially the same as the previous # example, so there is minimal discussion. import os, datetime, pickle # import the ClimateProcessor from pyhspf.preprocessing import ClimateProcessor # create an instance of the ClimateProcessor class processor = ClimateProcessor() # working directory location for all the data files output = 'HSPF_data' if not os.path.isdir(output): os.mkdir(output) # this is the bounding box of interest from the last example bbox = -77.2056, 38.2666, -76.4008, 39.3539 # start and end dates (just look at a few months to highlight the concept) start = datetime.datetime(1980, 4, 1) end = datetime.datetime(1980, 10, 2)
# start and end dates for data download
start = datetime.datetime(1988, 6, 1)
end = datetime.datetime(1988, 7, 1)
# use the "subbasin_catchments" shapefile to define the data processing area
filename = 'subbasin_catchments'
if not os.path.isfile(filename + '.shp'):
print('error: file {} does not exist!'.format(filename))
raise
# make an instance of the ClimateProcessor to fetch the climate data
processor = ClimateProcessor()
# the Penman-Monteith Equation requires temperature, humidity of dewpoint,
# wind speed, and solar radiation, which can be obtained from the processor
processor.download_shapefile(filename, start, end, output, space=0.)
# let's get the daily tmin, tmax, dewpoint, and wind speed from GSOD
tmax = processor.aggregate('GSOD', 'tmax', start, end)
tmin = processor.aggregate('GSOD', 'tmin', start, end)
dewt = processor.aggregate('GSOD', 'dewpoint', start, end)
wind = processor.aggregate('GSOD', 'wind', start, end)
# let's use the hourly METSTAT data from the NSRDB for solar radiation
# last updated: 02/15/2015 # # illustrates how to use the ClimateProcessor class to download data from # various online databases and then aggregate the time series using some # numpy features. The first part is the essentially the same as the previous # example, so there is minimal discussion. import os, datetime, pickle # import the ClimateProcessor from pyhspf.preprocessing import ClimateProcessor # create an instance of the ClimateProcessor class processor = ClimateProcessor() # working directory location for all the data files output = 'HSPF_data' if not os.path.isdir(output): os.mkdir(output) # this is the bounding box of interest from the last example bbox = -77.2056, 38.2666, -76.4008, 39.3539 # start and end dates (just look at a few months to highlight the concept) start = datetime.datetime(1980, 4, 1) end = datetime.datetime(1980, 10, 2)
# import the ClimateProcessor and PyShp reader
from pyhspf.preprocessing import ClimateProcessor
from shapefile import Reader
# path to existing shapefile defining the data region and processing information
filename = 'subbasin_catchments'
if not os.path.isfile(filename + '.shp'):
print(('error, {} does not exist!'.format(filename)))
raise
# create an instance of the ClimateProcessor class
processor = ClimateProcessor()
# working directory location for all the data files
output = 'HSPF_data'
if not os.path.isdir(output): os.mkdir(output)
# working directory for aggregated precipitation files
directory = '{}/subbasinprecipitation'.format(output)
if not os.path.isdir(directory): os.mkdir(directory)
# start and end dates (aggregate the whole 31 years)
# import the ClimateProcessor and PyShp reader
from pyhspf.preprocessing import ClimateProcessor
from shapefile import Reader
# path to existing shapefile defining the data region and processing information
filename = 'subbasin_catchments'
if not os.path.isfile(filename + '.shp'):
print('error, {} does not exist!'.format(filename))
raise
# create an instance of the ClimateProcessor class
processor = ClimateProcessor()
# working directory location for all the data files
output = 'HSPF_data'
if not os.path.isdir(output): os.mkdir(output)
# working directory for aggregated precipitation files
directory = '{}/subbasinprecipitation'.format(output)
if not os.path.isdir(directory): os.mkdir(directory)
# start and end dates (aggregate the whole 31 years)
# David J. Lampert ([email protected]) # # last updated: 02/21/2015 # # illustrates how to use the ClimateProcessor class to aggregate climate time # series from the NSRDB database (very similar to the last example) import os, datetime, pickle # import the ClimateProcessor from pyhspf.preprocessing import ClimateProcessor # create an instance of the ClimateProcessor class processor = ClimateProcessor() # working directory location for all the data files output = 'HSPF_data' if not os.path.isdir(output): os.mkdir(output) # this is the bounding box of interest from the last example bbox = -77.2056, 38.2666, -76.4008, 39.3539 # start and end dates (aggregate the whole 31 years) start = datetime.datetime(1980, 1, 1) end = datetime.datetime(2011, 1, 1)
# import the ClimateProcessor and PyShp reader
from pyhspf.preprocessing import ClimateProcessor
from shapefile import Reader
# path to existing shapefile defining the data region and processing information
filename = 'subbasin_catchments'
if not os.path.isfile(filename + '.shp'):
print('error, {} does not exist!'.format(filename))
raise
# create an instance of the ClimateProcessor class
processor = ClimateProcessor()
# working directory location for all the data files
output = 'HSPF_data'
if not os.path.isdir(output): os.mkdir(output)
# working directory for aggregated precipitation files
directory = '{}/subbasinprecipitation'.format(output)
if not os.path.isdir(directory): os.mkdir(directory)
# start and end dates (aggregate the whole 31 years)
# start and end dates
start = datetime.datetime(1980, 1, 1)
end = datetime.datetime(2010, 1, 1)
# use the "subbasin_catchments" shapefile to define the data processing area
filename = 'subbasin_catchments'
if not os.path.isfile(filename + '.shp'):
print('error: file {} does not exist!'.format(filename))
raise
# make an instance of the ClimateProcessor to fetch the climate data
processor = ClimateProcessor()
# the Penman-Monteith Equation requires temperature, humidity of dewpoint,
# wind speed, and solar radiation, which can be obtained from the processor
processor.download_shapefile(filename, start, end, output, space = 0.)
# let's get the daily tmin, tmax, dewpoint, and wind speed from GSOD
tmax = processor.aggregate('GSOD', 'tmax', start, end)
tmin = processor.aggregate('GSOD', 'tmin', start, end)
dewt = processor.aggregate('GSOD', 'dewpoint', start, end)
wind = processor.aggregate('GSOD', 'wind', start, end)
# let's use the hourly METSTAT data from the NSRDB for solar radiation
# David J. Lampert ([email protected]) # # last updated: 02/21/2015 # # illustrates how to use the ClimateProcessor class to aggregate climate time # series (using the same data as the previous example to minimize discussion). import os, datetime, pickle # import the ClimateProcessor from pyhspf.preprocessing import ClimateProcessor # create an instance of the ClimateProcessor class processor = ClimateProcessor() # working directory location for all the data files output = 'HSPF_data' if not os.path.isdir(output): os.mkdir(output) # this is the bounding box of interest from the last example bbox = -77.2056, 38.2666, -76.4008, 39.3539 # start and end dates (just look at a few months to highlight the concept) start = datetime.datetime(1980, 1, 1) end = datetime.datetime(1990, 1, 2)
# start and end dates for data download
start = datetime.datetime(2000, 1, 1)
end = datetime.datetime(2005, 1, 1)
# use the "subbasin_catchments" shapefile to define the data processing area
filename = "subbasin_catchments"
if not os.path.isfile(filename + ".shp"):
print("error: file {} does not exist!".format(filename))
raise
# make an instance of the ClimateProcessor to fetch the climate data
processor = ClimateProcessor()
# the Penman-Monteith Equation requires temperature, humidity of dewpoint,
# wind speed, and solar radiation, which can be obtained from the processor
processor.download_shapefile(filename, start, end, output, space=0.0)
# let's get the daily tmin, tmax, dewpoint, wind speed and solar
tmax = processor.aggregate("GSOD", "tmax", start, end)
tmin = processor.aggregate("GSOD", "tmin", start, end)
dewt = processor.aggregate("GSOD", "dewpoint", start, end)
wind = processor.aggregate("GSOD", "wind", start, end)
solar = processor.aggregate("NSRDB", "metstat", start, end)
# use the ETCalculator to estimate the evapotranspiration time series
# David J. Lampert ([email protected]) # # last updated: 02/21/2015 # # illustrates how to use the ClimateProcessor class to aggregate climate time # series from the GSOD database (very similar to the last example) import os, datetime, pickle # import the ClimateProcessor from pyhspf.preprocessing import ClimateProcessor # create an instance of the ClimateProcessor class processor = ClimateProcessor() # working directory location for all the data files output = 'HSPF_data' if not os.path.isdir(output): os.mkdir(output) # this is the bounding box of interest from the last example bbox = -77.2056, 38.2666, -76.4008, 39.3539 # start and end dates (just look at a few months to highlight the concept) start = datetime.datetime(1980, 1, 1) end = datetime.datetime(1990, 1, 2)
# David J. Lampert ([email protected]) # # last updated: 02/21/2015 # # illustrates how to use the ClimateProcessor class to aggregate climate time # series (using the same data as the previous example to minimize discussion). import os, datetime, pickle # import the ClimateProcessor from pyhspf.preprocessing import ClimateProcessor # create an instance of the ClimateProcessor class processor = ClimateProcessor() # working directory location for all the data files output = 'HSPF_data' if not os.path.isdir(output): os.mkdir(output) # this is the bounding box of interest from the last example bbox = -77.2056, 38.2666, -76.4008, 39.3539 # start and end dates (just look at a few months to highlight the concept) start = datetime.datetime(1980, 1, 1) end = datetime.datetime(1990, 1, 2)
# David J. Lampert ([email protected]) # # last updated: 02/21/2015 # # illustrates how to use the ClimateProcessor class to aggregate climate time # series from the NSRDB database (very similar to the last example) import os, datetime, pickle # import the ClimateProcessor from pyhspf.preprocessing import ClimateProcessor # create an instance of the ClimateProcessor class processor = ClimateProcessor() # working directory location for all the data files output = 'HSPF_data' if not os.path.isdir(output): os.mkdir(output) # this is the bounding box of interest from the last example bbox = -77.2056, 38.2666, -76.4008, 39.3539 # start and end dates (aggregate the whole 31 years) start = datetime.datetime(1980, 1, 1) end = datetime.datetime(2011, 1, 1)
# import the ClimateProcessor and PyShp reader
from pyhspf.preprocessing import ClimateProcessor
from shapefile import Reader
# path to existing shapefile defining the data region and processing information
filename = 'subbasin_catchments'
if not os.path.isfile(filename + '.shp'):
print('error, {} does not exist!'.format(filename))
raise
# create an instance of the ClimateProcessor class
processor = ClimateProcessor()
# working directory location for all the data files
output = 'HSPF_data'
if not os.path.isdir(output): os.mkdir(output)
# working directory for aggregated precipitation files
directory = '{}/subbasinprecipitation'.format(output)
if not os.path.isdir(directory): os.mkdir(directory)
# start and end dates (aggregate the whole 31 years)