# 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
solar = processor.aggregate('NSRDB', 'metstat', start, end)
# since the solar data are hourly and this example uses daily ET, the time
# series has to be aggregated to an average daily value (use a different
# variable for daily and hourly)
# start and end dates (aggregate the whole 31 years)
start = datetime.datetime(1980, 1, 1)
end = datetime.datetime(2011, 1, 1)
# the space argument increases the bounding box area for the data download and
# processing; let's use a larger area for this example to grab data from a
# few more stations
space = 0.5
# download/set the location of the data using the "download_shapefile" method
processor.download_shapefile(filename,
start,
end,
output,
datasets=['precip3240'],
space=0.5)
# open up and package the time series and locations for processing later
names, lons, lats = [], [], []
# make an empty numpy array for the data
precipitations = numpy.empty(
(len(processor.metadata.precip3240stations), (end - start).days * 24))
i = 0
for k, v in list(processor.metadata.precip3240stations.items()):
if not os.path.isdir(directory): os.mkdir(directory)
# start and end dates (aggregate the whole 31 years)
start = datetime.datetime(1980, 1, 1)
end = datetime.datetime(2011, 1, 1)
# the space argument increases the bounding box area for the data download and
# processing; let's use a larger area for this example to grab data from a
# few more stations
space = 0.5
# download/set the location of the data using the "download_shapefile" method
processor.download_shapefile(filename, start, end, output,
datasets = ['precip3240'], space = 0.5)
# the ClimateProcessor's aggregate method can be used with inverse-distance
# weighted average (IDWA) to interpolate between the stations at a given point
# using the "method," "latitude," and "longitude" keyword arguments. the
# result is the same as the previous example. as before, the subbasin_catchments
# shapefile will be used that contains the centroid for each aggregation.
sf = Reader(filename)
# index of the comid, latitude, and longitude records
comid_index = [f[0] for f in sf.fields].index('ComID') - 1
lon_index = [f[0] for f in sf.fields].index('CenX') - 1
lat_index = [f[0] for f in sf.fields].index('CenY') - 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
solar = processor.aggregate('NSRDB', 'metstat', start, end)
# since the solar data are hourly and this example uses daily ET, the time
# series has to be aggregated to an average daily value