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)
# make a time series
delta = datetime.timedelta(days=1)
times = [start + i * delta for i in range((end - start).days)]
# path to 7zip if using windows
processor.path_to_7z = r'C:/Program Files/7-Zip/7z.exe'
# download the data; this step will be skipped if this has already been done
# (from the last example) but is necessary to set the metadata
processor.download(bbox, start, end, output, datasets=['GSOD'])
# let's use the processor to aggregate the GSOD data together, including
# tmin, tmax, dewpoint, and wind speed. The GSOD database contains dew point
# and seems to be more complete than GHCND. It doesn't have snow or pan
# evaporation though--this is why they are both included.
tmax = processor.aggregate('GSOD', 'tmax', start, end)
tmin = processor.aggregate('GSOD', 'tmin', start, end)
dewpoint = processor.aggregate('GSOD', 'dewpoint', start, end)
wind = processor.aggregate('GSOD', 'wind', start, end)
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) # path to 7zip if using windows processor.path_to_7z = r'C:/Program Files/7-Zip/7z.exe' # download the data; this step will be skipped if this has already been done # (from the last example) but is necessary to set the metadata processor.download(bbox, start, end, output, datasets = ['GHCND']) # let's use GHCND to work with temperature data. let's use the metadata to # find the 10 stations with the longest temperature records and then aggregate # the data together to get a mean time series across the period of interest. n = 10 # make a list of the templengths = []
