def main():

    #--------------------------------------------

    # test procedures mostly for timing purposes

    #--------------------------------------------

    # TODO: set up a (temporary?) GRASS database to use for processing? code

    # currently assumes it's being run within an existing GRASS session

    # using an appropriately configured LOCATION...

    #

    # note the following trick to fix datum for modis sinu;

    # TODO: check that this works properly...compare to MRT output?

    # gs.run_command('g.proj', flags='c',

    #     proj4='+proj=sinu +a=6371007.181 +b=6371007.181 +ellps=sphere')

    ##    proj4='+proj=sinu +R=6371007.181 +nadgrids=@null +wktext')

    # (1) download then aggregate for one month

    tile = 'h09v04'

    year = 2005

    month = 1

    hdfdir = '.'

    # determine range of doys for the specified month

    start_doy, end_doy = get_doy_range(year, month)

    # download data

    ### [atlas 17-May-2012] Wall time: 111.62 s

    hdfs = download_mod11a1(hdfdir, tile, start_doy, end_doy, year)

    # generate monthly pixelwise mean & count of high-quality daytime LST

    ### [atlas 17-May-2012] Wall time: 53.79 s

    gs.os.environ['GRASS_OVERWRITE'] = '1'

    LST = [load_qc_adjusted_lst(hdf) for hdf in hdfs]

    clims = calc_clim(LST, 'LST_%s_%d_%02d' % (tile, year, month))

    # clean up

    gs.run_command('g.remove', rast=','.join(LST))

    gs.os.environ['GRASS_OVERWRITE'] = '0'

    # (2) aggregate all 12 months in one year, using local data

    tile = 'h09v04'

    year = 2005

    hdfdir = '/home/layers/data/climate/MOD11A1.004-OR-orig'

    ### [atlas 17-May-2012] Wall time: 802.86 s

    gs.os.environ['GRASS_OVERWRITE'] = '1'

    for month in range(1, 12+1):

        start_doy, end_doy = get_doy_range(year, month)

        hdfs = get_hdf_paths(hdfdir, tile, start_doy, end_doy, year)

        LST = [load_qc_adjusted_lst(hdf) for hdf in hdfs]

        clims = calc_clim(LST, 'LST_%s_%d_%02d' % (tile, year, month))

        gs.run_command('g.remove', rast=','.join(LST))

    gs.os.environ['GRASS_OVERWRITE'] = '0'

    # (3) download then aggregate for one month, tile h21v09

    tile = 'h21v09'

    year = 2005

    month = 1

    hdfdir = '.'

    # determine range of doys for the specified month

    # download data

    ### [atlas 17-May-2012] Wall time: 118.41 s

    hdfs = download_mod11a1(hdfdir, tile, start_doy, end_doy, year)

    # generate monthly pixelwise mean & count of high-quality daytime LST

    gs.os.environ['GRASS_OVERWRITE'] = '1'

    ### [atlas 17-May-2012] Wall time: 33.28 s

    ### [atlas 17-May-2012] Wall time: 15.09 s

    # clean up

    gs.os.environ['GRASS_OVERWRITE'] = '0'

    return None