Compute valid WOfS statistics from WOFLs

wofs_stats.py

# Source: https://bitbucket.org/kirill-ga/wofs-summary/src/0f1e4284085e2a65bd7a0b9fdb00e0f65c36f713/kk/wofs_summary/_wofs_stats.py?utm_term=file&utm_source=bb-slack&utm_medium=referral-external&atlOrigin=eyJpIjoiMWRiZjlmZjhkYmE3NDg0Mzk3NWI3ODZhZjczNGQyODQiLCJwIjoiYmItY2hhdHMtaW50ZWdyYXRpb24ifQ#lines-114:136

def wofs_stats(xx):
    attrs = {'crs': xx.crs}

    xx_wet = make_mask(xx.water, wet=True).sum(dim='time', dtype='int16')
    xx_dry = make_mask(xx.water, dry=True).sum(dim='time', dtype='int16')
    xx_clear = xx_wet + xx_dry
    xx_freq = xx_wet.astype('float32')/xx_clear

    # TODO: actually compute nodata pixels. In this context 0 clear observations
    #       is not the same as no observations at all. On NCI datacube-stats is
    #       not computing those either. So this will do for now.
    xx_wet.attrs['nodata'] = -1
    xx_clear.attrs['nodata'] = -1

    out = xr.Dataset({'count_wet': xx_wet,
                      'count_clear': xx_clear,
                      'frequency': xx_freq},
                     attrs=attrs)

    for var in out.data_vars.values():
        var.attrs.update(**attrs)

    return out