constructs thematic map from input information
    :param suvi_data: (header, data) for a suvi image, prefer 195
    :param responses: a list of hek responses
    :param config: a configuration object from suvitrainer
    :param include_human: if true, will automatically include all human labels
    :param origins: which systems to use in thematic map from hek
    :param themes: which hek labels to use
    :return: a (m,n) array of hek labels as thematic map image gridded to suvi_data

def make_thmap(suvi_data, responses, config, include_human=False,
               origins=('SPoCA', 'Flare Detective - Trigger Module'),
               themes=('AR', 'CH', 'FI', 'FL')):
    """
    constructs thematic map from input information
    :param suvi_data: (header, data) for a suvi image, prefer 195
    :param responses: a list of hek responses
    :param config: a configuration object from suvitrainer
    :param include_human: if true, will automatically include all human labels
    :param origins: which systems to use in thematic map from hek
    :param themes: which hek labels to use
    :return: a (m,n) array of hek labels as thematic map image gridded to suvi_data
    """
    theme_name_map = {'AR': "bright_region", "CH": "coronal_hole", "FI": "filament", "FL": "flare"}
    suvimap = sunpy.map.Map(suvi_data[1], suvi_data[0])  # scaled version of the above for nice display
    thmap = np.zeros_like(suvi_data[1])
    responses = sorted(responses, key=lambda e: e['event_type'])
    for response in responses:
        if response['event_type'] == 'FI' or response['event_type'] == 'FL':
            print(response['event_type'], response['frm_name'])
        if response['event_type'] in themes and \
                ((response['frm_name'] in origins) or (include_human and response['frm_humanflag']=='true')):
            if response['frm_name'] == "SPoCA":
                p1 = response['hpc_boundcc'][9:-2]
            else:
                p1 = response["hpc_bbox"][9:-2]
            p2 = p1.split(',')
            p3 = [v.split(" ") for v in p2]
            date = parse_time(response['event_starttime'])

            boundary = SkyCoord(
                [(float(v[0]), float(v[1])) * u.arcsec for v in p3],
                obstime=date,
                frame=frames.Helioprojective)
            xs, ys = boundary.to_pixel(suvimap.wcs)
            xx, yy = polygon(xs, ys)
            thmap[yy, xx] = config.solar_class_index[theme_name_map[response['event_type']]]
    return thmap