def run_clark2009(catchment, output_dem, hydro_year_to_take, met_inp_folder,
                  catchment_shp_folder):
    """
    wrapper to call the clark 2009 snow model for given area
    :param catchment: string giving catchment area to run model on
    :param output_dem: string identifying the grid to run model on
    :param hydro_year_to_take: integer specifying the hydrological year to run model over. 2001 = 1/4/2000 to 31/3/2001
    :return: st_swe, st_melt, st_acc, out_dt, mask. daily grids of SWE at day's end, total melt and accumulation over the previous day, and datetimes of ouput
    """
    print('loading met data')
    data_id = '{}_{}'.format(catchment, output_dem)
    inp_met = nc.Dataset(
        met_inp_folder +
        '/met_inp_{}_hy{}.nc'.format(data_id, hydro_year_to_take), 'r')
    inp_dt = nc.num2date(inp_met.variables['time'][:],
                         inp_met.variables['time'].units)
    inp_doy = convert_date_hydro_DOY(inp_dt)
    inp_hourdec = [datt.hour for datt in inp_dt]
    inp_ta = inp_met.variables['temperature'][:]
    inp_precip = inp_met.variables['precipitation'][:]
    print('met data loaded')

    mask = create_mask_from_shpfile(
        inp_met.variables['lat'][:], inp_met.variables['lon'][:],
        catchment_shp_folder + '/{}.shp'.format(catchment))
    # TODO: think about masking input data to speed up

    print('starting snow model')
    # start with no snow.
    # call main function once hourly/sub-hourly temp and precip data available.
    st_swe, st_melt, st_acc = snow_main_simple(inp_ta,
                                               inp_precip,
                                               inp_doy,
                                               inp_hourdec,
                                               dtstep=3600)
    out_dt = np.asarray(
        make_regular_timeseries(inp_dt[0], inp_dt[-1] + dt.timedelta(days=1),
                                86400))

    print('snow model finished')

    # mask out values outside of catchment
    st_swe[:, mask == False] = np.nan
    st_melt[:, mask == False] = np.nan
    st_acc[:, mask == False] = np.nan

    return st_swe, st_melt, st_acc, out_dt, mask
ts_mb = plt.cumsum(mb_dat[:, 0])
np.where(np.asarray(mb_dt) == dt.datetime(2011, 5, 13, 00, 00))
ts_mb -= ts_mb[199]
#

init_swe = np.ones(
    inp_ta.shape[1:]) * 0  # give initial value of swe as starts in spring
init_d_snow = np.ones(
    inp_ta.shape[1:]) * 30  # give initial value of days since snow

# call main function once hourly/sub-hourly temp and precip data available.
st_swe, st_melt, st_acc, st_alb = snow_main_simple(inp_ta,
                                                   inp_precip,
                                                   inp_doy,
                                                   inp_hourdec,
                                                   dtstep=1800,
                                                   init_swe=init_swe,
                                                   init_d_snow=init_d_snow,
                                                   inp_sw=inp_sw,
                                                   which_melt='clark2009',
                                                   **config)

st_swe1, st_melt1, st_acc1, st_alb1 = snow_main_simple(inp_ta,
                                                       inp_precip,
                                                       inp_doy,
                                                       inp_hourdec,
                                                       dtstep=1800,
                                                       init_swe=init_swe,
                                                       init_d_snow=init_d_snow,
                                                       inp_sw=inp_sw,
                                                       which_melt='dsc_snow',
                                                       **config)
    # plt.plot(plot_dt,inp_sw_obs/2, label = 'Observed radiation')
    # plt.plot(year_VC, rad_VC_year, label = 'VC rad')
    # plt.plot(year_VN, rad_VN_year, label = 'VN rad')
    # plt.gcf().autofmt_xdate()
    # months = mdates.MonthLocator()  # every month
    # monthsFmt = mdates.DateFormatter('%b')
    # manager = plt.get_current_fig_manager()
    # manager.window.showMaximized()
    # plt.ylabel('radiations in W/m2')
    # plt.legend()
    # plt.show()
    # plt.close

    # call main function once hourly/sub-hourly temp and precip data available.
    try :
        st_swe, st_melt, st_acc, st_alb = snow_main_simple(inp_ta, inp_precip, inp_doy, inp_hourdec, dtstep=3600,init_swe=init_swe,
                                                    init_d_snow=init_d_snow, inp_sw=inp_sw, which_melt='clark2009', **config)
        st_swe1, st_melt1, st_acc1, st_alb1 = snow_main_simple(inp_ta, inp_precip, inp_doy, inp_hourdec, dtstep=3600, init_swe=init_swe,
                                                    init_d_snow=init_d_snow, inp_sw=inp_sw, which_melt='dsc_snow', **config)
    except :
        print('No VC data', year)
    try :
        st_swea, st_melta, st_acca, st_alba = snow_main_simple(inp_ta_VN, inp_precip, inp_doy, inp_hourdec, dtstep=3600,init_swe=init_swe,
                                                    init_d_snow=init_d_snow, inp_sw=inp_sw_VN,which_melt='clark2009', **config)
        st_swe1a, st_melt1a, st_acc1a, st_alb1a = snow_main_simple(inp_ta_VN, inp_precip, inp_doy, inp_hourdec, dtstep=3600,init_swe=init_swe,
                                                    init_d_snow=init_d_snow, inp_sw=inp_sw_VN,which_melt='dsc_snow', **config)
    except :
        print('No VN data', year)

    # MUELLER SWE csv file
    # csv_file = "C:/Users/Bonnamourar/OneDrive - NIWA/CSV SWE/Mueller SWE.csv"
    # MAHANGA SWE csv file
stor_dict = {}

ii = 0
for i, tf in enumerate(tf_list):
    for j, tt in enumerate(tmelt_list):
        stor_dict[ii] = {}
        config['mf_mean'] = tf
        config['tmelt'] = tt
        # call main function once hourly/sub-hourly temp and precip data available.
        st_swe, st_melt, st_acc, st_alb = snow_main_simple(
            inp_ta,
            inp_precip,
            inp_doy,
            inp_hourdec,
            dtstep=3600,
            init_swe=init_swe,
            init_d_snow=init_d_snow,
            inp_sw=inp_sw,
            which_melt='clark2009',
            **config)

        run_dict = {
            'timestamp': inp_dt,
            'swe': np.squeeze(st_swe),
            'melt': np.squeeze(st_melt),
            'acc': np.squeeze(st_acc),
            'alb': np.squeeze(st_alb)
        }
        run_df = pd.DataFrame.from_dict(run_dict)
        run_df.set_index('timestamp', inplace=True)