def test_yearly_mb_temp_prcp(self): """Test the routine which returns the yearly mass balance relevant climate parameters, i.e. positive melting temperature and solid precipitation. The testing target is the output of the corresponding OGGM routine `get_yearly_mb_climate_on_glacier(gdir)`. """ # read the Hintereisferner DEM hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.read_file(hef_file).iloc[0] # initialize the GlacierDirectory gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) # define the local grid and glacier mask gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) # run centerline prepro tasks centerlines.compute_centerlines(gdir) centerlines.initialize_flowlines(gdir) centerlines.catchment_area(gdir) centerlines.catchment_intersections(gdir) centerlines.catchment_width_geom(gdir) centerlines.catchment_width_correction(gdir) # process the given climate file climate.process_custom_climate_data(gdir) # get yearly sums of terminus temperature and solid precipitation years, temp, prcp = vascaling.get_yearly_mb_temp_prcp(gdir) # use the OGGM methode to get the mass balance # relevant climate parameters years_oggm, temp_oggm, prcp_oggm = \ climate.mb_yearly_climate_on_glacier(gdir) # the energy input at the glacier terminus must be greater than (or # equal to) the glacier wide average, since the air temperature drops # with elevation, i.e. the mean deviation must be positive, using the # OGGM data as reference assert md(temp_oggm, temp) >= 0 # consequentially, the average mass input must be less than (or equal # to) the mass input integrated over the whole glacier surface, i.e. # the mean deviation must be negative, using the OGGM data as reference # TODO: does it actually?! And if so, why?! @ASK assert md(prcp_oggm, prcp) <= 0 # correlation must be higher than set threshold assert corrcoef(temp, temp_oggm) >= 0.94 assert corrcoef(prcp, prcp_oggm) >= 0.98 # get terminus temperature using the OGGM routine fpath = gdir.get_filepath('gridded_data') with ncDataset(fpath) as nc: mask = nc.variables['glacier_mask'][:] topo = nc.variables['topo'][:] heights = np.array([np.min(topo[np.where(mask == 1)])]) years_height, temp_height, _ = \ climate.mb_yearly_climate_on_height(gdir, heights, flatten=False) temp_height = temp_height[0] # both time series must be equal np.testing.assert_array_equal(temp, temp_height) # get solid precipitation averaged over the glacier # (not weighted with widths) fls = gdir.read_pickle('inversion_flowlines') heights = np.array([]) for fl in fls: heights = np.append(heights, fl.surface_h) years_height, _, prcp_height = \ climate.mb_yearly_climate_on_height(gdir, heights, flatten=True) # correlation must be higher than set threshold assert corrcoef(prcp, prcp_height) >= 0.99 # TODO: assert absolute values (or differences) of precipitation @ASK # test exception handling of out of bounds time/year range with self.assertRaises(climate.MassBalanceCalibrationError): # start year out of bounds year_range = [1500, 1980] _, _, _ = vascaling.get_yearly_mb_temp_prcp(gdir, year_range=year_range) with self.assertRaises(climate.MassBalanceCalibrationError): # end year oud of bounds year_range = [1980, 3000] _, _, _ = vascaling.get_yearly_mb_temp_prcp(gdir, year_range=year_range) with self.assertRaises(ValueError): # get not N full years t0 = datetime.datetime(1980, 1, 1) t1 = datetime.datetime(1980, 3, 1) time_range = [t0, t1] _, _, _ = vascaling.get_yearly_mb_temp_prcp(gdir, time_range=time_range) # TODO: assert gradient in climate file?! pass
prcp_t = [] ids = [] for gdir in gdirs: #Get the total precipitation of the glacier to store it later heights, widths = gdir.get_inversion_flowline_hw() df = gdir.read_json('local_mustar') tstar = df['t_star'] mu_hp = int(cfg.PARAMS['mu_star_halfperiod']) yr = [tstar - mu_hp, tstar + mu_hp] years, temp, prcp = mb_yearly_climate_on_height(gdir, heights, year_range=yr, flatten=False) prcp_avg = np.average(prcp, axis=1) # compute the area of each section fls = gdir.read_pickle('inversion_flowlines') area_sec = widths * fls[0].dx * gdir.grid.dx prcpsol = np.sum(prcp_avg * area_sec) rho = cfg.PARAMS['ice_density'] # We will save this! accu_ice = (prcpsol * 1e-9) / rho gdir.inversion_calving_rate = 0