Ejemplo n.º 1
0
    def setup_cache(self):

        setattr(full_workflow.setup_cache, "timeout", 360)

        utils.mkdir(self.testdir, reset=True)
        self.cfg_init()

        entity = gpd.read_file(get_demo_file('01_rgi60_Columbia.shp')).iloc[0]
        gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)

        tasks.define_glacier_region(gdir, entity=entity)
        tasks.glacier_masks(gdir)
        tasks.compute_centerlines(gdir)
        tasks.initialize_flowlines(gdir)
        tasks.compute_downstream_line(gdir)
        tasks.compute_downstream_bedshape(gdir)
        tasks.catchment_area(gdir)
        tasks.catchment_intersections(gdir)
        tasks.catchment_width_geom(gdir)
        tasks.catchment_width_correction(gdir)
        climate.process_dummy_cru_file(gdir, seed=0)

        # Test default k (it overshoots)
        df1 = utils.find_inversion_calving(gdir)

        # Test with smaller k (it doesn't overshoot)
        cfg.PARAMS['k_calving'] = 0.2
        df2 = utils.find_inversion_calving(gdir)

        return (df1.calving_flux.values, df1.mu_star.values,
                df2.calving_flux.values, df2.mu_star.values)
Ejemplo n.º 2
0
    def setup_cache(self):

        utils.mkdir(self.testdir, reset=True)
        self.cfg_init()

        hef_file = get_demo_file('Hintereisferner_RGI5.shp')
        entity = gpd.read_file(hef_file).iloc[0]

        gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)

        tasks.define_glacier_region(gdir, entity=entity)
        tasks.glacier_masks(gdir)
        tasks.compute_centerlines(gdir)
        tasks.initialize_flowlines(gdir)
        tasks.compute_downstream_line(gdir)
        tasks.compute_downstream_bedshape(gdir)
        tasks.catchment_area(gdir)
        tasks.catchment_intersections(gdir)
        tasks.catchment_width_geom(gdir)
        tasks.catchment_width_correction(gdir)
        tasks.process_custom_climate_data(gdir)
        tasks.mu_candidates(gdir)
        mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE']
        res = climate.t_star_from_refmb(gdir, mbdf)
        tasks.local_mustar(gdir, tstar=res['t_star'], bias=res['bias'])
        tasks.apparent_mb(gdir)

        tasks.prepare_for_inversion(gdir)
        tasks.mass_conservation_inversion(gdir)

        return gdir
Ejemplo n.º 3
0
cfg.PARAMS['auto_skip_task'] = True
cfg.PARAMS['run_mb_calibration'] = True

base_dir = os.path.join(os.path.expanduser('~/tmp'), 'OGGM_GMD', 'scenarios')
cfg.PATHS['working_dir'] = base_dir
mkdir(base_dir, reset=reset)

entity = gpd.read_file(get_demo_file('Hintereisferner_RGI5.shp')).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=base_dir)

tasks.define_glacier_region(gdir, entity=entity)
tasks.glacier_masks(gdir)
tasks.compute_centerlines(gdir)
tasks.initialize_flowlines(gdir)
tasks.compute_downstream_line(gdir)
tasks.compute_downstream_bedshape(gdir)
tasks.catchment_area(gdir)
tasks.catchment_intersections(gdir)
tasks.catchment_width_geom(gdir)
tasks.catchment_width_correction(gdir)
tasks.process_cru_data(gdir)
tasks.mu_candidates(gdir)
tasks.compute_ref_t_stars([gdir])
tasks.distribute_t_stars([gdir])
tasks.apparent_mb(gdir)
tasks.prepare_for_inversion(gdir)
tasks.volume_inversion(gdir, glen_a=cfg.A, fs=0)
tasks.filter_inversion_output(gdir)
tasks.init_present_time_glacier(gdir)

df = utils.glacier_characteristics([gdir], path=False)
Ejemplo n.º 4
0
    def setup_cache(self):

        setattr(full_workflow.setup_cache, "timeout", 360)

        utils.mkdir(self.testdir, reset=True)
        self.cfg_init()

        entity = gpd.read_file(get_demo_file('01_rgi60_Columbia.shp')).iloc[0]
        gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)

        tasks.define_glacier_region(gdir, entity=entity)
        tasks.glacier_masks(gdir)
        tasks.compute_centerlines(gdir)
        tasks.initialize_flowlines(gdir)
        tasks.compute_downstream_line(gdir)
        tasks.compute_downstream_bedshape(gdir)
        tasks.catchment_area(gdir)
        tasks.catchment_intersections(gdir)
        tasks.catchment_width_geom(gdir)
        tasks.catchment_width_correction(gdir)
        climate.process_dummy_cru_file(gdir, seed=0)

        rho = cfg.PARAMS['ice_density']
        i = 0
        calving_flux = []
        mu_star = []
        ite = []
        cfg.PARAMS['clip_mu_star'] = False
        cfg.PARAMS['min_mu_star'] = 0  # default is now 1
        while i < 12:

            # Calculates a calving flux from model output
            if i == 0:
                # First call we set to zero (not very necessary,
                # this first loop could be removed)
                f_calving = 0
            elif i == 1:
                # Second call we set a very small positive calving
                f_calving = utils.calving_flux_from_depth(gdir, water_depth=1)
            elif cfg.PARAMS['clip_mu_star']:
                # If we have to clip mu the calving becomes the real flux
                fl = gdir.read_pickle('inversion_flowlines')[-1]
                f_calving = fl.flux[-1] * (gdir.grid.dx**2) * 1e-9 / rho
            else:
                # Otherwise it is parameterized
                f_calving = utils.calving_flux_from_depth(gdir)

            # Give it back to the inversion and recompute
            gdir.inversion_calving_rate = f_calving

            # At this step we might raise a MassBalanceCalibrationError
            mu_is_zero = False
            try:
                climate.local_t_star(gdir)
                df = gdir.read_json('local_mustar')
            except MassBalanceCalibrationError as e:
                assert 'mu* out of specified bounds' in str(e)
                # When this happens we clip mu* to zero and store the
                # bad value (just for plotting)
                cfg.PARAMS['clip_mu_star'] = True
                df = gdir.read_json('local_mustar')
                df['mu_star_glacierwide'] = float(str(e).split(':')[-1])
                climate.local_t_star(gdir)

            climate.mu_star_calibration(gdir)
            tasks.prepare_for_inversion(gdir, add_debug_var=True)
            v_inv, _ = tasks.mass_conservation_inversion(gdir)

            # Store the data
            calving_flux = np.append(calving_flux, f_calving)
            mu_star = np.append(mu_star, df['mu_star_glacierwide'])
            ite = np.append(ite, i)

            # Do we have to do another_loop?
            if i > 0:
                avg_one = np.mean(calving_flux[-4:])
                avg_two = np.mean(calving_flux[-5:-1])
                difference = abs(avg_two - avg_one)
                conv = (difference < 0.05 * avg_two or calving_flux[-1] == 0
                        or calving_flux[-1] == calving_flux[-2])
                if mu_is_zero or conv:
                    break
            i += 1

        assert i < 8
        assert calving_flux[-1] < np.max(calving_flux)
        assert calving_flux[-1] > 2
        assert mu_star[-1] == 0

        mbmod = massbalance.MultipleFlowlineMassBalance
        mb = mbmod(gdir,
                   use_inversion_flowlines=True,
                   mb_model_class=massbalance.ConstantMassBalance,
                   bias=0)
        flux_mb = (mb.get_specific_mb() * gdir.rgi_area_m2) * 1e-9 / rho
        np.testing.assert_allclose(flux_mb, calving_flux[-1], atol=0.001)

        return calving_flux, mu_star