def test_distribute(self): hef_file = get_demo_file("Hintereisferner.shp") entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0] gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) climate.process_histalp_nonparallel([gdir]) climate.mu_candidates(gdir, div_id=0) hef_file = get_demo_file("mbdata_RGI40-11.00897.csv") mbdf = pd.read_csv(hef_file).set_index("YEAR") t_star, bias, prcp_fac = climate.t_star_from_refmb(gdir, mbdf["ANNUAL_BALANCE"]) t_star = t_star[-1] bias = bias[-1] climate.local_mustar_apparent_mb(gdir, tstar=t_star, bias=bias, prcp_fac=prcp_fac) # OK. Values from Fischer and Kuhn 2013 # Area: 8.55 # meanH = 67+-7 # Volume = 0.573+-0.063 # maxH = 242+-13 inversion.prepare_for_inversion(gdir) ref_v = 0.573 * 1e9 def to_optimize(x): glen_a = cfg.A * x[0] fs = cfg.FS * x[1] v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a) return (v - ref_v) ** 2 import scipy.optimize as optimization out = optimization.minimize(to_optimize, [1, 1], bounds=((0.01, 10), (0.01, 10)), tol=1e-1)["x"] glen_a = cfg.A * out[0] fs = cfg.FS * out[1] v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True) np.testing.assert_allclose(ref_v, v) inversion.distribute_thickness(gdir, how="per_altitude", add_nc_name=True) inversion.distribute_thickness(gdir, how="per_interpolation", add_slope=False, add_nc_name=True) grids_file = gdir.get_filepath("gridded_data") with netCDF4.Dataset(grids_file) as nc: t1 = nc.variables["thickness_per_altitude"][:] t2 = nc.variables["thickness_per_interpolation"][:] np.testing.assert_allclose(np.sum(t1), np.sum(t2)) if not HAS_NEW_GDAL: np.testing.assert_allclose(np.max(t1), np.max(t2), atol=30)
def test_find_tstars(self): hef_file = get_demo_file('Hintereisferner.shp') rgidf = gpd.GeoDataFrame.from_file(hef_file) # loop because for some reason indexing wont work gdirs = [] for index, entity in rgidf.iterrows(): gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) gdirs.append(gdir) climate.distribute_climate_data(gdirs) climate.mu_candidates(gdir, div_id=0) hef_file = get_demo_file('mbdata_RGI40-11.00897.csv') mbdf = pd.read_csv(hef_file).set_index('YEAR') t_stars, bias = climate.t_star_from_refmb(gdir, mbdf['ANNUAL_BALANCE']) y, t, p = climate.mb_yearly_climate_on_glacier(gdir, div_id=0) # which years to look at selind = np.searchsorted(y, mbdf.index) t = t[selind] p = p[selind] mu_yr_clim = gdir.read_pickle('mu_candidates', div_id=0) for t_s, rmd in zip(t_stars, bias): mb_per_mu = p - mu_yr_clim.loc[t_s] * t md = utils.md(mbdf['ANNUAL_BALANCE'], mb_per_mu) np.testing.assert_allclose(md, rmd) self.assertTrue(np.abs(md / np.mean(mbdf['ANNUAL_BALANCE'])) < 0.1) r = utils.corrcoef(mbdf['ANNUAL_BALANCE'], mb_per_mu) self.assertTrue(r > 0.8)
def test_find_tstars(self): hef_file = get_demo_file('Hintereisferner.shp') rgidf = gpd.GeoDataFrame.from_file(hef_file) # loop because for some reason indexing wont work gdirs = [] for index, entity in rgidf.iterrows(): gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) gdirs.append(gdir) climate.distribute_climate_data(gdirs) climate.mu_candidates(gdir, div_id=0) hef_file = get_demo_file('mbdata_RGI40-11.00897.csv') mbdf = pd.read_csv(hef_file).set_index('YEAR') t_stars, bias = climate.t_star_from_refmb(gdir, mbdf['ANNUAL_BALANCE']) y, t, p = climate.mb_yearly_climate_on_glacier(gdir, div_id=0) # which years to look at selind = np.searchsorted(y, mbdf.index) t = t[selind] p = p[selind] mu_yr_clim = gdir.read_pickle('mu_candidates', div_id=0) for t_s, rmd in zip(t_stars, bias): mb_per_mu = p - mu_yr_clim.loc[t_s] * t md = utils.md(mbdf['ANNUAL_BALANCE'], mb_per_mu) np.testing.assert_allclose(md, rmd) self.assertTrue(np.abs(md/np.mean(mbdf['ANNUAL_BALANCE'])) < 0.1) r = utils.corrcoef(mbdf['ANNUAL_BALANCE'], mb_per_mu) self.assertTrue(r > 0.8)
def test_local_mustar(self): hef_file = get_demo_file('Hintereisferner.shp') rgidf = gpd.GeoDataFrame.from_file(hef_file) # loop because for some reason indexing wont work for index, entity in rgidf.iterrows(): gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) climate.distribute_climate_data([gdir]) climate.mu_candidates(gdir, div_id=0) hef_file = get_demo_file('mbdata_RGI40-11.00897.csv') mbdf = pd.read_csv(hef_file).set_index('YEAR') t_star, bias = climate.t_star_from_refmb(gdir, mbdf['ANNUAL_BALANCE']) t_star = t_star[-1] bias = bias[-1] climate.local_mustar_apparent_mb(gdir, tstar=t_star, bias=bias) df = pd.read_csv(gdir.get_filepath('local_mustar', div_id=0)) mu_ref = gdir.read_pickle('mu_candidates', div_id=0).loc[t_star] np.testing.assert_allclose(mu_ref, df['mu_star'][0], atol=1e-3) # Check for apparent mb to be zeros for i in [0] + list(gdir.divide_ids): fls = gdir.read_pickle('inversion_flowlines', div_id=i) tmb = 0. for fl in fls: self.assertTrue(fl.apparent_mb.shape == fl.widths.shape) tmb += np.sum(fl.apparent_mb * fl.widths) np.testing.assert_allclose(tmb, 0., atol=0.01) if i == 0: continue np.testing.assert_allclose(fls[-1].flux[-1], 0., atol=0.01) # ------ Look for gradient # which years to look at fls = gdir.read_pickle('inversion_flowlines', div_id=0) mb_on_h = np.array([]) h = np.array([]) for fl in fls: y, t, p = climate.mb_yearly_climate_on_height(gdir, fl.surface_h) selind = np.searchsorted(y, mbdf.index) t = np.mean(t[:, selind], axis=1) p = np.mean(p[:, selind], axis=1) mb_on_h = np.append(mb_on_h, p - mu_ref * t) h = np.append(h, fl.surface_h) dfg = pd.read_csv(get_demo_file('mbgrads_RGI40-11.00897.csv'), index_col='ALTITUDE').mean(axis=1) # Take the altitudes below 3100 and fit a line dfg = dfg[dfg.index < 3100] pok = np.where(h < 3100) from scipy.stats import linregress slope_obs, _, _, _, _ = linregress(dfg.index, dfg.values) slope_our, _, _, _, _ = linregress(h[pok], mb_on_h[pok]) np.testing.assert_allclose(slope_obs, slope_our, rtol=0.1)
def test_invert_hef(self): hef_file = get_demo_file("Hintereisferner.shp") entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0] gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) climate.process_histalp_nonparallel([gdir]) climate.mu_candidates(gdir, div_id=0) hef_file = get_demo_file("mbdata_RGI40-11.00897.csv") mbdf = pd.read_csv(hef_file).set_index("YEAR") t_star, bias, prcp_fac = climate.t_star_from_refmb(gdir, mbdf["ANNUAL_BALANCE"]) t_star = t_star[-1] bias = bias[-1] climate.local_mustar_apparent_mb(gdir, tstar=t_star, bias=bias, prcp_fac=prcp_fac) # OK. Values from Fischer and Kuhn 2013 # Area: 8.55 # meanH = 67+-7 # Volume = 0.573+-0.063 # maxH = 242+-13 inversion.prepare_for_inversion(gdir) lens = [len(gdir.read_pickle("centerlines", div_id=i)) for i in [1, 2, 3]] pid = np.argmax(lens) + 1 # Check how many clips: cls = gdir.read_pickle("inversion_input", div_id=pid) nabove = 0 maxs = 0.0 npoints = 0.0 for cl in cls: # Clip slope to avoid negative and small slopes slope = cl["slope_angle"] nm = np.where(slope < np.deg2rad(2.0)) nabove += len(nm[0]) npoints += len(slope) _max = np.max(slope) if _max > maxs: maxs = _max self.assertTrue(nabove == 0) self.assertTrue(np.rad2deg(maxs) < 40.0) ref_v = 0.573 * 1e9 def to_optimize(x): glen_a = cfg.A * x[0] fs = cfg.FS * x[1] v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a) return (v - ref_v) ** 2 import scipy.optimize as optimization out = optimization.minimize(to_optimize, [1, 1], bounds=((0.01, 10), (0.01, 10)), tol=1e-4)["x"] self.assertTrue(out[0] > 0.1) self.assertTrue(out[1] > 0.1) self.assertTrue(out[0] < 1.1) self.assertTrue(out[1] < 1.1) glen_a = cfg.A * out[0] fs = cfg.FS * out[1] v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True) np.testing.assert_allclose(ref_v, v) lens = [len(gdir.read_pickle("centerlines", div_id=i)) for i in [1, 2, 3]] pid = np.argmax(lens) + 1 cls = gdir.read_pickle("inversion_output", div_id=pid) fls = gdir.read_pickle("inversion_flowlines", div_id=pid) maxs = 0.0 for cl, fl in zip(cls, fls): thick = cl["thick"] _max = np.max(thick) if _max > maxs: maxs = _max np.testing.assert_allclose(242, maxs, atol=40)
def test_local_mustar(self): hef_file = get_demo_file("Hintereisferner.shp") entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0] gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) climate.process_histalp_nonparallel([gdir]) climate.mu_candidates(gdir, div_id=0) hef_file = get_demo_file("mbdata_RGI40-11.00897.csv") mbdf = pd.read_csv(hef_file).set_index("YEAR") t_star, bias, prcp_fac = climate.t_star_from_refmb(gdir, mbdf["ANNUAL_BALANCE"]) self.assertEqual(prcp_fac, 2.5) t_star = t_star[-1] bias = bias[-1] climate.local_mustar_apparent_mb(gdir, tstar=t_star, bias=bias, prcp_fac=prcp_fac) df = pd.read_csv(gdir.get_filepath("local_mustar", div_id=0)) mu_ref = gdir.read_pickle("mu_candidates", div_id=0)[prcp_fac].loc[t_star] np.testing.assert_allclose(mu_ref, df["mu_star"][0], atol=1e-3) # Check for apparent mb to be zeros for i in [0] + list(gdir.divide_ids): fls = gdir.read_pickle("inversion_flowlines", div_id=i) tmb = 0.0 for fl in fls: self.assertTrue(fl.apparent_mb.shape == fl.widths.shape) tmb += np.sum(fl.apparent_mb * fl.widths) np.testing.assert_allclose(tmb, 0.0, atol=0.01) if i == 0: continue np.testing.assert_allclose(fls[-1].flux[-1], 0.0, atol=0.01) # ------ Look for gradient # which years to look at fls = gdir.read_pickle("inversion_flowlines", div_id=0) mb_on_h = np.array([]) h = np.array([]) for fl in fls: y, t, p = climate.mb_yearly_climate_on_height(gdir, fl.surface_h, prcp_fac) selind = np.searchsorted(y, mbdf.index) t = np.mean(t[:, selind], axis=1) p = np.mean(p[:, selind], axis=1) mb_on_h = np.append(mb_on_h, p - mu_ref * t) h = np.append(h, fl.surface_h) dfg = pd.read_csv(get_demo_file("mbgrads_RGI40-11.00897.csv"), index_col="ALTITUDE").mean(axis=1) # Take the altitudes below 3100 and fit a line dfg = dfg[dfg.index < 3100] pok = np.where(h < 3100) from scipy.stats import linregress slope_obs, _, _, _, _ = linregress(dfg.index, dfg.values) slope_our, _, _, _, _ = linregress(h[pok], mb_on_h[pok]) np.testing.assert_allclose(slope_obs, slope_our, rtol=0.1)
def test_find_tstars_prcp_fac(self): hef_file = get_demo_file("Hintereisferner.shp") entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0] mb_file = get_demo_file("RGI_WGMS_oetztal.csv") mb_file = os.path.join(os.path.dirname(mb_file), "mbdata", "mbdata_RGI40-11.00897.csv") cfg.PARAMS["prcp_auto_scaling_factor"] = True gdirs = [] gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) gdirs.append(gdir) climate.process_histalp_nonparallel(gdirs) climate.mu_candidates(gdir, div_id=0) mbdf = pd.read_csv(mb_file).set_index("YEAR")["ANNUAL_BALANCE"] t_stars, bias, prcp_fac = climate.t_star_from_refmb(gdir, mbdf) y, t, p = climate.mb_yearly_climate_on_glacier(gdir, prcp_fac, div_id=0) # which years to look at selind = np.searchsorted(y, mbdf.index) t = t[selind] p = p[selind] dffac = gdir.read_pickle("prcp_fac_optim").loc[prcp_fac] np.testing.assert_allclose(dffac["avg_bias"], np.mean(bias)) mu_yr_clim = gdir.read_pickle("mu_candidates", div_id=0)[prcp_fac] std_bias = [] for t_s, rmd in zip(t_stars, bias): mb_per_mu = p - mu_yr_clim.loc[t_s] * t md = utils.md(mbdf, mb_per_mu) np.testing.assert_allclose(md, rmd, rtol=1e-4) self.assertTrue(np.abs(md / np.mean(mbdf)) < 0.1) r = utils.corrcoef(mbdf, mb_per_mu) self.assertTrue(r > 0.8) std_bias.append(np.std(mb_per_mu) - np.std(mbdf)) np.testing.assert_allclose(dffac["avg_std_bias"], np.mean(std_bias), rtol=1e-4) # test crop years cfg.PARAMS["tstar_search_window"] = [1902, 0] climate.mu_candidates(gdir, div_id=0) t_stars, bias, prcp_fac = climate.t_star_from_refmb(gdir, mbdf) mu_yr_clim = gdir.read_pickle("mu_candidates", div_id=0)[prcp_fac] y, t, p = climate.mb_yearly_climate_on_glacier(gdir, prcp_fac, div_id=0) selind = np.searchsorted(y, mbdf.index) t = t[selind] p = p[selind] for t_s, rmd in zip(t_stars, bias): mb_per_mu = p - mu_yr_clim.loc[t_s] * t md = utils.md(mbdf, mb_per_mu) np.testing.assert_allclose(md, rmd, rtol=1e-4) self.assertTrue(np.abs(md / np.mean(mbdf)) < 0.1) r = utils.corrcoef(mbdf, mb_per_mu) self.assertTrue(r > 0.8) self.assertTrue(t_s >= 1902) # test distribute cfg.PATHS["wgms_rgi_links"] = get_demo_file("RGI_WGMS_oetztal.csv") climate.compute_ref_t_stars(gdirs) climate.distribute_t_stars(gdirs) cfg.PARAMS["tstar_search_window"] = [0, 0] df = pd.read_csv(gdir.get_filepath("local_mustar")) np.testing.assert_allclose(df["t_star"], t_s) np.testing.assert_allclose(df["bias"], rmd) np.testing.assert_allclose(df["prcp_fac"], prcp_fac) cfg.PARAMS["prcp_auto_scaling_factor"] = False
tasks.compute_centerlines(gdir) tasks.initialize_flowlines(gdir) tasks.catchment_area(gdir) tasks.catchment_width_geom(gdir) tasks.catchment_width_correction(gdir) cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc') tasks.process_custom_climate_data(gdir) tasks.mu_candidates(gdir) # For plots mu_yr_clim = gdir.read_pickle('mu_candidates')[pcp_fac] mbdf = gdir.get_ref_mb_data() years, temp_yr, prcp_yr = mb_yearly_climate_on_glacier(gdir, pcp_fac, div_id=0) # which years to look at selind = np.searchsorted(years, mbdf.index) temp_yr = np.mean(temp_yr[selind]) prcp_yr = np.mean(prcp_yr[selind]) # Average oberved mass-balance ref_mb = mbdf.ANNUAL_BALANCE.mean() mb_per_mu = prcp_yr - mu_yr_clim * temp_yr # Diff to reference diff = mb_per_mu - ref_mb pdf = pd.DataFrame() pdf[r'$\mu (t)$'] = mu_yr_clim pdf['bias'] = diff t_stars, bias, _ = t_star_from_refmb(gdir, mbdf.ANNUAL_BALANCE)
def init_hef(reset=False, border=40, invert_with_sliding=True, invert_with_rectangular=True): from oggm.core.preprocessing import gis, centerlines, geometry from oggm.core.preprocessing import climate, inversion import oggm import oggm.cfg as cfg from oggm.utils import get_demo_file # test directory testdir = os.path.join(cfg.PATHS['test_dir'], 'tmp_border{}'.format(border)) if not invert_with_sliding: testdir += '_withoutslide' if not invert_with_rectangular: testdir += '_withoutrectangular' if not os.path.exists(testdir): os.makedirs(testdir) reset = True # Init cfg.initialize() cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif') cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc') cfg.PARAMS['border'] = border cfg.PARAMS['use_optimized_inversion_params'] = True hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0] gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset) if not gdir.has_file('inversion_params'): reset = True gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset) if not reset: return gdir gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) centerlines.compute_downstream_lines(gdir) geometry.initialize_flowlines(gdir) centerlines.compute_downstream_bedshape(gdir) geometry.catchment_area(gdir) geometry.catchment_intersections(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) climate.process_histalp_nonparallel([gdir]) climate.mu_candidates(gdir, div_id=0) mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE'] res = climate.t_star_from_refmb(gdir, mbdf) climate.local_mustar_apparent_mb(gdir, tstar=res['t_star'][-1], bias=res['bias'][-1], prcp_fac=res['prcp_fac']) inversion.prepare_for_inversion(gdir, add_debug_var=True, invert_with_rectangular=invert_with_rectangular) ref_v = 0.573 * 1e9 if invert_with_sliding: def to_optimize(x): # For backwards compat _fd = 1.9e-24 * x[0] glen_a = (cfg.N+2) * _fd / 2. fs = 5.7e-20 * x[1] v, _ = inversion.mass_conservation_inversion(gdir, fs=fs, glen_a=glen_a) return (v - ref_v)**2 out = optimization.minimize(to_optimize, [1, 1], bounds=((0.01, 10), (0.01, 10)), tol=1e-4)['x'] _fd = 1.9e-24 * out[0] glen_a = (cfg.N+2) * _fd / 2. fs = 5.7e-20 * out[1] v, _ = inversion.mass_conservation_inversion(gdir, fs=fs, glen_a=glen_a, write=True) else: def to_optimize(x): glen_a = cfg.A * x[0] v, _ = inversion.mass_conservation_inversion(gdir, fs=0., glen_a=glen_a) return (v - ref_v)**2 out = optimization.minimize(to_optimize, [1], bounds=((0.01, 10),), tol=1e-4)['x'] glen_a = cfg.A * out[0] fs = 0. v, _ = inversion.mass_conservation_inversion(gdir, fs=fs, glen_a=glen_a, write=True) d = dict(fs=fs, glen_a=glen_a) d['factor_glen_a'] = out[0] try: d['factor_fs'] = out[1] except IndexError: d['factor_fs'] = 0. gdir.write_pickle(d, 'inversion_params') # filter inversion.filter_inversion_output(gdir) inversion.distribute_thickness(gdir, how='per_altitude', add_nc_name=True) inversion.distribute_thickness(gdir, how='per_interpolation', add_slope=False, smooth=False, add_nc_name=True) return gdir
def init_hef(reset=False, border=40, invert_with_sliding=True, invert_with_rectangular=True): from oggm.core.preprocessing import gis, centerlines, geometry from oggm.core.preprocessing import climate, inversion import oggm import oggm.cfg as cfg from oggm.utils import get_demo_file # test directory testdir = os.path.join(cfg.PATHS['test_dir'], 'tmp_border{}'.format(border)) if not invert_with_sliding: testdir += '_withoutslide' if not invert_with_rectangular: testdir += '_withoutrectangular' if not os.path.exists(testdir): os.makedirs(testdir) reset = True # Init cfg.initialize() cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif') cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc') cfg.PARAMS['border'] = border hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0] gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset) if not gdir.has_file('inversion_params'): reset = True gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset) if not reset: return gdir gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) centerlines.compute_downstream_lines(gdir) geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_intersections(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) climate.process_histalp_nonparallel([gdir]) climate.mu_candidates(gdir, div_id=0) mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE'] res = climate.t_star_from_refmb(gdir, mbdf) climate.local_mustar_apparent_mb(gdir, tstar=res['t_star'][-1], bias=res['bias'][-1], prcp_fac=res['prcp_fac']) inversion.prepare_for_inversion( gdir, add_debug_var=True, invert_with_rectangular=invert_with_rectangular) ref_v = 0.573 * 1e9 if invert_with_sliding: def to_optimize(x): # For backwards compat _fd = 1.9e-24 * x[0] glen_a = (cfg.N + 2) * _fd / 2. fs = 5.7e-20 * x[1] v, _ = inversion.mass_conservation_inversion(gdir, fs=fs, glen_a=glen_a) return (v - ref_v)**2 out = optimization.minimize(to_optimize, [1, 1], bounds=((0.01, 10), (0.01, 10)), tol=1e-4)['x'] _fd = 1.9e-24 * out[0] glen_a = (cfg.N + 2) * _fd / 2. fs = 5.7e-20 * out[1] v, _ = inversion.mass_conservation_inversion(gdir, fs=fs, glen_a=glen_a, write=True) else: def to_optimize(x): glen_a = cfg.A * x[0] v, _ = inversion.mass_conservation_inversion(gdir, fs=0., glen_a=glen_a) return (v - ref_v)**2 out = optimization.minimize(to_optimize, [1], bounds=((0.01, 10), ), tol=1e-4)['x'] glen_a = cfg.A * out[0] fs = 0. v, _ = inversion.mass_conservation_inversion(gdir, fs=fs, glen_a=glen_a, write=True) d = dict(fs=fs, glen_a=glen_a) d['factor_glen_a'] = out[0] try: d['factor_fs'] = out[1] except IndexError: d['factor_fs'] = 0. gdir.write_pickle(d, 'inversion_params') # filter inversion.filter_inversion_output(gdir) inversion.distribute_thickness(gdir, how='per_altitude', add_nc_name=True) inversion.distribute_thickness(gdir, how='per_interpolation', add_slope=False, smooth=False, add_nc_name=True) return gdir
def init_hef(reset=False, border=40, invert_with_sliding=True): # test directory testdir = TESTDIR_BASE + '_border{}'.format(border) if not invert_with_sliding: testdir += '_withoutslide' if not os.path.exists(testdir): os.makedirs(testdir) reset = True if not os.path.exists(os.path.join(testdir, 'RGI40-11.00897')): reset = True if not os.path.exists(os.path.join(testdir, 'RGI40-11.00897', 'inversion_params.pkl')): reset = True # Init cfg.initialize() cfg.set_divides_db(get_demo_file('HEF_divided.shp')) cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif') cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc') cfg.PARAMS['border'] = border # loop because for some reason indexing wont work hef_file = get_demo_file('Hintereisferner.shp') rgidf = gpd.GeoDataFrame.from_file(hef_file) for index, entity in rgidf.iterrows(): gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset) if not reset: return gdir gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) centerlines.compute_downstream_lines(gdir) geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) climate.distribute_climate_data([gdir]) climate.mu_candidates(gdir, div_id=0) hef_file = get_demo_file('mbdata_RGI40-11.00897.csv') mbdf = pd.read_csv(hef_file).set_index('YEAR') t_star, bias = climate.t_star_from_refmb(gdir, mbdf['ANNUAL_BALANCE']) climate.local_mustar_apparent_mb(gdir, tstar=t_star[-1], bias=bias[-1]) inversion.prepare_for_inversion(gdir) ref_v = 0.573 * 1e9 if invert_with_sliding: def to_optimize(x): # For backwards compat _fd = 1.9e-24 * x[0] glen_a = (cfg.N+2) * _fd / 2. fs = 5.7e-20 * x[1] v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a) return (v - ref_v)**2 import scipy.optimize as optimization out = optimization.minimize(to_optimize, [1, 1], bounds=((0.01, 10), (0.01, 10)), tol=1e-4)['x'] _fd = 1.9e-24 * out[0] glen_a = (cfg.N+2) * _fd / 2. fs = 5.7e-20 * out[1] v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True) else: def to_optimize(x): glen_a = cfg.A * x[0] v, _ = inversion.invert_parabolic_bed(gdir, fs=0., glen_a=glen_a) return (v - ref_v)**2 import scipy.optimize as optimization out = optimization.minimize(to_optimize, [1], bounds=((0.01, 10),), tol=1e-4)['x'] glen_a = cfg.A * out[0] fs = 0. v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True) d = dict(fs=fs, glen_a=glen_a) d['factor_glen_a'] = out[0] try: d['factor_fs'] = out[1] except IndexError: d['factor_fs'] = 0. gdir.write_pickle(d, 'inversion_params') inversion.distribute_thickness(gdir, how='per_altitude', add_nc_name=True) inversion.distribute_thickness(gdir, how='per_interpolation', add_slope=False, smooth=False, add_nc_name=True) return gdir
def init_hef(reset=False, border=40, invert_with_sliding=True): # test directory testdir = TESTDIR_BASE + '_border{}'.format(border) if not invert_with_sliding: testdir += '_withoutslide' if not os.path.exists(testdir): os.makedirs(testdir) reset = True if not os.path.exists(os.path.join(testdir, 'RGI40-11.00897')): reset = True if not os.path.exists(os.path.join(testdir, 'RGI40-11.00897', 'inversion_params.pkl')): reset = True # Init cfg.initialize() cfg.set_divides_db(get_demo_file('HEF_divided.shp')) cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif') cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc') cfg.PARAMS['border'] = border # loop because for some reason indexing wont work hef_file = get_demo_file('Hintereisferner.shp') rgidf = gpd.GeoDataFrame.from_file(hef_file) for index, entity in rgidf.iterrows(): gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset) if not reset: return gdir gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) centerlines.compute_downstream_lines(gdir) geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) climate.distribute_climate_data([gdir]) climate.mu_candidates(gdir, div_id=0) hef_file = get_demo_file('mbdata_RGI40-11.00897.csv') mbdf = pd.read_csv(hef_file).set_index('YEAR') t_star, bias = climate.t_star_from_refmb(gdir, mbdf['ANNUAL_BALANCE']) climate.local_mustar_apparent_mb(gdir, tstar=t_star[-1], bias=bias[-1]) inversion.prepare_for_inversion(gdir) ref_v = 0.573 * 1e9 if invert_with_sliding: def to_optimize(x): # For backwards compat _fd = 1.9e-24 * x[0] glen_a = (cfg.N+2) * _fd / 2. fs = 5.7e-20 * x[1] v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a) return (v - ref_v)**2 import scipy.optimize as optimization out = optimization.minimize(to_optimize, [1, 1], bounds=((0.01, 10), (0.01, 10)), tol=1e-4)['x'] _fd = 1.9e-24 * out[0] glen_a = (cfg.N+2) * _fd / 2. fs = 5.7e-20 * out[1] v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True) else: def to_optimize(x): glen_a = cfg.A * x[0] v, _ = inversion.invert_parabolic_bed(gdir, fs=0., glen_a=glen_a) return (v - ref_v)**2 import scipy.optimize as optimization out = optimization.minimize(to_optimize, [1], bounds=((0.01, 10),), tol=1e-4)['x'] glen_a = cfg.A * out[0] fs = 0. v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True) d = dict(fs=fs, glen_a=glen_a) d['factor_glen_a'] = out[0] try: d['factor_fs'] = out[1] except IndexError: d['factor_fs'] = 0. gdir.write_pickle(d, 'inversion_params') return gdir
def init_hef(reset=False, border=40, invert_with_sliding=True): from oggm.core.preprocessing import gis, centerlines, geometry from oggm.core.preprocessing import climate, inversion import oggm import oggm.cfg as cfg from oggm.utils import get_demo_file # test directory testdir = TESTDIR_BASE + "_border{}".format(border) if not invert_with_sliding: testdir += "_withoutslide" if not os.path.exists(testdir): os.makedirs(testdir) reset = True if not os.path.exists(os.path.join(testdir, "RGI40-11.00897")): reset = True if not os.path.exists(os.path.join(testdir, "RGI40-11.00897", "inversion_params.pkl")): reset = True # Init cfg.initialize() cfg.PATHS["dem_file"] = get_demo_file("hef_srtm.tif") cfg.PATHS["climate_file"] = get_demo_file("histalp_merged_hef.nc") cfg.PARAMS["border"] = border hef_file = get_demo_file("Hintereisferner.shp") entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0] gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset) if not reset: return gdir gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) centerlines.compute_downstream_lines(gdir) geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) climate.process_histalp_nonparallel([gdir]) climate.mu_candidates(gdir, div_id=0) hef_file = get_demo_file("mbdata_RGI40-11.00897.csv") mbdf = pd.read_csv(hef_file).set_index("YEAR") t_star, bias, prcp_fac = climate.t_star_from_refmb(gdir, mbdf["ANNUAL_BALANCE"]) climate.local_mustar_apparent_mb(gdir, tstar=t_star[-1], bias=bias[-1], prcp_fac=prcp_fac) inversion.prepare_for_inversion(gdir) ref_v = 0.573 * 1e9 if invert_with_sliding: def to_optimize(x): # For backwards compat _fd = 1.9e-24 * x[0] glen_a = (cfg.N + 2) * _fd / 2.0 fs = 5.7e-20 * x[1] v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a) return (v - ref_v) ** 2 out = optimization.minimize(to_optimize, [1, 1], bounds=((0.01, 10), (0.01, 10)), tol=1e-4)["x"] _fd = 1.9e-24 * out[0] glen_a = (cfg.N + 2) * _fd / 2.0 fs = 5.7e-20 * out[1] v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True) else: def to_optimize(x): glen_a = cfg.A * x[0] v, _ = inversion.invert_parabolic_bed(gdir, fs=0.0, glen_a=glen_a) return (v - ref_v) ** 2 out = optimization.minimize(to_optimize, [1], bounds=((0.01, 10),), tol=1e-4)["x"] glen_a = cfg.A * out[0] fs = 0.0 v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True) d = dict(fs=fs, glen_a=glen_a) d["factor_glen_a"] = out[0] try: d["factor_fs"] = out[1] except IndexError: d["factor_fs"] = 0.0 gdir.write_pickle(d, "inversion_params") inversion.distribute_thickness(gdir, how="per_altitude", add_nc_name=True) inversion.distribute_thickness(gdir, how="per_interpolation", add_slope=False, smooth=False, add_nc_name=True) return gdir
def test_invert_hef_nofs(self): hef_file = get_demo_file('Hintereisferner.shp') rgidf = gpd.GeoDataFrame.from_file(hef_file) # loop because for some reason indexing wont work for index, entity in rgidf.iterrows(): gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) climate.distribute_climate_data([gdir]) climate.mu_candidates(gdir, div_id=0) hef_file = get_demo_file('mbdata_RGI40-11.00897.csv') mbdf = pd.read_csv(hef_file).set_index('YEAR') t_star, bias = climate.t_star_from_refmb(gdir, mbdf['ANNUAL_BALANCE']) t_star = t_star[-1] bias = bias[-1] climate.local_mustar_apparent_mb(gdir, tstar=t_star, bias=bias) # OK. Values from Fischer and Kuhn 2013 # Area: 8.55 # meanH = 67+-7 # Volume = 0.573+-0.063 # maxH = 242+-13 inversion.prepare_for_inversion(gdir) ref_v = 0.573 * 1e9 def to_optimize(x): glen_a = cfg.A * x[0] fs = 0. v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a) return (v - ref_v)**2 import scipy.optimize as optimization out = optimization.minimize(to_optimize, [1], bounds=((0.00001, 100000),), tol=1e-4)['x'] self.assertTrue(out[0] > 0.1) self.assertTrue(out[0] < 10) glen_a = cfg.A * out[0] fs = 0. v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True) np.testing.assert_allclose(ref_v, v) lens = [len(gdir.read_pickle('centerlines', div_id=i)) for i in [1,2,3]] pid = np.argmax(lens) + 1 cls = gdir.read_pickle('inversion_output', div_id=pid) fls = gdir.read_pickle('inversion_flowlines', div_id=pid) maxs = 0. for cl, fl in zip(cls, fls): thick = cl['thick'] _max = np.max(thick) if _max > maxs: maxs = _max atol = 30 if HAS_NEW_GDAL else 10 np.testing.assert_allclose(242, maxs, atol=atol) # check that its not tooo sensitive to the dx cfg.PARAMS['flowline_dx'] = 1. geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) climate.distribute_climate_data([gdir]) climate.mu_candidates(gdir, div_id=0) hef_file = get_demo_file('mbdata_RGI40-11.00897.csv') mbdf = pd.read_csv(hef_file).set_index('YEAR') t_star, bias = climate.t_star_from_refmb(gdir, mbdf['ANNUAL_BALANCE']) t_star = t_star[-1] bias = bias[-1] climate.local_mustar_apparent_mb(gdir, tstar=t_star, bias=bias) inversion.prepare_for_inversion(gdir) v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True) np.testing.assert_allclose(ref_v, v, rtol=0.02) cls = gdir.read_pickle('inversion_output', div_id=pid) maxs = 0. for cl in cls: thick = cl['thick'] _max = np.max(thick) if _max > maxs: maxs = _max np.testing.assert_allclose(242, maxs, atol=atol)
def test_invert_hef(self): hef_file = get_demo_file('Hintereisferner.shp') rgidf = gpd.GeoDataFrame.from_file(hef_file) # loop because for some reason indexing wont work for index, entity in rgidf.iterrows(): gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) climate.distribute_climate_data([gdir]) climate.mu_candidates(gdir, div_id=0) hef_file = get_demo_file('mbdata_RGI40-11.00897.csv') mbdf = pd.read_csv(hef_file).set_index('YEAR') t_star, bias = climate.t_star_from_refmb(gdir, mbdf['ANNUAL_BALANCE']) t_star = t_star[-1] bias = bias[-1] climate.local_mustar_apparent_mb(gdir, tstar=t_star, bias=bias) # OK. Values from Fischer and Kuhn 2013 # Area: 8.55 # meanH = 67+-7 # Volume = 0.573+-0.063 # maxH = 242+-13 inversion.prepare_for_inversion(gdir) lens = [ len(gdir.read_pickle('centerlines', div_id=i)) for i in [1, 2, 3] ] pid = np.argmax(lens) + 1 # Check how many clips: cls = gdir.read_pickle('inversion_input', div_id=pid) nabove = 0 maxs = 0. npoints = 0. for cl in cls: # Clip slope to avoid negative and small slopes slope = cl['slope_angle'] nm = np.where(slope < np.deg2rad(2.)) nabove += len(nm[0]) npoints += len(slope) _max = np.max(slope) if _max > maxs: maxs = _max self.assertTrue(nabove == 0) self.assertTrue(np.rad2deg(maxs) < 40.) ref_v = 0.573 * 1e9 def to_optimize(x): glen_a = cfg.A * x[0] fs = cfg.FS * x[1] v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a) return (v - ref_v)**2 import scipy.optimize as optimization out = optimization.minimize(to_optimize, [1, 1], bounds=((0.01, 10), (0.01, 10)), tol=1e-4)['x'] self.assertTrue(out[0] > 0.1) self.assertTrue(out[1] > 0.1) self.assertTrue(out[0] < 1.1) self.assertTrue(out[1] < 1.1) glen_a = cfg.A * out[0] fs = cfg.FS * out[1] v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True) np.testing.assert_allclose(ref_v, v) lens = [ len(gdir.read_pickle('centerlines', div_id=i)) for i in [1, 2, 3] ] pid = np.argmax(lens) + 1 cls = gdir.read_pickle('inversion_output', div_id=pid) fls = gdir.read_pickle('inversion_flowlines', div_id=pid) maxs = 0. for cl, fl in zip(cls, fls): thick = cl['thick'] _max = np.max(thick) if _max > maxs: maxs = _max np.testing.assert_allclose(242, maxs, atol=21)
def test_invert_hef_nofs(self): hef_file = get_demo_file('Hintereisferner.shp') rgidf = gpd.GeoDataFrame.from_file(hef_file) # loop because for some reason indexing wont work for index, entity in rgidf.iterrows(): gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) climate.distribute_climate_data([gdir]) climate.mu_candidates(gdir, div_id=0) hef_file = get_demo_file('mbdata_RGI40-11.00897.csv') mbdf = pd.read_csv(hef_file).set_index('YEAR') t_star, bias = climate.t_star_from_refmb(gdir, mbdf['ANNUAL_BALANCE']) t_star = t_star[-1] bias = bias[-1] climate.local_mustar_apparent_mb(gdir, tstar=t_star, bias=bias) # OK. Values from Fischer and Kuhn 2013 # Area: 8.55 # meanH = 67+-7 # Volume = 0.573+-0.063 # maxH = 242+-13 inversion.prepare_for_inversion(gdir) ref_v = 0.573 * 1e9 def to_optimize(x): glen_a = cfg.A * x[0] fs = 0. v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a) return (v - ref_v)**2 import scipy.optimize as optimization out = optimization.minimize(to_optimize, [1], bounds=((0.00001, 100000), ), tol=1e-4)['x'] self.assertTrue(out[0] > 0.1) self.assertTrue(out[0] < 10) glen_a = cfg.A * out[0] fs = 0. v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True) np.testing.assert_allclose(ref_v, v) lens = [ len(gdir.read_pickle('centerlines', div_id=i)) for i in [1, 2, 3] ] pid = np.argmax(lens) + 1 cls = gdir.read_pickle('inversion_output', div_id=pid) fls = gdir.read_pickle('inversion_flowlines', div_id=pid) maxs = 0. for cl, fl in zip(cls, fls): thick = cl['thick'] _max = np.max(thick) if _max > maxs: maxs = _max atol = 30 if HAS_NEW_GDAL else 10 np.testing.assert_allclose(242, maxs, atol=atol) # check that its not tooo sensitive to the dx cfg.PARAMS['flowline_dx'] = 1. geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) climate.distribute_climate_data([gdir]) climate.mu_candidates(gdir, div_id=0) hef_file = get_demo_file('mbdata_RGI40-11.00897.csv') mbdf = pd.read_csv(hef_file).set_index('YEAR') t_star, bias = climate.t_star_from_refmb(gdir, mbdf['ANNUAL_BALANCE']) t_star = t_star[-1] bias = bias[-1] climate.local_mustar_apparent_mb(gdir, tstar=t_star, bias=bias) inversion.prepare_for_inversion(gdir) v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True) np.testing.assert_allclose(ref_v, v, rtol=0.02) cls = gdir.read_pickle('inversion_output', div_id=pid) maxs = 0. for cl in cls: thick = cl['thick'] _max = np.max(thick) if _max > maxs: maxs = _max np.testing.assert_allclose(242, maxs, atol=atol)
def test_invert_hef_nofs(self): hef_file = get_demo_file("Hintereisferner.shp") entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0] gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) climate.process_histalp_nonparallel([gdir]) climate.mu_candidates(gdir, div_id=0) hef_file = get_demo_file("mbdata_RGI40-11.00897.csv") mbdf = pd.read_csv(hef_file).set_index("YEAR") t_star, bias, prcp_fac = climate.t_star_from_refmb(gdir, mbdf["ANNUAL_BALANCE"]) t_star = t_star[-1] bias = bias[-1] climate.local_mustar_apparent_mb(gdir, tstar=t_star, bias=bias, prcp_fac=prcp_fac) # OK. Values from Fischer and Kuhn 2013 # Area: 8.55 # meanH = 67+-7 # Volume = 0.573+-0.063 # maxH = 242+-13 inversion.prepare_for_inversion(gdir) ref_v = 0.573 * 1e9 def to_optimize(x): glen_a = cfg.A * x[0] fs = 0.0 v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a) return (v - ref_v) ** 2 import scipy.optimize as optimization out = optimization.minimize(to_optimize, [1], bounds=((0.00001, 100000),), tol=1e-4)["x"] self.assertTrue(out[0] > 0.1) self.assertTrue(out[0] < 10) glen_a = cfg.A * out[0] fs = 0.0 v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True) np.testing.assert_allclose(ref_v, v) lens = [len(gdir.read_pickle("centerlines", div_id=i)) for i in [1, 2, 3]] pid = np.argmax(lens) + 1 cls = gdir.read_pickle("inversion_output", div_id=pid) fls = gdir.read_pickle("inversion_flowlines", div_id=pid) maxs = 0.0 for cl, fl in zip(cls, fls): thick = cl["thick"] _max = np.max(thick) if _max > maxs: maxs = _max np.testing.assert_allclose(242, maxs, atol=25) # check that its not tooo sensitive to the dx cfg.PARAMS["flowline_dx"] = 1.0 geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) climate.process_histalp_nonparallel([gdir]) climate.mu_candidates(gdir, div_id=0) hef_file = get_demo_file("mbdata_RGI40-11.00897.csv") mbdf = pd.read_csv(hef_file).set_index("YEAR") t_star, bias, prcp_fac = climate.t_star_from_refmb(gdir, mbdf["ANNUAL_BALANCE"]) t_star = t_star[-1] bias = bias[-1] climate.local_mustar_apparent_mb(gdir, tstar=t_star, bias=bias, prcp_fac=prcp_fac) inversion.prepare_for_inversion(gdir) v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True) np.testing.assert_allclose(ref_v, v, rtol=0.02) cls = gdir.read_pickle("inversion_output", div_id=pid) maxs = 0.0 for cl in cls: thick = cl["thick"] _max = np.max(thick) if _max > maxs: maxs = _max np.testing.assert_allclose(242, maxs, atol=25)
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.catchment_area(gdir) tasks.catchment_width_geom(gdir) tasks.catchment_width_correction(gdir) cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc') tasks.process_custom_climate_data(gdir) tasks.mu_candidates(gdir) mbdf = gdir.get_ref_mb_data() res = t_star_from_refmb(gdir, mbdf.ANNUAL_BALANCE) local_mustar_apparent_mb(gdir, tstar=res['t_star'][-1], bias=res['bias'][-1], prcp_fac=res['prcp_fac']) # For plots mu_yr_clim = gdir.read_pickle('mu_candidates')[pcp_fac] years, temp_yr, prcp_yr = mb_yearly_climate_on_glacier(gdir, pcp_fac, div_id=0) # which years to look at selind = np.searchsorted(years, mbdf.index) temp_yr = np.mean(temp_yr[selind]) prcp_yr = np.mean(prcp_yr[selind]) # Average oberved mass-balance
def test_distribute(self): hef_file = get_demo_file('Hintereisferner.shp') entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0] gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) geometry.initialize_flowlines(gdir) geometry.catchment_area(gdir) geometry.catchment_width_geom(gdir) geometry.catchment_width_correction(gdir) climate.distribute_climate_data([gdir]) climate.mu_candidates(gdir, div_id=0) hef_file = get_demo_file('mbdata_RGI40-11.00897.csv') mbdf = pd.read_csv(hef_file).set_index('YEAR') t_star, bias = climate.t_star_from_refmb(gdir, mbdf['ANNUAL_BALANCE']) t_star = t_star[-1] bias = bias[-1] climate.local_mustar_apparent_mb(gdir, tstar=t_star, bias=bias) # OK. Values from Fischer and Kuhn 2013 # Area: 8.55 # meanH = 67+-7 # Volume = 0.573+-0.063 # maxH = 242+-13 inversion.prepare_for_inversion(gdir) ref_v = 0.573 * 1e9 def to_optimize(x): glen_a = cfg.A * x[0] fs = cfg.FS * x[1] v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a) return (v - ref_v)**2 import scipy.optimize as optimization out = optimization.minimize(to_optimize, [1, 1], bounds=((0.01, 10), (0.01, 10)), tol=1e-1)['x'] glen_a = cfg.A * out[0] fs = cfg.FS * out[1] v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True) np.testing.assert_allclose(ref_v, v) inversion.distribute_thickness(gdir, how='per_altitude', add_nc_name=True) inversion.distribute_thickness(gdir, how='per_interpolation', add_slope=False, add_nc_name=True) grids_file = gdir.get_filepath('gridded_data') with netCDF4.Dataset(grids_file) as nc: t1 = nc.variables['thickness_per_altitude'][:] t2 = nc.variables['thickness_per_interpolation'][:] np.testing.assert_allclose(np.sum(t1), np.sum(t2)) if not HAS_NEW_GDAL: np.testing.assert_allclose(np.max(t1), np.max(t2), atol=30)