def test_mu_candidates(self): hef_file = get_demo_file("Hintereisferner.shp") entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0] 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) se = gdir.read_pickle("mu_candidates")[2.5] self.assertTrue(se.index[0] == 1802) self.assertTrue(se.index[-1] == 2003) df = pd.DataFrame() df["mu"] = se # Check that the moovin average of temp is negatively correlated # with the mus with netCDF4.Dataset(get_demo_file("histalp_merged_hef.nc")) as nc_r: ref_t = nc_r.variables["temp"][:, 1, 1] ref_t = np.mean(ref_t.reshape((len(df), 12)), 1) ma = np.convolve(ref_t, np.ones(31) / float(31), "same") df["temp"] = ma df = df.dropna() self.assertTrue(np.corrcoef(df["mu"], df["temp"])[0, 1] < -0.75)
def test_distribute_climate(self): hef_file = get_demo_file("Hintereisferner.shp") entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0] gdirs = [] gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) gis.define_glacier_region(gdir, entity=entity) gdirs.append(gdir) climate.process_histalp_nonparallel(gdirs) ci = gdir.read_pickle("climate_info") self.assertEqual(ci["hydro_yr_0"], 1802) self.assertEqual(ci["hydro_yr_1"], 2003) with netCDF4.Dataset(get_demo_file("histalp_merged_hef.nc")) as nc_r: ref_h = nc_r.variables["hgt"][1, 1] ref_p = nc_r.variables["prcp"][:, 1, 1] ref_t = nc_r.variables["temp"][:, 1, 1] with netCDF4.Dataset(os.path.join(gdir.dir, "climate_monthly.nc")) as nc_r: self.assertTrue(ref_h == nc_r.ref_hgt) np.testing.assert_allclose(ref_t, nc_r.variables["temp"][:]) np.testing.assert_allclose(ref_p, nc_r.variables["prcp"][:])
def test_mb_climate(self): hef_file = get_demo_file("Hintereisferner.shp") entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0] gdirs = [] gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) gis.define_glacier_region(gdir, entity=entity) gdirs.append(gdir) climate.process_histalp_nonparallel(gdirs) with netCDF4.Dataset(get_demo_file("histalp_merged_hef.nc")) as nc_r: ref_h = nc_r.variables["hgt"][1, 1] ref_p = nc_r.variables["prcp"][:, 1, 1] ref_t = nc_r.variables["temp"][:, 1, 1] ref_t = np.where(ref_t < 0, 0, ref_t) hgts = np.array([ref_h, ref_h, -8000, 8000]) time, temp, prcp = climate.mb_climate_on_height(gdir, hgts, 1.0) ref_nt = 202 * 12 self.assertTrue(len(time) == ref_nt) self.assertTrue(temp.shape == (4, ref_nt)) self.assertTrue(prcp.shape == (4, ref_nt)) np.testing.assert_allclose(temp[0, :], ref_t) np.testing.assert_allclose(temp[0, :], temp[1, :]) np.testing.assert_allclose(prcp[0, :], prcp[1, :]) np.testing.assert_allclose(prcp[3, :], ref_p) np.testing.assert_allclose(prcp[2, :], ref_p * 0) np.testing.assert_allclose(temp[3, :], ref_p * 0) yr = [1802, 1802] time, temp, prcp = climate.mb_climate_on_height(gdir, hgts, 1.0, year_range=yr) ref_nt = 1 * 12 self.assertTrue(len(time) == ref_nt) self.assertTrue(temp.shape == (4, ref_nt)) self.assertTrue(prcp.shape == (4, ref_nt)) np.testing.assert_allclose(temp[0, :], ref_t[0:12]) np.testing.assert_allclose(temp[0, :], temp[1, :]) np.testing.assert_allclose(prcp[0, :], prcp[1, :]) np.testing.assert_allclose(prcp[3, :], ref_p[0:12]) np.testing.assert_allclose(prcp[2, :], ref_p[0:12] * 0) np.testing.assert_allclose(temp[3, :], ref_p[0:12] * 0) yr = [1803, 1804] time, temp, prcp = climate.mb_climate_on_height(gdir, hgts, 1.0, year_range=yr) ref_nt = 2 * 12 self.assertTrue(len(time) == ref_nt) self.assertTrue(temp.shape == (4, ref_nt)) self.assertTrue(prcp.shape == (4, ref_nt)) np.testing.assert_allclose(temp[0, :], ref_t[12:36]) np.testing.assert_allclose(temp[0, :], temp[1, :]) np.testing.assert_allclose(prcp[0, :], prcp[1, :]) np.testing.assert_allclose(prcp[3, :], ref_p[12:36]) np.testing.assert_allclose(prcp[2, :], ref_p[12:36] * 0) np.testing.assert_allclose(temp[3, :], ref_p[12:36] * 0)
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_distribute_climate_cru(self): hef_file = get_demo_file("Hintereisferner.shp") entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0] gdirs = [] gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) gis.define_glacier_region(gdir, entity=entity) gdirs.append(gdir) gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir_cru) gis.define_glacier_region(gdir, entity=entity) gdirs.append(gdir) climate.process_histalp_nonparallel([gdirs[0]]) cru_dir = get_demo_file("cru_ts3.23.1901.2014.tmp.dat.nc") cru_dir = os.path.dirname(cru_dir) cfg.PATHS["climate_file"] = "~" cfg.PATHS["cru_dir"] = cru_dir climate.process_cru_data(gdirs[1]) cfg.PATHS["cru_dir"] = "~" cfg.PATHS["climate_file"] = get_demo_file("histalp_merged_hef.nc") ci = gdir.read_pickle("climate_info") self.assertEqual(ci["hydro_yr_0"], 1902) self.assertEqual(ci["hydro_yr_1"], 2014) gdh = gdirs[0] gdc = gdirs[1] with xr.open_dataset(os.path.join(gdh.dir, "climate_monthly.nc")) as nc_h: with xr.open_dataset(os.path.join(gdc.dir, "climate_monthly.nc")) as nc_c: # put on the same altitude # (using default gradient because better) temp_cor = nc_c.temp - 0.0065 * (nc_h.ref_hgt - nc_c.ref_hgt) totest = temp_cor - nc_h.temp self.assertTrue(totest.mean() < 0.5) # precip totest = nc_c.prcp - nc_h.prcp self.assertTrue(totest.mean() < 100)
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
def test_yearly_mb_climate(self): hef_file = get_demo_file("Hintereisferner.shp") entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0] gdirs = [] gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) gis.define_glacier_region(gdir, entity=entity) gdirs.append(gdir) climate.process_histalp_nonparallel(gdirs) with netCDF4.Dataset(get_demo_file("histalp_merged_hef.nc")) as nc_r: ref_h = nc_r.variables["hgt"][1, 1] ref_p = nc_r.variables["prcp"][:, 1, 1] ref_t = nc_r.variables["temp"][:, 1, 1] ref_t = np.where(ref_t < 0, 0, ref_t) # NORMAL -------------------------------------------------------------- hgts = np.array([ref_h, ref_h, -8000, 8000]) years, temp, prcp = climate.mb_yearly_climate_on_height(gdir, hgts, 1) ref_nt = 202 self.assertTrue(len(years) == ref_nt) self.assertTrue(temp.shape == (4, ref_nt)) self.assertTrue(prcp.shape == (4, ref_nt)) yr = [1802, 1802] years, temp, prcp = climate.mb_yearly_climate_on_height(gdir, hgts, 1, year_range=yr) ref_nt = 1 self.assertTrue(len(years) == ref_nt) self.assertTrue(years == 1802) self.assertTrue(temp.shape == (4, ref_nt)) self.assertTrue(prcp.shape == (4, ref_nt)) np.testing.assert_allclose(temp[0, :], np.sum(ref_t[0:12])) np.testing.assert_allclose(temp[0, :], temp[1, :]) np.testing.assert_allclose(prcp[0, :], prcp[1, :]) np.testing.assert_allclose(prcp[3, :], np.sum(ref_p[0:12])) np.testing.assert_allclose(prcp[2, :], np.sum(ref_p[0:12]) * 0) np.testing.assert_allclose(temp[3, :], np.sum(ref_p[0:12]) * 0) yr = [1803, 1804] years, temp, prcp = climate.mb_yearly_climate_on_height(gdir, hgts, 1, year_range=yr) ref_nt = 2 self.assertTrue(len(years) == ref_nt) np.testing.assert_allclose(years, yr) self.assertTrue(temp.shape == (4, ref_nt)) self.assertTrue(prcp.shape == (4, ref_nt)) np.testing.assert_allclose(prcp[2, :], [0, 0]) np.testing.assert_allclose(temp[3, :], [0, 0]) # FLATTEN ------------------------------------------------------------- hgts = np.array([ref_h, ref_h, -8000, 8000]) years, temp, prcp = climate.mb_yearly_climate_on_height(gdir, hgts, 1, flatten=True) ref_nt = 202 self.assertTrue(len(years) == ref_nt) self.assertTrue(temp.shape == (ref_nt,)) self.assertTrue(prcp.shape == (ref_nt,)) yr = [1802, 1802] hgts = np.array([ref_h]) years, temp, prcp = climate.mb_yearly_climate_on_height(gdir, hgts, 1, year_range=yr, flatten=True) ref_nt = 1 self.assertTrue(len(years) == ref_nt) self.assertTrue(years == 1802) self.assertTrue(temp.shape == (ref_nt,)) self.assertTrue(prcp.shape == (ref_nt,)) np.testing.assert_allclose(temp[:], np.sum(ref_t[0:12])) yr = [1802, 1802] hgts = np.array([8000]) years, temp, prcp = climate.mb_yearly_climate_on_height(gdir, hgts, 1, year_range=yr, flatten=True) np.testing.assert_allclose(prcp[:], np.sum(ref_p[0:12]))
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)
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): 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