def time_hef_run_until_and_store_with_nc(): mb_mod = massbalance.RandomMassBalance(gdir, bias=0, seed=0) fls = gdir.read_pickle('model_flowlines') model = flowline.FluxBasedModel(fls, mb_model=mb_mod, y0=0.) model.run_until_and_store(200, run_path=os.path.join(testdir, 'run.nc'), diag_path=os.path.join(testdir, 'diag.nc'))
def time_hef_run_until_in_steps(): mb_mod = massbalance.RandomMassBalance(gdir, bias=0, seed=0) fls = gdir.read_pickle('model_flowlines') model = flowline.FluxBasedModel(fls, mb_model=mb_mod, y0=0.) for yr in np.linspace(0, 200, 400): model.run_until(yr)
def time_1d_flux_simple_bed_adaptive_dt(): fls = dummy_constant_bed() mb = massbalance.LinearMassBalance(2600.) model = flowline.FluxBasedModel(fls, mb_model=mb, y0=0.) model.run_until(800)
def test_coxe(): testdir = os.path.join(get_test_dir(), 'tmp_coxe') utils.mkdir(testdir, reset=True) # Init cfg.initialize() cfg.PARAMS['use_intersects'] = False cfg.PATHS['dem_file'] = get_demo_file('dem_RGI50-01.10299.tif') cfg.PARAMS['border'] = 40 cfg.PARAMS['clip_tidewater_border'] = False cfg.PARAMS['use_multiple_flowlines'] = False cfg.PARAMS['use_kcalving_for_inversion'] = True cfg.PARAMS['use_kcalving_for_run'] = True cfg.PARAMS['trapezoid_lambdas'] = 1 hef_file = get_demo_file('rgi_RGI50-01.10299.shp') entity = gpd.read_file(hef_file).iloc[0] gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=True) gis.define_glacier_region(gdir) gis.glacier_masks(gdir) centerlines.compute_centerlines(gdir) centerlines.initialize_flowlines(gdir) centerlines.compute_downstream_line(gdir) centerlines.compute_downstream_bedshape(gdir) centerlines.catchment_area(gdir) centerlines.catchment_intersections(gdir) centerlines.catchment_width_geom(gdir) centerlines.catchment_width_correction(gdir) climate.apparent_mb_from_linear_mb(gdir) inversion.prepare_for_inversion(gdir) inversion.mass_conservation_inversion(gdir) inversion.filter_inversion_output(gdir) flowline.init_present_time_glacier(gdir) fls = gdir.read_pickle('model_flowlines') p = gdir.read_pickle('linear_mb_params') mb_mod = massbalance.LinearMassBalance(ela_h=p['ela_h'], grad=p['grad']) mb_mod.temp_bias = -0.3 model = flowline.FluxBasedModel(fls, mb_model=mb_mod, y0=0, inplace=True, is_tidewater=True) # run model.run_until(200) assert model.calving_m3_since_y0 > 0 fig, ax = plt.subplots() graphics.plot_modeloutput_map(gdir, ax=ax, model=model) fig.tight_layout() shutil.rmtree(testdir) return fig
def test_init_present_time_glacier(self): gdirs = up_to_inversion() # Inversion Results cfg.PARAMS['invert_with_sliding'] = True cfg.PARAMS['optimize_thick'] = True workflow.inversion_tasks(gdirs) fpath = os.path.join(cfg.PATHS['working_dir'], 'inversion_optim_results.csv') df = pd.read_csv(fpath, index_col=0) r1 = rmsd(df['ref_volume_km3'], df['oggm_volume_km3']) assert r1 < 0.1 cfg.PARAMS['invert_with_sliding'] = False cfg.PARAMS['optimize_thick'] = False workflow.inversion_tasks(gdirs) fpath = os.path.join(cfg.PATHS['working_dir'], 'inversion_optim_results.csv') df = pd.read_csv(fpath, index_col=0) r1 = rmsd(df['ref_volume_km3'], df['oggm_volume_km3']) assert r1 < 0.12 # Init glacier d = gdirs[0].read_pickle('inversion_params') fs = d['fs'] glen_a = d['glen_a'] for gdir in gdirs: flowline.init_present_time_glacier(gdir) mb_mod = massbalance.ConstantMassBalance(gdir) fls = gdir.read_pickle('model_flowlines') model = flowline.FluxBasedModel(fls, mb_model=mb_mod, y0=0., fs=fs, glen_a=glen_a) _vol = model.volume_km3 _area = model.area_km2 if gdir.rgi_id in df.index: gldf = df.loc[gdir.rgi_id] assert_allclose(gldf['oggm_volume_km3'], _vol, rtol=0.05) assert_allclose(gldf['ref_area_km2'], _area, rtol=0.05) maxo = max([fl.order for fl in model.fls]) for fl in model.fls: if len(model.fls) > 1: if fl.order == (maxo - 1): self.assertTrue(fl.flows_to is fls[-1]) # Test the glacier charac dfc = utils.glacier_characteristics(gdirs) self.assertTrue(np.all(dfc.terminus_type == 'Land-terminating')) cc = dfc[['flowline_mean_elev', 'tstar_avg_temp_mean_elev']].corr().values[0, 1] assert cc < -0.8 assert np.all(dfc.t_star > 1900)
def time_1d_flux_simple_bed_fixed_dt(): fls = dummy_constant_bed() mb = massbalance.LinearMassBalance(2600.) model = flowline.FluxBasedModel(fls, mb_model=mb, y0=0., fixed_dt=10 * cfg.SEC_IN_DAY) model.run_until(800)
def test_run_until_and_store(self): """Test the volume/area scaling model against the oggm.FluxBasedModel. Both models run the Hintereisferner over the entire HistAlp climate period, initialized with the 2003 RGI outline without spin up. The following two parameters for length, area and volume are tested: - correlation coefficient - relative RMSE, i.e. RMSE/mean(OGGM). Whereby the results from the VAS model are offset with the average differences to the OGGM results. """ # 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) # process the given climate file climate.process_custom_climate_data(gdir) # run center line preprocessing tasks centerlines.compute_centerlines(gdir) centerlines.initialize_flowlines(gdir) centerlines.compute_downstream_line(gdir) centerlines.compute_downstream_bedshape(gdir) centerlines.catchment_area(gdir) centerlines.catchment_intersections(gdir) centerlines.catchment_width_geom(gdir) centerlines.catchment_width_correction(gdir) # read reference glacier mass balance data mbdf = gdir.get_ref_mb_data() # compute the reference t* for the glacier # given the reference of mass balance measurements res = climate.t_star_from_refmb(gdir, mbdf=mbdf['ANNUAL_BALANCE']) t_star, bias = res['t_star'], res['bias'] # -------------------- # SCALING MODEL # -------------------- # compute local t* and the corresponding mu* vascaling.local_t_star(gdir, tstar=t_star, bias=bias) # instance the mass balance models vas_mbmod = vascaling.VAScalingMassBalance(gdir) # get reference area a0 = gdir.rgi_area_m2 # get reference year y0 = gdir.read_json('climate_info')['baseline_hydro_yr_0'] # get min and max glacier surface elevation h0, h1 = vascaling.get_min_max_elevation(gdir) vas_model = vascaling.VAScalingModel(year_0=y0, area_m2_0=a0, min_hgt=h0, max_hgt=h1, mb_model=vas_mbmod) # let model run over entire HistAlp climate period vas_ds = vas_model.run_until_and_store(2003) # ------ # OGGM # ------ # compute local t* and the corresponding mu* climate.local_t_star(gdir, tstar=t_star, bias=bias) climate.mu_star_calibration(gdir) # instance the mass balance models mb_mod = massbalance.PastMassBalance(gdir) # perform ice thickness inversion inversion.prepare_for_inversion(gdir) inversion.mass_conservation_inversion(gdir) inversion.filter_inversion_output(gdir) # initialize present time glacier flowline.init_present_time_glacier(gdir) # instance flowline model fls = gdir.read_pickle('model_flowlines') y0 = gdir.read_json('climate_info')['baseline_hydro_yr_0'] fl_mod = flowline.FluxBasedModel(flowlines=fls, mb_model=mb_mod, y0=y0) # run model and store output as xarray data set _, oggm_ds = fl_mod.run_until_and_store(2003) # temporal indices must be equal assert (vas_ds.time == oggm_ds.time).all() # specify which parameters to compare and their respective correlation # coefficients and rmsd values params = ['length_m', 'area_m2', 'volume_m3'] corr_coeffs = np.array([0.96, 0.90, 0.93]) rmsds = np.array([0.43e3, 0.14e6, 0.03e9]) # compare given parameters for param, cc, rmsd in zip(params, corr_coeffs, rmsds): # correlation coefficient assert corrcoef(oggm_ds[param].values, vas_ds[param].values) >= cc # root mean squared deviation rmsd_an = rmsd_bc(oggm_ds[param].values, vas_ds[param].values) assert rmsd_an <= rmsd
inversion.prepare_for_inversion(gdir) inversion.mass_conservation_inversion(gdir) inversion.filter_inversion_output(gdir) # ---------------- # DYNAMICAL PART # ---------------- from oggm.core import flowline # initialize present time glacier flowline.init_present_time_glacier(gdir) # instance flowline model fls = gdir.read_pickle('model_flowlines') y0 = gdir.read_pickle('climate_info')['baseline_hydro_yr_0'] fl_mod = flowline.FluxBasedModel(flowlines=fls, mb_model=mb_mod, y0=y0) # run model and store output as xarray data set run_ds, diag_ds = fl_mod.run_until_and_store(2003) # ----------------- # RESULTS & PLOTS # ----------------- # store results to file store = True if store: # define unit unit = 'km' unit_factor = 1e3 # get values years = diag_ds.hydro_year.values
def time_hef_run_until_and_store(): mb_mod = massbalance.RandomMassBalance(gdir, bias=0, seed=0) fls = gdir.read_pickle('model_flowlines') model = flowline.FluxBasedModel(fls, mb_model=mb_mod, y0=0.) model.run_until_and_store(200)
def compare(rgi_id, glacier_name): """ :param rgi_id: :param glacier_name: :return: """ # --------------------- # PREPROCESSING TASKS # --------------------- # create test directory wdir = os.path.join(os.path.abspath('.'), 'comparison_wdir') if not os.path.exists(wdir): os.makedirs(wdir) shutil.rmtree(wdir) os.makedirs(wdir) # load default parameter file cfg.initialize() # RGI entity # get/downlaod the rgi entity including the outline shapefile rgi_df = utils.get_rgi_glacier_entities([rgi_id]) # set name, since not delivered with RGI if rgi_df.loc[int(rgi_id[-5:])-1, 'Name'] is None: rgi_df.loc[int(rgi_id[-5:])-1, 'Name'] = glacier_name # select single entry rgi_entity = rgi_df.iloc[0] # GlacierDirectory # specify the working directory and define the glacier directory cfg.PATHS['working_dir'] = wdir gdir = oggm.GlacierDirectory(rgi_entity) # DEM and GIS tasks # get the path to the DEM file (will download if necessary) dem = utils.get_topo_file(gdir.cenlon, gdir.cenlat) # set path in config file cfg.PATHS['dem_file'] = dem[0][0] cfg.PARAMS['border'] = 10 cfg.PARAMS['use_intersects'] = False # run GIS tasks gis.define_glacier_region(gdir, entity=rgi_entity) gis.glacier_masks(gdir) # Climate data # using HistAlp cfg.PARAMS['baseline_climate'] = 'HISTALP' # climate records before 1850 are hardly reliable, which is not so drastic for # qualitative experiments (could be driven with random climate anyway) # cfg.PARAMS['baseline_y0'] = 1850 # change hyper parameters for HistAlp cfg.PARAMS['prcp_scaling_factor'] = 1.75 cfg.PARAMS['temp_melt'] = -1.75 # run climate task climate.process_histalp_data(gdir) # run center line preprocessing tasks centerlines.compute_centerlines(gdir) centerlines.initialize_flowlines(gdir) centerlines.compute_downstream_line(gdir) centerlines.compute_downstream_bedshape(gdir) centerlines.catchment_area(gdir) centerlines.catchment_intersections(gdir) centerlines.catchment_width_geom(gdir) centerlines.catchment_width_correction(gdir) # -------------------- # SCALING MODEL # -------------------- # compute local t* and the corresponding mu* vascaling.local_t_star(gdir) # instance the mass balance models vas_mb_mod = vascaling.VAScalingMassBalance(gdir) # get reference area a0 = gdir.rgi_area_m2 # get reference year y0 = gdir.read_pickle('climate_info')['baseline_hydro_yr_0'] y1 = gdir.read_pickle('climate_info')['baseline_hydro_yr_1'] # get min and max glacier surface elevation h0, h1 = vascaling.get_min_max_elevation(gdir) # instance VAS model vas_model = vascaling.VAScalingModel(year_0=y0, area_m2_0=a0, min_hgt=h0, max_hgt=h1, mb_model=vas_mb_mod) # run model over all HistAlp climate period vas_df = vas_model.run_and_store(y1, reset=True) # get relevant parameters years_vas = vas_df.index.values length_m_vas = vas_df.length_m.values area_m2_vas = vas_df.area_m2.values volume_m3_vas = vas_df.volume_m3.values # ------ # OGGM # ------ # compute local t* and the corresponding mu* climate.local_t_star(gdir) climate.mu_star_calibration(gdir) # instance the mass balance models mb_mod = massbalance.PastMassBalance(gdir) # run inversion tasks inversion.prepare_for_inversion(gdir) inversion.mass_conservation_inversion(gdir) inversion.filter_inversion_output(gdir) # initialize present time glacier flowline.init_present_time_glacier(gdir) # instance flowline model fls = gdir.read_pickle('model_flowlines') y0 = gdir.read_pickle('climate_info')['baseline_hydro_yr_0'] y1 = gdir.read_pickle('climate_info')['baseline_hydro_yr_1'] fl_mod = flowline.FluxBasedModel(flowlines=fls, mb_model=mb_mod, y0=y0) # run model and store output as xarray data set _, oggm_ds = fl_mod.run_until_and_store(y1) years_oggm = oggm_ds.hydro_year.values # annual index must be equal np.testing.assert_array_equal(years_oggm, years_vas) length_m_oggm = oggm_ds.length_m.values area_m2_oggm = oggm_ds.area_m2.values volume_m3_oggm = oggm_ds.volume_m3.values # define column names for DataFrame names = ['length_vas', 'length_oggm', 'area_vas', 'area_oggm', 'volume_vas', 'volume_oggm'] # combine glacier geometries into DataFrame df = pd.DataFrame(np.array([length_m_vas, length_m_oggm, area_m2_vas, area_m2_oggm, volume_m3_vas, volume_m3_oggm]).T, index=years_vas, columns=names) # save to file store = True if store: # define path and file names folder = '/Users/oberrauch/work/master/data/' df.to_csv(folder+'run_comparison.csv') def plot_both(vas_df, oggm_df, ref=None, correct_bias=False, title='', ylabel='', file_path='', exp=0): """ Plot geometric parameters of both models. If a `file_path` is given, the figure will be saved. :param vas_df: (pandas.Series) geometric glacier parameter of the VAS model :param oggm_df: (pandas.Series) geometric glacier parameter of the OGGM :param ref: (pandas.Series) measured glacier parameter, optional :param title: (string) figure title, optional :param ylabel: (string) label for y-axis, optional :param file_path: (string) where to store the figure, optional :param exp: (int) exponent for labels in scientific notation, optional """ beamer = True if beamer: mpl.rc('axes', titlesize=18) mpl.rc('axes', labelsize=14) mpl.rc('xtick', labelsize=14) mpl.rc('ytick', labelsize=14) mpl.rc('legend', fontsize=10) # create figure and first axes fig = plt.figure(figsize=[6, 4]) ax = fig.add_axes([0.15, 0.1, 0.8, 0.8]) # define colors c1 = 'C0' c2 = 'C1' c3 = 'C3' # plot vas and OGGM parameters ax.plot(oggm_df.index, oggm_df.values, c=c2, label='OGGM') ax.plot(vas_df.index, vas_df.values, c=c1, label='VAS') if ref: # plot reference parameter if given ax.plot(ref.index, ref.values, c=c3, label='measurements') if correct_bias: # plot bias corrected vas df_ = pd.DataFrame([oggm_df, vas_df]).T bias = vas_df.values - df_.mean().diff().iloc[1] ax.plot(vas_df.index, bias, c=c1, ls='--', label='VAS, bias corrected') # add RMSD as text ax.text(0.05, 0.05, 'RMSD: {:.1e}'.format(utils.rmsd(oggm_df, bias)), transform=plt.gca().transAxes) # add correlation coefficient as text ax.text(0.05, 0.11, 'Corr. Coef.: {:.2f}'.format( utils.corrcoef(oggm_df, vas_df)), transform=plt.gca().transAxes) # add title, labels, legend ax.set_title(title) ax.set_ylabel(ylabel) ax.legend() import matplotlib.ticker class OOMFormatter(matplotlib.ticker.ScalarFormatter): def __init__(self, order=0, fformat="%1.1f", offset=False, mathText=False): self.oom = order self.fformat = fformat matplotlib.ticker.ScalarFormatter.__init__(self, useOffset=offset, useMathText=mathText) def _set_orderOfMagnitude(self, nothing): self.orderOfMagnitude = self.oom def _set_format(self, vmin, vmax): self.format = self.fformat if self._useMathText: self.format = '$%s$' % matplotlib.ticker._mathdefault(self.format) # use scientific notation with fixed exponent according ax.yaxis.set_major_formatter(OOMFormatter(exp, "%1.2f")) # store to file if file_path: plt.savefig(file_path, bbox_inches='tight', format=file_path.split('.')[-1]) # specify plot directory folder = '/Users/oberrauch/work/master/plots/' # plot length plot_both(df.length_vas, df.length_oggm, correct_bias=True, title='Glacier length - {}'.format(glacier_name), ylabel=r'Length [m]', file_path=os.path.join(folder, '{}_length.pdf'.format(rgi_id)), exp=3) # plot area plot_both(df.area_vas, df.area_oggm, correct_bias=True, title='Surface area - {}'.format(glacier_name), ylabel=r'Area [m$^2$]', file_path=os.path.join(folder, '{}_area.pdf'.format(rgi_id)), exp=6) # plot volume plot_both(df.volume_vas, df.volume_oggm, correct_bias=True, title='Glacier volume - {}'.format(glacier_name), ylabel=r'Volume [m$^3$]', file_path=os.path.join(folder, '{}_volume.pdf'.format(rgi_id)), exp=9)