Merge ec74cf2 into b1e1623

docs/_code/prepare_climate.py

@@ -37,8 +37,6 @@
 cfg.PARAMS['baseline_climate'] = 'HISTALP'
 cfg.PARAMS['use_tstar_calibration'] = True
 cfg.PARAMS['use_winter_prcp_factor'] = False
-cfg.PARAMS['hydro_month_nh'] = 10
-cfg.PARAMS['hydro_month_sh'] = 4
 cfg.PARAMS['prcp_scaling_factor'] = 2.5
 tasks.process_histalp_data(gdir)
 with warnings.catch_warnings():

docs/api.rst

@@ -30,7 +30,6 @@ Tools to set-up and run OGGM.
     workflow.execute_entity_task
     workflow.gis_prepro_tasks
     workflow.download_ref_tstars
-    workflow.climate_tasks
     workflow.inversion_tasks
     workflow.merge_glacier_tasks
     workflow.calibrate_inversion_from_consensus
@@ -135,7 +134,6 @@ the majority of OGGM's tasks). They are parallelizable.
     tasks.process_climate_data
     tasks.process_custom_climate_data
     tasks.historical_delta_method
-    tasks.historical_climate_qc
     tasks.local_t_star
     tasks.mu_star_calibration
     tasks.apparent_mb_from_linear_mb
@@ -164,7 +162,7 @@ the majority of OGGM's tasks). They are parallelizable.
     tasks.run_from_climate_data
     tasks.run_constant_climate
     tasks.run_dynamic_spinup
-    tasks.run_dynamic_mu_star_calibration
+    tasks.run_dynamic_melt_f_calibration
     tasks.copy_to_basedir
     tasks.gdir_to_tar
 
@@ -182,7 +180,6 @@ but might use multiprocessing internally.
     :nosignatures:
 
     global_tasks.gis_prepro_tasks
-    global_tasks.climate_tasks
     global_tasks.inversion_tasks
     global_tasks.calibrate_inversion_from_consensus
     global_tasks.match_regional_geodetic_mb

docs/whats-new.rst

@@ -30,7 +30,7 @@ Enhancements
   By `Lilian Schuster <https://github.com/lilianschuster>`_
 - added support for a precipitation factor varying per glacier (:pull:`1435`).
   By `Lilian Schuster <https://github.com/lilianschuster>`_
-- Added a new entity task ``run_dynamic_mu_star_calibration``. This task
+- Added a new entity task ``run_dynamic_melt_f_calibration``. This task
   dynamically calibrates the temperature sensitivity mu star to a geodetic
   mass-balance observation. There are different options available how this is
   done, the default incorporates an inversion and a dynamic spinup in each
@@ -44,7 +44,7 @@ Enhancements
   able to globally define the used minimum ice thickness for the dynamic spinup
   (:pull:`1425`).
   By `Patrick Schmitt <https://github.com/pat-schmitt>`_
-- Rearanged the entity tasks ``run_dynamic_mu_star_calibration`` and
+- Rearanged the entity tasks ``run_dynamic_melt_f_calibration`` and
   ``run_dynamic_spinup`` with all help functions in new modul
   ``oggm.core.dynamic_spinup`` (:pull:`1425`).
   By `Patrick Schmitt <https://github.com/pat-schmitt>`_

oggm/cfg.py

@@ -114,8 +114,9 @@ class ParamsLoggingDict(ResettingOrderedDict):
 
     def __setitem__(self, key, value):
         # Overrides the original dic to log the change
-        self._check_input(key, value)
         if self.do_log:
+            if key not in self:
+                raise InvalidParamsError(f'Parameter `{key}` not in PARAMS')
             self._log_param_change(key, value)
         ResettingOrderedDict.__setitem__(self, key, value)
 
@@ -160,16 +161,6 @@ def _log_param_change(self, key, value):
                                                                       prev,
                                                                       value))
 
-    def _check_input(self, key, value):
-
-        if key == 'hydro_month_sh' and value == 1:
-            nh = self.get('hydro_month_nh')
-            if nh is not None and nh != 1:
-                msg = ("When setting PARAMS['hydro_month_sh'] to 1, please set "
-                       "PARAMS['hydro_month_nh'] to 1 first.")
-                raise InvalidWorkflowError(msg)
-
-
 # Globals
 IS_INITIALIZED = False
 PARAMS = ParamsLoggingDict()
@@ -201,27 +192,27 @@ def _check_input(self, key, value):
         ' value 0 at unglaciated points.')
 BASENAMES['glacier_mask'] = ('glacier_mask.tif', _doc)
 
-_doc = ('The glacier outlines in the local map projection (Transverse '
-        'Mercator).')
+_doc = ('The glacier outlines in the local map projection '
+        '(Transverse Mercator or UTM).')
 BASENAMES['outlines'] = ('outlines.shp', _doc)
 
-_doc = ('The glacier intersects in the local map projection (Transverse '
-        'Mercator).')
+_doc = 'The glacier intersects in the local map projection.'
 BASENAMES['intersects'] = ('intersects.shp', _doc)
 
 _doc = ('Each flowline has a catchment area computed from flow routing '
         'algorithms: this shapefile stores the catchment outlines (in the '
-        'local map projection (Transverse Mercator).')
+        'local map projection).')
 BASENAMES['flowline_catchments'] = ('flowline_catchments.shp', _doc)
 
 _doc = ('The intersections between catchments (shapefile) in the local map '
-        'projection (Transverse Mercator).')
+        'projection.')
 BASENAMES['catchments_intersects'] = ('catchments_intersects.shp', _doc)
 
 _doc = 'A ``salem.Grid`` handling the georeferencing of the local grid.'
 BASENAMES['glacier_grid'] = ('glacier_grid.json', _doc)
 
-_doc = 'A dictionary containing runtime diagnostics useful for debugging.'
+_doc = ('A dictionary containing runtime diagnostics useful for debugging or ' 
+        'logging of run parameters.')
 BASENAMES['diagnostics'] = ('diagnostics.json', _doc)
 
 _doc = ('A netcdf file containing several gridded data variables such as '
@@ -254,7 +245,7 @@ def _check_input(self, key, value):
 _doc = 'A list of :py:class:`oggm.Centerline` instances, sorted by flow order.'
 BASENAMES['centerlines'] = ('centerlines.pkl', _doc)
 
-_doc = ('A "better" version of the Centerlines, now on a regular spacing '
+_doc = ('A "better" version of the centerlines, now on a regular spacing '
         'i.e., not on the gridded (i, j) indices. The tails of the '
         'tributaries are cut out to make more realistic junctions. '
         'They are now "1.5D" i.e., with a width.')
@@ -263,26 +254,19 @@ def _check_input(self, key, value):
 _doc = 'The historical monthly climate timeseries stored in a netCDF file.'
 BASENAMES['climate_historical'] = ('climate_historical.nc', _doc)
 
-# so far, this is only ERA5_daily and does not work with the default OGGM
-# mass balance module
+# so far, this is only ERA5 or E5E5 daily and does not work with the default
+# OGGM mass balance module, only with sandbox
 _doc = ('The historical daily climate timeseries stored in a netCDF file.'
         '(only temperature is really changing on daily basis,'
         'precipitation is just assumed constant for every day')
 BASENAMES['climate_historical_daily'] = ('climate_historical_daily.nc', _doc)
 
-_doc = 'Deprecated: old name for `climate_historical`.'
-BASENAMES['climate_monthly'] = ('climate_monthly.nc', _doc)
-
-_doc = ('Some information (dictionary) about the mass '
-        'balance parameters for this glacier.')
-BASENAMES['climate_info'] = ('climate_info.json', _doc)
+_doc = "A dict containing the glacier's mass balance calibration parameters."
+BASENAMES['mb_calib'] = ('mb_calib.json', _doc)
 
 _doc = 'The monthly GCM climate timeseries stored in a netCDF file.'
 BASENAMES['gcm_data'] = ('gcm_data.nc', _doc)
 
-_doc = "A dict containing the glacier's t*, bias, and the flowlines' mu*"
-BASENAMES['local_mustar'] = ('local_mustar.json', _doc)
-
 _doc = 'List of dicts containing the data needed for the inversion.'
 BASENAMES['inversion_input'] = ('inversion_input.pkl', _doc)
 
@@ -296,11 +280,8 @@ def _check_input(self, key, value):
         'the optimal ela_h and grad.')
 BASENAMES['linear_mb_params'] = ('linear_mb_params.pkl', _doc)
 
-_doc = 'Deprecated: renamed to `model_geometry`.'
-BASENAMES['model_run'] = ('model_run.nc', _doc)
-
 _doc = ('A netcdf file containing enough information to reconstruct the '
-        'entire flowline glacier geometry along the run (can be expensive'
+        'entire flowline glacier geometry along the run (can be expensive '
         'in disk space).')
 BASENAMES['model_geometry'] = ('model_geometry.nc', _doc)
 
@@ -529,14 +510,10 @@ def initialize_minimal(file=None, logging_level='INFO', params=None,
     PARAMS['filter_min_slope'] = cp.as_bool('filter_min_slope')
     PARAMS['downstream_line_shape'] = cp['downstream_line_shape']
     PARAMS['auto_skip_task'] = cp.as_bool('auto_skip_task')
-    PARAMS['correct_for_neg_flux'] = cp.as_bool('correct_for_neg_flux')
-    PARAMS['filter_for_neg_flux'] = cp.as_bool('filter_for_neg_flux')
-    PARAMS['run_mb_calibration'] = cp.as_bool('run_mb_calibration')
     PARAMS['rgi_version'] = cp['rgi_version']
     PARAMS['use_rgi_area'] = cp.as_bool('use_rgi_area')
     PARAMS['compress_climate_netcdf'] = cp.as_bool('compress_climate_netcdf')
     PARAMS['use_tar_shapefiles'] = cp.as_bool('use_tar_shapefiles')
-    PARAMS['clip_mu_star'] = cp.as_bool('clip_mu_star')
     PARAMS['clip_tidewater_border'] = cp.as_bool('clip_tidewater_border')
     PARAMS['dl_verify'] = cp.as_bool('dl_verify')
     PARAMS['calving_line_extension'] = cp.as_int('calving_line_extension')
@@ -550,13 +527,10 @@ def initialize_minimal(file=None, logging_level='INFO', params=None,
     PARAMS['store_fl_diagnostics'] = cp.as_bool('store_fl_diagnostics')
 
     # Climate
-    PARAMS['use_tstar_calibration'] = cp.as_bool('use_tstar_calibration')
     PARAMS['baseline_climate'] = cp['baseline_climate'].strip().upper()
     PARAMS['hydro_month_nh'] = cp.as_int('hydro_month_nh')
     PARAMS['hydro_month_sh'] = cp.as_int('hydro_month_sh')
-    PARAMS['climate_qc_months'] = cp.as_int('climate_qc_months')
     PARAMS['temp_use_local_gradient'] = cp.as_bool('temp_use_local_gradient')
-    PARAMS['tstar_search_glacierwide'] = cp.as_bool('tstar_search_glacierwide')
     PARAMS['geodetic_mb_period'] = cp['geodetic_mb_period']
     PARAMS['use_winter_prcp_factor'] = cp.as_bool('use_winter_prcp_factor')
 
@@ -566,11 +540,8 @@ def initialize_minimal(file=None, logging_level='INFO', params=None,
     PARAMS[k] = [float(vk) for vk in cp.as_list(k)]
     k = 'temp_local_gradient_bounds'
     PARAMS[k] = [float(vk) for vk in cp.as_list(k)]
-    k = 'tstar_search_window'
-    PARAMS[k] = [int(vk) for vk in cp.as_list(k)]
     k = 'ref_mb_valid_window'
     PARAMS[k] = [int(vk) for vk in cp.as_list(k)]
-    PARAMS['use_bias_for_run'] = cp.as_bool('use_bias_for_run')
     k = 'free_board_marine_terminating'
     PARAMS[k] = [float(vk) for vk in cp.as_list(k)]
     k = 'store_diagnostic_variables'
@@ -599,22 +570,22 @@ def initialize_minimal(file=None, logging_level='INFO', params=None,
 
     # Delete non-floats
     ltr = ['working_dir', 'dem_file', 'climate_file', 'use_tar_shapefiles',
-           'grid_dx_method', 'run_mb_calibration', 'compress_climate_netcdf',
-           'mp_processes', 'use_multiprocessing', 'climate_qc_months',
+           'grid_dx_method', 'compress_climate_netcdf',
+           'mp_processes', 'use_multiprocessing',
            'temp_use_local_gradient', 'temp_local_gradient_bounds',
            'topo_interp', 'use_compression', 'bed_shape', 'continue_on_error',
-           'use_multiple_flowlines', 'tstar_search_glacierwide', 'border',
-           'mpi_recv_buf_size', 'hydro_month_nh', 'clip_mu_star', 'map_proj',
-           'tstar_search_window', 'use_bias_for_run', 'hydro_month_sh',
+           'use_multiple_flowlines', 'border',
+           'mpi_recv_buf_size', 'map_proj',
+           'hydro_month_sh', 'hydro_month_nh',
            'use_intersects', 'filter_min_slope', 'clip_tidewater_border',
-           'auto_skip_task', 'correct_for_neg_flux', 'filter_for_neg_flux',
+           'auto_skip_task','ref_mb_valid_window',
            'rgi_version', 'dl_verify', 'use_mp_spawn', 'calving_use_limiter',
-           'use_shape_factor_for_inversion', 'use_rgi_area', 'use_tstar_calibration',
+           'use_shape_factor_for_inversion', 'use_rgi_area',
            'use_shape_factor_for_fluxbasedmodel', 'baseline_climate',
            'calving_line_extension', 'use_kcalving_for_run', 'lru_maxsize',
            'free_board_marine_terminating', 'use_kcalving_for_inversion',
            'error_when_glacier_reaches_boundaries', 'glacier_length_method',
-           'use_inversion_params_for_run', 'ref_mb_valid_window',
+           'use_inversion_params_for_run',
            'tidewater_type', 'store_model_geometry', 'use_winter_prcp_factor',
            'store_diagnostic_variables', 'store_fl_diagnostic_variables',
            'geodetic_mb_period', 'store_fl_diagnostics', 'winter_prcp_factor_ab',

oggm/cli/benchmark.py

@@ -30,8 +30,9 @@ def _add_time_to_df(df, index, t):
 
 def run_benchmark(rgi_version=None, rgi_reg=None, border=None,
                   output_folder='', working_dir='', is_test=False,
-                  test_rgidf=None, test_intersects_file=None,
-                  override_params=None, test_topofile=None):
+                  logging_level='WORKFLOW', test_rgidf=None,
+                  test_intersects_file=None, override_params=None,
+                  test_topofile=None):
     """Does the actual job.
 
     Parameters
@@ -62,17 +63,14 @@ def run_benchmark(rgi_version=None, rgi_reg=None, border=None,
     log = logging.getLogger(__name__)
 
     # Params
-    params = {}
-
-    # Local paths
     if override_params is None:
         override_params = {}
 
     utils.mkdir(working_dir)
     override_params['working_dir'] = working_dir
 
     # Initialize OGGM and set up the run parameters
-    cfg.initialize(logging_level='WORKFLOW', params=override_params, future=True)
+    cfg.initialize(logging_level=logging_level, params=override_params, future=True)
 
     # Use multiprocessing?
     cfg.PARAMS['use_multiprocessing'] = True
@@ -126,7 +124,6 @@ def run_benchmark(rgi_version=None, rgi_reg=None, border=None,
     # Input
     if test_topofile:
         cfg.PATHS['dem_file'] = test_topofile
-    utils.apply_test_ref_tstars()
 
     # Initialize working directories
     start = time.time()
@@ -146,8 +143,8 @@ def run_benchmark(rgi_version=None, rgi_reg=None, border=None,
         tasks.catchment_intersections,
         tasks.catchment_width_geom,
         tasks.catchment_width_correction,
-        tasks.local_t_star,
-        tasks.mu_star_calibration,
+        tasks.mb_calibration_from_geodetic_mb,
+        tasks.apparent_mb_from_any_mb,
         tasks.prepare_for_inversion,
         tasks.mass_conservation_inversion,
         tasks.filter_inversion_output,
@@ -160,15 +157,16 @@ def run_benchmark(rgi_version=None, rgi_reg=None, border=None,
 
     # Runs
     start = time.time()
-    workflow.execute_entity_task(tasks.run_random_climate, gdirs,
-                                 nyears=250, bias=0, seed=0,
-                                 output_filesuffix='_tstar')
-    _add_time_to_df(odf, 'run_random_climate_tstar_250', time.time()-start)
+    workflow.execute_entity_task(tasks.run_constant_climate, gdirs,
+                                 nyears=250, y0=1995,
+                                 temperature_bias=-0.5,
+                                 output_filesuffix='_constant')
+    _add_time_to_df(odf, 'run_constant_climate_commit_250', time.time()-start)
 
     start = time.time()
     workflow.execute_entity_task(tasks.run_random_climate, gdirs,
                                  nyears=250, y0=1995, seed=0,
-                                 output_filesuffix='_commit')
+                                 output_filesuffix='_random')
     _add_time_to_df(odf, 'run_random_climate_commit_250', time.time()-start)
 
     # Compile results
@@ -182,12 +180,12 @@ def run_benchmark(rgi_version=None, rgi_reg=None, border=None,
     _add_time_to_df(odf, 'compile_climate_statistics', time.time()-start)
 
     start = time.time()
-    utils.compile_run_output(gdirs, input_filesuffix='_tstar')
-    _add_time_to_df(odf, 'compile_run_output_tstar', time.time()-start)
+    utils.compile_run_output(gdirs, input_filesuffix='_constant')
+    _add_time_to_df(odf, 'compile_run_output_constant', time.time()-start)
 
     start = time.time()
-    utils.compile_run_output(gdirs, input_filesuffix='_commit')
-    _add_time_to_df(odf, 'compile_run_output_commit', time.time()-start)
+    utils.compile_run_output(gdirs, input_filesuffix='_random')
+    _add_time_to_df(odf, 'compile_run_output_random', time.time()-start)
 
     # Log
     opath = os.path.join(base_dir, 'benchmarks_b{:03d}.csv'.format(border))