included max thickness due to min w0 in bed_h boundaries

Author: pat-schmitt

agile1d/core/inversion.py

@@ -302,7 +302,7 @@ def add_setting():
 
 @entity_task(log, writes=['inversion_input'])
 def prepare_for_agile_inversion(gdir, inversion_settings=None,
-                                  filesuffix='_agile'):
+                                filesuffix='_agile'):
     """TODO
     """
     if inversion_settings is None:
@@ -322,11 +322,18 @@ def get_control_var_bounds(data_logger):
             fl = data_logger.flowline_init
             ice_mask = data_logger.ice_mask
             bed_h_bounds = data_logger.bed_h_bounds
-            bounds[var_indices] = [(sfc_h - thick * bed_h_bounds[1],
-                                    sfc_h - thick * bed_h_bounds[0])
-                                   for sfc_h, thick in
+
+            h_max_w0 = (fl.widths_m[fl.thick > 0] - data_logger.min_w0_m) / \
+                       fl._lambdas[fl.thick > 0]
+
+            bounds[var_indices] = [(sfc_h - min(thick * bed_h_bounds[1],
+                                                thick_max),
+                                    sfc_h - thick * bed_h_bounds[0]
+                                    )
+                                   for sfc_h, thick, thick_max in
                                    zip(fl.surface_h[ice_mask],
-                                       fl.thick[ice_mask])]
+                                       fl.thick[ice_mask],
+                                       h_max_w0)]
 
             # Outdated calculation using GlabTop
             # upper_limits = get_adaptive_upper_ice_thickness_limit(
@@ -344,14 +351,19 @@ def get_control_var_bounds(data_logger):
             fl = data_logger.flowline_init
             ice_mask = data_logger.ice_mask
             bed_h_bounds = data_logger.bed_h_bounds
-            bounds[var_indices] = [((sfc_h - thick * bed_h_bounds[1]) *
-                                    width_m,
-                                    (sfc_h - thick * bed_h_bounds[0]) *
-                                    width_m)
-                                   for sfc_h, thick, width_m in
+
+            h_max_w0 = (fl.widths_m[fl.thick > 0] - data_logger.min_w0_m) / \
+                       fl._lambdas[fl.thick > 0]
+
+            bounds[var_indices] = [((sfc_h - min(thick * bed_h_bounds[1],
+                                                 thick_max)) * width_m,
+                                    (sfc_h - thick * bed_h_bounds[0]) * width_m
+                                    )
+                                   for sfc_h, thick, width_m, thick_max in
                                    zip(fl.surface_h[ice_mask],
                                        fl.thick[ice_mask],
-                                       fl.widths_m[ice_mask])]
+                                       fl.widths_m[ice_mask],
+                                       h_max_w0)]
 
             # Outdated calculation using GlabTop
             # upper_limits = get_adaptive_upper_ice_thickness_limit(

agile1d/tests/conftest.py

@@ -150,7 +150,7 @@ def data_logger_init(hef_gdir, control_vars, spinup_option, dynamic_models,
         inversion_settings['regularisation_terms'] = {'smoothed_bed': 1.,
                                                       'smoothed_flux': 10.}
     prepare_for_agile_inversion(hef_gdir, inversion_settings=inversion_settings,
-                                  filesuffix='_agile')
+                                filesuffix='_agile')
     data_logger = initialise_DataLogger(hef_gdir, inversion_input_filesuffix='_agile')
     return data_logger
 

agile1d/tests/test_inversion.py

@@ -6,9 +6,9 @@
 
 from agile1d.core.first_guess import get_first_guess
 from agile1d.core.inversion import (prepare_for_agile_inversion,
-                                      get_control_var_bounds,
-                                      get_default_inversion_settings,
-                                      agile_inversion)
+                                    get_control_var_bounds,
+                                    get_default_inversion_settings,
+                                    agile_inversion)
 from agile1d.core.data_logging import initialise_DataLogger
 from agile1d.core.cost_function import create_cost_fct, descale_unknown_parameters
 
@@ -27,7 +27,7 @@ def test_default_initialisation(self, hef_gdir):
 
         # add default inversion_input_agile and try again
         prepare_for_agile_inversion(hef_gdir, inversion_settings=None,
-                                      filesuffix='_agile')
+                                    filesuffix='_agile')
         data_logger = initialise_DataLogger(
             hef_gdir, inversion_input_filesuffix='_agile')
 
@@ -103,7 +103,7 @@ def test_get_control_var_bounds(self, data_logger):
                              ids=[#'flux_based',
                                   'implicit'])
     def test_agile_inversion(self, hef_gdir, control_vars, spinup_options,
-                               dynamic_model, all_supported_control_vars):
+                             dynamic_model, all_supported_control_vars):
         # Final integration test that the inversion runs with no errors, also
         # test the saving of the final past evolution
         inversion_settings = get_default_inversion_settings(get_doc=False)
@@ -131,11 +131,11 @@ def test_agile_inversion(self, hef_gdir, control_vars, spinup_options,
         inversion_settings['observations'][measure_key]['2000-2019'] = -1.
 
         prepare_for_agile_inversion(hef_gdir,
-                                      inversion_settings=inversion_settings,
-                                      filesuffix='_agile')
+                                    inversion_settings=inversion_settings,
+                                    filesuffix='_agile')
 
         data_logger = agile_inversion(hef_gdir, give_data_logger_back=True,
-                                        save_past_evolution=True)
+                                      save_past_evolution=True)
 
         # test if data_logger contains data
         assert data_logger.minimize_message is not None