[pre-commit.ci] pre-commit autoupdate

.gitignore

@@ -11,4 +11,4 @@ build/
 dist/
 /.pytest_cache/
 /sostrades_optimization_plugins/tests/__pycache__/
-/sostrades_optimization_plugins/tests/.pytest_cache/
+/sostrades_optimization_plugins/tests/.pytest_cache/

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ repos:
     hooks:
     - id: update-headers
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.0
+    rev: v0.9.2
     hooks:
       - id: ruff
         args: [
@@ -14,7 +14,7 @@ repos:
           --config=ruff.toml,
         ]
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
+    rev: v5.0.0
     hooks:
       - id: trailing-whitespace
       - id: end-of-file-fixer
@@ -41,11 +41,11 @@ repos:
       - id: rst-directive-colons
       - id: rst-inline-touching-normal
   - repo: https://github.com/kynan/nbstripout
-    rev: 0.7.1
+    rev: 0.8.1
     hooks:
       - id: nbstripout
   - repo: https://github.com/igorshubovych/markdownlint-cli
-    rev: v0.39.0
+    rev: v0.43.0
     hooks:
     - id: markdownlint
       args: [

headers_ignore_config.json

@@ -1,59 +1,59 @@
 {
-    "extension_to_ignore": [
-        "pkl",
-        "png",
-        "jpg",
-        "csv",
-        "md",
-        "markdown",
-        "avif",
-        "json",
-        "in",
-        "gitignore",
-        "cfg",
-        "puml",
-        "pdf",
-        "txt",
-        "ipynb",
-        "zip",
-        "rst",
-        "ini",
-        "coveragerc",
-        "yaml",
-        "bat",
-        "tex",
-        "toml"
-    ],
-    "files_to_ignore": [
-        "LICENSE",
-        ".readthedocs",
-        "docs/Makefile",
-        ".flake8",
-        ".prettierignore",
-        "sostrades_optimization_plugins/models/design_var/design_var.py",
-        "sostrades_optimization_plugins/models/design_var/design_var_disc.py",
-        "sostrades_optimization_plugins/models/func_manager/func_manager.py",
-        "sostrades_optimization_plugins/models/func_manager/func_manager_disc.py",
-        "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_design_var/__init__.py",
-        "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_design_var/process.py",
-        "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_design_var/usecase.py",
-        "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_func_manager/__init__.py",
-        "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_func_manager/process.py",
-        "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_func_manager/usecase.py",
-        "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_func_manager_faulty/__init__.py",
-        "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_func_manager_faulty/process.py",
-        "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_func_manager_faulty/usecase.py",
-        "sostrades_optimization_plugins/tests/l0_test_14_optim_scenario_with_func_manager.py",
-        "sostrades_optimization_plugins/tests/l0_test_44_func_manager.py",
-        "sostrades_optimization_plugins/tests/l0_test_62_design_var_in_sellar.py",
-        "sostrades_optimization_plugins/tests/l1s_test_all_usecases.py",
-        "sostrades_optimization_plugins/tools/cst_manager/common_config.py",
-        "sostrades_optimization_plugins/tools/cst_manager/constraint_manager.py",
-        "sostrades_optimization_plugins/tools/cst_manager/constraint_object.py",
-        "sostrades_optimization_plugins/tools/cst_manager/database.py",
-        "sostrades_optimization_plugins/tools/cst_manager/fileutils.py",
-        "sostrades_optimization_plugins/tools/cst_manager/func_manager_common.py",
-        "sostrades_optimization_plugins/tools/cst_manager/__init__.py"
-    ],
-    "airbus_rev_commit": "d833fdf740389587b876e30f084631430687fa5a"
-}
+  "extension_to_ignore": [
+    "pkl",
+    "png",
+    "jpg",
+    "csv",
+    "md",
+    "markdown",
+    "avif",
+    "json",
+    "in",
+    "gitignore",
+    "cfg",
+    "puml",
+    "pdf",
+    "txt",
+    "ipynb",
+    "zip",
+    "rst",
+    "ini",
+    "coveragerc",
+    "yaml",
+    "bat",
+    "tex",
+    "toml"
+  ],
+  "files_to_ignore": [
+    "LICENSE",
+    ".readthedocs",
+    "docs/Makefile",
+    ".flake8",
+    ".prettierignore",
+    "sostrades_optimization_plugins/models/design_var/design_var.py",
+    "sostrades_optimization_plugins/models/design_var/design_var_disc.py",
+    "sostrades_optimization_plugins/models/func_manager/func_manager.py",
+    "sostrades_optimization_plugins/models/func_manager/func_manager_disc.py",
+    "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_design_var/__init__.py",
+    "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_design_var/process.py",
+    "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_design_var/usecase.py",
+    "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_func_manager/__init__.py",
+    "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_func_manager/process.py",
+    "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_func_manager/usecase.py",
+    "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_func_manager_faulty/__init__.py",
+    "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_func_manager_faulty/process.py",
+    "sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_func_manager_faulty/usecase.py",
+    "sostrades_optimization_plugins/tests/l0_test_14_optim_scenario_with_func_manager.py",
+    "sostrades_optimization_plugins/tests/l0_test_44_func_manager.py",
+    "sostrades_optimization_plugins/tests/l0_test_62_design_var_in_sellar.py",
+    "sostrades_optimization_plugins/tests/l1s_test_all_usecases.py",
+    "sostrades_optimization_plugins/tools/cst_manager/common_config.py",
+    "sostrades_optimization_plugins/tools/cst_manager/constraint_manager.py",
+    "sostrades_optimization_plugins/tools/cst_manager/constraint_object.py",
+    "sostrades_optimization_plugins/tools/cst_manager/database.py",
+    "sostrades_optimization_plugins/tools/cst_manager/fileutils.py",
+    "sostrades_optimization_plugins/tools/cst_manager/func_manager_common.py",
+    "sostrades_optimization_plugins/tools/cst_manager/__init__.py"
+  ],
+  "airbus_rev_commit": "d833fdf740389587b876e30f084631430687fa5a"
+}

platform_version_required.txt

@@ -1 +1 @@
-v5.0.0
+v5.0.0

pytest.ini

@@ -2,4 +2,4 @@
 python_files = l*_test*.py
 testpaths =
   sostrades_optimization_plugins/tests
-addopts = --numprocesses=auto
+addopts = --numprocesses=auto

sostrades_optimization_plugins/models/design_var/design_var.py

@@ -145,7 +145,7 @@ def rebuild_input_array_with_activated_elements(self, inputs_dict, elem):
         return np.array(final_value), np.array(gradient)
 
     def update_gradient_with_deactivated_elements_first_value(self, initial_gradient):
-        ### TO DO generalize it with other deactivated elements
+        # TO DO generalize it with other deactivated elements
 
         result = np.delete(initial_gradient, 0, axis=1)
         return result

sostrades_optimization_plugins/models/design_var/design_var_disc.py

@@ -170,9 +170,8 @@ def run(self):
         # update output dictionary with dspace
         outputs_dict.update({'design_space_last_ite': dspace_out})
 
-
         if self.get_sosdisc_inputs(self.WRITE_XVECT):
-            #dspace_out.to_csv(f"dspace_ite_{self.iter}.csv", index=False)
+            # dspace_out.to_csv(f"dspace_ite_{self.iter}.csv", index=False)
 
             # write all iterations into a dictionnary
             self.all_iterations_dict.update({f"iteration {self.iter}": dict_current})

sostrades_optimization_plugins/models/design_var/documentation/design_var.markdown

@@ -1,4 +1,4 @@
-# Documentation 
+# Documentation
 
 The design variable discipline handles the variables that are used to drive the optimization.
 
@@ -9,5 +9,5 @@ A b-spline function is a continuous composite function, that creates smooth curv
 
 The optimizer has control over the poles, and moves them (on the y-axis) to modify the B-Spline function. Then the value
 of the design variables for each year are calculated using the B-Spline function.
-  
-![](BSpline_example.PNG)
+
+![](BSpline_example.PNG)

sostrades_optimization_plugins/models/func_manager/documentation/func_manager_disc.markdown

@@ -1,11 +1,11 @@
 ## Documentation
 
 Function manager is a model that aggregates weighted objectives, inequality and equality constraints using smoothing functions.
-It takes both dataframes/arrays and floats as objectives/constraints. 
+It takes both dataframes/arrays and floats as objectives/constraints.
 
 ![](scheme_funcmanager.png)
 
-### Scalarize functions 
+### Scalarize functions
 
 Objectives, inequality constraints and equality constraints functions enter the function manager with a numpy array containing a single (objective) or multiple (constraints) values.
 The first step towards the aggregation of these functions is to merge these numpy array into a single scalar value.
@@ -15,8 +15,8 @@ Each type of function is treated individually at first:
 	-objectives:
 		the values are either summed or the smooth_maximum function is applied on value, depending on the 'aggr_type' variable of the function.
 	-equality and inequality constraints:
-		the array goes through a function 'cst_func_smooth_positive' to scale the values between 0 and +inf, then the smooth_maximum value is returned. 
-		
+		the array goes through a function 'cst_func_smooth_positive' to scale the values between 0 and +inf, then the smooth_maximum value is returned.
+
 The scalar value of each function is then multiplied by the weight associated with the function
 
 #### Function 'smooth\_maximum'
@@ -45,7 +45,7 @@ This function loops on the numpy array and apply one of four functions on each e
 	- if self.smooth_log and val.real > self.eps2: res = res0 + 2 * np.log(val)
 
 The four parts of this function are designed to ensure a smooth continuous function, with a quadratic increase above 'eps'.
-If 'smoothlog' is set to True, the function is capped with a log increase above eps2, to avoid some numeric issues with constraints 
+If 'smoothlog' is set to True, the function is capped with a log increase above eps2, to avoid some numeric issues with constraints
 values going haywire.
 !['Continuous positive smoothing function'](residuals_wo_smoothlog.png)
 
@@ -57,5 +57,3 @@ It is calculated as the sum of the three aggregated functions multiplied by 100.
 
 
 [^1]: [Martins, J. R. R. A., and Nicholas MK Poon. "On structural optimization using constraint aggregation." VI World Congress on Structural and Multidisciplinary Optimization WCSMO6, Rio de Janeiro, Brasil. 2005.](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.140.3612&rep=rep1&type=pdf)
-
-

sostrades_optimization_plugins/models/func_manager/func_manager.py

@@ -63,7 +63,7 @@ def __init__(self):
         self.POS_FTYPE = [self.OBJECTIVE,
                           self.INEQ_CONSTRAINT, self.EQ_CONSTRAINT]
         self.reinit()
-        #-- We could use it to log?
+        # -- We could use it to log?
 
     def reinit(self):
         """
@@ -129,13 +129,13 @@ def scalarize_all_functions(self, eps=1e-3, alpha=3):
             dict_mod_func = {}
             dict_mod_func[self.FTYPE] = self.functions[tag][self.FTYPE]
             dict_mod_func[self.AGGR] = self.functions[tag][self.AGGR]
-            #-- All values are an np array even single values
-            #-- Weights are applied here to allow sign modification
+            # -- All values are an np array even single values
+            # -- Weights are applied here to allow sign modification
             values = weight * self.functions[tag][self.VALUE]
             aggr_type = dict_mod_func[self.AGGR]
             if self.functions[tag][self.FTYPE] == self.OBJECTIVE:
-                #-- smooth maximum of values return the value if it was a float
-                #-- return smooth maximum if objective was an array
+                # -- smooth maximum of values return the value if it was a float
+                # -- return smooth maximum if objective was an array
                 if aggr_type == self.AGGR_TYPE_SMAX:
                     res = smooth_maximum(values, alpha)
                 elif aggr_type == self.AGGR_TYPE_SUM:
@@ -145,7 +145,7 @@ def scalarize_all_functions(self, eps=1e-3, alpha=3):
                 else:
                     raise Exception(f"Unhandled aggr_type {aggr_type}")
             elif self.functions[tag][self.FTYPE] == self.INEQ_CONSTRAINT:
-                #-- scale between (0., +inf) and take smooth maximum
+                # -- scale between (0., +inf) and take smooth maximum
                 if aggr_type == self.INEQ_NEGATIVE_WHEN_SATIFIED:
                     res = keep_positive_only(values)
                 elif aggr_type == self.INEQ_POSITIVE_WHEN_SATIFIED:
@@ -189,12 +189,12 @@ def build_aggregated_functions(self, eps=1e-3, alpha=3):
             elif self.mod_functions[tag][self.FTYPE] == self.EQ_CONSTRAINT:
                 all_mod_eq_cst.append(self.mod_functions[tag])
 
-        #-- Objective aggregation: sum all the objectives
+        # -- Objective aggregation: sum all the objectives
         self.aggregated_functions[self.OBJECTIVE] = 0.
         for obj_dict in all_mod_obj:
             self.aggregated_functions[self.OBJECTIVE] += obj_dict[self.VALUE]
 
-        #-- Inequality constraint aggregation: takes the smooth maximum
+        # -- Inequality constraint aggregation: takes the smooth maximum
         ineq_cst_val = [ineq_dict[self.VALUE] for ineq_dict in all_mod_ineq_cst]
         ineq_cst_val = np.array(ineq_cst_val)
         if len(ineq_cst_val) > 0:
@@ -207,7 +207,7 @@ def build_aggregated_functions(self, eps=1e-3, alpha=3):
         else:
             self.aggregated_functions[self.INEQ_CONSTRAINT] = 0.
 
-        #-- Equality constraint aggregation: takes the smooth maximum
+        # -- Equality constraint aggregation: takes the smooth maximum
         eq_cst_val = [eq_dict[self.VALUE] for eq_dict in all_mod_eq_cst]
         eq_cst_val = np.array(eq_cst_val)
         if len(eq_cst_val) > 0:
@@ -219,7 +219,7 @@ def build_aggregated_functions(self, eps=1e-3, alpha=3):
         else:
             self.aggregated_functions[self.EQ_CONSTRAINT] = 0.
 
-        #--- Lagrangian objective calculation: sum the aggregated objective and constraints * 100.
+        # --- Lagrangian objective calculation: sum the aggregated objective and constraints * 100.
         self.mod_obj = 0.
         self.mod_obj += self.aggregated_functions[self.OBJECTIVE]
         self.mod_obj += self.aggregated_functions[self.INEQ_CONSTRAINT]
@@ -279,15 +279,15 @@ def cst_func_eq_lintoquad(self, values, eps=1e-3, tag='cst'):
         cst_result = np.zeros_like(values)
         for iii, val in enumerate(values):
             if val.real > eps:
-                #if val > eps: quadratic
+                # if val > eps: quadratic
                 res0 = eps * (np.exp(eps) - 1.)
                 res = res0 + val ** 2 - eps ** 2
             elif -eps < val.real < 0:
                 # if val < 0: linear
                 res = eps * (np.exp(-val) - 1.)
             elif val.real < -eps:
                 res0 = eps * (np.exp(eps) - 1.)
-                res= res0 + (-val) - eps
+                res = res0 + (-val) - eps
             else:
                 # if 0 < val < eps: linear
                 res = eps * (np.exp(val) - 1.)
@@ -389,7 +389,7 @@ def scale_function(val, val_range):
         :return: scaled function with 0 corresponding to ideal value and 1 to anti-ideal
         """
         # TODO: consider using a positive interval and adding a maximisation flag in the sake of clarity
-        return np.array([(val - val_range[0]) / (val_range[1] - val_range[0])]).reshape((-1,)) # NB: funcmanager demands arrays of shape (N, )
+        return np.array([(val - val_range[0]) / (val_range[1] - val_range[0])]).reshape((-1,))  # NB: funcmanager demands arrays of shape (N, )
 
     @staticmethod
     def scale_function_derivative(val_range) -> float:

sostrades_optimization_plugins/sos_processes/test/test_sellar_coupling_for_design_var/usecase.py

@@ -29,7 +29,7 @@ def setup_usecase(self):
 
         disc_dict = {}
         # Sellar inputs
-        disc_dict[f'{ns}.{coupling_name}.x'] = pd.DataFrame(data={'index': [0,1,2,3], 'value' : [1., 1., 1., 1.]})
+        disc_dict[f'{ns}.{coupling_name}.x'] = pd.DataFrame(data={'index': [0, 1, 2, 3], 'value': [1., 1., 1., 1.]})
         disc_dict[f'{ns}.{coupling_name}.y_1'] = 1.
         disc_dict[f'{ns}.{coupling_name}.y_2'] = 1.
         disc_dict[f'{ns}.{coupling_name}.z'] = array([5., 2.])

sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_design_var_sub/process.py

@@ -49,8 +49,8 @@ def get_builders(self):
 
                      }
 
-        ns_dict = {'ns_optim': self.ee.study_name ,
-                   'ns_OptimSellar': self.ee.study_name ,
+        ns_dict = {'ns_optim': self.ee.study_name,
+                   'ns_OptimSellar': self.ee.study_name,
                    'ns_functions': self.ee.study_name
                    }
 
@@ -61,5 +61,4 @@ def get_builders(self):
             "SellarCoupling")
         coupling_builder.set_builder_info('cls_builder', builder_list)
 
-
         return coupling_builder

sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_design_var_sub/usecase.py

@@ -70,7 +70,6 @@ def setup_usecase(self):
         disc_dict[
             f'{ns}.{self.coupling_name}.DesignVar.design_var_descriptor'] = design_var_descriptor
 
-
         # Sellar and design var inputs
         disc_dict[f'{ns}.x_in'] = array([1., 1., 1., 1.])
         disc_dict[f'{ns}.y_1'] = 5.

sostrades_optimization_plugins/sos_processes/test/test_sellar_opt_w_func_manager/process.py

@@ -21,6 +21,7 @@
 Generate an optimization scenario
 """
 
+
 class ProcessBuilder(BaseProcessBuilder):
 
     # ontology information
@@ -30,6 +31,7 @@ class ProcessBuilder(BaseProcessBuilder):
         'category': '',
         'version': '',
     }
+
     def get_builders(self):
         '''
         default initialisation test