[WIP] More Small changes to revive vector probability examples.

Author: Leguark

gempy_engine/core/data/raw_arrays_solution.py

@@ -19,8 +19,10 @@
 @dataclass(init=True)
 class RawArraysSolution:
     class BlockSolutionType(enum.Enum):
-        OCTREE = 0
-        DENSE_GRID = 1
+        NONE = 0
+        OCTREE = 1
+        DENSE_GRID = 2
+        
 
     # region Regular Grid
     lith_block: np.ndarray = field(default_factory=lambda: np.empty(0))
@@ -60,7 +62,7 @@ class BlockSolutionType(enum.Enum):
     # ? TODO: This could be just the init
     @classmethod
     def from_gempy_engine_solutions(cls, octrees_output: list[OctreeLevel], meshes: list[DualContouringMesh],
-                                    block_solution_type: BlockSolutionType) -> "RawArraysSolution":
+                                    block_solution_type: BlockSolutionType) -> "RawArraysSolution" | None:
         raw_arrays_solution = cls()
 
         first_level_octree: OctreeLevel = octrees_output[0]
@@ -78,6 +80,11 @@ def from_gempy_engine_solutions(cls, octrees_output: list[OctreeLevel], meshes:
             case cls.BlockSolutionType.DENSE_GRID:
                 _fill_block_solutions_with_dense_grid(stacks_output, raw_arrays_solution)
                 raw_arrays_solution.dense_ids = BackendTensor.t.to_numpy(collapsed_output.ids_block_dense_grid)
+            case cls.BlockSolutionType.NONE:
+                return None
+            case _:
+                raise ValueError(f"Block solution type {block_solution_type} not recognized")
+        
 
         # Endregion
 

gempy_engine/modules/activator/activator_interface.py

@@ -16,7 +16,7 @@ def activate_formation_block(exported_fields: ExportedFields, ids: np.ndarray,
         sigm = activate_formation_block_from_args(Z_x, ids, scalar_value_at_sp, sigmoid_slope)
     else:
         from .torch_activation import activate_formation_block_from_args_hard_sigmoid
-        sigm = activate_formation_block_from_args_hard_sigmoid(Z_x, ids, scalar_value_at_sp, sigmoid_slope)
+        sigm = activate_formation_block_from_args_hard_sigmoid(Z_x, ids, scalar_value_at_sp)
 
     return sigm
 

gempy_engine/modules/activator/torch_activation.py

@@ -13,7 +13,7 @@
 '''
 
 
-def activate_formation_block_from_args_hard_sigmoid(Z_x, ids, scalar_value_at_sp, sigmoid_slope):
+def activate_formation_block_from_args_hard_sigmoid(Z_x, ids, scalar_value_at_sp):
     element_0 = bt.t.array([0], dtype=BackendTensor.dtype_obj)
 
     min_Z_x = BackendTensor.t.min(Z_x, axis=0).reshape(-1)  # ? Is this as good as it gets?