[TEST] Reviving activation test for multiple layers

Author: Leguark

gempy_engine/modules/activator/activator_interface.py

@@ -33,7 +33,7 @@ def activate_formation_block_from_args(Z_x, ids, scalar_value_at_sp, sigmoid_slo
     sigm = bt.t.zeros((1, Z_x.shape[0]), dtype=BackendTensor.dtype_obj)
 
     for i in range(len(ids)):
-        if LEGACY:=False:
+        if LEGACY:=True:
             sigm += _compute_sigmoid(Z_x, scalar_0_v[i], scalar_1_v[i], drift_0_v[i], drift_1_v[i], ids[i], sigmoid_slope)
         else:
             sigm += HardSigmoid.apply(Z_x, scalar_0_v[i], scalar_1_v[i])

tests/fixtures/simple_models.py

@@ -327,6 +327,40 @@ def simple_model_values_block_output(simple_model, simple_grid_3d_more_points_gr
 
     return output
 
+@pytest.fixture(scope="session")
+def simple_model_3_layers_output(simple_model_3_layers):
+    interporlation_input = simple_model_3_layers[0]
+    options = simple_model_3_layers[1]
+    data_shape = simple_model_3_layers[2].tensors_structure
+    ids = np.array([1, 2, 3, 4])
+
+    interp_input: SolverInput = input_preprocess(data_shape, interporlation_input)
+    weights = _solve_interpolation(interp_input, options.kernel_options)
+
+    exported_fields = _evaluate_sys_eq(interp_input, weights, options)
+
+    exported_fields.set_structure_values(
+        reference_sp_position=data_shape.reference_sp_position,
+        slice_feature=interporlation_input.slice_feature,
+        grid_size=interporlation_input.grid.len_all_grids)
+
+    # -----------------
+    # Export and Masking operations can happen even in parallel
+    # TODO: [~X] Export block
+    values_block: np.ndarray = activate_formation_block(exported_fields, ids, sigmoid_slope=50000)
+
+    output = InterpOutput(
+        ScalarFieldOutput(
+            weights=weights,
+            grid=interporlation_input.grid,
+            exported_fields=exported_fields,
+            values_block=values_block,
+            stack_relation=interporlation_input.stack_relation,
+        ),
+    )
+
+    return output
+
 
 @pytest.fixture(scope="session")
 def unconformity_complex():

tests/test_common/test_modules/test_activator.py

@@ -1,8 +1,12 @@
+import dataclasses
 import os
 import pytest
 import matplotlib.pyplot as plt
 import numpy as np
 
+from gempy_engine.API.interp_single._interp_scalar_field import _solve_interpolation, _evaluate_sys_eq
+from gempy_engine.API.interp_single._interp_single_feature import input_preprocess
+from gempy_engine.core.data.internal_structs import SolverInput
 from gempy_engine.core.data.interp_output import InterpOutput
 from gempy_engine.modules.activator.activator_interface import activate_formation_block
 from gempy_engine.API.interp_single.interp_features import interpolate_single_field
@@ -35,21 +39,51 @@ def test_activator(simple_model_values_block_output):
         plt.show()
 
 
-@pytest.mark.skip(reason="This is unfinished I have to extract the 3 layers values")
-def test_activator_3_layers(simple_model_3_layers):
-    interpolation_input, options, structure = simple_model_3_layers
+# @pytest.mark.skip(reason="This is unfinished I have to extract the 3 layers values")
+def test_activator_3_layers(simple_model_3_layers, simple_grid_3d_more_points_grid):
+    interpolation_input = simple_model_3_layers[0]
+    options = simple_model_3_layers[1]
+    data_shape = simple_model_3_layers[2].tensors_structure
+    grid = dataclasses.replace(simple_grid_3d_more_points_grid)
+    interpolation_input.grid = grid
+    
+    ids = np.array([1, 2, 3, 4])
 
-    res = interpolation_input.grid.regular_grid.resolution
+    interp_input: SolverInput = input_preprocess(data_shape, interpolation_input)
+    weights = _solve_interpolation(interp_input, options.kernel_options)
 
-    output: InterpOutput = interpolate_single_field(interpolation_input, options, structure)
-    Z_x = output.exported_fields.scalar_field
+    exported_fields = _evaluate_sys_eq(interp_input, weights, options)
+
+    exported_fields.set_structure_values(
+        reference_sp_position=data_shape.reference_sp_position,
+        slice_feature=interpolation_input.slice_feature,
+        grid_size=interpolation_input.grid.len_all_grids)
+
+    Z_x: np.ndarray = exported_fields.scalar_field
+    sasp = exported_fields.scalar_field_at_surface_points
+    ids = np.array([1, 2, 3, 4])
+
+    print(Z_x, Z_x.shape[0])
+    print(sasp)
+
+
+    ids_block = activate_formation_block(
+        exported_fields=exported_fields,
+        ids= ids,
+        sigmoid_slope=50000
+    )[:, :-7]
 
     if plot:
-        plt.contourf(Z_x.reshape(res)[:, 0, :].T, N=40, cmap="autumn",
+        plt.contourf(Z_x.reshape(50, 5, 50)[:, 0, :].T, N=40, cmap="autumn",
                      extent=(.25, .75, .25, .75))
 
         xyz = interpolation_input.surface_points.sp_coords
         plt.plot(xyz[:, 0], xyz[:, 2], "o")
         plt.colorbar()
 
         plt.show()
+
+        plt.contourf(ids_block[0, :-4].reshape(50, 5, 50)[:, 2, :].T, N=40, cmap="viridis")
+        plt.colorbar()
+
+        plt.show()