[ENH] Refactor and enhance posterior analysis testing

Added 2D plotting functionality using gempy_viewer, improved test coverage by including posterior mean visualization, and adjusted geomodel refinement. Simplified readability by reorganizing imports, and replaced deprecated or less efficient operations.

Author: Leguark

tests/test_posterior_analysis/test_gempy_posterior_I.py

@@ -1,12 +1,13 @@
+import arviz as az
+import matplotlib.pyplot as plt
 import numpy as np
 import os
-import pyro.distributions as dist
-import torch
 
 import gempy as gp
+import gempy_viewer as gpv
 from gempy_engine.core.backend_tensor import BackendTensor
-import arviz as az
-import matplotlib.pyplot as plt
+from gempy_viewer.API._plot_2d_sections_api import plot_sections
+from gempy_viewer.core.data_to_show import DataToShow
 
 
 def test_gempy_posterior_I():
@@ -15,22 +16,64 @@ def test_gempy_posterior_I():
     geo_model = gp.create_geomodel(
         project_name='Wells',
         extent=[0, 12000, -500, 500, 0, 4000],
-        refinement=3,
+        refinement=4,
         importer_helper=gp.data.ImporterHelper(
             path_to_orientations=os.path.join(data_path, "2-layers", "2-layers_orientations.csv"),
             path_to_surface_points=os.path.join(data_path, "2-layers", "2-layers_surface_points.csv")
         )
     )
+    
+    BackendTensor.change_backend_gempy(engine_backend=gp.data.AvailableBackends.PYTORCH)
 
     geo_model.interpolation_options.uni_degree = 0
     geo_model.interpolation_options.mesh_extraction = False
-    geo_model.interpolation_options.sigmoid_slope = 1100.
 
     data = az.from_netcdf("../arviz_data.nc")
 
-    if PLOT:= False:
+    if PLOT := False:
         _plot_posterior(data)
 
+    gp.compute_model(gempy_model=geo_model)
+    p2d = gpv.plot_2d(
+        model=geo_model,
+        show_topography=False,
+        legend=False,
+        show_lith=False,
+        show=False
+    )
+
+    posterior_top_mean_z: np.ndarray = (data.posterior[r'$\mu_{top}$'].values[0, :])
+    xyz = np.zeros((posterior_top_mean_z.shape[0], 3))
+    xyz[:, 2] = posterior_top_mean_z
+    world_coord = geo_model.input_transform.apply_inverse(xyz)
+    i = 0
+    for i in range(0, 200, 5):
+        gp.modify_surface_points(
+            geo_model=geo_model,
+            slice=0,
+            Z=world_coord[i, 2]
+        )
+        gp.compute_model(gempy_model=geo_model)
+
+        plot_sections(
+            gempy_model=geo_model,
+            sections_data=p2d.section_data_list,
+            data_to_show=DataToShow(
+                n_axis=1,
+                show_data=True,
+                show_surfaces=True,
+                show_lith=False
+            ),
+            kwargs_boundaries={
+                    "linewidth": 0.5,
+                    "alpha"    : 0.1,
+            },
+        )
+
+    p2d.fig.show()
+
+    pass
+
 
 def _plot_posterior(data):
     az.plot_trace(data)