fix: updating lavavu to use new bounding box

Author: lachlangrose

LoopStructural/utils/_bounding_box.py

@@ -34,6 +34,10 @@ def __init__(
             "maxz": (1, 2),
         }
 
+    @property
+    def valid(self):
+        return self._origin is not None and self._maximum is not None
+
     @property
     def origin(self) -> np.ndarray:
         if self._origin is None:
@@ -87,6 +91,7 @@ def fit(self, locations: np.ndarray):
             raise LoopValueError(
                 f"locations array is {locations.shape[1]}D but bounding box is {self.dimensions}"
             )
+        print("fitting")
         self.origin = locations.min(axis=0)
         self.maximum = locations.max(axis=0)
         return self

LoopStructural/visualisation/lavavu.py

@@ -41,7 +41,7 @@ def __init__(self, model=None, bounding_box=None, nsteps=None, **kwargs):
         self.bounding_box = bounding_box
         self.nsteps = nsteps
         if model is not None:
-            self.bounding_box = model.bounding_box
+            self.bounding_box = model.bounding_box.bb
             self.nsteps = model.nsteps
             logger.debug("Using bounding box from model")
         if self.bounding_box is None or self.nsteps is None:
@@ -255,7 +255,6 @@ def save(self, fname, **kwargs):
         self.lv.image(fname, **kwargs)
 
     def export_to_webgl(self, fname, **kwargs):
-
         self.lv.webgl(fname, **kwargs)
 
     def display(self, fname=None, **kwargs):

LoopStructural/visualisation/model_plotter.py

@@ -41,7 +41,7 @@ def model(self):
     @model.setter
     def model(self, model):
         if model is not None:
-            self.bounding_box = np.array(model.bounding_box)
+            self.bounding_box = np.array(model.bounding_box.bb)
             self.nsteps = np.array(model.nsteps)
             self._model = model
             self._nelements = self.nsteps[0] * self.nsteps[1] * self.nsteps[2]
@@ -291,6 +291,7 @@ def add_isosurface(
         # update the feature to make sure its current
 
         # do isosurfacing of support using marching tetras/cubes
+        print(self.bounding_box)
         x = np.linspace(
             self.bounding_box[0, 0], self.bounding_box[1, 0], self.nsteps[0]
         )
@@ -652,7 +653,6 @@ def add_model_surfaces(
                 n_faults += 1
 
         if self.model.stratigraphic_column and cmap is None:
-
             colours = []
             boundaries = []
             data = []
@@ -675,7 +675,6 @@ def add_model_surfaces(
         if faults:
             n_surfaces += n_faults
         with tqdm(total=n_surfaces) as pbar:
-
             if strati and self.model.stratigraphic_column:
                 for g in self.model.stratigraphic_column.keys():
                     if g in self.model.feature_name_index:
@@ -826,7 +825,6 @@ def add_data(self, feature, disks=False, vectors=False, **kwargs):
         else:
             features = [feature]
         for feature in features:
-
             grad = feature.builder.get_gradient_constraints()
             norm = feature.builder.get_norm_constraints()
             value = feature.builder.get_value_constraints()