Resolve "(LearnerND) add iso-surface plot feature"

Closes #112 (closed)

adaptive/learner/learnerND.py

@@ -10,20 +10,11 @@ import scipy.spatial
 
 from .base_learner import BaseLearner
 
-from ..notebook_integration import ensure_holoviews
+from ..notebook_integration import ensure_holoviews, ensure_plotly
 from .triangulation import (Triangulation, point_in_simplex,
                             circumsphere, simplex_volume_in_embedding)
 from ..utils import restore, cache_latest
 
-try:
-    import plotly.graph_objs
-    import plotly.figure_factory
-    import plotly.offline
-
-    with_plotly = True
-except ModuleNotFoundError:
-    with_plotly = False
-
 
 def volume(simplex, ys=None):
     # Notice the parameter ys is there so you can use this volume method as
@@ -586,11 +577,6 @@ class LearnerND(BaseLearner):
         else:
             raise ValueError("Only 1 or 2-dimensional plots can be generated.")
 
-    def get_range(self):
-        r_min = min(self.data[v] for v in self.tri.vertices)
-        r_max = max(self.data[v] for v in self.tri.vertices)
-        return r_min, r_max
-
     def _get_isosurface(self, level=0.0):
         if self.ndim != 3 or self.vdim != 1:
             raise Exception('Isosurface plotting is only supported'
@@ -605,8 +591,8 @@ class LearnerND(BaseLearner):
                 return from_line_to_vertex[(a, b)]
 
             # Otherwise compute it and cache the result.
-            vertex_a = np.array(self.tri.vertices[a])
-            vertex_b = np.array(self.tri.vertices[b])
+            vertex_a = self.tri.vertices[a]
+            vertex_b = self.tri.vertices[b]
             value_a = self.data[vertex_a]
             value_b = self.data[vertex_b]
 
@@ -614,7 +600,7 @@ class LearnerND(BaseLearner):
             db = abs(value_b - level)
             dab = da + db
 
-            new_pt = db / dab * vertex_a + da / dab * vertex_b
+            new_pt = db / dab * np.array(vertex_a) + da / dab * np.array(vertex_b)
 
             new_index = len(vertices)
             vertices.append(new_pt)
@@ -623,10 +609,10 @@ class LearnerND(BaseLearner):
 
         for simplex in self.tri.simplices:
             plane = []
-            for a,b in itertools.combinations(simplex, 2):
+            for a, b in itertools.combinations(simplex, 2):
                 va = self.data[self.tri.vertices[a]]
                 vb = self.data[self.tri.vertices[b]]
-                if min(va,vb) < level <= max(va,vb):
+                if min(va, vb) < level <= max(va, vb):
                     vi = _get_vertex_index(a, b)
                     should_add = True
                     for pi in plane:
@@ -642,7 +628,8 @@ class LearnerND(BaseLearner):
                 faces.append(plane[1:])
 
         if len(faces) == 0:
-            r_min, r_max = self.get_range()
+            r_min = min(self.data[v] for v in self.tri.vertices)
+            r_max = max(self.data[v] for v in self.tri.vertices)
 
             raise ValueError(
                 f"Could not draw isosurface for level={level}, as"
@@ -653,7 +640,7 @@ class LearnerND(BaseLearner):
         return vertices, faces
 
     def plot_isosurface(self, level=0.0, hull_opacity=0.2):
-        """Plots the linearly interpolated iso-surface of the function, based on 
+        """Plots the linearly interpolated iso-surface of the function, based on
         the currently evaluated points. This is the 3d analog of an iso-line.
 
         Parameters
@@ -663,8 +650,7 @@ class LearnerND(BaseLearner):
         hull_opacity : float
             the opacity of the hull of the domain. Defaults to 0.2
         """
-        if not with_plotly:
-            raise Exception('plot_isosurface requires plotly to be installed')
+        plotly = ensure_plotly()
 
         vertices, faces = self._get_isosurface(level)
         x, y, z = zip(*vertices)
@@ -680,13 +666,15 @@ class LearnerND(BaseLearner):
         return plotly.offline.iplot([fig.data[0], hull_mesh])
 
     def _get_hull_mesh(self, opacity=0.2):
+        plotly = ensure_plotly()
         hull = scipy.spatial.ConvexHull(self._bounds_points)
 
         # Find the colors of each plane, giving triangles which are coplanar the
         # same color, such that a square face has the same color.
         color_dict = {}
+
         def _get_plane_color(simplex):
-            simplex= tuple(simplex)
+            simplex = tuple(simplex)
             # If the volume of the two triangles combined is zero then they
             # belong to the same plane.
             for simplex_key, color in color_dict.items():
@@ -695,7 +683,7 @@ class LearnerND(BaseLearner):
                     return color
                 if scipy.spatial.ConvexHull(points).volume < 1e-5:
                     return color
-            color_dict[simplex] = tuple(random.randint(0,255) for _ in range(3))
+            color_dict[simplex] = tuple(random.randint(0, 255) for _ in range(3))
             return color_dict[simplex]
 
         colors = [_get_plane_color(simplex) for simplex in hull.simplices]
@@ -704,4 +692,3 @@ class LearnerND(BaseLearner):
         i, j, k = hull.simplices.T
         return plotly.graph_objs.Mesh3d(x=x, y=y, z=z, i=i, j=j, k=k,
                                         facecolor=(colors), opacity=opacity)
-        
\ No newline at end of file