changed grid interpolation method and tested accuracy

utils/planner.py

@@ -6,6 +6,7 @@ from math import pi
 import trimesh as tm
 import pymeshfix as pf
 from scipy.spatial import KDTree
+from scipy.interpolate import LinearNDInterpolator
 
 
 class Planner:
@@ -57,11 +58,23 @@ class Planner:
         print("Interpolating pointcloud to grid stresses")
         # get mean stress at each voxel
 
-        self.centtree = KDTree(self.centers, leafsize=5) #kd tree for mapping grid points to fea mesh
-        print(time.time()- gs)
-        self.gridstresses = np.zeros(self.centers.shape[0])
-        dd, ii = self.centtree.query(self.centers, k=4)
-        self.gridstress = np.sum(1/dd * self.mesh['StressValues'][ii], axis=1)/np.sum(1/dd, axis=1)
-#        print(time.time()- gs)
+        self.interpolator = LinearNDInterpolator(self.mesh.points, self.mesh['StressValues'])
+        print("Completed generating LinearND lookup")
+        print(time.time()-gs) # up to 100s for mesh with 300k points
 
 
+    def check_interp1s(self, node_is):
+        """
+        Works my removing nodes in list node_is from mehs and linear interpolating their stresses
+        and then comparing to true values
+        return predicted values and true values
+        # called with node_is = [i for i in range(0,308539, 1000)]
+        """
+        s = time.time()
+        except_i = [i for i in np.arange(self.mesh.points.shape[0]) if i not in node_is]
+        interpolator = LinearNDInterpolator(self.mesh.points[except_i,:], self.mesh['StressValues'][except_i])
+        node_i_pred_stress = interpolator(self.mesh.points[node_is,:])
+        node_i_stress = self.mesh['StressValues'][node_is]
+        e = time.time()
+        print(e-s)
+        return node_i_pred_stress, node_i_stress