nearest neighbour / IDW interpolation using scipy kdTree

unsupported/utilities/__init__.py

@@ -0,0 +1,13 @@
+__version__ = "0.1"
+
+
+import warnings
+
+warnings.warn(
+"""\n
+The alchemy module is not supported.
+Questions should be addressed to sandd@student.unimelb.edu.au \n """
+)
+
+
+from interpolation import *

unsupported/utilities/interpolation.py

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+from scipy.spatial import cKDTree as kdTree
+import numpy as np
+
+def nn_evaluation(_fromCoords, _toCoords, n=1, weighted=False):
+
+    """
+    This function provides nearest neighbour information for uw swarms,
+    given the "_toCoords", which could be the .data handle (coordinates) of a mesh or a different swarm,
+    this function returns the indices of the n nearest neighbours in "_fromCoords" (will usually be swarm.particleCoordinates.data )
+    it also returns the inverse-distance weights if weighted=True.
+
+    The function works in parallel, if the example below is followed
+
+
+    Usage
+    ------------
+    #get the n indexes, weights and distances
+    ix, weights, d = nn_evaluation(swarm.particleCoordinates.data, toSwarm.particleCoordinates.data, n=n, weighted=False)
+
+    #apply to the 'toSwarm' variable in a parallel-safe way
+    if len(weights): #parallel safety
+        toSwarmVar.data[:,0] =  np.average(fromSwarmVar.evaluate(fromSwarm)[:,0][ix], weights=weights, axis=len((weights.shape)) - 1)
+
+    """
+
+    if len(_toCoords) > 0: #this is required for safety in parallel
+
+        tree = kdTree(_fromCoords)
+        d, ix = tree.query(_toCoords, n)
+        if n == 1:
+            weights = np.ones(_toCoords.shape[0])
+        elif not weighted:
+            weights = np.ones((_toCoords.shape[0], n))*(1./n)
+        else:
+            weights = (1./d[:])/(1./d[:]).sum(axis=1)[:,None]
+        return ix,  weights, d
+    else:
+        return  np.empty(0, dtype="int"),  np.empty(0, dtype="int"), np.empty(0, dtype="int")