def addRBF(self, newXt, n=1000, qhull_opts="", output=True, makeHull=True):
sampled=False; x=1.0;i=0
while not(sampled):
diff=self.Xt-newXt
diff[:,2]*=self.vref*x
diff*=diff
diff=diff.sum(1)
diff=np.sqrt(diff)
index=np.argpartition(diff, n)[:n] #provide the indices of the n closest points
points=self.array[index,:]
rsampled=len(set(points[:,0]))>5
zsampled=len(set(points[:,1]))>5
tsampled=len(set(points[:,2]))>5
if rsampled and zsampled and tsampled:
sampled=True
elif not(rsampled and zsampled):
x*=2;i+=1
else:
x/=1.3;i+=1
if i>=100:
sampled=True
print "max iterations reached, forcing convergence"
if output: print("creating RBF at ", newXt)
def dist_func(X1,X2):
X=X1[:-1,...]-X2[:-1,...]
T=(X1[-1,...]-X2[-1,...])*self.vref
return np.sqrt((X**2).sum(0)+T**2)
RBFu=Rbf(points[:,0], points[:,1], points[:,2], points[:,3],\
norm=dist_func, smooth=0, epsilon=5e2)
RBFw=Rbf(points[:,0], points[:,1], points[:,2], points[:,4],\
norm=dist_func, smooth=0, epsilon=5e2)
if makeHull:
hull=Delaunay(RBFu.xi.T, qhull_options="QJ10000 "+qhull_opts) #creates a convex hull around the points used to construct the RBF
RBFu.hull=hull #a point cant be tested to be interior to the hull with:
RBFw.hull=hull #RBF.hull.find_simplex([r,z,t])>=0
RBFu.centre=points.mean(0)
RBFw.centre=points.mean(0)
self.RBFs.appendleft([RBFu, RBFw])
