python - Finding closest three x,y points in three arrays -

in python, have 3 lists containing x , y coordinates. each list contains 128 points. how can find the closest 3 points in efficient way?

this working python code isn't efficient enough:

   def findclosest(c1, c2, c3):        mina = 999999999        in c1:           j in c2:              k in c3:                 # calculate sum of distances between points                 d = xy3dist(i,j,k)                 if d < mina:                    mina = d      def xy3dist(a, b, c):        l1 = math.sqrt((a[0]-b[0]) ** 2 + (a[1]-b[1]) ** 2 )           l2 = math.sqrt((b[0]-c[0]) ** 2 + (b[1]-c[1]) ** 2 )           l3 = math.sqrt((a[0]-c[0]) ** 2 + (a[1]-c[1]) ** 2 )               return l1+l2+l3 

any idea how can done using numpy?

you can use numpy's broadcasting features vectorize 2 inner loops:

 import numpy np  def findclosest(c1, c2, c3):    c1 = np.asarray(c1)    c2 = np.asarray(c2)    c3 = np.asarray(c3)     arr in (c1, c2, c3):        if not (arr.ndim == 2 , arr.shape[1] == 2):            raise valueerror("expected arrays of 2d coordinates")     min_val = np.inf    min_pos = none     a, in enumerate(c1):        d = xy3dist(i, c2.t[:,:,np.newaxis], c3.t[:,np.newaxis,:])        k = np.argmin(d)         if d.flat[k] < min_val:            min_val = d.flat[k]            b, c = np.unravel_index(k, d.shape)            min_pos = (a, b, c)         print a, min_val, d.min()     return min_val, min_pos  def xy3dist(a, b, c):    l1 = np.sqrt((a[0]-b[0]) ** 2 + (a[1]-b[1]) ** 2 )       l2 = np.sqrt((b[0]-c[0]) ** 2 + (b[1]-c[1]) ** 2 )       l3 = np.sqrt((a[0]-c[0]) ** 2 + (a[1]-c[1]) ** 2 )           return l1+l2+l3  np.random.seed(1234) c1 = np.random.rand(5, 2) c2 = np.random.rand(9, 2) c3 = np.random.rand(7, 2)  val, pos = findclosest(c1, c2, c3)  a, b, c = pos print val, xy3dist(c1[a], c2[b], c3[c]) 

it's possible vectorize of 3 loops

 def findclosest2(c1, c2, c3):     c1 = np.asarray(c1)     c2 = np.asarray(c2)     c3 = np.asarray(c3)     d = xy3dist(c1.t[:,:,np.newaxis,np.newaxis], c2.t[:,np.newaxis,:,np.newaxis], c3.t[:,np.newaxis,np.newaxis,:])     k = np.argmin(d)     min_val = d.flat[k]     a, b, c = np.unravel_index(k, d.shape)     min_pos = (a, b, c)     return min_val, min_pos 

if arrays big, findclosest may better findclosest2 uses less memory. (and if arrays huge, vectorize 1 innermost loop.)

you can google "numpy broadcasting" learn more np.newaxis does