title: Interpolation with Delaunay Triangulation (n-dim)
body: <p>I would like to use Delaunay Triangulation in Python to interpolate the points in 3D.</p>
  <p>What I have is</p>
  <pre><code># my array of points
  points = [[1,2,3], [2,3,4], ...]
  # my array of values
  values = [7, 8, ...]
  # an object with triangulation
  tri = Delaunay(points)        
  # a set of points at which I want to interpolate
  p = [[1.5, 2.5, 3.5], ...]
  # this gets simplexes that contain given points
  s = tri.find_simplex(p)
  # this gets vertices for the simplexes
  v = tri.vertices[s]
  </code></pre>
  <p>I was only able to find one <a href="https://stackoverflow.com/questions/27622663/interpolation-with-delaunay-triangulation">answer</a> here that suggest to use <code>transform</code> method for the interpolation, but without being any more specific.</p>
  <p>What I need to know is how to use the vertices of the containing simplex to get the weights for the linear interpolation. Let's assume a general n-dim case so that the answer does not depend on the dimension.</p>
  <p>EDIT: I do not want to use LinearNDInterpolator or similar approach because I do not have a number at each point as a value but something more complex (array/function).</p>