Python, load .csv in numpy arrays, return a list

I would like to improve this code in order to return a list of "solutions" at the end:

[bcoords[0, 1, 2, 3], R[0, 1, 2, 3], G[0, 1, 2, 3], B[0, 1, 2, 3]]

code:

import csv
import numpy as np
import scipy.spatial

points = np.array([(float(X), float(Y), float(Z))
              for R, G, B, X, Y, Z in csv.reader(open('XYZcolorlist_D65.csv'))])
    # load XYZ coordinates of 'points' in a np.array

tri = scipy.spatial.Delaunay(points)
# do the triangulation

indices = tri.simplices
# indices of vertices

vert = points[tri.simplices]
# the vertices for each tetrahedron

targets = np.array([(float(X), float(Y), float(Z))
           for X, Y, Z in csv.reader(open('targets.csv'))])
# load the XYZ target values in a np.array

tetrahedra = tri.find_simplex(targets)
# find which tetrahedron each point belong to

X = tri.transform[tetrahedra,:3]
Y = targets - tri.transform[tetrahedra,3]
b = np.einsum('ijk,ik->ij', X, Y)
bcoords = np.c_[b, 1 - b.sum(axis=1)]
# find the barycentric coordinates of each point

print bcoords

_

The code loads two .csv files in two np.array and use the module scipy.spatial.Delaunay to find the barycentric coordinates of a point in a tetrahedron.

XYZcolorlist.csv is a cloud of points R, G, B, X, Y, Z

and targets.csv is a set of targets X, Y, Z

XYZcolorlist.csv:

255,63,127,35.5344302104,21.380721966,20.3661095969
255,95,127,40.2074945517,26.5282949405,22.7094284437
255,127,127,43.6647438365,32.3482625492,23.6181801523
255,159,127,47.1225628354,39.1780944388,22.9366615044
255,223,159,61.7379149646,62.8387601708,32.3936200864
...

targets.csv:

49.72,5,8.64
50.06,5,8.64
50.4,5,8.64
50.74,5,8.64
51.08,5,8.64
51.42,5,8.64
51.76,5,8.64
...

For each point of targets.csv, I want to get:

• the 4 vertices containing the point

• the 4 float(R), float(G), float(B) associated with each vertex:

• the 4 barycentric coordinates associated with each point

and I want to do it fast, using numpy

The code gives all this, except for the 4 R, G, B's

alternatively, I could load the entire file's data with this code:

points = np.array([(float(R), float(G), float(B), float(X), float(Y), float(Z))
          for R, G, B, X, Y, Z in csv.reader(open('XYZcolorlist_D65.csv'))])
# load R,G,B,X,Y,Z coordinates of 'points' in a np.array

How can I return the list:

[bcoords[0, 1, 2, 3], R[0, 1, 2, 3], G[0, 1, 2, 3], B[0, 1, 2, 3]] ?

Is it possible to build a dict[] ?

Thanks