1 files changed, 92 insertions, 3 deletions
diff --git a/firmware/Spectrum Measurement.ipynb b/firmware/Spectrum Measurement.ipynb
index 6d904c3..c77709a 100644
--- a/firmware/Spectrum Measurement.ipynb
+++ b/firmware/Spectrum Measurement.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 277,
    "metadata": {
     "collapsed": false
    },
@@ -13,6 +13,7 @@
     "from IPython import display\n",
     "from datetime import datetime\n",
     "import scipy.interpolate as inter\n",
+    "from scipy import integrate\n",
     "\n",
     "import numpy as np\n",
     "from matplotlib import pyplot as plt\n",
@@ -6652,12 +6653,100 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 271,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# CIE XYZ Color matching functions\n",
+    "# rows are: λ[nm], x, y, z\n",
+    "CMFs = { fn[:-4]: np.genfromtxt(fn, delimiter=',')\n",
+    "        for fn in ['cie_xyz_1931.csv', 'cie_xyz_judd_1951.csv', 'cie_xyz_judd_vos_1978.csv'] }\n",
+    "CMFs = { name: np.hstack([inter.interp1d(d[:,0], d[:,i]) for i in range(1,4)])\n",
+    "        for name, d in CMFs.items() }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+    "def integrate_tristimulus_response(data, colorspace='cie_xyz_1931'):\n",
+    "    a = np.array([[\n",
+    "        integrate.simps(\n",
+    "            np.multiply(CMFs[colorspace][j](data[i][0]), data[i][1]),\n",
+    "            data[i][0])\n",
+    "        for j in range(3) ]\n",
+    "        for i in range(len(data)) ])\n",
+    "    # normalize by largest component\n",
+    "    return a / np.max(np.sum(a, axis=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 289,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  3.46142003e-01,   1.73335974e-01,  -7.18827590e-05],\n",
+       "       [  9.01721797e-02,   1.69512416e-01,   2.15830281e-02],\n",
+       "       [  1.75128165e-01,   2.49230694e-01,   9.78488855e-01]])"
+      ]
+     },
+     "execution_count": 289,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tristimulus_data = integrate_tristimulus_response(data_rgb)\n",
+    "tristimulus_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 330,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def led_setpoint_from_xyz(x, y, z):\n",
+    "    # returns [r, g, b] array.\n",
+    "    # Note that many xyz tristimulus values cannot be produced because one component is outside [0, 1]\n",
+    "    #return np.linalg.solve(tristimulus_data.T, np.array([x, y, z]))\n",
+    "    return np.dot(np.linalg.inv(tristimulus_data.T), np.array([x, y, z]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 332,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.72869072,  0.1386238 ,  0.20139265])"
+      ]
+     },
+     "execution_count": 332,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "led_setpoint_from_xyz(0.3, 0.2,  0.2)"
+   ]
   }
  ],
  "metadata": {