ormike/.gitignore

## .gitignore
.ipynb_checkpoints


## cie1931.csv
"wavelength, nm" "x-bar" "y-bar" "z-bar" "little-x" "little-y"
380 0.001368 0.000039 0.006450 0.17411 0.00496
385 0.002236 0.000064 0.010550 0.17401 0.00498
390 0.004243 0.000120 0.020050 0.17380 0.00492
395 0.007650 0.000217 0.036210 0.17356 0.00492
400 0.014310 0.000396 0.067850 0.17334 0.00480
405 0.023190 0.000640 0.110200 0.17302 0.00478
410 0.043510 0.001210 0.207400 0.17258 0.00480
415 0.077630 0.002180 0.371300 0.17209 0.00483
420 0.134380 0.004000 0.645600 0.17141 0.00510
425 0.214770 0.007300 1.039050 0.17030 0.00579
430 0.283900 0.011600 1.385600 0.16888 0.00690
435 0.328500 0.016840 1.622960 0.16690 0.00856
440 0.348280 0.023000 1.747060 0.16441 0.01086
445 0.348060 0.029800 1.782600 0.16110 0.01379
450 0.336200 0.038000 1.772110 0.15664 0.01770
455 0.318700 0.048000 1.744100 0.15099 0.02274
460 0.290800 0.060000 1.669200 0.14396 0.02970
465 0.251100 0.073900 1.528100 0.13550 0.03988
470 0.195360 0.090980 1.287640 0.12412 0.05780
475 0.142100 0.112600 1.041900 0.10959 0.08684
480 0.095640 0.139020 0.812950 0.09129 0.13270
485 0.057950 0.169300 0.616200 0.06871 0.20072
490 0.032010 0.208020 0.465180 0.04539 0.29498
495 0.014700 0.258600 0.353300 0.02346 0.41270
500 0.004900 0.323000 0.272000 0.00817 0.53842
505 0.002400 0.407300 0.212300 0.00386 0.65482
510 0.009300 0.503000 0.158200 0.01387 0.75019
515 0.029100 0.608200 0.111700 0.03885 0.81202
520 0.063270 0.710000 0.078250 0.07430 0.83380
525 0.109600 0.793200 0.057250 0.11416 0.82621
530 0.165500 0.862000 0.042160 0.15472 0.80586
535 0.225750 0.914850 0.029840 0.19288 0.78163
540 0.290400 0.954000 0.020300 0.22962 0.75433
545 0.359700 0.980300 0.013400 0.26578 0.72432
550 0.433450 0.994950 0.008750 0.30160 0.69231
555 0.512050 1.000000 0.005750 0.33736 0.65885
560 0.594500 0.995000 0.003900 0.37310 0.62445
565 0.678400 0.978600 0.002750 0.40874 0.58961
570 0.762100 0.952000 0.002100 0.44406 0.55471
575 0.842500 0.915400 0.001800 0.47877 0.52020
580 0.916300 0.870000 0.001650 0.51249 0.48659
585 0.978600 0.816300 0.001400 0.54479 0.45443
590 1.026300 0.757000 0.001100 0.57515 0.42423
595 1.056700 0.694900 0.001000 0.60293 0.39650
600 1.062200 0.631000 0.000800 0.62704 0.37249
605 1.045600 0.566800 0.000600 0.64823 0.35139
610 1.002600 0.503000 0.000340 0.66576 0.33401
615 0.938400 0.441200 0.000240 0.68008 0.31975
620 0.854450 0.381000 0.000190 0.69150 0.30834
625 0.751400 0.321000 0.000100 0.70061 0.29930
630 0.642400 0.265000 0.000050 0.70792 0.29203
635 0.541900 0.217000 0.000030 0.71403 0.28593
640 0.447900 0.175000 0.000020 0.71903 0.28093
645 0.360800 0.138200 0.000010 0.72303 0.27695
650 0.283500 0.107000 0.000000 0.72599 0.27401
655 0.218700 0.081600 0.000000 0.72827 0.27173
660 0.164900 0.061000 0.000000 0.72997 0.27003
665 0.121200 0.044580 0.000000 0.73109 0.26891
670 0.087400 0.032000 0.000000 0.73199 0.26801
675 0.063600 0.023200 0.000000 0.73272 0.26728
680 0.046770 0.017000 0.000000 0.73342 0.26658
685 0.032900 0.011920 0.000000 0.73405 0.26595
690 0.022700 0.008210 0.000000 0.73439 0.26561
695 0.015840 0.005723 0.000000 0.73459 0.26541
700 0.011359 0.004102 0.000000 0.73469 0.26531
705 0.008111 0.002929 0.000000 0.73469 0.26531
710 0.005790 0.002091 0.000000 0.73469 0.26531
715 0.004109 0.001484 0.000000 0.73469 0.26531
720 0.002899 0.001047 0.000000 0.73469 0.26531
725 0.002049 0.000740 0.000000 0.73469 0.26531
730 0.001440 0.000520 0.000000 0.73469 0.26531
735 0.001000 0.000361 0.000000 0.73469 0.26531
740 0.000690 0.000249 0.000000 0.73469 0.26531
745 0.000476 0.000172 0.000000 0.73469 0.26531
750 0.000332 0.000120 0.000000 0.73469 0.26531
755 0.000235 0.000085 0.000000 0.73469 0.26531
760 0.000166 0.000060 0.000000 0.73469 0.26531

## cie1931.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The CIE 1931 Standard Observer\n",
    "Some charts and calculations based on color matching functions and spectral power distribution numbers from CIE 15: Technical Report: Colorimetry, 3rd edition. CIE 15:2004"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get color matching functions from CIE 15:2004.  This is Table T.4. _CIE 1931 standard colorimetric observer_, copied from the pdf and pasted into the csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmf = np.loadtxt(\"cie1931.csv\", skiprows=1).reshape((-1,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get spectral power distribution for various standard illuminants.  This is Table T.1. _Relative spectral power distributions of CIE illuminants_, copied from the pdf and pasted into the csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "spd = np.loadtxt(\"cie1931_spd.csv\", skiprows=1).reshape((-1,7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recommendation ITU-R BT.709 color primaries and reference white "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_primaries_709 = np.array([[0.64, 0.33, 0.03], [0.3, 0.6, 0.1], [0.15, 0.06, 0.79]]).transpose()\n",
    "reference_white_709 = np.array([0.312727, 0.329024, 0.358249])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "J is the relative weighting of color primaries to get the reference white color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.64446254,  0.        ,  0.        ],\n",
       "       [ 0.        ,  1.19192148,  0.        ],\n",
       "       [ 0.        ,  0.        ,  1.20290795]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "J = np.matmul(np.linalg.inv(color_primaries_709), reference_white_709) / reference_white_709[1]\n",
    "np.diag(J)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The middle row of color_primaries_709 is for luminance, Y.  When multiplied by the relative weights of color primaries J the result is normalized to unity.  This is a check on the computation of J"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.inner(color_primaries_709[1,:], J)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the same calculation but for all three XYZ components.  If you start with RGB = (1, 1, 1) which is white then what values do you get for XYZ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.95046866,  1.        ,  1.08882331])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.matmul(np.matmul(color_primaries_709, np.diag(J)), np.array([1.0, 1.0, 1.0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By the way, this is the same as reference white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.95046866,  1.        ,  1.08882331])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reference_white_709 / reference_white_709[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sum all the values in the color matching functions for X, Y, Z.  They are normalized to have equal sums:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.371223000000001"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(cmf[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.371218999999996"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(cmf[:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.37154"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(cmf[:,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the color matching functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BlovIWyLSsKCTiEg5EUkLbLhVCk2UvPUWtGnjumIOPRSmToUnn4SUFPd6xYrQqZNrHZk8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff4f84880f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6.0, 3.0), dpi=100)\n",
    "fig.subplots_adjust(hspace=0.5)\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(cmf[:,0],cmf[:,1], label=r'$\\overline{x}$', color='red')\n",
    "ax.plot(cmf[:,0],cmf[:,2], label=r'$\\overline{y}$', color='green')\n",
    "ax.plot(cmf[:,0],cmf[:,3], label=r'$\\overline{z}$', color='blue')\n",
    "ax.set_xlabel(r'wavelength of light $\\lambda$ (nm)')\n",
    "ax.set_ylabel('relative sensitivity')\n",
    "ax.set_title(r'CIE 1931 $2\\degree$ color matching functions')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the spectral power distribution for D65 illuminant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8II/M/BKiwoIZ0aVVY70M5ceSYiJ4+rxB3PD+Kt74ZScn9k5kXE9tFK1UQ2mJ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\nvKHARERERPLG/wLjarNzSIIN1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff4f6346940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6.0, 3.0), dpi=100)\n",
    "fig.subplots_adjust(hspace=0.5)\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(spd[:,0],spd[:,2], label=r'$M_{rel}(\\lambda)$')\n",
    "ax.set_xlabel(r'wavelength of light $\\lambda$ (nm)')\n",
    "ax.set_ylabel('relative power')\n",
    "ax.set_title(r'spectral power distribution for $D_{65}$ illuminant')\n",
    "ax.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute X, Y, Z for the D65 illuminant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0059112269402001e-05, 1.0593608756991503e-05, 1.150575810585e-05)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.inner(spd[16:93,2],cmf[:,1]) * 5e-9\n",
    "Y = np.inner(spd[16:93,2],cmf[:,2]) * 5e-9\n",
    "Z = np.inner(spd[16:93,2],cmf[:,3]) * 5e-9\n",
    "(X, Y, Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize by dividing by X + Y + Z to get x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.31279813414174473, 0.32941883580464121)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = X / (X + Y + Z)\n",
    "y = Y / (X + Y + Z)\n",
    "(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the same as the reference white position on the CIE 1931 Chromaticity Diagram"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

## cie1931_spd.csv
"lambda. nm" "Standard IIluminant A" "Standard Iliuminant D65" "Illuminant C" "IIluminant D50" "Illuminant D55" "Illuminant D75"
300 0.930483 0.0341000 0.00 0.019 0.024 0.043
305 1.12821 1.66430 0.00 1.035 1.048 2.588
310 1.35769 3.29450 0.00 2.051 2.072 5.133
315 1.62219 11.7652 0.00 4.914 6.648 17.470
320 1.92508 20.2360 0.01 7.778 11.224 29.808
325 2.26980 28.6447 0.20 11.263 15.936 42.369
330 2.65981 37.0535 0.40 14.748 20.647 54.930
335 3.09861 38.5011 1.55 16.348 22.266 56.095
340 3.58968 39.9488 2.70 17.948 23.885 57.259
345 4.13648 42.4302 4.85 19.479 25.851 60.000
350 4.74238 44.9117 7.00 21.010 27.817 62.740
355 5.41070 45.7750 9.95 22.476 29.219 62.861
360 6.14462 46.6383 12.90 23.942 30.621 62.982
365 6.94720 49.3637 17.20 25.451 32.464 66.647
370 7.82135 52.0891 21.40 26.961 34.308 70.312
375 8.76980 51.0323 27.50 25.724 33.446 68.507
380 9.79510 49.9755 33.00 24.488 32.584 66.703
385 10.8996 52.3118 39.92 27.179 35.335 68.333
390 12.0853 54.6482 47.40 29.871 38.087 69.963
395 13.3543 68.7015 55.17 39.589 49.518 85.946
400 14.7080 82.7549 63.30 49.308 60.949 101.929
405 16.1480 87.1204 71.81 52.910 64.751 106.911
410 17.6753 91.4860 80.60 56.513 68.554 111.894
415 19.2907 92.4589 89.53 58.273 70.065 112.346
420 20.9950 93.4318 98.10 60.034 71.577 112.798
425 22.7883 90.0570 105.80 58.926 69.746 107.945
430 24.6709 86.6823 112.40 57.818 67.914 103.092
435 26.6425 95.7736 117.75 66.321 76.760 112.145
440 28.7027 104.865 121.50 74.825 85.605 121.198
445 30.8508 110.936 123.45 81.036 91.799 127.104
450 33.0859 117.008 124.00 87.247 97.993 133.010
455 35.4068 117.410 123.60 88.930 99.228 132.682
460 37.8121 117.812 123.10 90.612 100.463 132.355
465 40.3002 116.336 123.30 90.990 100.188 129.838
470 42.8693 114.861 123.80 91.368 99.913 127.322
475 45.5174 115.392 124.09 93.238 101.326 127.061
480 48.2423 115.923 123.90 95.109 102.739 126.800
485 51.0418 112.367 122.92 93.536 100.409 122.291
490 53.9132 108.811 120.70 91.963 98.078 117.783
495 56.8539 109.082 116.90 93.843 99.379 117.186
500 59.8611 109.354 112.10 95.724 100.680 116.589
505 62.9320 108.578 106.98 96.169 100.688 115.146
510 66.0635 107.802 102.30 96.613 100.695 113.702
515 69.2525 106.296 98.81 96.871 100.341 111.181
520 72.4959 104.790 96.90 97.129 99.987 108.659
525 75.7903 106.239 96.78 99.614 102.098 109.552
530 79.1326 107.689 98.00 102.099 104.210 110.445
535 82.5193 106.047 99.94 101.427 103.156 108.367
540 85.9470 104.405 102.10 100.755 102.102 106.289
545 89.4124 104.225 103.95 101.536 102.535 105.596
550 92.9120 104.046 105.20 102.317 102.968 104.904
555 96.4423 102.023 105.67 101.159 101.484 102.452
560 100.000 105.30 100.000 100.000 100.000 100.000
565 103.582 98.1671 104.11 98.868 98.608 97.808
570 107.184 96.3342 102.30 97.735 97.216 95.616
575 110.803 96.0611 100.15 98.327 97.482 94.914
580 114.436 95.7880 97.80 98.918 97.749 94.213
585 118.080 92.2368 95.43 96.208 94.590 90.605
590 121.731 88.6856 93.20 93.499 91.432 86.997
595 125.386 89.3459 91.22 95.593 92.926 87.112
600 129.043 90.0062 89.70 97.688 94.419 87.227
605 132.697 89.8026 88.83 98.478 94.780 86.684
610 136.346 89.5991 88.40 99.269 95.140 86.140
615 139.988 88.6489 88.19 99.155 94.680 84.861
620 143.618 87.6987 88.10 99.042 94.220 83.581
625 147.235 85.4936 88.06 97.382 92.334 81.164
630 150.836 83.2886 88.00 95.722 90.448 78.747
635 154.418 83.4939 87.86 97.290 91.389 78.587
640 157.979 83.6992 87.80 98.857 92.330 78.428
645 161.516 81.8630 87.99 97.262 90.592 76.614
650 165.028 80.0268 88.20 95.667 88.854 74.801
655 168.510 80.1207 88.20 96.929 89.586 74.562
660 171.963 80.2146 87.90 98.190 90.317 74.324
665 175.383 81.2462 87.22 100.597 92.133 74.873
670 178.769 82.2778 86.30 103.003 93.950 75.422
675 182.118 80.2810 85.30 101.068 91.953 73.499
680 185.429 78.2842 84.00 99.133 89.956 71.576
685 188.701 74.0027 82.21 93.257 84.817 67.714
690 191.931 69.7213 80.20 87.381 79.677 63.852
695 195.118 70.6652 78.24 81.258 64.464 66.697
700 198.261 71.6091 76.30 91.604 82.840 65.076
705 201.359 72.9790 74.36 92.246 83.842 66.573
710 204.409 74.3490 72.40 92.889 84.844 68.070
715 207.411 67.9765 70.40 84.872 77.539 62.256
720 210.365 61.6040 68.30 76.854 70.235 56.443
725 213.268 65.7448 66.30 81.683 74.768 60.343
730 216.120 69.8856 64.40 86.511 79.301 64.242
735 218.920 72.4863 62.80 89.546 82.147 66.697
740 221.667 75.0870 61.50 92.580 84.993 69.151
745 224.361 69.3398 60.20 85.405 78.437 63.890
750 227.000 63.5927 59.20 78.230 71.880 58.629
755 229.585 55.0054 58.50 67.961 62.337 50.623
760 232.115 46.4182 58.10 57.692 52.793 42.617
765 234.589 56.6118 58.00 70.307 64.360 51.985
770 237.008 66.8054 58.20 82.923 75.927 61.352
775 239.370 65.0941 58.50 80.599 73.872 59.838
780 241.675 63.3828 59.10 78.274 71.818 58.324
	"wavelength, nm" "x-bar" "y-bar" "z-bar" "little-x" "little-y"
	380 0.001368 0.000039 0.006450 0.17411 0.00496
	385 0.002236 0.000064 0.010550 0.17401 0.00498
	390 0.004243 0.000120 0.020050 0.17380 0.00492
	395 0.007650 0.000217 0.036210 0.17356 0.00492
	400 0.014310 0.000396 0.067850 0.17334 0.00480
	405 0.023190 0.000640 0.110200 0.17302 0.00478
	410 0.043510 0.001210 0.207400 0.17258 0.00480
	415 0.077630 0.002180 0.371300 0.17209 0.00483
	420 0.134380 0.004000 0.645600 0.17141 0.00510
	425 0.214770 0.007300 1.039050 0.17030 0.00579
	430 0.283900 0.011600 1.385600 0.16888 0.00690
	435 0.328500 0.016840 1.622960 0.16690 0.00856
	440 0.348280 0.023000 1.747060 0.16441 0.01086
	445 0.348060 0.029800 1.782600 0.16110 0.01379
	450 0.336200 0.038000 1.772110 0.15664 0.01770
	455 0.318700 0.048000 1.744100 0.15099 0.02274
	460 0.290800 0.060000 1.669200 0.14396 0.02970
	465 0.251100 0.073900 1.528100 0.13550 0.03988
	470 0.195360 0.090980 1.287640 0.12412 0.05780
	475 0.142100 0.112600 1.041900 0.10959 0.08684
	480 0.095640 0.139020 0.812950 0.09129 0.13270
	485 0.057950 0.169300 0.616200 0.06871 0.20072
	490 0.032010 0.208020 0.465180 0.04539 0.29498
	495 0.014700 0.258600 0.353300 0.02346 0.41270
	500 0.004900 0.323000 0.272000 0.00817 0.53842
	505 0.002400 0.407300 0.212300 0.00386 0.65482
	510 0.009300 0.503000 0.158200 0.01387 0.75019
	515 0.029100 0.608200 0.111700 0.03885 0.81202
	520 0.063270 0.710000 0.078250 0.07430 0.83380
	525 0.109600 0.793200 0.057250 0.11416 0.82621
	530 0.165500 0.862000 0.042160 0.15472 0.80586
	535 0.225750 0.914850 0.029840 0.19288 0.78163
	540 0.290400 0.954000 0.020300 0.22962 0.75433
	545 0.359700 0.980300 0.013400 0.26578 0.72432
	550 0.433450 0.994950 0.008750 0.30160 0.69231
	555 0.512050 1.000000 0.005750 0.33736 0.65885
	560 0.594500 0.995000 0.003900 0.37310 0.62445
	565 0.678400 0.978600 0.002750 0.40874 0.58961
	570 0.762100 0.952000 0.002100 0.44406 0.55471
	575 0.842500 0.915400 0.001800 0.47877 0.52020
	580 0.916300 0.870000 0.001650 0.51249 0.48659
	585 0.978600 0.816300 0.001400 0.54479 0.45443
	590 1.026300 0.757000 0.001100 0.57515 0.42423
	595 1.056700 0.694900 0.001000 0.60293 0.39650
	600 1.062200 0.631000 0.000800 0.62704 0.37249
	605 1.045600 0.566800 0.000600 0.64823 0.35139
	610 1.002600 0.503000 0.000340 0.66576 0.33401
	615 0.938400 0.441200 0.000240 0.68008 0.31975
	620 0.854450 0.381000 0.000190 0.69150 0.30834
	625 0.751400 0.321000 0.000100 0.70061 0.29930
	630 0.642400 0.265000 0.000050 0.70792 0.29203
	635 0.541900 0.217000 0.000030 0.71403 0.28593
	640 0.447900 0.175000 0.000020 0.71903 0.28093
	645 0.360800 0.138200 0.000010 0.72303 0.27695
	650 0.283500 0.107000 0.000000 0.72599 0.27401
	655 0.218700 0.081600 0.000000 0.72827 0.27173
	660 0.164900 0.061000 0.000000 0.72997 0.27003
	665 0.121200 0.044580 0.000000 0.73109 0.26891
	670 0.087400 0.032000 0.000000 0.73199 0.26801
	675 0.063600 0.023200 0.000000 0.73272 0.26728
	680 0.046770 0.017000 0.000000 0.73342 0.26658
	685 0.032900 0.011920 0.000000 0.73405 0.26595
	690 0.022700 0.008210 0.000000 0.73439 0.26561
	695 0.015840 0.005723 0.000000 0.73459 0.26541
	700 0.011359 0.004102 0.000000 0.73469 0.26531
	705 0.008111 0.002929 0.000000 0.73469 0.26531
	710 0.005790 0.002091 0.000000 0.73469 0.26531
	715 0.004109 0.001484 0.000000 0.73469 0.26531
	720 0.002899 0.001047 0.000000 0.73469 0.26531
	725 0.002049 0.000740 0.000000 0.73469 0.26531
	730 0.001440 0.000520 0.000000 0.73469 0.26531
	735 0.001000 0.000361 0.000000 0.73469 0.26531
	740 0.000690 0.000249 0.000000 0.73469 0.26531
	745 0.000476 0.000172 0.000000 0.73469 0.26531
	750 0.000332 0.000120 0.000000 0.73469 0.26531
	755 0.000235 0.000085 0.000000 0.73469 0.26531
	760 0.000166 0.000060 0.000000 0.73469 0.26531
	"lambda. nm" "Standard IIluminant A" "Standard Iliuminant D65" "Illuminant C" "IIluminant D50" "Illuminant D55" "Illuminant D75"
	300 0.930483 0.0341000 0.00 0.019 0.024 0.043
	305 1.12821 1.66430 0.00 1.035 1.048 2.588
	310 1.35769 3.29450 0.00 2.051 2.072 5.133
	315 1.62219 11.7652 0.00 4.914 6.648 17.470
	320 1.92508 20.2360 0.01 7.778 11.224 29.808
	325 2.26980 28.6447 0.20 11.263 15.936 42.369
	330 2.65981 37.0535 0.40 14.748 20.647 54.930
	335 3.09861 38.5011 1.55 16.348 22.266 56.095
	340 3.58968 39.9488 2.70 17.948 23.885 57.259
	345 4.13648 42.4302 4.85 19.479 25.851 60.000
	350 4.74238 44.9117 7.00 21.010 27.817 62.740
	355 5.41070 45.7750 9.95 22.476 29.219 62.861
	360 6.14462 46.6383 12.90 23.942 30.621 62.982
	365 6.94720 49.3637 17.20 25.451 32.464 66.647
	370 7.82135 52.0891 21.40 26.961 34.308 70.312
	375 8.76980 51.0323 27.50 25.724 33.446 68.507
	380 9.79510 49.9755 33.00 24.488 32.584 66.703
	385 10.8996 52.3118 39.92 27.179 35.335 68.333
	390 12.0853 54.6482 47.40 29.871 38.087 69.963
	395 13.3543 68.7015 55.17 39.589 49.518 85.946
	400 14.7080 82.7549 63.30 49.308 60.949 101.929
	405 16.1480 87.1204 71.81 52.910 64.751 106.911
	410 17.6753 91.4860 80.60 56.513 68.554 111.894
	415 19.2907 92.4589 89.53 58.273 70.065 112.346
	420 20.9950 93.4318 98.10 60.034 71.577 112.798
	425 22.7883 90.0570 105.80 58.926 69.746 107.945
	430 24.6709 86.6823 112.40 57.818 67.914 103.092
	435 26.6425 95.7736 117.75 66.321 76.760 112.145
	440 28.7027 104.865 121.50 74.825 85.605 121.198
	445 30.8508 110.936 123.45 81.036 91.799 127.104
	450 33.0859 117.008 124.00 87.247 97.993 133.010
	455 35.4068 117.410 123.60 88.930 99.228 132.682
	460 37.8121 117.812 123.10 90.612 100.463 132.355
	465 40.3002 116.336 123.30 90.990 100.188 129.838
	470 42.8693 114.861 123.80 91.368 99.913 127.322
	475 45.5174 115.392 124.09 93.238 101.326 127.061
	480 48.2423 115.923 123.90 95.109 102.739 126.800
	485 51.0418 112.367 122.92 93.536 100.409 122.291
	490 53.9132 108.811 120.70 91.963 98.078 117.783
	495 56.8539 109.082 116.90 93.843 99.379 117.186
	500 59.8611 109.354 112.10 95.724 100.680 116.589
	505 62.9320 108.578 106.98 96.169 100.688 115.146
	510 66.0635 107.802 102.30 96.613 100.695 113.702
	515 69.2525 106.296 98.81 96.871 100.341 111.181
	520 72.4959 104.790 96.90 97.129 99.987 108.659
	525 75.7903 106.239 96.78 99.614 102.098 109.552
	530 79.1326 107.689 98.00 102.099 104.210 110.445
	535 82.5193 106.047 99.94 101.427 103.156 108.367
	540 85.9470 104.405 102.10 100.755 102.102 106.289
	545 89.4124 104.225 103.95 101.536 102.535 105.596
	550 92.9120 104.046 105.20 102.317 102.968 104.904
	555 96.4423 102.023 105.67 101.159 101.484 102.452
	560 100.000 105.30 100.000 100.000 100.000 100.000
	565 103.582 98.1671 104.11 98.868 98.608 97.808
	570 107.184 96.3342 102.30 97.735 97.216 95.616
	575 110.803 96.0611 100.15 98.327 97.482 94.914
	580 114.436 95.7880 97.80 98.918 97.749 94.213
	585 118.080 92.2368 95.43 96.208 94.590 90.605
	590 121.731 88.6856 93.20 93.499 91.432 86.997
	595 125.386 89.3459 91.22 95.593 92.926 87.112
	600 129.043 90.0062 89.70 97.688 94.419 87.227
	605 132.697 89.8026 88.83 98.478 94.780 86.684
	610 136.346 89.5991 88.40 99.269 95.140 86.140
	615 139.988 88.6489 88.19 99.155 94.680 84.861
	620 143.618 87.6987 88.10 99.042 94.220 83.581
	625 147.235 85.4936 88.06 97.382 92.334 81.164
	630 150.836 83.2886 88.00 95.722 90.448 78.747
	635 154.418 83.4939 87.86 97.290 91.389 78.587
	640 157.979 83.6992 87.80 98.857 92.330 78.428
	645 161.516 81.8630 87.99 97.262 90.592 76.614
	650 165.028 80.0268 88.20 95.667 88.854 74.801
	655 168.510 80.1207 88.20 96.929 89.586 74.562
	660 171.963 80.2146 87.90 98.190 90.317 74.324
	665 175.383 81.2462 87.22 100.597 92.133 74.873
	670 178.769 82.2778 86.30 103.003 93.950 75.422
	675 182.118 80.2810 85.30 101.068 91.953 73.499
	680 185.429 78.2842 84.00 99.133 89.956 71.576
	685 188.701 74.0027 82.21 93.257 84.817 67.714
	690 191.931 69.7213 80.20 87.381 79.677 63.852
	695 195.118 70.6652 78.24 81.258 64.464 66.697
	700 198.261 71.6091 76.30 91.604 82.840 65.076
	705 201.359 72.9790 74.36 92.246 83.842 66.573
	710 204.409 74.3490 72.40 92.889 84.844 68.070
	715 207.411 67.9765 70.40 84.872 77.539 62.256
	720 210.365 61.6040 68.30 76.854 70.235 56.443
	725 213.268 65.7448 66.30 81.683 74.768 60.343
	730 216.120 69.8856 64.40 86.511 79.301 64.242
	735 218.920 72.4863 62.80 89.546 82.147 66.697
	740 221.667 75.0870 61.50 92.580 84.993 69.151
	745 224.361 69.3398 60.20 85.405 78.437 63.890
	750 227.000 63.5927 59.20 78.230 71.880 58.629
	755 229.585 55.0054 58.50 67.961 62.337 50.623
	760 232.115 46.4182 58.10 57.692 52.793 42.617
	765 234.589 56.6118 58.00 70.307 64.360 51.985
	770 237.008 66.8054 58.20 82.923 75.927 61.352
	775 239.370 65.0941 58.50 80.599 73.872 59.838
	780 241.675 63.3828 59.10 78.274 71.818 58.324