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@cgobat
Last active August 16, 2022 19:35
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Color stuff with spectra
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{
"cells": [
{
"cell_type": "markdown",
"id": "d3905291",
"metadata": {},
"source": [
"Utilities for calculating the apparent (RGB) color given an intensity spectrum as a function of photon wavelength."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "theoretical-coupon",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd,numpy as np,matplotlib.pyplot as plt\n",
"from scipy import interpolate"
]
},
{
"cell_type": "markdown",
"id": "molecular-abortion",
"metadata": {},
"source": [
"The XYZ color space is more readily translated to and from spectral colors than RGB. First we acquire spectral color matching functions: [GitHub/mocabe/CIE-1931-XYZ-Color-Space-Standard-Observer-Color-Matching-Functions](https://github.com/mocabe/CIE-1931-XYZ-Color-Space-Standard-Observer-Color-Matching-Functions) and define spectral response functions for X, Y, and Z."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "mental-multimedia",
"metadata": {},
"outputs": [],
"source": [
"xyz = pd.read_csv(\"xyz_CIE1931.csv\")\n",
"\n",
"x = interpolate.interp1d(xyz[\"lam\"],xyz[\"x\"],bounds_error=False,fill_value=0)\n",
"y = interpolate.interp1d(xyz[\"lam\"],xyz[\"y\"],bounds_error=False,fill_value=0)\n",
"z = interpolate.interp1d(xyz[\"lam\"],xyz[\"z\"],bounds_error=False,fill_value=0)"
]
},
{
"cell_type": "markdown",
"id": "d5ed4254",
"metadata": {},
"source": [
"We can now move on to working in RGB."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "patient-latest",
"metadata": {},
"outputs": [],
"source": [
"def RGB_to_XYZ(RGB):\n",
" 'http://www.brucelindbloom.com/index.html?Eqn_RGB_XYZ_Matrix.html'\n",
" M = np.array([[0.4887180, 0.3106803, 0.2006017],\n",
" [0.1762044, 0.8129847, 0.0108109],\n",
" [0.0000000, 0.0102048, 0.9897952]])\n",
" XYZ = M @ np.array(RGB) # transform\n",
" return XYZ\n",
"\n",
"def XYZ_to_RGB(XYZ):\n",
" 'http://www.brucelindbloom.com/index.html?Eqn_RGB_XYZ_Matrix.html'\n",
" M_inv = np.array([[ 2.3706743, -0.9000405, -0.4706338],\n",
" [-0.5138850, 1.4253036, 0.0885814],\n",
" [ 0.0052982, -0.0146949, 1.0093968]])\n",
" RGB = np.array(XYZ) @ M_inv # transform\n",
" return RGB\n",
"\n",
"def RGB_from_spec(λ,vals,normalize=True,as_hex=False):\n",
" λ = np.array(λ)\n",
" vals = np.array(vals)\n",
" assert len(vals) == len(λ) # spectrum and wavelength dimensions must match\n",
" assert len(λ.shape) == len(vals.shape) == 1 # arrays should be 1D\n",
" N = len(λ) # number of points in spectrum\n",
" vals = vals/vals.max() # normalize spectrum\n",
" X = Y = Z = 0 # initialize X,Y,Z color triplet values to 0\n",
" \n",
" for i in range(N-1):\n",
" dλ = λ[i+1]-λ[i] # step width in wavelength\n",
" val = 0.5*(vals[i]+vals[i+1]) # spectrum values (arbitrary units)\n",
" \n",
" # evaluate the CIE observer weight functions at this spot in the spectrum\n",
" x_here = (x(λ[i])+x(λ[i+1]))/2\n",
" y_here = (y(λ[i])+y(λ[i+1]))/2\n",
" z_here = (z(λ[i])+z(λ[i+1]))/2\n",
" \n",
" # numerically integrate\n",
" X += x_here*val*dλ\n",
" Y += y_here*val*dλ\n",
" Z += z_here*val*dλ\n",
" \n",
" R,G,B = XYZ_to_RGB([X,Y,Z]) # convert XYZ color triplet to RGB\n",
" if normalize:\n",
" peak = max([R,G,B])\n",
" R /= peak\n",
" G /= peak\n",
" B /= peak\n",
" if as_hex:\n",
" return '#%02x%02x%02x' % (round(R),round(G),round(B))\n",
" else:\n",
" return np.array([R,G,B])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "earned-distribution",
"metadata": {},
"outputs": [],
"source": [
"spec = pd.read_csv(\"sample_spectra.csv\")\n",
"spec.columns = [\"lambda\",\"a\",\"b\"]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "permanent-mumbai",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for wl in range(360,790):\n",
" plt.plot([wl]*2,[0,2],linewidth=1,\n",
" color=np.sqrt(np.abs(RGB_from_spec([wl-1,wl,wl+1],[0,1,0],normalize=True))))\n",
"plt.plot(spec[\"lambda\"],spec[\"a\"],color=RGB_from_spec(spec[\"lambda\"],spec[\"a\"]),label=\"Spectrum A\")\n",
"plt.plot(spec[\"lambda\"],spec[\"b\"],color=RGB_from_spec(spec[\"lambda\"],spec[\"b\"]),label=\"Spectrum B\")\n",
"plt.plot(spec[\"lambda\"],x(spec[\"lambda\"]),\"r:\",label=r\"$\\bar{x}(\\lambda)$\")\n",
"plt.plot(spec[\"lambda\"],y(spec[\"lambda\"]),\"g:\",label=r\"$\\bar{y}(\\lambda)$\")\n",
"plt.plot(spec[\"lambda\"],z(spec[\"lambda\"]),\"b:\",label=r\"$\\bar{z}(\\lambda)$\")\n",
"plt.legend()\n",
"plt.xlabel(r\"$\\lambda$ [nm]\")\n",
"plt.ylabel(\"Spectral intensity\")\n",
"plt.xlim(300,790)\n",
"plt.ylim(0,2)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "intimate-traveler",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.13 64-bit (windows store)",
"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": "3.9.13"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
},
"vscode": {
"interpreter": {
"hash": "ab71228b3e09297cc4e0c1b6adea629ded25da184e6754fb14ae42947e6aab48"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Wavelength Sample 1 Absorbance Sample 2 Absorbance
2.20E+02 1.13E+00 1.38E+00
2.21E+02 1.16E+00 1.43E+00
2.22E+02 1.17E+00 1.45E+00
2.23E+02 1.21E+00 1.48E+00
2.24E+02 1.22E+00 1.47E+00
2.25E+02 1.24E+00 1.47E+00
2.26E+02 1.24E+00 1.45E+00
2.27E+02 1.24E+00 1.48E+00
2.28E+02 1.24E+00 1.48E+00
2.29E+02 1.24E+00 1.48E+00
2.30E+02 1.23E+00 1.48E+00
2.31E+02 1.24E+00 1.49E+00
2.32E+02 1.25E+00 1.49E+00
2.33E+02 1.26E+00 1.50E+00
2.34E+02 1.26E+00 1.51E+00
2.35E+02 1.27E+00 1.52E+00
2.36E+02 1.27E+00 1.53E+00
2.37E+02 1.29E+00 1.53E+00
2.38E+02 1.29E+00 1.53E+00
2.39E+02 1.29E+00 1.54E+00
2.40E+02 1.29E+00 1.53E+00
2.41E+02 1.30E+00 1.54E+00
2.42E+02 1.30E+00 1.53E+00
2.43E+02 1.31E+00 1.55E+00
2.44E+02 1.31E+00 1.55E+00
2.45E+02 1.31E+00 1.55E+00
2.46E+02 1.31E+00 1.55E+00
2.47E+02 1.32E+00 1.56E+00
2.48E+02 1.31E+00 1.55E+00
2.49E+02 1.32E+00 1.55E+00
2.50E+02 1.32E+00 1.55E+00
2.51E+02 1.32E+00 1.56E+00
2.52E+02 1.32E+00 1.56E+00
2.53E+02 1.32E+00 1.57E+00
2.54E+02 1.33E+00 1.57E+00
2.55E+02 1.33E+00 1.58E+00
2.56E+02 1.33E+00 1.58E+00
2.57E+02 1.33E+00 1.59E+00
2.58E+02 1.34E+00 1.60E+00
2.59E+02 1.33E+00 1.60E+00
2.60E+02 1.33E+00 1.60E+00
2.61E+02 1.33E+00 1.61E+00
2.62E+02 1.33E+00 1.61E+00
2.63E+02 1.33E+00 1.61E+00
2.64E+02 1.33E+00 1.61E+00
2.65E+02 1.33E+00 1.61E+00
2.66E+02 1.33E+00 1.61E+00
2.67E+02 1.33E+00 1.61E+00
2.68E+02 1.34E+00 1.61E+00
2.69E+02 1.34E+00 1.62E+00
2.70E+02 1.34E+00 1.61E+00
2.71E+02 1.34E+00 1.62E+00
2.72E+02 1.34E+00 1.62E+00
2.73E+02 1.34E+00 1.62E+00
2.74E+02 1.34E+00 1.63E+00
2.75E+02 1.34E+00 1.63E+00
2.76E+02 1.34E+00 1.63E+00
2.77E+02 1.34E+00 1.64E+00
2.78E+02 1.34E+00 1.64E+00
2.79E+02 1.35E+00 1.64E+00
2.80E+02 1.35E+00 1.65E+00
2.81E+02 1.35E+00 1.65E+00
2.82E+02 1.36E+00 1.65E+00
2.83E+02 1.36E+00 1.65E+00
2.84E+02 1.36E+00 1.65E+00
2.85E+02 1.37E+00 1.66E+00
2.86E+02 1.37E+00 1.66E+00
2.87E+02 1.38E+00 1.67E+00
2.88E+02 1.38E+00 1.67E+00
2.89E+02 1.39E+00 1.68E+00
2.90E+02 1.39E+00 1.68E+00
2.91E+02 1.40E+00 1.69E+00
2.92E+02 1.40E+00 1.69E+00
2.93E+02 1.41E+00 1.69E+00
2.94E+02 1.41E+00 1.70E+00
2.95E+02 1.41E+00 1.70E+00
2.96E+02 1.41E+00 1.70E+00
2.97E+02 1.42E+00 1.71E+00
2.98E+02 1.42E+00 1.71E+00
2.99E+02 1.43E+00 1.71E+00
3.00E+02 1.43E+00 1.71E+00
3.01E+02 1.44E+00 1.72E+00
3.02E+02 1.44E+00 1.72E+00
3.03E+02 1.44E+00 1.73E+00
3.04E+02 1.45E+00 1.73E+00
3.05E+02 1.45E+00 1.74E+00
3.06E+02 1.46E+00 1.74E+00
3.07E+02 1.47E+00 1.74E+00
3.08E+02 1.47E+00 1.75E+00
3.09E+02 1.47E+00 1.75E+00
3.10E+02 1.48E+00 1.76E+00
3.11E+02 1.48E+00 1.76E+00
3.12E+02 1.48E+00 1.76E+00
3.13E+02 1.49E+00 1.77E+00
3.14E+02 1.49E+00 1.77E+00
3.15E+02 1.49E+00 1.77E+00
3.16E+02 1.50E+00 1.77E+00
3.17E+02 1.50E+00 1.77E+00
3.18E+02 1.50E+00 1.78E+00
3.19E+02 1.51E+00 1.78E+00
3.20E+02 1.51E+00 1.78E+00
3.21E+02 1.51E+00 1.79E+00
3.22E+02 1.52E+00 1.79E+00
3.23E+02 1.52E+00 1.79E+00
3.24E+02 1.52E+00 1.80E+00
3.25E+02 1.52E+00 1.80E+00
3.26E+02 1.52E+00 1.79E+00
3.27E+02 1.52E+00 1.79E+00
3.28E+02 1.52E+00 1.80E+00
3.29E+02 1.53E+00 1.80E+00
3.30E+02 1.53E+00 1.80E+00
3.31E+02 1.53E+00 1.80E+00
3.32E+02 1.53E+00 1.80E+00
3.33E+02 1.53E+00 1.80E+00
3.34E+02 1.54E+00 1.81E+00
3.35E+02 1.53E+00 1.82E+00
3.36E+02 1.54E+00 1.82E+00
3.37E+02 1.54E+00 1.82E+00
3.38E+02 1.54E+00 1.82E+00
3.39E+02 1.54E+00 1.82E+00
3.40E+02 1.54E+00 1.83E+00
3.41E+02 1.55E+00 1.83E+00
3.42E+02 1.55E+00 1.83E+00
3.43E+02 1.55E+00 1.83E+00
3.44E+02 1.55E+00 1.83E+00
3.45E+02 1.55E+00 1.83E+00
3.46E+02 1.55E+00 1.83E+00
3.47E+02 1.56E+00 1.83E+00
3.48E+02 1.56E+00 1.83E+00
3.49E+02 1.56E+00 1.83E+00
3.50E+02 1.57E+00 1.83E+00
3.51E+02 1.57E+00 1.83E+00
3.52E+02 1.58E+00 1.83E+00
3.53E+02 1.58E+00 1.83E+00
3.54E+02 1.59E+00 1.83E+00
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7.62E+02 6.06E-02 8.17E-02
7.63E+02 6.05E-02 8.17E-02
7.64E+02 6.05E-02 8.18E-02
7.65E+02 6.05E-02 8.20E-02
7.66E+02 6.05E-02 8.22E-02
7.67E+02 6.06E-02 8.23E-02
7.68E+02 6.07E-02 8.25E-02
7.69E+02 6.10E-02 8.26E-02
7.70E+02 6.08E-02 8.27E-02
7.71E+02 6.09E-02 8.28E-02
7.72E+02 6.09E-02 8.29E-02
7.73E+02 6.10E-02 8.30E-02
7.74E+02 6.10E-02 8.32E-02
7.75E+02 6.11E-02 8.31E-02
7.76E+02 6.11E-02 8.31E-02
7.77E+02 6.12E-02 8.31E-02
7.78E+02 6.11E-02 8.30E-02
7.79E+02 6.10E-02 8.30E-02
7.80E+02 6.10E-02 8.31E-02
7.81E+02 6.11E-02 8.31E-02
7.82E+02 6.11E-02 8.31E-02
7.83E+02 6.10E-02 8.30E-02
7.84E+02 6.09E-02 8.29E-02
7.85E+02 6.08E-02 8.29E-02
7.86E+02 6.07E-02 8.29E-02
7.87E+02 6.04E-02 8.28E-02
7.88E+02 6.03E-02 8.28E-02
7.89E+02 6.03E-02 8.28E-02
7.90E+02 6.01E-02 8.26E-02
7.91E+02 5.99E-02 8.24E-02
7.92E+02 5.97E-02 8.24E-02
7.93E+02 5.97E-02 8.23E-02
7.94E+02 5.97E-02 8.21E-02
7.95E+02 5.96E-02 8.21E-02
7.96E+02 5.96E-02 8.19E-02
7.97E+02 5.95E-02 8.19E-02
7.98E+02 5.95E-02 8.19E-02
7.99E+02 5.95E-02 8.18E-02
8.00E+02 5.93E-02 8.17E-02
8.01E+02 5.93E-02 8.17E-02
8.02E+02 5.92E-02 8.16E-02
8.03E+02 5.90E-02 8.13E-02
8.04E+02 5.88E-02 8.11E-02
8.05E+02 5.86E-02 8.10E-02
8.06E+02 5.85E-02 8.08E-02
8.07E+02 5.83E-02 8.07E-02
8.08E+02 5.82E-02 8.06E-02
8.09E+02 5.81E-02 8.03E-02
8.10E+02 5.78E-02 8.02E-02
8.11E+02 5.78E-02 8.00E-02
8.12E+02 5.76E-02 7.97E-02
8.13E+02 5.76E-02 7.95E-02
8.14E+02 5.75E-02 7.95E-02
8.15E+02 5.73E-02 7.93E-02
8.16E+02 5.72E-02 7.91E-02
8.17E+02 5.70E-02 7.89E-02
8.18E+02 5.68E-02 7.86E-02
8.19E+02 5.66E-02 7.84E-02
8.20E+02 5.65E-02 7.83E-02
8.21E+02 5.64E-02 7.81E-02
8.22E+02 5.61E-02 7.79E-02
8.23E+02 5.58E-02 7.78E-02
8.24E+02 5.56E-02 7.76E-02
8.25E+02 5.54E-02 7.73E-02
8.26E+02 5.53E-02 7.71E-02
8.27E+02 5.50E-02 7.69E-02
8.28E+02 5.49E-02 7.66E-02
8.29E+02 5.46E-02 7.64E-02
8.30E+02 5.45E-02 7.60E-02
8.31E+02 5.41E-02 7.58E-02
8.32E+02 5.38E-02 7.54E-02
8.33E+02 5.36E-02 7.51E-02
8.34E+02 5.34E-02 7.49E-02
8.35E+02 5.32E-02 7.45E-02
8.36E+02 5.31E-02 7.44E-02
8.37E+02 5.28E-02 7.41E-02
8.38E+02 5.27E-02 7.38E-02
8.39E+02 5.24E-02 7.36E-02
8.40E+02 5.21E-02 7.33E-02
8.41E+02 5.19E-02 7.31E-02
8.42E+02 5.16E-02 7.28E-02
8.43E+02 5.13E-02 7.27E-02
8.44E+02 5.11E-02 7.24E-02
8.45E+02 5.07E-02 7.20E-02
8.46E+02 5.05E-02 7.18E-02
8.47E+02 5.02E-02 7.13E-02
8.48E+02 4.99E-02 7.10E-02
8.49E+02 4.96E-02 7.06E-02
8.50E+02 4.93E-02 7.04E-02
8.51E+02 4.92E-02 7.01E-02
8.52E+02 4.89E-02 6.97E-02
8.53E+02 4.86E-02 6.96E-02
8.54E+02 4.86E-02 6.93E-02
8.55E+02 4.84E-02 6.90E-02
8.56E+02 4.83E-02 6.88E-02
8.57E+02 4.81E-02 6.85E-02
8.58E+02 4.78E-02 6.84E-02
8.59E+02 4.75E-02 6.82E-02
8.60E+02 4.73E-02 6.81E-02
8.61E+02 4.70E-02 6.77E-02
8.62E+02 4.66E-02 6.73E-02
8.63E+02 4.63E-02 6.70E-02
8.64E+02 4.61E-02 6.67E-02
8.65E+02 4.57E-02 6.63E-02
8.66E+02 4.54E-02 6.60E-02
8.67E+02 4.51E-02 6.56E-02
8.68E+02 4.49E-02 6.53E-02
8.69E+02 4.46E-02 6.49E-02
8.70E+02 4.44E-02 6.47E-02
8.71E+02 4.41E-02 6.43E-02
8.72E+02 4.38E-02 6.40E-02
8.73E+02 4.36E-02 6.38E-02
8.74E+02 4.34E-02 6.34E-02
8.75E+02 4.31E-02 6.31E-02
8.76E+02 4.29E-02 6.27E-02
8.77E+02 4.26E-02 6.24E-02
8.78E+02 4.23E-02 6.21E-02
8.79E+02 4.20E-02 6.17E-02
8.80E+02 4.16E-02 6.13E-02
8.81E+02 4.14E-02 6.10E-02
8.82E+02 4.11E-02 6.06E-02
8.83E+02 4.08E-02 6.03E-02
8.84E+02 4.06E-02 6.00E-02
8.85E+02 4.03E-02 5.97E-02
8.86E+02 4.00E-02 5.93E-02
8.87E+02 3.98E-02 5.90E-02
8.88E+02 3.95E-02 5.87E-02
8.89E+02 3.92E-02 5.83E-02
8.90E+02 3.89E-02 5.81E-02
8.91E+02 3.87E-02 5.78E-02
8.92E+02 3.84E-02 5.75E-02
8.93E+02 3.82E-02 5.71E-02
8.94E+02 3.79E-02 5.67E-02
8.95E+02 3.76E-02 5.64E-02
8.96E+02 3.73E-02 5.60E-02
8.97E+02 3.71E-02 5.58E-02
8.98E+02 3.68E-02 5.54E-02
8.99E+02 3.66E-02 5.50E-02
9.00E+02 3.62E-02 5.46E-02
9.01E+02 3.60E-02 5.42E-02
9.02E+02 3.57E-02 5.39E-02
9.03E+02 3.55E-02 5.36E-02
9.04E+02 3.52E-02 5.32E-02
9.05E+02 3.49E-02 5.29E-02
9.06E+02 3.47E-02 5.25E-02
9.07E+02 3.44E-02 5.22E-02
9.08E+02 3.41E-02 5.18E-02
9.09E+02 3.38E-02 5.15E-02
9.10E+02 3.35E-02 5.12E-02
9.11E+02 3.33E-02 5.09E-02
9.12E+02 3.30E-02 5.05E-02
9.13E+02 3.27E-02 5.02E-02
9.14E+02 3.24E-02 4.99E-02
9.15E+02 3.22E-02 4.95E-02
9.16E+02 3.19E-02 4.92E-02
9.17E+02 3.16E-02 4.89E-02
9.18E+02 3.14E-02 4.86E-02
9.19E+02 3.11E-02 4.82E-02
9.20E+02 3.08E-02 4.79E-02
9.21E+02 3.05E-02 4.75E-02
9.22E+02 3.02E-02 4.72E-02
9.23E+02 3.00E-02 4.68E-02
9.24E+02 2.97E-02 4.65E-02
9.25E+02 2.95E-02 4.62E-02
9.26E+02 2.92E-02 4.58E-02
9.27E+02 2.89E-02 4.55E-02
9.28E+02 2.86E-02 4.52E-02
9.29E+02 2.84E-02 4.48E-02
9.30E+02 2.81E-02 4.45E-02
9.31E+02 2.79E-02 4.42E-02
9.32E+02 2.77E-02 4.38E-02
9.33E+02 2.74E-02 4.35E-02
9.34E+02 2.72E-02 4.32E-02
9.35E+02 2.70E-02 4.29E-02
9.36E+02 2.68E-02 4.26E-02
9.37E+02 2.65E-02 4.23E-02
9.38E+02 2.62E-02 4.20E-02
9.39E+02 2.60E-02 4.17E-02
9.40E+02 2.58E-02 4.13E-02
9.41E+02 2.55E-02 4.10E-02
9.42E+02 2.53E-02 4.07E-02
9.43E+02 2.50E-02 4.03E-02
9.44E+02 2.47E-02 4.00E-02
9.45E+02 2.45E-02 3.97E-02
9.46E+02 2.42E-02 3.94E-02
9.47E+02 2.39E-02 3.90E-02
9.48E+02 2.37E-02 3.87E-02
9.49E+02 2.34E-02 3.84E-02
9.50E+02 2.32E-02 3.81E-02
9.51E+02 2.29E-02 3.78E-02
9.52E+02 2.27E-02 3.75E-02
9.53E+02 2.25E-02 3.72E-02
9.54E+02 2.23E-02 3.69E-02
9.55E+02 2.20E-02 3.66E-02
9.56E+02 2.18E-02 3.63E-02
9.57E+02 2.15E-02 3.60E-02
9.58E+02 2.13E-02 3.57E-02
9.59E+02 2.11E-02 3.54E-02
9.60E+02 2.08E-02 3.51E-02
9.61E+02 2.06E-02 3.48E-02
9.62E+02 2.04E-02 3.45E-02
9.63E+02 2.01E-02 3.42E-02
9.64E+02 1.99E-02 3.39E-02
9.65E+02 1.97E-02 3.36E-02
9.66E+02 1.94E-02 3.33E-02
9.67E+02 1.92E-02 3.30E-02
9.68E+02 1.90E-02 3.27E-02
9.69E+02 1.88E-02 3.25E-02
9.70E+02 1.86E-02 3.22E-02
9.71E+02 1.84E-02 3.19E-02
9.72E+02 1.82E-02 3.16E-02
9.73E+02 1.80E-02 3.13E-02
9.74E+02 1.78E-02 3.10E-02
9.75E+02 1.76E-02 3.07E-02
9.76E+02 1.74E-02 3.04E-02
9.77E+02 1.72E-02 3.02E-02
9.78E+02 1.70E-02 2.99E-02
9.79E+02 1.68E-02 2.96E-02
9.80E+02 1.66E-02 2.93E-02
9.81E+02 1.64E-02 2.91E-02
9.82E+02 1.62E-02 2.88E-02
9.83E+02 1.59E-02 2.85E-02
9.84E+02 1.57E-02 2.83E-02
9.85E+02 1.56E-02 2.80E-02
9.86E+02 1.54E-02 2.78E-02
9.87E+02 1.52E-02 2.75E-02
9.88E+02 1.50E-02 2.73E-02
9.89E+02 1.48E-02 2.70E-02
9.90E+02 1.46E-02 2.67E-02
9.91E+02 1.44E-02 2.65E-02
9.92E+02 1.42E-02 2.62E-02
9.93E+02 1.41E-02 2.60E-02
9.94E+02 1.39E-02 2.58E-02
9.95E+02 1.37E-02 2.55E-02
9.96E+02 1.36E-02 2.53E-02
9.97E+02 1.34E-02 2.50E-02
9.98E+02 1.33E-02 2.48E-02
9.99E+02 1.32E-02 2.47E-02
1.00E+03 1.31E-02 2.45E-02
lam x y z
360 0.0001299 3.917e-06 0.0006061
361 0.000145847 4.393581e-06 0.0006808792
362 0.0001638021 4.929604e-06 0.0007651456
363 0.0001840037 5.532136e-06 0.0008600124
364 0.0002066902 6.208245e-06 0.0009665928
365 0.0002321 6.965e-06 0.001086
366 0.000260728 7.813219e-06 0.001220586
367 0.000293075 8.767336e-06 0.001372729
368 0.000329388 9.839844e-06 0.001543579
369 0.000369914 1.104323e-05 0.001734286
370 0.0004149 1.239e-05 0.001946
371 0.0004641587 1.388641e-05 0.002177777
372 0.000518986 1.555728e-05 0.002435809
373 0.000581854 1.744296e-05 0.002731953
374 0.0006552347 1.958375e-05 0.003078064
375 0.0007416 2.202e-05 0.003486
376 0.0008450296 2.483965e-05 0.003975227
377 0.0009645268 2.804126e-05 0.00454088
378 0.001094949 3.153104e-05 0.00515832
379 0.001231154 3.521521e-05 0.005802907
380 0.001368 3.9e-05 0.006450001
381 0.00150205 4.28264e-05 0.007083216
382 0.001642328 4.69146e-05 0.007745488
383 0.001802382 5.15896e-05 0.008501152
384 0.001995757 5.71764e-05 0.009414544
385 0.002236 6.4e-05 0.01054999
386 0.002535385 7.234421e-05 0.0119658
387 0.002892603 8.221224e-05 0.01365587
388 0.003300829 9.350816e-05 0.01558805
389 0.003753236 0.0001061361 0.01773015
390 0.004243 0.00012 0.02005001
391 0.004762389 0.000134984 0.02251136
392 0.005330048 0.000151492 0.02520288
393 0.005978712 0.000170208 0.02827972
394 0.006741117 0.000191816 0.03189704
395 0.00765 0.000217 0.03621
396 0.008751373 0.0002469067 0.04143771
397 0.01002888 0.00028124 0.04750372
398 0.0114217 0.00031852 0.05411988
399 0.01286901 0.0003572667 0.06099803
400 0.01431 0.000396 0.06785001
401 0.01570443 0.0004337147 0.07448632
402 0.01714744 0.000473024 0.08136156
403 0.01878122 0.000517876 0.08915364
404 0.02074801 0.0005722187 0.09854048
405 0.02319 0.00064 0.1102
406 0.02620736 0.00072456 0.1246133
407 0.02978248 0.0008255 0.1417017
408 0.03388092 0.00094116 0.1613035
409 0.03846824 0.00106988 0.1832568
410 0.04351 0.00121 0.2074
411 0.0489956 0.001362091 0.2336921
412 0.0550226 0.001530752 0.2626114
413 0.0617188 0.001720368 0.2947746
414 0.069212 0.001935323 0.3307985
415 0.07763 0.00218 0.3713
416 0.08695811 0.0024548 0.4162091
417 0.09717672 0.002764 0.4654642
418 0.1084063 0.0031178 0.5196948
419 0.1207672 0.0035264 0.5795303
420 0.13438 0.004 0.6456
421 0.1493582 0.00454624 0.7184838
422 0.1653957 0.00515932 0.7967133
423 0.1819831 0.00582928 0.8778459
424 0.198611 0.00654616 0.959439
425 0.21477 0.0073 1.0390501
426 0.2301868 0.008086507 1.1153673
427 0.2448797 0.00890872 1.1884971
428 0.2587773 0.00976768 1.2581233
429 0.2718079 0.01066443 1.3239296
430 0.2839 0.0116 1.3856
431 0.2949438 0.01257317 1.4426352
432 0.3048965 0.01358272 1.4948035
433 0.3137873 0.01462968 1.5421903
434 0.3216454 0.01571509 1.5848807
435 0.3285 0.01684 1.62296
436 0.3343513 0.01800736 1.6564048
437 0.3392101 0.01921448 1.6852959
438 0.3431213 0.02045392 1.7098745
439 0.3461296 0.02171824 1.7303821
440 0.34828 0.023 1.74706
441 0.3495999 0.02429461 1.7600446
442 0.3501474 0.02561024 1.7696233
443 0.350013 0.02695857 1.7762637
444 0.349287 0.02835125 1.7804334
445 0.34806 0.0298 1.7826
446 0.3463733 0.03131083 1.7829682
447 0.3442624 0.03288368 1.7816998
448 0.3418088 0.03452112 1.7791982
449 0.3390941 0.03622571 1.7758671
450 0.3362 0.038 1.77211
451 0.3331977 0.03984667 1.7682589
452 0.3300411 0.041768 1.764039
453 0.3266357 0.043766 1.7589438
454 0.3228868 0.04584267 1.7524663
455 0.3187 0.048 1.7441
456 0.3140251 0.05024368 1.7335595
457 0.308884 0.05257304 1.7208581
458 0.3032904 0.05498056 1.7059369
459 0.2972579 0.05745872 1.6887372
460 0.2908 0.06 1.6692
461 0.2839701 0.06260197 1.6475287
462 0.2767214 0.06527752 1.6234127
463 0.2689178 0.06804208 1.5960223
464 0.2604227 0.07091109 1.564528
465 0.2511 0.0739 1.5281
466 0.2408475 0.077016 1.4861114
467 0.2298512 0.0802664 1.4395215
468 0.2184072 0.0836668 1.3898799
469 0.2068115 0.0872328 1.3387362
470 0.19536 0.09098 1.28764
471 0.1842136 0.09491755 1.2374223
472 0.1733273 0.09904584 1.1878243
473 0.1626881 0.1033674 1.1387611
474 0.1522833 0.1078846 1.090148
475 0.1421 0.1126 1.0419
476 0.1321786 0.117532 0.9941976
477 0.1225696 0.1226744 0.9473473
478 0.1132752 0.1279928 0.9014531
479 0.1042979 0.1334528 0.8566193
480 0.09564 0.13902 0.8129501
481 0.08729955 0.1446764 0.7705173
482 0.07930804 0.1504693 0.7294448
483 0.07171776 0.1564619 0.6899136
484 0.06458099 0.1627177 0.6521049
485 0.05795001 0.1693 0.6162
486 0.05186211 0.1762431 0.5823286
487 0.04628152 0.1835581 0.5504162
488 0.04115088 0.1912735 0.5203376
489 0.03641283 0.199418 0.4919673
490 0.03201 0.20802 0.46518
491 0.0279172 0.2171199 0.4399246
492 0.0241444 0.2267345 0.4161836
493 0.020687 0.2368571 0.3938822
494 0.0175404 0.2474812 0.3729459
495 0.0147 0.2586 0.3533
496 0.01216179 0.2701849 0.3348578
497 0.00991996 0.2822939 0.3175521
498 0.00796724 0.2950505 0.3013375
499 0.006296346 0.308578 0.2861686
500 0.0049 0.323 0.272
501 0.003777173 0.3384021 0.2588171
502 0.00294532 0.3546858 0.2464838
503 0.00242488 0.3716986 0.2347718
504 0.002236293 0.3892875 0.2234533
505 0.0024 0.4073 0.2123
506 0.00292552 0.4256299 0.2011692
507 0.00383656 0.4443096 0.1901196
508 0.00517484 0.4633944 0.1792254
509 0.00698208 0.4829395 0.1685608
510 0.0093 0.503 0.1582
511 0.01214949 0.5235693 0.1481383
512 0.01553588 0.544512 0.1383758
513 0.01947752 0.56569 0.1289942
514 0.02399277 0.5869653 0.1200751
515 0.0291 0.6082 0.1117
516 0.03481485 0.6293456 0.1039048
517 0.04112016 0.6503068 0.09666748
518 0.04798504 0.6708752 0.08998272
519 0.05537861 0.6908424 0.08384531
520 0.06327 0.71 0.07824999
521 0.07163501 0.7281852 0.07320899
522 0.08046224 0.7454636 0.06867816
523 0.08973996 0.7619694 0.06456784
524 0.09945645 0.7778368 0.06078835
525 0.1096 0.7932 0.05725001
526 0.1201674 0.8081104 0.05390435
527 0.1311145 0.8224962 0.05074664
528 0.1423679 0.8363068 0.04775276
529 0.1538542 0.8494916 0.04489859
530 0.1655 0.862 0.04216
531 0.1772571 0.8738108 0.03950728
532 0.18914 0.8849624 0.03693564
533 0.2011694 0.8954936 0.03445836
534 0.2133658 0.9054432 0.03208872
535 0.2257499 0.9148501 0.02984
536 0.2383209 0.9237348 0.02771181
537 0.2510668 0.9320924 0.02569444
538 0.2639922 0.9399226 0.02378716
539 0.2771017 0.9472252 0.02198925
540 0.2904 0.954 0.0203
541 0.3038912 0.9602561 0.01871805
542 0.3175726 0.9660074 0.01724036
543 0.3314384 0.9712606 0.01586364
544 0.3454828 0.9760225 0.01458461
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