ColorResearch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def h(value):\n",
    "    value = value.lstrip('#')\n",
    "    lv = len(value)\n",
    "    return tuple(int(value[i:i + lv // 3], 16) for i in range(0, lv, lv // 3))\n",
    "\n",
    "night = np.array(list(map(h,[\"02070d\",\"0a0a0b\",\"232933\",\"414b5e\",\"7587a8\"])))\n",
    "twilight = np.array(list(map(h,[\"10083e\",\"10083e\",\"331f51\",\"3b294d\",\"493b46\"])))\n",
    "nice = np.array(list(map(h,[\"f1b3fc\",\"eaa9c5\",\"c58a98\",\"a67072\",\"6f432f\"])))\n",
    "day = np.array(list(map(h,[\"fa841b\",\"f66914\",\"ec5317\",\"e43f1a\",\"d61e1f\"])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.array([night,twilight,nice,day]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "[[ -0.48251774   2.08993557   4.10404861   6.00213514   9.09279104]\n",
      " [ -0.86920582  -4.34282797  -6.9084863   -9.33471512 -13.27677648]\n",
      " [  0.31519505  -0.86906037  -1.78353003  -2.64474495  -4.04706643]\n",
      " [  1.30385945   1.99698603   2.43517366   2.85612583   3.53443806]\n",
      " [  2.62294456   5.38966201   7.30294295   9.12348321  12.07169752]] [[241 179 252]\n",
      " [234 169 197]\n",
      " [197 138 152]\n",
      " [166 112 114]\n",
      " [111  67  47]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fe066e6ea60>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fit_matrix(A,B): # Tries to find X to solve AX = B\n",
    "    X,residuals,rank,s = np.linalg.lstsq(A,B,rcond=None)\n",
    "    print(residuals)\n",
    "    return X\n",
    "X = fit_matrix(nice.T,day.T)\n",
    "print(X,nice)\n",
    "plt.imshow(np.array( ((X @ nice),) ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}