numpy_images.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Обязательные библиотеки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from typing import List, Any, Callable, Tuple\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy.ndimage import interpolation as ip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Общий код"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Задание №1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb219c636d0>"
      ]
     },
     "execution_count": 2,
     "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": [
    "matrix: np.ndarray = np.zeros((100, 100))\n",
    "matrix = np.vstack((matrix, 255-matrix))\n",
    "plt.imshow(matrix, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb219c0bbd0>"
      ]
     },
     "execution_count": 3,
     "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": [
    "matrix: np.ndarray = np.full((100, 100), 255)\n",
    "matrix = np.vstack((matrix, 255-matrix))\n",
    "matrix = np.hstack((matrix, 255-matrix))\n",
    "plt.imshow(matrix, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb219b85090>"
      ]
     },
     "execution_count": 4,
     "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": [
    "matrix: np.ndarray = np.full((100, 100), 255)\n",
    "matrix[25:75, 25:75] = 0\n",
    "plt.imshow(matrix, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb219b5fed0>"
      ]
     },
     "execution_count": 5,
     "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": [
    "matrix:np.ndarray = np.full((100, 100), 255)\n",
    "matrix[25:75, 25:75] = 0\n",
    "matrix = np.hstack((matrix, 255-matrix))\n",
    "plt.imshow(matrix, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb219ad3950>"
      ]
     },
     "execution_count": 6,
     "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": [
    "matrix1: np.ndarray = np.full((100, 100), 0)\n",
    "matrix1[25:75, 25:75] = 255\n",
    "matrix2: np.ndarray = np.full((100, 100), 255)\n",
    "matrix2[45:55, :] = 0\n",
    "matrix2[:, 45:55] = 0\n",
    "color_converter: Callable[[int, int], int] = lambda x, y: 0 if 0 < (x + y) < 256 else 255 \n",
    "plt.imshow(np.vectorize(color_converter)(matrix1, matrix2), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Задание №2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb2159b8f90>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQcAAAD8CAYAAAB6iWHJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAMvUlEQVR4nO3cT4xd9XnG8e9jO7AgSEAJyDVWIZErhSzqWBZFooqoUBPwxmRBBYvEipCcBUiJlC6cZBE2kdKqSSSkFskRKKZKoUgJwgvahlqRUBcQbETMH5fgEAoTW7gpFUGNlBTydjHHye34Hc/YM2fuHfX7ka7uub/7O+c+PhmenHPun1QVkrTQhmkHkDSbLAdJLctBUstykNSyHCS1LAdJrdHKIcnNSV5OcjzJvrFeR9I4MsbnHJJsBH4M/BkwBzwD3FFVL636i0kaxVhHDtcBx6vq1ar6NfAwsHuk15I0gk0jbXcL8MbE4zngjxebnKQWPO7mrOjxWq0zxjZm8d/6/+U11/J1F46d6/NLzT9y5MjPq+oDZ0xaxFjlcGZKWFgAe4G9E49JwoYNG367PHlbjfHTY8D/eX5yeTnPbdiw4Yx5ABs3bmy3cbY8Z5u73JwL15/Mstz9da4ZJ7d/tixn2/a55lxqf51tG2uVcTl/f1PM+O/L/094vHKYA7ZOPL4KODE5oar2A/vhzCMHSdM31jWHZ4BtSa5JcgFwO3BwpNeSNIJRjhyq6t0kdwP/DGwEHqiqF8d4LUnjGOu0gqp6HHh8rO1LGpefkJTUshwktSwHSS3LQVLLcpDUshwktSwHSS3LQVLLcpDUshwktSwHSS3LQVLLcpDUshwktSwHSS3LQVLLcpDUshwktSwHSS3LQVLLcpDUshwktSwHSS3LQVLLcpDUshwktSwHSS3LQVLLcpDUshwktSwHSS3LQVLLcpDUshwktTatZOUkrwHvAO8B71bVziSXAf8AXA28Bvx5Vf3XymJKWmurceTwp1W1vap2Do/3AYeqahtwaHgsaZ0Z47RiN3BgWD4A3DrCa0ga2UrLoYDvJzmSZO8wdmVVnQQY7q/oVkyyN8nhJIdXmEHSCFZ0zQG4oapOJLkCeCLJvy13xaraD+wHSFIrzCFpla3oyKGqTgz3p4BHgeuAN5NsBhjuT600pKS1d97lkOSiJBefXgY+DrwAHAT2DNP2AI+tNKSktbeS04orgUeTnN7O31fVPyV5BngkyZ3A68BtK48paa2ddzlU1avAHzXj/wnctJJQkqbPT0hKalkOklqWg6SW5SCpZTlIalkOklqWg6SW5SCpZTlIalkOklqWg6SW5SCpZTlIalkOklqWg6SW5SCpZTlIalkOklqWg6SW5SCpZTlIalkOklqWg6SW5SCpZTlIalkOklqWg6SW5SCpZTlIalkOklqWg6SW5SCpZTlIai1ZDkkeSHIqyQsTY5cleSLJK8P9pcN4ktyb5HiSo0l2jBle0niWc+TwbeDmBWP7gENVtQ04NDwGuAXYNtz2AvetTkxJa23JcqiqJ4G3FgzvBg4MyweAWyfGH6x5TwGXJNm8WmElrZ3zveZwZVWdBBjurxjGtwBvTMybG8YkrTObVnl7acaqnZjsZf7UQ9IMOt8jhzdPny4M96eG8Tlg68S8q4AT3Qaqan9V7ayqneeZQdKIzrccDgJ7huU9wGMT458e3rW4Hnj79OmHpPVlydOKJA8BNwKXJ5kDvgJ8DXgkyZ3A68Btw/THgV3AceCXwGdGyCxpDSxZDlV1xyJP3dTMLeCulYaSNH1+QlJSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1FqyHJI8kORUkhcmxu5J8rMkzw23XRPPfTHJ8SQvJ/nEWMEljWs5Rw7fBm5uxr9ZVduH2+MASa4Fbgc+Mqzzt0k2rlZYSWtnyXKoqieBt5a5vd3Aw1X1q6r6KXAcuG4F+SRNyUquOdyd5Ohw2nHpMLYFeGNiztwwJmmdOd9yuA/4ELAdOAl8fRhPM7e6DSTZm+RwksPnmUHSiM6rHKrqzap6r6p+A3yL3506zAFbJ6ZeBZxYZBv7q2pnVe08nwySxnVe5ZBk88TDTwKn38k4CNye5MIk1wDbgB+uLKKkadi01IQkDwE3ApcnmQO+AtyYZDvzpwyvAZ8FqKoXkzwCvAS8C9xVVe+NE13SmJYsh6q6oxm+/yzzvwp8dSWhJE2fn5CU1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLWWLIckW5P8IMmxJC8m+dwwflmSJ5K8MtxfOownyb1Jjic5mmTH2P8ISatvOUcO7wJfqKoPA9cDdyW5FtgHHKqqbcCh4THALcC24bYXuG/VU0sa3ZLlUFUnq+rZYfkd4BiwBdgNHBimHQBuHZZ3Aw/WvKeAS5JsXvXkkkZ1TtccklwNfBR4Griyqk7CfIEAVwzTtgBvTKw2N4wt3NbeJIeTHD732JLGtmm5E5O8H/gu8Pmq+kWSRac2Y3XGQNV+YP+w7TOelzRdyzpySPI+5ovhO1X1vWH4zdOnC8P9qWF8Dtg6sfpVwInViStprSzn3YoA9wPHquobE08dBPYMy3uAxybGPz28a3E98Pbp0w9J68dyTituAD4FPJ/kuWHsS8DXgEeS3Am8Dtw2PPc4sAs4DvwS+MyqJpa0JpYsh6r6V/rrCAA3NfMLuGuFuSRNmZ+QlNSyHCS1LAdJLctBUstykNSyHCS1LAdJLctBUstykNSyHCS1LAdJLctBUstykNSyHCS1LAdJLctBUstykNSyHCS1LAdJLctBUstykNSyHCS1LAdJLctBUstykNSyHCS1LAdJLctBUstykNSyHCS1LAdJLctBUstykNRashySbE3ygyTHkryY5HPD+D1JfpbkueG2a2KdLyY5nuTlJJ8Y8x8gaRybljHnXeALVfVskouBI0meGJ77ZlX99eTkJNcCtwMfAX4f+Jckf1hV761mcEnjWvLIoapOVtWzw/I7wDFgy1lW2Q08XFW/qqqfAseB61YjrKS1c07XHJJcDXwUeHoYujvJ0SQPJLl0GNsCvDGx2hxnLxNJM2jZ5ZDk/cB3gc9X1S+A+4APAduBk8DXT09tVq9me3uTHE5y+JxTSxrdssohyfuYL4bvVNX3AKrqzap6r6p+A3yL3506zAFbJ1a/CjixcJtVtb+qdlbVzpX8AySNYznvVgS4HzhWVd+YGN88Me2TwAvD8kHg9iQXJrkG2Ab8cPUiS1oLy3m34gbgU8DzSZ4bxr4E3JFkO/OnDK8BnwWoqheTPAK8xPw7HXf5ToW0/qTqjMsBax8i+Q/gv4GfTzvLMlzO+sgJZh3DeskJZ2b9g6r6wHJXnolyAEhyeD1cf1gvOcGsY1gvOWHlWf34tKSW5SCpNUvlsH/aAZZpveQEs45hveSEFWadmWsOkmbLLB05SJohUy+HJDcPX+0+nmTftPMslOS1JM8PX0s/PIxdluSJJK8M95cutZ2Rsj2Q5FSSFybG2myZd++wn48m2THlnDP3lf+z/DzBLO7T8X9KoaqmdgM2Aj8BPghcAPwIuHaamZqMrwGXLxj7K2DfsLwP+MspZfsYsAN4YalswC7gH5n/7sv1wNNTznkP8BfN3GuHv4MLgWuGv4+Na5RzM7BjWL4Y+PGQZxb36WJZV22/TvvI4TrgeFW9WlW/Bh5m/ivfs243cGBYPgDcOo0QVfUk8NaC4cWy7QYerHlPAZcs+Aj8WudczNS+8l+L/zzBLO7T0X9KYdrlsB6+3l3A95McSbJ3GLuyqk7C/P9IwBVTS3emxbLN4r6e2a/8L/h5gpnep2P9lMK0y2FZX++eshuqagdwC3BXko9NO9B5mrV9vaKv/I+p+XmCRac2Y9POumr7ddrlsKyvd09TVZ0Y7k8BjzJ/KPbm6cPH4f7U9BKeYbFsM7Wva4Vf+R9L9/MEzOg+HeOnFCZNuxyeAbYluSbJBcz/9uTBKWf6rSQXDb+bSZKLgI8z/9X0g8CeYdoe4LHpJGwtlu0g8OnhCvv1wNunD5WnYRa/8r/YzxMwg/t0sayrul/X6urqWa667mL+SutPgC9PO8+CbB9k/grvj4AXT+cDfg84BLwy3F82pXwPMX/o+D/M/z/DnYtlY/6w8m+G/fw8sHPKOf9uyHF0+MPdPDH/y0POl4Fb1jDnnzB/qH0UeG647ZrRfbpY1lXbr35CUlJr2qcVkmaU5SCpZTlIalkOklqWg6SW5SCpZTlIalkOklr/CyteVCuzWPV8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrix: np.ndarray = np.meshgrid(np.arange(0, 255),np.arange(0, 255))[0]\n",
    "plt.imshow(matrix, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 300)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb219a19b50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQcAAAD8CAYAAAB6iWHJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAMvUlEQVR4nO3cT4xd9XnG8e9jO7AgSEAJyDVWIZErhSzqWBZFooqoUBPwxmRBBYvEipCcBUiJlC6cZBE2kdKqSSSkFskRKKZKoUgJwgvahlqRUBcQbETMH5fgEAoTW7gpFUGNlBTydjHHye34Hc/YM2fuHfX7ka7uub/7O+c+PhmenHPun1QVkrTQhmkHkDSbLAdJLctBUstykNSyHCS1LAdJrdHKIcnNSV5OcjzJvrFeR9I4MsbnHJJsBH4M/BkwBzwD3FFVL636i0kaxVhHDtcBx6vq1ar6NfAwsHuk15I0gk0jbXcL8MbE4zngjxebnKQWPO7mrOjxWq0zxjZm8d/6/+U11/J1F46d6/NLzT9y5MjPq+oDZ0xaxFjlcGZKWFgAe4G9E49JwoYNG367PHlbjfHTY8D/eX5yeTnPbdiw4Yx5ABs3bmy3cbY8Z5u73JwL15/Mstz9da4ZJ7d/tixn2/a55lxqf51tG2uVcTl/f1PM+O/L/094vHKYA7ZOPL4KODE5oar2A/vhzCMHSdM31jWHZ4BtSa5JcgFwO3BwpNeSNIJRjhyq6t0kdwP/DGwEHqiqF8d4LUnjGOu0gqp6HHh8rO1LGpefkJTUshwktSwHSS3LQVLLcpDUshwktSwHSS3LQVLLcpDUshwktSwHSS3LQVLLcpDUshwktSwHSS3LQVLLcpDUshwktSwHSS3LQVLLcpDUshwktSwHSS3LQVLLcpDUshwktSwHSS3LQVLLcpDUshwktSwHSS3LQVLLcpDUshwktTatZOUkrwHvAO8B71bVziSXAf8AXA28Bvx5Vf3XymJKWmurceTwp1W1vap2Do/3AYeqahtwaHgsaZ0Z47RiN3BgWD4A3DrCa0ga2UrLoYDvJzmSZO8wdmVVnQQY7q/oVkyyN8nhJIdXmEHSCFZ0zQG4oapOJLkCeCLJvy13xaraD+wHSFIrzCFpla3oyKGqTgz3p4BHgeuAN5NsBhjuT600pKS1d97lkOSiJBefXgY+DrwAHAT2DNP2AI+tNKSktbeS04orgUeTnN7O31fVPyV5BngkyZ3A68BtK48paa2ddzlU1avAHzXj/wnctJJQkqbPT0hKalkOklqWg6SW5SCpZTlIalkOklqWg6SW5SCpZTlIalkOklqWg6SW5SCpZTlIalkOklqWg6SW5SCpZTlIalkOklqWg6SW5SCpZTlIalkOklqWg6SW5SCpZTlIalkOklqWg6SW5SCpZTlIalkOklqWg6SW5SCpZTlIai1ZDkkeSHIqyQsTY5cleSLJK8P9pcN4ktyb5HiSo0l2jBle0niWc+TwbeDmBWP7gENVtQ04NDwGuAXYNtz2AvetTkxJa23JcqiqJ4G3FgzvBg4MyweAWyfGH6x5TwGXJNm8WmElrZ3zveZwZVWdBBjurxjGtwBvTMybG8YkrTObVnl7acaqnZjsZf7UQ9IMOt8jhzdPny4M96eG8Tlg68S8q4AT3Qaqan9V7ayqneeZQdKIzrccDgJ7huU9wGMT458e3rW4Hnj79OmHpPVlydOKJA8BNwKXJ5kDvgJ8DXgkyZ3A68Btw/THgV3AceCXwGdGyCxpDSxZDlV1xyJP3dTMLeCulYaSNH1+QlJSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1FqyHJI8kORUkhcmxu5J8rMkzw23XRPPfTHJ8SQvJ/nEWMEljWs5Rw7fBm5uxr9ZVduH2+MASa4Fbgc+Mqzzt0k2rlZYSWtnyXKoqieBt5a5vd3Aw1X1q6r6KXAcuG4F+SRNyUquOdyd5Ohw2nHpMLYFeGNiztwwJmmdOd9yuA/4ELAdOAl8fRhPM7e6DSTZm+RwksPnmUHSiM6rHKrqzap6r6p+A3yL3506zAFbJ6ZeBZxYZBv7q2pnVe08nwySxnVe5ZBk88TDTwKn38k4CNye5MIk1wDbgB+uLKKkadi01IQkDwE3ApcnmQO+AtyYZDvzpwyvAZ8FqKoXkzwCvAS8C9xVVe+NE13SmJYsh6q6oxm+/yzzvwp8dSWhJE2fn5CU1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLUsB0kty0FSy3KQ1LIcJLWWLIckW5P8IMmxJC8m+dwwflmSJ5K8MtxfOownyb1Jjic5mmTH2P8ISatvOUcO7wJfqKoPA9cDdyW5FtgHHKqqbcCh4THALcC24bYXuG/VU0sa3ZLlUFUnq+rZYfkd4BiwBdgNHBimHQBuHZZ3Aw/WvKeAS5JsXvXkkkZ1TtccklwNfBR4Griyqk7CfIEAVwzTtgBvTKw2N4wt3NbeJIeTHD732JLGtmm5E5O8H/gu8Pmq+kWSRac2Y3XGQNV+YP+w7TOelzRdyzpySPI+5ovhO1X1vWH4zdOnC8P9qWF8Dtg6sfpVwInViStprSzn3YoA9wPHquobE08dBPYMy3uAxybGPz28a3E98Pbp0w9J68dyTituAD4FPJ/kuWHsS8DXgEeS3Am8Dtw2PPc4sAs4DvwS+MyqJpa0JpYsh6r6V/rrCAA3NfMLuGuFuSRNmZ+QlNSyHCS1LAdJLctBUstykNSyHCS1LAdJLctBUstykNSyHCS1LAdJLctBUstykNSyHCS1LAdJLctBUstykNSyHCS1LAdJLctBUstykNSyHCS1LAdJLctBUstykNSyHCS1LAdJLctBUstykNSyHCS1LAdJLctBUstykNRashySbE3ygyTHkryY5HPD+D1JfpbkueG2a2KdLyY5nuTlJJ8Y8x8gaRybljHnXeALVfVskouBI0meGJ77ZlX99eTkJNcCtwMfAX4f+Jckf1hV761mcEnjWvLIoapOVtWzw/I7wDFgy1lW2Q08XFW/qqqfAseB61YjrKS1c07XHJJcDXwUeHoYujvJ0SQPJLl0GNsCvDGx2hxnLxNJM2jZ5ZDk/cB3gc9X1S+A+4APAduBk8DXT09tVq9me3uTHE5y+JxTSxrdssohyfuYL4bvVNX3AKrqzap6r6p+A3yL3506zAFbJ1a/CjixcJtVtb+qdlbVzpX8AySNYznvVgS4HzhWVd+YGN88Me2TwAvD8kHg9iQXJrkG2Ab8cPUiS1oLy3m34gbgU8DzSZ4bxr4E3JFkO/OnDK8BnwWoqheTPAK8xPw7HXf5ToW0/qTqjMsBax8i+Q/gv4GfTzvLMlzO+sgJZh3DeskJZ2b9g6r6wHJXnolyAEhyeD1cf1gvOcGsY1gvOWHlWf34tKSW5SCpNUvlsH/aAZZpveQEs45hveSEFWadmWsOkmbLLB05SJohUy+HJDcPX+0+nmTftPMslOS1JM8PX0s/PIxdluSJJK8M95cutZ2Rsj2Q5FSSFybG2myZd++wn48m2THlnDP3lf+z/DzBLO7T8X9KoaqmdgM2Aj8BPghcAPwIuHaamZqMrwGXLxj7K2DfsLwP+MspZfsYsAN4YalswC7gH5n/7sv1wNNTznkP8BfN3GuHv4MLgWuGv4+Na5RzM7BjWL4Y+PGQZxb36WJZV22/TvvI4TrgeFW9WlW/Bh5m/ivfs243cGBYPgDcOo0QVfUk8NaC4cWy7QYerHlPAZcs+Aj8WudczNS+8l+L/zzBLO7T0X9KYdrlsB6+3l3A95McSbJ3GLuyqk7C/P9IwBVTS3emxbLN4r6e2a/8L/h5gpnep2P9lMK0y2FZX++eshuqagdwC3BXko9NO9B5mrV9vaKv/I+p+XmCRac2Y9POumr7ddrlsKyvd09TVZ0Y7k8BjzJ/KPbm6cPH4f7U9BKeYbFsM7Wva4Vf+R9L9/MEzOg+HeOnFCZNuxyeAbYluSbJBcz/9uTBKWf6rSQXDb+bSZKLgI8z/9X0g8CeYdoe4LHpJGwtlu0g8OnhCvv1wNunD5WnYRa/8r/YzxMwg/t0sayrul/X6urqWa667mL+SutPgC9PO8+CbB9k/grvj4AXT+cDfg84BLwy3F82pXwPMX/o+D/M/z/DnYtlY/6w8m+G/fw8sHPKOf9uyHF0+MPdPDH/y0POl4Fb1jDnnzB/qH0UeG647ZrRfbpY1lXbr35CUlJr2qcVkmaU5SCpZTlIalkOklqWg6SW5SCpZTlIalkOklr/CyteVCuzWPV8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrix: np.ndarray = np.arange(0, 255)\n",
    "matrix = np.repeat(matrix, 255).reshape(255, 255).T\n",
    "plt.imshow(matrix, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Задание №3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def d_angle(angle: Tuple[int, str, bool]) -> Tuple[int, str]:\n",
    "    if angle[1] == \"l\":\n",
    "        return (angle[0], \"по часовой\")\n",
    "    else:\n",
    "        return (360 - angle[0], \"против часовой\")\n",
    "\n",
    "        \n",
    "angles: List[Tuple[int, str, bool]] = [(30, \"l\", False), (60, \"r\", True)]\n",
    "matrix: np.ndarray = np.meshgrid(np.arange(0, 255),np.arange(0, 255))[0]\n",
    "\n",
    "ax: Any = plt.subplots(ncols=2, nrows=1, figsize=(20, 10))[1]\n",
    "index: int \n",
    "angle: int\n",
    "for index, angle in enumerate(angles):\n",
    "    new_angle: int\n",
    "    direction: str\n",
    "    new_angle, direction = d_angle(angle)\n",
    "    ax[index].set_title(str(angle[0]) + \" градусов \" + direction)\n",
    "    ax[index].imshow(ip.rotate(matrix, new_angle, reshape=angle[2]), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Задание №4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb2198e30d0>"
      ]
     },
     "execution_count": 10,
     "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": [
    "center: Tuple[int, int] = (100, 100)\n",
    "matrix1: np.ndarray\n",
    "matrix2: np.ndarray\n",
    "matrix1, matrix2 = np.meshgrid(np.arange(0, 200), np.arange(0, 200))\n",
    "rad: Callable[[int, int], float] = lambda x, y: np.sqrt((x - center[0]) ** 2 + (y - center[1]) ** 2)\n",
    "matrix: np.ndarray = np.vectorize(rad)(matrix1, matrix2)\n",
    "plt.imshow(matrix, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb2198bcf90>"
      ]
     },
     "execution_count": 11,
     "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": [
    "center: Tuple[int, int] = (100, 100)\n",
    "matrix1: np.ndarray\n",
    "matrix2: np.ndarray\n",
    "matrix1, matrix2 = np.meshgrid(np.arange(0, 200), np.arange(0, 200))\n",
    "matrix: np.ndarray = np.sqrt((matrix1 - center[0]) ** 2 + (matrix2 - center[1]) ** 2)\n",
    "plt.imshow(matrix, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Задание №5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb21982ecd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OBe5MAqN5+5mq+mKSB4A7klwD/AB41xz7uC1JbgMuB16SZA34EPARNh/H3cCVjE6a/RR478w7fAa2GNvlSXYx2tU/CFwLUFWPJbkD+CZwDPhAVR2fR7+H4l8MSo2b9+GApDkzBKTGGQJS4wwBqXGGgNQ4Q0BqnCEgNc4QkBr3/4UprKAeYonLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "center: Tuple[int, int] = (100, 100)\n",
    "radius: int = 50\n",
    "matrix1: np.ndarray\n",
    "matrix2: np.ndarray\n",
    "matrix1, matrix2 = np.meshgrid(np.arange(0, 200), np.arange(0, 200))\n",
    "rad: Callable[[int, int], int] = lambda x, y: 1 if np.sqrt((x - center[0]) ** 2 + (y - center[1]) ** 2) < radius else 0\n",
    "matrix: np.ndarray = np.vectorize(rad)(matrix1, matrix2)\n",
    "plt.imshow(matrix, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Задание №6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb219808190>"
      ]
     },
     "execution_count": 13,
     "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": [
    "center: Tuple[int, int] = (150, 150)\n",
    "\n",
    "\n",
    "def rad(x: int, y: int, radius: int) -> bool:\n",
    "    return True if np.sqrt((x - center[0]) ** 2 + (y - center[1]) ** 2) < radius else False\n",
    "\n",
    "\n",
    "def rad_counter(x: int, y: int, radiuses: Tuple[int, int]) -> int:\n",
    "    if rad(x, y, radiuses[0]):\n",
    "        return 255\n",
    "    elif rad(x, y, radiuses[1]):\n",
    "        return 127\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "    \n",
    "radiuses: Tuple[int, int] = (50, 100)\n",
    "matrix1: np.ndarray\n",
    "matrix2: np.ndarray\n",
    "matrix1, matrix2 = np.meshgrid(np.arange(0, 300), np.arange(0, 300))\n",
    "radius: Callable[[int, int], int] = lambda x, y: rad_counter(x, y, radiuses)\n",
    "matrix: np.ndarray = np.vectorize(radius)(matrix1, matrix2)\n",
    "plt.imshow(matrix, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb21977db50>"
      ]
     },
     "execution_count": 14,
     "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": [
    "np.set_printoptions(threshold=sys.maxsize)\n",
    "center: Tuple[int, int] = (150, 150)\n",
    "radiuses: Tuple[int, int] = (50, 100)\n",
    "matrix1: np.ndarray\n",
    "matrix2: np.ndarray\n",
    "matrix1, matrix2 = np.meshgrid(np.arange(0, 300), np.arange(0, 300))\n",
    "D: np.ndarray = np.sqrt((matrix1 - center[0]) ** 2 + (matrix2 - center[1]) ** 2)\n",
    "matrix: np.ndarray = (D < radiuses[0]) * 255 + (D >= radiuses[0]) * (D < radiuses[1]) * 127\n",
    "plt.imshow(matrix, cmap='gray')"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}