Silaev/auto/3.2.5/325.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5dabef1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11293824972675794\n",
      "0.01226740102904371\n",
      "0.0938096382728547\n",
      "0.009262073610238137\n",
      "0.40123743304422665\n",
      "0.041224279574892234\n",
      "0.40493839466080334\n",
      "0.019640130500072135\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x1ff88333ac0>"
      ]
     },
     "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": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "nu = [1588, 1581, 1571, 1561, 1552, 1541, 1531, 1522, 1601, 1596, 1612, 1620, 1630, 1640, 1651, 1663]\n",
    "v = [30, 26.5, 21, 17.5, 14, 11.5, 10, 9, 30, 31, 25, 21.5, 17.5, 14.5, 12.5, 10.5]\n",
    "\n",
    "for i in range(len(v)):\n",
    "    nu[i] /= 1596\n",
    "    v[i] /= 31\n",
    "    \n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "plt.scatter(nu, v)\n",
    "\n",
    "nu_100 = [1597, 1583, 1571, 1528, 1505, 1480, 1447, 1407, 1349, 1553, 1653, 1685, 1718, 1759, 1817, 1905, 2073]\n",
    "u_100 = [3, 2.9, 2.9, 2.4, 2.1, 1.8, 1.5, 1.2, 0.9, 2.7, 2.7, 2.4, 2.1, 1.8, 1.5, 1.2, 0.9]\n",
    "\n",
    "for i in range(len(u_100)):\n",
    "    nu_100[i] /= 1597\n",
    "    u_100[i] /= 3\n",
    "    \n",
    "plt.scatter(nu_100, u_100)\n",
    "\n",
    "n = [0] * 4\n",
    "u = [0] * 4\n",
    "\n",
    "n[0] = [0, 2, 10, 19, 12, 4, 7]\n",
    "u[0] = [1.12, 0.2, 0.72, 1, 0.8, 0.38, 0.58]\n",
    "\n",
    "n[1] = [0, 2, 4, 7, 12, 19, 27]\n",
    "u[1] = [1.12, 1, 0.78, 0.6, 0.38, 0.2, 0.1]\n",
    "\n",
    "n[2] = [0, 1, 2, 3, 4, 5, 6]\n",
    "u[2] = [280, 65, 135, 185, 215, 240, 250]\n",
    "\n",
    "n[3] = [0, 1, 2, 3, 4, 5, 6]\n",
    "u[3] = [280, 230, 150, 100, 70, 45, 30]\n",
    "\n",
    "for i in range(len(u[1])-1):\n",
    "    u[1][i+1] = u[1][0] - u[1][i+1]\n",
    "for i in range(len(u[3])-1):\n",
    "    u[3][i+1] = u[3][0] - u[3][i+1]\n",
    "\n",
    "theta = [[], [], [], []]\n",
    "avg_theta = [0] * 4\n",
    "sigma_theta = [0] * 4\n",
    "\n",
    "for k in range(4):\n",
    "    for i in range(len(n[k]) - 1):\n",
    "        for j in range(len(n[k]) - i - 2):\n",
    "            theta[k].append(np.abs(np.log((u[k][0] - u[k][i+1]) / (u[k][0] - u[k][j+i+2])) / (n[k][j+i+2] - n[k][i+1])))\n",
    "            avg_theta[k] += theta[k][-1] \n",
    "        \n",
    "    avg_theta[k] /= len(theta[k])\n",
    "    print(avg_theta[k])\n",
    "    for i in range(len(theta[k])):\n",
    "        sigma_theta[k] += (avg_theta[k] - theta[k][i]) **2\n",
    "    sigma_theta[k] /= (len(theta[k])-1) \n",
    "    print(np.sqrt(sigma_theta[k]))\n",
    "    \n",
    "ax.grid()\n",
    "ax.hlines(0.707, 0.8, 1.3, color = 'red', linewidth = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b8c0678",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13da0f9f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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