中间脉冲光场-数据处理.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import scipy.signal as signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取数据至字典键值对\n",
    "with open('new.txt','r') as f:\n",
    "    data = {}\n",
    "    line = f.readline()\n",
    "    while line:\n",
    "        if line != '\\n':\n",
    "            tmp = list(map(lambda x: float(x), list(line.split())))\n",
    "#             print('test', tmp[1], tmp[3], tmp[4])\n",
    "            if tmp[3] in data:\n",
    "                data[tmp[3]] += tmp[4]\n",
    "            else:\n",
    "                data[tmp[3]] = tmp[4]\n",
    "        line = f.readline()\n",
    "    data[0.0] /= 2  # 与数据结构有关，第一个值重发"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 中间变量，用于求极值点\n",
    "x = np.array(list(data.keys()))\n",
    "y = np.array(list(data.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05235988, 0.68067841, 1.30899694, 1.93731547, 2.565634  ,\n",
       "       3.19395253, 3.82227106, 4.45058959, 5.07890812, 5.70722665])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 极值点 角度位置，单位弧度\n",
    "dd = x[signal.argrelextrema(y, np.greater)]\n",
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 极值点间隔，单位 弧度\n",
    "v = np.array([abs(dd[i+1] - dd[i]) for i in range(len(dd)-1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.62831853, 0.62831853, 0.62831853, 0.62831853, 0.62831853,\n",
       "       0.62831853, 0.62831853, 0.62831853, 0.62831853, 0.62831853])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 最后一个间隔数据\n",
    "v = np.append(v, np.pi * 2 - dd[-1] + dd[0])\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f787e02c4e0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kSV0WhSSpy6KQJHVZFJKkLotCktRlUUiSuiwKSVKXRSFJ6rIoJEldFoUkqcuikCR1WRSSpC6LQpLUZVFIkrosCklSl0UhSeqyKCRJXRaFJKnLopAkdVkUkqQui0KS1DVnUSQ5Mcl9SXYmeTjJdW38U0m+k2RHW9bPMv+DSR5qy+VTxi9I8q02vjnJCW08ST6aZHeSB5Kcu1A7K0mav2E+UTwNXFBVrwXWAxuTnNeeu7aq1rdlx/SJSX4POLfN+03gT5K8JMkvAJuBK6rqbOB7wKY27Y3AmW25Crjx6HdPknSs5iyKGjjcHq5sSw35+q8Bvl5Vz1TVPwAPABuBU4CfVdWutt1W4C1t/RLg0+197wXWJFk75PtJkhbYUNcokqxIsgPYD2ytqm3tqRva6aGPJFk1w9SdDD6BvDjJqcD5wOnAD4ATkoy37S5r4wCnAU9MeY09bUySNAJDFUVVHamq9cA6YEOSs4H3AmcBvwGcDPzpDPO+CnwF+Fvgs8A3gSNVVcAVwEeS3Af8BDgyn+BJrkoykWTiwIED85kqSZqHed31VFWHgLuBjVW1r50eehr4a2DDLHNuaNcwLgQC7Grj36yq36mqDcDXJ8eBvfzjpwsYlNPeGV73pqoar6rxsbGx+eyGJGkehrnraSzJmra+GrgQeHTyukGSAJcCD80wd0WSU9r6OcA5wFfb45e2P1cx+DTysTbtduCt7e6n84AfV9W+Y9pLSdJRO2GIbdYCm5OsYFAst1TVHUnuSjLG4FPCDuBqgHbd4eqqupLBhe9vDLqEJ4E/qqpn2utem+TN7TVvrKq72vhXgDcBu4H/C/zrBdhPSdJRyuBywdI2Pj5eExMTo44hSUtKku1VNT7Xdv5ktiSpy6KQJHVZFJKkLotCktRlUUiSuiwKSVKXRSFJ6rIoJEldFoUkqcuikCR1WRSSpC6LQpLUZVFIkrpeEP96bJIDwPfmOe1UBr+SdSky+/G3VHOD2UdlKWT/1aqa8ze/vSCK4mgkmRjmn9ddjMx+/C3V3GD2UVnK2afz1JMkqcuikCR1LeeiuGnUAY6B2Y+/pZobzD4qSzn7P7Fsr1FIkoaznD9RSJKGsOyKIsnGJI8l2Z3kPaPOM5ck303yYJIdSSba2MlJtiZ5vP150qhzAiS5Ocn+JA9NGZsxawY+2r4ODyQ5d3TJZ83+/iR727HfkeRNU557b8v+WJKLR5P6uSynJ7k7yf9K8nCSf9fGF/2x72Rf9Mc+yYlJ7kuys2W/ro2/PMm2lvHzSV7Uxle1x7vb82eMKvu8VdWyWYAVwP8GXgG8CNgJvGbUuebI/F3g1GljHwLe09bfA3xw1Dlblt8FzgUemisr8CZgCxDgPGDbIsz+fuBPZtj2Ne3vzirg5e3v1IoRZl8LnNvWfwnY1TIu+mPfyb7oj307fr/Y1lcC29rxvAW4oo1/DLimrb8D+FhbvwL4/KiO+3yX5faJYgOwu6r+T1X9DPgccMmIMx2NS4DNbX0zcOkIszynqr4OHJw2PFvWS4BP18C9wJoka49P0v/fLNlncwnwuap6uqq+A+xm8HdrJKpqX1V9q63/BHgEOI0lcOw72WezaI59O36H28OVbSngAuALbXz6cZ/8enwBeEOSHKe4x2S5FcVpwBNTHu+h/5dyMSjgq0m2J7mqjb2sqva19b8HXjaaaEOZLetS+Vq8s52euXnKKb5Fm72dzvh1Bv93u6SO/bTssASOfZIVSXYA+4GtDD7hHKqqZ2bI91z29vyPgVOOb+Kjs9yKYin67ao6F3gj8G+T/O7UJ2vwOXZJ3Lq2lLI2NwKvBNYD+4C/HG2cviS/CPx34N9X1ZNTn1vsx36G7Evi2FfVkapaD6xj8MnmrBFHel4st6LYC5w+5fG6NrZoVdXe9ud+4IsM/jJ+f/JUQftz/+gSzmm2rIv+a1FV32/fCJ4FPs4/nuJYdNmTrGTwjfYzVXVrG14Sx36m7Evp2ANU1SHgbuC3GJzKO6E9NTXfc9nb878M/PA4Rz0qy60o7gfObHclvIjBBaXbR5xpVkn+WZJfmlwHLgIeYpB5U9tsE/Cl0SQcymxZbwfe2u7AOQ/48ZTTJIvCtPP2f8Dg2MMg+xXtLpaXA2cC9x3vfJPaee5PAo9U1X+e8tSiP/azZV8Kxz7JWJI1bX01cCGDayx3A5e1zaYf98mvx2XAXe2T3uI36qvpx3thcMfHLgbnEv9s1HnmyPoKBnd47AQenszL4Lzm14DHgf8BnDzqrC3XZxmcJvg5g3Ozb58tK4M7Rv5r+zo8CIwvwux/07I9wOA/8rVTtv+zlv0x4I0jzv7bDE4rPQDsaMublsKx72Rf9MceOAf4dsv4EPDnbfwVDMprN/DfgFVt/MT2eHd7/hWj/Hszn8WfzJYkdS23U0+SpHmyKCRJXRaFJKnLopAkdVkUkqQui0KS1GVRSJK6LApJUtf/AzwEyuOPin1LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图，使用角度制\n",
    "plt.scatter(dd*57.3,v*57.3)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
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 },
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 "nbformat_minor": 2
}