justhomework/AIandML/e1_python_basics/e1.2_matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 人工智能与机器学习-实验1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part.III Matplotlib绘图练习\n",
    "\n",
    "|学号       |姓名    |\n",
    "|----------|--------|\n",
    "|***REMOVED***|***REMOVED***|\n",
    "|2020113874|何一涛|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 曲线\n",
    "\n",
    "* 用蓝线绘制函数$𝑓(𝑥) = 𝑥^2−2𝑥+3$的图表，并在坐标(1,2)位置处标上一个红色的点\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "x = np.linspace(-1, 3, 50)\n",
    "y = x**2 - 2*x + 3\n",
    "plt.plot(x, y, color='blue', linewidth=1.0)\n",
    "plt.scatter(1, 2, color='red')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. x-y图\n",
    "\n",
    "* 使用`np.linspace()`函数生成$t\\in [0, 2\\pi]$。\n",
    "* 给定$𝑥=16*sin⁡(𝑡)^3$和$𝑦=13*cos⁡(𝑡)−5*cos⁡(2𝑡)−2*cos⁡(3𝑡)−cos⁡(4𝑡)$，画出x-y图表，\n",
    "* 并给图标添加一个题目：Heart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "t = np.linspace(0, 2*np.pi, 1000)\n",
    "x = 16*np.sin(t)**3\n",
    "y = 13*np.cos(t) - 5*np.cos(2*t) - 2*np.cos(3*t) - np.cos(4*t)\n",
    "plt.plot(x, y, color='red', linewidth=1.0)\n",
    "plt.title('Heart')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "1f0d395e06aa83586067b19165efc9b683889967164248deef4bbf1fa27cfb00"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}