DA08_BaiTap1_An.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BÀI TẬP MATPLOTLIB CƠ BẢN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Đề Bài"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vẽ phương trình y = ax + b (cho phép gán giá trị khác nhau của a, b)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Script"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure that the input is always an number\n",
    "def get_input_num(cap='Nhập số: '):\n",
    "    while True:\n",
    "        try:\n",
    "            return float(input(cap))\n",
    "        except:\n",
    "            print('Dữ liệu nhập vào phải là số, xin vui lòng thử lại...')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Float try compare to int\n",
    "def float_try_int(num=0):\n",
    "    temp = int(num)\n",
    "    if temp == float(num):\n",
    "        return temp\n",
    "    return num"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Main\n",
    "def main():\n",
    "    # import the values of a and b\n",
    "    a = get_input_num(\"Nhập a = \")\n",
    "    b = get_input_num(\"Nhập b = \")\n",
    "    # draw horizontal and vertical lines\n",
    "    plt.axhline(y=0, color='gray')\n",
    "    plt.axvline(x=0, color='gray')\n",
    "    # set the x and y axis labels and the title\n",
    "    plt.xlabel('x')\n",
    "    plt.ylabel('y')\n",
    "    plt.title(f'Đồ thị của phương trình y = {float_try_int(a)}x {b > 0 and \"+\" or \"-\"} {float_try_int(abs(b))}')\n",
    "    # set the limit\n",
    "    plt.xlim(-5, 5)\n",
    "    # plot the graph\n",
    "    x = np.linspace(-6, 6, 100)\n",
    "    y = a * x + b\n",
    "    plt.plot(x, y, linewidth=2)\n",
    "    # add grid\n",
    "    plt.grid(linestyle='--', alpha=0.5)\n",
    "    # display the plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nhập a = a\n",
      "Dữ liệu nhập vào phải là số, xin vui lòng thử lại...\n",
      "Nhập a = 0.1\n",
      "Nhập b = -3.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run\n",
    "main()"
   ]
  }
 ],
 "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": "3.9.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "583404eccf5213a0b6dfd4a05f26ebb583b04870550067811cef66bba1a732ae"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}