{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9b1c44d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('3.8.10', '1.21.2', '3.4.3')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from platform import python_version\n",
    "\n",
    "python_version(), np.__version__,matplotlib.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64220c36",
   "metadata": {},
   "source": [
    "## Horizontal line at "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b926e843",
   "metadata": {},
   "outputs": [
    {
     "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",
    "\n",
    "plt.clf()\n",
    "\n",
    "height = 5\n",
    "\n",
    "plt.axhline(y=height)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e030c2c",
   "metadata": {},
   "source": [
    "## Vertical line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dfc80ee1",
   "metadata": {},
   "outputs": [
    {
     "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",
    "\n",
    "plt.clf()\n",
    "\n",
    "x_level = 5\n",
    "\n",
    "plt.axvline(x=x_level)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7808109e",
   "metadata": {},
   "source": [
    "## Vertical line, string axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c0d9c72f",
   "metadata": {},
   "outputs": [
    {
     "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",
    "\n",
    "plt.clf()\n",
    "\n",
    "\n",
    "days   = ['sun','mon','tue','wed','thu','fri','sat']\n",
    "values = [1    , 4   , 4   , 8   , 3   , 4   , 3   ]\n",
    "\n",
    "# plotting a bar just for comparison\n",
    "plt.bar(days, values)\n",
    "\n",
    "plt.axvline('thu', color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "633661a5",
   "metadata": {},
   "source": [
    "## Line 45 degree angle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d899a9b",
   "metadata": {},
   "outputs": [
    {
     "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",
    "plt.clf()\n",
    "\n",
    "xs = np.random.uniform(0,100,100)\n",
    "ys = np.random.uniform(0,10,100)\n",
    "\n",
    "# just to populate the plot\n",
    "plt.scatter(xs,ys,s=0.6)\n",
    "\n",
    "# angle of 45 degrees means a slope of 1 (relatively)\n",
    "slope = (ys.max() - ys.min()) / (xs.max() - xs.min())\n",
    "\n",
    "plt.axline((1,2),slope=slope)\n",
    "\n",
    "plt.plot(1,2,'bo')\n",
    "plt.annotate(\"(1,2)\",(1,2),xytext=(0.05,0.15), textcoords='axes fraction')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "981956ac",
   "metadata": {},
   "source": [
    "## Using Coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f123ac4",
   "metadata": {},
   "outputs": [
    {
     "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",
    "\n",
    "plt.clf()\n",
    "\n",
    "plt.axline((1,2),(5,7))\n",
    "\n",
    "plt.plot(1,2,'bo')\n",
    "plt.plot(5,7,'bo')\n",
    "plt.annotate(\"(1,2)\",(1,2),xytext=(-10,10),textcoords='offset points')\n",
    "plt.annotate(\"(5,7)\",(5,7),xytext=(-10,-15),textcoords='offset points')\n",
    "plt.xlim(0,10)\n",
    "plt.ylim(0,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ce03ee3",
   "metadata": {},
   "source": [
    "## Dotted line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c1352bec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eDTxQVXdPd6d5vaRTVY8leS/wdTrvwK+uqluSXAiMVdVa4L/ofFu2g86bFGfN50yD0uNe/CvwLOCq7vvWu6vqjIENPU963Ism9LgXXwdOS7INeBz426o65r4L7nEvPgh8Jslf03kD953H4gvEJFfQ+SK/pPt+xT8BvwJQVZfQef/idGAH8BDwrp4e9xjcK0nSJPxNW0lqhMGXpEYYfElqhMGXpEYYfElqhMGXpEYYfElqxP8B6u0DixR4CJMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.clf()\n",
    "\n",
    "plt.axhline(y=5,linestyle='dotted')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b0ddfe5",
   "metadata": {},
   "source": [
    "## Dashed line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d5283e64",
   "metadata": {},
   "outputs": [
    {
     "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",
    "\n",
    "plt.clf()\n",
    "\n",
    "plt.axvline(x=5,linestyle='dashed')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5409011",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}