matplot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%config InlineBacken.figure_format='retina'\n",
    "x=np.arange(0,100)\n",
    "y=x*2\n",
    "z=x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'XY')"
      ]
     },
     "execution_count": 14,
     "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": [
    "fig = plt.figure()\n",
    "ax =fig.add_axes([0,0,1,1])\n",
    "ax.plot(x,y)\n",
    "# plt.plot(x,y)\n",
    "ax.set_xlabel(\"x\")\n",
    "ax.set_ylabel(\"y\")\n",
    "ax.set_title('XY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe368706080>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig =plt.figure()\n",
    "ax1 = fig.add_axes([0,0,1,1])\n",
    "ax2 = fig.add_axes([0.2,0.5,.2,.2])\n",
    "ax1.plot(x,y)\n",
    "ax2.plot(x,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 2 Axes>,\n",
       " array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fe3682b7198>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe368235550>],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}