udemy-ML/04-Matplotlib/02-Matplotlib-SubPlots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
    "___\n",
    "# Matplotlib Sub Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the `matplotlib.pyplot` module under the name `plt` (the tidy way):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# COMMON MISTAKE!\n",
    "# DON'T FORGET THE .PYPLOT part\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE: For users running .py scripts in an IDE like PyCharm or Sublime Text Editor. You will not see the plots in a notebook, instead if you are using another editor, you'll use: *plt.show()* at the end of all your plotting commands to have the figure pop up in another window.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.linspace(0,10,11)\n",
    "b = a ** 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.000e+00, 1.000e+00, 1.600e+01, 8.100e+01, 2.560e+02, 6.250e+02,\n",
       "       1.296e+03, 2.401e+03, 4.096e+03, 6.561e+03, 1.000e+04])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(0,10)\n",
    "y = 2 * x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plt.subplots()\n",
    "\n",
    "**NOTE: Make sure you put the commands all together in the same cell as we do in this notebook and video!**\n",
    "\n",
    "The plt.subplots() object will act as a more automatic axis manager. This makes it much easier to show multiple plots side by side.\n",
    "\n",
    "Note how we use tuple unpacking to grba both the Figure object and a numpy array of axes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3deXxU5dn/8c/lQi2iYoVKRS3uNSAgRFx4cEUL1u1n1aK27uICrbiWR+tSrTuiKCirKxQXEAXZBQRUBBL2RYQiBQQligIKKoHr+eMef4Y4wRAyc2dmvu/XK6/MnHOSfI3KxXXOvZi7IyIiUtoOsQOIiEjVpAIhIiJJqUCIiEhSKhAiIpKUCoSIiCSlAiEiIkmpQIhUAjPb38y+NrMdt3KNm9nB6cwlsj1UIEQqyMyWmFlLAHdf6u413H1T4tw7ZnZV3IQi20cFQkREklKBEKkAM3sJ2B8Ykri1dFviFtJOZnY/0ALomjjXNcnX/8LMOpnZUjP7zMy6m9kv0/3PIbI1KhAiFeDufwGWAme6ew3g1RLn7gAmAu0Tt53aJ/kWDwOHAo2Bg4G6wF0pDy6yDVQgRNLMzAy4GrjR3Ve7+zrgAaBN3GQiW9opdgCRHFQbqA4UhloBgAFljoASiUEFQqTitrYU8tbOfQ5sAOq7+yeVG0mk8ugWk0jFfQYcuK3n3H0z0At43Mx+DWBmdc3s9ylJKVJBKhAiFfcg8A8z+wo4r9S5LsB5ZvalmT2Z5Gv/DiwCPjCztcDbwGEpTSuyjUwbBomISDLqIEREJCkVCBERSUoFQkREklKBEBGRpLJqHkStWrW8Xr16sWOIiGSMwsLCz929drJzWVUg6tWrR0FBQewYIiIZw8z+W9Y53WISEZGkVCBERCQpFQgREUlKBUJERJJSgRARkaRUIEREJCkVCBERSUoFQkQkk737LjzySEq+tQqEiEgmWrcO2reHFi2gRw/45ptK/xEqECIimWbECGjQAJ5+Gm64AWbOhF13rfQfowIhIpIpvvgCLr0UWrcOBeG99+CJJ6BGjZT8uJStxWRmzwJnAKvcvUHi2Cv8uK1iTeArd2+c5GuXAOuATUCxu+enKqeISJXnDgMHQrt2sHo1/OMf4eMXv0jpj03lYn3PA12BF3844O5/+uG1mT0GrNnK15/k7p+nLJ2ISCZYuTIUhkGDoGlTGDUKGjVKy49O2S0md58ArE52zswMuADon6qfLyKS0dzhuecgLw+GDw8jlT74IG3FAeI9g2gBfObuC8s478AoMys0s7Zb+0Zm1tbMCsysoKioqNKDioik3ccfw2mnwRVXQMOG4SH0rbfCTundoSFWgbiQrXcPzd29CdAaaGdmx5d1obv3dPd8d8+vXTvpnhciIplh0ybo0iWMUJo8GZ55BsaNg0MPjRIn7RsGmdlOwLlA07KucfcVic+rzGwQ0AyYkJ6EIiIRzJsHV10FkyaFUUo9esB++0WNFKODaAl86O7Lk500s13NbLcfXgOnAXPSmE9EJH02boR//QuOPBI++gj69oWhQ6MXB0hhgTCz/sAk4DAzW25mVyZOtaHU7SUz28fMhiXe7g28a2YzgSnAUHcfkaqcIiLRFBZCfj7ceSece27oIi6+GMxiJwNSeIvJ3S8s4/hlSY6tAE5PvF4MpO8xvYhIum3YAPfcA506QZ068OabcNZZsVP9RNqfQYiI5LTx48OzhkWL4Oqrw/DVmjVjp0pKS22IiKTD2rVw3XVw4omweTOMGQM9e1bZ4gAqECIiqTdsGNSvHwrCTTfBrFlw8smxU/0sFQgRkVT5/HP485/hD3+APfaA99+Hxx5LycqrqaACISJS2dzh5Zfh8MPh1Vfh7rth2jQ4+ujYybaJHlKLiFSmTz6B66+HwYPhqKOgTx844ojYqSpEHYSISGVwh169wuJ6o0eHIayTJmVscQB1ECIi2+8//wlDVseNC6OUevWCgw+OnWq7qYMQEamoTZugc+fQJRQWhlFKY8dmRXEAdRAiIhUzZw5ceSVMmQJnnhlWXq1bN3aqSqUOQkRkW3z/Pfzzn9CkCSxeDP37h6Uysqw4gDoIEZHymzIldA1z5sBFF4W9G2rVip0qZdRBiIj8nPXr4ZZb4Nhj4csvYcgQ6Ncvq4sDqIMQEdm6cePC4nqLF8O118JDD4VZ0TlAHYSISDJr1sA114Q1k3bYAd55JzyIzpHiACoQIiI/NWRImPDWuzfceivMnAknnBA7VdqpQIiI/KCoCC68MGzes9deMHly2K+hevXYyaJQgRARcYd//zssrjdwINx7LxQUhO1Ac1gq96R+1sxWmdmcEsfuMbNPzGxG4uP0Mr62lZktMLNFZtYxVRlFRFi2LEx0u/jiMAN6+vSwR3S1arGTRZfKDuJ5oFWS44+7e+PEx7DSJ81sR6Ab0BrIAy40s7wU5hSRXLR5M/ToETbyGTcOHn8c3nsvvBcghQXC3ScAqyvwpc2ARe6+2N2/B14Gzq7UcCKS2xYuDKOTrr0WmjWD2bOhQwfYccfYyaqUGM8g2pvZrMQtqD2TnK8LLCvxfnniWFJm1tbMCsysoKioqLKzikg2KS4Oy3A3bAgzZoRRSqNHw4EHxk5WJaW7QDwDHAQ0BlYCjyW5xpIc87K+obv3dPd8d8+vXbt25aQUkewza1aYCX3rrfD738O8eWHZDEv2R45AmguEu3/m7pvcfTPQi3A7qbTlwH4l3u8LrEhHPhHJQt99B3fdBU2bwtKlYQvQQYNgn31iJ6vy0logzOw3Jd7+P2BOksumAoeY2QFmVg1oAwxORz4RyTIffBBWXb3vvjC/Yd48OP98dQ3llMphrv2BScBhZrbczK4EHjGz2WY2CzgJuDFx7T5mNgzA3YuB9sBIYD7wqrvPTVVOEclC33wDN94Ixx0H69bBsGHw4oth8puUm7mXeXs/4+Tn53tBQUHsGCIS05gxYfvPjz+G66+HBx+E3XePnarKMrNCd086I1AzqUUkO3z1VVh1tWVL2GknGD8eunVTcdgOKhAikvnefDMsrvf88/D3v4fF9Y4/PnaqjKf9IEQkc332Gfztb2FkUqNGYRXWpk1jp8oa6iBEJPO4Q9++oWt44w24/36YOlXFoZKpgxCRzLJ0aVgiY/jwMPGtT5+wCqtUOnUQIpIZNm+Gp58Oi+mNHw9dusDEiSoOKaQOQkSqvo8+CiOUJk6EU0+Fnj2hXr3YqbKeOggRqbqKi+Hhh8PierNnw3PPwciRKg5pog5CRKqmGTPCYnrTpsG554Y5DXXqxE6VU9RBiEjV8u23cMcdYbvPTz6BAQPCNqAqDmmnDkJEqo733w9dw4cfwqWXQufO8KtfxU6Vs9RBiEh8X38dJrz9z//Ahg0wYkSYFa3iEJU6CBGJa9QoaNs2zG9o3x4eeABq1IidSlAHISKxrF4Nl18ednfbZZcwhPXJJ1UcqhAVCBFJv4EDwzIZL70Et98eRiw1bx47lZSiW0wikj6ffhpuIw0cCEceGZ41NG4cO5WUQR2EiKSee3jonJcHb70FDz0EkyerOFRx6iBEJLWWLAkPoUePDqOUeveGww6LnUrKIZV7Uj9rZqvMbE6JY4+a2YdmNsvMBplZzTK+dkli7+oZZqY9REUy0ebN8NRT0KABTJoUZkKPH6/ikEFSeYvpeaBVqWOjgQbu3hD4CPjfrXz9Se7euKy9UkWkCps/H1q0CHMbWrSAOXPC/tA76K52JknZvy13nwCsLnVslLsXJ95+AOybqp8vIhFs3BjmMTRuHGZDv/giDBsGv/1t7GRSATHL+RXA8DLOOTDKzArNrG0aM4lIRU2bBkcdFdZROuccmDcP/vIXMIudTCooSoEwszuAYqBfGZc0d/cmQGugnZmVufu4mbU1swIzKygqKkpBWhHZqg0boGNHaNYs7BE9aBC88grsvXfsZLKd0l4gzOxS4AzgYnf3ZNe4+4rE51XAIKBZWd/P3Xu6e76759euXTsVkUWkLBMnhttJDz8Ml10WuoZzzomdSipJWguEmbUC/g6c5e7ry7hmVzPb7YfXwGnAnGT
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use similar to plt.figure() except use tuple unpacking to grab fig and axes\n",
    "fig, axes = plt.subplots()\n",
    "\n",
    "# Now use the axes object to add stuff to plot\n",
    "axes.plot(x, y, 'r')\n",
    "axes.set_xlabel('x')\n",
    "axes.set_ylabel('y')\n",
    "axes.set_title('title'); #; hides Out[]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding rows and columns\n",
    "\n",
    "Then you can specify the number of rows and columns when creating the subplots() object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Empty canvas of 1 by 2 subplots\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000023521E20588>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x0000023521E5D8C8>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Axes is an array of axes to plot on\n",
    "axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Empty canvas of 2 by 2 subplots\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000023521ED5E48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000023521F09D88>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000023521F45308>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000023521F79D88>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axes.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting on axes objects\n",
    "\n",
    "Just as before, we simple .plot() on the axes objects, and we can also use the .set_ methods on each axes.\n",
    "\n",
    "Let's explore this, make sure this is all in the same cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(nrows=1,ncols=2)\n",
    "\n",
    "for axe in axes:\n",
    "    axe.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2352216ce88>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(nrows=1,ncols=2)\n",
    "\n",
    "axes[0].plot(a,b)\n",
    "axes[1].plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2352229c648>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NOTE! This returns 2 dimensional array\n",
    "fig,axes = plt.subplots(nrows=2,ncols=2)\n",
    "\n",
    "axes[0][0].plot(a,b)\n",
    "axes[1][1].plot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A common issue with matplolib is overlapping subplots or figures. We ca use **fig.tight_layout()** or **plt.tight_layout()** method, which automatically adjusts the positions of the axes on the figure canvas so that there is no overlapping content:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NOTE! This returns 2 dimensional array\n",
    "fig,axes = plt.subplots(nrows=2,ncols=2)\n",
    "\n",
    "axes[0][0].plot(a,b)\n",
    "axes[1][1].plot(x,y)  \n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters on subplots()\n",
    "\n",
    "Recall we have both the Figure object and the axes. Meaning we can edit properties at both levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(nrows=2,ncols=2,figsize=(12,8))\n",
    "\n",
    "# SET YOUR AXES PARAMETERS FIRST\n",
    "\n",
    "# Parameters at the axes level\n",
    "axes[0][0].plot(a,b)\n",
    "axes[0][0].set_title('0 0 Title')\n",
    "\n",
    "\n",
    "axes[1][1].plot(x,y)\n",
    "axes[1][1].set_title('1 1 Title')\n",
    "axes[1][1].set_xlabel('1 1 X Label')\n",
    "\n",
    "axes[0][1].plot(y,x)\n",
    "axes[1][0].plot(b,a)\n",
    "\n",
    "# THEN SET OVERALL FIGURE PARAMETERS\n",
    "\n",
    "# Parameters at the Figure level\n",
    "fig.suptitle(\"Figure Level\",fontsize=16)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manual spacing on subplots()\n",
    "\n",
    "Use .subplots_adjust to adjust spacing manually.\n",
    "\n",
    "Full Details Here: https://matplotlib.org/3.2.2/api/_as_gen/matplotlib.pyplot.subplots_adjust.html\n",
    "\n",
    "Example from link:\n",
    "\n",
    "* left = 0.125  # the left side of the subplots of the figure\n",
    "* right = 0.9   # the right side of the subplots of the figure\n",
    "* bottom = 0.1  # the bottom of the subplots of the figure\n",
    "* top = 0.9     # the top of the subplots of the figure\n",
    "* wspace = 0.2  # the amount of width reserved for space between subplots,\n",
    "              # expressed as a fraction of the average axis width\n",
    "* hspace = 0.2  # the amount of height reserved for space between subplots,\n",
    "              # expressed as a fraction of the average axis height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAHSCAYAAADBgiw3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdeXxU5d3+8c83CRCWsISENez7voUA8ad1qYpLiwtuVVkFl9r6dNHaPm3tU2tb+3T1aasihkUFVNyoCkrVVhFZwh42CWELSUhCWBIgZJn790cGGzFsySRnZnK9Xy9eM3PPmckVkZwrZ+5zH3POISIiIiIiFybC6wAiIiIiIqFIRVpEREREpBpUpEVEREREqkFFWkRERESkGlSkRURERESqQUVaRERERKQaorwOUF1xcXGua9euXscQkUrWrFmT75yL9zqHSCjSfk0kOJ1t3xayRbpr166kpqZ6HUNEKjGzPV5nEAlV2q+JBKez7ds0tUNEREREpBpUpEVEREREqkFFWkRERESkGlSkRURERESq4ZxF2sxSzCzXzNIqjcWa2VIz2+G/beUfNzN7yszSzWyjmQ2v9JqJ/u13mNnESuMjzGyT/zVPmZkF+psUEREREQm08zkiPRsYe9rYo8AHzrlewAf+xwDXAL38f6YDT0NF8QYeA0YBScBjp8q3f5vplV53+tcSEREREQk65yzSzrmPgYLThscBc/z35wA3VBqf6yqsAFqaWXvgamCpc67AOXcIWAqM9T/X3Dn3mXPOAXMrvZeIBIF3Nmbz8ed5XscQEREJCOccMz/JIL/oZI3fq7rrSLd1zmX7w2SbWRv/eEdgX6XtMv1jZxvPrGK8SmY2nYqj13Tu3Lma0UXkfDnn+M3irfRs04xLeus6KyIiEtpKy3385PVNvLomk9Jyx/2X9qjR+wX6ZMOq5je7aoxXyTk3wzmX6JxLjI/XTl2ktm3OOkrmoRNcM7Cd11FERERq5HhJGdPnpvLqmkweuqIX932te43fs7pF+oB/Wgb+21z/eCbQqdJ2CUDWOcYTqhgXkSCwOC2byAjjyv4q0iIiEroOFp3kjudW8u/P8/j1jYP43pW9CcT6FtUt0ouAUytvTATeqjQ+wb96x2jgiH8KyHvAVWbWyn+S4VXAe/7nCs1stH+1jgmV3ktEPOScY3FaDqO6xRLbtKHXcURERKplX8Fxxj/zGduyj/LMXSP41qjATQ8+5xxpM5sPXArEmVkmFatv/BZ4xcymAnuBW/ybvwtcC6QDx4HJAM65AjN7HFjt3+6XzrlTJzDeT8XKII2Bxf4/IuKxHblFZOQdY3JyV6+jiIiIVEva/iNMnr2akjIf86aNYkSX2IC+/zmLtHPujjM8dUUV2zrg22d4nxQgpYrxVGDguXKISN1avCkHM7h6gKZ1iIhI6Fm2I5/7XlxD8+go5t8/hp5tYgL+Naq7aoeIhLnFadmM6NyKNs2jvY4iIiJyQd5av58fvrqBHvHNmD05iXYtamdfpkuEi8hX7M4/xracQsZqtQ4REQkxMz/J4KEF6xneuRUv3zum1ko06Ii0iFRhcVoOgIq0iIiEDJ/P8et3tzJz2S6uHdSOP946lOgGkbX6NVWkReQrlqRlMzihBQmtmngdRURE5JxKynz88NUNLNqQxcQxXfj5NwYQGVHz5e3ORVM7RORL9h8+wYbMIzoaLSIiIaGwuJQps1ezaEMWj4ztwy++WTclGnREWkROs8Q/reOage09TiIiInJ2uYXFTJ61mm05hfz+liGMH5Fw7hcFkIq0iHzJkrRs+raLoVtcU6+jiIiInFFGXhETZ60iv7CEmRMTuaxPmzrPoKkdIvKF3MJiUvcc0rQOEREJauv3HWb8M59x/GQ5C6aP9qREg45Ii0gl720+gHOa1iEiIsHro225PPDSWuJiGjJ3yihPP0FVkRaRLyxJy6Z7XFN6t23mdRQREZGveDV1H4++vol+7WOYNSmJ+JhGnubR1A4RAeDQsRJWZBQwdmA7zOrmbGcREZHz4Zzjbx+l8/DCjST3aM2C6WM8L9GgI9Ii4rd0ywHKfU7TOkREJKiU+xz/84/NzP1sDzcM7cDvxg+hYVRwHAtWkRYRABanZZPQqjEDOzb3OopI2DCz7wH3AA7YBEx2zhV7m0okdBSXlvO9l9ezOC2H6Zd059GxfYmoozWiz0dw1HkR8dTR4lKWpeczdoCmdYgEipl1BL4LJDrnBgKRwO3ephIJHUdOlDIhZRWL03L46XX9+Mm1/YKqRIOOSIsI8OHWXErLHdcM0rJ3IgEWBTQ2s1KgCZDlcR6RkJB95ASTUlaTkV/EU3cM45tDOngdqUoq0iLC4rRs2jZvxLBOrbyOIhI2nHP7zez3wF7gBPC+c+79ytuY2XRgOkDnzp3rPqRIENpxoJAJKasoLC5jzuQkknvGeR3pjDS1Q6SeO15Sxr8/z+PqAe2C7iMzkVBmZq2AcUA3oAPQ1MzuqryNc26Gcy7ROZcYHx/vRUyRoJK6u4Dxz3xGmc/x8r2jg7pEg4q0SL33r+15FJf6dDVDkcD7OrDLOZfnnCsFXgeSPc4kErTe25zDnTNX0rppQ16/P5kBHVp4HemcVKRF6rnFaTnENm1IUtdYr6OIhJu9wGgza2IVZ/FeAWz1OJNIUHpxxR7uf3EN/do3Z+H9yXSKbeJ1pPOiOdIi9VhxaTkfbj3AN4Z0ICpSv1eLBJJzbqWZLQTWAmXAOmCGt6lEgotzjj8t/ZynPkzn8r5t+Ou3htGkYejU09BJKiIBt2xHPsdKyjWtQ6SWOOceAx7zOodIMCor9/Hfb6Txcuo+bk1M4Nc3Dgq5gzoq0iL12OK0HGKio0juEdwnc4iISHg5UVLOg/PW8sG2XL5zeU++f2XvkLyOgYq0SD1VWu7jn1sPcGW/tkFzqVUREQl/BcdKmDpnNev3HebxGwZy9+guXkeqNhVpkXrqs50HOXKiVNM6RESkzuwrOM7EWavIPHSCp+8cEfL7IBVpkXpqcVoOTRpGcklvrV0rIiK1b0vWUSbOWsXJ0nJeumcUI8NgtSgVaZF6qNznWLolh8v6tiG6QaTXcUREJMwtT8/n3hfW0Cw6ipfuT6Z32xivIwWEirRIPbR6dwH5RSVcE+IfqYmISPD7x4Ysvv/KerrFNWXOlCTat2jsdaSAUZEWqYeWpOXQKCqCy/q08TqKiIiEseeX7eLxt7eQ1DWW5yYk0qJJA68jBVSNTtU3s++Z2WYzSzOz+WYWbWbdzGylme0ws5fNrKF/20b+x+n+57tWep8f+8e3m9nVNfuWRORsfD7HkrQcLukdT9NG+l1aREQCz+dz/ObdrTz+9hbGDmjH3KlJYVeioQZF2sw6At8FEp1zA4FI4HbgSeBPzrlewCFgqv8lU4FDzrmewJ/822Fm/f2vGwCMBf5uZpq0KVJL1mceJudosaZ1iIhIrSgp8/H9V9bz7McZ3D26C3+7c3jYno9T08Vjo4DGZhYFNAGygcuBhf7n5wA3+O+P8z/G//wVVrHy9jhggXPupHNuF5AOJNUwl4icwZK0HBpEGlf0a+t1FBERCTNFJ8uYOmc1b67P4odX9eaX4wYQGRF6F1o5X9X+XNc5t9/Mfg/sBU4A7wNrgMPOuTL/ZplAR//9jsA+/2vLzOwI0No/vqLSW1d+jYgEkHOOxWnZJPeIo0Xj8PuITUREvJNXeJLJs1exNbuQ340fzK2JnbyOVOtqMrWjFRVHk7sBHYCmwDVVbOpOveQMz51pvKqvOd3MUs0sNS8v78JDi9Rzm7OOsq/ghKZ1iIhIQO3KP8bNTy9nZ+4xnpswol6UaKjZ1I6vA7ucc3nOuVLgdSAZaOmf6gGQAGT572cCnQD8z7cACiqPV/GaL3HOzXDOJTrnEuPjdREJkQu1JC2HCIMr+2tah4iIBMaGfYcZ//RyCotLmTdtFJf3rT/7mJoU6b3AaDNr4p/rfAWwBfgIGO/fZiLwlv/+Iv9j/M9/6Jxz/vHb/at6dAN6AatqkEtEzmBxWjajurWmdbNGXkcREZEw8K/tudz
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(nrows=2,ncols=2,figsize=(12,8))\n",
    "\n",
    "# Parameters at the axes level\n",
    "axes[0][0].plot(a,b)\n",
    "axes[1][1].plot(x,y)\n",
    "axes[0][1].plot(y,x)\n",
    "axes[1][0].plot(b,a)\n",
    "\n",
    "# Use left,right,top, bottom to stretch subplots\n",
    "# Use wspace,hspace to add spacing between subplots\n",
    "fig.subplots_adjust(left=None,\n",
    "    bottom=None,\n",
    "    right=None,\n",
    "    top=None,\n",
    "    wspace=0.9,\n",
    "    hspace=0.1,)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exporting plt.subplots()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NOTE! This returns 2 dimensional array\n",
    "fig,axes = plt.subplots(nrows=2,ncols=2,figsize=(12,8))\n",
    "\n",
    "axes[0][0].plot(a,b)\n",
    "axes[1][1].plot(x,y)\n",
    "axes[0][1].plot(y,x)\n",
    "axes[1][0].plot(b,a)\n",
    "\n",
    "fig.savefig('subplots.png',bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}