Matplotlib/matplotlib_question.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to use matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize one type of data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot data on a graph\n",
    "* Creating a graph using matplotlib\n",
    "    * ** generate graph with ``matplotlib.pyplot.plot(data corresponding to X-axis, data corresponding to Y-axis)`** *** display on screen with ``matplotlib.pyplot.show()`** *** plot data on screen with ``matplotlib.pyplot.show()\n",
    "    * **`matplotlib.pyplot.show()`** to display on screen\n",
    "    * In Jupyter Notebook, write **`%matplotlib inline`** once before displaying the graph.\n",
    "        * If not, the graph will not be displayed on the first run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### グラフ作成の例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "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",
    "## Required for Jupyter Notebook\n",
    "%matplotlib inline\n",
    "\n",
    "## X axis data\n",
    "x = np.linspace(-15, 15) ## Get data equally spaced from -15 to 15\n",
    "## Y axis data\n",
    "y = 1.5 * x + 5\n",
    "\n",
    "## Plot data x,y on graph\n",
    "# Here is the code to create the graph\n",
    "\n",
    "# Display graph\n",
    "# Here is the code to display the graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set a name for a graph element\n",
    "* Set graph title\n",
    "    * **`matplot.pyplot.title(\"title\")`**\n",
    "* **name for the x-axis\n",
    "    * **`matplot.pyplot.xlabel(\"name of X axis\")`**\n",
    "* ** name of Y axis\n",
    "    * **`matplot.pyplot.ylabel(\"Y axis name\")`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "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",
    "%matplotlib inline\n",
    "\n",
    "x = np.linspace(-15, 15)\n",
    "\n",
    "y = 1.5 * x + 5\n",
    "\n",
    "## Here is the code to set the graph title\n",
    "\n",
    "## Here is the code to set the x-axis\n",
    "\n",
    "## Here is the code to set the y-axis\n",
    "\n",
    "\n",
    "plt.plot(x, y)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display grid on graph\n",
    "* Grid (grid) display is hidden by default\n",
    "* Grid display: **`matplotlib.pyplot.grid(True)`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "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",
    "%matplotlib inline\n",
    "\n",
    "x = np.linspace(-15, 15)\n",
    "\n",
    "y = 1.5 * x + 10\n",
    "\n",
    "## Here is the code to set up the grid display\n",
    "\n",
    "plt.plot(x, y)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set tick marks on graph axes\n",
    "* When creating a graph, X and Y axes are automatically set to tick marks.\n",
    "* To set tick marks manually\n",
    "    * X axis: **`matplotlib.pyplot.xticks(position to insert ticks, ticks to insert)`**\n",
    "    * Y axis: **`matplotlib.pyplot.yticks(position to insert ticks, ticks to insert)`**\n",
    "    * If there are multiple ticks to insert, each position and tick is a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "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",
    "%matplotlib inline\n",
    "\n",
    "x = np.linspace(-15, 15)\n",
    "\n",
    "y = 1.5 * x + 10\n",
    "\n",
    "plt.grid(True)\n",
    "\n",
    "xpos = [-10.0, -5.0, 0, 1.0, 3.0, 5.0, 7.0] # Position to insert tick marks\n",
    "xlabel = [\"-10\", \"-5\", \"0\", \"1\", \"3\", \"5\", \"7\"] # Tick to insert\n",
    "\n",
    "## Write the code to set the X-axis ticks\n",
    "\n",
    "plt.plot(x, y)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize multiple data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting two types of data on one graph\n",
    "* Plotting multiple data on one graph\n",
    "    * **Call ``matplotlib.pyplot.plot()`** repeatedly with different data to plot\n",
    "* **Specify plot colors so that multiple data can be distinguished on the graph\n",
    "    * **`matplotlib.pyplot.plot(x, y, color=\"specify color\")`**\n",
    "        * **The color specified for color is specified by **HTML color code**.\n",
    "            * Specify by **#(hexadecimal 6 digits)**.            * Search google for \"color swatch\" to find the color code corresponding to each color\n",
    "        * Representative colors can also be specified by letter\n",
    "            * \"b\": blue, \"g\": green, \"r\": red, \"c\": cyan, \"m\": magenta, \"y\": yellow, \"k\": black, \"w\": white\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "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",
    "%matplotlib inline\n",
    "\n",
    "x = np.linspace(-15, 15)\n",
    "## Y-axis data 1\n",
    "y1 = 1.5 * x + 10\n",
    "## Y-axis data 2\n",
    "y2 = -1.5 * x + 15\n",
    "\n",
    "plt.grid(True)\n",
    "\n",
    "## Here is the code to plot data x,y1 on the graph\n",
    "# Specify red (#aa0000) in HTML color code\n",
    "\n",
    "# Here is the code to plot data x,y2 on the graph\n",
    "# Specify green in the text specification\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting up series labels\n",
    "* Display multiple series data on a single graph\n",
    "    * You can set labels (legends) to indicate what each series data represents\n",
    "        * **`matplotlib.pyplot.legend([\"label name 1\", \"label name 2\",...])`**\n",
    "        * **label=\"label name \"** can be specified when plotting series data (**`matplotlib.pyplot.plot()`**), **`matplotlib.pyplot.legend()`** (without argument) can be used to set labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "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",
    "%matplotlib inline\n",
    "\n",
    "x = np.linspace(-15, 15)\n",
    "\n",
    "y1 = 1.5 * x + 10\n",
    "\n",
    "y2 = -1.5 * x + 15\n",
    "\n",
    "plt.grid(True)\n",
    "\n",
    "plt.plot(x, y1, color=\"#aa0000\", label=\"Y = 1.5 * X + 10\")\n",
    "\n",
    "plt.plot(x, y2, color=\"g\", label=\"Y = -1.5 * X + 15\")\n",
    "\n",
    "# Here is the code to set the series label (legend) display\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting the size of the figure\n",
    "* **Figure: the entire area where matplotlib draws (multiple) graphs**.\n",
    "* **Settings for the figure area\n",
    "    ** **Use ``matplotlib.pyplot.figure()`**.\n",
    "* Specifying the size of the figure\n",
    "    * ** utilize ``matplotlib.pyplot.figure(figsize=(horizontal size, vertical size))`**\n",
    "        * The unit of size is inches (1 inch = 2.54 cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAAEvCAYAAACZqb84AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAt3UlEQVR4nO3deXxV9b3v/9c3O3NCCCEQQgKEJCDzGFEcIInziKgoWu2kxQHsefxObx+1p+eetrfDtT1DWxMQuc5WS1ut1Vo9WtkJkwiCIqKi2RlJAiQEEjIne+/v7w/ivbQFq7CSlb3zfj4eeSR7r2R/335crLxZe++FsdYiIiIiIs6JcDuAiIiISLhRwRIRERFxmAqWiIiIiMNUsEREREQcpoIlIiIi4jAVLBERERGHRbod4ESpqak2Kyur39dpb28nISGh39cZKjRP52mmztI8naeZOkvzdN5AzHTXrl2HrbWjTrZtUBWsrKwsdu7c2e/rlJaWkp+f3+/rDBWap/M0U2dpns7TTJ2leTpvIGZqjKk+1TY9RSgiIiLiMBUsEREREYepYImIiIg4TAVLRERExGEqWCIiIiIOU8ESERERcZgKloiIiIjDVLBEREREHKaCJSIiIuIwFSwREREJG9Za3vQdZk+j39Ucg+qfyhERERE5HdZaSj9ppNjrY1f1Uc4aEcE3XcyjgiUiIiIhKxi0vPHRIYpLfOypbSEjOY4fXTeDtPYKV3OpYImIiEjICQQtr+49QLHXx76DrUwYGc/PbpjJ0rmZREdGUFpa6Wo+FSwREREJGf5AkJfeq2d1iY/yxnZyRiXwi5tnc82ssUR6Bs9Ly1WwREREZNDr8Qd54d1a1pSWU93UwZQxw1h96zwunzEGT4RxO97fUcESERGRQaurN8Dvd+5n7cYK6po7mZU5nHW3z+fiqWlEDMJi9SkVLBERERl0OnsCPLO9mnWbKmho7Wb+hBH8eOkM8iePwpjBW6w+pYIlIiIig0Zbt5+nt1XzyOYKmtp7WJg9kl/ePIeFOSNDolh9SgVLREREXNfS2csTW6t4bGslLZ29LJo8im8W5pKXleJ2tNOigiUiIiKuOdLew2NbKnnyzSpau/1cPDWNVYW5zBmX7Ha0M3LGBcsYEwtsAmL6Hu85a+33jTEpwG+BLKAKuMlae/RM1xMREZHQ19DaxSObK/n1W9V09ga4YsYYVhbkMn3scLejOcKJM1jdQKG1ts0YEwVsMca8ClwPbLDWPmCMuR+4H/iOA+uJiIhIiDrQ0snDGyv4zY4aegNBrp09lpUFuUxKG+Z2NEedccGy1lqgre9mVN+HBZYA+X33PwmUooIlIiIyJO0/0sFDG8t5bmctQWu5fl4G9+TnMjE1we1o/cKR12AZYzzALiAXWG2t3W6MSbPWHgCw1h4wxox2Yi0REREJHZWH21ld4uOFd+vwGMOyvEzuXpzDuJR4t6P1K3P8BJRDD2ZMMvACcB+wxVqbfMK2o9baESf5mRXACoC0tLT569evdyzPqbS1tZGYmNjv6wwVmqfzNFNnaZ7O00ydFY7zrGsN8qeKHrYfCBAZAfnjIrlyYhQjYgfmn7MZiJkWFBTsstbmnWybo+8itNY2G2NKgcuBQ8aY9L6zV+lAwyl+Zh2wDiAvL8/m5+c7GemkSktLGYh1hgrN03maqbM0T+dpps4Kp3nurWthdYmPV/ceJD7aw4pF2dx5YTajhsUMaA63Z+rEuwhHAb195SoOuBj4GfAS8BXggb7PL57pWiIiIjI47d7fTNGGMjbsa2BYTCT3Feby9fMnMiIh2u1ornDiDFY68GTf67AigN9Za182xmwDfmeMuQOoAZY5sJaIiIgMIjsqj1DkLWNz2WGS46P450sm85XzshgeF+V2NFc58S7CPcDck9zfBFx0po8vIiIig4u1ljfLm3hwQxnbK4+QmhjN/VdM4bZzJ5AYo2uYg67kLiIiIp+TtZbSjxsp8pbxTk0zaUkx/NvV07hlwXjioj1uxxtUVLBERETkMwWDlr98dIhir4/361rISI7jx9fN4Mb5mcRGqVidjAqWiIiInFQgaHnl/QOsLvGx72ArE0bG8/MbZrF0XgZRnoG53EKoUsESERGRv+IPBHnpvXpWl/gob2wnd3Qiv7x5DlfPSidSxepzUcESERERAHr8Qf7wTi1rSsupOdLBlDHDWH3rPK6YMYaICON2vJCigiUiIjLEdfUG+P3O/azdWEFdcyezMofzP6/O46Ipo1WsTpMKloiIyBDV0ePn2e01rNtUQUNrN/MnjOAnS2ewePIojFGxOhMqWCIiIkNMW7efp7ZV8ejmSprae1iYPZJfLp/DwuyRKlYOUcESEREZIlo6ennizSoe21pJS2cviyeP4r7CXPKyUtyOFnZUsERERMLckfYeHt1SwVNvVtPa7eeSaWmsKshl9rhkt6OFLRUsERGRMNXQ2sUjmyv59VvVdPYGuHJGOisLcpk2NsntaGFPBUtERCTM1Dd3sm5TBb/ZUUNvIMiSORncm5/DpLRhbkcbMlSwREREwsT+Ix2sKS3nuV37sRaun5fBvfm5ZKUmuB1tyFHBEhERCXEVjW2sKS3nhXfr8BjDTXnjuHtxDuNS4t2ONmSpYImIiISoTw61Uuz18fKeeqI8EXx54QTuWpTDmOGxbkcb8lSwREREQszeuhaKvT7++4ODxEd7+MaibO68IJtRw2LcjiZ9VLBERERCxLs1Ryn2+tiwr4FhMZHcV5jL18+fyIiEaLejyd9QwRIRERnkdlQeochbxuaywyTHR/GtSybz5fOyGB4X5XY0OQUVLBERkUHIWstWXxMPesvYUXmE1MRo7r9iCredO4HEGP36Huz0f0hERGQQsdZS+nEjD3rLeLemmbSkGP7t6mncsmA8cdEet+PJ56SCJSIiMggEg5bXPzxEcUkZe+uOkZEcx4+vm8GyvExiIlWsQo0KloiIiIsCQcv2A34eeHAz+w62kjUynp/fOIulczOI8kS4HU9OkwqWiIiIC/yBIC/urmd1qY+Kxm5yR0fxy5vncPWsdCJVrEKeCpaIiMgA6vEHef6dWtaU+th/pJOp6UmsnBPDt25aRESEcTueOEQFS0REZAB09Qb47dv7WbuxnAMtXczKHM6/XT2di6eOZuPGjSpXYUYFS0REpB919Ph55q0a1m2uoLG1m7wJI3jghlksmpSKMSpV4UoFS0REpB+0dvXy1LZqHt1SyZH2Hs7LGcmvls9hYfZIFashQAVLRETEQS0dvTy2tZLHt1ZyrMvP4smjuK8wl7ysFLejyQBSwRIREXFAU1s3j2yp5Olt1bR1+7lkWhr3FeYyKzPZ7WjiAhUsERGRM9BwrIt1myp4ZnsNXf4AV85IZ1VhLlPTk9yOJi5SwRIRETkN9c2drN1Yzvq39+MPBFkyJ4OVBTnkjh7mdjQZBFSwREREvoCapg4e2ujjuV21WAs3zMvknvwcslIT3I4mg4gKloiIyOdQ3tjG6hIfL+6ux2MMN589jrsX55A5It7taDIIqWCJiIh8ho8PtlJc4uPlPfXEREbwlYVZ3LU4m7SkWLejySCmgiUiInISe+taKPKW8doHh0iI9rBiUTZ3XpDNqGExbkeTEKCCJSIicoJ3ao5StKGMko8bGRYbyTcLc/na+RMZkRDtdjQJISpYIiIiwFsVTRR7fWzxHSY5Por/celkbl+YxfC4KLejSQhSwRIRkSHLWssW32GKNvjYUXWE1MRovnvFFG47dwIJMfoVKadPe4+IiAw51lq8+xoo8vrYvb+ZMUmxfP+aadyyYDyxUR6340kYUMESEZEhIxi0vP7hQYq8Pj6oP0bmiDh+snQGN87PJCZSxUqco4IlIiJhLxC0vLynntUlPj451MbE1AT+/cZZXDc3gyhPhNvxJAypYImISNjqDQR5cXc9a0p8VBxuZ9LoRH61fA5XzxqLJ8K4HU/CmAqWiIiEnW5/gOd31fHQRh/7j3QyLT2Jh740j8umjyFCxUoGgAqWiIiEja7eAOt31PDwpgoOtHQxe1wyP7hmOoVTRmOMipUMHBUsEREJee3dfp7dXsO6zRU0tnazICuFn984iwtyU1WsxBUqWCIiErJau3p5als1j2yu4GhHL+fnjqTolrmcmz3S7WgyxJ1xwTLGjAOeAsYAQWCdtfZXxpgU4LdAFlAF3GStPXqm64mIiDR39PD41ioe31rJsS4/BWeNYlXhJOZPGOF2NBHAmTNYfuBb1tp3jDHDgF3GmL8AXwU2WGsfMMbcD9wPfMeB9UREZIhqauvmkS2VPL2tmrZuP5dOS+O+wknMzBzudjSRv3LGBctaewA40Pd1qzHmIyADWALk933bk0ApKlgiInIaGo51sW5TBc9sr6HLH+CqmemsKsxlypgkt6OJnJSjr8EyxmQBc4HtQFpf+cJae8AYM9rJtUREJPzVN3eydmM569/eTyBoWTJnLCsLcskZleh2NJHPZKy1zjyQMYnARuAn1to/GGOarbXJJ2w/aq39uyfHjTErgBUAaWlp89evX+9Ins/S1tZGYqL+cDpF83SeZuoszdN5/T3Tho4gL1f0srXOD8D5GZFcnR3F6PjwvOq69lHnDcRMCwoKdllr8062zZEzWMaYKOB54Blr7R/67j5kjEnvO3uVDjSc7GetteuAdQB5eXk2Pz/fiUifqbS0lIFYZ6jQPJ2nmTpL83Ref820vLGN1SU+XtxdjyfC8KVzJ3DX4hwykuMcX2sw0T7qPLdn6sS7CA3wKPCRtfa/Ttj0EvAV4IG+zy+e6VoiIhKe9h08RrHXx5/fP0BMZARfPS+LFYuySUuKdTuayGlx4gzW+cDtwPvGmN199/0Lx4vV74wxdwA1wDIH1hIRkTDyfm0LRd4yXv/wEAnRHu5enMOdF0xkZGKM29FEzogT7yLcApzqMrkXnenji4hI+NlVfZRibxklHzeSFBvJP100ia+dn0VyfLTb0UQcoSu5i4jIgHmrookibxlbfU2MiI/i25edxe0LJ5AUG+V2NBFHqWCJiEi/stayuewwxV4fO6qOkJoYw/eunMqt54wnIUa/hiQ8ac8WEZF+Ya3Fu6+BB70+3tvfzJikWH5wzTSWLxhPbJTH7Xgi/UoFS0REHBUMWl774CBFXh8fHjhG5og4frp0JjfMzyAmUsVKhgYVLBERcUQgaHl5Tz3FXh9lDW1MTE3gP5bNZsmcsUR5wvMCoSKnooIlIiJnpDcQ5I/v1rGmtJzKw+1MTkvkV8vncPWssXgiTvUmc5HwpoIlIiKnpdsf4Plddawp9VF7tJPpY5NYe9s8Lp02hggVKxniVLBEROQL6eoN8JfqXr67rZQDLV3MHpfMD6+dTuGU0Rz/xz1ERAVLREQ+l/ZuP89sr2bdpkoOt/WwICuFn984iwtyU1WsRP6GCpaIiHymY129PL2tmkc2V3C0o5cLclO5MMVw1/UL3Y4mMmipYImIyEk1d/Tw2NYqnthaybEuP4VTRrOyIJf5E0ZQWlrqdjyRQU0FS0RE/srhtm4e3VLJU29W0d4T4LLpadxXOIkZGcPdjiYSMlSwREQEgIZjXTy8qYJntlfT7Q9y1cx0VhXmMmVMktvRREKOCpaIyBBX19zJ2tJyfrtzP4GgZcmcsawsyCVnVKLb0URClgqWiMgQVdPUwZpSH8+/UwvAjfMzuWdxLuNHxrucTCT0qWCJiAwxvoY21pT4ePG9ejwRhlsWjOeuxTlkJMe5HU0kbKhgiYgMEfsOHqPY6+PP7x8gNtLD187LYsWibEYnxbodTSTsqGCJiIS592tbKPKW8fqHh0iI9nD34hzuvGAiIxNj3I4mErZUsEREwtSu6qMUecso/biRpNhI/umiSXzt/CyS46PdjiYS9lSwRETCiLWWtyqOUFxSxlZfEykJ0Xz7srP48sIJDIuNcjueyJChgiUiEgastWwuO0yRt4y3q46SmhjD966cypfOHU98tA71IgNNf+pEREKYtZYNHzVQVOLjvf3NpA+P5YfXTufms8cRG+VxO57IkKWCJSISgoJBy39/cJAir4+PDhwjc0QcP106kxvmZxATqWIl4jYVLBGREBIIWl7eU0+x10dZQxvZqQn8x7LZLJkzlihPhNvxRKSPCpaISAjoDQR54d06Hiotp/JwO5PTEnnwlrlcNTMdT4RxO56I/A0VLBGRQazbH+C5XbU8VFpO7dFOpo9NYu1t87h02hgiVKxEBi0VLBGRQairN8BvdtTw8MYKDh7rYs64ZP7XkukUnDUaY1SsRAY7FSwRkUGkvdvPM9urWbepksNt3SzISuHfl83igtxUFSuREKKCJSIyCBzr6uWpN6t4dEslRzt6uXBSKqsK5nJO9ki3o4nIaVDBEhFxUXNHD49tqeTxN6to7fJTOGU0qwpzmTd+hNvRROQMqGCJiLjgcFs3j2yu5OltVbT3BLh8+hhWFeYyI2O429FExAEqWCIiA+jQsS4e3ljBszuq6fYHuXrWWFYV5HLWmGFuRxMRB6lgiYgMgNqjHTy8sYLf7txPIGi5bk4G9xbkkDMq0e1oItIPVLBERPpRdVM7a0rKef6dWoyBG+dncs/iXMaPjHc7moj0IxUsEZF+4GtoZXVJOS/uriPSE8GXzhnPXYtzGJsc53Y0ERkAKlgiIg766MAxir0+Xtl7gNhID3dcMJFvXJjN6KRYt6OJyABSwRIRccCe2maKvD7+8uEhEmMiuWdxDndcMJGRiTFuRxMRF6hgiYicgV3VR3hwg4+NnzSSFBvJP100ia+dn0VyfLTb0UTERSpYIiJfkLWWbRVNFG3wsa2iiZSEaL592Vl8eeEEhsVGuR1PRAYBFSwRkc/JWsvGTxop9vrYWX2UUcNi+NerpnLrOeOJj9bhVET+Hx0RRET+AWstb3zUQJG3jD21LaQPj+WH107n5rPHERvlcTueiAxCKlgiIqcQDFpe3XuQIm8Z+w62Mi4ljv99/UxumJdJdGSE2/FEZBBTwRIR+Rv+QJA/7alndUk5voY2slMT+I9ls1kyZyxRHhUrEfnHVLBERPr0+IP88d06Vpf6qG7qYHJaIg/eMperZqbjiTBuxxOREKKCJSJDXldvgN/vqmVtaTl1zZ1MH5vE2tvmc+m0NCJUrETkNKhgiciQ1dkT4NkdNazbVM6hY93MGZfMj66bTsFZozFGxUpETp8jBcsY8xhwNdBgrZ3Rd18K8FsgC6gCbrLWHnViPRGRM9HW7efXb1XzyOYKDrf1sGBiCv+5bA7n545UsRIRRzh1BusJoBh46oT77gc2WGsfMMbc33f7Ow6tJyLyhbV09vLkm1U8trWS5o5eLpyUyqqCXM7JHul2NBEJM44ULGvtJmNM1t/cvQTI7/v6SaAUFSwRcUFbj+U/X/+YJ7ZW0drt56Ipo1lZmMu88SPcjiYiYao/X4OVZq09AGCtPWCMGd2Pa4mI/J3G1m4e2VzBE1s76A74uGLGGFYW5DIjY7jb0UQkzBlrrTMPdPwM1ssnvAar2VqbfML2o9bav/vrojFmBbACIC0tbf769esdyfNZ2traSExM7Pd1hgrN03ma6Zk52hXklcpeNu730xuEeaMs10+OJ2OYrmHlFO2jztI8nTcQMy0oKNhlrc072bb+PIN1yBiT3nf2Kh1oONk3WWvXAesA8vLybH5+fj9GOq60tJSBWGeo0Dydp5mentqjHTxUWs7vd9YSsJalczO5Nz+Hmg92ap4O0z7qLM3TeW7PtD8L1kvAV4AH+j6/2I9ricgQVnW4nTWlPv7wTh3GwLK8cdyzOIdxKfEA1LicT0SGHqcu0/Abjr+gPdUYUwt8n+PF6nfGmDs4fnxb5sRaIiKf8jW0Uuz18dJ79UR5Irjt3AnctTib9OFxbkcTkSHOqXcR3nKKTRc58fgiIif6sP4YxSVlvLr3IHFRHu68MJs7L5zI6GGxbkcTEQF0JXcRCSHv7W+myOvjjY8OMSwmkpX5uXz9gomkJES7HU1E5K+oYInIoLez6ggPen1s+qSR4XFR/H8XT+ar52cxPC7K7WgiIielgiUig5K1lm3lTRR5fWyraGJkQjTfuXwKty+cQGKMDl0iMrjpKCUig4q1lo2fNFLk9bGr+iijh8Xwr1dN5dZzxhMfrUOWiIQGHa1EZFCw1vKXDw9RXOJjT20LY4fH8qMl01mWN47YKI/b8UREvhAVLBFxVTBoeXXvQYq8Zew72Mr4lHh+dsNMls7NJDpSV14XkdCkgiUirvAHgvxpTz2rS8rxNbSRPSqB/7ppNtfOHkukR8VKREKbCpaIDKgef5A/vlvH6lIf1U0dTBkzjOJb53LFjHQ8EcbteCIijlDBEpEB0dUb4Pe7allbWk5dcyczM4bz8O3zuWRqGhEqViISZlSwRKRfdfYEeHZHDes2lXPoWDfzxifz46UzyJ88CmNUrEQkPKlgiUi/aOv28/S2ah7ZXEFTew/nZqfwi5vmsDBnpIqViIQ9FSwRcVRLZy9PvlnFY1srae7oZdHkUdxXmMvZWSluRxMRGTAqWCLiiCPtPTy2pZIn36yitdvPxVNHs6pwEnPGJbsdTURkwKlgicgZaWzt5pHNFTz9VjWdvQGumDGGlQW5TB873O1oIiKuUcESkdNysKWLtRvL+c2OGnoDQa6ZPZZVBblMShvmdjQREdepYInIF7L/SAcPbSznuZ21BK1l6dwM7i3IZWJqgtvRREQGDRUsEflcKg+3s6bExwvv1hFhDDfmZXLP4hzGpcS7HU1EZNBRwRKRz1R2qJXiEh9/eq+eKE8Et507gbsWZ5M+PM7taCIig5YKloic1Af1Lawu8fHq3oPERXn4xoXZ3HHhREYPi3U7mojIoKeCJSJ/5b39zRR5y3jjowaGxUSyMj+Xr18wkZSEaLejiYiEDBUsEQHg7aojFHl9bPqkkeFxUfzzJZP5ynlZDI+LcjuaiEjIUcESGcKstWwrb+JBbxlvVRxhZEI037l8CrcvnEBijA4PIiKnS0dQkSHIWkvpJ40Ue33sqj7K6GEx/M+rp3HrgvHERXvcjiciEvJUsESGkGDQ8sZHhygu8bGntoWM5Dh+tGQ6y/LGERulYiUi4hQVLJEhIBC0vLr3AMVeH/sOtjI+JZ6f3TCTpXMziY6McDueiEjYUcESCWP+QJA/7amn2OujvLGdnFEJ/OLm2VwzayyRHhUrEZH+ooIlEoZ6/EFeeLeWNaXlVDd1MGXMMIpvncsVM9LxRBi344mIhD0VLJEw0tUb4Pe7allbWk5dcyczM4az7vb5XDw1jQgVKxGRAaOCJRIGOnsCPLujhnWbyjl0rJt545P58dIZ5E8ehTEqViIiA00FSySEtXX7eXpbNY9srqCpvYdzs1P4xU1zWJgzUsVKRMRFKlgiIails5cn36zisa2VNHf0smjyKO4rzOXsrBS3o4mICCpYIiHlSHsPj22p5Mk3q2jt9nPx1DRWFeYyZ1yy29FEROQEKlgiIaChtYtHNlfy67eq6ewNcMWMMawsyGX62OFuRxMRkZNQwRIZxA60dPLwxgp+s6OG3kCQa2ePZWVBLpPShrkdTUREPoMKlsgg1NgR5F9eeJ/ndtYStJbr52VwT34uE1MT3I4mIiKfgwqWyCBSebidNSU+/vBOJ56IWpblZXL34hzGpcS7HU1ERL4AFSyRQaDsUCvFJT7+9F49UZ4ICsdH8qNbFzNmeKzb0URE5DSoYIm46IP6FlaX+Hh170Hiojx848Js7rwwmw92bVO5EhEJYSpYIi7Yvb+ZYm8Zb3zUwLCYSFbm53LHBRMZkRDtdjQREXGACpbIAHq76ggPbihjc9lhkuOj+NYlk/nyeVkMj4tyO5qIiDhIBUukn1lrebO8iQc3lLG98gipidHcf8UUbjt3Aokx+iMoIhKOdHQX6SfWWko/aaRoQxnv1DSTlhTDv109jVsWjCcu2uN2PBER6UcqWCIOCwYtf/noEMVeH+/XtZCRHMePrpvBsvmZxEapWImIDAUqWCIOCQQtr7x/gNUlPvYdbGXCyHh+dsNMls7NJDoywu14IiIygFSwRM6QPxDkpffqWV3io7yxnZxRCfzi5tlcM2sskR4VKxGRoUgFS+Q09fiD/OGdWtaUllNzpIMpY4ax+tZ5XD5jDJ4I43Y8ERFxUb8XLGPM5cCvAA/wiLX2gf5eU6Q/dfUG+N3O/awtLae+pYtZmcP516vmc/HUNCJUrEREhH4uWMYYD7AauASoBd42xrxkrf2wP9cV6Q8dPX6e3V7Duk0VNLR2M3/CCH56/UwWTx6FMSpWIiLy//T3GawFgM9aWwFgjFkPLAFUsCRktHb18tS2ah7dUsmR9h7OzU7hlzfPYWHOSBUrERE5qf4uWBnA/hNu1wLn9POaIo5o6ejl8TcreXxrFS2dvSyaPIpvFuaSl5XidjQRERnkjLW2/x7cmGXAZdbaO/tu3w4ssNbed8L3rABWAKSlpc1fv359v+X5VFtbG4mJif2+zlARbvM81mN5vaqXN6p76QrA3NEersmJInv4wF3DKtxm6jbN03maqbM0T+cNxEwLCgp2WWvzTratv89g1QLjTridCdSf+A3W2nXAOoC8vDybn5/fz5GgtLSUgVhnqAiXeTYc6+L/bK7g12/V0OUPcMWMMawqmMS0sUkDniVcZjpYaJ7O00ydpXk6z+2Z9nfBehuYZIyZCNQBy4Fb+3lNkS+kvrmThzeW85u39+MPBLl29lhWFuQyKW2Y29FERCRE9WvBstb6jTGrgNc4fpmGx6y1H/TnmiKfV01TBw9t9PHcrlqshaVzM7i3IJeJqQluRxMRkRDX79fBsta+ArzS3+uIfF7ljW2sKSnnj7vr8BjDTXnjuHtxDuNS4t2OJiIiYUJXcpch4+ODrRSX+PjznnqiPBF8eeEE7lqUw5jhsW5HExGRMKOCJWFvb10LRd4yXvvgEPHRHr6xKJs7L8hm1LAYt6OJiEiYUsGSsPVuzVGKvD68+xoYFhPJfYW5fP38iYxIiHY7moiIhDkVLAk72yuaKPL62OI7THJ8FN+6ZDJfPi+L4XFRbkcTEZEhQgVLwoK1lq2+Jh70lrGj8gipidF894op3HbuBBJitJuLiMjA0m8eCWnWWko+bqDI6+PdmmbGJMXy/Wumsfzs8cRFD9yV10VERE6kgiUhKRi0vP7hIYpLythbd4yM5Dh+snQGN87PJCZSxUpERNylgiUhJRC0/Pn9A6z2+vj4UCtZI+P5+Y2zWDo3gyhPhNvxREREABUsCRH+QJA/7q5nTYmPisPt5I5O5FfL53DVzHQiVaxERGSQUcGSQa3HH+T5d2pZU+pj/5FOpqYnseZL87h8+hgiIozb8URERE5KBUsGpa7eAL99ez9rN5ZzoKWL2ZnD+f7V07lo6miMUbESEZHBTQVLBpWOHj/Pbq/h4U0VNLZ2c3bWCH52wywunJSqYiUiIiFDBUsGhdauXp7aVs2jWyo50t7DeTkjeXD5XM7NTlGxEhGRkKOCJa5q6ejl8TcreXxrFS2dveSfNYr7CnOZPyHF7WgiIiKnTQVLXNHU1s2jWyp5als1bd1+Lp2WxqrCXGZlJrsdTURE5IypYMmAajjWxbpNFTyzvYYuf4ArZ6azqiCXqelJbkcTERFxjAqWDIj65k7Wbixn/dv7CQQtS2aP5d6CXHJHJ7odTURExHEqWNKvapo6eGijj+d21WIt3DAvk3sLcpgwMsHtaCIiIv1GBUv6RXljG6tLfLy4ux6PMSw/ezx35+eQkRzndjQREZF+p4Iljvr4YCvFJT5e3lNPTGQEXz0vixWLsklLinU7moiIyIBRwRJH7K1rochbxmsfHCIh2sNdi3K488KJpCbGuB1NRERkwKlgyRl5p+Yov9jVxXv/vYVhsZF886JJfO28LEYkRLsdTURExDUqWHJatlc0UeT1scV3mMQo+PZlZ3H7wgkkxUa5HU1ERMR1KljyuVlr2eI7TNEGHzuqjpCaGMO/XDmF8T01XF6Q63Y8ERGRQUMFS/4hay3efQ0UeX3s3t/MmKRYfnDNNJYvGE9slIfS0v1uRxQRERlUVLDklIJBy+sfHqTI6+OD+mNkjojjJ0tncOP8TGIiPW7HExERGbRUsOTvBIKWl/fUs7rExyeH2piYmsC/3ziL6+ZmEOWJcDueiIjIoKeCJf9XbyDIi7vrWVPio+JwO5NGJ/Kr5XO4etZYPBHG7XgiIiIhQwVL6PYHeH5XHQ9t9LH/SCfT0pN46EvzuGz6GCJUrERERL4wFawhrKs3wG/f3s/ajeUcaOli9rhkfnDNdAqnjMYYFSsREZHTpYI1BHX0+HnmrRrWba6gsbWbs7NG8LMbZnHhpFQVKxEREQeoYA0hrV29PLWtmke3VHKkvYfzc0dSdMtczs0e6XY0ERGRsKKCNQS0dPTy2NZKHt9aybEuPwVnjWJV4STmTxjhdjQREZGwpIIVxpraunlkSyVPb6umrdvPpdPSuK9wEjMzh7sdTUREJKypYIWhhmNdrNtUwTPba+jyB7hqZjorC3KZmp7kdjQREZEhQQUrjNQ3d7J2Yznr395PIGhZMnss9xbkkjs60e1oIiIiQ4oKVhioaepgTamP59+pBeCGeZnck5/DhJEJLicTEREZmlSwQlh5YxurS3y8uLseT4Rh+dnjuTs/h4zkOLejiYiIDGkqWCFo38FjFHt9/Pn9A8RERvDV87JYsSibtKRYt6OJiIgIKlghZW9dCw9uKOP1Dw+REO3h7sU53HHBRFITY9yOJiIiIidQwQoB79QcpWhDGSUfNzIsNpJvXjSJr5+fRXJ8tNvRRERE5CRUsAaxtyqaKPKWsdXXxIj4KL592VncvnACSbFRbkcTERGRz6CCNchYa9niO0zRBh87qo6QmhjD966cyq3njCchRv+7REREQoF+Yw8S1lq8+xoo8vrYvb+ZMUmx/OCaaSxfMJ7YKI/b8UREROQLUMFyWTBoee2DgxR5fXx44BiZI+L46dKZ3DA/g5hIFSsREZFQpILlkkDQ8vKeelaX+PjkUBsTUxP4j2WzWTJnLFGeCLfjiYiIyBk4o4JljFkG/ACYCiyw1u48Ydt3gTuAAPBNa+1rZ7JWuOgNBPnju3WsKS2n8nA7k9MS+dXyOVw9ayyeCON2PBEREXHAmZ7B2gtcDzx84p3GmGnAcmA6MBZ4wxgz2VobOMP1Qla3P8Dzu+pYU+qj9mgn09KTWHvbPC6dNoYIFSsREZGwckYFy1r7EYAxf1cQlgDrrbXdQKUxxgcsALadyXqhqKs3wPodNTy8qYIDLV3MHpfMD6+dTuGU0Sebm4iIiISB/noNVgbw1gm3a/vuGzLau/08s72adZsqOdzWzYKsFH5+4ywuyE1VsRIREQlzxlr72d9gzBvAmJNs+p619sW+7ykF/senr8EyxqwGtllrf913+1HgFWvt8yd5/BXACoC0tLT569evP/3/ms+pra2NxMTEfnnsTr/ljepeXqvqpa0Xpo+M4JqcaKakhO87AvtznkOVZuoszdN5mqmzNE/nDcRMCwoKdllr80627R+ewbLWXnwaa9YC4064nQnUn+Lx1wHrAPLy8mx+fv5pLPfFlJaW4vQ6zR09PLa1iie2VnKsy0/BWaNYVTiJ+RNGOLrOYNQf8xzqNFNnaZ7O00ydpXk6z+2Z9tdThC8Bzxpj/ovjL3KfBOzop7Vc1dTWzSNbKnl6WzVt3X4um57GfYWTmJEx3O1oIiIi4pIzvUzDUqAIGAX82Riz21p7mbX2A2PM74APAT+wMtzeQdhwrIuHN1XwzPZquv1BrpqZzqrCXKaMSXI7moiIiLjsTN9F+ALwwim2/QT4yZk8/mBU19zJwxvLWf/2fgJBy5I5Y1lZkEvOKD13LiIiIsfpSu6fU01TB2tKfTz/Ti0AN87P5J7FuYwfGe9yMhERERlsVLD+AV9DG2tKfLz4Xj2eCMMtC8Zz1+IcMpLj3I4mIiIig5QK1insO3iMIq+PV94/QGykh6+dl8U3FmWTlhTrdjQREREZ5FSw/sb7tS0Uect4/cNDJER7uHtxDndeMJGRiTFuRxMREZEQoYLVZ1f1UYq8ZZR+3EhSbCT/dNEkvnZ+Fsnx0W5HExERkRAz5AvWWxVNFHnL2OprIiUhmm9fdha3L5xAUmyU29FEREQkRA3JgmWtZdMnjRR7feyoOkJqYgzfu3IqXzp3PPHRQ3IkIiIi4qAh1SastXj3NfCTt7qoaNlB+vBYfnjtdG4+exyxUeH7bwWKiIjIwBpSBav2aCcrnt7FiBj46dKZ3DA/g5hIFSsRERFx1pAqWONS4nn2znNordrDxeeMdzuOiIiIhKkItwMMtHOyRxIZYdyOISIiImFsyBUsERERkf6mgiUiIiLiMBUsEREREYepYImIiIg4TAVLRERExGEqWCIiIiIOU8ESERERcZgKloiIiIjDVLBEREREHKaCJSIiIuIwY611O8P/ZYxpBKoHYKlU4PAArDNUaJ7O00ydpXk6TzN1lubpvIGY6QRr7aiTbRhUBWugGGN2Wmvz3M4RLjRP52mmztI8naeZOkvzdJ7bM9VThCIiIiIOU8ESERERcdhQLVjr3A4QZjRP52mmztI8naeZOkvzdJ6rMx2Sr8ESERER6U9D9QyWiIiISL8ZMgXLGLPMGPOBMSZojMk74f4sY0ynMWZ338daN3OGklPNtG/bd40xPmPMx8aYy9zKGMqMMT8wxtSdsG9e6XamUGSMubxvP/QZY+53O0+oM8ZUGWPe79snd7qdJxQZYx4zxjQYY/aecF+KMeYvxpiyvs8j3MwYak4xU1ePoUOmYAF7geuBTSfZVm6tndP3cfcA5wplJ52pMWYasByYDlwOrDHGeAY+Xlj4xQn75ituhwk1ffvdauAKYBpwS9/+KWemoG+f1GUFTs8THD82nuh+YIO1dhKwoe+2fH5P8PczBRePoUOmYFlrP7LWfux2jnDyGTNdAqy31nZbaysBH7BgYNOJAMf3O5+1tsJa2wOs5/j+KeIaa+0m4Mjf3L0EeLLv6yeB6wYyU6g7xUxdNWQK1j8w0RjzrjFmozHmQrfDhIEMYP8Jt2v77pMvbpUxZk/f6W89ZfDFaV90ngVeN8bsMsascDtMGEmz1h4A6Ps82uU84cK1Y2hYFSxjzBvGmL0n+fisv7EeAMZba+cC/ww8a4xJGpjEg99pztSc5D69XfUk/sF8HwJygDkc30//082sIUr7ovPOt9bO4/jTriuNMYvcDiRyCq4eQyMHcrH+Zq29+DR+phvo7vt6lzGmHJgM6MWbnN5MOX6WYNwJtzOBemcShZfPO19jzP8BXu7nOOFI+6LDrLX1fZ8bjDEvcPxp2JO9tlW+mEPGmHRr7QFjTDrQ4HagUGetPfTp124cQ8PqDNbpMMaM+vQF2MaYbGASUOFuqpD3ErDcGBNjjJnI8ZnucDlTyOk7yH5qKcffVCBfzNvAJGPMRGNMNMfffPGSy5lCljEmwRgz7NOvgUvRfumUl4Cv9H39FeBFF7OEBbePoWF1BuuzGGOWAkXAKODPxpjd1trLgEXA/zLG+IEAcLe1dlC9UG6wOtVMrbUfGGN+B3wI+IGV1tqAm1lD1M+NMXM4/pRWFXCXq2lCkLXWb4xZBbwGeIDHrLUfuBwrlKUBLxhj4Pjvj2ettf/tbqTQY4z5DZAPpBpjaoHvAw8AvzPG3AHUAMvcSxh6TjHTfDePobqSu4iIiIjDhvxThCIiIiJOU8ESERERcZgKloiIiIjDVLBEREREHKaCJSIiIuIwFSwRERERh6lgiYiIiDhMBUtERETEYf8/Vco9gPSUGDwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "x = np.linspace(-15, 15)\n",
    "\n",
    "y = 1.5 * x + 10\n",
    "\n",
    "# Here is the code to set the figure size\n",
    "\n",
    "\n",
    "plt.grid(True)\n",
    "        \n",
    "plt.plot(x, y)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Subplots\n",
    "**Subplots: Areas in a figure where individual graphs are drawn** **Create a subplot\n",
    "**Create a subplot\n",
    "    **Specify the layout into which the figure will be divided and the position of the relevant subplot within it** **Subplot: area within the figure where the individual graphs will be drawn\n",
    "    ** For figure fig, to split the figure into a 2x3 layout and create a subplot in the second row from the top and the second column from the left\n",
    "        * **`fig.add_subplot(2, 3, 5)`**\n",
    "            * The arguments are (the number of vertical subplots, the number of horizontal subplots, and the position of the subplot counting from the top left)\n",
    "            * The return value is the subplot in the specified region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlsAAAEzCAYAAAAGisbbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAnp0lEQVR4nO3deXiU9b338c+XQICA7GEPm6wBl2pEXGpVVDYVe9qeR22rpVoen6qt7TmeYiu4oKdWzznaRUs5VsWnC+3TeiAiirjbuoFWMQkJhD2AEPY9kOT7/JFprxiTySSZe+6ZzPt1Xbkyc889mQ9hJr9P7vlmxtxdAAAACEabsAMAAAC0ZpQtAACAAFG2AAAAAkTZAgAACBBlCwAAIECULQAAgAA1WrbM7Akz22lmBQ1cbmb2MzMrNbNVZnZG/GMCAJIV6wQQXSxHtp6SNDnK5VMkjYh8zJT0y5bHAgCkkKfEOgE0qNGy5e5vSNoTZZfpkp72Gu9I6mZm/eIVEACQ3FgngOjiMbM1QNKWWufLItsAAJBYJ5Dm2sbha1g92+p9DyAzm6maQ8jq1KnTmaNHj47DzQMt9/777+9y9+wwM/Tq1cuHDBkSZgTgH+L8mGCdQMpryWMiHmWrTFJOrfMDJW2rb0d3ny9pviTl5eX5ypUr43DzQMuZ2aawMwwZMkQ8JpAs4vyYYJ1AymvJYyIeTyPmS7ou8tcmEyTtd/ftcfi6AIDWgXUCaa3RI1tm9ntJF0rqZWZlku6S1E6S3H2epKWSpkoqlXRE0oygwgIAkg/rBBBdo2XL3a9p5HKXdHPcEgEAUgrrBBAdryAPAAAQIMoWAABAgChbAAAAAaJsAQAABIiyBQAAECDKFgAAQIAoWwCQhv6wYrM27z4SdgwgLVC2ACDNFG07oDue+ViP/2V92FGAtEDZAoA0Ul3tmr24QN2yMvX9S0eGHQdIC5QtAEgjf/6gTO9v2qtZU0arW1Zm2HGAtEDZAoA0sf/ICT3wfLHOHNxdXz5jYNhxgLTR6HsjAgBah/94sUR7jxzX09PHq00bCzsOkDY4sgUAaeDjsv36zbubdN05QzS2f9ew4wBphbIFAK1cdbXrzsUF6tmpvb5/GUPxQKJRtgCglfvDyi36aMs+/WjaaHXp0C7sOEDaoWwBQCu25/Bx/eSFYo0f2kNXnT4g7DhAWqJsAUAr9uALxTp4rFJzp4+TGUPxQBgoW0ALmFkHM3vPzD4ys0IzuyeyvYeZLTeztZHP3cPOivTzt817tXDFFs04d4hG9T0p7DhA2qJsAS1TIelidz9N0umSJpvZBEmzJL3s7iMkvRw5DyRMVeSV4vt0aa/beKV4IFSULaAFvMahyNl2kQ+XNF3Sgsj2BZKuSnw6pLPfvbtJBVsP6M5puercnpdUBMJE2QJayMwyzOxDSTslLXf3dyX1cfftkhT53DvEiEgzuw5V6KFlJTr35J66/NR+YccB0h5lC2ghd69y99MlDZQ03szGxXpdM5tpZivNbGV5eXlgGZFeHni+WEdPVOlehuKBpEDZAuLE3fdJek3SZEk7zKyfJEU+72zgOvPdPc/d87KzsxMVFa3Yyo179Kf3y3TD+cM0vHfnsOMAEGULaBEzyzazbpHTHSVdIqlYUr6k6yO7XS9pcSgBkVYqq6p156IC9e/aQd+ZODzsOAAimJoEWqafpAVmlqGaX17+6O5LzOxtSX80sxskbZb0lTBDIj08/fYmFX9yUPO+doayMvnxDiQLHo1AC7j7Kkmfq2f7bkkTE58I6WrngWN6ePkaXTAyW5PG9g07DoBaeBoRAFqBf1+6WhWV1brnyrEMxQNJhrIFACnunfW7tejDbfrfXximob06hR0HQB2ULQBIYSeqqjVncYEGdOuob1/IUDyQjJjZAoAU9uRfN2jNjkP67+vy1DEzI+w4AOrBkS0ASFGf7D+mR15aq4mje+vS3D5hxwHQAMoWAKSouc8VqaradfeVY8OOAiAKyhYApKC/rN2l51Zt180XDVdOj6yw4wCIgrIFACmmorJKc/ILNLhnlmZeMCzsOAAawYA8AKSYx9/coPXlh/XkjLPUoR1D8UCy48gWAKSQrfuO6uevrNWksX100ajeYccBEAPKFgCkkHufLZQkzbmCoXggVVC2ACBFvFqyU8sKd+jWi0doQLeOYccBECPKFgCkgGMnqnR3fqGG9eqkGz8/NOw4AJqAAXkASAG/en29Nu0+ot/ccLbat2UoHkglHNkCgCS3efcRPfZaqaad2k/nj+gVdhwATUTZAoAkd++SQmW0Md05bUzYUQA0A2ULAJLYS0U79NLqnbrtkhHq15WheCAVUbYAIEkdPV6lu58t1IjenTXjPIbigVTFgDwAJKlfvlaqsr1H9ftvTVC7DH43BlIVj14ASEIbdh3WvNfX66rT++uck3uGHQdAC8RUtsxsspmVmFmpmc2q5/KuZvasmX1kZoVmNiP+UQEgPbi77sovVPu2bfTDqakxFM86ATSs0bJlZhmSHpU0RVKupGvMLLfObjdLKnL30yRdKOk/zSwzzlkBIC0sK/xEb6wp1/cuHaneXTqEHadRrBNAdLEc2RovqdTd17v7cUkLJU2vs49LOsnMTFJnSXskVcY1KQCkgSPHK3Xvs0Ua3fckXXfO4LDjxIp1AogilrI1QNKWWufLIttq+4WkMZK2SfpY0nfdvbruFzKzmWa20sxWlpeXNzMyALReP3+lVNv2H9Pcq8apbeoMxbNOAFHE8ki2erZ5nfOTJH0oqb+k0yX9wsy6fOZK7vPdPc/d87Kzs5sYFQBat9Kdh/T4m+v1pTMG6qwhPcKO0xSsE0AUsZStMkk5tc4PVM1vJrXNkPSM1yiVtEHS6PhEBIDWr2YovkAd22Xojqkp9+OTdQKIIpaytULSCDMbGhlmvFpSfp19NkuaKElm1kfSKEnr4xkUAFqzJau266+lu3X7pFHq1bl92HGainUCiKLRFzV190ozu0XSMkkZkp5w90Izuyly+TxJcyU9ZWYfq+Zw8g/cfVeAuQGg1ThUUan7nivSuAFddO3ZKTMU/w+sE0B0Mb2CvLsvlbS0zrZ5tU5vk3RZfKMBQHr46UtrtPNgheZ97UxltKlv/Cn5sU4ADUuZP3UBgNao5JODeuKvG3X1WTn63KDuYccBEADKFgCExN01e3GBTurQVrdPYlYcaK0oWwAQkkUfbtV7G/boB5NHq0cnXkwdaK0oWwAQgv1HT+j+54p1Wk43/a+8nMavACBlxTQgDwCIr4eXr9HuwxV68htnqU2KDsUDiA1HtgAgwYq2HdDTb2/UV88epFMGdg07DoCAUbYAIIGqq2uG4rtlZer2yxiKB9IBZQsAEuhPH5Tp/U17NWvKaHXNahd2HAAJQNkCgATZf+SEHni+WGcO7q4vnzEw7DgAEoSyBQAJ8tCLxdp35LjmTh/HUDyQRihbAJAAq8r26bfvbtZ15wxRbv8uYccBkECULQAIWHW1a/aiAvXq3F7fv2xk2HEAJBhlCwACtnDFFn1Utl8/mjpGXTowFA+kG8oWAARoz+HjenBZscYP7aHpp/cPOw6AEFC2ACBAD75QrIPHKnXfVeNkxlA8kI4oWwAQkA8279XCFVv0zfOGaGSfk8KOAyAklC0ACEBVtWvO4gL16dJe372EoXggnVG2gBYwsxwze9XMVptZoZl9N7K9h5ktN7O1kc/dw86KxPrdu5tUsPWA7pyWq87t24YdB0CIKFtAy1RK+hd3HyNpgqSbzSxX0ixJL7v7CEkvR84jTew6VKGHlpXo3JN76vJT+4UdB0DIKFtAC7j7dnf/IHL6oKTVkgZImi5pQWS3BZKuCiUgQvHjpcU6eqJK905nKB4AZQuIGzMbIulzkt6V1Mfdt0s1hUxS7xCjIYFWbNyjP39Qphs/P0zDe3cOOw6AJEDZAuLAzDpL+rOk29z9QBOuN9PMVprZyvLy8uACIiEqq6o1e1GB+nftoFsvHh52HABJgrIFtJCZtVNN0fqtuz8T2bzDzPpFLu8naWd913X3+e6e5+552dnZiQmMwCx4e5OKPzmoOVfkKiuToXgANShbQAtYzUDOryWtdvf/qnVRvqTrI6evl7Q40dmQWDsOHNPDy9fogpHZmjS2b9hxACQRfvUCWuY8SV+X9LGZfRjZ9kNJD0j6o5ndIGmzpK+EEw+J8u9LV+t4ZbXuuXIsQ/EAPoWyBbSAu/9FUkMr68REZkF43l63W4s/3KbvXDxcQ3t1CjsOgCTD04gA0AInqqo1Z3GBBnbvqP9zIUPxAD6LI1sA0AJP/GWD1u48pMevy1PHzIyw4wBIQhzZAoBm2r7/qH768lpNHN1bl+T2CTsOgCRF2QKAZrpvyWpVVbvuvnJs2FEAJDHKFgA0wxtryvXcx9t180XDldMjK+w4AJIYZQsAmqiiskp35xdqcM8szbxgWNhxACQ5BuQBoIkef3OD1u86rKdmnKUO7RiKBxAdR7YAoAnK9h7Rz19Zq0lj++jCUby/OIDGUbYAoAnufbZIJtOcKxiKBxAbyhYAxOjV4p16sWiHbp04XAO6dQw7DoAUQdkCgBgcO1Glu58t1LDsTrrxfIbiAcSOAXkAiMGvXl+vTbuP6Dc3nK3MtvyeCiB2/MQAgEZs3n1Ej71Wqmmn9tP5I3qFHQdAiqFsAUAj7nm2UBltTLOn5YYdBUAKomwBQBTLi3bo5eKduu2SEerbtUPYcQCkIMoWADTg6PGaV4of2aezZpw3NOw4AFIUA/IA0IDHXivV1n1HtXDmBLXL4HdTAM3DTw8AqMeGXYf1q9fX66rT+2vCsJ5hxwGQwihbAFCHu+uu/EK1b9tGP5w6Juw4AFJcTGXLzCabWYmZlZrZrAb2udDMPjSzQjN7Pb4xASBxXij4RG+sKdf3Lxup3l0Yio8F6wTQsEZntswsQ9Kjki6VVCZphZnlu3tRrX26SXpM0mR332xmvDsrgJR05Hil7l1SpDH9uujrEwaHHSclsE4A0cVyZGu8pFJ3X+/uxyUtlDS9zj7XSnrG3TdLkrvvjG9MAEiMn71cqu37j2nu9LFqy1B8rFgngChi+UkyQNKWWufLIttqGympu5m9Zmbvm9l18QoIAIlSuvOgHn9zvb585kDlDekRdpxUwjoBRBHLSz9YPdu8nq9zpqSJkjpKetvM3nH3NZ/6QmYzJc2UpEGDBjU9LQAExN01Z3GhsjIzNGvK6LDjpBrWCSCKWI5slUnKqXV+oKRt9ezzgrsfdvddkt6QdFrdL+Tu8909z93zsrOzm5sZAOJuyartemvdbt0+ebR6dW4fdpxUwzoBRBFL2VohaYSZDTWzTElXS8qvs89iSZ83s7ZmliXpbEmr4xsVAIJxqKJS9z1XpFMGdNW14zma0gysE0AUjT6N6O6VZnaLpGWSMiQ94e6FZnZT5PJ57r7azF6QtEpStaTH3b0gyOAAEC+PLF+jnQcr9Kuv5ymjTX3PiCEa1gkgupjersfdl0paWmfbvDrnH5L0UPyiAUDwSj45qCff2qirz8rR6Tndwo6TslgngIbxd80A0pa7a/biAnXp0Fb/NomheADBoGwBSFuLPtyq9zbs0Q8mj1b3TplhxwHQSlG2AKSl/UdP6P7ninV6Tjf9c15O41cAgGaKaWYLAFqbh5ev0e7DFXpqxllqw1A8gABxZAtA2inctl9Pv71RXzt7sMYN6Bp2HACtHGULQFqprnbNXlSg7lmZ+tfLRoUdB0AaoGwBSCt/+qBMH2zep1lTRqtrVruw4wBIA5QtAGlj35HjeuD5YuUN7q4vnTEw7DgA0gRlC0Da+I8XS7T/6AnNvWocQ/EAEoayBSAtrCrbp9++u1nXnTNYY/p1CTsOgDRC2QLQ6v19KL5X5/b63qUjw44DIM1QtgC0egtXbNFHZfv1o6lj1KUDQ/EAEouyBaBV23P4uB5cVqyzh/bQ9NP7hx0HQBqibAFo1R58oViHjlVq7lXjZMZQPIDEo2wBaLU+2LxXC1ds0Yzzhmhkn5PCjgMgTVG2ALRKVZGh+D5d2uu7lzAUDyA8lC0ArdJv392kwm0HNPvyXHVu3zbsOADSGGULQKuz61CFHlpWovOH99K0U/qFHQdAmqNsAS1kZk+Y2U4zK6i1rYeZLTeztZHP3cPMmG5+vLRYx05U6Z7pYxmKBxA6yhbQck9Jmlxn2yxJL7v7CEkvR84jAVZs3KM/f1Cmb31+mE7O7hx2HACgbAEt5e5vSNpTZ/N0SQsipxdIuiqRmdJVZVW1Zi8q0IBuHXXLxcPDjgMAkihbQFD6uPt2SYp87h1ynrSw4O1NKv7koGZfnqusTIbiASQHyhYQIjObaWYrzWxleXl52HFS2s4Dx/Tw8jW6cFS2Jo3tE3YcAPgHyhYQjB1m1k+SIp931reTu8939zx3z8vOzk5owNbm/qWrdbyqWndfwVA8gORC2QKCkS/p+sjp6yUtDjFLq/fWul1a/OE23XTBMA3p1SnsOADwKZQtoIXM7PeS3pY0yszKzOwGSQ9IutTM1kq6NHIeAThRVa05iwuV06Ojvn0RQ/EAkg8TpEALufs1DVw0MaFB0tSTf92g0p2H9Ovr89ShXUbYcQDgMziyBSBlbd9/VI+8tFaXjOmtiWMYigeQnChbAFLWfc+tVlW1664rxoYdBQAaRNkCkJL+snaXnlu1XTdfNFw5PbLCjgMADaJsAUg5FZVVmrO4QEN6ZmnmBcPCjgMAUTEgDyDlPP7mBq3fdVhPzTiLoXgASY8jWwBSypY9R/TzV9Zq8ti+unAU74IEIPlRtgCklLlLimQyzb4iN+woABATyhaAlPFq8U69WLRDt04crgHdOoYdBwBiQtkCkBKOnajSXfmFGpbdSTeez1A8gNTBgDyAlDDv9XXavOeIfnvj2cpsy++JAFIHP7EAJL3Nu4/osdfW6fJT++m84b3CjgMATULZApDU3F13P1uodm1Md05jKB5A6qFsAUhqL63eqVeKd+q2S0aqb9cOYccBgCajbAFIWkePV+nu/EKN7NNZ3zhvSNhxAKBZGJAHkLQefbVUW/cd1R9mTlC7DH43BJCa+OkFICmtLz+k+W+s1xc/N0BnD+sZdhwAaDbKFoCk4+66K79Q7du20R1TR4cdBwBahLIFIOk8X/CJ3ly7S9+/bKR6n8RQPIDUFlPZMrPJZlZiZqVmNivKfmeZWZWZfTl+EQGkk8MVlZq7pEhj+nXR1ycMDjsOYsQ6ATSs0bJlZhmSHpU0RVKupGvM7DMvdhPZ7yeSlsU7JID08bNX1mr7/mO676qxastQfEpgnQCii+Un2XhJpe6+3t2PS1ooaXo9+90q6c+SdsYxH4A0UrrzoH795gZ95cyBOnNwj7DjIHasE0AUsZStAZK21DpfFtn2D2Y2QNIXJc2LXzQA6cTdNWdxobIyMzRrCkPxKYZ1AogilrJl9WzzOucfkfQDd6+K+oXMZprZSjNbWV5eHmNEAOng2VXb9da63bp98mj17Nw+7DhoGtYJIIpYXtS0TFJOrfMDJW2rs0+epIVmJkm9JE01s0p3X1R7J3efL2m+JOXl5dV9IAJIUwePndB9S4p0yoCuunb8oLDjoOlYJ4AoYilbKySNMLOhkrZKulrStbV3cPehfz9tZk9JWlL3AQQADfnpS2tVfqhC86/LU0ab+g6SIMmxTgBRNFq23L3SzG5RzV+PZEh6wt0LzeymyOU8/w6g2Yo/OaAn39qoq88apNNzuoUdB83AOgFEF9N7I7r7UklL62yr98Hj7t9oeSwA6cDdNWdRobp0aKt/mzQq7DhoAdYJoGG8iA2A0PzP37bqvY179IPJo9W9U2bYcQAgEJQtAKHYf/SE/n3pap2e003/nJfT+BUAIEXF9DQiAMTbw8vXaM/h43pqxni1YSgeQCvGkS0ACVewdb+efnujvjZhsMYN6Bp2HAAIFGULQEJVV7vmLC5Q96xM/culDMUDaP0oWwAS6k/vl+mDzft0x9Qx6prVLuw4ABA4yhaAhNl35LgeeKFYeYO760tnDGj8CgDQClC2ACTMQ8tKtP/oCc29apwib9sCAK0eZQtAQny0ZZ9+995mXX/OEI3p1yXsOACQMJQtAIGrqnbNXlygXp3b67ZLR4QdBwASirIFIHALV2zWqrL9unPaGHXpwFA8gPRC2QIQqN2HKvTgCyWaMKyHrjytf9hxACDhKFsAAvXgCyU6XFGpudMZigeQnihbAALzwea9+sPKLbrh/KEa0eeksOMAQCgoWwACUVXtmr2oQH27dNB3JjIUDyB9UbYABOI372xS4bYDmn15rjq15z3vAaQvyhaAuCs/WKH/eLFE5w/vpamn9A07DgCEirIFIO5+/PxqHTtRpXumj2UoHkDao2wBiKv3NuzRMx9s1cwLhunk7M5hxwGA0FG2gICY2WQzKzGzUjObFXaeRDhRVa3Ziwo0oFtH3XzR8LDjAEBSoGwBATCzDEmPSpoiKVfSNWaWG26q4C14a6NKdhzUnCtylZXJUDwASJQtICjjJZW6+3p3Py5poaTpIWcK1I4Dx/TIS2t14ahsXZbbJ+w4AJA0KFtAMAZI2lLrfFlkW6t1/3OrdbyqWvdcyVA8ANRG2QKCUV/b8M/sZDbTzFaa2cry8vIExArGW+t2Kf+jbbrpCydrcM9OYccBgKRC2QKCUSYpp9b5gZK21d3J3ee7e56752VnZycsXDwdr6zWnMWFyunRUd++8OSw4wBA0qFsAcFYIWmEmQ01s0xJV0vKDzlTIJ786waV7jyku68Yqw7tMsKOAwBJhz8XAgLg7pVmdoukZZIyJD3h7oUhx4q77fuP6qcvr9UlY3pr4hiG4gGgPpQtICDuvlTS0rBzBGnukiJVVbvuumJs2FEAIGnxNCKAZnljTbmWfvyJbrlouHJ6ZIUdBwCSFmULQJNVVFbprvxCDemZpZlfGBZ2HABIajyNCKDJHn9zgzbsOqwF3xyv9m0ZigeAaDiyBaBJtuw5op+/slaTx/bVF0am5stVAEAiUbYANMncJUUymeZc0erf6hEA4oKyBSBmrxbv1ItFO/SdiSPUv1vHsOMAQEqgbAGIybETNUPxJ2d30g3nDw07DgCkDAbkAcRk3uvrtHnPEf32xrOV2Zbf0wAgVvzEBNCoTbsP67HX1unyU/vpvOG9wo4DACmFsgUgKnfX3fmFatfGdOc0huIBoKkoWwCiWl60Q6+WlOt7l45U364dwo4DACmHsgWgQUePV+meZ4s0sk9nXX/ukLDjAEBKYkAeQIMefbVUW/cd1R9mTlC7DH43A4Dm4KcngHqtLz+k+W+s1xc/N0BnD+sZdhwASFmULQCf4e66K79Q7du20R1TR4cdBwBSGmULwGc8X/CJ3ly7S/9y2Uj1PomheABoCcoWgE85XFGpe58tUm6/LvrahMFhxwGAlMeAPIBP+dkra/XJgWN69KufU1uG4gGgxWL6SWpmk82sxMxKzWxWPZd/1cxWRT7eMrPT4h8VQNDW7jioX7+5QV85c6DOHNwj7DhIIawTQMMaLVtmliHpUUlTJOVKusbM6r6M9AZJX3D3UyXNlTQ/3kEBBMvdNXtxgbIyMzRrCkPxiB3rBBBdLEe2xksqdff17n5c0kJJ02vv4O5vufveyNl3JA2Mb0wAQcv/aJveWb9Ht08erZ6d24cdB6mFdQKIIpayNUDSllrnyyLbGnKDpOfru8DMZprZSjNbWV5eHntKAIE6eOyE7n9utU4d2FXXjh8UdhykHtYJIIpYypbVs83r3dHsItU8iH5Q3+XuPt/d89w9Lzs7O/aUAAL18PK1Kj9UobnTxymjTX0PeSAq1gkgilj+GrFMUk6t8wMlbau7k5mdKulxSVPcfXd84gEI2urtB7Tg7Y26+qxBOi2nW9hxkJpYJ4AoYjmytULSCDMbamaZkq6WlF97BzMbJOkZSV939zXxjwkgCO6uOYsL1KVDW/3bpFFhx0HqYp0Aomj0yJa7V5rZLZKWScqQ9IS7F5rZTZHL50maI6mnpMfMTJIq3T0vuNgA4uGZD7Zqxca9euCfTlH3Tplhx0GKYp0AoovpRU3dfamkpXW2zat1+kZJN8Y3GoAg7T96Qj9+frVOz+mmf87LafwKQBSsE0DDeAV5IE3914sl2nP4uJ6aMV5tGIoHgMDwXhxAGirYul//951N+urZgzVuQNew4wBAq0bZAtJMdXXNK8V3z8rUv17GUDwABI2yBaSZ//f+Fv1t8z7dMXWMuma1CzsOALR6lC0gjew9fFwPPF+ss4Z015fOiPYC3wCAeKFsAWnkoRdLdOBYpe6dPk6RP78HAASMsgWkiY+27NPv39us688ZojH9uoQdBwDSBmULSANVkaH4Xp3b63uXjgg7DgCkFcoWkAZ+/95mrSrbrzunjdFJHRiKB4BEomwBrdzuQxV6aFmJJgzroStP6x92HABIO5QtoJX7yQvFOlxRqbkMxQNAKChbQCv2/qa9+uPKMn3z/KEa0eeksOMAQFqibAGtVGVVtWYvKlDfLh303YkMxQNAWChbQCv1m3c2qWj7Ac2+PFed2vOe8wAQFsoW0AqVH6zQf764RucP76Wpp/QNOw4ApDXKFtAK/Xjpah2rrNI908cyFA8AIaNsAa3Mu+t365m/bdW3Pj9MJ2d3DjsOAKQ9yhbQipyoqtacxYUa0K2jbrl4eNhxAACibAGtyoK3Nqpkx0HNuSJXWZkMxQNAMqBsAc1kZl8xs0IzqzazvDqX3WFmpWZWYmaTEpFnx4FjeuSltbpwVLYuy+2TiJsEAMSAX32B5iuQ9E+SflV7o5nlSrpa0lhJ/SW9ZGYj3b0qyDD3P7dax6uqdc+VDMUDQDLhyBbQTO6+2t1L6rlouqSF7l7h7hsklUoaH2SWt0p3Kf+jbbrpCydrcM9OQd4UAKCJKFtA/A2QtKXW+bLItkAcr6zW7MUFyunRUd++8OSgbgYA0Ew8jQhEYWYvSarvVUF/5O6LG7paPdu8ga8/U9JMSRo0aFCzMj7x1w1aV35YT3wjTx3aZTTrawAAgkPZAqJw90uacbUySTm1zg+UtK2Brz9f0nxJysvLq7eQRbNt31H99KW1umRMH108mqF4AEhGPI0IxF++pKvNrL2ZDZU0QtJ7QdzQ3CVFcrnuuiI3iC8PAIgDyhbQTGb2RTMrk3SOpOfMbJkkuXuhpD9KKpL0gqSbg/hLxNfXlOv5gk90y0XDldMjK95fHgAQJzyNCDSTu/+PpP9p4LL7Jd0f1G1XVFbp7vxCDe3VSd+6YFhQNwMAiAPKFpCC/vuN9dqw67Ce/uZ4tW/LUDwAJDOeRgRSzJY9R/SLV0s19ZS+umBkdthxAACNoGwBKebeJUVqY6Y7pzEUDwCpgLIFpJBXindoedEO3XrxCPXv1jHsOACAGFC2gBRx7ESV7sov1PDenXXD+UPDjgMAiBED8kCK+OVr67Rlz1H97sazldmW35MAIFXwExtIAZt2H9YvX1+nK07rr3OH9wo7DgCgCShbQJJzd92VX6h2bUx3ThsTdhwAQBNRtoAk91HZfr1WUq7vXTpSfbp0CDsOAKCJmNkCktzpOd30h5kTdMbg7mFHAQA0A2ULSAFnD+sZdgQAQDPxNCIAAECAKFsAAAABomwBAAAEiLIFAAAQIMoWAABAgChbAAAAAYqpbJnZZDMrMbNSM5tVz+VmZj+LXL7KzM6If1QAQLJinQAa1mjZMrMMSY9KmiIpV9I1ZpZbZ7cpkkZEPmZK+mWccwIAkhTrBBBdLEe2xksqdff17n5c0kJJ0+vsM13S017jHUndzKxfnLMCAJIT6wQQRSxla4CkLbXOl0W2NXUfAEDrxDoBRBHL2/VYPdu8GfvIzGaq5vCxJFWYWUEMtx+kXpJ2kSH0DGHfviSNCvn29f777+8ys00hRkiG/4d44N8RH4ObsC/rBBla++1LLVgnYilbZZJyap0fKGlbM/aRu8+XNF+SzGylu+c1KW2ckSE5MoR9+3/PEObtS5K7Z4d5+8nw/xAP/DtCwTpBhlZ9+3/P0NzrxvI04gpJI8xsqJllSrpaUn6dffIlXRf5a5MJkva7+/bmhgIApBTWCSCKRo9suXulmd0iaZmkDElPuHuhmd0UuXyepKWSpkoqlXRE0ozgIgMAkgnrBBBdLE8jyt2XquaBUnvbvFqnXdLNTbzt+U3cPwhkqBF2hrBvX0qODGFrLd8D/h0hYJ0IFBnCv32pBRms5v4PAACAIPB2PQAAAAEKvGwlw1s4xJDhq5HbXmVmb5nZaYm8/Vr7nWVmVWb25XjefqwZzOxCM/vQzArN7PVEZzCzrmb2rJl9FMkQ15kOM3vCzHY29Kfk6fp2Imb2lcj3u9rM8upcdkfk+1FiZpPCyhirWB9ryaa++6aZ9TCz5Wa2NvK5e5gZg8Q6wToRa4aUXSfcPbAP1QxKrpM0TFKmpI8k5dbZZ6qk51XzGiwTJL0bQoZzJXWPnJ4Szwyx3H6t/V5RzczDl0P4HnSTVCRpUOR87xAy/FDSTyKnsyXtkZQZxwwXSDpDUkEDlwd6X0zWD0ljVPP6Ma9Jyqu1PTfy/9Re0tDI/19G2Hlbch9L1o/67puSHpQ0K3J61t8fG63tg3WCdaKJGVJynQj6yFYyvIVDoxnc/S133xs5+45qXv8lYbcfcaukP0vaGcfbbkqGayU94+6bJcnd450jlgwu6SQzM0mdVfMgqoxXAHd/I/I1G5KWbyfi7qvdvaSei6ZLWujuFe6+QTV/RTY+semaJNbHWtJp4L45XdKCyOkFkq5KZKYEYp1gnWhKhpRcJ4IuW8nwFg5N/fo3qKa1Juz2zWyApC9KmqdgxPI9GCmpu5m9Zmbvm9l1IWT4hWqOsmyT9LGk77p7dZxzRMPbiXxaqn0/Ui1vY/p45HWoIp97h5wnKKwTrBNNyZCS60RML/3QAnF7C4eAM9TsaHaRah5E5yf49h+R9AN3r6op63EXS4a2ks6UNFFSR0lvm9k77r4mgRkmSfpQ0sWSTpa03MzedPcDccrQmKDvi6Exs5ck9a3noh+5++KGrlbPtmT+fqRaXtRgnWCdaEqGlFwngi5bcXsLh4AzyMxOlfS4pCnuvjvBt58naWHkAdRL0lQzq3T3RQnMUCZpl7sflnTYzN6QdJqkeD2IYskwQ9IDXvPEeKmZbZA0WtJ7ccrQmKDvi6Fx90uacbVU+36kWt7G7DCzfu6+PfI0RRBPHSUD1gnWiaZkSM11Il5DZQ0MkrWVtF41w7V/H3YbW2efafr0sNl7IWQYpJp5lHPD+B7U2f8pxX/wMZbvwRhJL0f2zZJUIGlcgjP8UtLdkdN9JG2V1CvO34shanjwMdD7YrJ/6LMD8mP16QH59UruAfkmPdaS7aPufVPSQ/r0gPyDYWcM6/+NdeIz+7NOeGqtE3G9wzQQbKpqWu861TxlIUk3SbopctokPRq5/OPaP+wTmOFxSXtVc2jyQ0krE3n7dfaN+4Mo1gySblfNX5oUSLothP+H/pJejNwPCiR9Lc63/3tJ2yWdUM1vJzck+r6YjB+qmQMpk1QhaYekZbUu+1Hk+1Gimt/mQ8/b1PtYKnw0cN/sqZqFbW3kc4+wcyby/411gnWiNa0TvII8AABAgHgFeQAAgABRtgAAAAJE2QIAAAgQZQsAACBAlC0AAIAAUbYAAAACRNkCAAAIEGULAAAgQP8fkOeh/ZMgvrkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "x = np.linspace(-15, 15)\n",
    "\n",
    "y = 1.5 * x + 10\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "\n",
    "# Here is the code to create a subplot for fig\n",
    "# Create a subplot object in the second column from the left in a layout with 1 vertical row and 3 horizontal columns\n",
    "\n",
    "\n",
    "ax.plot(x, y)\n",
    "\n",
    "for i in range(3):\n",
    "    if i == 1: if i == 1\n",
    "        continue\n",
    "    else: if i == 1: continue\n",
    "        fig.add_subplot(1, 3, i+1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjusting margins around subplots\n",
    "* When subplots are placed in a figure, the name or title of an axis may overlap with the neighboring subplot\n",
    "    * This can be avoided by adjusting the margins around the subplots\n",
    "    * **`matplotlib.pyplot.subplots_adjust(wspace=percentage of horizontal spacing, hspace=percentage of vertical spacing)`**\n",
    "        * Percentages are based on the vertical and horizontal axes of the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "x = np.linspace(-15, 15)\n",
    "\n",
    "y = 1.5 * x + 10\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 2)\n",
    "\n",
    "# Here is the code to space the horizontal spacing of the subplots by a ratio of 0.75\n",
    "\n",
    "\n",
    "ax.plot(x, y)\n",
    "\n",
    "for i in range(3):\n",
    "    if i == 1:.\n",
    "        continue\n",
    "    else:\n",
    "        fig.add_subplot(1, 3, i+1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting the display range of a graph in a subplot\n",
    "* Set the display range of a graph in units of subplots\n",
    "    * Set for a subplot object (**ax**)\n",
    "        * Specify range of X axis: **`ax.set_xlim([min, max])`**\n",
    "        * range specification for Y axis: **`ax.set_ylim([min, max])`**\n",
    "* Note that the method of specifying the range is different from that of setting the display range of a graph when no subplot is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "x = np.linspace(-15, 15)\n",
    "\n",
    "y = 1.5 * x + 10\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 2)\n",
    "\n",
    "plt.subplots_adjust(wspace = 0.75)\n",
    "\n",
    "# Here is the code to set the display range of the y-axis of the subplot ax to [10,20\n",
    "\n",
    "\n",
    "ax.plot(x, y)\n",
    "\n",
    "for i in range(3):\n",
    "    if i == 1:\n",
    "        continue\n",
    "    else:\n",
    "        fig.add_subplot(1, 3, i+1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set element names for graphs in subplots\n",
    "* Set the display range of a graph in units of subplots\n",
    "    * Set settings for subplot object (ax)\n",
    "        * Set graph title\n",
    "            * **`ax.set_title(\"title\")`**\n",
    "        * name of the x-axis\n",
    "            * **`ax.set_xlabel(\"name of X axis\")`**\n",
    "        * name of Y axis\n",
    "            * **`ax.set_ylabel(\"Y axis name\")`**\n",
    "* Note that the method of specifying the name is different from setting the name when no subplot is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "x = np.linspace(-15, 15)\n",
    "\n",
    "y = 1.5 * x + 10\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 2)\n",
    "\n",
    "plt.subplots_adjust(wspace = 0.75)\n",
    "\n",
    "## Here is the code to set the graph title\n",
    "\n",
    "## Here is the code to set the X axis\n",
    "\n",
    "## Here is the code to set the y-axis\n",
    "\n",
    "\n",
    "ax.plot(x, y)\n",
    "\n",
    "for i in range(3):\n",
    "    if i == 1:\n",
    "        continue\n",
    "    else:\n",
    "        fig.add_subplot(1, 3, i+1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display grid on graphs in subplots\n",
    "* Graphs in subplots (ax) also have grid (lattice) display hidden by default\n",
    "* Grid display: **`ax.grid(True)`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "x = np.linspace(-15, 15)\n",
    "\n",
    "y = 1.5 * x + 10\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 2)\n",
    "\n",
    "plt.subplots_adjust(wspace = 0.75)\n",
    "\n",
    "\n",
    "ax.set_title(\"y = 1.5 * x + 10\")\n",
    "\n",
    "ax.set_xlabel(\"X\")\n",
    "\n",
    "ax.set_ylabel(\"Y\")\n",
    "\n",
    "## Here is the code to set the grid display\n",
    "\n",
    "\n",
    "ax.plot(x, y)\n",
    "\n",
    "for i in range(3):\n",
    "    if i == 1:\n",
    "        continue\n",
    "    else:\n",
    "        fig.add_subplot(1, 3, i+1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set ticks on axes of graphs in subplots\n",
    "* Scales can also be set manually on subplots (ax)\n",
    "    * Note that the setting method is different from that without subplots\n",
    "    * Position of ticks to be inserted on the x-axis\n",
    "        * **`ax.set_xticks(list of insertion points)`**\n",
    "    * Information on labels to be inserted on the X axis\n",
    "        * **`ax.set_xticklabels(list of ticks)`**\n",
    "    * ** position of ticks to insert on Y-axis.\n",
    "        * **`ax.set_yticks(list of insertion points)`**\n",
    "    * information of labels to be inserted on the Y-axis\n",
    "        * **`ax.set_yticklabels(list of ticks)`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as pltimport numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "x = np.linspace(-15, 15)\n",
    "\n",
    "y = 1.5 * x + 10\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 2)\n",
    "\n",
    "plt.subplots_adjust(wspace = 0.75)\n",
    "\n",
    "\n",
    "ax.set_title(\"y = 1.5 * x + 10\")\n",
    "\n",
    "ax.set_xlabel(\"X\")\n",
    "\n",
    "ax.set_ylabel(\"Y\")\n",
    "\n",
    "## Here is the code to specify the X-axis tick position\n",
    "\n",
    "ax.set_xticklabels([\"▲10\", \"0\", \"10\"])\n",
    "\n",
    "ax.grid(True)\n",
    "\n",
    "ax.plot(x, y)\n",
    "\n",
    "for i in range(3):\n",
    "    if i == 1:\n",
    "        continue\n",
    "    else:\n",
    "        fig.add_subplot(1, 3, i+1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue.\n",
    "\n",
    "Please visualize three data in a subplot.\n",
    "Any display format is acceptable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}