src/notebooks/513-add-logo-matplotlib.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "tags": []
      },
      "source": [
        "## Introduction\n",
        "\n",
        "**Adding a logo** to your [matplotlib graphics](https://python-graph-gallery.com/matplotlib/) can be a bit tricky when you've never done it before. With this in mind, let's take a look at what to do if you want to add an image or logo to your beautiful [matplotlib graphics](https://python-graph-gallery.com/matplotlib/)."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Libraries\n",
        "\n",
        "First, you need to install the following librairies:\n",
        "- [matplotlib](https://python-graph-gallery.com/matplotlib/) for displaying the chart and the logo\n",
        "- `numpy` is used for manipulating the image as an array\n",
        "- `PIL` for opening the image\n",
        "- `requests` is used to send an HTTP GET request to the specified URL to download the image content\n",
        "- `io` is used to create a file-like object in memory to hold the image content that is downloaded using the requests library\n",
        "\n",
        "Don't forget to install PIL if you haven't already done so with the `pip install Pillow` command."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Libraries\n",
        "from PIL import Image\n",
        "import requests\n",
        "from io import BytesIO\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Open or Load the image\n",
        "\n",
        "We'll look at two cases: using an **image stored locally** on your computer and using an **image from the Internet** using only the url address.\n",
        "\n",
        "- Image stored locally: make sure you write the **right path** (in the following example, `/Users/josephbarbier/Desktop/python graph gallery/github_logo.png` is the location of the image on my computer). The image I chosed is just a basic png github logo. \n",
        "\n",
        "- Image from the Internet: we need to make sure we complete the objective with **some test**. With the `if` statement, we are checking whether the **HTTP response status code is equal to 200**. In the context of HTTP, a status code of 200 means *\"OK\"*, indicating that the request was successful and the server has responded with the **requested content**. If this test is passed, then we just have to get the content from the request and open the image!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Open an image from a computer \n",
        "def open_image_local(path_to_image):\n",
        "    image = Image.open(path_to_image) # Open the image\n",
        "    image_array = np.array(image) # Convert to a numpy array\n",
        "    return image_array # Output\n",
        "\n",
        "# Open the image from my computer\n",
        "image = open_image_local('/Users/josephbarbier/Desktop/python graph gallery/github_logo.png')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Open an image from the Internet\n",
        "def open_image_internet(url_adress):\n",
        "    response = requests.get(url_adress) # Send http request to the url adress\n",
        "    \n",
        "    # Make sure everything went right\n",
        "    if response.status_code == 200:\n",
        "        image_bytes = BytesIO(response.content) # Get content \n",
        "        image = Image.open(image_bytes) # Open the image\n",
        "        return image # Output\n",
        "    else:\n",
        "        print(\"Failed to download image:\", response.status_code)\n",
        "\n",
        "# Open image from the Internet\n",
        "image = open_image_internet('https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create a simple plot with a logo\n",
        "\n",
        "Now let's create a **simple graph** with a **logo** at bottom left. First, we have to define some data and **plot them**. In our case, we will create a [scatter plot](https://python-graph-gallery.com/scatter-plot/) since it's a very simple chart.\n",
        "\n",
        "Once we've done that, we need to define some parameters:\n",
        "- `x-axis position`\n",
        "- `y-axis position`\n",
        "- `Image width`\n",
        "- `Image height`\n",
        "\n",
        "It's **difficult to set these automatically**, as the appropriate values **may vary** from one graph to another. Unless you have something else, the *simplest thing to do* is to test several positions and dimensions until you find **the best one**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {},
      "outputs": [
        {
          "data": {
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Yhxcew4snAABAuYmJDlPvqFDetIYKhcALAADKlbeXoc7N6nq6DMCBKQ0AAACwNI8G3o0bNyo2Nlbh4eEyDENLlixxOm4YRqHbSy+9VOQ1582bV+g5WVms+wcAAFAVeTTwZmZmqnXr1po5c2ahx1NSUpy2uXPnyjAMDRo0qNjrBgYGFjjXz491/wAAAKoij87h7devn/r161fk8dDQUKf9pUuXqmfPnmratGmx1zUMo8C5AAAAqJoqzRze33//XStWrNCdd95ZYt9Tp06pcePGatiwoa6//nrt2LGj2P7Z2dmy2+1OGwAAAKyh0gTe9957TwEBAbr55puL7XfllVdq3rx5WrZsmT7++GP5+fmpa9eu2rt3b5HnxMfHy2azObaIiIjyLh8AAAAeYpimaXq6COn8NITFixdr4MCBhR6/8sor1bt3b82YMaNM183Ly1O7du3UvXt3TZ8+vdA+2dnZys7Oduzb7XZFREQoPT1dgYGBZfo8AAAAuJ7dbpfNZitVXqsU6/B+8803+vXXX7VgwYIyn+vl5aWrr7662BFeX19f+fr6XkqJAAAAqKAqxZSGd955R3/729/UunXrMp9rmqYSExMVFsbrDAEAAKoij47wnjp1Svv27XPsJycnKzExUUFBQWrUqJGk88PVn332mV5++eVCrzFixAg1aNBA8fHxkqS4uDh16tRJzZs3l91u1/Tp05WYmKhZs2a5/oYAAABQ4Xg08P7www/q2bOnY3/8+PGSpJEjR2revHmSpE8++USmaWrYsGGFXuPQoUPy8vrfQPXJkyf1j3/8Q6mpqbLZbGrbtq02btyoDh06uO5GAAAAUGFVmIfWKpKyTIIGAACA+5Ulr1WKObwAAADAxSLwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNJ8PF0AAABWlJtnamvycaVlZCk4wE8dIoPk7WV4uiygSvLoCO/GjRsVGxur8PBwGYahJUuWOB0fNWqUDMNw2jp16lTidRcuXKioqCj5+voqKipKixcvdtEdAABQ0KqkFHWbulbD5nynsZ8katic79Rt6lqtSkrxdGlAleTRwJuZmanWrVtr5syZRfaJiYlRSkqKY/vyyy+LveaWLVt0yy23aPjw4dq5c6eGDx+uoUOH6vvvvy/v8gEAKGBVUorum5+glPQsp/bU9CzdNz+B0At4gGGapunpIiTJMAwtXrxYAwcOdLSNGjVKJ0+eLDDyW5xbbrlFdrtdK1eudLTFxMSoTp06+vjjj0t1DbvdLpvNpvT0dAUGBpb6swEAVVtunqluU9cWCLv5DEmhNj9teuJapjcAl6gsea3CP7S2fv16BQcH6/LLL9fdd9+ttLS0Yvtv2bJFffr0cWrr27evNm/eXOQ52dnZstvtThsAAGW1Nfl4kWFXkkxJKelZ2pp83H1FAajYD63169dPQ4YMUePGjZWcnKyJEyfq2muv1fbt2+Xr61voOampqQoJCXFqCwkJUWpqapGfEx8fr7i4uHKtHbCSvLw8nT17VtnZ2Tp37pzy8vLk5eWl6tWrOzYvrwr/72fA5dIyig67F9MPQPmo0IH3lltucfzn6OhotW/fXo0bN9aKFSt08803F3meYTj/mcg0zQJtfzVhwgSNHz/esW+32xUREXEJlQOV25kzZ3TgwAHt2rVLu3bt0v59+/R7WppOnTqls9nZ/wu8vr4KCAhQSHCwIps101VXXaXWrVurcePG8vf39/RtAG4XHOBXrv0AlI8KHXgvFBYWpsaNG2vv3r1F9gkNDS0wmpuWllZg1PevfH19ixwxBqqKc+fOKSkpSV9++aU2rFun5ORkZWZmqjTT/Hf/9JPWr18vSapZs6aaNm2qa3r2VP/+/dWyZUtVq1bNxdUDFUOHyCCF2fyUmp6lwv6Xkz+Ht0NkkLtLA6q0SvU3yGPHjunw4cMKCwsrsk/nzp21evVqp7avvvpKXbp0cXV5QKV05swZffnllxpxxx0aMmiQZk6frh9//FGnTp0qVdjNZ5qmTNPUqVOntGvXLs14/XUNvvlmjRw+XKtWrVJWFn/ChfV5exmaFBsl6Xy4/av8/UmxUTywBriZR0d4T506pX379jn2k5OTlZiYqKCgIAUFBWny5MkaNGiQwsLCdODAAT311FOqV6+ebrrpJsc5I0aMUIMGDRQfHy9JGjt2rLp3766pU6fqxhtv1NKlS7VmzRpt2rTJ7fcHVGS5ubnatGmTZrz+urZt26azZ8+W+2dkZmZq/bp1+vbbb9WhY0c9NHasunTpIm9v73L/LKCiiIkO0+w72ilu+W6nB9hCbX6aFBulmOiiB20AuIZHlyVbv369evbsWaB95MiRmj17tgYOHKgdO3bo5MmTCgsLU8+ePfX88887za/t0aOHmjRponnz5jnaPv/8cz3zzDPav3+/mjVrphdffLHYOb8XYlkyWF1qaqpeeeUVLfr8c50+fbpMI7mXolatWho0eLAeHj++2GlGgBXwpjXAtcqS1yrMOrwVCYEXVmWapjZv3qyJzzyjn3fv9kgNhmEoKipKL0yZoo4dOxb7QCkAAEWx1Dq8AMpHbm6uPvroI919110eC7vS+dD9008/6a7Ro/XZp58qLy/PY7UAAKqGSrVKA4CLk5OTo3+/+aZeefllnTlzxtPlSDr/EOpTEyboxMmTuuuuu5jXCwBwGUZ4AYvLzc3Vv//9b/3rpZd05swZt83XLY3Tp09rany85s6dy0gvAMBlCLyAhZmmqU8//VSv/Otfys7OrlBhVzpf35msLE375z+1eNEiT5cDALAoAi9gYd9//71efP75Qkd2DcNQWFiYQkJCXP7iFV9fXwUHBys0NLTgQ2qmqVOZmXouLk7bt293aR0AgKqJObyARaWlpenZZ57RsWPHCj3eqFEjffr55/L391dycrJWr16tFcuX6+DBg46XSFys/FAbERGh/tdfrz59+qhZs2bKzMzUkMGDdfTIEecTTFNpaWmaNHGi3p8/X0FBvIUKAFB+CLyABeXm5ur1119XUlJSoccNw1C7v/1NERERMgxD9evXV4cOHXTPPffo008/1dx33nGEUm9vb/n7+ysgIEABgYGq4e8vbx8f5ebk6PSZM8qw25WRkaEzZ84oNzdXkhTeoIFGjhqlW265RcHBwY7PzcvLU+s2bfTb0aOFBuqEhATNnj1bEyZMkJcXf4ACAJQPAi9gQVu3btVnCxYUOUprmqZaRkcXmF5Qr1493X///erfv7/mzZsnm82mVq1aqXHjxgoKClKNGjXk4+MjwzBkmqbOnTunM2fO6NixYzp48KB27dqlU6dOadSoUWrcuHGBz/Xy8lJ0dLS+XLGiyLo+/OADDRgwQG3atLnknwMAABKBF7Cc7OxszZw+XRkZGUX2MQxDYaGhRR5v0qSJJk+eXOJnVatWTTVq1FDdunV1+eWXq3fv3iWeExoaKhUzXeLEiRN6Y9YsvTF7tnx8+IoCAFw6/mYIWMy3336rzZs3e7qMS7Ju7Vrt2LHD02UAACyCwAtYSG5urj6cP19ZWVnF9jNNUympqW6qyllqaqpUwuuET506pY8/+qjCLaMGAKicCLyAhfz3v//V5m+/LbGfISlh+3a3v+whNzdXCdu3FzulId/6deuUkpLihqoAAFZH4AUsZM3q1Tp58mTJHQ1DNpvN7SOopmmqfv36BdfiLcTvv/+uTd9844aqAABWR+AFLCInJ0fr1q0rMUwahqHLL79cjz/xhLy9vd1U3Xk+Pj566umn1apVqxL75uXlad26dUxrAIBKIDfP1Jb/HtPSxKPa8t9jys2rWN/dPAINWMTvv/+uX37+ucSA6O3trXvuu+/8agkeUK9ePY158EE9cP/9OnfuXLF9d+3cqYyMDAUGBrqpOgBAWa1KSlHc8t1KSf/f8yNhNj9Nio1STHSYByv7H0Z4AYvYs2dPqaYzNGnSRDExMa4vqBg9e/ZU08suK7Hf77//rsOHD7uhIgDAxViVlKL75ic4hV1JSk3P0n3zE7QqqWI8i0HgBSzi559/Vk5OTon9OnXpojp16rihoqIFBASoU8eOJa7WcObMGR04cMA9RQEAyiQ3z1Tc8t0q7O+K+W1xy3dXiOkNBF7AIpL37y8xQBqGobZt27qpouJFt2pV4moNeXl5OnTokJsqAgCUxdbk4wVGdv/KlJSSnqWtycfdV1QRCLyABZimqd/T0koMkN7e3goPD3dTVcULDQkp+aE5w1BaWpp7CgIAlElaRvFrvpe1nysReAELyM3NlT09vcR+hmHIz8/PDRWVzNfXt+TlyUxTGXa7ewoCAJRJcEDp/v+ktP1cicALWEBeXp7Onj1bYj/TNEvVzx3Onj1bqiXHzpViXjIAwP06RAYpzOanooYuDJ1fraFDZJA7yyoUgReoQvLy8pReipFgd0hPTy/Vm968vfiaAoCKyNvL0KTYKEkqEHrz9yfFRsnbq+SXDbka/08CWICXl5eqV69eYr+8vDwdOXLEDRWV7MjRoyWO8BqSatSs6Z6CAABlFhMdptl3tFOozXnaQqjNT7PvaFdh1uHlxROABXh7eyvQZitV359375ZpmqV6va+rmKapX37+WYZhFBt6TUlBQZ7/UxgAoGgx0WHqHRWqrcnHlZaRpeCA89MYKsLIbj4CL2ABhmEoNCSkxAApSTt37tSpU6cUEBDgpuoKOnXqlJKSkkoe4TUMNWjQwE1VAQAulreXoc7N6nq6jCIxpQGwiKbNmpXqIbCDBw4oISHBDRUVLSEhQYcOHiyxX/Xq1dWkSRPXFwQAsDQCL2ARLVq0ULVq1Ursl52drQ/ef1/nzp1zQ1UF5eTk6OOPPlJ2dnaJfW02mxo1auSGqgAAVkbgBSzi8ssvL/Urg9d+/bXWrFnj4ooKt27dOq3+6qsS+xmGoWaXXaZ69eq5oSoAgJUReAGLqF+/vqKiokr1MNqZM2f03OTJ2r17txsq+5+9e/cqbvJknT59usS+pmmqU+fOpRq1BgCgOARewCJ8fHx07XXXlbr/gQMHdP9992n79u2lmvt7qX788Ufdf++9+u++faXq7+/vr2uvvdbFVQEAqgICL2AhvXr1Up3ClvH6/6O+F47+/vrLL/q/kSP1xqxZOn78uEtqSk9P19tvv62Rw4crKSmpVOcYhqGoli3VsmVLl9QEAKhaWJYMsJAmTZqoe/fuWrJ4sVP73//+d3Xu3FnJycn6ZuNGpaamOo79+eefmvLii1qwYIEGDhyo3n36qFmzZvL397+otXpN01RWVpaSk5P19ddfa/GiRdrz66/KLcVb1fIZhqGbbr5Z/v7+Zf58AAAuZJju+FtmJWO322Wz2ZSenq7AwEBPlwOUyebNmzX89tud5sneeOONejE+XkFBQTpw4ICei4vTV//5j9OrffPX8K1Zs6YiGjVSixYtdGWLFoqNjVVkZGSJn7tnzx59+eWX2vPrr/rll190+NAhnTp1qsz1G4ahJk2aaMmyZQoODi7z+QCAqqEseY0pDYDFXH311ep5wdzXZcuW6bZhw7Rz505FRkbq9enT1euC+b75//bNzMzULz//rMWLFmn9unWylfINbv7+/vr800+1eNEi/bx790WFXel84L1jxAjCLgCg3BB4AYupVq2aHnjwQaclykzT1K6dO/XgmDHat2+fAgMDNfHZZ8+/xayIaQt+fn4a9/DDpX61b0REhEaMGnVJryw2DEMtW7bUsGHDLvoaAABciMALWFDr1q018v/+T15ezv8T37dvn6ZMmaKzZ8+qefPmeu6FF1S3kEBrGIYaNGyotm3blulzr7vuulKPCBfG399fjz7+eKnXEwYAoDQIvIAFGYahe+65Rx06dixwbN3XX2vTpk2SpH79+mne+++r34ABCg8PV+3atRUeHq5uf/+7xo4bp1q1apXpc0NDQy96KoJhGLrt9tvVq1evizofAICi8NBaIXhoDVaRlJSkUaNG6ejhw07tfWJiNGfOHFWvXl2SlJubq5MnT+rMmTPy9/eXzWaTj0/ZF3E5d+6cbho4UNt/+KHM53bp2lVvv/MOo7sAgFLhoTUAkqTo6GhNmTJFtevU0V9n1m5Yt06fLljgeFDN29tbdevWVcOGDVW3bt2LCrvS+VHaC6dRlMYVV16paS+9RNgFALgEgRewuN69e2vy5Mmq9Zd//Z49e1YvPP+83nrrLWVmZnqwOimyaVO9Pn26mjVr5tE6AADWxYsnAIszDENDhg6Vl5eXJj37rE6cOCHTNGW32/XCc89p+dKluv6GG9S2bVvHigx2u11nz55Vp06dLmnVhZJc0aKFXn/9dbVu3dplnwEAAIEXqAK8vLw0eMgQ1alTR0899ZQOHzok0zSVk5OjhIQEbd++XdWrV1e1atUkSTk5OWp/9dVa8Omn8vb2Lvd6DMNQl65dNe2ll9S0adNyvz4AAH/FlAagijAMQ9f17q33P/hA3f7+d8dc2/x5vGfPnlVmZqYyMzOVnZ0tVz3PWqNGDY2+6y69/c47hF0AgFsQeIEq5sorr9Tcd9/Vo48/rrp167rtc728vBQdHa033nxTcXFxql27tts+GwBQtTGlAaiCAgICNG7cOPXp00ezZs7U6q++uuhXARcnf/5vw4gIjRg5Urfddlup39wGAEB5IfACVVT+a3ynz5ihxMREffDee1q7dq2OHTsm0zTl7+9f5gfWvLy8VK9ePUmSr6+vLr/8ct00aJBuuukmhYaGuuI2AAAoES+eKAQvnkBVlJeXp8OHD+vrr7/W/v37dfPNN6tdu3Zlvs7+/fv19ddfq0WLFmrbtq1q1qzpgmoBAFVdWfIagbcQBF4AAICKjTetAQAAAP8fgRcAAACWRuAFAACApXk08G7cuFGxsbEKDw+XYRhasmSJ49i5c+f0xBNPqFWrVqpZs6bCw8M1YsQI/fbbb8Vec968eTIMo8CWlZXl4rsBAABAReTRwJuZmanWrVtr5syZBY6dPn1aCQkJmjhxohISErRo0SLt2bNHN9xwQ4nXDQwMVEpKitPm5+fnilsAAABABefRdXj79eunfv36FXrMZrNp9erVTm0zZsxQhw4ddOjQITVq1KjI6xqGwZqfAAAAkFTJ5vCmp6fLMIwSX0l66tQpNW7cWA0bNtT111+vHTt2FNs/OztbdrvdaQMAAIA1VJrAm5WVpSeffFK33XZbsWutXXnllZo3b56WLVumjz/+WH5+furatav27t1b5Dnx8fGy2WyOLSIiwhW3AAAAAA+oMC+eMAxDixcv1sCBAwscO3funIYMGaJDhw5p/fr1ZXoZRF5entq1a6fu3btr+vTphfbJzs5Wdna2Y99utysiIoIXTwAAAFRQZXnxhEfn8JbGuXPnNHToUCUnJ2vt2rVlDqBeXl66+uqrix3h9fX1la+v76WWCgAAgAqoQk9pyA+7e/fu1Zo1a1S3bt0yX8M0TSUmJiosLMwFFQIAAKCi8+gI76lTp7Rv3z7HfnJyshITExUUFKTw8HANHjxYCQkJ+uKLL5Sbm6vU1FRJUlBQkKpXry5JGjFihBo0aKD4+HhJUlxcnDp16qTmzZvLbrdr+vTpSkxM1KxZs9x/gwAAAPA4jwbeH374QT179nTsjx8/XpI0cuRITZ48WcuWLZMktWnTxum8devWqUePHpKkQ4cOycvrfwPVJ0+e1D/+8Q+lpqbKZrOpbdu22rhxozp06ODamwEAAECFVGEeWqtIyjIJGgAAAO5nqYfWAACVW26eqa3Jx5WWkaXgAD91iAySt5fh6bIAVCEEXgCAy6xKSlHc8t1KSc9ytIXZ/DQpNkox0TxMDMA9KvQqDQCAymtVUorum5/gFHYlKTU9S/fNT9CqpBQPVQagqiHwAgDKXW6eqbjlu1XYQyL5bXHLdys3j8dIALgegRcAUO62Jh8vMLL7V6aklPQsbU0+7r6iAFRZBF4AQLlLyyg67F5MPwC4FAReAEC5Cw7wK9d+AHApCLwAgHLXITJIYTY/FbX4mKHzqzV0iAxyZ1kAqigCLwCg3Hl7GZoUGyVJBUJv/v6k2CjW4wXgFgReAIBLxESHafYd7RRqc562EGrz0+w72rEOLwC34cUTAACXiYkOU++oUN60BsCjCLwAAJfy9jLUuVldT5cBoApjSgMAAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsrcyBd9WqVdq0aZNjf9asWWrTpo1uu+02nThxolyLAwAAAC5VmQPvY489JrvdLkn68ccf9cgjj6h///7av3+/xo8fX6Zrbdy4UbGxsQoPD5dhGFqyZInTcdM0NXnyZIWHh8vf3189evTQTz/9VOJ1Fy5cqKioKPn6+ioqKkqLFy8uU10AAACwjjIH3uTkZEVFRUk6Hyyvv/56TZkyRW+88YZWrlxZpmtlZmaqdevWmjlzZqHHp02bpldeeUUzZ87Utm3bFBoaqt69eysjI6PIa27ZskW33HKLhg8frp07d2r48OEaOnSovv/++zLVBgAAAGswTNM0y3JCUFCQNm3apKioKHXr1k0jRozQP/7xDx04cEBRUVE6ffr0xRViGFq8eLEGDhwo6fzobnh4uMaNG6cnnnhCkpSdna2QkBBNnTpV99xzT6HXueWWW2S3253Cd0xMjOrUqaOPP/64VLXY7XbZbDalp6crMDDwou4HAAAArlOWvFbmEd5u3bpp/Pjxev7557V161YNGDBAkrRnzx41bNjw4iouRHJyslJTU9WnTx9Hm6+vr6655hpt3ry5yPO2bNnidI4k9e3bt9hzsrOzZbfbnTYAAABYQ5kD78yZM+Xj46PPP/9cs2fPVoMGDSRJK1euVExMTLkVlpqaKkkKCQlxag8JCXEcK+q8sp4THx8vm83m2CIiIi6hcgAAAFQkPmU9oVGjRvriiy8KtL/66qvlUtCFDMNw2jdNs0DbpZ4zYcIEpwfu7HY7oRcAAMAiShV47Xa7Y25ESX/uL685r6GhoZLOj9iGhYU52tPS0gqM4F543oWjuSWd4+vrK19f30usGAAAABVRqaY01KlTR2lpaZKk2rVrq06dOgW2/PbyEhkZqdDQUK1evdrRdvbsWW3YsEFdunQp8rzOnTs7nSNJX331VbHnAAAAwLpKNcK7du1aBQUFOf5zSVMKSuvUqVPat2+fYz85OVmJiYkKCgpSo0aNNG7cOE2ZMkXNmzdX8+bNNWXKFNWoUUO33Xab45wRI0aoQYMGio+PlySNHTtW3bt319SpU3XjjTdq6dKlWrNmjdPLMgAAAFB1lCrwXnPNNY7/3KNHj3L78B9++EE9e/Z07OfPox05cqTmzZunxx9/XGfOnNH999+vEydOqGPHjvrqq68UEBDgOOfQoUPy8vrfQHWXLl30ySef6JlnntHEiRPVrFkzLViwQB07diy3ugEAAFB5lHkd3okTJ2ry5Mny9vZ2ak9PT9e9995b6rVuKzLW4QUAAKjYXLoO7/vvv6+uXbvqv//9r6Nt/fr1atWqlQ4cOFDmYgEAAABXKnPg3bVrl5o0aaI2bdpozpw5euyxx9SnTx+NGjWKebIAAACocMq8Dq/NZtMnn3yip59+Wvfcc498fHy0cuVK9erVyxX1AQAAAJekzCO8kjRjxgy9+uqrGjZsmJo2baqHHnpIO3fuLO/aAAAAgEtW5sDbr18/xcXF6f3339eHH36oHTt2qHv37urUqZOmTZvmihoBAACAi1bmwJuTk6Ndu3Zp8ODBkiR/f3/Nnj1bn3/+ucteLwwAAABcrDIvS1acP//8U/Xq1Suvy3kMy5IBAABUbC5dlqw4Vgi7AAAAsJYyr9KQm5urV199VZ9++qkOHTqks2fPOh0/fvx4uRUHAAAAXKoyj/DGxcXplVde0dChQ5Wenq7x48fr5ptvlpeXlyZPnuyCEgEAAICLV+bA++GHH2rOnDl69NFH5ePjo2HDhuntt9/Ws88+q++++84VNQIAAAAXrcyBNzU1Va1atZIk1apVS+np6ZKk66+/XitWrCjf6gAAAIBLVObA27BhQ6WkpEiSLrvsMn311VeSpG3btsnX17d8qwMAAAAuUZkD70033aSvv/5akjR27FhNnDhRzZs314gRIzR69OhyLxAAAAC4FJe8Du93332nzZs367LLLtMNN9xQXnV5FOvwAgAAVGxlyWtlXpbsQp06dVKnTp0u9TIAAACAS1zSiycCAwO1f//+8qoFAAAAKHelDrxHjhwp0FaObyUGAAAAXKLUgTc6OloffPCBK2sBAAAAyl2pA++UKVM0ZswYDRo0SMeOHZMk3XHHHTzUBQAAgAqt1IH3/vvv186dO3XixAm1bNlSy5Yt0+zZs1WvXj1X1gcAAABckjKt0hAZGam1a9dq5syZGjRokFq0aCEfH+dLJCQklGuBAAAAwKUo87JkBw8e1MKFCxUUFKQbb7yxQOAFAAAAKpIypdU5c+bokUce0XXXXaekpCTVr1/fVXUBAAAA5aLUgTcmJkZbt27VzJkzNWLECFfWBAAAAJSbUgfe3Nxc7dq1Sw0bNnRlPQAAAEC5KnXgXb16tSvrAAAAAFzikl4tDAAAAFR0BF4AAABYGoEXAAAAlkbgBQAAgKUReAEAAGBpBF4AAABYGoEXAAAAlkbgBQAAgKUReAEAAGBpBF4AAABYGoEXAAAAlkbgBQAAgKUReAEAAGBpBF4AAABYGoEXAAAAlkbgBQAAgKUReAEAAGBpBF4AAABYGoEXAAAAlkbgBQAAgKUReAEAAGBpBF4AAABYGoEXAAAAlkbgBQAAgKVV+MDbpEkTGYZRYBszZkyh/devX19o/19++cXNlQMAAKAi8PF0ASXZtm2bcnNzHftJSUnq3bu3hgwZUux5v/76qwIDAx379evXd1mNAAAAqLgqfOC9MKj+85//VLNmzXTNNdcUe15wcLBq167twsoAAABQGVT4KQ1/dfbsWc2fP1+jR4+WYRjF9m3btq3CwsLUq1cvrVu3rti+2dnZstvtThsAAACsoVIF3iVLlujkyZMaNWpUkX3CwsL01ltvaeHChVq0aJGuuOIK9erVSxs3bizynPj4eNlsNscWERHhguoBAADgCYZpmqaniyitvn37qnr16lq+fHmZzouNjZVhGFq2bFmhx7Ozs5Wdne3Yt9vtioiIUHp6utM8YAAAAFQMdrtdNputVHmtws/hzXfw4EGtWbNGixYtKvO5nTp10vz584s87uvrK19f30spDwAAABVUpZnS8O677yo4OFgDBgwo87k7duxQWFiYC6oCAABARVcpRnjz8vL07rvvauTIkfLxcS55woQJOnr0qN5//31J0muvvaYmTZqoZcuWjofcFi5cqIULF3qidAAAAHhYpQi8a9as0aFDhzR69OgCx1JSUnTo0CHH/tmzZ/Xoo4/q6NGj8vf3V8uWLbVixQr179/fnSUDAACggqhUD625S1kmQQMAAMD9LPnQGoDKLzfP1Nbk40rLyFJwgJ86RAbJ26v4NbUBALhUBF4AbrEqKUVxy3crJT3L0RZm89Ok2CjFRPNQKQDAdSrNKg0AKq9VSSm6b36CU9iVpNT0LN03P0GrklI8VBkAoCog8AJwqdw8U3HLd6uwhwXy2+KW71ZuHo8TAABcg8ALwKW2Jh8vMLL7V6aklPQsbU0+7r6iAABVCoEXgEulZRQddi+mHwAAZUXgBeBSwQF+5doPAICyIvACcKkOkUEKs/mpqMXHDJ1fraFDZJA7ywIAVCEEXgAu5e1laFJslCQVCL35+5Nio1iPFwDgMgReAC4XEx2m2Xe0U6jNedpCqM1Ps+9oxzq8AACX4sUTANwiJjpMvaNCedMaAMDtCLwA3Mbby1DnZnU9XQYAoIphSgMAAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAAS/PxdAGounLzTG1NPq60jCwFB/ipQ2SQvL0MT5cFAAAspkKP8E6ePFmGYThtoaGhxZ6zYcMG/e1vf5Ofn5+aNm2qN998003VoixWJaWo29S1GjbnO439JFHD5nynblPXalVSiqdLAwAAFlOhA68ktWzZUikpKY7txx9/LLJvcnKy+vfvr7///e/asWOHnnrqKT300ENauHChGytGSVYlpei++QlKSc9yak9Nz9J98xMIvQAAoFxV+CkNPj4+JY7q5nvzzTfVqFEjvfbaa5KkFi1a6IcfftC//vUvDRo0yIVVorRy80zFLd8ts5BjpiRDUtzy3eodFcr0BgAAUC4q/Ajv3r17FR4ersjISN16663av39/kX23bNmiPn36OLX17dtXP/zwg86dO1fkednZ2bLb7U4bXGNr8vECI7t/ZUpKSc/S1uTj7isKAABYWoUOvB07dtT777+v//znP5ozZ45SU1PVpUsXHTt2rND+qampCgkJcWoLCQlRTk6O/vzzzyI/Jz4+XjabzbFFRESU633gf9Iyig67F9MPAACgJBU68Pbr10+DBg1Sq1atdN1112nFihWSpPfee6/IcwzD+c/gpmkW2v5XEyZMUHp6umM7fPhwOVSPwgQH+JVrPwAAgJJU+Dm8f1WzZk21atVKe/fuLfR4aGioUlNTndrS0tLk4+OjunXrFnldX19f+fr6lmutKFyHyCCF2fyUmp5V6DxeQ1Ko7fwSZQAAAOWhQo/wXig7O1s///yzwsLCCj3euXNnrV692qntq6++Uvv27VWtWjV3lIgSeHsZmhQbJel8uP2r/P1JsVE8sAYAAMpNhQ68jz76qDZs2KDk5GR9//33Gjx4sOx2u0aOHCnp/FSEESNGOPrfe++9OnjwoMaPH6+ff/5Zc+fO1TvvvKNHH33UU7eAQsREh2n2He0UanOethBq89PsO9opJrrwf9AAAABcjAo9peHIkSMaNmyY/vzzT9WvX1+dOnXSd999p8aNG0uSUlJSdOjQIUf/yMhIffnll3r44Yc1a9YshYeHa/r06SxJVgHFRIepd1Qob1oDAAAuZ5j5T3XBwW63y2azKT09XYGBgZ4uBwAAABcoS16r0FMaAAAAgEtF4AUAAIClEXgBAABgaQReAAAAWBqBFwAAAJZG4AUAAIClEXgBAABgaQReAAAAWBqBFwAAAJZG4AUAAIClEXgBAABgaQReAAAAWBqBFwAAAJZG4AUAAIClEXgBAABgaQReAAAAWBqBFwAAAJZG4AUAAIClEXgBAABgaQReAAAAWBqBFwAAAJZG4AUAAIClEXgBAABgaQReAAAAWBqBFwAAAJZG4AUAAIClEXgBAABgaQReAAAAWBqBFwAAAJZG4AUAAIClEXgBAABgaQReAAAAWBqBFwAAAJZG4AUAAIClEXgBAABgaQReAAAAWBqBFwAAAJbm4+kCqrrcPFNbk48rLSNLwQF+6hAZJG8vw9NlAQAAWAaB14NWJaUobvlupaRnOdrCbH6aFBulmOgwD1YGAABgHUxp8JBVSSm6b36CU9iVpNT0LN03P0GrklI8VBkAAIC1EHg9IDfPVNzy3TILOZbfFrd8t3LzCusBAACAsiDwesDW5OMFRnb/ypSUkp6lrcnH3VcUAACARRF4PSAto+iwezH9AAAAUDQCrwcEB/iVaz8AAAAUjcDrAR0igxRm81NRi48ZOr9aQ4fIIHeWBQAAYEkEXg/w9jI0KTZKkgqE3vz9SbFRrMcLAABQDgi8HhITHabZd7RTqM152kKozU+z72jHOrwAAADlhBdPeFBMdJh6R4XypjUAAAAXIvB6mLeXoc7N6nq6DAAAAMtiSgMAAAAsjcALAAAAS6vQgTc+Pl5XX321AgICFBwcrIEDB+rXX38t9pz169fLMIwC2y+//OKmqgEAAFCRVOjAu2HDBo0ZM0bfffedVq9erZycHPXp00eZmZklnvvrr78qJSXFsTVv3twNFQMAAKCiqdAPra1atcpp/91331VwcLC2b9+u7t27F3tucHCwateu7cLqAAAAUBlU6BHeC6Wnp0uSgoJKfgNZ27ZtFRYWpl69emndunXF9s3OzpbdbnfaAAAAYA2VJvCapqnx48erW7duio6OLrJfWFiY3nrrLS1cuFCLFi3SFVdcoV69emnjxo1FnhMfHy+bzebYIiIiXHELAAAA8ADDNE3T00WUxpgxY7RixQpt2rRJDRs2LNO5sbGxMgxDy5YtK/R4dna2srOzHft2u10RERFKT09XYGDgJdUNAACA8me322Wz2UqV1yrFCO+DDz6oZcuWad26dWUOu5LUqVMn7d27t8jjvr6+CgwMdNoAAABgDRX6oTXTNPXggw9q8eLFWr9+vSIjIy/qOjt27FBYWFg5VwcAAIDKoEIH3jFjxuijjz7S0qVLFRAQoNTUVEmSzWaTv7+/JGnChAk6evSo3n//fUnSa6+9piZNmqhly5Y6e/as5s+fr4ULF2rhwoUeuw8AAAB4ToUOvLNnz5Yk9ejRw6n93Xff1ahRoyRJKSkpOnTokOPY2bNn9eijj+ro0aPy9/dXy5YttWLFCvXv399dZQMAAKACqTQPrblTWSZBAwAAwP3Kktcq9AgvYFW5eaa2Jh9XWkaWggP81CEySN5ehqfLAgBcAr7bKy4CL+Bmq5JSFLd8t1LSsxxtYTY/TYqNUkw0D1cCQGXEd3vFVimWJQOsYlVSiu6bn+D0hShJqelZum9+glYlpXioMgDAxeK7veIj8AJukptnKm75bhU2aT6/LW75buXmMa0eACoLvtsrBwIv4CZbk48X+Nf/X5mSUtKztDX5uPuKAgBcEr7bKwcCL+AmaRlFfyFeTD8AgOfx3V45EHgBNwkO8CvXfgAAz+O7vXIg8AJu0iEySGE2PxW1QI2h80/0dogMcmdZAIBLwHd75UDgBdzE28vQpNgoSSrwxZi/Pyk2ijUbAaAS4bu9ciDwAm4UEx2m2Xe0U6jN+U9boTY/zb6jHWs1AkAlxHd7xcerhQvBq4XharyNBwCsh+929+LVwkAF5+1lqHOzup4uAwBQjvhur7iY0gAAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABL8/F0AQBQFeTmmdqafFxpGVkKDvBTh8ggeXsZni4LAKoEAi8AuNiqpBTFLd+tlPQsR1uYzU+TYqMUEx3mwcoAoGpgSgMAuNCqpBTdNz/BKexKUmp6lu6bn6BVSSkeqgwAqg4CLwC4SG6eqbjlu2UWciy/LW75buXmFdYDAFBeCLwA4CJbk48XGNn9K1NSSnqWtiYfd19RAFAFEXgBwEXSMooOuxfTDwBwcQi8AOAiwQF+5doPAHBxCLwA4CIdIoMUZvNTUYuPGTq/WkOHyCB3lgUAVQ6BFwBcxNvL0KTYKEkqEHrz9yfFRrEeLwC4GIEXAFwoJjpMs+9op1Cb87SFUJufZt/RjnV4AcANePEEALhYTHSYekeF8qY1APAQAi8AuIG3l6HOzep6ugwAqJKY0gAAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABL8/F0ARWRaZqSJLvd7uFKAAAAUJj8nJaf24pD4C1ERkaGJCkiIsLDlQAAAKA4GRkZstlsxfYxzNLE4iomLy9Pv/32mwICAmQYhqfLsTS73a6IiAgdPnxYgYGBni4HbsDvvGri91718Duvetz9OzdNUxkZGQoPD5eXV/GzdBnhLYSXl5caNmzo6TKqlMDAQL4Qqxh+51UTv/eqh9951ePO33lJI7v5eGgNAAAAlkbgBQAAgKUReOFRvr6+mjRpknx9fT1dCtyE33nVxO+96uF3XvVU5N85D60BAADA0hjhBQAAgKUReAEAAGBpBF4AAABYGoEXAAAAlkbghUfEx8fr6quvVkBAgIKDgzVw4ED9+uuvni4LbhQfHy/DMDRu3DhPlwIXOnr0qO644w7VrVtXNWrUUJs2bbR9+3ZPlwUXycnJ0TPPPKPIyEj5+/uradOmeu6555SXl+fp0lCONm7cqNjYWIWHh8swDC1ZssTpuGmamjx5ssLDw+Xv768ePXrop59+8kyx/x+BFx6xYcMGjRkzRt99951Wr16tnJwc9enTR5mZmZ4uDW6wbds2vfXWW7rqqqs8XQpc6MSJE+ratauqVaumlStXavfu3Xr55ZdVu3ZtT5cGF5k6darefPNNzZw5Uz///LOmTZuml156STNmzPB0aShHmZmZat26tWbOnFno8WnTpumVV17RzJkztW3bNoWGhqp3797KyMhwc6X/w7JkqBD++OMPBQcHa8OGDerevbuny4ELnTp1Su3atdMbb7yhF154QW3atNFrr73m6bLgAk8++aS+/fZbffPNN54uBW5y/fXXKyQkRO+8846jbdCgQapRo4Y++OADD1YGVzEMQ4sXL9bAgQMlnR/dDQ8P17hx4/TEE09IkrKzsxUSEqKpU6fqnnvu8UidjPCiQkhPT5ckBQUFebgSuNqYMWM0YMAAXXfddZ4uBS62bNkytW/fXkOGDFFwcLDatm2rOXPmeLosuFC3bt309ddfa8+ePZKknTt3atOmTerfv7+HK4O7JCcnKzU1VX369HG0+fr66pprrtHmzZs9VpePxz4Z+P9M09T48ePVrVs3RUdHe7ocuNAnn3yihIQEbdu2zdOlwA3279+v2bNna/z48Xrqqae0detWPfTQQ/L19dWIESM8XR5c4IknnlB6erquvPJKeXt7Kzc3Vy+++KKGDRvm6dLgJqmpqZKkkJAQp/aQkBAdPHjQEyVJIvCiAnjggQe0a9cubdq0ydOlwIUOHz6ssWPH6quvvpKfn5+ny4Eb5OXlqX379poyZYokqW3btvrpp580e/ZsAq9FLViwQPPnz9dHH32kli1bKjExUePGjVN4eLhGjhzp6fLgRoZhOO2bplmgzZ0IvPCoBx98UMuWLdPGjRvVsGFDT5cDF9q+fbvS0tL0t7/9zdGWm5urjRs3aubMmcrOzpa3t7cHK0R5CwsLU1RUlFNbixYttHDhQg9VBFd77LHH9OSTT+rWW2+VJLVq1UoHDx5UfHw8gbeKCA0NlXR+pDcsLMzRnpaWVmDU152YwwuPME1TDzzwgBYtWqS1a9cqMjLS0yXBxXr16qUff/xRiYmJjq19+/a6/fbblZiYSNi1oK5duxZYbnDPnj1q3LixhyqCq50+fVpeXs7Rwtvbm2XJqpDIyEiFhoZq9erVjrazZ89qw4YN6tKli8fqYoQXHjFmzBh99NFHWrp0qQICAhxzfmw2m/z9/T1cHVwhICCgwBztmjVrqm7duszdtqiHH35YXbp00ZQpUzR06FBt3bpVb731lt566y1PlwYXiY2N1YsvvqhGjRqpZcuW2rFjh1555RWNHj3a06WhHJ06dUr79u1z7CcnJysxMVFBQUFq1KiRxo0bpylTpqh58+Zq3ry5pkyZoho1aui2227zWM0sSwaPKGoez7vvvqtRo0a5txh4TI8ePViWzOK++OILTZgwQXv37lVkZKTGjx+vu+++29NlwUUyMjI0ceJELV68WGlpaQoPD9ewYcP07LPPqnr16p4uD+Vk/fr16tmzZ4H2kSNHat68eTJNU3Fxcfr3v/+tEydOqGPHjpo1a5ZHBzcIvAAAALA05vACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAJysX79ehmHo5MmTni4FAMoFgRcAKqjc3Fx16dJFgwYNcmpPT09XRESEnnnmGZd8bpcuXZSSkiKbzeaS6wOAu/FqYQCowPbu3as2bdrorbfe0u233y5JGjFihHbu3Klt27apevXqHq4QACo+RngBoAJr3ry54uPj9eCDD+q3337T0qVL9cknn+i9994rMuw+8cQTuvzyy1WjRg01bdpUEydO1Llz5yRJpmnquuuuU0xMjPLHO06ePKlGjRrp6aefllRwSsPBgwcVGxurOnXqqGbNmmrZsqW+/PJL1988AJQTH08XAAAo3oMPPqjFixdrxIgR+vHHH/Xss8+qTZs2RfYPCAjQvHnzFB4erh9//FF33323AgIC9Pjjj8swDL333ntq1aqVpk+frrFjx+ree+9VSEiIJk+eXOj1xowZo7Nnz2rjxo2qWbOmdu/erVq1arnmZgHABZjSAACVwC+//KIWLVqoVatWSkhIkI9P6ccrXnrpJS1YsEA//PCDo+2zzz7T8OHDNX78eL3++uvasWOHLr/8cknnR3h79uypEydOqHbt2rrqqqs0aNAgTZo0qdzvCwDcgSkNAFAJzJ07VzVq1FBycrKOHDkiSbr33ntVq1Ytx5bv888/V7du3RQaGqpatWpp4sSJOnTokNP1hgwZoptvvlnx8fF6+eWXHWG3MA899JBeeOEFde3aVZMmTdKuXbtcc5MA4CIEXgCo4LZs2aJXX31VS5cuVefOnXXnnXfKNE0999xzSkxMdGyS9N133+nWW29Vv3799MUXX2jHjh16+umndfbsWadrnj59Wtu3b5e3t7f27t1b7Offdddd2r9/v4YPH64ff/xR7du314wZM1x1uwBQ7gi8AFCBnTlzRiNHjtQ999yj6667Tm+//ba2bdumf//73woODtZll13m2CTp22+/VePGjfX000+rffv2at68uQ4ePFjguo888oi8vLy0cuVKTZ8+XWvXri22joiICN17771atGiRHnnkEc2ZM8cl9wsArkDgBYAK7Mknn1ReXp6mTp0qSWrUqJFefvllPfbYYzpw4ECB/pdddpkOHTqkTz75RP/97381ffp0LV682KnPihUrNHfuXH344Yfq3bu3nnzySY0cOVInTpwotIZx48bpP//5j5KTk5WQkKC1a9eqRYsW5X6vAOAqPLQGABXUhg0b1KtXL61fv17dunVzOta3b1/l5ORozZo1MgzD6djjjz+uuXPnKjs7WwMGDFCnTp00efJknTx5Un/88YdatWqlsWPHasKECZKknJwcde3aVU2aNNGCBQsKPLT24IMPauXKlTpy5IgCAwMVExOjV199VXXr1nXbzwIALgWBFwAAAJbGlAYAAABYGoEXAAAAlkbgBQAAgKUReAEAAGBpBF4AAABYGoEXAAAAlkbgBQAAgKUReAEAAGBpBF4AAABYGoEXAAAAlkbgBQAAgKX9P4yLLRuFwsZ/AAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Some data\n",
        "x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
        "y = [3, 5, 2, 7, 1, 12, 15, 18, 2, 14]\n",
        "\n",
        "# Create scatter plot\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "\n",
        "# Scatter plot\n",
        "ax.scatter(x, y)\n",
        "\n",
        "# Add labels and title\n",
        "ax.set_xlabel('X-axis')\n",
        "ax.set_ylabel('Y-axis')\n",
        "ax.set_title('Simple Scatter Plot')\n",
        "\n",
        "# Define the position and size parameters\n",
        "image_xaxis = 0.3\n",
        "image_yaxis = 0.6\n",
        "image_width = 0.15\n",
        "image_height = 0.15  # Same as width since our logo is a square\n",
        "\n",
        "# Define the position for the image axes\n",
        "ax_image = fig.add_axes([image_xaxis,\n",
        "                         image_yaxis,\n",
        "                         image_width,\n",
        "                         image_height]\n",
        "                       )\n",
        "\n",
        "# Display the image\n",
        "ax_image.imshow(image)\n",
        "ax_image.axis('off')  # Remove axis of the image\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Going further\n",
        "\n",
        "This article showed how to __add an image__ or logo into a Matplotlib chart. \n",
        "\n",
        "You can see a direct application of this technique in this [beautiful stacked area chart](https://python-graph-gallery.com/web-stacked-line-chart-with-labels) where a __github logo__ is inserted in the caption.\n",
        "\n",
        "Last but not least, feel free to visit the [matplotlib section](https://python-graph-gallery.com/matplotlib) for more general matplotlib tips and tricks! 🔥"
      ]
    }
  ],
  "metadata": {
    "chartType": "colors",
    "description": "This post explains how to insert an image into your <a href='https://python-graph-gallery.com/matplotlib/'>matplotlib</a> plot using the <code>PIL</code> and <code>io</code> libraries.<br/>Two use cases are considered: your image beeing <b>stored locally</b> or somewhere <b>on the web</b>.",
    "family": "general",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "keywords": "python, chart, image, plot, logo, add",
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
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