antibiotic_recovery.ipynb

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    "colab": {
      "name": "antibiotic_recovery.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMsUV8lkRLeGysByMOp1SBK",
      "include_colab_link": true
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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Gibbons-Lab/2021_data_literacy/blob/main/antibiotic_recovery.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nMFKpitutvfx"
      },
      "source": [
        "# Antiobiotic recovery\n",
        "\n",
        "Here we will have a look at gut microbiome data from individuals that were put on antibiotics. Most of the individuals were sampled before antibiotic exposure (pre), during antibiotic exposure, and several weeks after exposure (post). The study included individuals from several countries. Here will look at the data from Singapore and Canada. \n",
        "\n",
        "To get an idea what the original data looked like, let's have a look at it. For that we will start by downloading the excel file for the study.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uUP2qYOi7lV3",
        "outputId": "d3b9b0e6-f496-4bc8-cfe3-52fa5fc09ce0"
      },
      "source": [
        "!wget https://git.io/JuaSY -O antibiotics.xlsx"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-09-10 23:06:15--  https://git.io/JuaSY\n",
            "Resolving git.io (git.io)... 54.157.58.70, 18.205.36.100, 54.162.128.250, ...\n",
            "Connecting to git.io (git.io)|54.157.58.70|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github.com/Gibbons-Lab/2021_data_literacy/raw/main/antibiotics_recovery/antibiotics.xlsx [following]\n",
            "--2021-09-10 23:06:15--  https://github.com/Gibbons-Lab/2021_data_literacy/raw/main/antibiotics_recovery/antibiotics.xlsx\n",
            "Resolving github.com (github.com)... 140.82.113.4\n",
            "Connecting to github.com (github.com)|140.82.113.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/Gibbons-Lab/2021_data_literacy/main/antibiotics_recovery/antibiotics.xlsx [following]\n",
            "--2021-09-10 23:06:15--  https://raw.githubusercontent.com/Gibbons-Lab/2021_data_literacy/main/antibiotics_recovery/antibiotics.xlsx\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 109548 (107K) [application/octet-stream]\n",
            "Saving to: ‘antibiotics.xlsx’\n",
            "\n",
            "antibiotics.xlsx    100%[===================>] 106.98K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2021-09-10 23:06:16 (7.21 MB/s) - ‘antibiotics.xlsx’ saved [109548/109548]\n",
            "\n"
          ]
        }
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hGYjbdOrALmD"
      },
      "source": [
        "This will us an excel file with several tables. You can have a look at the file by clicking on the folder symbol to the left. \n",
        "\n",
        "## The Singapore Data\n",
        "\n",
        "For now we will load the original data for Singapore from the study. We will use the `pandas` library which has a lot of functions to read data from tabular data. Let's have a look at the first five rows afterwards."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "boophFZAAfWp",
        "outputId": "db86a9bd-8a40-4915-f40b-f759a3cbc764",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        }
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "singapore = pd.read_excel(\"antibiotics.xlsx\", \"Singapore\")\n",
        "singapore.head()"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.163570</td>\n",
              "      <td>7.03122</td>\n",
              "      <td>0.150540</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.924190</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00017</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.01380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.2852</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.016720</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.78052</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0322</td>\n",
              "      <td>0.092850</td>\n",
              "      <td>0.014520</td>\n",
              "      <td>0.006440</td>\n",
              "      <td>0.015800</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.01225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.023580</td>\n",
              "      <td>0.006610</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>148JV_MBS092_B_CLOSING</td>\n",
              "      <td>148JV</td>\n",
              "      <td>during</td>\n",
              "      <td>R</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00511</td>\n",
              "      <td>1.910700</td>\n",
              "      <td>0.975340</td>\n",
              "      <td>0.138840</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00372</td>\n",
              "      <td>0.008100</td>\n",
              "      <td>0.20017</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.553790</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00261</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.93038</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 333 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                       ID  ... Yersinia_unclassified\n",
              "0  135EA_MBS303_A_INITIAL  ...                     0\n",
              "1  135EA_MBS304_B_CLOSING  ...                     0\n",
              "2     135EA_MBS074_C_POST  ...                     0\n",
              "3  148JV_MBS088_A_INITIAL  ...                     0\n",
              "4  148JV_MBS092_B_CLOSING  ...                     0\n",
              "\n",
              "[5 rows x 333 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ZwFQWh6A3mz"
      },
      "source": [
        "As you can see there is data for 329 bacterial species and each value in the table is a percentage (so 1 means the bacteria contributed 1% of all DNA in the sample). To get an idea how that looks, let's have a look at one of the most abundant bacteria *Bacteroides vulgatus*. For that we use `seaborn` again, in particular the `stripplot` function that draws a dot for each data point and also jitters the data arund the x-axis a bit so we don't have that many overlapping points."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FR3j1AZEBRP8",
        "outputId": "318bf56b-0894-4387-dfce-cfcfb7553759",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.stripplot(x=\"stage\", y=\"Bacteroides_vulgatus\", hue=\"stage\", data=singapore)"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb2e381a2d0>"
            ]
          },
          "metadata": {},
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "evM4amIXBtpm"
      },
      "source": [
        "So we see that the bacteria is present in most individuals before antibiotics, gets eradicated in most individuals during antibiotics and pop back up after. But how about the entire ecosystem?\n",
        "\n",
        "## Alpha Diversity\n",
        "\n",
        "Alpha diversity is measure how diverse an ecosystem is, it takes the number of species but also the abundances into account and will give you a single positive number where higher values mean more diverse and lower values mean less diverse. So let's have a look at alpha diversity in data from Singapore and Canada. Here individuals were also classified into \"recoverers\" (R) and \"non-recovereres\" (NR)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Ub72Bfr-tnTW",
        "outputId": "ff02ee5a-3a93-4446-b16c-000084bf61ed"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "adiv = pd.read_excel(\"antibiotics.xlsx\", \"Alpha Diversity (long)\")\n",
        "adiv"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>stage</th>\n",
              "      <th>sample_id</th>\n",
              "      <th>Status</th>\n",
              "      <th>country</th>\n",
              "      <th>diversity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>pre</td>\n",
              "      <td>135EA</td>\n",
              "      <td>R</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>1.237418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>during</td>\n",
              "      <td>135EA</td>\n",
              "      <td>R</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>1.528318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>post</td>\n",
              "      <td>135EA</td>\n",
              "      <td>R</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>3.176266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>pre</td>\n",
              "      <td>148JV</td>\n",
              "      <td>R</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>3.076906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>during</td>\n",
              "      <td>148JV</td>\n",
              "      <td>R</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>2.220140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>during</td>\n",
              "      <td>P4</td>\n",
              "      <td>NR</td>\n",
              "      <td>Canada</td>\n",
              "      <td>0.899459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>post</td>\n",
              "      <td>P4</td>\n",
              "      <td>NR</td>\n",
              "      <td>Canada</td>\n",
              "      <td>1.537152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>pre</td>\n",
              "      <td>P5</td>\n",
              "      <td>NR</td>\n",
              "      <td>Canada</td>\n",
              "      <td>1.972920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>during</td>\n",
              "      <td>P5</td>\n",
              "      <td>NR</td>\n",
              "      <td>Canada</td>\n",
              "      <td>1.652205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>post</td>\n",
              "      <td>P5</td>\n",
              "      <td>NR</td>\n",
              "      <td>Canada</td>\n",
              "      <td>2.139639</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>79 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     stage sample_id Status    country  diversity\n",
              "0      pre     135EA      R  Singapore   1.237418\n",
              "1   during     135EA      R  Singapore   1.528318\n",
              "2     post     135EA      R  Singapore   3.176266\n",
              "3      pre     148JV      R  Singapore   3.076906\n",
              "4   during     148JV      R  Singapore   2.220140\n",
              "..     ...       ...    ...        ...        ...\n",
              "74  during        P4     NR     Canada   0.899459\n",
              "75    post        P4     NR     Canada   1.537152\n",
              "76     pre        P5     NR     Canada   1.972920\n",
              "77  during        P5     NR     Canada   1.652205\n",
              "78    post        P5     NR     Canada   2.139639\n",
              "\n",
              "[79 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ygbi_26RA2LK"
      },
      "source": [
        "So you see there is a single value for each measurement now. We will visualize this with the `relplot` function from `seaborn` using a line plot. This will draw a line for the mean value and shaded areas for the standard deviation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "dtyv2mHfuvtz",
        "outputId": "8585a1f4-d391-432a-d4e1-726ada1cd8c2"
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.relplot(x=\"stage\", y=\"diversity\", hue=\"Status\", kind=\"line\", col=\"country\", data=adiv)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fb2e3bc5d50>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 770.375x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aTGgjyRmDYDl"
      },
      "source": [
        "So we the same behavior for Singapore again. Most people start at decent gut microbial diversity, then dip during antibiotic treatment and recover after. However you see that the \"recoverers\" end up at a higher diversity then the non-recoverers. This is similar in the Canadian population but the recoverers also tend to have a more diverse microbiome before the antibiotic treatment.\n",
        "\n",
        "The one thing that is similar between the two countries are the differences after antibiotics. This becomes super clear if we only visualize the post-antibiotics samples."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0IXYVx1V-cME",
        "outputId": "69164d62-4915-4c97-ccc6-5a73a8de458a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        }
      },
      "source": [
        "sns.catplot(x=\"Status\", y=\"diversity\", data=adiv[adiv.stage == \"post\"], col=\"country\")"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fb2e62af4d0>"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xURJWRVEEQl_"
      },
      "source": [
        "## Keystone taxa\n",
        "\n",
        "So what could be driving those differences? Well let's have a look at the abundances of two species of interest. What we can ask is if those species are maybe more abundant in recoverers than non-recoverers. If that were true we might postulate that those bacteria help us to recover after antibiotics. \n",
        "\n",
        "Let's start with *Bifidobacterium adolescentis* which is in general pretty low abundance (~0.5% of bacteria in our gut). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "7UQDSqJNwmtT",
        "outputId": "35d0f415-c967-4825-8476-2e5c113a1925"
      },
      "source": [
        "keystone = pd.read_excel(\"https://git.io/JuaSY\", \"Keystone Taxa Singapore\")\n",
        "\n",
        "sns.barplot(x=\"Status\", y=\"Bifidobacterium_adolescentis\", data=keystone, log=True)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb2e34618d0>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vwhBc86MFaGL"
      },
      "source": [
        "Okay that looks like a difference for sure. The bars denote the average abundance and the lines the standard error. Let's also have a look at another species, *Bacteroides thetaiotaomicron*. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "3YVicLoMxk5H",
        "outputId": "21e15193-e01c-4c88-fc16-c3f9739dd49f"
      },
      "source": [
        "sns.barplot(x=\"Status\", y=\"Bacteroides_thetaiotaomicron\", data=keystone, log=True)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb2e342edd0>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eW6IaousFwnJ"
      },
      "source": [
        "Similar behavior but a bit more noisy."
      ]
    }
  ]
}