{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "NUTS-LIGO-catalog.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "All the packages we need..."
      ],
      "metadata": {
        "id": "XcZRZbyaQJNN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "import numpy as np\n",
        "import scipy.interpolate\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as pp\n",
        "import matplotlib.mlab as mlab\n",
        "\n",
        "import h5py\n",
        "import requests\n",
        "import json"
      ],
      "metadata": {
        "id": "vAg6bnTFeF0a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Get catalogs"
      ],
      "metadata": {
        "id": "Reedau4YhJRg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We're going to download the content of pages like https://www.gw-openscience.org/eventapi/html/GWTC-3-confident, but in machine-readable form"
      ],
      "metadata": {
        "id": "W6mfmsQkQe6w"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "svn_frSFC62w"
      },
      "outputs": [],
      "source": [
        "!wget -q -O gwtc-3.csv https://www.gw-openscience.org/eventapi/csv/GWTC-3-confident"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -q -O gwtc-21.csv https://www.gw-openscience.org/eventapi/csv/GWTC-2.1-confident"
      ],
      "metadata": {
        "id": "v0cyEyrHDBsG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -q -O gwtc-1.csv https://www.gw-openscience.org/eventapi/csv/GWTC-1-confident"
      ],
      "metadata": {
        "id": "VKQk3L_XDM0C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We read the three CSV files, merge them, and index them by event name."
      ],
      "metadata": {
        "id": "YkNMig8oQi_S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gwtc = pd.concat([pd.read_csv(filename)\n",
        "                  for filename in ['gwtc-1.csv', 'gwtc-21.csv', 'gwtc-3.csv']]).set_index('commonName').sort_index()"
      ],
      "metadata": {
        "id": "hLtS6LGjDQey"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "It's a huge table..."
      ],
      "metadata": {
        "id": "IRgNo69wQomb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gwtc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 684
        },
        "id": "Ihx7Ht2JeCuS",
        "outputId": "b96ab6a0-a016-4d37-d211-5d51b816792c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 id  version catalog.shortName           GPS  \\\n",
              "commonName                                                                     \n",
              "GW150914                GW150914-v3        3  GWTC-1-confident  1.126259e+09   \n",
              "GW151012                GW151012-v3        3  GWTC-1-confident  1.128679e+09   \n",
              "GW151226                GW151226-v2        2  GWTC-1-confident  1.135136e+09   \n",
              "GW170104                GW170104-v2        2  GWTC-1-confident  1.167560e+09   \n",
              "GW170608                GW170608-v3        3  GWTC-1-confident  1.180922e+09   \n",
              "...                             ...      ...               ...           ...   \n",
              "GW200306_093714  GW200306_093714-v1        1  GWTC-3-confident  1.267523e+09   \n",
              "GW200308_173609  GW200308_173609-v1        1  GWTC-3-confident  1.267724e+09   \n",
              "GW200311_115853  GW200311_115853-v1        1  GWTC-3-confident  1.267963e+09   \n",
              "GW200316_215756  GW200316_215756-v1        1  GWTC-3-confident  1.268431e+09   \n",
              "GW200322_091133  GW200322_091133-v1        1  GWTC-3-confident  1.268904e+09   \n",
              "\n",
              "                                         reference  \\\n",
              "commonName                                           \n",
              "GW150914         https://doi.org/10.7935/82H3-HH23   \n",
              "GW151012         https://doi.org/10.7935/82H3-HH23   \n",
              "GW151226         https://doi.org/10.7935/82H3-HH23   \n",
              "GW170104         https://doi.org/10.7935/82H3-HH23   \n",
              "GW170608         https://doi.org/10.7935/82H3-HH23   \n",
              "...                                            ...   \n",
              "GW200306_093714                           /GWTC-3/   \n",
              "GW200308_173609                           /GWTC-3/   \n",
              "GW200311_115853                           /GWTC-3/   \n",
              "GW200316_215756                           /GWTC-3/   \n",
              "GW200322_091133                           /GWTC-3/   \n",
              "\n",
              "                                                           jsonurl  \\\n",
              "commonName                                                           \n",
              "GW150914         https://www.gw-openscience.org/eventapi/json/G...   \n",
              "GW151012         https://www.gw-openscience.org/eventapi/json/G...   \n",
              "GW151226         https://www.gw-openscience.org/eventapi/json/G...   \n",
              "GW170104         https://www.gw-openscience.org/eventapi/json/G...   \n",
              "GW170608         https://www.gw-openscience.org/eventapi/json/G...   \n",
              "...                                                            ...   \n",
              "GW200306_093714  https://www.gw-openscience.org/eventapi/json/G...   \n",
              "GW200308_173609  https://www.gw-openscience.org/eventapi/json/G...   \n",
              "GW200311_115853  https://www.gw-openscience.org/eventapi/json/G...   \n",
              "GW200316_215756  https://www.gw-openscience.org/eventapi/json/G...   \n",
              "GW200322_091133  https://www.gw-openscience.org/eventapi/json/G...   \n",
              "\n",
              "                 mass_1_source  mass_1_source_lower  mass_1_source_upper  \\\n",
              "commonName                                                                 \n",
              "GW150914                  35.6                 -3.1                  4.7   \n",
              "GW151012                  23.2                 -5.5                 14.9   \n",
              "GW151226                  13.7                 -3.2                  8.8   \n",
              "GW170104                  30.8                 -5.6                  7.3   \n",
              "GW170608                  11.0                 -1.7                  5.5   \n",
              "...                        ...                  ...                  ...   \n",
              "GW200306_093714           28.3                 -7.7                 17.1   \n",
              "GW200308_173609           36.4                 -9.6                 11.2   \n",
              "GW200311_115853           34.2                 -3.8                  6.4   \n",
              "GW200316_215756           13.1                 -2.9                 10.2   \n",
              "GW200322_091133           34.0                -18.0                 48.0   \n",
              "\n",
              "                 mass_2_source  ...  redshift_upper           far  far_lower  \\\n",
              "commonName                      ...                                            \n",
              "GW150914                  30.6  ...            0.03  1.000000e-07        NaN   \n",
              "GW151012                  13.6  ...            0.09  7.920000e-03        NaN   \n",
              "GW151226                   7.7  ...            0.04  1.000000e-07        NaN   \n",
              "GW170104                  20.0  ...            0.08  1.000000e-07        NaN   \n",
              "GW170608                   7.6  ...            0.02  1.000000e-07        NaN   \n",
              "...                        ...  ...             ...           ...        ...   \n",
              "GW200306_093714           14.8  ...            0.24  2.400000e+01        NaN   \n",
              "GW200308_173609           13.8  ...            0.32  2.400000e+00        NaN   \n",
              "GW200311_115853           27.7  ...            0.05  1.000000e-05        NaN   \n",
              "GW200316_215756            7.8  ...            0.08  1.000000e-05        NaN   \n",
              "GW200322_091133           14.0  ...            0.84  1.400000e+02        NaN   \n",
              "\n",
              "                 far_upper   p_astro  p_astro_lower  p_astro_upper  \\\n",
              "commonName                                                           \n",
              "GW150914               NaN  1.000000            NaN            NaN   \n",
              "GW151012               NaN  1.000000            NaN            NaN   \n",
              "GW151226               NaN  1.000000            NaN            NaN   \n",
              "GW170104               NaN  1.000000            NaN            NaN   \n",
              "GW170608               NaN  1.000000            NaN            NaN   \n",
              "...                    ...       ...            ...            ...   \n",
              "GW200306_093714        NaN  0.240040            NaN            NaN   \n",
              "GW200308_173609        NaN  0.856600            NaN            NaN   \n",
              "GW200311_115853        NaN  0.990000            NaN            NaN   \n",
              "GW200316_215756        NaN  0.990000            NaN            NaN   \n",
              "GW200322_091133        NaN  0.077572            NaN            NaN   \n",
              "\n",
              "                 final_mass_source  final_mass_source_lower  \\\n",
              "commonName                                                    \n",
              "GW150914                      63.1                     -3.0   \n",
              "GW151012                      35.6                     -3.8   \n",
              "GW151226                      20.5                     -1.5   \n",
              "GW170104                      48.9                     -4.0   \n",
              "GW170608                      17.8                     -0.7   \n",
              "...                            ...                      ...   \n",
              "GW200306_093714               41.7                     -6.9   \n",
              "GW200308_173609               47.4                     -7.7   \n",
              "GW200311_115853               59.0                     -3.9   \n",
              "GW200316_215756               20.2                     -1.9   \n",
              "GW200322_091133               53.0                    -26.0   \n",
              "\n",
              "                 final_mass_source_upper  \n",
              "commonName                                \n",
              "GW150914                             3.4  \n",
              "GW151012                            10.8  \n",
              "GW151226                             6.4  \n",
              "GW170104                             5.1  \n",
              "GW170608                             3.4  \n",
              "...                                  ...  \n",
              "GW200306_093714                     12.3  \n",
              "GW200308_173609                     11.1  \n",
              "GW200311_115853                      4.8  \n",
              "GW200316_215756                      7.4  \n",
              "GW200322_091133                     38.0  \n",
              "\n",
              "[90 rows x 42 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a5960cfa-f9a5-4f37-8b77-79897fdb6a1b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>version</th>\n",
              "      <th>catalog.shortName</th>\n",
              "      <th>GPS</th>\n",
              "      <th>reference</th>\n",
              "      <th>jsonurl</th>\n",
              "      <th>mass_1_source</th>\n",
              "      <th>mass_1_source_lower</th>\n",
              "      <th>mass_1_source_upper</th>\n",
              "      <th>mass_2_source</th>\n",
              "      <th>...</th>\n",
              "      <th>redshift_upper</th>\n",
              "      <th>far</th>\n",
              "      <th>far_lower</th>\n",
              "      <th>far_upper</th>\n",
              "      <th>p_astro</th>\n",
              "      <th>p_astro_lower</th>\n",
              "      <th>p_astro_upper</th>\n",
              "      <th>final_mass_source</th>\n",
              "      <th>final_mass_source_lower</th>\n",
              "      <th>final_mass_source_upper</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>commonName</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>GW150914</th>\n",
              "      <td>GW150914-v3</td>\n",
              "      <td>3</td>\n",
              "      <td>GWTC-1-confident</td>\n",
              "      <td>1.126259e+09</td>\n",
              "      <td>https://doi.org/10.7935/82H3-HH23</td>\n",
              "      <td>https://www.gw-openscience.org/eventapi/json/G...</td>\n",
              "      <td>35.6</td>\n",
              "      <td>-3.1</td>\n",
              "      <td>4.7</td>\n",
              "      <td>30.6</td>\n",
              "      <td>...</td>\n",
              "      <td>0.03</td>\n",
              "      <td>1.000000e-07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>63.1</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>3.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GW151012</th>\n",
              "      <td>GW151012-v3</td>\n",
              "      <td>3</td>\n",
              "      <td>GWTC-1-confident</td>\n",
              "      <td>1.128679e+09</td>\n",
              "      <td>https://doi.org/10.7935/82H3-HH23</td>\n",
              "      <td>https://www.gw-openscience.org/eventapi/json/G...</td>\n",
              "      <td>23.2</td>\n",
              "      <td>-5.5</td>\n",
              "      <td>14.9</td>\n",
              "      <td>13.6</td>\n",
              "      <td>...</td>\n",
              "      <td>0.09</td>\n",
              "      <td>7.920000e-03</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35.6</td>\n",
              "      <td>-3.8</td>\n",
              "      <td>10.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GW151226</th>\n",
              "      <td>GW151226-v2</td>\n",
              "      <td>2</td>\n",
              "      <td>GWTC-1-confident</td>\n",
              "      <td>1.135136e+09</td>\n",
              "      <td>https://doi.org/10.7935/82H3-HH23</td>\n",
              "      <td>https://www.gw-openscience.org/eventapi/json/G...</td>\n",
              "      <td>13.7</td>\n",
              "      <td>-3.2</td>\n",
              "      <td>8.8</td>\n",
              "      <td>7.7</td>\n",
              "      <td>...</td>\n",
              "      <td>0.04</td>\n",
              "      <td>1.000000e-07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.5</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>6.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GW170104</th>\n",
              "      <td>GW170104-v2</td>\n",
              "      <td>2</td>\n",
              "      <td>GWTC-1-confident</td>\n",
              "      <td>1.167560e+09</td>\n",
              "      <td>https://doi.org/10.7935/82H3-HH23</td>\n",
              "      <td>https://www.gw-openscience.org/eventapi/json/G...</td>\n",
              "      <td>30.8</td>\n",
              "      <td>-5.6</td>\n",
              "      <td>7.3</td>\n",
              "      <td>20.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1.000000e-07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>48.9</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>5.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GW170608</th>\n",
              "      <td>GW170608-v3</td>\n",
              "      <td>3</td>\n",
              "      <td>GWTC-1-confident</td>\n",
              "      <td>1.180922e+09</td>\n",
              "      <td>https://doi.org/10.7935/82H3-HH23</td>\n",
              "      <td>https://www.gw-openscience.org/eventapi/json/G...</td>\n",
              "      <td>11.0</td>\n",
              "      <td>-1.7</td>\n",
              "      <td>5.5</td>\n",
              "      <td>7.6</td>\n",
              "      <td>...</td>\n",
              "      <td>0.02</td>\n",
              "      <td>1.000000e-07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.8</td>\n",
              "      <td>-0.7</td>\n",
              "      <td>3.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GW200306_093714</th>\n",
              "      <td>GW200306_093714-v1</td>\n",
              "      <td>1</td>\n",
              "      <td>GWTC-3-confident</td>\n",
              "      <td>1.267523e+09</td>\n",
              "      <td>/GWTC-3/</td>\n",
              "      <td>https://www.gw-openscience.org/eventapi/json/G...</td>\n",
              "      <td>28.3</td>\n",
              "      <td>-7.7</td>\n",
              "      <td>17.1</td>\n",
              "      <td>14.8</td>\n",
              "      <td>...</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.400000e+01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.240040</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>41.7</td>\n",
              "      <td>-6.9</td>\n",
              "      <td>12.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GW200308_173609</th>\n",
              "      <td>GW200308_173609-v1</td>\n",
              "      <td>1</td>\n",
              "      <td>GWTC-3-confident</td>\n",
              "      <td>1.267724e+09</td>\n",
              "      <td>/GWTC-3/</td>\n",
              "      <td>https://www.gw-openscience.org/eventapi/json/G...</td>\n",
              "      <td>36.4</td>\n",
              "      <td>-9.6</td>\n",
              "      <td>11.2</td>\n",
              "      <td>13.8</td>\n",
              "      <td>...</td>\n",
              "      <td>0.32</td>\n",
              "      <td>2.400000e+00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.856600</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>47.4</td>\n",
              "      <td>-7.7</td>\n",
              "      <td>11.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GW200311_115853</th>\n",
              "      <td>GW200311_115853-v1</td>\n",
              "      <td>1</td>\n",
              "      <td>GWTC-3-confident</td>\n",
              "      <td>1.267963e+09</td>\n",
              "      <td>/GWTC-3/</td>\n",
              "      <td>https://www.gw-openscience.org/eventapi/json/G...</td>\n",
              "      <td>34.2</td>\n",
              "      <td>-3.8</td>\n",
              "      <td>6.4</td>\n",
              "      <td>27.7</td>\n",
              "      <td>...</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1.000000e-05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>59.0</td>\n",
              "      <td>-3.9</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GW200316_215756</th>\n",
              "      <td>GW200316_215756-v1</td>\n",
              "      <td>1</td>\n",
              "      <td>GWTC-3-confident</td>\n",
              "      <td>1.268431e+09</td>\n",
              "      <td>/GWTC-3/</td>\n",
              "      <td>https://www.gw-openscience.org/eventapi/json/G...</td>\n",
              "      <td>13.1</td>\n",
              "      <td>-2.9</td>\n",
              "      <td>10.2</td>\n",
              "      <td>7.8</td>\n",
              "      <td>...</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1.000000e-05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.2</td>\n",
              "      <td>-1.9</td>\n",
              "      <td>7.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GW200322_091133</th>\n",
              "      <td>GW200322_091133-v1</td>\n",
              "      <td>1</td>\n",
              "      <td>GWTC-3-confident</td>\n",
              "      <td>1.268904e+09</td>\n",
              "      <td>/GWTC-3/</td>\n",
              "      <td>https://www.gw-openscience.org/eventapi/json/G...</td>\n",
              "      <td>34.0</td>\n",
              "      <td>-18.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.84</td>\n",
              "      <td>1.400000e+02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.077572</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53.0</td>\n",
              "      <td>-26.0</td>\n",
              "      <td>38.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>90 rows × 42 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a5960cfa-f9a5-4f37-8b77-79897fdb6a1b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a5960cfa-f9a5-4f37-8b77-79897fdb6a1b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a5960cfa-f9a5-4f37-8b77-79897fdb6a1b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With this we can make plots already."
      ],
      "metadata": {
        "id": "Zf2iXm5_QqaB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gwtc.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mDxfyyUbQuiZ",
        "outputId": "8511b530-7d3a-40f2-c629-72b6b1b16ac8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'version', 'catalog.shortName', 'GPS', 'reference', 'jsonurl',\n",
              "       'mass_1_source', 'mass_1_source_lower', 'mass_1_source_upper',\n",
              "       'mass_2_source', 'mass_2_source_lower', 'mass_2_source_upper',\n",
              "       'network_matched_filter_snr', 'network_matched_filter_snr_lower',\n",
              "       'network_matched_filter_snr_upper', 'luminosity_distance',\n",
              "       'luminosity_distance_lower', 'luminosity_distance_upper', 'chi_eff',\n",
              "       'chi_eff_lower', 'chi_eff_upper', 'total_mass_source',\n",
              "       'total_mass_source_lower', 'total_mass_source_upper',\n",
              "       'chirp_mass_source', 'chirp_mass_source_lower',\n",
              "       'chirp_mass_source_upper', 'chirp_mass', 'chirp_mass_lower',\n",
              "       'chirp_mass_upper', 'redshift', 'redshift_lower', 'redshift_upper',\n",
              "       'far', 'far_lower', 'far_upper', 'p_astro', 'p_astro_lower',\n",
              "       'p_astro_upper', 'final_mass_source', 'final_mass_source_lower',\n",
              "       'final_mass_source_upper'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gwtc.plot.scatter('mass_1_source', 'mass_2_source', marker='.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "Afcje8FuDi-g",
        "outputId": "13bfdeb9-09b3-4231-9ef1-d40caef9751d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b01f05790>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gwtc.plot.scatter('chirp_mass_source', 'redshift', marker='.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "eNNbljQqnYbY",
        "outputId": "4b7a39fc-a086-4f42-ff89-94d0546b8ba6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b01defe90>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gwtc.plot.scatter('chirp_mass_source', 'luminosity_distance', marker='.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "SwCIGFR7nh8s",
        "outputId": "00b09354-f064-4ac2-b857-0ae110d6c99c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b0193ad10>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pp.loglog(gwtc.chirp_mass_source * (1 + gwtc.redshift), gwtc.luminosity_distance, '.')\n",
        "pp.loglog(np.linspace(1,100),5 * np.linspace(1,100)**(5/3));\n",
        "pp.xlabel('redshifted chirp mass')\n",
        "pp.ylabel('luminosity distance')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "cwhutIntoqrR",
        "outputId": "a9973866-a424-4f0f-b744-4b2c697c386f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'luminosity distance')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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zceejWkgNa7BKqpn9y8w6VnveycxGhPlwMxthZl+b2cwa2w81szlmNtfMrgVw9w/dfShwArBXbZ8nkgk1K4JWNjsU2o+vdqOq8NmQmjFVFq27c9IcTnlgStNWGlu7CiZcDf8+Glq2hXOeh/2vrTMh1Px7lcwr46lpC6rWR2hRWBAqIaQtfmmSMM1HO7r78son7l5mZruE/PyRwD+Ahys3mFkhMAw4GFgITDWzce4+28yOBM4H/h3y80XSqqFZtpUJobL0Qlzt1E0tWlenhSUweggsnQu7n8/0bS7hrY9XMbCirM6RRTX/XlNKl1KRCFKCAccP6N5gLOoEzh5hkkKBmXVy9zIAM9s45Ptw91fNrHeNzbsBc929NPl5jwNHAbPdfRwwzsyeAf4T7lcQaZz6mikammVb20kwrnbqphSt+5F15fDKn+C1O6FdNzh9HCWFOzbYNFbb36tmojyuf/cGD69O4OwR5uR+J/CWmT1FMvED/9eEY24OLKj2fCGwu5ntDxwLtAYm1PVmMxsCDAHo2bNnE8KQ5qihPoCGTk61nQQvPKBP7IXvKu8cnp6+EKt1j3p8/SGMPg8Wvw87nQyDboMNOjAlRJmJ2v5eA3p14rdHbM+zMxczqF+3Ji3nKZkXpqP5YTMrAQ5IbjrW3WenOxB3fxl4OcR+w4HhEExeS3cckt8aaqZo6ORUfclKM6NT21aRxtvYjuxRyWUtnw6zrGUiAVPugRdvhdYbwS8fge0GV70c5uq9tgRQMq+sqpDd1M+Xse2m7VJezlMyL2zto4+Assr9zaynu89P8ZiLgB7VnndPbgtNazRLqsKe6Oo7Oe2zdVde+uhr1iWcW8fPCn3SS0Vj2tob1S5fNg/GXADzXodtD4PBd8FGP1lvlzBX77UlAPUP5LYwtY8uBm4CviKYyVy5fOuOKR5zKrC1mW1BkAxOBE5uzAe4ezFQXFRUdG6KMUgz1ZRmisqr9sqhlhD9Sa8xbe2h9nWHdx+B564Lnh81DHY+Baz2RqeGEmSYPgX1D+SWMHcKlwLbunujx9qZ2WPA/kAXM1sI3OTuD5rZRcBEoBAY4e6zGvvZIqmq60TXUNv9lNKl6yUEAwoLC1i0/AdK5tU+OicdsYZNYg3u+93XUHwpzJkAvfaGo++BTr2aFF9dfQrqH8hdYQriTQYOdveKzITUsGrNR+d+8skncYcjeSBM2/1/3p7P9aNnVD3frXcn3luwnIqEx17Xp0EfFgcJYc13cOBvYeAFDZapCCtbCt5JeE0tiFcKvJwcJrqmcqO7/yVN8TWamo9yR66cMMK0g5etWkuBQcKDWbqtWxZSkfDI2s7T8rdbvQKevQbefwy67QTHDIef/DRtMYI6iPNNmKQwP/nTKvkjEko2VchsSJh28Jr7DOrXjamfL4uk7Twtf7vSl2HMhfDtYtj3atjv6tBlKqT5CjMk9ZZMBCL5J5dGoYRpB69tn8rRNum+E2rS3678B3jhFnj7XujcB86eBN1rbSkQ+ZEwo4+6AlcD2wMbVG53959FGFdDMWlIag7ItVEoDTWD1NacE1XTScp/u0XTg4loSz4OylsfdDO0apv2+CR/helongQ8AVxJUEb7V8A37n5N9OHVTyuvZb9c6VNoSBxNYY36260rD0pUvPInaLdpMNR0qwPqf480W03taO6cHEZ6qbu/ArxiZlPTG6Lkq3zphIyjKSz03+6bj4Midl+8CzueCINuhzYdG35fI+VLgpf6hUkK5cl/F5vZ4cAXQO1r74nkqaxsCksk4J374YWbgxLXJzwMfY+K5FC5NGhAmiZMUvi9mXUAfg38HWgPXBZpVA1Qn0J+SNeVZ6YWZ8mqCVnLF8DYC+CzV2GbQ2Hw3dBuk8gOl0uDBqRpwiSFMndfAawgWRTPzGJdBEfzFHJfuq48M3kFmxVNYe7w/uPBEpmeCJJB/9PrLFORLll5pySRCJMU/g70D7FNJLR0XXk2qyvY75cEs5I/Gg8994Rj7oVOvTNy6Ky7U5LI1JkUzGwPYE+gq5ldUe2l9gQ1i0RSlq4rz2ZzBfvRM0FCWL0CDvl9skxFZv83zIo7JYlcfXcKrYCNkvu0q7Z9JcFCOyIpS9eVZ95fwa5eGVQ0fe8R2HQHOH0cbNI37qgkj4WZp9DL3eclHxcAG7n7ykwEV09MKognGZfxIZmfvRasebByIex9Bex3DbRQpRlpuvrmKYQpk/hHM2tvZhsCM4HZZnZVWiNsJHcvdvchHTp0iDMMaUYqO7TvnDSHUx6YQsm8sugOVr4anrse/nVEUKvorElw4I05kRBK5pUxbPLcaP8+EqkwHc193X2lmZ0CPAtcC5QAd0QamUgWyViH9hfvwuih8M1HsOu5cPAt0GrD9B8nAprLkB/C3Cm0NLOWwNHAOHcvB7Q2suSFsFe2lR3ahUY0HdrrKoISFQ8cFPQjnDoKDv9zziQEqD1xSu4Jc6dwP/A58D7wqpn1IuhsFslpjbmyjbRDe8knQRG7RSWwwy/gsDugTe5dYTebkWB5Lkzp7LuBu6ttmmdmsVba0oxmSYfGNgmlfUhmIgFT/wnP/xZatoHjH4J+x6bv8zMs70eCNRP1zVM41d0fqTFHoTqtvCY5LdYr2xULg5FFn70CWx8CR/49qG6a4zSXIffVd6dQ2ZjZrp59RHJWfVe2kQ0/dYcPnoAJV0OiAgbfBf1/FXmZCpGwGpynkM20noJEIbJRNN8vgfGXwYfF0GNgUKZi4y2b/rkijZTSegpmdnddrwG4+yVNDUwkW1S/M4hk+OmcZ2HcJbB6ORx0C+x5ccbLVIiEUV/zUUny372AvgSrrwH8ApgdZVAimVTzzuC3R2yfvr6G1Sth4vXw7r9hk35w2mjYtF/6ghdJszqTgrv/C8DMzgf2dveK5PP7gNcyE55I9GreGZStWpueUTSfvwFjhgadyntfAftfCy1apzd4kTQLM0+hE0Fl1GXJ5xslt0kO0BKKDattFFKTRtGUr4aXfgdvDQtKW5/5HPTcPa0xi0QlTFK4DXjXzCYDBuwL3BxlUA3RPIVwVHYgnLSOr1/8Pow6D775EIrOhoNvhdYbpS9YkYiFmbz2kJk9C1Re6lzj7l9GG1aDMWmeQgjNagGaJmry+Pp1FfDGX+Hl26BtFzjladj6oPQFKJIhYe4USCaBsRHHImmmsgMZsmRuskzFNNj+WDj8Tmi7cdxRiaQkVFKQ3KSyAxFzh6kPwKQbgw7k4x6EHbT+lOQ2JYU8p7IDEVmxCMZeCKWToc9BcOQ/oH23uKMSabIGk4KZ3QmMcPdZGYhHpNEyOsLKHWY8Bc9cCYlyOPwvUHSWylRI3ghzp/AhMNzMWgAPAY+5+4powxIJJ6MjrL5fCs9cDrPHQvfd4Jj7oPNW0RxLJCYNLrLj7g+4+17A6UBv4AMz+0/c5bNFIIMLu3w8Ee7dAz6aAAfeBGc9p4QgeSnMymuYWSHw0+TPEoIFd64ws8cjjE2kQZGviLbm26Bm0X9OCIaaDpkM+1yhukWSt8L0KfwVOAJ4CfiDu7+TfOl2M5sTZXAiDYl0hNW8t4Khpsvnw16XwQHXq0yF5L0wfQofADe4+/e1vLZbmuMJRTOapbq0j7CqWAMv/R7e/Dt06gVnPgu99kjf54tksTDNR6fWTAhm9iJAXB3O7l7s7kM6dOgQx+Elny3+AIbvD2/eDQN+BUNfV0KQZqW+9RQ2ANoCXcysE0HdIwiK422egdhEMmddBbx5F0z+YzAb+eSnYJtD4o5KJOPqaz46D7gM2AyYXm37SuAfUQYlklFLP4XRQ2HhO9D3aDjirypTIc1Wfesp3AXcZWYXu/vfMxiTSGa4w7QRMOkGKGwZlKnod5wmokmzVl/z0c/c/SVgkZkdW/N1dx8VaWQiUVr5BYy9CD59Ebb6GRw1DNpvFndUIrGrr/loP4JhqINrec0BJYV6aHGbLDbjv/DMr4NRRof9GXY9R3cHIkn1NR/dlPz3zMyFkx+0uE2WWrUsSAazRkH3XeGY+zUrWaSGBoekmtmlZtbeAg+Y2XQz07CMemSs9IKE98kLcM8e8OE4+NmNwRKZSggiPxJmnsJZ7r4SOAToDJxGsESn1CHy0gsS3prvYPzl8Ohx0KYTnPsS7HslFKpqvEhtwvyfUdnYehjwsLvPMlMDbH20uE2WmP92UKai7HPY82I44AZouUHcUYlktTBJocTMJgFbANeZWTsgEW1YuU+L28SoYg28/Ed44y7o0B3OeAZ67xV3VCI5IUxSOBvYGSh191Vm1hlQ57Nkpy9nBncHX82E/qfDz/8ArdvFHZVIzmgwKbh7wsy6AycnW41ecffiyCMTaYzEuqCA3eT/gw06wklPwLaHxh2VSM4JUzr7NmBX4NHkpkvMbA93vz7SyETCWlYKo8+HBVNguyPhiL/BhurcF0lFmOajw4Cd3T0BYGb/At4F0p4UzOxo4HCConsPuvukdB9D8og7lIyEib+BghZwzHDY8QRNRBNpglArrwEdqz1uVL1qMxthZl+b2cwa2w81szlmNtfMrgVw9zHufi4wFPhlY44jzcy3XwaroY2/DLoXwQVvwk6/VEIQaaIwdwp/BN41s8kEw1P3Ba5txDFGElRVfbhyQ3J5z2HAwcBCYKqZjXP32cldbki+LvJjs0YHcw/KV8OgO4IyFQVhr29EpD5hOpofM7OXCfoVAK5x9y/DHsDdXzWz3jU27wbMdfdSgORaz0eZ2YcEE+Oedffp1MLMhgBDAHr27Bk2DMkHP5TBhKtgxlOw+YCgTEWXreOOSiSvhJ3WWQAsSe6/jZlt4+6vNuG4mwMLqj1fCOwOXAwcBHQwsz7ufl/NN7r7cGA4QFFRkTchBsklc1+EsRfC998Ek9D2vlyzkkUiEGb00e0E7fuz+N+kNQeakhRq5e53A3en+3Mlh639Hp7/LUx9ALr+FE56HDbbOe6oRPJWmEuto4Ft3X1NGo+7COhR7Xn35LZQzGwwMLhPnz5pDEmyzoJ3goloyz6DPS4KCtmpTIVIpML0zpUCLdN83KnA1ma2hZm1Ak4ExoV9s7sXu/uQDh0aNRBKckXFWnjxVhjx82Dt5F8Vw8//TwlBJAPC3CmsAt4zsxeBqrsFd78kzAHM7DFgf6CLmS0EbnL3B83sImAiUAiMcPdZjQ1e8tBXs2H0EPhyBuxyKvz8j7BB+7ijEmk2wiSFcTTiKr4mdz+pju0TgAmpfKaaj/JQYh28NQxe+h1s0AFOfAx+eljcUYk0O+aeuwN4ioqKfNq0aXGHIU1V9nlQpmL+m/DTI2DwXbBhl7ijEslbZlbi7kW1vVbnnYKZPenuJ5jZDILRRutx9x3TGKM0R+4w/WGYeD1YARx9H+x0omYli8SovuajS5P/HpGJQBpDzUd54NuvoPgS+Pg52GJfOOoe6Nij4feJSKRCNx+ZWXuqJRF3XxZVUGGp+ShHzR4LxZdB+So46BbYbYjKVIhkUErNR9XefB5wC7Ca/zUjObBl2iKU5uGH5fDs1fDBE7DZLkFV067bxB2ViFQTZvTRlUA/d18SdTCSxz6dHJSp+PZL2P962OcKKEz39BcRaaowSeFTgrkKWUN9Cjlk7Sp44WZ4537osg2c8wJs3j/uqESkDg32KZjZLsBDwNukMHktSupTyHILS4KJaEvnwsAL4MDfQss2cUcl0uw1qU8BuB94CZjB/wriidStYi28ege8die03ywoU7HFvnFHJSIhhEkKLd39isgjkfzw9YdBEbvF78POp8ChfwxmKItITgiTFJ5NLmxTzPrNR7EPSZUskkjAlHuCQnat28EvH4Xtsm6Ki4g0IExSqKxddF21bbEOSVVHc5YpmwdjLoB5r8O2hwdlKjbqGndUIpIC1T6S1LnDu4/Ac8nrhUG3w84nq0yFSJZr6uS102vb7u4PNzUwyWHffQ3Fl8KcCdB7Hzj6HuioNbNFcl2Y5qNdqz3eADgQmA4oKTRXHxYHCWHNd8F6B7sPVZkKkTzRYFJw94urPzezjsDjkUWUhUrmlTGldCkDt+zMgF6d4g4nPqtXwLPXwPuPQbedgjIVP/lp3FGJSBqFuVOo6Xtgi3QHkq1K5pVxygNTWFuRoFWLAh49Z2DzTAylL+htDdYAAA4tSURBVMOYC+HbxbDfNbDvVSpTIZKHwvQpFPO/QngFQF/gySiDakgmRx9NKV3K2ooECYfyigRTSpc2r6RQ/gO8cAu8fS907gNnPw/dB8QdlYhEJMydwp+rPa4A5rn7wojiCcXdi4HioqKic6M+1sAtO9OqRQHlFQlatihg4Jadoz5k9lhUAqOHwpKPg36DA2+CVm3jjkpEIhSmT+GVTASSrQb06sSj5wxsXn0K68rh1T8HpSrabQqnjYGtDog7KhHJgPqW4/yWWpbhBAxwd28fWVRZZkCvTs0jGQB8MwdGDYHF78GOJwZzD9p0jDsqEcmQOpOCu7fLZCASs0QC3r4vKHPdakM44WHoe1TcUYlIhqUy+kjyzfL5QZmKz1+DbQ6FwXdDu03ijkpEYqCk0Jy5w3v/CeYe4EEy6H+6ylSINGM5mRRUEC8NvvsGxl8GH42HXnsFZSo69Y47KhGJWU7WJnD3Yncf0qGD6vSn5KNn4J6B8MkkOOT38KvxSggiAuTonYKkaPWKoKLpe4/CpjvCMcWwSd+4oxKRLKKk0Fx89mrQmbxyUVCiYt+roUWruKMSkSyjpJDvyn+AF38HU4bBxlvBWZOgx64Nv09EmiUlhXz2xbsw6jxYMgd2PRcOviWYgyAiUgclhXy0rhxe+wu8+ifY8Cdw6ijoc2DcUYlIDlBSyDdLPgnKVHwxHXY4AQ77E7RpJiU6RKTJlBTyRSIB7wyHF26Clm3gFyNh+2PijkpEcoySQj5YvgDGXhCMMNr6EDjy70F1UxGRRsrJpKAZzUnu8METMOEqSKyDwXdB/1+pTIWIpEwzmnPV90vgydNg9HmwyfZw/hsw4AwlBBFpkpy8U2j2PpoAxZcEM5QPvhX2uAgKCuOOSkTygJJCLlm9EiZeB+8+ApvsAKePDe4SRETSREkhV3z+Oow5H1YshL2vgP2vhRat445KRPKMkkK2K18NL/0O3hoWVDI98znouXvcUYlInlJSyGZfvBd0JH/zERSdHfQftN4o7qhEJI8pKWSjdRXw+l/hldugbRc45WnY+qC4oxKRZkBJIdssmRvcHSyaBv2Og8P+DG03jjsqEWkmlBSyRSIB0x6ESTcGHcjHPQg7HB93VCLSzCgpZIMVi2DshVA6GfocHJSpaN8t7qhEpBlSUoiTO8x4Cp65EhLlcMRfYcCZmpUsIrFRUojL90vhmcth9ljosTscfS903iruqESkmVNSiMOc52DcxfBDGRx0M+x5icpUiEhWUFLIpDXfwsTrYfrD8JPt4bRRsOkOcUclIlIla5KCmW0J/Abo4O75N+xm3psweigsnw97XQYHXK8yFSKSdSItnW1mI8zsazObWWP7oWY2x8zmmtm1AO5e6u5nRxlPLMpXB8NMHzos6EA+81k4+BYlBBHJSlGvpzASOLT6BjMrBIYBg4C+wElm1jfiOOKx+AP45wHw5t3BWgdD34Bee8QdlYhInSJtPnL3V82sd43NuwFz3b0UwMweB44CZof5TDMbAgwB6NmzZ9piTat1FfDG3+Dl24LZyCc/BdscEndUIiINimPltc2BBdWeLwQ2N7POZnYfsIuZXVfXm919uLsXuXtR165do4618ZZ+Cg8NCiqb/vRwuGCKEoKI5Iys6Wh296XA0LjjSJn7/8pUFLYMylT0O04T0UQkp8SRFBYBPao9757cFpqZDQYG9+nTJ51xpW7lFzD2Ivj0RdjqZ3DUMGi/WdxRiYg0WhzNR1OBrc1sCzNrBZwIjGvMB7h7sbsP6dChQyQBNsqM/8I9A4Mhp4f9GU4dpYQgIjkr0jsFM3sM2B/oYmYLgZvc/UEzuwiYCBQCI9x9VpRxRGLVMnjmCpg1GrrvCsfcrzIVIpLzoh59dFId2ycAE1L93Nibjz6eBOMuglVL4Wc3BpPRCrOme0ZEJGVxNB81WWzNR2u+g+JL4T+/gDYbw7kvwb5XKiGISN7Q2Sys+VOCFdHK5sGeF8MBN0DLDeKOSkQkrXIyKWS0+ahiDUz+A7xxF3TsAWc8A733iv64IiIxUPNRfb6cAcMPCGYn9z8Nzn9TCUFE8lpO3ilELrEuuDOY/Ado0wlOegK2PbTh94mI5DglhZqWlQYlrhe8DdsdCUf8DTbsHHdUIiIZkZNJIZI+BXcoeQgm3gAFLeDYf8IOv1CZChFpVtSnALByMTx6PIy/HHrsChe8CTueoIQgIs1OTt4ppNXMp2H8FcEoo0F3wK7nQEFO5koRkSZrvklh1TKYcBXM/C9sPiAoU9Fl67ijEhGJVfNMCnNfhLEXwvffBJPQ9r5cs5JFRMjRpNDkjuaVi2CDDnDS47DZzmmNTUQkl5m7xx1DyoqKinzatGmNf6M7rFsLLVqnPygRkSxnZiXuXlTba82zR9VMCUFEpBbNMymIiEitlBRERKSKkoKIiFTJyaRgZoPNbPiKFSviDkVEJK/kZFKIbeU1EZE8l5NJQUREoqGkICIiVXJ68pqZfQMsB+rrXOhQx+tdgCVRxBWRun6PbDxGqp/TmPeF3beh/ep7vb7Xcun7o+9Oavs3dZ9s/v70cveutb7i7jn9AwxP5XVgWtyxp/P3zKZjpPo5jXlf2H1T/X6EeC1nvj/67qS2f1P3ydXvTz40HxU38fVckYnfI13HSPVzGvO+sPs25fuh707mj5GJ707Y/Zu6T05+f3K6+agpzGya11H7Q6Qh+v5IU2Tz9ycf7hRSNTzuACSn6fsjTZG1359me6cgIiI/1pzvFEREpAYlBRERqaKkICIiVZQUksxsQzP7l5n908xOiTseyS1mtqWZPWhm/407FsktZnZ08rzzhJkdEnc8eZ0UzGyEmX1tZjNrbD/UzOaY2Vwzuza5+Vjgv+5+LnBkxoOVrNOY74+7l7r72fFEKtmmkd+dMcnzzlDgl3HEW11eJwVgJHBo9Q1mVggMAwYBfYGTzKwv0B1YkNxtXQZjlOw1kvDfH5HqRtL4784NyddjlddJwd1fBZbV2LwbMDd5ZbcWeBw4ClhIkBggz/8uEk4jvz8iVRrz3bHA7cCz7j4907HW1BxPfpvzvzsCCJLB5sAo4Dgzu5ccnZ4uGVHr98fMOpvZfcAuZnZdPKFJlqvr3HMxcBBwvJkNjSOw6lrEHUC2cPfvgTPjjkNyk7svJWgTFmkUd78buDvuOCo1xzuFRUCPas+7J7eJhKHvj6QqJ747zTEpTAW2NrMtzKwVcCIwLuaYJHfo+yOpyonvTl4nBTN7DHgL2NbMFprZ2e5eAVwETAQ+BJ5091lxxinZSd8fSVUuf3dUEE9ERKrk9Z2CiIg0jpKCiIhUUVIQEZEqSgoiIlJFSUFERKooKYiISBUlBclJZnazmV2Zyutm9ma1x3eY2azkv2eY2WaNjKN3zfLIDez/spkV1bK9yMyyptSBNF+qfSRZw8yMYO5MIsrjuPue1Z4OATZ293Vm9jIwE/giyuPXEdM0YFrN7WbWIjnpSSQjdKcgsUpeac8xs4cJTsg9zOwqM5tqZh+Y2S3V9v2NmX1sZq8D21bbfomZzU7u/3i1j++bvDIvNbNLqu3/XfLfccBGQImZ/RIoAh41s/fMrI2ZDTCzV8ysxMwmmlm35PsGmNn7ZvY+cGE9v9s1ZjYjue9t1V76hZm9k/xd9knuu7+ZjU8+vtnM/m1mbwD/Tt7BjE3+Lp+Y2U11HO+7anc+L5jZbtV+/yOr/b1fM7PpyZ89k9u7mdmryd99ppntY2aFZjYy+XyGmV3e4H9QyX3urh/9xPYD9AYSwMDk80OA4YARXLSMB/YFBgAzgLZAe2AucGXyPV8ArZOPOyb/vRl4E2gNdAGWAi2Tr31X7fjVH78MFCUft0y+v2vy+S+BEcnHHwD7Jh/fAcys5fcalHx/2+Tzjasd487k48OAF5KP9wfGV4u9BGiTfH4GsBjoDLQhSJ5FtRzTgUHJx6OBScnfYyfgveT2tsAGycdbA9OSj38N/Cb5uBBol/ybP1/t8zvG/X3RT/Q/aj6SbDDP3ackHx+S/Hk3+XwjgpNXO2C0u6+Cqqv8Sh8QXOGPAcZU2/6Mu68B1pjZ18AmBDXsw9gW6Ac8H7RqUQgsNrOOBCfHV5P7/ZsgAdR0EPBQZbzuXn3BlVHJf0sIkmJtxrn7D9WeP+9BeW7MbBSwNz9ubloLPJd8PANY4+7lZjaj2nFaAv8ws50JVhjcJrl9KjDCzFoCY9z9PTMrBbY0s78DzxAkGclzaj6SbPB9tccG/NHdd07+9HH3Bxt4/+EEyxj2B6aaWeXFzppq+6yjcX1oBsyqFscO7p6uRdUr46ovpu9rPK9ZpKy2omXl7l65PVF5HA/6aCqPcznwFcHdQxHQKrnPqwR3ZIuAkWZ2uruXJfd7mWCtiAca+sUk9ykpSLaZCJxlZhsBmNnmZvYT4FXg6GRbfztgcPL1AqCHu08GrgE6ENxdpOJbgjsSgDlAVzPbI3mclma2vbsvB5ab2d7J/U6p47OeB840s7bJ92+cYkyVDjazjc2sDXA08EaKn9MBWJxMFKcR3AFhZr2Ar9z9nwQn//5m1gUocPenCdYP7t/E30FygJqPJKu4+yQz2w54K9ls8x1wqrtPN7MngPeBrwmaOyA4qT1iZh0Iru7vdvflyfc21kjgPjP7AdgDOB64O/nZLYC/AbMIVugbYWZOHU0q7v5csolmmpmtBSYA16cSVNI7wNMEC7M84sFopVTcAzxtZqcTNDVV3pHsD1xlZuUEf/PTCZaKfCiZeAG0zGgzoNLZIlnOzM4g6Fi+KO5YJP+p+UhERKroTkFERKroTkFERKooKYiISBUlBRERqaKkICIiVZQURESkipKCiIhU+X/3jw/kyTto8QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plot GW150914 strain"
      ],
      "metadata": {
        "id": "LGJrpXYjhDVB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The event record includes a link to a JSON file with more information. We download it and decode it."
      ],
      "metadata": {
        "id": "6jodT4D5Rz1L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gwtc.loc['GW150914']['jsonurl']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 57
        },
        "id": "yoPj5aK0g8NU",
        "outputId": "01614cc0-3068-4ca2-c275-c904e4d12ffa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gw150914_json = json.loads(requests.get(gwtc.loc['GW150914']['jsonurl']).content)"
      ],
      "metadata": {
        "id": "VhZLtk-chT8X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gw150914_json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NNJ6PpeCjFG9",
        "outputId": "3609acf4-9d31-49a0-a636-a25826b9d751"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'events': {'GW150914-v3': {'GPS': 1126259462.4,\n",
              "   'catalog.shortName': 'GWTC-1-confident',\n",
              "   'chi_eff': -0.01,\n",
              "   'chi_eff_lower': -0.13,\n",
              "   'chi_eff_unit': '',\n",
              "   'chi_eff_upper': 0.12,\n",
              "   'chirp_mass': None,\n",
              "   'chirp_mass_lower': None,\n",
              "   'chirp_mass_source': 28.6,\n",
              "   'chirp_mass_source_lower': -1.5,\n",
              "   'chirp_mass_source_unit': 'M_sun',\n",
              "   'chirp_mass_source_upper': 1.7,\n",
              "   'chirp_mass_unit': None,\n",
              "   'chirp_mass_upper': None,\n",
              "   'commonName': 'GW150914',\n",
              "   'far': 1e-07,\n",
              "   'far_lower': None,\n",
              "   'far_unit': 'yr^-1',\n",
              "   'far_upper': None,\n",
              "   'final_mass_source': 63.1,\n",
              "   'final_mass_source_lower': -3.0,\n",
              "   'final_mass_source_unit': 'M_sun',\n",
              "   'final_mass_source_upper': 3.4,\n",
              "   'gracedb_id': 'G184098_R1',\n",
              "   'jsonurl': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3',\n",
              "   'luminosity_distance': 440.0,\n",
              "   'luminosity_distance_lower': -170.0,\n",
              "   'luminosity_distance_unit': 'Mpc',\n",
              "   'luminosity_distance_upper': 150.0,\n",
              "   'mass_1_source': 35.6,\n",
              "   'mass_1_source_lower': -3.1,\n",
              "   'mass_1_source_unit': 'M_sun',\n",
              "   'mass_1_source_upper': 4.7,\n",
              "   'mass_2_source': 30.6,\n",
              "   'mass_2_source_lower': -4.4,\n",
              "   'mass_2_source_unit': 'M_sun',\n",
              "   'mass_2_source_upper': 3.0,\n",
              "   'network_matched_filter_snr': 24.4,\n",
              "   'network_matched_filter_snr_lower': None,\n",
              "   'network_matched_filter_snr_unit': '',\n",
              "   'network_matched_filter_snr_upper': None,\n",
              "   'p_astro': 1.0,\n",
              "   'p_astro_lower': None,\n",
              "   'p_astro_unit': '',\n",
              "   'p_astro_upper': None,\n",
              "   'parameters': {'GWTC-1-confident_GW150914_R2_pe_combined': {'chi_eff': -0.04,\n",
              "     'chi_eff_lower': -0.14,\n",
              "     'chi_eff_unit': '',\n",
              "     'chi_eff_upper': 0.12,\n",
              "     'chirp_mass_source': 27.9,\n",
              "     'chirp_mass_source_lower': -1.5,\n",
              "     'chirp_mass_source_unit': 'M_sun',\n",
              "     'chirp_mass_source_upper': 1.7,\n",
              "     'data_url': 'https://zenodo.org/api/files/ecf41927-9275-47da-8b37-e299693fe5cb/IGWN-GWTC2p1-v2-GW150914_095045_PEDataRelease_mixed_cosmo.h5',\n",
              "     'date_added': '2022-05-13',\n",
              "     'final_mass_source': 61.5,\n",
              "     'final_mass_source_lower': -2.9,\n",
              "     'final_mass_source_unit': 'M_sun',\n",
              "     'final_mass_source_upper': 3.4,\n",
              "     'final_spin': 0.68,\n",
              "     'final_spin_lower': -0.05,\n",
              "     'final_spin_unit': '',\n",
              "     'final_spin_upper': 0.05,\n",
              "     'is_preferred': False,\n",
              "     'luminosity_distance': 470.0,\n",
              "     'luminosity_distance_lower': -160.0,\n",
              "     'luminosity_distance_unit': 'Mpc',\n",
              "     'luminosity_distance_upper': 140.0,\n",
              "     'mass_1_source': 34.6,\n",
              "     'mass_1_source_lower': -2.6,\n",
              "     'mass_1_source_unit': 'M_sun',\n",
              "     'mass_1_source_upper': 4.4,\n",
              "     'mass_2_source': 30.0,\n",
              "     'mass_2_source_lower': -4.6,\n",
              "     'mass_2_source_unit': 'M_sun',\n",
              "     'mass_2_source_upper': 2.9,\n",
              "     'network_matched_filter_snr': 26.0,\n",
              "     'network_matched_filter_snr_lower': -0.2,\n",
              "     'network_matched_filter_snr_unit': '',\n",
              "     'network_matched_filter_snr_upper': 0.1,\n",
              "     'pipeline_type': 'pe',\n",
              "     'redshift': 0.1,\n",
              "     'redshift_lower': -0.03,\n",
              "     'redshift_unit': '',\n",
              "     'redshift_upper': 0.03,\n",
              "     'sky_area': 250.0,\n",
              "     'sky_area_lower': None,\n",
              "     'sky_area_unit': 'deg^2',\n",
              "     'sky_area_upper': None,\n",
              "     'total_mass_source': 64.5,\n",
              "     'total_mass_source_lower': -3.2,\n",
              "     'total_mass_source_unit': 'M_sun',\n",
              "     'total_mass_source_upper': 3.7},\n",
              "    'gwtc1_cwb_GW150914': {'data_url': '',\n",
              "     'date_added': '2020-02-19',\n",
              "     'far': 0.000163,\n",
              "     'far_lower': None,\n",
              "     'far_unit': 'yr^-1',\n",
              "     'far_upper': None,\n",
              "     'is_preferred': False,\n",
              "     'network_matched_filter_snr': 25.2,\n",
              "     'network_matched_filter_snr_lower': None,\n",
              "     'network_matched_filter_snr_unit': '',\n",
              "     'network_matched_filter_snr_upper': None,\n",
              "     'p_astro': 1.0,\n",
              "     'p_astro_lower': None,\n",
              "     'p_astro_unit': '',\n",
              "     'p_astro_upper': None,\n",
              "     'pipeline_type': 'search'},\n",
              "    'gwtc1_gstlal_GW150914': {'data_url': '',\n",
              "     'date_added': '2020-02-19',\n",
              "     'far': 1e-07,\n",
              "     'far_lower': None,\n",
              "     'far_unit': 'yr^-1',\n",
              "     'far_upper': None,\n",
              "     'is_preferred': True,\n",
              "     'network_matched_filter_snr': 24.4,\n",
              "     'network_matched_filter_snr_lower': None,\n",
              "     'network_matched_filter_snr_unit': '',\n",
              "     'network_matched_filter_snr_upper': None,\n",
              "     'p_astro': 1.0,\n",
              "     'p_astro_lower': None,\n",
              "     'p_astro_unit': '',\n",
              "     'p_astro_upper': None,\n",
              "     'pipeline_type': 'search'},\n",
              "    'gwtc1_pe_GW150914': {'E_rad': 3.1,\n",
              "     'E_rad_lower': -0.4,\n",
              "     'E_rad_unit': 'M_sun c^2',\n",
              "     'E_rad_upper': 0.4,\n",
              "     'L_peak': 3.6,\n",
              "     'L_peak_lower': -0.4,\n",
              "     'L_peak_unit': '10^56 erg s^-1',\n",
              "     'L_peak_upper': 0.4,\n",
              "     'a_final': 0.69,\n",
              "     'a_final_lower': -0.04,\n",
              "     'a_final_unit': '',\n",
              "     'a_final_upper': 0.05,\n",
              "     'chi_eff': -0.01,\n",
              "     'chi_eff_lower': -0.13,\n",
              "     'chi_eff_unit': '',\n",
              "     'chi_eff_upper': 0.12,\n",
              "     'chirp_mass_source': 28.6,\n",
              "     'chirp_mass_source_lower': -1.5,\n",
              "     'chirp_mass_source_unit': 'M_sun',\n",
              "     'chirp_mass_source_upper': 1.7,\n",
              "     'data_url': 'https://dcc.ligo.org/public/0157/P1800370/005/GW150914_GWTC-1.hdf5',\n",
              "     'date_added': '2020-02-19',\n",
              "     'final_mass_source': 63.1,\n",
              "     'final_mass_source_lower': -3.0,\n",
              "     'final_mass_source_unit': 'M_sun',\n",
              "     'final_mass_source_upper': 3.4,\n",
              "     'geocent_time': 1126259462.4,\n",
              "     'geocent_time_lower': None,\n",
              "     'geocent_time_unit': '',\n",
              "     'geocent_time_upper': None,\n",
              "     'is_preferred': True,\n",
              "     'luminosity_distance': 440.0,\n",
              "     'luminosity_distance_lower': -170.0,\n",
              "     'luminosity_distance_unit': 'Mpc',\n",
              "     'luminosity_distance_upper': 150.0,\n",
              "     'mass_1_source': 35.6,\n",
              "     'mass_1_source_lower': -3.1,\n",
              "     'mass_1_source_unit': 'M_sun',\n",
              "     'mass_1_source_upper': 4.7,\n",
              "     'mass_2_source': 30.6,\n",
              "     'mass_2_source_lower': -4.4,\n",
              "     'mass_2_source_unit': 'M_sun',\n",
              "     'mass_2_source_upper': 3.0,\n",
              "     'pipeline_type': 'pe',\n",
              "     'redshift': 0.09,\n",
              "     'redshift_lower': -0.03,\n",
              "     'redshift_unit': '',\n",
              "     'redshift_upper': 0.03,\n",
              "     'sky_area': 182.0,\n",
              "     'sky_area_lower': None,\n",
              "     'sky_area_unit': 'deg^2',\n",
              "     'sky_area_upper': None},\n",
              "    'gwtc1_pycbc_GW150914': {'data_url': '',\n",
              "     'date_added': '2020-02-19',\n",
              "     'far': 1.5300000000000003e-05,\n",
              "     'far_lower': None,\n",
              "     'far_unit': 'yr^-1',\n",
              "     'far_upper': None,\n",
              "     'is_preferred': False,\n",
              "     'network_matched_filter_snr': 23.6,\n",
              "     'network_matched_filter_snr_lower': None,\n",
              "     'network_matched_filter_snr_unit': '',\n",
              "     'network_matched_filter_snr_upper': None,\n",
              "     'p_astro': 1.0,\n",
              "     'p_astro_lower': None,\n",
              "     'p_astro_unit': '',\n",
              "     'p_astro_upper': None,\n",
              "     'pipeline_type': 'search'}},\n",
              "   'redshift': 0.09,\n",
              "   'redshift_lower': -0.03,\n",
              "   'redshift_unit': '',\n",
              "   'redshift_upper': 0.03,\n",
              "   'reference': 'https://doi.org/10.7935/82H3-HH23',\n",
              "   'strain': [{'GPSstart': 1126259447,\n",
              "     'detector': 'H1',\n",
              "     'duration': 32,\n",
              "     'format': 'gwf',\n",
              "     'sampling_rate': 16384,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/H-H1_GWOSC_16KHZ_R1-1126259447-32.gwf'},\n",
              "    {'GPSstart': 1126259447,\n",
              "     'detector': 'H1',\n",
              "     'duration': 32,\n",
              "     'format': 'hdf5',\n",
              "     'sampling_rate': 16384,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/H-H1_GWOSC_16KHZ_R1-1126259447-32.hdf5'},\n",
              "    {'GPSstart': 1126259447,\n",
              "     'detector': 'H1',\n",
              "     'duration': 32,\n",
              "     'format': 'txt',\n",
              "     'sampling_rate': 16384,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/H-H1_GWOSC_16KHZ_R1-1126259447-32.txt.gz'},\n",
              "    {'GPSstart': 1126259447,\n",
              "     'detector': 'H1',\n",
              "     'duration': 32,\n",
              "     'format': 'gwf',\n",
              "     'sampling_rate': 4096,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/H-H1_GWOSC_4KHZ_R1-1126259447-32.gwf'},\n",
              "    {'GPSstart': 1126259447,\n",
              "     'detector': 'H1',\n",
              "     'duration': 32,\n",
              "     'format': 'hdf5',\n",
              "     'sampling_rate': 4096,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/H-H1_GWOSC_4KHZ_R1-1126259447-32.hdf5'},\n",
              "    {'GPSstart': 1126259447,\n",
              "     'detector': 'H1',\n",
              "     'duration': 32,\n",
              "     'format': 'txt',\n",
              "     'sampling_rate': 4096,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/H-H1_GWOSC_4KHZ_R1-1126259447-32.txt.gz'},\n",
              "    {'GPSstart': 1126257415,\n",
              "     'detector': 'H1',\n",
              "     'duration': 4096,\n",
              "     'format': 'gwf',\n",
              "     'sampling_rate': 16384,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/H-H1_GWOSC_16KHZ_R1-1126257415-4096.gwf'},\n",
              "    {'GPSstart': 1126257415,\n",
              "     'detector': 'H1',\n",
              "     'duration': 4096,\n",
              "     'format': 'hdf5',\n",
              "     'sampling_rate': 16384,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/H-H1_GWOSC_16KHZ_R1-1126257415-4096.hdf5'},\n",
              "    {'GPSstart': 1126257415,\n",
              "     'detector': 'H1',\n",
              "     'duration': 4096,\n",
              "     'format': 'txt',\n",
              "     'sampling_rate': 16384,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/H-H1_GWOSC_16KHZ_R1-1126257415-4096.txt.gz'},\n",
              "    {'GPSstart': 1126257415,\n",
              "     'detector': 'H1',\n",
              "     'duration': 4096,\n",
              "     'format': 'gwf',\n",
              "     'sampling_rate': 4096,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/H-H1_GWOSC_4KHZ_R1-1126257415-4096.gwf'},\n",
              "    {'GPSstart': 1126257415,\n",
              "     'detector': 'H1',\n",
              "     'duration': 4096,\n",
              "     'format': 'hdf5',\n",
              "     'sampling_rate': 4096,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/H-H1_GWOSC_4KHZ_R1-1126257415-4096.hdf5'},\n",
              "    {'GPSstart': 1126257415,\n",
              "     'detector': 'H1',\n",
              "     'duration': 4096,\n",
              "     'format': 'txt',\n",
              "     'sampling_rate': 4096,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/H-H1_GWOSC_4KHZ_R1-1126257415-4096.txt.gz'},\n",
              "    {'GPSstart': 1126259447,\n",
              "     'detector': 'L1',\n",
              "     'duration': 32,\n",
              "     'format': 'gwf',\n",
              "     'sampling_rate': 16384,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/L-L1_GWOSC_16KHZ_R1-1126259447-32.gwf'},\n",
              "    {'GPSstart': 1126259447,\n",
              "     'detector': 'L1',\n",
              "     'duration': 32,\n",
              "     'format': 'hdf5',\n",
              "     'sampling_rate': 16384,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/L-L1_GWOSC_16KHZ_R1-1126259447-32.hdf5'},\n",
              "    {'GPSstart': 1126259447,\n",
              "     'detector': 'L1',\n",
              "     'duration': 32,\n",
              "     'format': 'txt',\n",
              "     'sampling_rate': 16384,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/L-L1_GWOSC_16KHZ_R1-1126259447-32.txt.gz'},\n",
              "    {'GPSstart': 1126259447,\n",
              "     'detector': 'L1',\n",
              "     'duration': 32,\n",
              "     'format': 'gwf',\n",
              "     'sampling_rate': 4096,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/L-L1_GWOSC_4KHZ_R1-1126259447-32.gwf'},\n",
              "    {'GPSstart': 1126259447,\n",
              "     'detector': 'L1',\n",
              "     'duration': 32,\n",
              "     'format': 'hdf5',\n",
              "     'sampling_rate': 4096,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/L-L1_GWOSC_4KHZ_R1-1126259447-32.hdf5'},\n",
              "    {'GPSstart': 1126259447,\n",
              "     'detector': 'L1',\n",
              "     'duration': 32,\n",
              "     'format': 'txt',\n",
              "     'sampling_rate': 4096,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/L-L1_GWOSC_4KHZ_R1-1126259447-32.txt.gz'},\n",
              "    {'GPSstart': 1126257415,\n",
              "     'detector': 'L1',\n",
              "     'duration': 4096,\n",
              "     'format': 'gwf',\n",
              "     'sampling_rate': 16384,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/L-L1_GWOSC_16KHZ_R1-1126257415-4096.gwf'},\n",
              "    {'GPSstart': 1126257415,\n",
              "     'detector': 'L1',\n",
              "     'duration': 4096,\n",
              "     'format': 'hdf5',\n",
              "     'sampling_rate': 16384,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/L-L1_GWOSC_16KHZ_R1-1126257415-4096.hdf5'},\n",
              "    {'GPSstart': 1126257415,\n",
              "     'detector': 'L1',\n",
              "     'duration': 4096,\n",
              "     'format': 'txt',\n",
              "     'sampling_rate': 16384,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/L-L1_GWOSC_16KHZ_R1-1126257415-4096.txt.gz'},\n",
              "    {'GPSstart': 1126257415,\n",
              "     'detector': 'L1',\n",
              "     'duration': 4096,\n",
              "     'format': 'gwf',\n",
              "     'sampling_rate': 4096,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/L-L1_GWOSC_4KHZ_R1-1126257415-4096.gwf'},\n",
              "    {'GPSstart': 1126257415,\n",
              "     'detector': 'L1',\n",
              "     'duration': 4096,\n",
              "     'format': 'hdf5',\n",
              "     'sampling_rate': 4096,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/L-L1_GWOSC_4KHZ_R1-1126257415-4096.hdf5'},\n",
              "    {'GPSstart': 1126257415,\n",
              "     'detector': 'L1',\n",
              "     'duration': 4096,\n",
              "     'format': 'txt',\n",
              "     'sampling_rate': 4096,\n",
              "     'url': 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/L-L1_GWOSC_4KHZ_R1-1126257415-4096.txt.gz'}],\n",
              "   'total_mass_source': None,\n",
              "   'total_mass_source_lower': None,\n",
              "   'total_mass_source_unit': None,\n",
              "   'total_mass_source_upper': None,\n",
              "   'version': 3}}}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The JSON includes links to many strain files. We select the short, low-cadence files for H1 and L1 in HDF5 format,  download them, and parse them."
      ],
      "metadata": {
        "id": "LbLTAYvrR9Jm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hdffiles = [file['url'] for file in gw150914_json['events']['GW150914-v3']['strain']\n",
        "                        if file['duration'] == 32 and file['sampling_rate'] == 4096 and file['format'] == 'hdf5']"
      ],
      "metadata": {
        "id": "-Zh1nCzqhkuV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hdffiles"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y3f34EhIhX-N",
        "outputId": "2e92bdd8-337d-4faf-f4f9-7e18198e7473"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/H-H1_GWOSC_4KHZ_R1-1126259447-32.hdf5',\n",
              " 'https://www.gw-openscience.org/eventapi/json/GWTC-1-confident/GW150914/v3/L-L1_GWOSC_4KHZ_R1-1126259447-32.hdf5']"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for hdffile in hdffiles:\n",
        "    with open(os.path.basename(hdffile), 'wb') as outfile:\n",
        "        outfile.write(requests.get(hdffile).content)"
      ],
      "metadata": {
        "id": "DfCYFLX_htXN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "h1file = h5py.File('H-H1_GWOSC_4KHZ_R1-1126259447-32.hdf5', 'r')"
      ],
      "metadata": {
        "id": "eI0dwZKPh0bS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "h1file"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RVf7iZh7h3CP",
        "outputId": "65af1e7a-541f-455d-e49c-3177af7e8c07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<HDF5 file \"H-H1_GWOSC_4KHZ_R1-1126259447-32.hdf5\" (mode r)>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The HDF file has a hierarchical structure. We find the strain vector and the attributes that describe its time axis."
      ],
      "metadata": {
        "id": "NUWg8ffbSLOs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "h1file.visititems(print)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sGiBnNAfh87r",
        "outputId": "15957bd5-d9f1-4f54-9fb1-48d072ce6152"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "meta <HDF5 group \"/meta\" (8 members)>\n",
            "meta/Description <HDF5 dataset \"Description\": shape (), type \"|O\">\n",
            "meta/DescriptionURL <HDF5 dataset \"DescriptionURL\": shape (), type \"|O\">\n",
            "meta/Detector <HDF5 dataset \"Detector\": shape (), type \"|O\">\n",
            "meta/Duration <HDF5 dataset \"Duration\": shape (), type \"<i8\">\n",
            "meta/GPSstart <HDF5 dataset \"GPSstart\": shape (), type \"<i8\">\n",
            "meta/Observatory <HDF5 dataset \"Observatory\": shape (), type \"|O\">\n",
            "meta/Type <HDF5 dataset \"Type\": shape (), type \"|O\">\n",
            "meta/UTCstart <HDF5 dataset \"UTCstart\": shape (), type \"|O\">\n",
            "quality <HDF5 group \"/quality\" (3 members)>\n",
            "quality/detail <HDF5 group \"/quality/detail\" (0 members)>\n",
            "quality/injections <HDF5 group \"/quality/injections\" (3 members)>\n",
            "quality/injections/InjDescriptions <HDF5 dataset \"InjDescriptions\": shape (5,), type \"|S29\">\n",
            "quality/injections/InjShortnames <HDF5 dataset \"InjShortnames\": shape (5,), type \"|S17\">\n",
            "quality/injections/Injmask <HDF5 dataset \"Injmask\": shape (32,), type \"<u4\">\n",
            "quality/simple <HDF5 group \"/quality/simple\" (3 members)>\n",
            "quality/simple/DQDescriptions <HDF5 dataset \"DQDescriptions\": shape (7,), type \"|S22\">\n",
            "quality/simple/DQShortnames <HDF5 dataset \"DQShortnames\": shape (7,), type \"|S10\">\n",
            "quality/simple/DQmask <HDF5 dataset \"DQmask\": shape (32,), type \"<u4\">\n",
            "strain <HDF5 group \"/strain\" (1 members)>\n",
            "strain/Strain <HDF5 dataset \"Strain\": shape (131072,), type \"<f8\">\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "h1strain = h1file['strain/Strain']\n",
        "gpstime = h1strain.attrs['Xstart'] + h1strain.attrs['Xspacing'] * np.arange(h1strain.attrs['Npoints'])"
      ],
      "metadata": {
        "id": "d6i7yhUKiBKp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "OK, looks very noisy though. Is that the event at 66? No..."
      ],
      "metadata": {
        "id": "4wnJEEfWSTe6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tevent = gwtc.loc['GW150914']['GPS']"
      ],
      "metadata": {
        "id": "kG6nOL-tSyQB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pp.plot(gpstime, h1strain[:])\n",
        "pp.axvline(tevent, c='C1')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "23TKraTjiLjT",
        "outputId": "9e6c3579-837c-4fc8-a74f-ac3700f2ab0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7f2b00b81550>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's see the noise spectrum, using Welch averaging..."
      ],
      "metadata": {
        "id": "V4ajeAuPS3Fz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "psd, freqs = mlab.psd(h1strain, NFFT=4096*4, Fs=4096)\n",
        "\n",
        "pp.loglog(freqs, np.sqrt(psd))\n",
        "\n",
        "pp.xlabel('f [Hz]'); pp.ylabel('PSD [1/sqrt(Hz)]')\n",
        "pp.axis(xmin=10, xmax=1024, ymin=1e-24, ymax=1e-19);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "sY0ZQI69TCY5",
        "outputId": "6e77af2a-45c5-4d6e-9fa4-0960a54d215d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then set up a function to *whiten* the data by dividing by sqrt(PSD), and zeroing out frequencies outside a bandpass."
      ],
      "metadata": {
        "id": "7sYsc5G6TMCm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def whiten(strain, sampling=4096, bandpass=[20,350]):\n",
        "    # compute the spectrum\n",
        "    psd, psd_freqs = mlab.psd(strain, NFFT=sampling*4, Fs=sampling)\n",
        "\n",
        "    # max out the spectrum outside the bandpass\n",
        "    psd[(psd_freqs < bandpass[0]) | (psd_freqs > bandpass[1])] = np.max(psd)\n",
        "    \n",
        "    dt = 1/sampling\n",
        "\n",
        "    # compute the real FFT and the corresponding frequencies\n",
        "    strain_fft = np.fft.rfft(strain)\n",
        "    fft_freqs = np.fft.rfftfreq(len(strain), dt)\n",
        "\n",
        "    # interpolate the spectrum to the FFT frequencies\n",
        "    psd_fft = scipy.interpolate.interp1d(psd_freqs, psd)(fft_freqs)\n",
        "    \n",
        "    # whiten and transform back to real space\n",
        "    whitened = np.fft.irfft(strain_fft / np.sqrt(psd_fft) * np.sqrt(2*dt))\n",
        "    \n",
        "    return whitened"
      ],
      "metadata": {
        "id": "frmUxoJnTLRL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mmmh... artifacts at the edges, but what's in the middle?"
      ],
      "metadata": {
        "id": "ZBbNnCzjThkn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pp.figure(figsize=(12,3))\n",
        "pp.plot(gpstime, whiten(h1strain))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "14FGwxIETa_r",
        "outputId": "a8529ce0-a313-4154-b7e4-6eef604ddd16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2afe7ea310>]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x216 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's zoom in. Bingo!"
      ],
      "metadata": {
        "id": "rByO91z2TlcA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "around = (gpstime > tevent - 0.1) & (gpstime < tevent + 0.1)"
      ],
      "metadata": {
        "id": "cl27yzMciY7O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pp.figure(figsize=(12,3))\n",
        "pp.plot(gpstime[around] - tevent, whiten(h1strain)[around])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "id": "wLvmh-Kbit8B",
        "outputId": "40316fab-b079-424e-d973-be82ab5ea219"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2afe739150>]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x216 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's try L1 also?"
      ],
      "metadata": {
        "id": "TBCgVXSXTs3Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "l1file = h5py.File('L-L1_GWOSC_4KHZ_R1-1126259447-32.hdf5', 'r')\n",
        "l1strain = l1file['strain/Strain']"
      ],
      "metadata": {
        "id": "_aO7UDivi1xu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pp.figure(figsize=(12,3))\n",
        "pp.plot(gpstime[around] - tevent, whiten(l1strain, bandpass=[20,350])[around], 'C1')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "LG2LsXLKi4u4",
        "outputId": "0128bb6e-b8c6-40fe-d663-bfb8181e2187"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2afe730790>]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x216 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Demonstrating speed of light!"
      ],
      "metadata": {
        "id": "MrqFdiuwT6cr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pp.figure(figsize=(12,3))\n",
        "pp.plot(gpstime[around] - tevent, whiten(h1strain)[around])\n",
        "pp.plot(gpstime[around] - tevent + 0.007, -whiten(l1strain)[around])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "id": "j2eXYpNyi6yj",
        "outputId": "95ac6fe3-3564-4f47-f28b-cdfac69e6bff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2afe6a7c90>]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x216 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plot NS posteriors"
      ],
      "metadata": {
        "id": "Xoe5E4oDhFt6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "An annoyingly awkward function to find the location of MCMC samples for an event. We need to dig into the JSON file to find one of several HDF files; locate the dataset in the HDF, and read it as a Pandas table."
      ],
      "metadata": {
        "id": "t587cJkuT_jG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_posterior(row, flavor='combined', dataset='Overall'):\n",
        "  # get JSON details for event\n",
        "  js = json.loads(requests.get(row.jsonurl).content)\n",
        "  \n",
        "  # display and choose PE analysis\n",
        "  print(\"PE flavors:\")\n",
        "  for a in js['events'][row.id]['parameters']:\n",
        "    print(f'  {a}') \n",
        "  analysis = [a for a in js['events'][row.id]['parameters'] if flavor in a][0]\n",
        "  \n",
        "  # download and load h5\n",
        "  h5file = js['events'][row.id]['parameters'][analysis]['data_url']\n",
        "  !wget -q -O samples.h5 {h5file}\n",
        "  h5 = h5py.File('samples.h5')\n",
        "\n",
        "  # display and choose PE dataset\n",
        "  print(\"Datasets:\")\n",
        "  for a in h5:\n",
        "    print(f'   {a}')\n",
        "  posterior = [a for a in h5 if dataset in a or 'Mixed' in a][0]\n",
        "  h5.close()\n",
        "\n",
        "  # convert to Pandas dataframe and return\n",
        "  try:\n",
        "    return pd.read_hdf('samples.h5', posterior)\n",
        "  except:\n",
        "    return pd.read_hdf('samples.h5', posterior + '/posterior_samples')"
      ],
      "metadata": {
        "id": "SrwnuhYjGl0E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'll get all NS events..."
      ],
      "metadata": {
        "id": "-Fmx4QQwUOBh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gw170817 = get_posterior(gwtc.loc['GW170817'], flavor='pe', dataset='IMRPhenomPv2NRT_lowSpin_posterior')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oteTKlNhmnBr",
        "outputId": "0c76b0f4-ca33-4676-d86e-eb2d8d9fd857"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PE flavors:\n",
            "  gwtc1_gstlal_GW170817\n",
            "  gwtc1_pe_GW170817\n",
            "  gwtc1_pycbc_GW170817\n",
            "Datasets:\n",
            "   IMRPhenomPv2NRT_highSpin_posterior\n",
            "   IMRPhenomPv2NRT_highSpin_prior\n",
            "   IMRPhenomPv2NRT_lowSpin_posterior\n",
            "   IMRPhenomPv2NRT_lowSpin_prior\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gw190814 = get_posterior(gwtc.loc['GW190814'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xSHX2BaSZag-",
        "outputId": "84fbc3f4-12d0-4dd7-d0fa-ad13ae341e2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PE flavors:\n",
            "  GWTC-2.1-confident_GW190814_R2_pe_combined\n",
            "  GWTC-2.1-confident_gstlal_GW190814\n",
            "  GWTC-2.1-confident_mbta_GW190814\n",
            "  GWTC-2.1-confident_pycbc_GW190814\n",
            "Datasets:\n",
            "   C01:IMRPhenomXPHM\n",
            "   C01:Mixed\n",
            "   history\n",
            "   version\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gw190425 = get_posterior(gwtc.loc['GW190425'], dataset='C01:IMRPhenomPv2_NRTidal:LowSpin')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PdexRj_gnrA5",
        "outputId": "93262cab-6c0e-4bbb-a26e-1f77e26d4c1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PE flavors:\n",
            "  GWTC-2.1-confident_GW190425_R2_pe_combined\n",
            "  GWTC-2.1-confident_gstlal_GW190425\n",
            "Datasets:\n",
            "   C01:IMRPhenomPv2_NRTidal:HighSpin\n",
            "   C01:IMRPhenomPv2_NRTidal:LowSpin\n",
            "   history\n",
            "   version\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tables/path.py:137: NaturalNameWarning: object name is not a valid Python identifier: \"'geocent'_time\"; it does not match the pattern ``^[a-zA-Z_][a-zA-Z0-9_]*$``; you will not be able to use natural naming to access this object; using ``getattr()`` will still work, though\n",
            "  check_attribute_name(name)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gw200115 = get_posterior(gwtc.loc['GW200115_042309'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MsA-9kj-dY_y",
        "outputId": "f33c0704-be02-48f7-f9ed-d189f911d24b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PE flavors:\n",
            "  GWTC-3-confident_GW200115_042309_R1_pe_combined\n",
            "  GWTC-3-confident_gstlal_GW200115_042309\n",
            "  GWTC-3-confident_mbta_GW200115_042309\n",
            "  GWTC-3-confident_pycbc_broad_GW200115_042309\n",
            "Datasets:\n",
            "   C01:IMRPhenomNSBH:HighSpin\n",
            "   C01:IMRPhenomNSBH:LowSpin\n",
            "   C01:IMRPhenomXPHM:HighSpin\n",
            "   C01:IMRPhenomXPHM:LowSpin\n",
            "   C01:Mixed\n",
            "   C01:Mixed:NSBH:HighSpin\n",
            "   C01:Mixed:NSBH:LowSpin\n",
            "   C01:SEOBNRv4PHM\n",
            "   C01:SEOBNRv4_ROM_NRTidalv2_NSBH:HighSpin\n",
            "   C01:SEOBNRv4_ROM_NRTidalv2_NSBH:LowSpin\n",
            "   history\n",
            "   version\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gw200210 = get_posterior(gwtc.loc['GW200210_092254'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-7kZUxFVcRnQ",
        "outputId": "d74d73cd-80b9-4302-e664-a5996755fd81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PE flavors:\n",
            "  GWTC-3-confident_GW200210_092254_R1_pe_combined\n",
            "  GWTC-3-confident_gstlal_GW200210_092254\n",
            "  GWTC-3-confident_pycbc_bbh_GW200210_092254\n",
            "  GWTC-3-confident_pycbc_broad_GW200210_092254\n",
            "Datasets:\n",
            "   C01:IMRPhenomXPHM\n",
            "   C01:Mixed\n",
            "   C01:SEOBNRv4PHM\n",
            "   history\n",
            "   version\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gw190924 = get_posterior(gwtc.loc['GW190924_021846'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DQga_KJJc0-S",
        "outputId": "6c5fbca7-04a3-4cde-dc71-68215a47a34c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PE flavors:\n",
            "  GWTC-2.1-confident_GW190924_021846_R2_pe_combined\n",
            "  GWTC-2.1-confident_gstlal_GW190924_021846\n",
            "  GWTC-2.1-confident_mbta_GW190924_021846\n",
            "  GWTC-2.1-confident_pycbc_GW190924_021846\n",
            "  GWTC-2.1-confident_pycbc_bbh_GW190924_021846\n",
            "Datasets:\n",
            "   C01:IMRPhenomXPHM\n",
            "   C01:Mixed\n",
            "   C01:SEOBNRv4PHM\n",
            "   history\n",
            "   version\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gw170817.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dPQay3TNUXz9",
        "outputId": "a380fec8-5799-4eaa-9725-1a3aab7f483c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 8078 entries, 0 to 8077\n",
            "Data columns (total 12 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   costheta_jn              8078 non-null   float64\n",
            " 1   luminosity_distance_Mpc  8078 non-null   float64\n",
            " 2   right_ascension          8078 non-null   float64\n",
            " 3   declination              8078 non-null   float64\n",
            " 4   m1_detector_frame_Msun   8078 non-null   float64\n",
            " 5   m2_detector_frame_Msun   8078 non-null   float64\n",
            " 6   lambda1                  8078 non-null   float64\n",
            " 7   lambda2                  8078 non-null   float64\n",
            " 8   spin1                    8078 non-null   float64\n",
            " 9   spin2                    8078 non-null   float64\n",
            " 10  costilt1                 8078 non-null   float64\n",
            " 11  costilt2                 8078 non-null   float64\n",
            "dtypes: float64(12)\n",
            "memory usage: 820.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pp.figure(figsize=(6,3))\n",
        "pp.hist(gw190814.mass_2_source, histtype='step', density=True, bins=20, label='gw190814');\n",
        "pp.hist(gw190425.mass_2_source, histtype='step', density=True, bins=20, label='gw190425');\n",
        "pp.hist(gw190924.mass_2_source, histtype='step', density=True, bins=20, label='gw190924');\n",
        "pp.hist(gw200115.mass_1_source, histtype='step', density=True, bins=20, label='gw200115 (primary)');\n",
        "pp.hist(gw200210.mass_2_source, histtype='step', density=True, bins=20, label='gw200210');\n",
        "pp.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "id": "_i5xToe6awu5",
        "outputId": "7cf60319-9b77-42fb-a675-a05b7d0c67ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f2afd63fbd0>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "58XkfXgW_n7C"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}