Vis_prior_work_dissertation_Colley.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/M-Colley/colley-dissertation/blob/main/Vis_prior_work_dissertation_Colley.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uLlF1H9mvYOw"
      },
      "source": [
        "# Supplementary Material for the Dissertation of [Mark Colley](https://scholar.google.de/citations?user=Kt5I7wYAAAAJ&hl=de&oi=ao)\n",
        "Source Code for some visualizations of the dissertation of Mark Colley entitled \"Calibrating Trust in Automated Vehicles - Theoretical, Design, and Empirical Insights into Effects of Visualizations on Trust\" (tbd)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uh6qG4utIuV3",
        "outputId": "040db46f-fd9e-4283-bc1e-7bd823459e50"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.3.post1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.23.5)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.5/48.5 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting pdflatex\n",
            "  Downloading pdflatex-0.1.3-py3-none-any.whl (8.3 kB)\n",
            "Collecting attrs<19.0,>=18.2 (from pdflatex)\n",
            "  Downloading attrs-18.2.0-py2.py3-none-any.whl (34 kB)\n",
            "Installing collected packages: attrs, pdflatex\n",
            "  Attempting uninstall: attrs\n",
            "    Found existing installation: attrs 23.1.0\n",
            "    Uninstalling attrs-23.1.0:\n",
            "      Successfully uninstalled attrs-23.1.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires fastapi, which is not installed.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\n",
            "lida 0.0.10 requires uvicorn, which is not installed.\n",
            "fiona 1.9.5 requires attrs>=19.2.0, but you have attrs 18.2.0 which is incompatible.\n",
            "jsonschema 4.19.2 requires attrs>=22.2.0, but you have attrs 18.2.0 which is incompatible.\n",
            "referencing 0.30.2 requires attrs>=22.2.0, but you have attrs 18.2.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed attrs-18.2.0 pdflatex-0.1.3\n",
            "Collecting adjustText\n",
            "  Downloading adjustText-0.8-py3-none-any.whl (9.1 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from adjustText) (1.23.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from adjustText) (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->adjustText) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->adjustText) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->adjustText) (4.44.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->adjustText) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->adjustText) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->adjustText) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->adjustText) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->adjustText) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->adjustText) (1.16.0)\n",
            "Installing collected packages: adjustText\n",
            "Successfully installed adjustText-0.8\n",
            "Requirement already satisfied: openpyxl in /usr/local/lib/python3.10/dist-packages (3.1.2)\n",
            "Requirement already satisfied: et-xmlfile in /usr/local/lib/python3.10/dist-packages (from openpyxl) (1.1.0)\n",
            "Requirement already satisfied: matplotlib==3.7.1 in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.7.1) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.7.1) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.7.1) (4.44.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.7.1) (1.4.5)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.7.1) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.7.1) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.7.1) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.7.1) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.7.1) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib==3.7.1) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "#!pip install tikzplotlib --upgrade\n",
        "!pip install pandas\n",
        "!pip install --upgrade -q gspread\n",
        "!pip install pdflatex\n",
        "!pip install adjustText\n",
        "!pip install openpyxl\n",
        "!pip install matplotlib==3.7.1 #--upgrade #with version higher than 3.7.1, LaTeX (version TeXLive 2023) has issues"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OsqfS1AFcE70",
        "outputId": "87a28488-a7f6-4274-b164-aaf0ca972550"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  cm-super-minimal dvisvgm fonts-droid-fallback fonts-lato fonts-lmodern\n",
            "  fonts-noto-mono fonts-texgyre fonts-urw-base35 ghostscript\n",
            "  libapache-pom-java libcommons-logging-java libcommons-parent-java\n",
            "  libfontbox-java libfontenc1 libgs9 libgs9-common libidn12 libijs-0.35\n",
            "  libjbig2dec0 libkpathsea6 libpdfbox-java libptexenc1 libruby3.0 libsynctex2\n",
            "  libteckit0 libtexlua53 libtexluajit2 libwoff1 libzzip-0-13 lmodern\n",
            "  pfb2t1c2pfb poppler-data preview-latex-style rake ruby ruby-net-telnet\n",
            "  ruby-rubygems ruby-webrick ruby-xmlrpc ruby3.0 rubygems-integration t1utils\n",
            "  tex-common tex-gyre texlive-base texlive-binaries texlive-latex-base\n",
            "  texlive-latex-recommended texlive-pictures texlive-plain-generic tipa\n",
            "  xfonts-encodings xfonts-utils\n",
            "Suggested packages:\n",
            "  fonts-noto fonts-freefont-otf | fonts-freefont-ttf ghostscript-x\n",
            "  libavalon-framework-java libcommons-logging-java-doc\n",
            "  libexcalibur-logkit-java liblog4j1.2-java poppler-utils\n",
            "  fonts-japanese-mincho | fonts-ipafont-mincho fonts-japanese-gothic\n",
            "  | fonts-ipafont-gothic fonts-arphic-ukai fonts-arphic-uming fonts-nanum ri\n",
            "  ruby-dev bundler debhelper perl-tk xpdf | pdf-viewer xzdec\n",
            "  texlive-fonts-recommended-doc texlive-latex-base-doc python3-pygments\n",
            "  icc-profiles libfile-which-perl libspreadsheet-parseexcel-perl\n",
            "  texlive-latex-extra-doc texlive-latex-recommended-doc texlive-luatex\n",
            "  texlive-pstricks dot2tex prerex texlive-pictures-doc vprerex\n",
            "  default-jre-headless tipa-doc\n",
            "The following NEW packages will be installed:\n",
            "  cm-super cm-super-minimal dvipng dvisvgm fonts-droid-fallback fonts-lato\n",
            "  fonts-lmodern fonts-noto-mono fonts-texgyre fonts-urw-base35 ghostscript\n",
            "  libapache-pom-java libcommons-logging-java libcommons-parent-java\n",
            "  libfontbox-java libfontenc1 libgs9 libgs9-common libidn12 libijs-0.35\n",
            "  libjbig2dec0 libkpathsea6 libpdfbox-java libptexenc1 libruby3.0 libsynctex2\n",
            "  libteckit0 libtexlua53 libtexluajit2 libwoff1 libzzip-0-13 lmodern\n",
            "  pfb2t1c2pfb poppler-data preview-latex-style rake ruby ruby-net-telnet\n",
            "  ruby-rubygems ruby-webrick ruby-xmlrpc ruby3.0 rubygems-integration t1utils\n",
            "  tex-common tex-gyre texlive-base texlive-binaries texlive-fonts-recommended\n",
            "  texlive-latex-base texlive-latex-extra texlive-latex-recommended\n",
            "  texlive-pictures texlive-plain-generic tipa xfonts-encodings xfonts-utils\n",
            "0 upgraded, 57 newly installed, 0 to remove and 19 not upgraded.\n",
            "Need to get 195 MB of archives.\n",
            "After this operation, 613 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-droid-fallback all 1:6.0.1r16-1.1build1 [1,805 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-lato all 2.0-2.1 [2,696 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu jammy/main amd64 poppler-data all 0.4.11-1 [2,171 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tex-common all 6.17 [33.7 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libkpathsea6 amd64 2021.20210626.59705-1ubuntu0.1 [60.3 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libptexenc1 amd64 2021.20210626.59705-1ubuntu0.1 [39.1 kB]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libsynctex2 amd64 2021.20210626.59705-1ubuntu0.1 [55.5 kB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libtexlua53 amd64 2021.20210626.59705-1ubuntu0.1 [120 kB]\n",
            "Get:9 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libtexluajit2 amd64 2021.20210626.59705-1ubuntu0.1 [267 kB]\n",
            "Get:10 http://archive.ubuntu.com/ubuntu jammy/main amd64 t1utils amd64 1.41-4build2 [61.3 kB]\n",
            "Get:11 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libteckit0 amd64 2.5.11+ds1-1 [421 kB]\n",
            "Get:12 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libzzip-0-13 amd64 0.13.72+dfsg.1-1.1 [27.0 kB]\n",
            "Get:13 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 texlive-binaries amd64 2021.20210626.59705-1ubuntu0.1 [9,848 kB]\n",
            "Get:14 http://archive.ubuntu.com/ubuntu jammy/universe amd64 texlive-base all 2021.20220204-1 [21.0 MB]\n",
            "Get:15 http://archive.ubuntu.com/ubuntu jammy/universe amd64 fonts-lmodern all 2.004.5-6.1 [4,532 kB]\n",
            "Get:16 http://archive.ubuntu.com/ubuntu jammy/universe amd64 texlive-latex-base all 2021.20220204-1 [1,128 kB]\n",
            "Get:17 http://archive.ubuntu.com/ubuntu jammy/universe amd64 texlive-latex-recommended all 2021.20220204-1 [14.4 MB]\n",
            "Get:18 http://archive.ubuntu.com/ubuntu jammy/universe amd64 cm-super-minimal all 0.3.4-17 [5,777 kB]\n",
            "Get:19 http://archive.ubuntu.com/ubuntu jammy/universe amd64 pfb2t1c2pfb amd64 0.3-11 [9,342 B]\n",
            "Get:20 http://archive.ubuntu.com/ubuntu jammy/universe amd64 cm-super all 0.3.4-17 [20.2 MB]\n",
            "Get:21 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-urw-base35 all 20200910-1 [6,367 kB]\n",
            "Get:22 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libgs9-common all 9.55.0~dfsg1-0ubuntu5.5 [752 kB]\n",
            "Get:23 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libidn12 amd64 1.38-4ubuntu1 [60.0 kB]\n",
            "Get:24 http://archive.ubuntu.com/ubuntu jammy/main amd64 libijs-0.35 amd64 0.35-15build2 [16.5 kB]\n",
            "Get:25 http://archive.ubuntu.com/ubuntu jammy/main amd64 libjbig2dec0 amd64 0.19-3build2 [64.7 kB]\n",
            "Get:26 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libgs9 amd64 9.55.0~dfsg1-0ubuntu5.5 [5,030 kB]\n",
            "Get:27 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 ghostscript amd64 9.55.0~dfsg1-0ubuntu5.5 [49.5 kB]\n",
            "Get:28 http://archive.ubuntu.com/ubuntu jammy/universe amd64 dvipng amd64 1.15-1.1 [78.9 kB]\n",
            "Get:29 http://archive.ubuntu.com/ubuntu jammy/main amd64 libwoff1 amd64 1.0.2-1build4 [45.2 kB]\n",
            "Get:30 http://archive.ubuntu.com/ubuntu jammy/universe amd64 dvisvgm amd64 2.13.1-1 [1,221 kB]\n",
            "Get:31 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-noto-mono all 20201225-1build1 [397 kB]\n",
            "Get:32 http://archive.ubuntu.com/ubuntu jammy/universe amd64 fonts-texgyre all 20180621-3.1 [10.2 MB]\n",
            "Get:33 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libapache-pom-java all 18-1 [4,720 B]\n",
            "Get:34 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libcommons-parent-java all 43-1 [10.8 kB]\n",
            "Get:35 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libcommons-logging-java all 1.2-2 [60.3 kB]\n",
            "Get:36 http://archive.ubuntu.com/ubuntu jammy/main amd64 libfontenc1 amd64 1:1.1.4-1build3 [14.7 kB]\n",
            "Get:37 http://archive.ubuntu.com/ubuntu jammy/main amd64 rubygems-integration all 1.18 [5,336 B]\n",
            "Get:38 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 ruby3.0 amd64 3.0.2-7ubuntu2.4 [50.1 kB]\n",
            "Get:39 http://archive.ubuntu.com/ubuntu jammy/main amd64 ruby-rubygems all 3.3.5-2 [228 kB]\n",
            "Get:40 http://archive.ubuntu.com/ubuntu jammy/main amd64 ruby amd64 1:3.0~exp1 [5,100 B]\n",
            "Get:41 http://archive.ubuntu.com/ubuntu jammy/main amd64 rake all 13.0.6-2 [61.7 kB]\n",
            "Get:42 http://archive.ubuntu.com/ubuntu jammy/main amd64 ruby-net-telnet all 0.1.1-2 [12.6 kB]\n",
            "Get:43 http://archive.ubuntu.com/ubuntu jammy/universe amd64 ruby-webrick all 1.7.0-3 [51.8 kB]\n",
            "Get:44 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 ruby-xmlrpc all 0.3.2-1ubuntu0.1 [24.9 kB]\n",
            "Get:45 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libruby3.0 amd64 3.0.2-7ubuntu2.4 [5,113 kB]\n",
            "Get:46 http://archive.ubuntu.com/ubuntu jammy/main amd64 xfonts-encodings all 1:1.0.5-0ubuntu2 [578 kB]\n",
            "Get:47 http://archive.ubuntu.com/ubuntu jammy/main amd64 xfonts-utils amd64 1:7.7+6build2 [94.6 kB]\n",
            "Get:48 http://archive.ubuntu.com/ubuntu jammy/universe amd64 lmodern all 2.004.5-6.1 [9,471 kB]\n",
            "Get:49 http://archive.ubuntu.com/ubuntu jammy/universe amd64 preview-latex-style all 12.2-1ubuntu1 [185 kB]\n",
            "Get:50 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tex-gyre all 20180621-3.1 [6,209 kB]\n",
            "Get:51 http://archive.ubuntu.com/ubuntu jammy/universe amd64 texlive-fonts-recommended all 2021.20220204-1 [4,972 kB]\n",
            "Get:52 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libfontbox-java all 1:1.8.16-2 [207 kB]\n",
            "Get:53 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libpdfbox-java all 1:1.8.16-2 [5,199 kB]\n",
            "Get:54 http://archive.ubuntu.com/ubuntu jammy/universe amd64 texlive-pictures all 2021.20220204-1 [8,720 kB]\n",
            "Get:55 http://archive.ubuntu.com/ubuntu jammy/universe amd64 texlive-latex-extra all 2021.20220204-1 [13.9 MB]\n",
            "Get:56 http://archive.ubuntu.com/ubuntu jammy/universe amd64 texlive-plain-generic all 2021.20220204-1 [27.5 MB]\n",
            "Get:57 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tipa all 2:1.3-21 [2,967 kB]\n",
            "Fetched 195 MB in 16s (12.0 MB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 57.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package fonts-droid-fallback.\n",
            "(Reading database ... 120874 files and directories currently installed.)\n",
            "Preparing to unpack .../00-fonts-droid-fallback_1%3a6.0.1r16-1.1build1_all.deb ...\n",
            "Unpacking fonts-droid-fallback (1:6.0.1r16-1.1build1) ...\n",
            "Selecting previously unselected package fonts-lato.\n",
            "Preparing to unpack .../01-fonts-lato_2.0-2.1_all.deb ...\n",
            "Unpacking fonts-lato (2.0-2.1) ...\n",
            "Selecting previously unselected package poppler-data.\n",
            "Preparing to unpack .../02-poppler-data_0.4.11-1_all.deb ...\n",
            "Unpacking poppler-data (0.4.11-1) ...\n",
            "Selecting previously unselected package tex-common.\n",
            "Preparing to unpack .../03-tex-common_6.17_all.deb ...\n",
            "Unpacking tex-common (6.17) ...\n",
            "Selecting previously unselected package libkpathsea6:amd64.\n",
            "Preparing to unpack .../04-libkpathsea6_2021.20210626.59705-1ubuntu0.1_amd64.deb ...\n",
            "Unpacking libkpathsea6:amd64 (2021.20210626.59705-1ubuntu0.1) ...\n",
            "Selecting previously unselected package libptexenc1:amd64.\n",
            "Preparing to unpack .../05-libptexenc1_2021.20210626.59705-1ubuntu0.1_amd64.deb ...\n",
            "Unpacking libptexenc1:amd64 (2021.20210626.59705-1ubuntu0.1) ...\n",
            "Selecting previously unselected package libsynctex2:amd64.\n",
            "Preparing to unpack .../06-libsynctex2_2021.20210626.59705-1ubuntu0.1_amd64.deb ...\n",
            "Unpacking libsynctex2:amd64 (2021.20210626.59705-1ubuntu0.1) ...\n",
            "Selecting previously unselected package libtexlua53:amd64.\n",
            "Preparing to unpack .../07-libtexlua53_2021.20210626.59705-1ubuntu0.1_amd64.deb ...\n",
            "Unpacking libtexlua53:amd64 (2021.20210626.59705-1ubuntu0.1) ...\n",
            "Selecting previously unselected package libtexluajit2:amd64.\n",
            "Preparing to unpack .../08-libtexluajit2_2021.20210626.59705-1ubuntu0.1_amd64.deb ...\n",
            "Unpacking libtexluajit2:amd64 (2021.20210626.59705-1ubuntu0.1) ...\n",
            "Selecting previously unselected package t1utils.\n",
            "Preparing to unpack .../09-t1utils_1.41-4build2_amd64.deb ...\n",
            "Unpacking t1utils (1.41-4build2) ...\n",
            "Selecting previously unselected package libteckit0:amd64.\n",
            "Preparing to unpack .../10-libteckit0_2.5.11+ds1-1_amd64.deb ...\n",
            "Unpacking libteckit0:amd64 (2.5.11+ds1-1) ...\n",
            "Selecting previously unselected package libzzip-0-13:amd64.\n",
            "Preparing to unpack .../11-libzzip-0-13_0.13.72+dfsg.1-1.1_amd64.deb ...\n",
            "Unpacking libzzip-0-13:amd64 (0.13.72+dfsg.1-1.1) ...\n",
            "Selecting previously unselected package texlive-binaries.\n",
            "Preparing to unpack .../12-texlive-binaries_2021.20210626.59705-1ubuntu0.1_amd64.deb ...\n",
            "Unpacking texlive-binaries (2021.20210626.59705-1ubuntu0.1) ...\n",
            "Selecting previously unselected package texlive-base.\n",
            "Preparing to unpack .../13-texlive-base_2021.20220204-1_all.deb ...\n",
            "Unpacking texlive-base (2021.20220204-1) ...\n",
            "Selecting previously unselected package fonts-lmodern.\n",
            "Preparing to unpack .../14-fonts-lmodern_2.004.5-6.1_all.deb ...\n",
            "Unpacking fonts-lmodern (2.004.5-6.1) ...\n",
            "Selecting previously unselected package texlive-latex-base.\n",
            "Preparing to unpack .../15-texlive-latex-base_2021.20220204-1_all.deb ...\n",
            "Unpacking texlive-latex-base (2021.20220204-1) ...\n",
            "Selecting previously unselected package texlive-latex-recommended.\n",
            "Preparing to unpack .../16-texlive-latex-recommended_2021.20220204-1_all.deb ...\n",
            "Unpacking texlive-latex-recommended (2021.20220204-1) ...\n",
            "Selecting previously unselected package cm-super-minimal.\n",
            "Preparing to unpack .../17-cm-super-minimal_0.3.4-17_all.deb ...\n",
            "Unpacking cm-super-minimal (0.3.4-17) ...\n",
            "Selecting previously unselected package pfb2t1c2pfb.\n",
            "Preparing to unpack .../18-pfb2t1c2pfb_0.3-11_amd64.deb ...\n",
            "Unpacking pfb2t1c2pfb (0.3-11) ...\n",
            "Selecting previously unselected package cm-super.\n",
            "Preparing to unpack .../19-cm-super_0.3.4-17_all.deb ...\n",
            "Unpacking cm-super (0.3.4-17) ...\n",
            "Selecting previously unselected package fonts-urw-base35.\n",
            "Preparing to unpack .../20-fonts-urw-base35_20200910-1_all.deb ...\n",
            "Unpacking fonts-urw-base35 (20200910-1) ...\n",
            "Selecting previously unselected package libgs9-common.\n",
            "Preparing to unpack .../21-libgs9-common_9.55.0~dfsg1-0ubuntu5.5_all.deb ...\n",
            "Unpacking libgs9-common (9.55.0~dfsg1-0ubuntu5.5) ...\n",
            "Selecting previously unselected package libidn12:amd64.\n",
            "Preparing to unpack .../22-libidn12_1.38-4ubuntu1_amd64.deb ...\n",
            "Unpacking libidn12:amd64 (1.38-4ubuntu1) ...\n",
            "Selecting previously unselected package libijs-0.35:amd64.\n",
            "Preparing to unpack .../23-libijs-0.35_0.35-15build2_amd64.deb ...\n",
            "Unpacking libijs-0.35:amd64 (0.35-15build2) ...\n",
            "Selecting previously unselected package libjbig2dec0:amd64.\n",
            "Preparing to unpack .../24-libjbig2dec0_0.19-3build2_amd64.deb ...\n",
            "Unpacking libjbig2dec0:amd64 (0.19-3build2) ...\n",
            "Selecting previously unselected package libgs9:amd64.\n",
            "Preparing to unpack .../25-libgs9_9.55.0~dfsg1-0ubuntu5.5_amd64.deb ...\n",
            "Unpacking libgs9:amd64 (9.55.0~dfsg1-0ubuntu5.5) ...\n",
            "Selecting previously unselected package ghostscript.\n",
            "Preparing to unpack .../26-ghostscript_9.55.0~dfsg1-0ubuntu5.5_amd64.deb ...\n",
            "Unpacking ghostscript (9.55.0~dfsg1-0ubuntu5.5) ...\n",
            "Selecting previously unselected package dvipng.\n",
            "Preparing to unpack .../27-dvipng_1.15-1.1_amd64.deb ...\n",
            "Unpacking dvipng (1.15-1.1) ...\n",
            "Selecting previously unselected package libwoff1:amd64.\n",
            "Preparing to unpack .../28-libwoff1_1.0.2-1build4_amd64.deb ...\n",
            "Unpacking libwoff1:amd64 (1.0.2-1build4) ...\n",
            "Selecting previously unselected package dvisvgm.\n",
            "Preparing to unpack .../29-dvisvgm_2.13.1-1_amd64.deb ...\n",
            "Unpacking dvisvgm (2.13.1-1) ...\n",
            "Selecting previously unselected package fonts-noto-mono.\n",
            "Preparing to unpack .../30-fonts-noto-mono_20201225-1build1_all.deb ...\n",
            "Unpacking fonts-noto-mono (20201225-1build1) ...\n",
            "Selecting previously unselected package fonts-texgyre.\n",
            "Preparing to unpack .../31-fonts-texgyre_20180621-3.1_all.deb ...\n",
            "Unpacking fonts-texgyre (20180621-3.1) ...\n",
            "Selecting previously unselected package libapache-pom-java.\n",
            "Preparing to unpack .../32-libapache-pom-java_18-1_all.deb ...\n",
            "Unpacking libapache-pom-java (18-1) ...\n",
            "Selecting previously unselected package libcommons-parent-java.\n",
            "Preparing to unpack .../33-libcommons-parent-java_43-1_all.deb ...\n",
            "Unpacking libcommons-parent-java (43-1) ...\n",
            "Selecting previously unselected package libcommons-logging-java.\n",
            "Preparing to unpack .../34-libcommons-logging-java_1.2-2_all.deb ...\n",
            "Unpacking libcommons-logging-java (1.2-2) ...\n",
            "Selecting previously unselected package libfontenc1:amd64.\n",
            "Preparing to unpack .../35-libfontenc1_1%3a1.1.4-1build3_amd64.deb ...\n",
            "Unpacking libfontenc1:amd64 (1:1.1.4-1build3) ...\n",
            "Selecting previously unselected package rubygems-integration.\n",
            "Preparing to unpack .../36-rubygems-integration_1.18_all.deb ...\n",
            "Unpacking rubygems-integration (1.18) ...\n",
            "Selecting previously unselected package ruby3.0.\n",
            "Preparing to unpack .../37-ruby3.0_3.0.2-7ubuntu2.4_amd64.deb ...\n",
            "Unpacking ruby3.0 (3.0.2-7ubuntu2.4) ...\n",
            "Selecting previously unselected package ruby-rubygems.\n",
            "Preparing to unpack .../38-ruby-rubygems_3.3.5-2_all.deb ...\n",
            "Unpacking ruby-rubygems (3.3.5-2) ...\n",
            "Selecting previously unselected package ruby.\n",
            "Preparing to unpack .../39-ruby_1%3a3.0~exp1_amd64.deb ...\n",
            "Unpacking ruby (1:3.0~exp1) ...\n",
            "Selecting previously unselected package rake.\n",
            "Preparing to unpack .../40-rake_13.0.6-2_all.deb ...\n",
            "Unpacking rake (13.0.6-2) ...\n",
            "Selecting previously unselected package ruby-net-telnet.\n",
            "Preparing to unpack .../41-ruby-net-telnet_0.1.1-2_all.deb ...\n",
            "Unpacking ruby-net-telnet (0.1.1-2) ...\n",
            "Selecting previously unselected package ruby-webrick.\n",
            "Preparing to unpack .../42-ruby-webrick_1.7.0-3_all.deb ...\n",
            "Unpacking ruby-webrick (1.7.0-3) ...\n",
            "Selecting previously unselected package ruby-xmlrpc.\n",
            "Preparing to unpack .../43-ruby-xmlrpc_0.3.2-1ubuntu0.1_all.deb ...\n",
            "Unpacking ruby-xmlrpc (0.3.2-1ubuntu0.1) ...\n",
            "Selecting previously unselected package libruby3.0:amd64.\n",
            "Preparing to unpack .../44-libruby3.0_3.0.2-7ubuntu2.4_amd64.deb ...\n",
            "Unpacking libruby3.0:amd64 (3.0.2-7ubuntu2.4) ...\n",
            "Selecting previously unselected package xfonts-encodings.\n",
            "Preparing to unpack .../45-xfonts-encodings_1%3a1.0.5-0ubuntu2_all.deb ...\n",
            "Unpacking xfonts-encodings (1:1.0.5-0ubuntu2) ...\n",
            "Selecting previously unselected package xfonts-utils.\n",
            "Preparing to unpack .../46-xfonts-utils_1%3a7.7+6build2_amd64.deb ...\n",
            "Unpacking xfonts-utils (1:7.7+6build2) ...\n",
            "Selecting previously unselected package lmodern.\n",
            "Preparing to unpack .../47-lmodern_2.004.5-6.1_all.deb ...\n",
            "Unpacking lmodern (2.004.5-6.1) ...\n",
            "Selecting previously unselected package preview-latex-style.\n",
            "Preparing to unpack .../48-preview-latex-style_12.2-1ubuntu1_all.deb ...\n",
            "Unpacking preview-latex-style (12.2-1ubuntu1) ...\n",
            "Selecting previously unselected package tex-gyre.\n",
            "Preparing to unpack .../49-tex-gyre_20180621-3.1_all.deb ...\n",
            "Unpacking tex-gyre (20180621-3.1) ...\n",
            "Selecting previously unselected package texlive-fonts-recommended.\n",
            "Preparing to unpack .../50-texlive-fonts-recommended_2021.20220204-1_all.deb ...\n",
            "Unpacking texlive-fonts-recommended (2021.20220204-1) ...\n",
            "Selecting previously unselected package libfontbox-java.\n",
            "Preparing to unpack .../51-libfontbox-java_1%3a1.8.16-2_all.deb ...\n",
            "Unpacking libfontbox-java (1:1.8.16-2) ...\n",
            "Selecting previously unselected package libpdfbox-java.\n",
            "Preparing to unpack .../52-libpdfbox-java_1%3a1.8.16-2_all.deb ...\n",
            "Unpacking libpdfbox-java (1:1.8.16-2) ...\n",
            "Selecting previously unselected package texlive-pictures.\n",
            "Preparing to unpack .../53-texlive-pictures_2021.20220204-1_all.deb ...\n",
            "Unpacking texlive-pictures (2021.20220204-1) ...\n",
            "Selecting previously unselected package texlive-latex-extra.\n",
            "Preparing to unpack .../54-texlive-latex-extra_2021.20220204-1_all.deb ...\n",
            "Unpacking texlive-latex-extra (2021.20220204-1) ...\n",
            "Selecting previously unselected package texlive-plain-generic.\n",
            "Preparing to unpack .../55-texlive-plain-generic_2021.20220204-1_all.deb ...\n",
            "Unpacking texlive-plain-generic (2021.20220204-1) ...\n",
            "Selecting previously unselected package tipa.\n",
            "Preparing to unpack .../56-tipa_2%3a1.3-21_all.deb ...\n",
            "Unpacking tipa (2:1.3-21) ...\n",
            "Setting up pfb2t1c2pfb (0.3-11) ...\n",
            "Setting up fonts-lato (2.0-2.1) ...\n",
            "Setting up fonts-noto-mono (20201225-1build1) ...\n",
            "Setting up libwoff1:amd64 (1.0.2-1build4) ...\n",
            "Setting up libtexlua53:amd64 (2021.20210626.59705-1ubuntu0.1) ...\n",
            "Setting up libijs-0.35:amd64 (0.35-15build2) ...\n",
            "Setting up libtexluajit2:amd64 (2021.20210626.59705-1ubuntu0.1) ...\n",
            "Setting up libfontbox-java (1:1.8.16-2) ...\n",
            "Setting up rubygems-integration (1.18) ...\n",
            "Setting up libzzip-0-13:amd64 (0.13.72+dfsg.1-1.1) ...\n",
            "Setting up fonts-urw-base35 (20200910-1) ...\n",
            "Setting up poppler-data (0.4.11-1) ...\n",
            "Setting up tex-common (6.17) ...\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78.)\n",
            "debconf: falling back to frontend: Readline\n",
            "update-language: texlive-base not installed and configured, doing nothing!\n",
            "Setting up libfontenc1:amd64 (1:1.1.4-1build3) ...\n",
            "Setting up libjbig2dec0:amd64 (0.19-3build2) ...\n",
            "Setting up libteckit0:amd64 (2.5.11+ds1-1) ...\n",
            "Setting up libapache-pom-java (18-1) ...\n",
            "Setting up ruby-net-telnet (0.1.1-2) ...\n",
            "Setting up xfonts-encodings (1:1.0.5-0ubuntu2) ...\n",
            "Setting up t1utils (1.41-4build2) ...\n",
            "Setting up libidn12:amd64 (1.38-4ubuntu1) ...\n",
            "Setting up fonts-texgyre (20180621-3.1) ...\n",
            "Setting up libkpathsea6:amd64 (2021.20210626.59705-1ubuntu0.1) ...\n",
            "Setting up ruby-webrick (1.7.0-3) ...\n",
            "Setting up fonts-lmodern (2.004.5-6.1) ...\n",
            "Setting up fonts-droid-fallback (1:6.0.1r16-1.1build1) ...\n",
            "Setting up ruby-xmlrpc (0.3.2-1ubuntu0.1) ...\n",
            "Setting up libsynctex2:amd64 (2021.20210626.59705-1ubuntu0.1) ...\n",
            "Setting up libgs9-common (9.55.0~dfsg1-0ubuntu5.5) ...\n",
            "Setting up libpdfbox-java (1:1.8.16-2) ...\n",
            "Setting up libgs9:amd64 (9.55.0~dfsg1-0ubuntu5.5) ...\n",
            "Setting up preview-latex-style (12.2-1ubuntu1) ...\n",
            "Setting up libcommons-parent-java (43-1) ...\n",
            "Setting up dvisvgm (2.13.1-1) ...\n",
            "Setting up libcommons-logging-java (1.2-2) ...\n",
            "Setting up ghostscript (9.55.0~dfsg1-0ubuntu5.5) ...\n",
            "Setting up xfonts-utils (1:7.7+6build2) ...\n",
            "Setting up libptexenc1:amd64 (2021.20210626.59705-1ubuntu0.1) ...\n",
            "Setting up texlive-binaries (2021.20210626.59705-1ubuntu0.1) ...\n",
            "update-alternatives: using /usr/bin/xdvi-xaw to provide /usr/bin/xdvi.bin (xdvi.bin) in auto mode\n",
            "update-alternatives: using /usr/bin/bibtex.original to provide /usr/bin/bibtex (bibtex) in auto mode\n",
            "Setting up lmodern (2.004.5-6.1) ...\n",
            "Setting up texlive-base (2021.20220204-1) ...\n",
            "/usr/bin/ucfr\n",
            "/usr/bin/ucfr\n",
            "/usr/bin/ucfr\n",
            "/usr/bin/ucfr\n",
            "mktexlsr: Updating /var/lib/texmf/ls-R-TEXLIVEDIST... \n",
            "mktexlsr: Updating /var/lib/texmf/ls-R-TEXMFMAIN... \n",
            "mktexlsr: Updating /var/lib/texmf/ls-R... \n",
            "mktexlsr: Done.\n",
            "tl-paper: setting paper size for dvips to a4: /var/lib/texmf/dvips/config/config-paper.ps\n",
            "tl-paper: setting paper size for dvipdfmx to a4: /var/lib/texmf/dvipdfmx/dvipdfmx-paper.cfg\n",
            "tl-paper: setting paper size for xdvi to a4: /var/lib/texmf/xdvi/XDvi-paper\n",
            "tl-paper: setting paper size for pdftex to a4: /var/lib/texmf/tex/generic/tex-ini-files/pdftexconfig.tex\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78.)\n",
            "debconf: falling back to frontend: Readline\n",
            "Setting up tex-gyre (20180621-3.1) ...\n",
            "Setting up dvipng (1.15-1.1) ...\n",
            "Setting up texlive-plain-generic (2021.20220204-1) ...\n",
            "Setting up texlive-latex-base (2021.20220204-1) ...\n",
            "Setting up texlive-latex-recommended (2021.20220204-1) ...\n",
            "Setting up texlive-pictures (2021.20220204-1) ...\n",
            "Setting up texlive-fonts-recommended (2021.20220204-1) ...\n",
            "Setting up tipa (2:1.3-21) ...\n",
            "Setting up cm-super-minimal (0.3.4-17) ...\n",
            "Setting up texlive-latex-extra (2021.20220204-1) ...\n",
            "Setting up cm-super (0.3.4-17) ...\n",
            "Creating fonts. This may take some time... done.\n",
            "Setting up rake (13.0.6-2) ...\n",
            "Setting up libruby3.0:amd64 (3.0.2-7ubuntu2.4) ...\n",
            "Setting up ruby3.0 (3.0.2-7ubuntu2.4) ...\n",
            "Setting up ruby (1:3.0~exp1) ...\n",
            "Setting up ruby-rubygems (3.3.5-2) ...\n",
            "Processing triggers for man-db (2.10.2-1) ...\n",
            "Processing triggers for fontconfig (2.13.1-4.2ubuntu5) ...\n",
            "Processing triggers for libc-bin (2.35-0ubuntu3.1) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
            "\n",
            "Processing triggers for tex-common (6.17) ...\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78.)\n",
            "debconf: falling back to frontend: Readline\n",
            "Running updmap-sys. This may take some time... done.\n",
            "Running mktexlsr /var/lib/texmf ... done.\n",
            "Building format(s) --all.\n",
            "\tThis may take some time... done.\n"
          ]
        }
      ],
      "source": [
        "!sudo apt-get install texlive-latex-extra texlive-fonts-recommended dvipng cm-super"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "0iVEzGzA_L6C"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# setup matplotlib to use LaTeX\n",
        "import matplotlib\n",
        "matplotlib.use(\"pgf\")\n",
        "matplotlib.rcParams.update({\n",
        "    \"pgf.texsystem\": \"pdflatex\",\n",
        "    'font.family': 'serif',\n",
        "    'text.usetex': True,\n",
        "    'pgf.rcfonts': False,\n",
        "    'legend.fontsize': 'x-large',\n",
        "    'figure.figsize': (15, 5),\n",
        "    'axes.labelsize': 'x-large',\n",
        "    'axes.titlesize':'x-large',\n",
        "    'xtick.labelsize':'x-large',\n",
        "    'ytick.labelsize':'x-large'\n",
        "})\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.colors as mcolors\n",
        "\n",
        "from matplotlib.patches import Patch\n",
        "from matplotlib.collections import PatchCollection\n",
        "from matplotlib.colors import ListedColormap\n",
        "\n",
        "# Import necessary modules\n",
        "from matplotlib.ticker import FuncFormatter\n",
        "from matplotlib.dates import YearLocator, DateFormatter\n",
        "\n",
        "import pdflatex\n",
        "\n",
        "import numpy as np\n",
        "from adjustText import adjust_text\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.metrics import r2_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0N5BJvcFJeVu"
      },
      "source": [
        "#Get Data from Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "gTHUHFv6Ky29"
      },
      "outputs": [],
      "source": [
        "#from google.colab import auth\n",
        "#auth.authenticate_user()\n",
        "\n",
        "#import gspread\n",
        "#from google.auth import default\n",
        "#creds, _ = default()\n",
        "\n",
        "#gc = gspread.authorize(creds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "t3pudp9SK5ka"
      },
      "outputs": [],
      "source": [
        "#worksheet = gc.open('Dissertation-Evaluation-Prior-Work').sheet1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rr3RORQY8Xsm"
      },
      "source": [
        "# Get data from public [GitHub](https://github.com/M-Colley/colley-dissertation/tree/main/data) repository"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "QB0riXRALBko",
        "outputId": "e5f4fb15-79b5-480f-9b1c-e3fa25383c45"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Paper                                                URL  \\\n",
              "0    locken2016autoambicar     https://dl.acm.org/doi/10.1145/3379336.3381502   \n",
              "1   wilbrink2020reflecting          http://dx.doi.org/10.1145/3004323.3004329   \n",
              "2       lindemann2018catch                        https://www.mdpi.com/349948   \n",
              "3       lindemann2018catch                        https://www.mdpi.com/349948   \n",
              "4        currano2021little                                                NaN   \n",
              "..                     ...                                                ...   \n",
              "76       schneider2023dont  https://dl.acm.org/doi/pdf/10.1145/3581641.358...   \n",
              "77           omeiza2021not  https://ieeexplore.ieee.org/stamp/stamp.jsp?ar...   \n",
              "78           omeiza2021not  https://ieeexplore.ieee.org/stamp/stamp.jsp?ar...   \n",
              "79           omeiza2021not  https://ieeexplore.ieee.org/stamp/stamp.jsp?ar...   \n",
              "80           omeiza2021not  https://ieeexplore.ieee.org/stamp/stamp.jsp?ar...   \n",
              "\n",
              "    Country  Mean    SD  scale-min  scale-max    year      N  \\\n",
              "0   Germany   NaN   NaN        NaN        NaN     NaN    NaN   \n",
              "1   Germany   NaN   NaN        NaN        NaN     NaN    NaN   \n",
              "2   Germany  4.62  1.41        1.0        7.0  2018.0   32.0   \n",
              "3   Germany  5.53  1.08        1.0        7.0  2018.0   32.0   \n",
              "4       USA   NaN   NaN        NaN        NaN     NaN    NaN   \n",
              "..      ...   ...   ...        ...        ...     ...    ...   \n",
              "76  Germany   NaN   NaN        1.0        5.0  2023.0  113.0   \n",
              "77      USA  2.50  0.50        1.0        5.0  2021.0  101.0   \n",
              "78      USA  2.90  0.40        1.0        5.0  2021.0  101.0   \n",
              "79      USA  3.00  0.50        1.0        5.0  2021.0  101.0   \n",
              "80      USA  2.80  0.50        1.0        5.0  2021.0  101.0   \n",
              "\n",
              "   Visualization-level  SAE Uncertainty Own  Exclude            Reason  \\\n",
              "0                  NaN  NaN         NaN  No     True  No mean provided   \n",
              "1                  NaN  NaN         NaN  No     True  No mean provided   \n",
              "2                 None  5.0          No  No    False               NaN   \n",
              "3             Multiple  5.0          No  No    False               NaN   \n",
              "4                  NaN  NaN         NaN  No     True          No Trust   \n",
              "..                 ...  ...         ...  ..      ...               ...   \n",
              "76                 NaN  4.0          No  No     True               NaN   \n",
              "77            Multiple  4.0          No  No    False               NaN   \n",
              "78            Multiple  4.0          No  No    False               NaN   \n",
              "79            Multiple  4.0          No  No    False               NaN   \n",
              "80            Multiple  4.0          No  No    False               NaN   \n",
              "\n",
              "                                Remarks               Unnamed: 16 Unnamed: 17  \n",
              "0                                   NaN                       NaN         NaN  \n",
              "1                                   NaN                       NaN         NaN  \n",
              "2                  Took high visibility                       NaN         NaN  \n",
              "3                  Took high visibility                       NaN         NaN  \n",
              "4                                   NaN                       NaN         NaN  \n",
              "..                                  ...                       ...         ...  \n",
              "76  between-subject; trust not reported                       NaN         NaN  \n",
              "77                                 What  values are guessed; post         NaN  \n",
              "78                              What if  values are guessed; post         NaN  \n",
              "79                                  Why  values are guessed; post         NaN  \n",
              "80                              Why not  values are guessed; post         NaN  \n",
              "\n",
              "[81 rows x 18 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2b1fa858-5065-4129-9408-d88217fca177\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Paper</th>\n",
              "      <th>URL</th>\n",
              "      <th>Country</th>\n",
              "      <th>Mean</th>\n",
              "      <th>SD</th>\n",
              "      <th>scale-min</th>\n",
              "      <th>scale-max</th>\n",
              "      <th>year</th>\n",
              "      <th>N</th>\n",
              "      <th>Visualization-level</th>\n",
              "      <th>SAE</th>\n",
              "      <th>Uncertainty</th>\n",
              "      <th>Own</th>\n",
              "      <th>Exclude</th>\n",
              "      <th>Reason</th>\n",
              "      <th>Remarks</th>\n",
              "      <th>Unnamed: 16</th>\n",
              "      <th>Unnamed: 17</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>locken2016autoambicar</td>\n",
              "      <td>https://dl.acm.org/doi/10.1145/3379336.3381502</td>\n",
              "      <td>Germany</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>True</td>\n",
              "      <td>No mean provided</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>wilbrink2020reflecting</td>\n",
              "      <td>http://dx.doi.org/10.1145/3004323.3004329</td>\n",
              "      <td>Germany</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>True</td>\n",
              "      <td>No mean provided</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>lindemann2018catch</td>\n",
              "      <td>https://www.mdpi.com/349948</td>\n",
              "      <td>Germany</td>\n",
              "      <td>4.62</td>\n",
              "      <td>1.41</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>None</td>\n",
              "      <td>5.0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Took high visibility</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>lindemann2018catch</td>\n",
              "      <td>https://www.mdpi.com/349948</td>\n",
              "      <td>Germany</td>\n",
              "      <td>5.53</td>\n",
              "      <td>1.08</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>Multiple</td>\n",
              "      <td>5.0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Took high visibility</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>currano2021little</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>True</td>\n",
              "      <td>No Trust</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>schneider2023dont</td>\n",
              "      <td>https://dl.acm.org/doi/pdf/10.1145/3581641.358...</td>\n",
              "      <td>Germany</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>between-subject; trust not reported</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>omeiza2021not</td>\n",
              "      <td>https://ieeexplore.ieee.org/stamp/stamp.jsp?ar...</td>\n",
              "      <td>USA</td>\n",
              "      <td>2.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>Multiple</td>\n",
              "      <td>4.0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>What</td>\n",
              "      <td>values are guessed; post</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>omeiza2021not</td>\n",
              "      <td>https://ieeexplore.ieee.org/stamp/stamp.jsp?ar...</td>\n",
              "      <td>USA</td>\n",
              "      <td>2.90</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>Multiple</td>\n",
              "      <td>4.0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>What if</td>\n",
              "      <td>values are guessed; post</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>omeiza2021not</td>\n",
              "      <td>https://ieeexplore.ieee.org/stamp/stamp.jsp?ar...</td>\n",
              "      <td>USA</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>Multiple</td>\n",
              "      <td>4.0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Why</td>\n",
              "      <td>values are guessed; post</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>omeiza2021not</td>\n",
              "      <td>https://ieeexplore.ieee.org/stamp/stamp.jsp?ar...</td>\n",
              "      <td>USA</td>\n",
              "      <td>2.80</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>Multiple</td>\n",
              "      <td>4.0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Why not</td>\n",
              "      <td>values are guessed; post</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>81 rows × 18 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2b1fa858-5065-4129-9408-d88217fca177')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-2b1fa858-5065-4129-9408-d88217fca177 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-2b1fa858-5065-4129-9408-d88217fca177');\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",
              "\n",
              "\n",
              "<div id=\"df-8eaa6d44-8db2-4af4-a349-3eefb72e43a1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8eaa6d44-8db2-4af4-a349-3eefb72e43a1')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8eaa6d44-8db2-4af4-a349-3eefb72e43a1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# get_all_values gives a list of rows.\n",
        "#rows = worksheet.get_all_values()\n",
        "#print(rows)\n",
        "\n",
        "#main_df = pd.DataFrame.from_records(rows)\n",
        "\n",
        "\n",
        "#url1 = 'https://github.com/M-Colley/colley-dissertation/blob/main/data/Dissertation-Evaluation-Prior-Work.csv'\n",
        "url1 = 'https://github.com/M-Colley/colley-dissertation/blob/main/data/Dissertation-Evaluation-Prior-Work.xlsx?raw=true'\n",
        "main_df = pd.read_excel(url1,  engine='openpyxl')\n",
        "main_df\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2bm-X56herS7"
      },
      "source": [
        "# Define own functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "2pxclv9xep96"
      },
      "outputs": [],
      "source": [
        "# Function to normalize a value\n",
        "def normalize(x_vector, old_min, old_max, new_min, new_max):\n",
        "    return new_min + ((x_vector - old_min) / (old_max - old_min)) * (new_max - new_min)\n",
        "\n",
        "# Function to adjust standard deviation\n",
        "#def adjust_sd(sd_vector, old_range, new_range):\n",
        "#    return sd_vector * (new_range / old_range)\n",
        "\n",
        "def adjust_sd(sd_vector, old_range, new_range, omit_na=False, make_na_to_one=False):\n",
        "    #sd_vector = sd_vector.copy()\n",
        "    if omit_na:\n",
        "        # If any sd is NA, replace with NA\n",
        "        sd_vector = sd_vector.where(pd.notna(sd_vector), np.nan)\n",
        "    if make_na_to_one:\n",
        "        # If any sd is NA, replace with 1.0\n",
        "        sd_vector = sd_vector.where(pd.notna(sd_vector), 1.0)\n",
        "    return sd_vector * (new_range / old_range)\n",
        "\n",
        "# Set color palette\n",
        "cavalcanti1 = mcolors.LinearSegmentedColormap.from_list(\"Cavalcanti1\", [\"#F2D7D5\", \"#D4AC0D\"], N=2)\n",
        "ownPalette = mcolors.LinearSegmentedColormap.from_list(\"Cavalcanti1\", [\"#000000\", \"#E69F00\"], N=2)\n",
        "visualization_levels = [\"None\", \"Sit. Detection\", \"Sit. Prediction\", \"Traj. Planning\", \"Multiple\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osDQ07GEehiS"
      },
      "source": [
        "# Make first row to header (not necessary from `.csv` but for Google Drive)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "qylGcd5HMGzB"
      },
      "outputs": [],
      "source": [
        "#main_df.rename(columns=main_df.iloc[0])\n",
        "\n",
        "#headers = main_df.iloc[0]\n",
        "#main_df  = pd.DataFrame(main_df.values[1:], columns=headers)\n",
        "#main_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "jo3EddykLd57"
      },
      "outputs": [],
      "source": [
        "# Convert data types\n",
        "#main_df['Mean'] = pd.to_numeric(main_df['Mean'].str.replace(',', '.'), errors='coerce')\n",
        "#main_df['SD'] = pd.to_numeric(main_df['SD'].str.replace(',', '.'), errors='coerce')\n",
        "\n",
        "main_df['scale-min'] = pd.to_numeric(main_df['scale-min'], errors='coerce')\n",
        "main_df['scale-max'] = pd.to_numeric(main_df['scale-max'], errors='coerce')\n",
        "\n",
        "\n",
        "# Filter out rows with Exclude is True\n",
        "#main_df = main_df[main_df['Exclude'] != \"TRUE\"]\n",
        "# Filter out rows with Exclude is True and create an explicit copy\n",
        "main_df = main_df[main_df['Exclude'] != \"TRUE\"].copy()\n",
        "\n",
        "\n",
        "# show data?\n",
        "#main_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 859
        },
        "id": "HcUNsS4kNglu",
        "outputId": "7b991b97-d3dc-443a-ffc3-ef9483a1c144"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Paper                                                URL  \\\n",
              "0    locken2016autoambicar     https://dl.acm.org/doi/10.1145/3379336.3381502   \n",
              "1   wilbrink2020reflecting          http://dx.doi.org/10.1145/3004323.3004329   \n",
              "2       lindemann2018catch                        https://www.mdpi.com/349948   \n",
              "3       lindemann2018catch                        https://www.mdpi.com/349948   \n",
              "4        currano2021little                                                NaN   \n",
              "..                     ...                                                ...   \n",
              "76       schneider2023dont  https://dl.acm.org/doi/pdf/10.1145/3581641.358...   \n",
              "77           omeiza2021not  https://ieeexplore.ieee.org/stamp/stamp.jsp?ar...   \n",
              "78           omeiza2021not  https://ieeexplore.ieee.org/stamp/stamp.jsp?ar...   \n",
              "79           omeiza2021not  https://ieeexplore.ieee.org/stamp/stamp.jsp?ar...   \n",
              "80           omeiza2021not  https://ieeexplore.ieee.org/stamp/stamp.jsp?ar...   \n",
              "\n",
              "    Country  Mean    SD  scale-min  scale-max    year      N  \\\n",
              "0   Germany   NaN   NaN        NaN        NaN     NaN    NaN   \n",
              "1   Germany   NaN   NaN        NaN        NaN     NaN    NaN   \n",
              "2   Germany  4.62  1.41        1.0        7.0  2018.0   32.0   \n",
              "3   Germany  5.53  1.08        1.0        7.0  2018.0   32.0   \n",
              "4       USA   NaN   NaN        NaN        NaN     NaN    NaN   \n",
              "..      ...   ...   ...        ...        ...     ...    ...   \n",
              "76  Germany   NaN   NaN        1.0        5.0  2023.0  113.0   \n",
              "77      USA  2.50  0.50        1.0        5.0  2021.0  101.0   \n",
              "78      USA  2.90  0.40        1.0        5.0  2021.0  101.0   \n",
              "79      USA  3.00  0.50        1.0        5.0  2021.0  101.0   \n",
              "80      USA  2.80  0.50        1.0        5.0  2021.0  101.0   \n",
              "\n",
              "   Visualization-level  ...  Uncertainty Own Exclude            Reason  \\\n",
              "0                  NaN  ...          NaN  No    True  No mean provided   \n",
              "1                  NaN  ...          NaN  No    True  No mean provided   \n",
              "2                 None  ...           No  No   False               NaN   \n",
              "3             Multiple  ...           No  No   False               NaN   \n",
              "4                  NaN  ...          NaN  No    True          No Trust   \n",
              "..                 ...  ...          ...  ..     ...               ...   \n",
              "76                 NaN  ...           No  No    True               NaN   \n",
              "77            Multiple  ...           No  No   False               NaN   \n",
              "78            Multiple  ...           No  No   False               NaN   \n",
              "79            Multiple  ...           No  No   False               NaN   \n",
              "80            Multiple  ...           No  No   False               NaN   \n",
              "\n",
              "                                Remarks               Unnamed: 16 Unnamed: 17  \\\n",
              "0                                   NaN                       NaN         NaN   \n",
              "1                                   NaN                       NaN         NaN   \n",
              "2                  Took high visibility                       NaN         NaN   \n",
              "3                  Took high visibility                       NaN         NaN   \n",
              "4                                   NaN                       NaN         NaN   \n",
              "..                                  ...                       ...         ...   \n",
              "76  between-subject; trust not reported                       NaN         NaN   \n",
              "77                                 What  values are guessed; post         NaN   \n",
              "78                              What if  values are guessed; post         NaN   \n",
              "79                                  Why  values are guessed; post         NaN   \n",
              "80                              Why not  values are guessed; post         NaN   \n",
              "\n",
              "   normalized_trust  normalized_sd                      Paper_adj  \n",
              "0               NaN            NaN   \\cite{locken2016autoambicar}  \n",
              "1               NaN            NaN  \\cite{wilbrink2020reflecting}  \n",
              "2          3.413333           0.94      \\cite{lindemann2018catch}  \n",
              "3          4.020000           0.72      \\cite{lindemann2018catch}  \n",
              "4               NaN            NaN       \\cite{currano2021little}  \n",
              "..              ...            ...                            ...  \n",
              "76              NaN           1.00       \\cite{schneider2023dont}  \n",
              "77         2.500000           0.50           \\cite{omeiza2021not}  \n",
              "78         2.900000           0.40           \\cite{omeiza2021not}  \n",
              "79         3.000000           0.50           \\cite{omeiza2021not}  \n",
              "80         2.800000           0.50           \\cite{omeiza2021not}  \n",
              "\n",
              "[81 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a80489d5-55ae-450d-b904-7c449b508cc9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Paper</th>\n",
              "      <th>URL</th>\n",
              "      <th>Country</th>\n",
              "      <th>Mean</th>\n",
              "      <th>SD</th>\n",
              "      <th>scale-min</th>\n",
              "      <th>scale-max</th>\n",
              "      <th>year</th>\n",
              "      <th>N</th>\n",
              "      <th>Visualization-level</th>\n",
              "      <th>...</th>\n",
              "      <th>Uncertainty</th>\n",
              "      <th>Own</th>\n",
              "      <th>Exclude</th>\n",
              "      <th>Reason</th>\n",
              "      <th>Remarks</th>\n",
              "      <th>Unnamed: 16</th>\n",
              "      <th>Unnamed: 17</th>\n",
              "      <th>normalized_trust</th>\n",
              "      <th>normalized_sd</th>\n",
              "      <th>Paper_adj</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>locken2016autoambicar</td>\n",
              "      <td>https://dl.acm.org/doi/10.1145/3379336.3381502</td>\n",
              "      <td>Germany</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>True</td>\n",
              "      <td>No mean provided</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>\\cite{locken2016autoambicar}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>wilbrink2020reflecting</td>\n",
              "      <td>http://dx.doi.org/10.1145/3004323.3004329</td>\n",
              "      <td>Germany</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>True</td>\n",
              "      <td>No mean provided</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>\\cite{wilbrink2020reflecting}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>lindemann2018catch</td>\n",
              "      <td>https://www.mdpi.com/349948</td>\n",
              "      <td>Germany</td>\n",
              "      <td>4.62</td>\n",
              "      <td>1.41</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>None</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Took high visibility</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.413333</td>\n",
              "      <td>0.94</td>\n",
              "      <td>\\cite{lindemann2018catch}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>lindemann2018catch</td>\n",
              "      <td>https://www.mdpi.com/349948</td>\n",
              "      <td>Germany</td>\n",
              "      <td>5.53</td>\n",
              "      <td>1.08</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>Multiple</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Took high visibility</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.020000</td>\n",
              "      <td>0.72</td>\n",
              "      <td>\\cite{lindemann2018catch}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>currano2021little</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>True</td>\n",
              "      <td>No Trust</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>\\cite{currano2021little}</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>76</th>\n",
              "      <td>schneider2023dont</td>\n",
              "      <td>https://dl.acm.org/doi/pdf/10.1145/3581641.358...</td>\n",
              "      <td>Germany</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>between-subject; trust not reported</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "      <td>\\cite{schneider2023dont}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>omeiza2021not</td>\n",
              "      <td>https://ieeexplore.ieee.org/stamp/stamp.jsp?ar...</td>\n",
              "      <td>USA</td>\n",
              "      <td>2.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>Multiple</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>What</td>\n",
              "      <td>values are guessed; post</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>0.50</td>\n",
              "      <td>\\cite{omeiza2021not}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>omeiza2021not</td>\n",
              "      <td>https://ieeexplore.ieee.org/stamp/stamp.jsp?ar...</td>\n",
              "      <td>USA</td>\n",
              "      <td>2.90</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>Multiple</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>What if</td>\n",
              "      <td>values are guessed; post</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>0.40</td>\n",
              "      <td>\\cite{omeiza2021not}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>omeiza2021not</td>\n",
              "      <td>https://ieeexplore.ieee.org/stamp/stamp.jsp?ar...</td>\n",
              "      <td>USA</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>Multiple</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Why</td>\n",
              "      <td>values are guessed; post</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.50</td>\n",
              "      <td>\\cite{omeiza2021not}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>omeiza2021not</td>\n",
              "      <td>https://ieeexplore.ieee.org/stamp/stamp.jsp?ar...</td>\n",
              "      <td>USA</td>\n",
              "      <td>2.80</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>Multiple</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Why not</td>\n",
              "      <td>values are guessed; post</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>0.50</td>\n",
              "      <td>\\cite{omeiza2021not}</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>81 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a80489d5-55ae-450d-b904-7c449b508cc9')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-a80489d5-55ae-450d-b904-7c449b508cc9 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-a80489d5-55ae-450d-b904-7c449b508cc9');\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",
              "\n",
              "\n",
              "<div id=\"df-db81888d-184c-4685-8ae3-41a5191fd81d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-db81888d-184c-4685-8ae3-41a5191fd81d')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-db81888d-184c-4685-8ae3-41a5191fd81d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Normalize trust and adjust SD\n",
        "main_df['normalized_trust'] = normalize(main_df['Mean'], main_df['scale-min'], main_df['scale-max'], new_min=1, new_max=5)\n",
        "main_df['normalized_sd'] = adjust_sd(main_df['SD'], old_range=main_df['scale-max'] - main_df['scale-min'], new_range=4, make_na_to_one=True)\n",
        "\n",
        "# Reorder the 'Visualization-level' column\n",
        "main_df['Visualization-level'] = pd.Categorical(main_df['Visualization-level'], categories=visualization_levels, ordered=True)\n",
        "\n",
        "# Convert columns to factor\n",
        "main_df['Uncertainty'] = main_df['Uncertainty'].astype('category')\n",
        "main_df['Own'] = main_df['Own'].astype('category')\n",
        "\n",
        "# Creating the citation labels\n",
        "main_df['Paper_adj'] = \"\\\\cite{\" + main_df['Paper'] + \"}\"\n",
        "\n",
        "\n",
        "main_df\n",
        "#main_df['Paper_adj_2'] = \"[\" + main_df['Paper'] + \"]\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "UOBIsqHcTb4n"
      },
      "outputs": [],
      "source": [
        "# ordering for the x-axis\n",
        "main_df = main_df.sort_values(by=\"Visualization-level\", ascending=True)\n",
        "\n",
        "# Creating a color map for the 'Own' column\n",
        "unique_owns = main_df['Own'].unique()\n",
        "colors = {own: ownPalette(i) for i, own in enumerate(unique_owns)}\n",
        "\n",
        "# Define the mapping from visualization levels to numbers\n",
        "vis_level_mapping = {level: i for i, level in enumerate(main_df['Visualization-level'].unique())}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jbqyq-R3tSRr"
      },
      "source": [
        "# This would be the correct one, for the thesis this was done in R\n",
        "\n",
        "Do not execute, is buggy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "0Z3LWKXxUf9a",
        "outputId": "4e62e1d0-1120-4cf6-bb8c-3c7a53b6156f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nfig, ax = plt.subplots(figsize=(7,9))\\n\\n# Define the mapping from visualization levels to numbers\\nvis_level_mapping = {level: i for i, level in enumerate(main_df['Visualization-level'].unique())}\\n\\nplt.style.use('seaborn-v0_8-colorblind')\\n\\ntexts = []  # Create an empty list to hold the text objects\\npoints = []  # Create an empty list to hold the points\\n\\nfor i, row in main_df.iterrows():\\n\\n    x = vis_level_mapping[row['Visualization-level']]\\n    y = row['normalized_trust']\\n\\n    ax.scatter(x, y, s=row['normalized_sd']*100, color=colors[row['Own']], alpha=0.8)\\n    text = ax.text(x, y, row['Paper_adj'])\\n\\n    texts.append(text)  # Add the text object to the list\\n    points.append((x, y))  # Add the point to the list\\n\\n# Use adjust_text to iterate through the text objects and adjust their position to minimize overlaps\\nadjust_text(texts)\\n\\n# Draw arrows from points to their corresponding adjusted texts\\nfor text, (x, y) in zip(texts, points):\\n    x_text, y_text = text.get_position()\\n    ax.annotate('', xy=(x, y), xytext=(x_text, y_text),\\n                arrowprops=dict(arrowstyle='-', color='black'))\\n\\n# Set the x-ticks to be the names of the visualization levels\\nax.set_xticks(range(len(vis_level_mapping)))\\nax.set_xticklabels(vis_level_mapping.keys())\\n\\nax.set_ylim(1.5,4.5)\\nax.set_ylabel('Trust in Automation (normalized)')\\nax.set_axisbelow(True)\\nax.yaxis.grid(color='gray', linestyle='dashed')\\nax.grid(True)\\n\\n#plt.savefig('prior_work_categorized.pgf')\\n\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "\"\"\"\n",
        "fig, ax = plt.subplots(figsize=(7,9))\n",
        "\n",
        "# Define the mapping from visualization levels to numbers\n",
        "vis_level_mapping = {level: i for i, level in enumerate(main_df['Visualization-level'].unique())}\n",
        "\n",
        "plt.style.use('seaborn-v0_8-colorblind')\n",
        "\n",
        "texts = []  # Create an empty list to hold the text objects\n",
        "points = []  # Create an empty list to hold the points\n",
        "\n",
        "for i, row in main_df.iterrows():\n",
        "\n",
        "    x = vis_level_mapping[row['Visualization-level']]\n",
        "    y = row['normalized_trust']\n",
        "\n",
        "    ax.scatter(x, y, s=row['normalized_sd']*100, color=colors[row['Own']], alpha=0.8)\n",
        "    text = ax.text(x, y, row['Paper_adj'])\n",
        "\n",
        "    texts.append(text)  # Add the text object to the list\n",
        "    points.append((x, y))  # Add the point to the list\n",
        "\n",
        "# Use adjust_text to iterate through the text objects and adjust their position to minimize overlaps\n",
        "adjust_text(texts)\n",
        "\n",
        "# Draw arrows from points to their corresponding adjusted texts\n",
        "for text, (x, y) in zip(texts, points):\n",
        "    x_text, y_text = text.get_position()\n",
        "    ax.annotate('', xy=(x, y), xytext=(x_text, y_text),\n",
        "                arrowprops=dict(arrowstyle='-', color='black'))\n",
        "\n",
        "# Set the x-ticks to be the names of the visualization levels\n",
        "ax.set_xticks(range(len(vis_level_mapping)))\n",
        "ax.set_xticklabels(vis_level_mapping.keys())\n",
        "\n",
        "ax.set_ylim(1.5,4.5)\n",
        "ax.set_ylabel('Trust in Automation (normalized)')\n",
        "ax.set_axisbelow(True)\n",
        "ax.yaxis.grid(color='gray', linestyle='dashed')\n",
        "ax.grid(True)\n",
        "\n",
        "#plt.savefig('prior_work_categorized.pgf')\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ni0ibAMv_4ov"
      },
      "source": [
        "# Now visualize the own work in their relation to the Levels of Capability"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OXiyxhALt1d4"
      },
      "source": [
        "##Prepare Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Svb931pJ-5c8"
      },
      "outputs": [],
      "source": [
        "#worksheet2 = gc.open('Dissertation-Own-Work').sheet1\n",
        "\n",
        "# get_all_values gives a list of rows.\n",
        "#rows = worksheet2.get_all_values()\n",
        "#print(rows)\n",
        "\n",
        "#main_df_2 = pd.DataFrame.from_records(rows)\n",
        "\n",
        "\n",
        "url2 = 'https://github.com/M-Colley/colley-dissertation/blob/main/data/Dissertation-Own-Work.xlsx?raw=true'\n",
        "main_df_2 = pd.read_excel(url2)\n",
        "\n",
        "#headers = main_df_2.iloc[0]\n",
        "#main_df_2  = pd.DataFrame(main_df_2.values[1:], columns=headers)\n",
        "\n",
        "# Convert data types\n",
        "#main_df_2['Mean'] = pd.to_numeric(main_df_2['Mean'].str.replace(',', '.'), errors='coerce')\n",
        "#main_df_2['SD'] = pd.to_numeric(main_df_2['SD'].str.replace(',', '.'), errors='coerce')\n",
        "\n",
        "#main_df_2['scale-min'] = pd.to_numeric(main_df_2['scale-min'], errors='coerce')\n",
        "#main_df_2['scale-max'] = pd.to_numeric(main_df_2['scale-max'], errors='coerce')\n",
        "\n",
        "#main_df_2['metric'] = pd.to_numeric(main_df_2['metric'].str.replace(',', '.'), errors='coerce')\n",
        "\n",
        "# Filter out rows with Exclude is True\n",
        "main_df_2 = main_df_2[main_df_2['Exclude'] != \"TRUE\"]\n",
        "\n",
        "\n",
        "\n",
        "# Normalize trust and adjust SD\n",
        "main_df_2['normalized_trust'] = normalize(main_df_2['Mean'], main_df_2['scale-min'], main_df_2['scale-max'], new_min=1, new_max=5)\n",
        "main_df_2['normalized_sd'] = adjust_sd(main_df_2['SD'], old_range=main_df_2['scale-max'] - main_df_2['scale-min'], new_range=4, make_na_to_one=True)\n",
        "\n",
        "main_df_2['Visualization-level'] = pd.Categorical(main_df_2['Visualization-level'], categories=visualization_levels, ordered=True)\n",
        "\n",
        "# Convert columns to factor\n",
        "main_df_2['Uncertainty'] = main_df_2['Uncertainty'].astype('category')\n",
        "main_df_2['Own'] = main_df_2['Own'].astype('category')\n",
        "\n",
        "\n",
        "# Creating the citation labels\n",
        "main_df_2['Paper_adj'] = \"\\\\cite{\" + main_df_2['Paper'] + \"}\"\n",
        "#main_df_2['publication_adj'] = \"\\\\cite{\" + main_df_2['publication'] + \"}\"\n",
        "#main_df_2['text_combined'] = main_df_2.apply(lambda row: str(row['remarks_display']) + \"; \" + str(row['publication']) if row['publication'] else str(row['remarks_display']), axis=1)\n",
        "\n",
        "main_df_2['text_combined'] = main_df_2.apply(\n",
        "    lambda row: \"; \".join(filter(pd.notna, [row['remarks_display'], row['publication']])),\n",
        "    axis=1\n",
        ")\n",
        "\n",
        "# ordering for the x-axis\n",
        "main_df_2 = main_df_2.sort_values(by=\"Visualization-level\", ascending=True)\n",
        "\n",
        "# Creating a color map for the 'Own' column\n",
        "unique_owns = main_df_2['Own'].unique()\n",
        "colors = {own: ownPalette(i) for i, own in enumerate(unique_owns)}\n",
        "\n",
        "# Define the mapping from visualization levels to numbers\n",
        "vis_level_mapping = {level: i for i, level in enumerate(main_df_2['Visualization-level'].unique())}\n",
        "\n",
        "#print(vis_level_mapping)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "86oTAtaJt5s_"
      },
      "source": [
        "## Try with matplotlib\n",
        "\n",
        "Do not run, is currently not needed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "qXfFJ9sHA3oR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "9f9b688a-8ddd-43a1-e29e-8a9ace3dd465"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\n# available palettes: https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html\\nplt.style.use('seaborn-v0_8-colorblind')\\n\\n# Define the mapping from visualization levels to numbers\\nvis_level_mapping_2 = {level: i for i, level in enumerate(main_df_2['metric'].unique())}\\n\\n# Get the unique visualization levels\\nvis_levels = main_df_2['Visualization-level'].unique()\\n\\n# Determine the number of rows for the subplot grid\\nn = len(vis_levels)\\n\\nfig, axs = plt.subplots(n, 1, figsize=(10, 5*n), sharex=True, sharey=True)\\n\\nfor ax, level in zip(axs, vis_levels):\\n    data = main_df_2[main_df_2['Visualization-level'] == level]\\n\\n    for i, row in data.iterrows():\\n        ax.scatter(vis_level_mapping_2[row['metric']], row['normalized_trust'], s=row['normalized_sd']*100, alpha=0.8)\\n        ax.text(vis_level_mapping_2[row['metric']], row['normalized_trust'], row['Paper_adj'])\\n        ax.text(vis_level_mapping_2[row['metric']] + 0.8, row['normalized_trust'], row['text_combined'])\\n\\n    ax.set_title('Visualization-level: ' + str(level))\\n    ax.set_ylim(1,5)\\n    ax.set_ylabel('Trust in Automation')\\n    ax.set_axisbelow(True)\\n    ax.yaxis.grid(color='gray', linestyle='dashed')\\n    ax.grid(True)\\n\\nplt.xlabel('Metric')\\nplt.tight_layout()\\n\\nplt.savefig('prior_work_categorized_only_own.pgf')\\n\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "\"\"\"\n",
        "# available palettes: https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html\n",
        "plt.style.use('seaborn-v0_8-colorblind')\n",
        "\n",
        "# Define the mapping from visualization levels to numbers\n",
        "vis_level_mapping_2 = {level: i for i, level in enumerate(main_df_2['metric'].unique())}\n",
        "\n",
        "# Get the unique visualization levels\n",
        "vis_levels = main_df_2['Visualization-level'].unique()\n",
        "\n",
        "# Determine the number of rows for the subplot grid\n",
        "n = len(vis_levels)\n",
        "\n",
        "fig, axs = plt.subplots(n, 1, figsize=(10, 5*n), sharex=True, sharey=True)\n",
        "\n",
        "for ax, level in zip(axs, vis_levels):\n",
        "    data = main_df_2[main_df_2['Visualization-level'] == level]\n",
        "\n",
        "    for i, row in data.iterrows():\n",
        "        ax.scatter(vis_level_mapping_2[row['metric']], row['normalized_trust'], s=row['normalized_sd']*100, alpha=0.8)\n",
        "        ax.text(vis_level_mapping_2[row['metric']], row['normalized_trust'], row['Paper_adj'])\n",
        "        ax.text(vis_level_mapping_2[row['metric']] + 0.8, row['normalized_trust'], row['text_combined'])\n",
        "\n",
        "    ax.set_title('Visualization-level: ' + str(level))\n",
        "    ax.set_ylim(1,5)\n",
        "    ax.set_ylabel('Trust in Automation')\n",
        "    ax.set_axisbelow(True)\n",
        "    ax.yaxis.grid(color='gray', linestyle='dashed')\n",
        "    ax.grid(True)\n",
        "\n",
        "plt.xlabel('Metric')\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.savefig('prior_work_categorized_only_own.pgf')\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tRk0dW3Gt7_k"
      },
      "source": [
        "## Try with Seaborn\n",
        "\n",
        "This generates one plot with 3 subplots"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "LL0vqRBHBtZ3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "596380f4-c3c4-464f-c816-6ce44733ae03"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\n# Define the mapping from visualization levels to numbers\\nvis_level_mapping_2 = {level: i for i, level in enumerate(main_df_2['metric'].unique())}\\n\\n# Create a facet grid\\ng = sns.FacetGrid(main_df_2, col='Visualization-level', col_wrap=2, height=5)\\n\\n\\ntexts = []  # Create an empty list to hold the text objects\\n\\n\\n# Define the function to create a scatterplot on an Axes\\ndef scatterplot(data, color, label=None):\\n    ax = plt.gca()\\n    for _, row in data.iterrows():\\n        x = row['metric']#vis_level_mapping_2.get(row['metric'], 1)\\n        y = row['normalized_trust']\\n        ax.scatter(x, y, s=row['normalized_sd']*100, alpha=0.8)\\n\\n        text = ax.text(x, y, row['Paper_adj'])\\n        texts.append(text)  # Add the text object to the list\\n\\n\\n        ax.text(x, y, row['Paper_adj'])\\n        ax.text(x + 14, y, row['text_combined'])\\n\\n        # Add x value of the data point as a text label on the x-axis\\n        #ax.text(x, 0, row['metric'], ha='center', va='top')\\n\\n        # Add a vertical line from the data point down to the x-axis\\n        ax.axvline(x, 0, y, color=color, alpha=0.15)\\n\\n\\n    # Use adjust_text to iterate through the text objects and adjust their position to minimize overlaps\\n    #adjust_text(texts)\\n\\n    # Draw arrows from points to their corresponding adjusted texts\\n    #for text, (x, y) in zip(texts, points):\\n    #    x_text, y_text = text.get_position()\\n    #    ax.annotate('', xy=(x, y), xytext=(x_text, y_text),\\n    #                arrowprops=dict(arrowstyle='-', color='black'))\\n    ax.set_ylim(1, 5)\\n    ax.set_ylabel('Trust in Automation')\\n    ax.set_axisbelow(True)\\n    ax.yaxis.grid(color='gray', linestyle='dashed')\\n    ax.grid(True)\\n\\n    ax.set_xlim(0, 100)\\n\\n\\n# Apply this function to each Axes in the grid\\ng.map_dataframe(scatterplot)\\n\\n# Automatically adjust subplot params so that the subplots fits in to the figure area.\\nplt.tight_layout()\\n\\nplt.savefig('prior_work_categorized_only_own_test_sns.pgf')\\n\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "\"\"\"\n",
        "# Define the mapping from visualization levels to numbers\n",
        "vis_level_mapping_2 = {level: i for i, level in enumerate(main_df_2['metric'].unique())}\n",
        "\n",
        "# Create a facet grid\n",
        "g = sns.FacetGrid(main_df_2, col='Visualization-level', col_wrap=2, height=5)\n",
        "\n",
        "\n",
        "texts = []  # Create an empty list to hold the text objects\n",
        "\n",
        "\n",
        "# Define the function to create a scatterplot on an Axes\n",
        "def scatterplot(data, color, label=None):\n",
        "    ax = plt.gca()\n",
        "    for _, row in data.iterrows():\n",
        "        x = row['metric']#vis_level_mapping_2.get(row['metric'], 1)\n",
        "        y = row['normalized_trust']\n",
        "        ax.scatter(x, y, s=row['normalized_sd']*100, alpha=0.8)\n",
        "\n",
        "        text = ax.text(x, y, row['Paper_adj'])\n",
        "        texts.append(text)  # Add the text object to the list\n",
        "\n",
        "\n",
        "        ax.text(x, y, row['Paper_adj'])\n",
        "        ax.text(x + 14, y, row['text_combined'])\n",
        "\n",
        "        # Add x value of the data point as a text label on the x-axis\n",
        "        #ax.text(x, 0, row['metric'], ha='center', va='top')\n",
        "\n",
        "        # Add a vertical line from the data point down to the x-axis\n",
        "        ax.axvline(x, 0, y, color=color, alpha=0.15)\n",
        "\n",
        "\n",
        "    # Use adjust_text to iterate through the text objects and adjust their position to minimize overlaps\n",
        "    #adjust_text(texts)\n",
        "\n",
        "    # Draw arrows from points to their corresponding adjusted texts\n",
        "    #for text, (x, y) in zip(texts, points):\n",
        "    #    x_text, y_text = text.get_position()\n",
        "    #    ax.annotate('', xy=(x, y), xytext=(x_text, y_text),\n",
        "    #                arrowprops=dict(arrowstyle='-', color='black'))\n",
        "    ax.set_ylim(1, 5)\n",
        "    ax.set_ylabel('Trust in Automation')\n",
        "    ax.set_axisbelow(True)\n",
        "    ax.yaxis.grid(color='gray', linestyle='dashed')\n",
        "    ax.grid(True)\n",
        "\n",
        "    ax.set_xlim(0, 100)\n",
        "\n",
        "\n",
        "# Apply this function to each Axes in the grid\n",
        "g.map_dataframe(scatterplot)\n",
        "\n",
        "# Automatically adjust subplot params so that the subplots fits in to the figure area.\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.savefig('prior_work_categorized_only_own_test_sns.pgf')\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TRuNiExmREvp"
      },
      "source": [
        "# Visualization of Trust in Automation Based on Different Visualization Levels\n",
        "\n",
        "This script is designed to visualize trust in automation based on various visualization levels, using a dataset that includes specific metrics and normalized values. The process includes:\n",
        "\n",
        "1. **Exclusion of Specific Values:**\n",
        "   - The data is filtered to exclude specific values such as 'None' and 'Multiple', resulting in a DataFrame that contains only the desired visualization levels.\n",
        "\n",
        "2. **Categorization and Ordering:**\n",
        "   - The visualization levels are categorized into specific groups like \"Sit. Detection,\" \"Sit. Prediction,\" and \"Traj. Planning,\" and are ordered accordingly.\n",
        "\n",
        "3. **Creation of FacetGrid:**\n",
        "   - A Seaborn FacetGrid is created, dividing the data into different facets based on the 'Visualization-level'. The grid is configured with specific dimensions and settings.\n",
        "\n",
        "4. **Custom Scatterplot Function:**\n",
        "   - A custom function, `scatterplot_1`, is defined to create scatterplots for each facet.\n",
        "   - Each scatterplot represents different metrics and normalized trust values, with point sizes based on the normalized standard deviation.\n",
        "   - Additional annotations, texts, and vertical lines are added to enhance readability and provide context for each data point.\n",
        "\n",
        "5. **Mapping Scatterplot Function to FacetGrid:**\n",
        "   - The custom scatterplot function is applied to each facet in the grid, resulting in a multi-faceted visualization.\n",
        "\n",
        "6. **Final Adjustments and Saving:**\n",
        "   - The layout is automatically adjusted to ensure that the subplots fit properly.\n",
        "   - The final plot is saved as a PGF file named 'prior_work_categorized_only_own_test_sns_1.pgf'.\n",
        "\n",
        "The resulting visualization provides an insightful representation of trust in automation, categorized by different visualization levels. It offers a comprehensive view of the relationship between various metrics and trust levels, facilitating further analysis and interpretation.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "# This makes singular plots\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# List of values to exclude\n",
        "exclude_values = ['None', 'Multiple']\n",
        "\n",
        "# Create a filtered DataFrame\n",
        "filtered_df = main_df_2[~main_df_2['Visualization-level'].isin(exclude_values)].copy()\n",
        "\n",
        "# Remove rows where 'normalized_trust' is NA\n",
        "filtered_df = filtered_df.dropna(subset=['normalized_trust'])\n",
        "\n",
        "# Specify the order for 'Visualization-level'\n",
        "visualization_levels_1 = [\"Sit. Detection\", \"Sit. Prediction\", \"Traj. Planning\"]\n",
        "filtered_df['Visualization-level'] = pd.Categorical(filtered_df['Visualization-level'], categories=visualization_levels_1, ordered=True)\n",
        "\n",
        "# Define a dictionary to hold xlim values for each level\n",
        "xlim_dict = {\n",
        "    \"Sit. Detection\": (67, 88),\n",
        "    \"Sit. Prediction\": (0, 85),\n",
        "    \"Traj. Planning\": (69, 85)\n",
        "}\n",
        "\n",
        "color_dict = {\n",
        "    \"Sit. Detection\": 'blue',\n",
        "    \"Sit. Prediction\": 'orange',\n",
        "    \"Traj. Planning\": 'purple'\n",
        "}\n",
        "\n",
        "xname_dict = {\n",
        "    \"Sit. Detection\": \"mIoU\",\n",
        "    \"Sit. Prediction\": \"AP or minFDE\",\n",
        "    \"Traj. Planning\": \"Trajectory Planning capability\"\n",
        "}\n",
        "\n",
        "\n",
        "# Define the function to create a scatterplot on an Axes\n",
        "def scatterplot_1(data, level):\n",
        "    # Create a new figure and axes\n",
        "    fig, ax = plt.subplots()\n",
        "\n",
        "    # Loop through each row in the DataFrame\n",
        "    for _, row in data.iterrows():\n",
        "        x = row['metric']\n",
        "        y = row['normalized_trust']\n",
        "\n",
        "        # Plot the scatter point\n",
        "        ax.scatter(x, y, s=row['normalized_sd']*100, alpha=0.8, color=color_dict[level])\n",
        "\n",
        "        # Add text labels\n",
        "        fontsize = 13\n",
        "        ax.text(x, y, row['Paper_adj'], fontsize=fontsize)\n",
        "        # Only add this line when it's not 'Sit. Detection'\n",
        "        if row['Visualization-level'] == 'Sit. Prediction':\n",
        "            ax.text(x + 5, y, row['text_combined'], fontsize=fontsize)\n",
        "        if row['Visualization-level'] == 'Traj. Planning':\n",
        "            ax.text(x + 1, y, row['text_combined'], fontsize=fontsize)\n",
        "\n",
        "        # Add vertical line from data point to x-axis\n",
        "        # removed bc of visual clutter\n",
        "        # ax.axvline(x, 0, y, color=color_dict[level], alpha=0.1, zorder=0, linestyle='dashed')\n",
        "\n",
        "    # Additional plot settings\n",
        "    ax.set_ylim(2, 5)\n",
        "    ax.set_ylabel('Trust in Automation (Low to High)')\n",
        "    ax.set_axisbelow(True)\n",
        "    ax.yaxis.grid(color='gray', linestyle='dashed')\n",
        "    ax.grid(True)\n",
        "    # Set axis labels and limits\n",
        "    ax.set_xlabel(xname_dict[level])\n",
        "    ax.set_xlim(xlim_dict[level])\n",
        "\n",
        "    # Save the figure\n",
        "    plt.savefig(f'prior_work_{data.iloc[0][\"Visualization-level\"]}.pgf')\n",
        "\n",
        "# Loop through unique 'Visualization-level' categories and plot each one\n",
        "for level in visualization_levels_1:\n",
        "    sub_df = filtered_df[filtered_df['Visualization-level'] == level]\n",
        "    #scatterplot_1(sub_df, level) # comment in if needed"
      ],
      "metadata": {
        "id": "oMfSEdsLSezW"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# This is the one I use in the disserstation"
      ],
      "metadata": {
        "id": "U-ZrkyiHmkTj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.lines as mlines\n",
        "\n",
        "\n",
        "# List of values to exclude\n",
        "exclude_values = ['None', 'Multiple']\n",
        "\n",
        "# Create a filtered DataFrame\n",
        "filtered_df = main_df_2[~main_df_2['Visualization-level'].isin(exclude_values)].copy()\n",
        "\n",
        "# Remove rows where 'normalized_trust' is NA\n",
        "filtered_df = filtered_df.dropna(subset=['normalized_trust'])\n",
        "\n",
        "# Specify the order for 'Visualization-level'\n",
        "visualization_levels_1 = [\"Sit. Detection\", \"Sit. Prediction\", \"Traj. Planning\"]\n",
        "filtered_df['Visualization-level'] = pd.Categorical(filtered_df['Visualization-level'], categories=visualization_levels_1, ordered=True)\n",
        "\n",
        "# Define a dictionary to hold xlim values for each level\n",
        "xlim_dict = {\n",
        "    \"Sit. Detection\": (67, 88),\n",
        "    \"Sit. Prediction\": (0, 85),\n",
        "    \"Traj. Planning\": (69, 85)\n",
        "}\n",
        "\n",
        "color_dict = {\n",
        "    \"Sit. Detection\": 'blue',\n",
        "    \"Sit. Prediction\": 'orange',\n",
        "    \"Traj. Planning\": 'purple'\n",
        "}\n",
        "\n",
        "xname_dict = {\n",
        "    \"Sit. Detection\": \"Sit. Detection - mIoU\",\n",
        "    \"Sit. Prediction\": \"Sit. Prediction - AP or minFDE\",\n",
        "    \"Traj. Planning\": \"Traj. Planning capability\"\n",
        "}\n",
        "\n",
        "\n",
        "# Define a list of distinct colors\n",
        "distinct_colors = [\n",
        "    '#e6194B', '#3cb44b', '#4363d8', '#f58231', '#911eb4', '#46f0f0',\n",
        "    '#f032e6', '#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324', '#fffac8',\n",
        "    '#800000', '#aaffc3', '#808000', '#ffd8b1', '#000075', '#808080', '#000000'\n",
        "]\n",
        "\n",
        "\n",
        "# Generate a color dictionary for each unique 'Paper_adj'\n",
        "unique_papers = filtered_df['Paper_adj'].unique()\n",
        "#color_dict = {paper: plt.cm.tab20(i) for i, paper in enumerate(unique_papers)}\n",
        "# Generate a color dictionary for each unique 'Paper_adj'\n",
        "color_dict = {paper: distinct_colors[i % len(distinct_colors)] for i, paper in enumerate(unique_papers)}\n",
        "\n",
        "\n",
        "# Function to create a scatter plot on a given Axes\n",
        "def scatterplot_1(ax, data, level, is_middle):\n",
        "    # Loop through each unique paper to plot\n",
        "    for paper in unique_papers:\n",
        "        subset = data[data['Paper_adj'] == paper]\n",
        "\n",
        "        ax.scatter(subset['metric'], subset['normalized_trust'],\n",
        "                   s=np.pi * ((subset['normalized_sd']*10) ** 2), alpha=0.8,\n",
        "                   color=color_dict[paper], label=paper)\n",
        "    # subset['normalized_sd']*100\n",
        "\n",
        "    # Set y-axis label only for the middle subplot\n",
        "    if is_middle:\n",
        "        ax.set_ylabel('Trust in Automation (Low to High)', labelpad=21, fontsize=23)  # Increased labelpad for distance\n",
        "\n",
        "    # Set x-axis labels and limits\n",
        "    ax.set_xlabel(xname_dict[level], fontsize=19)\n",
        "    ax.set_xlim(xlim_dict[level])\n",
        "\n",
        "    # Additional plot settings\n",
        "    ax.set_ylim(2, 5)\n",
        "    ax.set_axisbelow(True)\n",
        "    ax.yaxis.grid(color='gray', linestyle='dashed')\n",
        "    ax.grid(True)\n",
        "\n",
        "# Create a new figure with subplots\n",
        "fig, axes = plt.subplots(3, 1, figsize=(15, 15), sharey=True)  # Share y-axis\n",
        "\n",
        "# Plot each level on a separate subplot\n",
        "for i, level in enumerate(visualization_levels_1):\n",
        "    sub_df = filtered_df[filtered_df['Visualization-level'] == level]\n",
        "    is_middle = i == 1  # Only the middle subplot\n",
        "    scatterplot_1(axes[i], sub_df, level, is_middle)\n",
        "\n",
        "# Create a legend with all unique papers from all subplots\n",
        "handles, labels = [], []\n",
        "for ax in axes:\n",
        "    for handle, label in zip(*ax.get_legend_handles_labels()):\n",
        "        if label not in labels:\n",
        "            handles.append(handle)\n",
        "            labels.append(label)\n",
        "\n",
        "\n",
        "# Create custom legend handles with the desired marker size\n",
        "custom_handles = [mlines.Line2D([], [], color=color_dict[paper], marker='o', linestyle='None',\n",
        "                                markersize=np.sqrt(np.pi*7**2), # Size of the legend marker\n",
        "                                label=paper) for paper in unique_papers]\n",
        "\n",
        "# Place the legend above the subplots and store the legend object in a variable\n",
        "legend = fig.legend(handles=custom_handles,\n",
        "                    loc='upper center', ncol=len(labels),\n",
        "                    title='Paper Reference', fontsize=21,\n",
        "                    labelspacing=1.0, columnspacing=5.0,\n",
        "                    title_fontsize=23)\n",
        "#legend = fig.legend(handles, labels, loc='upper center', ncol=len(labels), title='Paper Reference', fontsize='x-large')\n",
        "\n",
        "# Remove the border of the legend by setting the edge color to 'none'\n",
        "legend.get_frame().set_edgecolor('none')  # This will work now\n",
        "\n",
        "# Adjust the layout to make room for the legend and prevent overlap\n",
        "# Modify the rect parameter to leave more space at the top for the legend\n",
        "plt.tight_layout(rect=[0, 0.03, 1, 0.925])  # Adjust the bottom and top\n",
        "\n",
        "# Place the legend above the subplots and further away from the plot\n",
        "# Adjust the 'bbox_to_anchor' to move the legend up\n",
        "#fig.legend(handles, labels, loc='lower center', bbox_to_anchor=(0.5, 1.02),\n",
        "#           ncol=len(labels), title='Paper Reference', fontsize='small')\n",
        "\n",
        "# Save the figure\n",
        "plt.savefig('combined_prior_work.pgf', bbox_inches='tight')\n",
        "\n",
        "#plt.show()  # Display the combined plot\n"
      ],
      "metadata": {
        "id": "Is5tt5uAh_Ky"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# This is the one to have one single figure"
      ],
      "metadata": {
        "id": "F0BjK36kSfPl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "40MDWasV8t-i"
      },
      "outputs": [],
      "source": [
        "# List of values to exclude\n",
        "exclude_values = ['None', 'Multiple']\n",
        "\n",
        "# Create a filtered DataFrame\n",
        "filtered_df = main_df_2[~main_df_2['Visualization-level'].isin(exclude_values)].copy()\n",
        "\n",
        "visualization_levels_1 = [\"Sit. Detection\", \"Sit. Prediction\", \"Traj. Planning\"]\n",
        "filtered_df['Visualization-level'] = pd.Categorical(filtered_df['Visualization-level'], categories=visualization_levels_1, ordered=True)\n",
        "\n",
        "# Now use the filtered DataFrame to create your FacetGrid\n",
        "# TODO adjust col_wrap\n",
        "g = sns.FacetGrid(filtered_df, col='Visualization-level', col_wrap=2, height=5)\n",
        "\n",
        "\n",
        "texts = []  # Create an empty list to hold the text objects\n",
        "\n",
        "\n",
        "# Define the function to create a scatterplot on an Axes\n",
        "def scatterplot_1(data, color, label=None):\n",
        "    ax = plt.gca()\n",
        "    for _, row in data.iterrows():\n",
        "        x = row['metric']#vis_level_mapping_2.get(row['metric'], 1)\n",
        "        y = row['normalized_trust']\n",
        "        ax.scatter(x, y, s=row['normalized_sd']*100, alpha=0.8)\n",
        "\n",
        "        fontsize = 13  # Adjust this value to set the desired font size\n",
        "\n",
        "        text = ax.text(x, y, row['Paper_adj'], fontsize=fontsize)\n",
        "        texts.append(text)  # Add the text object to the list\n",
        "\n",
        "\n",
        "        ax.text(x, y, row['Paper_adj'], fontsize=fontsize)\n",
        "        #ax.text(x + 20, y, row['text_combined'], fontsize=fontsize)\n",
        "\n",
        "        # Add x value of the data point as a text label on the x-axis\n",
        "        #ax.text(x, 0, row['metric'], ha='center', va='top')\n",
        "\n",
        "        # Add a vertical line from the data point down to the x-axis\n",
        "        ax.axvline(x, 0, y, color=color, alpha=0.1, zorder=0, linestyle='dashed')\n",
        "\n",
        "\n",
        "    # Use adjust_text to iterate through the text objects and adjust their position to minimize overlaps\n",
        "    #adjust_text(texts)\n",
        "\n",
        "    # Draw arrows from points to their corresponding adjusted texts\n",
        "    #for text, (x, y) in zip(texts, points):\n",
        "    #    x_text, y_text = text.get_position()\n",
        "    #    ax.annotate('', xy=(x, y), xytext=(x_text, y_text),\n",
        "    #                arrowprops=dict(arrowstyle='-', color='black'))\n",
        "    ax.set_ylim(2, 5)\n",
        "    #ax.set_ylim(1, 5) # original version\n",
        "    ax.set_ylabel('Trust in Automation (Low to High)')\n",
        "    ax.set_axisbelow(True)\n",
        "    ax.yaxis.grid(color='gray', linestyle='dashed')\n",
        "    ax.grid(True)\n",
        "\n",
        "    ax.set_xlim(0, 100)\n",
        "\n",
        "\n",
        "# Apply this function to each Axes in the grid\n",
        "g.map_dataframe(scatterplot_1)\n",
        "\n",
        "# Automatically adjust subplot params so that the subplots fits in to the figure area.\n",
        "plt.tight_layout()\n",
        "\n",
        "\n",
        "plt.savefig('prior_work_categorized_only_own_test_sns_1.pgf')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5YGJb2Wr_xyI"
      },
      "source": [
        "# Visualization of Interest, Ease, and Expected Reality for Automated Vehicles (AVs)\n",
        "\n",
        "This script performs an analysis and visualization of the expectation that autonomous vehicles (AVs) will become a reality in the next 10 years, whether it will ease the lives of users, and their interest. The process includes the following key steps:\n",
        "\n",
        "1. **Data Grouping:** The data is grouped by year, citation, and a core categorization, and the mean and standard deviation of a specified numeric variable are computed for each group.\n",
        "\n",
        "2. **Color Mapping:** A colormap is generated to represent unique core categorizations, and each unique core value is mapped to a specific color.\n",
        "\n",
        "3. **Scatter Plot Creation:** A scatter plot is created to visualize the mean expectation reality over different years. The plot includes:\n",
        "   - Points representing the mean values, with sizes based on the standard deviation.\n",
        "   - Colors representing different core categorizations.\n",
        "   - Custom labels, grid lines, and axis ticks.\n",
        "   - A regression line that fits the trend of the data.\n",
        "\n",
        "4. **Legend Construction:** A legend is added to the plot, with each color corresponding to a unique core categorization.\n",
        "\n",
        "5. **Saving the Plot:** The final plot is adjusted for layout and saved as a PGF file, providing a visual representation for different citations over the years.\n",
        "\n",
        "\n",
        "## The combined dataset (dissertation_colley_combined_interest_ease_reality) over the years is looked at here\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Xf0iuu6X_vg1",
        "outputId": "1f671e67-2c72-45c4-c39c-1e6d9fd9c095"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      interest  ease  reality  year Core                      citation  \\\n",
              "0            5     3        2  2020  Yes  \\cite{core-colley2020effect}   \n",
              "1            5     5        4  2020  Yes  \\cite{core-colley2020effect}   \n",
              "2            5     4        5  2020  Yes  \\cite{core-colley2020effect}   \n",
              "3            3     1        4  2020  Yes  \\cite{core-colley2020effect}   \n",
              "4            4     4        4  2020  Yes  \\cite{core-colley2020effect}   \n",
              "...        ...   ...      ...   ...  ...                           ...   \n",
              "1673         5     5        4  2023  Yes      \\cite{core-colley2024bo}   \n",
              "1674         4     4        4  2023  Yes      \\cite{core-colley2024bo}   \n",
              "1675         4     5        5  2023  Yes      \\cite{core-colley2024bo}   \n",
              "1676         5     5        3  2023  Yes      \\cite{core-colley2024bo}   \n",
              "1677         3     4        4  2023  Yes      \\cite{core-colley2024bo}   \n",
              "\n",
              "                          doi  \n",
              "0     10.1145/3409120.3410648  \n",
              "1     10.1145/3409120.3410648  \n",
              "2     10.1145/3409120.3410648  \n",
              "3     10.1145/3409120.3410648  \n",
              "4     10.1145/3409120.3410648  \n",
              "...                       ...  \n",
              "1673                      NaN  \n",
              "1674                      NaN  \n",
              "1675                      NaN  \n",
              "1676                      NaN  \n",
              "1677                      NaN  \n",
              "\n",
              "[1678 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c0b53b1f-962c-457d-9173-4385ee383feb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>interest</th>\n",
              "      <th>ease</th>\n",
              "      <th>reality</th>\n",
              "      <th>year</th>\n",
              "      <th>Core</th>\n",
              "      <th>citation</th>\n",
              "      <th>doi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2020</td>\n",
              "      <td>Yes</td>\n",
              "      <td>\\cite{core-colley2020effect}</td>\n",
              "      <td>10.1145/3409120.3410648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2020</td>\n",
              "      <td>Yes</td>\n",
              "      <td>\\cite{core-colley2020effect}</td>\n",
              "      <td>10.1145/3409120.3410648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>2020</td>\n",
              "      <td>Yes</td>\n",
              "      <td>\\cite{core-colley2020effect}</td>\n",
              "      <td>10.1145/3409120.3410648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2020</td>\n",
              "      <td>Yes</td>\n",
              "      <td>\\cite{core-colley2020effect}</td>\n",
              "      <td>10.1145/3409120.3410648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2020</td>\n",
              "      <td>Yes</td>\n",
              "      <td>\\cite{core-colley2020effect}</td>\n",
              "      <td>10.1145/3409120.3410648</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1673</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2023</td>\n",
              "      <td>Yes</td>\n",
              "      <td>\\cite{core-colley2024bo}</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1674</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2023</td>\n",
              "      <td>Yes</td>\n",
              "      <td>\\cite{core-colley2024bo}</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1675</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2023</td>\n",
              "      <td>Yes</td>\n",
              "      <td>\\cite{core-colley2024bo}</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1676</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2023</td>\n",
              "      <td>Yes</td>\n",
              "      <td>\\cite{core-colley2024bo}</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1677</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2023</td>\n",
              "      <td>Yes</td>\n",
              "      <td>\\cite{core-colley2024bo}</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1678 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c0b53b1f-962c-457d-9173-4385ee383feb')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-c0b53b1f-962c-457d-9173-4385ee383feb 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-c0b53b1f-962c-457d-9173-4385ee383feb');\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",
              "\n",
              "\n",
              "<div id=\"df-84e2f319-87be-4215-aca8-fbf7464f286b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-84e2f319-87be-4215-aca8-fbf7464f286b')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-84e2f319-87be-4215-aca8-fbf7464f286b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "#worksheet3 = gc.open('combined_interest_ease_reality').sheet1\n",
        "\n",
        "\n",
        "# get_all_values gives a list of rows.\n",
        "#rows = worksheet3.get_all_values()\n",
        "#print(rows)\n",
        "\n",
        "#main_df_3 = pd.DataFrame.from_records(rows)\n",
        "\n",
        "\n",
        "url3 = 'https://github.com/M-Colley/colley-dissertation/blob/main/data/dissertation_colley_combined_interest_ease_reality.csv?raw=true'\n",
        "main_df_3 = pd.read_csv(url3, sep=';')\n",
        "\n",
        "#headers = main_df_3.iloc[0]\n",
        "#main_df_3  = pd.DataFrame(main_df_3.values[1:], columns=headers)\n",
        "\n",
        "main_df_3['year'] = main_df_3['year'].astype(int)\n",
        "main_df_3['interest'] = main_df_3['interest'].astype(int)\n",
        "main_df_3['ease'] = main_df_3['ease'].astype(int)\n",
        "main_df_3['reality'] = main_df_3['reality'].astype(int)\n",
        "\n",
        "main_df_3\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RCjwkEjNN4l3"
      },
      "outputs": [],
      "source": [
        "# Compute the mean and standard deviation of 'numeric_variable' for each year and citation\n",
        "grouped_df = main_df_3.groupby(['year', 'citation', 'Core'])['interest'].agg(['mean', 'std']).reset_index()\n",
        "\n",
        "# uncomment to print\n",
        "#print(grouped_df)\n",
        "\n",
        "# Create a colormap with as many colors as there are unique citations\n",
        "#colormap = ListedColormap(sns.color_palette('husl', n_colors=grouped_df['citation'].nunique()))\n",
        "colormap = ListedColormap(sns.color_palette('coolwarm', n_colors=grouped_df['Core'].nunique()))\n",
        "\n",
        "\n",
        "# Create a dictionary mapping each unique citation to a color\n",
        "color_dict = dict(zip(grouped_df['Core'].unique(), colormap.colors))\n",
        "\n",
        "# Create a new column in the DataFrame with the color corresponding to each citation\n",
        "grouped_df['color'] = grouped_df['Core'].map(color_dict)\n",
        "\n",
        "# Create a scatter plot\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Increase the size multiplier for the standard deviation\n",
        "scatter = ax.scatter(grouped_df['year'], grouped_df['mean'], s=grouped_df['std']*100, color=grouped_df['color'], alpha=0.8)\n",
        "\n",
        "# Adding citation as text annotation for each point\n",
        "for i in range(len(grouped_df)):\n",
        "    ax.text(grouped_df.loc[i, 'year']+0.05, grouped_df.loc[i, 'mean'], grouped_df.loc[i, 'citation'], fontsize='large')\n",
        "\n",
        "plt.xlabel('Year', fontsize='x-large')\n",
        "plt.ylabel('Mean of interest', fontsize='x-large')\n",
        "plt.title('Mean of interest for each citation over years', fontsize='x-large')\n",
        "plt.grid(True, linestyle='dashed')\n",
        "\n",
        "# Dynamically set y-ticks based on data and round to two decimal places\n",
        "ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.2f}'.format(y)))\n",
        "plt.yticks(np.linspace(grouped_df['mean'].min(), grouped_df['mean'].max(), 5))\n",
        "\n",
        "# Adjust the x-axis to only show the values 2019, 2020, and 2021\n",
        "plt.xticks([2019, 2020, 2021, 2023])\n",
        "# Adjust the x-axis limits to add padding on the left and right\n",
        "ax.set_xlim(2018.5, 2023.5)\n",
        "\n",
        "plt.savefig('interest.pgf')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hdbeu4qb-Ir6"
      },
      "outputs": [],
      "source": [
        "# Compute the mean and standard deviation of 'numeric_variable' for each year and citation\n",
        "grouped_df = main_df_3.groupby(['year', 'citation', 'Core'])['interest'].agg(['mean', 'std']).reset_index()\n",
        "\n",
        "# Create a colormap with as many colors as there are unique citations\n",
        "#colormap = ListedColormap(sns.color_palette('husl', n_colors=grouped_df['citation'].nunique()))\n",
        "colormap = ListedColormap(sns.color_palette('coolwarm', n_colors=grouped_df['Core'].nunique()))\n",
        "\n",
        "\n",
        "# Create a dictionary mapping each unique citation to a color\n",
        "color_dict = dict(zip(grouped_df['Core'].unique(), colormap.colors))\n",
        "\n",
        "# Create a new column in the DataFrame with the color corresponding to each citation\n",
        "grouped_df['color'] = grouped_df['Core'].map(color_dict)\n",
        "\n",
        "\n",
        "# Create a scatter plot\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "scatter = ax.scatter(grouped_df['year'], grouped_df['mean'], s=grouped_df['std']*100, color=grouped_df['color'], alpha=0.8)\n",
        "\n",
        "# Adding citation as text annotation for each point\n",
        "#for i in range(len(grouped_df)):\n",
        "#    ax.text(grouped_df.loc[i, 'year']+0.05, grouped_df.loc[i, 'mean'], grouped_df.loc[i, 'citation'], fontsize='x-large')\n",
        "\n",
        "plt.xlabel('Year', fontsize='x-large')\n",
        "plt.ylabel('Mean of interest', fontsize='x-large')\n",
        "plt.title('Mean of interest for each citation over years', fontsize='x-large')\n",
        "\n",
        "\n",
        "plt.grid(True, linestyle='dashed')\n",
        "\n",
        "# Dynamically set y-ticks based on data and round to two decimal places\n",
        "ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.2f}'.format(y)))\n",
        "plt.yticks(np.linspace(grouped_df['mean'].min(), grouped_df['mean'].max(), 5))\n",
        "\n",
        "# Adjust the x-axis to only show the values 2019, 2020, and 2021\n",
        "plt.xticks([2019, 2020, 2021, 2023])\n",
        "# Adjust the x-axis limits to add padding on the left and right\n",
        "ax.set_xlim(2018.5, 2023.5)\n",
        "\n",
        "# Compute the parameters of the regression line\n",
        "slope, intercept = np.polyfit(grouped_df['year'], grouped_df['mean'], 1)\n",
        "\n",
        "# Add the regression line to the plot\n",
        "plt.plot(grouped_df['year'], slope*grouped_df['year'] + intercept, color='red')\n",
        "\n",
        "# Create legend handles manually\n",
        "legend_elements = [Patch(facecolor=color_dict[c], edgecolor=color_dict[c], label=c) for c in grouped_df['Core'].unique()]\n",
        "\n",
        "# Create the legend\n",
        "ax.legend(handles=legend_elements, title='Core')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig('interest_2.pgf')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0PMhd7s6QdX_"
      },
      "outputs": [],
      "source": [
        "# Compute the mean and standard deviation of 'numeric_variable' for each year and citation\n",
        "grouped_df = main_df_3.groupby(['year', 'citation', 'Core'])['ease'].agg(['mean', 'std']).reset_index()\n",
        "\n",
        "# Create a colormap with as many colors as there are unique citations\n",
        "#colormap = ListedColormap(sns.color_palette('husl', n_colors=grouped_df['citation'].nunique()))\n",
        "colormap = ListedColormap(sns.color_palette('coolwarm', n_colors=grouped_df['Core'].nunique()))\n",
        "\n",
        "\n",
        "# Create a dictionary mapping each unique citation to a color\n",
        "color_dict = dict(zip(grouped_df['Core'].unique(), colormap.colors))\n",
        "\n",
        "# Create a new column in the DataFrame with the color corresponding to each citation\n",
        "grouped_df['color'] = grouped_df['Core'].map(color_dict)\n",
        "\n",
        "# Create a scatter plot\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Increase the size multiplier for the standard deviation\n",
        "scatter = ax.scatter(grouped_df['year'], grouped_df['mean'], s=grouped_df['std']*100, color=grouped_df['color'], alpha=0.8)\n",
        "\n",
        "# Adding citation as text annotation for each point\n",
        "#for i in range(len(grouped_df)):\n",
        "#    ax.text(grouped_df.loc[i, 'year'], grouped_df.loc[i, 'mean'], grouped_df.loc[i, 'citation'], fontsize='x-large')\n",
        "\n",
        "plt.xlabel('Year', fontsize='x-large')\n",
        "plt.ylabel('Mean of easing of life', fontsize='x-large')\n",
        "plt.title('Mean of easing of life for each citation over years', fontsize='x-large')\n",
        "plt.grid(True, linestyle='dashed')\n",
        "#plt.yticks([3, 4, 5])\n",
        "\n",
        "# Dynamically set y-ticks based on data and round to two decimal places\n",
        "ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.2f}'.format(y)))\n",
        "plt.yticks(np.linspace(grouped_df['mean'].min(), grouped_df['mean'].max(), 5))\n",
        "\n",
        "# Adjust the x-axis to only show the values 2019, 2020, and 2021\n",
        "plt.xticks([2019, 2020, 2021, 2023])\n",
        "# Adjust the x-axis limits to add padding on the left and right\n",
        "ax.set_xlim(2018.5, 2023.5)\n",
        "\n",
        "\n",
        "# Compute the parameters of the regression line\n",
        "slope, intercept = np.polyfit(grouped_df['year'], grouped_df['mean'], 1)\n",
        "\n",
        "# Add the regression line to the plot\n",
        "plt.plot(grouped_df['year'], slope*grouped_df['year'] + intercept, color='red')\n",
        "\n",
        "\n",
        "# Create legend handles manually\n",
        "legend_elements = [Patch(facecolor=color_dict[c], edgecolor=color_dict[c], label=c) for c in grouped_df['Core'].unique()]\n",
        "\n",
        "# Create the legend\n",
        "ax.legend(handles=legend_elements, title='Core')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig('ease.pgf')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VDlQeHx0Qp0D"
      },
      "outputs": [],
      "source": [
        "# Compute the mean and standard deviation of 'numeric_variable' for each year and citation\n",
        "grouped_df = main_df_3.groupby(['year', 'citation', 'Core'])['reality'].agg(['mean', 'std']).reset_index()\n",
        "\n",
        "# Create a colormap with as many colors as there are unique citations\n",
        "#colormap = ListedColormap(sns.color_palette('husl', n_colors=grouped_df['citation'].nunique()))\n",
        "colormap = ListedColormap(sns.color_palette('coolwarm', n_colors=grouped_df['Core'].nunique()))\n",
        "\n",
        "\n",
        "# Create a dictionary mapping each unique citation to a color\n",
        "color_dict = dict(zip(grouped_df['Core'].unique(), colormap.colors))\n",
        "\n",
        "# Create a new column in the DataFrame with the color corresponding to each citation\n",
        "grouped_df['color'] = grouped_df['Core'].map(color_dict)\n",
        "\n",
        "# Create a scatter plot\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Increase the size multiplier for the standard deviation\n",
        "scatter = ax.scatter(grouped_df['year'], grouped_df['mean'], s=grouped_df['std']*100, color=grouped_df['color'], alpha=0.8)\n",
        "\n",
        "# Adding citation as text annotation for each point\n",
        "#for i in range(len(grouped_df)):\n",
        "    #ax.text(grouped_df.loc[i, 'year'], grouped_df.loc[i, 'mean'], grouped_df.loc[i, 'citation'], fontsize='x-large')\n",
        "\n",
        "plt.xlabel('Year', fontsize='x-large')\n",
        "plt.ylabel('Mean of expectation reality in 10 years', fontsize='x-large')\n",
        "plt.title('Mean of expectation that AVs are reality in 10 years for each citation over years', fontsize='x-large')\n",
        "plt.grid(True, linestyle='dashed')\n",
        "\n",
        "# Dynamically set y-ticks based on data and round to two decimal places\n",
        "ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.2f}'.format(y)))\n",
        "plt.yticks(np.linspace(grouped_df['mean'].min(), grouped_df['mean'].max(), 5))\n",
        "\n",
        "# Adjust the x-axis to only show the values 2019, 2020, and 2021\n",
        "plt.xticks([2019, 2020, 2021, 2023])\n",
        "# Adjust the x-axis limits to add padding on the left and right\n",
        "ax.set_xlim(2018.5, 2023.5)\n",
        "\n",
        "\n",
        "# Compute the parameters of the regression line\n",
        "slope, intercept = np.polyfit(grouped_df['year'], grouped_df['mean'], 1)\n",
        "\n",
        "# Compute the predicted values and the R-squared score\n",
        "predicted = slope*grouped_df['year'] + intercept\n",
        "r_squared = r2_score(grouped_df['mean'], predicted)\n",
        "\n",
        "# Add the regression line to the plot\n",
        "plt.plot(grouped_df['year'], predicted, color='red')\n",
        "\n",
        "# Create legend handles manually\n",
        "legend_elements = [Patch(facecolor=color_dict[c], edgecolor=color_dict[c], label=c) for c in grouped_df['Core'].unique()]\n",
        "\n",
        "# Create the legend\n",
        "ax.legend(handles=legend_elements, title='Core')\n",
        "\n",
        "\n",
        "# Add textual details about the regression\n",
        "#regression_text = f'y = {slope:.2f}x + {intercept:.2f}, R^2 = {r_squared:.2f}'\n",
        "#ax.text(0.05, 0.95, regression_text, transform=ax.transAxes, fontsize='large')\n",
        "\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig('reality.pgf')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "Jbqyq-R3tSRr",
        "86oTAtaJt5s_",
        "tRk0dW3Gt7_k"
      ],
      "provenance": [],
      "authorship_tag": "ABX9TyPmCkXw+9qUHAhg/geYcsMc",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}