ipynb/Chad.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Chad\n",
        "\n",
        "* Homepage of project: https://oscovida.github.io\n",
        "* Plots are explained at http://oscovida.github.io/plots.html\n",
        "* [Execute this Jupyter Notebook using myBinder](https://mybinder.org/v2/gh/oscovida/binder/master?filepath=ipynb/Chad.ipynb)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "import datetime\n",
        "import time\n",
        "\n",
        "start = datetime.datetime.now()\n",
        "print(f\"Notebook executed on: {start.strftime('%d/%m/%Y %H:%M:%S%Z')} {time.tzname[time.daylight]}\")"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "%config InlineBackend.figure_formats = ['svg']\n",
        "from oscovida import *"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "overview(\"Chad\", weeks=5);"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "overview(\"Chad\");"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "compare_plot(\"Chad\", normalise=True);\n"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# load the data\n",
        "cases, deaths = get_country_data(\"Chad\")\n",
        "\n",
        "# get population of the region for future normalisation:\n",
        "inhabitants = population(\"Chad\")\n",
        "print(f'Population of \"Chad\": {inhabitants} people')\n",
        "\n",
        "# compose into one table\n",
        "table = compose_dataframe_summary(cases, deaths)\n",
        "\n",
        "# show tables with up to 500 rows\n",
        "pd.set_option(\"max_rows\", 500)\n",
        "\n",
        "# display the table\n",
        "table"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Explore the data in your web browser\n",
        "\n",
        "- If you want to execute this notebook, [click here to use myBinder](https://mybinder.org/v2/gh/oscovida/binder/master?filepath=ipynb/Chad.ipynb)\n",
        "- and wait (~1 to 2 minutes)\n",
        "- Then press SHIFT+RETURN to advance code cell to code cell\n",
        "- See http://jupyter.org for more details on how to use Jupyter Notebook"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Acknowledgements:\n",
        "\n",
        "- Johns Hopkins University provides data for countries\n",
        "- Robert Koch Institute provides data for within Germany\n",
        "- Atlo Team for gathering and providing data from Hungary (https://atlo.team/koronamonitor/)\n",
        "- Open source and scientific computing community for the data tools\n",
        "- Github for hosting repository and html files\n",
        "- Project Jupyter for the Notebook and binder service\n",
        "- The H2020 project Photon and Neutron Open Science Cloud ([PaNOSC](https://www.panosc.eu/))\n",
        "\n",
        "--------------------"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "print(f\"Download of data from Johns Hopkins university: cases at {fetch_cases_last_execution()} and \"\n",
        "      f\"deaths at {fetch_deaths_last_execution()}.\")"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# to force a fresh download of data, run \"clear_cache()\""
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "print(f\"Notebook execution took: {datetime.datetime.now()-start}\")\n"
      ],
      "outputs": [],
      "execution_count": null
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.1"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}