{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%config InlineBackend.figure_formats = ['svg']\n", "import oscovida as ov" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "[Execute this notebook with Binder](https://mybinder.org/v2/gh/oscovida/binder/master?filepath=ipynb/tutorial-overview-graphs.ipynb)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ov.display_binder_link(\"tutorial-overview-graphs.ipynb\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview function explained\n", "\n", "The most important function of `oscovida` package is `overview`. It takes the following parameters:\n", "1. `country` — a country to analyse (mandatory, `str`);\n", "2. `region` — a region of the country (optional, `str`);\n", "3. `subregion` — a subregion of the country (optional, `str`);\n", "4. `savefig` — whether to save a sigure (optional, `bool`, default is `False`);\n", "5. `dates` — a range of dates in a format \"2020-05-15:2020-10-20\"\n", "6. `weeks` — how many last weeks to show (optional, `int`, default is zero, which means \"all\").\n", "\n", "The function returns a triple:\n", "(`pyplot` graph, a `pandas` series for _cases_, a `pandas` series for _deaths_).\n", "\n", "This function provides six graphs:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n" ], "text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ov.overview('Russia');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see how exactly we obtain all these graphs.\n", "\n", "Under the hood we \n", "* retrieve the data with `get_country_data()` function (see the [tutorial](tutorial-accessing-cases-and-deaths.nbdata.html))\n", "* then we optionally narrow the time range using either `weeks=N` for the last `N` weeks or\n", " `dates=\"2020-05-01:2020-10-01\"` for the specific range of dates. Note that one cannot use\n", " both `dates` and `weeks` together.\n", "* finally, we feed this data for *cases* and *deaths* to a set of plotting functions:\n", " - `plot_time_step` for total numbers (plot 1, see the [tutorial](tutorial-overview-plot_time_step.nbdata.html))\n", " - `plot_daily_change` for daily changes (plots 2 and 3, see the [tutorial](tutorial-overview-plot_daily_change.nbdata.html))\n", " - `plot_reproduction_number` for `R`-value and the growth factor (plots 4 and 5, see the [tutorial](tutorial-overview-plot_reproduction_number.nbdata.html))\n", " - `plot_doubling_time` for the doubling times (plot 6, see the [tutorial](tutorial-overview-plot_doubling_time.nbdata.html)).\n", "\n", "That's exactly how we fetch the data using `weeks` inside the `overview` function:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "country = \"Iran\"\n", "weeks = 30\n", "cases, deaths = ov.get_country_data(country)\n", "cases = cases[- weeks * 7:] # cut off unwanted data\n", "deaths = deaths[- weeks * 7:] # cut off unwanted data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What we have in `cases` and `deaths` are Pandas time series: it is a sort of a two-row array with dates in one row and COVID cases / deaths in the other:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020-03-31 44605\n", "2020-04-01 47593\n", "2020-04-02 50468\n", "2020-04-03 53183\n", "2020-04-04 55743\n", " ... \n", "2020-10-22 550757\n", "2020-10-23 556891\n", "2020-10-24 562705\n", "2020-10-25 568896\n", "2020-10-26 574856\n", "Freq: D, Name: Iran cases, Length: 210, dtype: object" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cases" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here is the example with `dates`:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n" ], "text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "country = \"Germany\"\n", "region=\"Hamburg\"\n", "dates = \"2020-09-15:2020-10-25\"\n", "cases, deaths = ov.get_country_data(country, region)\n", "\n", "date_start, date_end = dates.split(':')\n", "cases = cases[date_start:date_end]\n", "deaths = deaths[date_start:date_end]\n", "\n", "ov.overview(country=country, region=region, dates=dates);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Other tutorials\n", "\n", "You can find [more tutorials here](tag-tutorial.html)." ] } ], "metadata": { "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": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }