ipynb/tutorial-overview-plot_daily_change.ipynb

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       "[Execute this notebook with Binder](https://mybinder.org/v2/gh/oscovida/binder/master?filepath=ipynb/tutorial-overview-plot_daily_change.ipynb)"
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    "%config InlineBackend.figure_formats = ['svg']\n",
    "import oscovida as ov\n",
    "\n",
    "ov.display_binder_link(\"tutorial-overview-plot_daily_change.ipynb\")"
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   "source": [
    "### Daily change\n",
    "\n",
    "The second and the third graphs returned by the `overview` function \n",
    "(see the [overview](tutorial-overview-graphs.nbdata.html)\n",
    "and the explanation of the [observables](tutorial-observables.nbdata.html))\n",
    "show the daily changes for COVID cases and deaths respectively. Here we show how it is created internally.\n",
    "We also meed to import the plotting framework:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.dates import DateFormatter, MONDAY, WeekdayLocator, DayLocator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also we will need an empty Matplotlib graph object to pass onto the plotting functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
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   "outputs": [
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    "fig2, ax2 = plt.subplots();    # create an empty graph\n",
    "country = \"US\"\n",
    "region = \"California\"\n",
    "weeks = 8\n",
    "\n",
    "cases, deaths = ov.get_country_data(country, region)    # get actual data\n",
    "cases = cases[- weeks * 7:]   # cut off unwanted data\n",
    "\n",
    "# pass the data to the plotting function\n",
    "ov.plot_daily_change(ax=ax2, series=cases, labels=(\"USA: California\",\"cases\"), color=\"C2\", \n",
    "                     country=\"US\", region=\"California\");\n",
    "\n",
    "# nicer X-axis formatting: put a tick every Monday\n",
    "ax2.get_xaxis().set_major_locator(WeekdayLocator(byweekday=MONDAY)) \n",
    "ax2.get_xaxis().set_major_formatter(DateFormatter('%d %b'))"
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    "And if it we wave the population data for this region (see [here](link-here))\n",
    "then we can plot a normalised graph with the data per 100K capita.\n",
    "For doing that we pass regional identifiers to the plotting function. And here we will use deaths data:"
   ]
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   "cell_type": "code",
   "execution_count": 4,
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    "pycharm": {
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     "name": "stdout",
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     "text": [
      "The population of Germany, Bayern is 13124737 people\n"
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       " <defs>\n",
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       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, ax2 = plt.subplots();    # an empty graph again\n",
    "country = \"Germany\"\n",
    "region = \"Bayern\"\n",
    "weeks = 8\n",
    "\n",
    "cases, deaths = ov.get_country_data(country, region)\n",
    "dates = \"2020-05-01:2020-07-15\"\n",
    "date_start, date_end = dates.split(':')\n",
    "deaths = deaths[date_start: date_end] # cut off unwanted data\n",
    "\n",
    "habitants = ov.population(country, region)\n",
    "print(f\"The population of {country}, {region} is {habitants} people\")\n",
    "\n",
    "ov.plot_daily_change(ax=ax2, series=deaths, color=\"C2\", labels=(\"Germany: Bavaria\", \"deaths\"), \n",
    "                     country=country, region=region);\n",
    "\n",
    "# nicer X-axis formatting: put a tick every Monday\n",
    "ax2.get_xaxis().set_major_locator(DayLocator(bymonthday=1)) \n",
    "ax2.get_xaxis().set_major_formatter(DateFormatter('%d %b'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other tutorials\n",
    "\n",
    "You can find [more tutorials here](tag-tutorial.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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