{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Anomaly calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "from earthkit import transforms as ekt\n",
    "from earthkit import data as ekd\n",
    "from earthkit.data.testing import earthkit_remote_test_data_file\n",
    "ekd.settings.set(\"cache-policy\", \"user\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load some test data\n",
    "\n",
    "In this example we will use hourly ERA5 2m temperature data on a 0.5x0.5 spatial grid for the year 2015 as\n",
    "our physical data; and we will use the NUTS geometries which are stored in a geojson file.\n",
    "\n",
    "All `earthkit-transforms` methods can be called with `earthkit-data` objects (Readers and Wrappers) or with a pre-loaded `xarray`. To reduce the number of conversions in the example, we will convert to xarray in the first cell and use that data object for all subsequent steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get some demonstration ERA5 data, this could be any url or path to an ERA5 grib or netCDF file.\n",
    "remote_era5_file = earthkit_remote_test_data_file(\"era5_temperature_france_2015_2016_2017_3deg.grib\")\n",
    "era5_data = ekd.from_source(\"url\", remote_era5_file)\n",
    "\n",
    "# convert to xarray to save repeated conversion in further steps\n",
    "era5_xr = era5_data.to_xarray(time_dim_mode=\"valid_time\")\n",
    "era5_xr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the daily climatology of the ERA5 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "climatology_daily_mean = ekt.climatology.daily_mean(era5_xr)\n",
    "climatology_daily_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the anomaly and relative anomaly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "anomaly = ekt.climatology.anomaly(era5_xr, climatology_daily_mean)\n",
    "anomaly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "relative_anomaly = ekt.climatology.relative_anomaly(\n",
    "    era5_xr, climatology_daily_mean\n",
    ")\n",
    "relative_anomaly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the output for a random location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "\n",
    "start, end = datetime(2015,1,1), datetime(2015,3,31)\n",
    "\n",
    "isel_kwargs = {\"latitude\":2, \"longitude\":4}\n",
    "sel_kwargs = {\"valid_time\": slice(start, end)}\n",
    "\n",
    "fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(10,5))\n",
    "\n",
    "for data in [anomaly, relative_anomaly]:\n",
    "    var_name = list(data.data_vars.keys())[0]\n",
    "    p_data = data[var_name].isel(**isel_kwargs).sel(**sel_kwargs)\n",
    "    p_data.plot(ax=ax, label=var_name)\n",
    "\n",
    "ax.set_xlim(start, end)\n",
    "ax.set_ylabel(\"2m temperature anomaly [K] and relative anomaly [%]\")\n",
    "ax.hlines(0, xmin=start, xmax=end, color=\"black\", linestyle=\"--\")\n",
    "ax.legend()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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