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 "cells": [
  {
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Climatology 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 climatologies of the ERA5 data\n",
    "\n",
    "Monthly mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "climatology_monthly_mean = ekt.climatology.monthly_mean(era5_xr)\n",
    "# # The following line would also work, but we have already converted the data to xarray,\n",
    "# #  so do not need to do it again.\n",
    "# climatology = ek_aggregate.climatology.monthly_mean(era5_xr)\n",
    "climatology_monthly_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Climatology of the daily mean:"
   ]
  },
  {
   "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": [
    "Repeat for the monthly maximum, minimum  and standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clim_max = ekt.climatology.monthly_max(era5_xr)\n",
    "clim_min = ekt.climatology.monthly_min(era5_xr)\n",
    "clim_std = ekt.climatology.monthly_std(era5_xr)\n",
    "clim_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantiles\n",
    "\n",
    "Please note the api for quantiles is slightly different, it requires an additional argument `q` which is a list of the quantiles to return.\n",
    "\n",
    "Additionally, the returned object has `quantiles` dimension which is for each of the quantiles returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "quantiles = ekt.climatology.quantiles(era5_xr, [0.1, 0.5, 0.9], frequency='month')\n",
    "quantiles"
   ]
  },
  {
   "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, timedelta\n",
    "import xarray as xr\n",
    "isel_kwargs = {\"latitude\":2, \"longitude\":4}\n",
    "\n",
    "fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(10,5))\n",
    "\n",
    "for data in [climatology_monthly_mean, clim_min, clim_max]:\n",
    "    var_name = list(data.data_vars.keys())[0]\n",
    "    c_point = data[var_name].isel(**isel_kwargs)\n",
    "    c_time = [datetime(2000,i,15) for i in c_point.month.values]\n",
    "    ax.plot(c_time, c_point.values.flat, label=f'Monthly {var_name}')\n",
    "\n",
    "\n",
    "data = climatology_daily_mean\n",
    "var_name = list(data.data_vars.keys())[0]\n",
    "c_point = data[var_name].isel(**isel_kwargs)\n",
    "c_time = [datetime(2000,1,1)+timedelta(days=int(i-1)) for i in c_point.dayofyear.values]\n",
    "ax.plot(c_time, c_point.values.flat, label=f'Daily {var_name}')\n",
    "\n",
    "ax.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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