{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cd110cd9-f40e-4ccf-b702-b40d611f60bf",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## MIR: regridding numpy arrays"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e6004fb6-603c-4671-977c-3ce72e16a42d",
   "metadata": {
    "editable": true,
    "raw_mimetype": "text/restructuredtext",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "This example shows how to interpolate numpy arrays using the :ref:`mir <mir-regrid-array>` backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5854d65c-ef1e-4c99-8c09-fa8e7612f399",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from earthkit.regrid.array import regrid\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de55e418-ffa0-4609-b6dc-a9b23ebe77b8",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "First, we generate random data matching the size of an O32 octahedral reduced Gaussian grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a71db0be-2233-4133-b03f-9219dc84e3f3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "values = np.random.random(5248)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "c1a951b8-f550-45f1-aef4-3bed884a7bdf",
   "metadata": {
    "editable": true,
    "raw_mimetype": "text/restructuredtext",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Next, interpolate the array onto a 1x1 degree global regular latitude-longitude grid using :ref:`regrid() <mir-regrid-array>`. The input and output grids are defined by a :ref:`gridspec <gridspec>`. Both the input and the output values are numpy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "24ccc6b5-90c2-4e4d-8175-677c5d0f12ed",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((181, 360),\n",
       " {'east': 359, 'grid': [1, 1], 'north': 90, 'south': -90, 'west': 0})"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res, res_grid = regrid(values, in_grid={\"grid\": \"O32\"}, out_grid={\"grid\": [1,1]}) \n",
    "res.shape, res_grid"
   ]
  },
  {
   "cell_type": "raw",
   "id": "537e772f-d965-42dc-b395-62a3be7eb7f5",
   "metadata": {
    "editable": true,
    "raw_mimetype": "text/restructuredtext",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    ":ref:`regrid() <mir-regrid-array>` returns a tuple with the regridded array and the resulting gridspec. This latter might be different than the one specified in ``out_grid`` because MIR can perform normalisation and other adjustments on it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13cac978-b11a-463d-a877-9b3bdfb9b7b0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mir",
   "language": "python",
   "name": "mir"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}