{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Demo on the \"most common\" regridder\n",
"\n",
"For regridding categorical data to a coarser resolution, normal regridding methods don't work.\n",
"This can be data like land cover or vegetation type.\n",
"\n",
"For this reason we implemented a \"most common\" regridder, which takes the most common value of the source data cells that overlap with the target grid cells.\n",
"\n",
"If you want to regrid data to a finer resolution, the \"nearest-neighbor\" regridder can be used."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We start with importing the required packages:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import xarray as xr\n",
"import xarray_regrid\n",
"\n",
"import dask.distributed\n",
"\n",
"client = dask.distributed.Client()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we need a high resolution dataset to regrid. We used the LCCS land cover data which is available from the [Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover).\n",
"\n",
"Note the data is loaded in as a dask arrays, allowing for lazy computation."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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