Adding example with xarray accessors.

examples/notebooks/accessors.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2b61cda9-cd2d-4487-b656-93374fa406d6",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Using the enstools-compression accessors for Xarray\n",
+    "\n",
+    "An example on how to use the different accessors available for Xarray."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc1750af-27da-4ec1-a9a4-9b9646532a6f",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Get some dummy data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d00551a0-599d-4362-b0dd-9861f4919c11",
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import xarray\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9aff74be-8f44-40f1-966b-6c23a33f748f",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "dataset_name = \"air_temperature\"\n",
+    "dataset = xarray.tutorial.open_dataset(dataset_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a022c4ef-e697-42c1-a73e-86c3769635ba",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Define a path for the temporary file\n",
+    "output_file = Path(\"compressed.nc\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1684f6e",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Import the accessors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2f5f6571",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from enstools.compression import xr_accessor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26f148e6-ad70-45f3-85e9-c4403ffd7a6a",
+   "metadata": {},
+   "source": [
+    "# Use the accessors\n",
+    "## Method to_compressed_netcdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0906b0d0-90b7-4d77-969b-6ca30ced0bd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Dataset' object has no attribute 'to_compressed_netcdf'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_compressed_netcdf\u001b[49m(output_file, compression\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlossy,sz,pw_rel,1.e-5\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m/scratch/o/Oriol.Tinto/compression-kernel/venv/lib/python3.10/site-packages/xarray/core/common.py:278\u001b[0m, in \u001b[0;36mAttrAccessMixin.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    276\u001b[0m         \u001b[38;5;28;01mwith\u001b[39;00m suppress(\u001b[38;5;167;01mKeyError\u001b[39;00m):\n\u001b[1;32m    277\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m source[name]\n\u001b[0;32m--> 278\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m    279\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    280\u001b[0m )\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'Dataset' object has no attribute 'to_compressed_netcdf'"
+     ]
+    }
+   ],
+   "source": [
+    "dataset.to_compressed_netcdf(output_file, compression=\"lossy,sz,pw_rel,1.e-5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "204ae92f",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Remove Temporary files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "462f511c",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Remove temporary files\n",
+    "if output_file.exists():\n",
+    "    output_file.unlink()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "enstools-compression-2022.12.1",
+   "language": "python",
+   "name": "enstools-compression-2022.12.1"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}