Add cubed notebook for hourly climatology example using "map-reduce" method (#356)

* Add cubed notebook for hourly climatology example using "map-reduce" method

* Add cubed dependencies to docs build

* Use Cubed version with HTML repr fix

Author: tomwhite

ci/docs.yml

@@ -2,6 +2,8 @@ name: flox-doc
 channels:
   - conda-forge
 dependencies:
+  - cubed>=0.14.3
+  - cubed-xarray
   - dask-core
   - pip
   - xarray

docs/source/user-stories.md

@@ -7,6 +7,7 @@
    user-stories/overlaps.md
    user-stories/climatology.ipynb
    user-stories/climatology-hourly.ipynb
+   user-stories/climatology-hourly-cubed.ipynb
    user-stories/custom-aggregations.ipynb
    user-stories/nD-bins.ipynb
 ```

docs/source/user-stories/climatology-hourly-cubed.ipynb

@@ -0,0 +1,106 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# More climatology reductions using Cubed\n",
+    "\n",
+    "This is the Cubed equivalent of [More climatology reductions](climatology-hourly.ipynb).\n",
+    "\n",
+    "The task is to compute an hourly climatology from an hourly dataset with 744 hours in each chunk, using the \"map-reduce\" strategy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cubed\n",
+    "import cubed.array_api as xp\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "\n",
+    "import flox.xarray"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2",
+   "metadata": {},
+   "source": [
+    "## Create data\n",
+    "\n",
+    "Note that we use fewer lat/long points so the computation can be run locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spec = cubed.Spec(allowed_mem=\"2GB\")\n",
+    "ds = xr.Dataset(\n",
+    "    {\n",
+    "        \"tp\": (\n",
+    "            (\"time\", \"latitude\", \"longitude\"),\n",
+    "            xp.ones((8760, 72, 144), chunks=(744, 5, 144), dtype=np.float32, spec=spec),\n",
+    "        )\n",
+    "    },\n",
+    "    coords={\"time\": pd.date_range(\"2021-01-01\", \"2021-12-31 23:59\", freq=\"h\")},\n",
+    ")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4",
+   "metadata": {},
+   "source": [
+    "## Computation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hourly = flox.xarray.xarray_reduce(ds.tp, ds.time.dt.hour, func=\"mean\", reindex=True)\n",
+    "hourly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hourly.compute()"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}