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

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
+}