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add a plotting example (#61)
* add a plotting example * add matplotlib to the dependencies * configure nbsphinx * fail RTD build on warnings * use the correct name for matplotlib * also depend on ipykernel and jupyter_client * use alert-warning for the note * use "alert alert-info" instead * try to fix the info block * try if removing the ' helps * fix the alert markup
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‎docs/conf.py

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"sphinx_autosummary_accessors",
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"IPython.sphinxext.ipython_directive",
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"IPython.sphinxext.ipython_console_highlighting",
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"nbsphinx",
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]
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# Add any paths that contain templates here, relative to this directory.
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"unit-like": ":term:`unit-like`",
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}
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# nbsphinx
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nbsphinx_timeout = 600
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nbsphinx_execute = "always"
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# -- Options for intersphinx extension ---------------------------------------
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intersphinx_mapping = {

‎docs/examples.rst

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Examples
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========
3-
There are no examples yet.
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.. toctree::
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:maxdepth: 1
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examples/plotting

‎docs/examples/plotting.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "round-optimization",
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"metadata": {},
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"source": [
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"# plotting quantified data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "greatest-smart",
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"metadata": {},
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"outputs": [],
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"source": [
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"import xarray as xr\n",
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"import pint_xarray"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fuzzy-maintenance",
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"metadata": {},
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"source": [
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"## load the data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "proved-racing",
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"metadata": {},
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"outputs": [],
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"source": [
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"ds = xr.tutorial.open_dataset(\"air_temperature\")\n",
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"data = ds.air\n",
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"data"
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]
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},
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{
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"cell_type": "markdown",
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"id": "medium-backup",
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"metadata": {},
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"source": [
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"## convert units into a format understood by pint\n",
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"\n",
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"<div class=\"alert alert-info\">\n",
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"<strong>Note:</strong> this example uses the data provided by the <code>xarray.tutorial</code> functions. As such, the <code>units</code> attributes follow the CF conventions, which <code>pint</code> does not understand by default. To work around that, we are modifying the <code>units</code> attributes here, but in general it is better to use a library that adds support for the units used by the CF conventions to <code>pint</code>.\n",
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"</div>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "published-powell",
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"metadata": {},
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"outputs": [],
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"source": [
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"data.lat.attrs[\"units\"] = \"degree\"\n",
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"data.lon.attrs[\"units\"] = \"degree\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "banned-tolerance",
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"metadata": {},
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"source": [
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"## quantify the data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "divine-boost",
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"metadata": {},
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"outputs": [],
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"source": [
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"quantified = data.pint.quantify()\n",
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"quantified"
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]
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},
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{
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"cell_type": "markdown",
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"id": "whole-momentum",
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"metadata": {},
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"source": [
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"## work with the data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dried-friday",
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"metadata": {},
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"outputs": [],
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"source": [
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"monthly_means = (\n",
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" quantified\n",
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" .pint.to(\"degC\")\n",
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" .sel(time=\"2013\")\n",
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" .groupby(\"time.month\").mean()\n",
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")\n",
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"monthly_means"
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]
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},
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{
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"cell_type": "markdown",
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"id": "still-ebony",
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"metadata": {},
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"source": [
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"## plot\n",
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"\n",
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"`xarray`'s plotting functions will cast the data to `numpy.ndarray`, so we need to \"dequantify\" first."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "united-machine",
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"metadata": {},
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"outputs": [],
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"source": [
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"monthly_means.pint.dequantify(format=\"~P\").plot.imshow(col=\"month\", col_wrap=4)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎docs/requirements.txt

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sphinx>=3.2
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sphinx_rtd_theme
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ipython
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ipykernel
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jupyter_client
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nbsphinx
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matplotlib
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sphinx-autosummary-accessors

‎readthedocs.yml

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- requirements: docs/requirements.txt
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- method: pip
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path: .
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sphinx:
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fail_on_warning: true

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