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refactor: use nbsphinx as the default execution lib (#1482)
* refactor: use nbsphinx as the default execution lib * add nbstripout to the pre-commits' * add pandoc to the readthedocs deps * refactor: clean the notebook * move the example to the correct folder * fix: solve link issue * install pandoc in the test environment * fix: display of large table in executed cells * avoid Userwarnings from matplotlib * hide the matplotlib wrning management cell * Update readthedocs.yml * build: use pandoc_binary to install pandoc * docs: add reference to pandoc in the setup * update docs * remove pypandoc_binary * Update pyproject.toml Co-authored-by: gabalafou <gabriel@fouasnon.com> * ci: use back setup-pandoc * Trigger CI build --------- Co-authored-by: Gabriel Fouasnon <gabriel@fouasnon.com>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# PyData Library Styles\n", | ||
| "\n", | ||
| "This theme has built-in support and special styling for several major visualization libraries in the PyData ecosystem.\n", | ||
| "This ensures that the images and output generated by these libraries looks good for both light and dark modes.\n", | ||
| "Below are examples of each that we use as a benchmark for reference.\n", | ||
| "\n", | ||
| "## Pandas" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import string\n", | ||
| "\n", | ||
| "import numpy as np\n", | ||
| "import pandas as pd\n", | ||
| "\n", | ||
| "rng = np.random.default_rng()\n", | ||
| "data = rng.standard_normal((100, 26))\n", | ||
| "df = pd.DataFrame(data, columns=list(string.ascii_lowercase))\n", | ||
| "df" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Matplotlib" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "nbsphinx": "hidden" | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import matplotlib\n", | ||
| "\n", | ||
| "# avoid warnings upon doc build\n", | ||
| "matplotlib.set_loglevel(\"critical\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import matplotlib.pyplot as plt\n", | ||
| "\n", | ||
| "fig, ax = plt.subplots()\n", | ||
| "ax.scatter(df[\"a\"], df[\"b\"], c=df[\"b\"], s=3)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import matplotlib.pyplot as plt\n", | ||
| "import numpy as np\n", | ||
| "\n", | ||
| "matplotlib.set_loglevel(\"critical\")\n", | ||
| "\n", | ||
| "rng = np.random.default_rng()\n", | ||
| "data = rng.standard_normal((3, 100))\n", | ||
| "fig, ax = plt.subplots()\n", | ||
| "ax.scatter(data[0], data[1], c=data[2], s=3)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Plotly\n", | ||
| "\n", | ||
| "The HTML below shouldn't display, but it uses RequireJS to make sure that all\n", | ||
| "works as expected. If the widgets don't show up, RequireJS may be broken." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import plotly.io as pio\n", | ||
| "import plotly.express as px\n", | ||
| "import plotly.offline as py\n", | ||
| "\n", | ||
| "pio.renderers.default = \"notebook\"\n", | ||
| "\n", | ||
| "df = px.data.iris()\n", | ||
| "fig = px.scatter(df, x=\"sepal_width\", y=\"sepal_length\", color=\"species\", size=\"sepal_length\")\n", | ||
| "fig" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Xarray\n", | ||
| "\n", | ||
| "Here we demonstrate `xarray` to ensure that it shows up properly." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import xarray as xr\n", | ||
| "data = xr.DataArray(\n", | ||
| " np.random.randn(2, 3),\n", | ||
| " dims=(\"x\", \"y\"),\n", | ||
| " coords={\"x\": [10, 20]}, attrs={\"foo\": \"bar\"}\n", | ||
| " )\n", | ||
| "data" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## ipyleaflet\n", | ||
| "\n", | ||
| "`ipyleaflet` is a **Jupyter**/**Leaflet** bridge enabling interactive maps in the Jupyter notebook environment. this demonstrate how you can integrate maps in your documentation." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from ipyleaflet import Map, basemaps\n", | ||
| "\n", | ||
| "# display a map centered on France\n", | ||
| "m = Map(basemap=basemaps.Esri.WorldImagery, zoom=5, center=[46.21, 2.21])\n", | ||
| "m" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "name": "python", | ||
| "version": "3.10.8" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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