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Adding documentation for ArviZ support (#146)
* refactoring docs * rtds version * invalid key * adding tutorial for arviz
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,63 +1,42 @@ | ||
| # -*- coding: utf-8 -*- | ||
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| import os | ||
| import sys | ||
|
|
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| d = os.path.dirname | ||
| sys.path.insert(0, d(d(os.path.abspath(__file__)))) | ||
| import corner # NOQA | ||
| import corner | ||
|
|
||
| extensions = [ | ||
| "sphinx.ext.autodoc", | ||
| "sphinx.ext.mathjax", | ||
| "sphinx.ext.napoleon", | ||
| "myst_nb", | ||
| ] | ||
| templates_path = ["_templates"] | ||
| source_suffix = ".rst" | ||
| master_doc = "index" | ||
|
|
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| # General information about the project. | ||
| project = u"corner.py" | ||
| copyright = u"2013-2016 Dan Foreman-Mackey & contributors" | ||
| copyright = u"2013-2021 Dan Foreman-Mackey & contributors" | ||
|
|
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| version = corner.__version__ | ||
| release = corner.__version__ | ||
|
|
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| exclude_patterns = ["_build", "_static/notebooks/profile"] | ||
| pygments_style = "sphinx" | ||
|
|
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| # Sphinx-gallery | ||
| sphinx_gallery_conf = dict( | ||
| examples_dirs="../examples", | ||
| gallery_dirs="examples", | ||
| ) | ||
|
|
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| # Readthedocs. | ||
| on_rtd = os.environ.get("READTHEDOCS", None) == "True" | ||
| if not on_rtd: | ||
| import sphinx_rtd_theme | ||
|
|
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| html_theme = "sphinx_rtd_theme" | ||
| html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] | ||
|
|
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| # htmp_theme_options = dict( | ||
| # analytics_id="analytics_id", | ||
| # ) | ||
| # html_context = dict( | ||
| # display_github=True, | ||
| # github_user="dfm", | ||
| # github_repo="corner.py", | ||
| # github_version="corner.py", | ||
| # conf_py_path="/docs/", | ||
| # favicon="favicon.png", | ||
| # script_files=[ | ||
| # "_static/jquery.js", | ||
| # "_static/underscore.js", | ||
| # "_static/doctools.js", | ||
| # "//cdn.mathjax.org/mathjax/latest/MathJax.js" | ||
| # "?config=TeX-AMS-MML_HTMLorMML", | ||
| # "_static/js/analytics.js", | ||
| # ], | ||
| # ) | ||
| exclude_patterns = ["_build"] | ||
| html_theme = "sphinx_book_theme" | ||
| html_title = "corner.py" | ||
| html_static_path = ["_static"] | ||
| html_show_sourcelink = False | ||
| html_theme_options = { | ||
| "path_to_docs": "docs", | ||
| "repository_url": "https://github.com/dfm/corner.py", | ||
| "repository_branch": "main", | ||
| "launch_buttons": { | ||
| "binderhub_url": "https://mybinder.org", | ||
| "colab_url": "https://colab.research.google.com/", | ||
| "notebook_interface": "jupyterlab", | ||
| }, | ||
| "use_edit_page_button": True, | ||
| "use_issues_button": True, | ||
| "use_repository_button": True, | ||
| "use_download_button": True, | ||
| } | ||
| html_baseurl = "https://corner.readthedocs.io/en/latest/" | ||
|
|
||
| jupyter_execute_notebooks = "force" |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,80 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# ArviZ support\n", | ||
| "\n", | ||
| "As of version 2.2, corner supports plotting [ArviZ](https://arviz-devs.github.io/arviz/) samples, providing a consistent interface to [many MCMC samplers](https://arviz-devs.github.io/arviz/getting_started/CreatingInferenceData.html).\n", | ||
| "\n", | ||
| "Here is a simple example of this:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import corner\n", | ||
| "import numpy as np\n", | ||
| "import arviz as az\n", | ||
| "\n", | ||
| "np.random.seed(11234)\n", | ||
| "\n", | ||
| "x = np.random.randn(2, 2000)\n", | ||
| "data = az.from_dict(\n", | ||
| " posterior={\n", | ||
| " \"x\": x,\n", | ||
| " \"y\": np.random.randn(2, 2000, 2)\n", | ||
| " },\n", | ||
| " sample_stats={\n", | ||
| " \"diverging\": x < -1.2\n", | ||
| " }\n", | ||
| ")\n", | ||
| "\n", | ||
| "figure = corner.corner(data, divergences=True)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "When using this interface, it is probably best to provide the `truths` parameter using a dictionary keyed on the variable names:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "figure = corner.corner(data, truths={\"x\": 1.5, \"y\": [-0.3, 0.1]})" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.9.1" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 4 | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,132 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Custom plotting\n", | ||
| "\n", | ||
| "It can sometimes be useful to add custom annotations or plot elements to the output of `corner.corner`.\n", | ||
| "This tutorial shows how you can do this.\n", | ||
| "\n", | ||
| "For example, let's say that we want to overplot a few different values for comparison.\n", | ||
| "The `truths` interface isn't sufficient for supporting this use case, so here's how you can do it.\n", | ||
| "\n", | ||
| "First, let's generate some fake data with a mode at the origin and another randomly sampled mode:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import corner\n", | ||
| "import numpy as np\n", | ||
| "\n", | ||
| "ndim, nsamples = 4, 50000\n", | ||
| "np.random.seed(1234)\n", | ||
| "data1 = np.random.randn(ndim * 4 * nsamples // 5).reshape([4 * nsamples // 5, ndim])\n", | ||
| "mean = 4*np.random.rand(ndim)\n", | ||
| "data2 = (mean[None, :] + np.random.randn(ndim * nsamples // 5).reshape([nsamples // 5, ndim]))\n", | ||
| "samples = np.vstack([data1, data2])\n", | ||
| "\n", | ||
| "figure = corner.corner(samples)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Now let's overplot the empirical mean of the samples and the true mean of the second mode." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# This is the true mean of the second mode that we used above:\n", | ||
| "value1 = mean\n", | ||
| "\n", | ||
| "# This is the empirical mean of the sample:\n", | ||
| "value2 = np.mean(samples, axis=0)\n", | ||
| "\n", | ||
| "# Make the base corner plot\n", | ||
| "figure = corner.corner(samples)\n", | ||
| "\n", | ||
| "# Extract the axes\n", | ||
| "axes = np.array(figure.axes).reshape((ndim, ndim))\n", | ||
| "\n", | ||
| "# Loop over the diagonal\n", | ||
| "for i in range(ndim):\n", | ||
| " ax = axes[i, i]\n", | ||
| " ax.axvline(value1[i], color=\"g\")\n", | ||
| " ax.axvline(value2[i], color=\"r\")\n", | ||
| " \n", | ||
| "# Loop over the histograms\n", | ||
| "for yi in range(ndim):\n", | ||
| " for xi in range(yi):\n", | ||
| " ax = axes[yi, xi]\n", | ||
| " ax.axvline(value1[xi], color=\"g\")\n", | ||
| " ax.axvline(value2[xi], color=\"r\")\n", | ||
| " ax.axhline(value1[yi], color=\"g\")\n", | ||
| " ax.axhline(value2[yi], color=\"r\")\n", | ||
| " ax.plot(value1[xi], value1[yi], \"sg\")\n", | ||
| " ax.plot(value2[xi], value2[yi], \"sr\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "A similar procedure could be used to add anything to the axes that you can normally do with matplotlib.\n", | ||
| "\n", | ||
| "This being said, there is actually an even easier way to do this using the `overplot_lines` and `overplot_points` functions:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# This is the true mean of the second mode that we used above:\n", | ||
| "value1 = mean\n", | ||
| "\n", | ||
| "# This is the empirical mean of the sample:\n", | ||
| "value2 = np.mean(samples, axis=0)\n", | ||
| "\n", | ||
| "# Make the base corner plot\n", | ||
| "figure = corner.corner(samples)\n", | ||
| "\n", | ||
| "corner.overplot_lines(figure, value1, color=\"C1\")\n", | ||
| "corner.overplot_points(figure, value1[None], marker=\"s\", color=\"C1\")\n", | ||
| "corner.overplot_lines(figure, value2, color=\"C2\")\n", | ||
| "corner.overplot_points(figure, value2[None], marker=\"s\", color=\"C2\")" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.9.1" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 4 | ||
| } |
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