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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,139 @@ | ||
| { | ||
| "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\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "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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| savefig.dpi: 100 | ||
| figure.dpi: 100 | ||
| font.size: 14 | ||
| font.family: sans-serif | ||
| font.sans-serif: Liberation Sans | ||
| font.cursive: Liberation Sans | ||
| mathtext.fontset: custom |
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