diff --git a/notebooks/cauchy.ipynb b/notebooks/cauchy.ipynb new file mode 100644 index 0000000..85a9223 --- /dev/null +++ b/notebooks/cauchy.ipynb @@ -0,0 +1,73 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 21, + "id": "763459b8-d00f-44ad-891f-b21a8c4eda6a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from scipy.stats import cauchy\n", + "\n", + "fig, ax = plt.subplots(1, 1)\n", + "mean, var, skew, kurt = cauchy.stats(moments='mvsk')\n", + "scale = 0.01\n", + "x = np.linspace(cauchy.ppf(0.01, scale=scale), cauchy.ppf(0.99, scale=scale), 100)\n", + "ax.set_ylim((0.0, 0.5))\n", + "ax.plot(x, cauchy.pdf(x, scale=scale), \"r-\", lw=5, alpha=0.6, label=\"cauchy pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ccd3a71d-44e3-431e-bc02-3b47da15e653", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "emevo-lab", + "language": "python", + "name": "emevo-lab" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}