{ "cells": [ { "cell_type": "markdown", "id": "liquid-cosmetic", "metadata": {}, "source": [ "# Example of MCMC in Python" ] }, { "cell_type": "code", "execution_count": 1, "id": "worst-husband", "metadata": {}, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import pandas as pd\n", "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "from scipy.optimize import minimize\n", "import emcee as mc" ] }, { "cell_type": "markdown", "id": "naval-circuit", "metadata": {}, "source": [ "Read data in" ] }, { "cell_type": "code", "execution_count": 2, "id": "progressive-source", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'C:\\\\LocalData\\\\aipentti\\\\OneDrive - University of Helsinki\\\\Kurssit\\\\Oma opetus\\\\Tähtitieteen inversiomenetelmät (ja data-analyysi)\\\\Moniste\\\\Jupyter notebooks'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 3, "id": "western-workstation", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | phase angle (deg) | \n", "polarization (%) | \n", "
---|---|---|
0 | \n", "3.10 | \n", "-1.00 | \n", "
1 | \n", "5.25 | \n", "-1.33 | \n", "
2 | \n", "6.90 | \n", "-0.40 | \n", "
3 | \n", "15.30 | \n", "-1.10 | \n", "
4 | \n", "15.30 | \n", "-1.00 | \n", "
... | \n", "... | \n", "... | \n", "
164 | \n", "48.60 | \n", "16.01 | \n", "
165 | \n", "48.75 | \n", "15.61 | \n", "
166 | \n", "49.07 | \n", "13.40 | \n", "
167 | \n", "49.07 | \n", "16.00 | \n", "
168 | \n", "49.07 | \n", "17.59 | \n", "
169 rows × 2 columns
\n", "