diff --git a/code/A_test_fit.ipynb b/code/A_test_fit.ipynb index bf5590a6ae80372f363194c9ae6b91adecd6f12d..e142c2791bb1ac08991d363b7b5a2e71ea32dd22 100644 --- a/code/A_test_fit.ipynb +++ b/code/A_test_fit.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -28,6 +28,7 @@ "plt.rcParams['font.family'] = 'DejaVu Sans'\n", "rc('text', usetex=True)\n", "plt.rcParams.update({'font.size': 19})\n", + "import pandas as pd\n", "import ptemcee\n", "import qnm\n", "import random\n", @@ -2256,6 +2257,65 @@ "print([0, 0, 0, 0, 0.978518, 4.29435, -2.11289, 1.38519])" ] }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Test Bayesian</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>logevidence</th>\n", + " <td>-21.358361</td>\n", + " </tr>\n", + " <tr>\n", + " <th>error</th>\n", + " <td>4.898100</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Test Bayesian\n", + "logevidence -21.358361\n", + "error 4.898100" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#PLEAST RERUN THIS CELL IT HAS NOT BEEN RUN SINCE AFTER BEING PASTED HERE\n", + "logevidence = sampler.log_evidence_estimate(fburnin=0.2)\n", + "pd.DataFrame(logevidence,index = [r'logevidence', r'error'], columns=[r'Test Bayesian'])" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2485,6 +2545,64 @@ "print([0, 0, 0, 0, 0.978518, 4.29435, -2.11289, 1.38519])" ] }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Test Bayesian</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>logevidence</th>\n", + " <td>-21.358361</td>\n", + " </tr>\n", + " <tr>\n", + " <th>error</th>\n", + " <td>4.898100</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Test Bayesian\n", + "logevidence -21.358361\n", + "error 4.898100" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logevidence = sampler.log_evidence_estimate(fburnin=0.2)\n", + "pd.DataFrame(logevidence,index = [r'logevidence', r'error'], columns=[r'Test Bayesian'])" + ] + }, { "cell_type": "markdown", "metadata": {},