{ "cells": [ { "cell_type": "code", "execution_count": 79, "id": "571f726e", "metadata": {}, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import pandas as pd\n", "import emcee\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from matplotlib import rcParams\n", "from multiprocessing import Pool\n", "import time\n", "import corner\n", "\n", "rcParams.update({'figure.autolayout': True})\n", "sns.set_style(\"white\", {'legend.frameon': True})\n", "sns.set_style(\"ticks\", {'legend.frameon': True})\n", "sns.set_context(\"talk\")\n", "sns.set_palette('Dark2', desat=1)\n", "cc = sns.color_palette()" ] }, { "cell_type": "code", "execution_count": 137, "id": "a1f6a1f1", "metadata": {}, "outputs": [], "source": [ "def model(x, a, b, c):\n", " # gaussian\n", " return a * np.exp(-0.5 * ((x - b) / c) ** 2)" ] }, { "cell_type": "code", "execution_count": 138, "id": "1e057409", "metadata": {}, "outputs": [], "source": [ "# Generate x values\n", "x = np.arange(100) # 0 to 99\n", "\n", "# Generate y values with a bit of random noise\n", "np.random.seed(0) # for reproducibility\n", "noise = np.random.normal(0, 5, size=len(x)) # mean=0, std=5\n", "y = model(x, 200, 52, 5) + noise\n", "\n", "# Create dataframe\n", "df = pd.DataFrame({'x': x, 'y': y})\n", "\n", "df['yerr'] = np.random.uniform(4, 6, size=len(df))" ] }, { "cell_type": "code", "execution_count": 139, "id": "ae8cd91b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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xyyerr
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112.0007865.212786
224.8936904.038386
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449.3377905.320347
............
95953.5328665.662097
96960.0525005.257964
97978.9293525.745301
98980.6345604.547084
99992.0099475.596094
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100 rows × 3 columns

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" ], "text/plain": [ " x y yerr\n", "0 0 8.820262 4.847710\n", "1 1 2.000786 5.212786\n", "2 2 4.893690 4.038386\n", "3 3 11.204466 4.603150\n", "4 4 9.337790 5.320347\n", ".. .. ... ...\n", "95 95 3.532866 5.662097\n", "96 96 0.052500 5.257964\n", "97 97 8.929352 5.745301\n", "98 98 0.634560 4.547084\n", "99 99 2.009947 5.596094\n", "\n", "[100 rows x 3 columns]" ] }, "execution_count": 139, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 140, "id": "d39aaf28", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 140, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 6))\n", "ax.plot(df['x'], df['y'], color='k', drawstyle='steps-mid', label='data')\n", "ax.plot(df['x'], df['yerr'], color='blue', drawstyle='steps-mid', label='y_err', alpha=0.5)\n", "ax.legend()" ] }, { "cell_type": "code", "execution_count": null, "id": "f74498fe", "metadata": {}, "outputs": [], "source": [ "# theta is a vector that contains the parameters of the model\n", "def lnprior(theta):\n", " a, b, c = theta\n", " \n", " # flat prior, boundaries\n", " if 0 < a < 500 and 0 < b < 100 and 2 < c < 7:\n", " return 0.0\n", " return -np.inf\n", "\n", "\n", "def lnlike(theta, x, y, yerr):\n", " a, b, c = theta\n", " model_y = model(x, a, b, c)\n", "\n", " # gaussian likelihood\n", " return -0.5 * np.sum(np.log(2 * np.pi * yerr ** 2) +\n", " (y - model_y) ** 2 /\n", " yerr ** 2)\n", "\n", "\n", "def lnprob(theta, x, y, yerr):\n", " lp = lnprior(theta)\n", " if not np.isfinite(lp):\n", " return -np.inf\n", "\n", " lnMeasured = lnlike(\n", " theta,\n", " x,\n", " y,\n", " yerr)\n", " if not np.isfinite(lnMeasured):\n", " return -np.inf\n", "\n", " return lp + lnMeasured" ] }, { "cell_type": "code", "execution_count": 142, "id": "85b9eead", "metadata": {}, "outputs": [], "source": [ "x = df['x'].values\n", "y = df['y'].values\n", "yerr = df['yerr'].values\n", "\n", "\n", "ndim = 3\n", "nwalkers = 100\n", "nsteps = 1000\n", "nburn = 200 # steps to discard at the beginning of the chain" ] }, { "cell_type": "code", "execution_count": 143, "id": "8ade65d3", "metadata": {}, "outputs": [], "source": [ "starting_guesses = []\n", "\n", "for i in range(nwalkers):\n", " aux = [\n", " np.random.uniform(100, 150), # a\n", " np.random.uniform(50, 55), # b\n", " np.random.uniform(3, 4) # c\n", " ]\n", " starting_guesses.append(aux)" ] }, { "cell_type": "code", "execution_count": 144, "id": "475e5823", "metadata": {}, "outputs": [], "source": [ "# with Pool(os.cpu_count()) as pool:\n", "\n", "sampler = emcee.EnsembleSampler(\n", " nwalkers,\n", " ndim,\n", " lnprob,\n", " args=(x, y, yerr)\n", ")\n", "\n", "# pool=pool" ] }, { "cell_type": "code", "execution_count": 145, "id": "d8fc7a2d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Step: 1 / 1000\n", "Mean acceptance fraction: 0.440000\n", "Mean lnprob and Max lnprob values: -2524.061052 -1104.886876\n", "Time to run previous set of walkers (seconds): 0.005702\n", "Step: 2 / 1000\n", "Mean acceptance fraction: 0.415000\n", "Mean lnprob and Max lnprob values: -2414.958075 -1104.886876\n", "Time to run previous set of walkers (seconds): 0.002354\n", "Step: 3 / 1000\n", "Mean acceptance fraction: 0.400000\n", "Mean lnprob and Max lnprob values: -2319.566832 -415.861376\n", "Time to run previous set of walkers (seconds): 0.002219\n", "Step: 4 / 1000\n", "Mean acceptance fraction: 0.422500\n", "Mean lnprob and Max lnprob values: -2205.139719 -415.861376\n", "Time to run previous set of walkers (seconds): 0.002102\n", "Step: 5 / 1000\n", "Mean acceptance fraction: 0.420000\n", "Mean lnprob and Max lnprob values: -2113.967966 -415.861376\n", "Time to run previous set of walkers (seconds): 0.002038\n", "Step: 6 / 1000\n", "Mean acceptance fraction: 0.420000\n", "Mean lnprob and Max lnprob values: -2021.566916 -382.091775\n", "Time to run previous set of walkers (seconds): 0.001889\n", "Step: 7 / 1000\n", "Mean acceptance fraction: 0.421429\n", "Mean lnprob and Max lnprob values: -1938.324930 -373.093229\n", "Time to run previous set of walkers (seconds): 0.002131\n", "Step: 8 / 1000\n", "Mean acceptance fraction: 0.413750\n", "Mean lnprob and Max lnprob values: -1868.781507 -371.057840\n", "Time to run previous set of walkers (seconds): 0.002314\n", "Step: 9 / 1000\n", "Mean acceptance fraction: 0.406667\n", "Mean lnprob and Max lnprob values: -1775.627681 -371.057840\n", "Time to run previous set of walkers (seconds): 0.002072\n", "Step: 10 / 1000\n", "Mean acceptance fraction: 0.410000\n", "Mean lnprob and Max lnprob values: -1691.588664 -350.383399\n", "Time to run previous set of walkers (seconds): 0.002416\n", "Step: 11 / 1000\n", "Mean acceptance fraction: 0.416364\n", "Mean lnprob and Max lnprob values: -1586.403919 -321.822319\n", "Time to run previous set of walkers (seconds): 0.002278\n", "Step: 12 / 1000\n", "Mean acceptance fraction: 0.418333\n", "Mean lnprob and Max lnprob values: -1499.432780 -321.822319\n", "Time to run previous set of walkers (seconds): 0.002160\n", "Step: 13 / 1000\n", "Mean acceptance fraction: 0.417692\n", "Mean lnprob and Max lnprob values: -1413.220144 -321.822319\n", "Time to run previous set of walkers (seconds): 0.002335\n", "Step: 14 / 1000\n", "Mean acceptance fraction: 0.415000\n", "Mean lnprob and Max lnprob values: -1348.081757 -321.822319\n", "Time to run previous set of walkers (seconds): 0.003057\n", "Step: 15 / 1000\n", "Mean acceptance fraction: 0.410667\n", "Mean lnprob and Max lnprob values: -1306.855023 -321.760261\n", "Time to run previous set of walkers (seconds): 0.002346\n", "Step: 16 / 1000\n", "Mean acceptance fraction: 0.411250\n", "Mean lnprob and Max lnprob values: -1232.606121 -321.760261\n", "Time to run previous set of walkers (seconds): 0.001879\n", "Step: 17 / 1000\n", "Mean acceptance fraction: 0.409412\n", "Mean lnprob and Max lnprob values: -1165.372047 -312.725861\n", "Time to run previous set of walkers (seconds): 0.002806\n", "Step: 18 / 1000\n", "Mean acceptance fraction: 0.406667\n", "Mean lnprob and Max lnprob values: -1107.832638 -312.725861\n", "Time to run previous set of walkers (seconds): 0.001806\n", "Step: 19 / 1000\n", "Mean acceptance fraction: 0.406842\n", "Mean lnprob and Max lnprob values: -1021.045989 -307.862553\n", "Time to run previous set of walkers (seconds): 0.001879\n", "Step: 20 / 1000\n", "Mean acceptance fraction: 0.409000\n", "Mean lnprob and Max lnprob values: -965.209726 -306.665292\n", "Time to run previous set of walkers (seconds): 0.001920\n", "Step: 21 / 1000\n", "Mean acceptance fraction: 0.407143\n", "Mean lnprob and Max lnprob values: -906.362973 -306.665292\n", "Time to run previous set of walkers (seconds): 0.002787\n", "Step: 22 / 1000\n", "Mean acceptance fraction: 0.405000\n", "Mean lnprob and Max lnprob values: -867.228449 -306.665292\n", "Time to run previous set of walkers (seconds): 0.002160\n", "Step: 23 / 1000\n", "Mean acceptance fraction: 0.403913\n", "Mean lnprob and Max lnprob values: -818.374292 -304.991709\n", "Time to run previous set of walkers (seconds): 0.002966\n", "Step: 24 / 1000\n", "Mean acceptance fraction: 0.401250\n", "Mean lnprob and Max lnprob values: -773.098107 -304.967826\n", "Time to run previous set of walkers (seconds): 0.002081\n", "Step: 25 / 1000\n", "Mean acceptance fraction: 0.401200\n", "Mean lnprob and Max lnprob values: -736.275318 -304.967826\n", "Time to run previous set of walkers (seconds): 0.001847\n", "Step: 26 / 1000\n", "Mean acceptance fraction: 0.400000\n", "Mean lnprob and Max lnprob values: -692.412968 -304.967826\n", "Time to run previous set of walkers (seconds): 0.001724\n", "Step: 27 / 1000\n", "Mean acceptance fraction: 0.400370\n", "Mean lnprob and Max lnprob values: -661.501316 -304.967826\n", "Time to run previous set of walkers (seconds): 0.002133\n", "Step: 28 / 1000\n", "Mean acceptance fraction: 0.397143\n", "Mean lnprob and Max lnprob values: -630.041570 -304.967826\n", "Time to run previous set of walkers (seconds): 0.001704\n", "Step: 29 / 1000\n", "Mean acceptance fraction: 0.397931\n", "Mean lnprob and Max lnprob values: -590.673905 -304.967826\n", "Time to run previous set of walkers (seconds): 0.001682\n", "Step: 30 / 1000\n", "Mean acceptance fraction: 0.396667\n", "Mean lnprob and Max lnprob values: -563.327829 -303.964459\n", "Time to run previous set of walkers (seconds): 0.001714\n", "Step: 31 / 1000\n", "Mean acceptance fraction: 0.397097\n", "Mean lnprob and Max lnprob values: -531.405442 -303.964459\n", "Time to run previous set of walkers (seconds): 0.001971\n", "Step: 32 / 1000\n", "Mean acceptance fraction: 0.393125\n", "Mean lnprob and Max lnprob values: -512.359022 -304.967826\n", "Time to run previous set of walkers (seconds): 0.001813\n", "Step: 33 / 1000\n", "Mean acceptance fraction: 0.393636\n", "Mean lnprob and Max lnprob values: -492.275734 -301.999548\n", "Time to run previous set of walkers (seconds): 0.001820\n", "Step: 34 / 1000\n", "Mean acceptance fraction: 0.394412\n", "Mean lnprob and Max lnprob values: -474.284558 -301.354701\n", "Time to run previous set of walkers (seconds): 0.001882\n", "Step: 35 / 1000\n", "Mean acceptance fraction: 0.394571\n", "Mean lnprob and Max lnprob values: -454.237936 -301.354701\n", "Time to run previous set of walkers (seconds): 0.001865\n", "Step: 36 / 1000\n", "Mean acceptance fraction: 0.395833\n", "Mean lnprob and Max lnprob values: -431.928648 -301.354701\n", "Time to run previous set of walkers (seconds): 0.001924\n", "Step: 37 / 1000\n", "Mean acceptance fraction: 0.396757\n", "Mean lnprob and Max lnprob values: -414.014250 -301.210245\n", "Time to run previous set of walkers (seconds): 0.001741\n", "Step: 38 / 1000\n", "Mean acceptance fraction: 0.397632\n", "Mean lnprob and Max lnprob values: -398.641864 -301.210245\n", "Time to run previous set of walkers (seconds): 0.001770\n", "Step: 39 / 1000\n", "Mean acceptance fraction: 0.397949\n", "Mean lnprob and Max lnprob values: -390.783853 -301.210245\n", "Time to run previous set of walkers (seconds): 0.001668\n", "Step: 40 / 1000\n", "Mean acceptance fraction: 0.398000\n", "Mean lnprob and Max lnprob values: -374.436065 -300.967254\n", "Time to run previous set of walkers (seconds): 0.002027\n", "Step: 41 / 1000\n", "Mean acceptance fraction: 0.398537\n", "Mean lnprob and Max lnprob values: -364.189225 -300.933872\n", "Time to run previous set of walkers (seconds): 0.001752\n", "Step: 42 / 1000\n", "Mean acceptance fraction: 0.400000\n", "Mean lnprob and Max lnprob values: -354.843195 -300.933872\n", "Time to run previous set of walkers (seconds): 0.001998\n", "Step: 43 / 1000\n", "Mean acceptance fraction: 0.400465\n", "Mean lnprob and Max lnprob values: -351.469808 -300.967254\n", "Time to run previous set of walkers (seconds): 0.002161\n", "Step: 44 / 1000\n", "Mean acceptance fraction: 0.400455\n", "Mean lnprob and Max lnprob values: -342.404270 -300.967254\n", "Time to run previous set of walkers (seconds): 0.002046\n", "Step: 45 / 1000\n", "Mean acceptance fraction: 0.400444\n", "Mean lnprob and Max lnprob values: -335.589862 -301.046565\n", "Time to run previous set of walkers (seconds): 0.002203\n", "Step: 46 / 1000\n", "Mean acceptance fraction: 0.402391\n", "Mean lnprob and Max lnprob values: -330.093495 -300.996487\n", "Time to run previous set of walkers (seconds): 0.002108\n", "Step: 47 / 1000\n", "Mean acceptance fraction: 0.404894\n", "Mean lnprob and Max lnprob values: -326.720804 -300.996487\n", "Time to run previous set of walkers (seconds): 0.001877\n", "Step: 48 / 1000\n", "Mean acceptance fraction: 0.404792\n", "Mean lnprob and Max lnprob values: -322.271748 -300.996487\n", "Time to run previous set of walkers (seconds): 0.001947\n", "Step: 49 / 1000\n", "Mean acceptance fraction: 0.407551\n", "Mean lnprob and Max lnprob values: -319.844966 -301.141179\n", "Time to run previous set of walkers (seconds): 0.001993\n", "Step: 50 / 1000\n", "Mean acceptance fraction: 0.410000\n", "Mean lnprob and Max lnprob values: -316.206448 -301.141179\n", "Time to run previous set of walkers (seconds): 0.001778\n", "Step: 51 / 1000\n", "Mean acceptance fraction: 0.410196\n", "Mean lnprob and Max lnprob values: -313.285579 -300.966354\n", "Time to run previous set of walkers (seconds): 0.002308\n", "Step: 52 / 1000\n", "Mean acceptance fraction: 0.412692\n", "Mean lnprob and Max lnprob values: -311.107250 -300.966354\n", "Time to run previous set of walkers (seconds): 0.002058\n", "Step: 53 / 1000\n", "Mean acceptance fraction: 0.414151\n", "Mean lnprob and Max lnprob values: -309.832943 -301.189977\n", "Time to run previous set of walkers (seconds): 0.002470\n", "Step: 54 / 1000\n", "Mean acceptance fraction: 0.414444\n", "Mean lnprob and Max lnprob values: -308.639997 -301.189977\n", "Time to run previous set of walkers (seconds): 0.001847\n", "Step: 55 / 1000\n", "Mean acceptance fraction: 0.416727\n", "Mean lnprob and Max lnprob values: -307.602032 -301.189977\n", "Time to run previous set of walkers (seconds): 0.002189\n", "Step: 56 / 1000\n", "Mean acceptance fraction: 0.418393\n", "Mean lnprob and Max lnprob values: -307.230056 -301.189977\n", "Time to run previous set of walkers (seconds): 0.002276\n", "Step: 57 / 1000\n", "Mean acceptance fraction: 0.419298\n", "Mean lnprob and Max lnprob values: -306.716589 -300.956247\n", "Time to run previous set of walkers (seconds): 0.001822\n", "Step: 58 / 1000\n", "Mean acceptance fraction: 0.420690\n", "Mean lnprob and Max lnprob values: -306.176487 -300.948260\n", "Time to run previous set of walkers (seconds): 0.001760\n", "Step: 59 / 1000\n", "Mean acceptance fraction: 0.421864\n", "Mean lnprob and Max lnprob values: -305.255746 -300.948260\n", "Time to run previous set of walkers (seconds): 0.002367\n", "Step: 60 / 1000\n", "Mean acceptance fraction: 0.422500\n", "Mean lnprob and Max lnprob values: -304.967722 -301.058376\n", "Time to run previous set of walkers (seconds): 0.002328\n", "Step: 61 / 1000\n", "Mean acceptance fraction: 0.423607\n", "Mean lnprob and Max lnprob values: -304.489096 -300.879701\n", "Time to run previous set of walkers (seconds): 0.001837\n", "Step: 62 / 1000\n", "Mean acceptance fraction: 0.424194\n", "Mean lnprob and Max lnprob values: -304.213052 -301.107027\n", "Time to run previous set of walkers (seconds): 0.002475\n", "Step: 63 / 1000\n", "Mean acceptance fraction: 0.426825\n", "Mean lnprob and Max lnprob values: -303.741352 -300.891225\n", "Time to run previous set of walkers (seconds): 0.002501\n", "Step: 64 / 1000\n", "Mean acceptance fraction: 0.428438\n", "Mean lnprob and Max lnprob values: -303.687372 -300.891225\n", "Time to run previous set of walkers (seconds): 0.003408\n", "Step: 65 / 1000\n", "Mean acceptance fraction: 0.430154\n", "Mean lnprob and Max lnprob values: -303.710929 -300.891225\n", "Time to run previous set of walkers (seconds): 0.002135\n", "Step: 66 / 1000\n", "Mean acceptance fraction: 0.433485\n", "Mean lnprob and Max lnprob values: -303.444080 -300.871628\n", "Time to run previous set of walkers (seconds): 0.001968\n", "Step: 67 / 1000\n", "Mean acceptance fraction: 0.435224\n", "Mean lnprob and Max lnprob values: -303.246832 -301.101923\n", "Time to run previous set of walkers (seconds): 0.002038\n", "Step: 68 / 1000\n", "Mean acceptance fraction: 0.437647\n", "Mean lnprob and Max lnprob values: -303.086155 -300.983531\n", "Time to run previous set of walkers (seconds): 0.001934\n", "Step: 69 / 1000\n", "Mean acceptance fraction: 0.441594\n", "Mean lnprob and Max lnprob values: -303.065608 -301.043306\n", "Time to run previous set of walkers (seconds): 0.001805\n", "Step: 70 / 1000\n", "Mean acceptance fraction: 0.445143\n", "Mean lnprob and Max lnprob values: -302.797032 -300.898135\n", "Time to run previous set of walkers (seconds): 0.002205\n", "Step: 71 / 1000\n", "Mean acceptance fraction: 0.447324\n", "Mean lnprob and Max lnprob values: -302.780461 -300.909118\n", "Time to run previous set of walkers (seconds): 0.002235\n", "Step: 72 / 1000\n", "Mean acceptance fraction: 0.450139\n", "Mean lnprob and Max lnprob values: -302.900332 -300.909118\n", "Time to run previous set of walkers (seconds): 0.001842\n", "Step: 73 / 1000\n", "Mean acceptance fraction: 0.451918\n", "Mean lnprob and Max lnprob values: -302.909097 -300.913741\n", "Time to run previous set of walkers (seconds): 0.001805\n", "Step: 74 / 1000\n", "Mean acceptance fraction: 0.455135\n", "Mean lnprob and Max lnprob values: -302.843913 -300.856772\n", "Time to run previous set of walkers (seconds): 0.002599\n", "Step: 75 / 1000\n", "Mean acceptance fraction: 0.457733\n", "Mean lnprob and Max lnprob values: -302.715540 -300.856772\n", "Time to run previous set of walkers (seconds): 0.002074\n", "Step: 76 / 1000\n", "Mean acceptance fraction: 0.458947\n", "Mean lnprob and Max lnprob values: -302.691298 -300.856772\n", "Time to run previous set of walkers (seconds): 0.002832\n", "Step: 77 / 1000\n", "Mean acceptance fraction: 0.461039\n", "Mean lnprob and Max lnprob values: -302.555931 -300.856772\n", "Time to run previous set of walkers (seconds): 0.002044\n", "Step: 78 / 1000\n", "Mean acceptance fraction: 0.463590\n", "Mean lnprob and Max lnprob values: -302.525515 -300.856772\n", "Time to run previous set of walkers (seconds): 0.002781\n", "Step: 79 / 1000\n", "Mean acceptance fraction: 0.464430\n", "Mean lnprob and Max lnprob values: -302.415789 -300.856772\n", "Time to run previous set of walkers (seconds): 0.001816\n", "Step: 80 / 1000\n", "Mean acceptance fraction: 0.466875\n", "Mean lnprob and Max lnprob values: -302.368186 -300.856772\n", "Time to run previous set of walkers (seconds): 0.003374\n", "Step: 81 / 1000\n", "Mean acceptance fraction: 0.469012\n", "Mean lnprob and Max lnprob values: -302.298506 -300.859054\n", "Time to run previous set of walkers (seconds): 0.002881\n", "Step: 82 / 1000\n", "Mean acceptance fraction: 0.471585\n", "Mean lnprob and Max lnprob values: -302.321730 -300.896023\n", "Time to run previous set of walkers (seconds): 0.001971\n", "Step: 83 / 1000\n", "Mean acceptance fraction: 0.473855\n", "Mean lnprob and Max lnprob values: -302.456986 -300.877014\n", "Time to run previous set of walkers (seconds): 0.002223\n", "Step: 84 / 1000\n", "Mean acceptance fraction: 0.475952\n", "Mean lnprob and Max lnprob values: -302.444084 -300.933022\n", "Time to run previous set of walkers (seconds): 0.002018\n", "Step: 85 / 1000\n", "Mean acceptance fraction: 0.477176\n", "Mean lnprob and Max lnprob values: -302.327562 -300.933022\n", "Time to run previous set of walkers (seconds): 0.001936\n", "Step: 86 / 1000\n", "Mean acceptance fraction: 0.478721\n", "Mean lnprob and Max lnprob values: -302.404766 -300.959959\n", "Time to run previous set of walkers (seconds): 0.001849\n", "Step: 87 / 1000\n", "Mean acceptance fraction: 0.479655\n", "Mean lnprob and Max lnprob values: -302.331126 -300.899524\n", "Time to run previous set of walkers (seconds): 0.001831\n", "Step: 88 / 1000\n", "Mean acceptance fraction: 0.481818\n", "Mean lnprob and Max lnprob values: -302.310282 -300.899524\n", "Time to run previous set of walkers (seconds): 0.002300\n", "Step: 89 / 1000\n", "Mean acceptance fraction: 0.484607\n", "Mean lnprob and Max lnprob values: -302.353529 -300.899524\n", "Time to run previous set of walkers (seconds): 0.002165\n", "Step: 90 / 1000\n", "Mean acceptance fraction: 0.486556\n", "Mean lnprob and Max lnprob values: -302.344854 -300.910834\n", "Time to run previous set of walkers (seconds): 0.002096\n", "Step: 91 / 1000\n", "Mean acceptance fraction: 0.489011\n", "Mean lnprob and Max lnprob values: -302.212740 -300.875204\n", "Time to run previous set of walkers (seconds): 0.002182\n", "Step: 92 / 1000\n", "Mean acceptance fraction: 0.490978\n", "Mean lnprob and Max lnprob values: -302.250847 -300.875025\n", "Time to run previous set of walkers (seconds): 0.001970\n", "Step: 93 / 1000\n", "Mean acceptance fraction: 0.492581\n", "Mean lnprob and Max lnprob values: -302.255740 -300.875025\n", "Time to run previous set of walkers (seconds): 0.002830\n", "Step: 94 / 1000\n", "Mean acceptance fraction: 0.494787\n", "Mean lnprob and Max lnprob values: -302.342617 -300.875025\n", "Time to run previous set of walkers (seconds): 0.001879\n", "Step: 95 / 1000\n", "Mean acceptance fraction: 0.496526\n", "Mean lnprob and Max lnprob values: -302.467066 -300.869149\n", "Time to run previous set of walkers (seconds): 0.001813\n", "Step: 96 / 1000\n", "Mean acceptance fraction: 0.497812\n", "Mean lnprob and Max lnprob values: -302.575400 -301.006148\n", "Time to run previous set of walkers (seconds): 0.002886\n", "Step: 97 / 1000\n", "Mean acceptance fraction: 0.499485\n", "Mean lnprob and Max lnprob values: -302.555406 -300.926323\n", "Time to run previous set of walkers (seconds): 0.002141\n", "Step: 98 / 1000\n", "Mean acceptance fraction: 0.500510\n", "Mean lnprob and Max lnprob values: -302.648371 -300.964949\n", "Time to run previous set of walkers (seconds): 0.003658\n", "Step: 99 / 1000\n", "Mean acceptance fraction: 0.502020\n", "Mean lnprob and Max lnprob values: -302.638412 -300.911509\n", "Time to run previous set of walkers (seconds): 0.002643\n", "Step: 100 / 1000\n", "Mean acceptance fraction: 0.503000\n", "Mean lnprob and Max lnprob values: -302.586321 -300.849839\n", "Time to run previous set of walkers (seconds): 0.002005\n", "Step: 101 / 1000\n", "Mean acceptance fraction: 0.504356\n", "Mean lnprob and Max lnprob values: -302.583938 -300.902062\n", "Time to run previous set of walkers (seconds): 0.002018\n", "Step: 102 / 1000\n", "Mean acceptance fraction: 0.505392\n", "Mean lnprob and Max lnprob values: -302.580405 -300.902062\n", "Time to run previous set of walkers (seconds): 0.001988\n", "Step: 103 / 1000\n", "Mean acceptance fraction: 0.506408\n", "Mean lnprob and Max lnprob values: -302.585576 -300.866021\n", "Time to run previous set of walkers (seconds): 0.002117\n", "Step: 104 / 1000\n", "Mean acceptance fraction: 0.507692\n", "Mean lnprob and Max lnprob values: -302.429344 -300.971510\n", "Time to run previous set of walkers (seconds): 0.002184\n", "Step: 105 / 1000\n", "Mean acceptance fraction: 0.508857\n", "Mean lnprob and Max lnprob values: -302.396891 -300.929823\n", "Time to run previous set of walkers (seconds): 0.002212\n", "Step: 106 / 1000\n", "Mean acceptance fraction: 0.510377\n", "Mean lnprob and Max lnprob values: -302.380640 -300.883864\n", "Time to run previous set of walkers (seconds): 0.001837\n", "Step: 107 / 1000\n", "Mean acceptance fraction: 0.511682\n", "Mean lnprob and Max lnprob values: -302.202120 -300.870994\n", "Time to run previous set of walkers (seconds): 0.002293\n", "Step: 108 / 1000\n", "Mean acceptance fraction: 0.512963\n", "Mean lnprob and Max lnprob values: -302.118063 -300.873058\n", "Time to run previous set of walkers (seconds): 0.002374\n", "Step: 109 / 1000\n", "Mean acceptance fraction: 0.514954\n", "Mean lnprob and Max lnprob values: -302.187396 -300.860097\n", "Time to run previous set of walkers (seconds): 0.002189\n", "Step: 110 / 1000\n", "Mean acceptance fraction: 0.516727\n", "Mean lnprob and Max lnprob values: -302.324197 -300.976544\n", "Time to run previous set of walkers (seconds): 0.001955\n", "Step: 111 / 1000\n", "Mean acceptance fraction: 0.517928\n", "Mean lnprob and Max lnprob values: -302.248749 -300.902244\n", "Time to run previous set of walkers (seconds): 0.002342\n", "Step: 112 / 1000\n", "Mean acceptance fraction: 0.518750\n", "Mean lnprob and Max lnprob values: -302.249022 -300.870377\n", "Time to run previous set of walkers (seconds): 0.001911\n", "Step: 113 / 1000\n", "Mean acceptance fraction: 0.519912\n", "Mean lnprob and Max lnprob values: -302.231663 -300.882585\n", "Time to run previous set of walkers (seconds): 0.001785\n", "Step: 114 / 1000\n", "Mean acceptance fraction: 0.521140\n", "Mean lnprob and Max lnprob values: -302.253018 -300.886062\n", "Time to run previous set of walkers (seconds): 0.001867\n", "Step: 115 / 1000\n", "Mean acceptance fraction: 0.522522\n", "Mean lnprob and Max lnprob values: -302.226087 -300.889083\n", "Time to run previous set of walkers (seconds): 0.002409\n", "Step: 116 / 1000\n", "Mean acceptance fraction: 0.523017\n", "Mean lnprob and Max lnprob values: -302.248127 -300.920054\n", "Time to run previous set of walkers (seconds): 0.002000\n", "Step: 117 / 1000\n", "Mean acceptance fraction: 0.524017\n", "Mean lnprob and Max lnprob values: -302.242627 -300.880117\n", "Time to run previous set of walkers (seconds): 0.001790\n", "Step: 118 / 1000\n", "Mean acceptance fraction: 0.524492\n", "Mean lnprob and Max lnprob values: -302.314018 -300.936730\n", "Time to run previous set of walkers (seconds): 0.001961\n", "Step: 119 / 1000\n", "Mean acceptance fraction: 0.525378\n", "Mean lnprob and Max lnprob values: -302.293623 -300.917723\n", "Time to run previous set of walkers (seconds): 0.002480\n", "Step: 120 / 1000\n", "Mean acceptance fraction: 0.526083\n", "Mean lnprob and Max lnprob values: -302.322440 -300.879192\n", "Time to run previous set of walkers (seconds): 0.001824\n", "Step: 121 / 1000\n", "Mean acceptance fraction: 0.526033\n", "Mean lnprob and Max lnprob values: -302.235399 -300.879192\n", "Time to run previous set of walkers (seconds): 0.002608\n", "Step: 122 / 1000\n", "Mean acceptance fraction: 0.527541\n", "Mean lnprob and Max lnprob values: -302.186171 -300.875224\n", "Time to run previous set of walkers (seconds): 0.002273\n", "Step: 123 / 1000\n", "Mean acceptance fraction: 0.528049\n", "Mean lnprob and Max lnprob values: -302.174673 -300.875224\n", "Time to run previous set of walkers (seconds): 0.002161\n", "Step: 124 / 1000\n", "Mean acceptance fraction: 0.529597\n", "Mean lnprob and Max lnprob values: -302.167358 -300.933449\n", "Time to run previous set of walkers (seconds): 0.001998\n", "Step: 125 / 1000\n", "Mean acceptance fraction: 0.529600\n", "Mean lnprob and Max lnprob values: -302.401934 -300.915183\n", "Time to run previous set of walkers (seconds): 0.002262\n", "Step: 126 / 1000\n", "Mean acceptance fraction: 0.530556\n", "Mean lnprob and Max lnprob values: -302.318694 -300.909706\n", "Time to run previous set of walkers (seconds): 0.002302\n", "Step: 127 / 1000\n", "Mean acceptance fraction: 0.531575\n", "Mean lnprob and Max lnprob values: -302.246981 -300.884003\n", "Time to run previous set of walkers (seconds): 0.001948\n", "Step: 128 / 1000\n", "Mean acceptance fraction: 0.532188\n", "Mean lnprob and Max lnprob values: -302.211612 -300.886016\n", "Time to run previous set of walkers (seconds): 0.001815\n", "Step: 129 / 1000\n", "Mean acceptance fraction: 0.533256\n", "Mean lnprob and Max lnprob values: -302.337980 -300.861261\n", "Time to run previous set of walkers (seconds): 0.001795\n", "Step: 130 / 1000\n", "Mean acceptance fraction: 0.534154\n", "Mean lnprob and Max lnprob values: -302.321582 -300.877564\n", "Time to run previous set of walkers (seconds): 0.002140\n", "Step: 131 / 1000\n", "Mean acceptance fraction: 0.535344\n", "Mean lnprob and Max lnprob values: -302.454517 -300.869015\n", "Time to run previous set of walkers (seconds): 0.002153\n", "Step: 132 / 1000\n", "Mean acceptance fraction: 0.536970\n", "Mean lnprob and Max lnprob values: -302.497152 -300.865736\n", "Time to run previous set of walkers (seconds): 0.002195\n", "Step: 133 / 1000\n", "Mean acceptance fraction: 0.538045\n", "Mean lnprob and Max lnprob values: -302.443409 -300.865736\n", "Time to run previous set of walkers (seconds): 0.001842\n", "Step: 134 / 1000\n", "Mean acceptance fraction: 0.538433\n", "Mean lnprob and Max lnprob values: -302.379990 -300.900306\n", "Time to run previous set of walkers (seconds): 0.001820\n", "Step: 135 / 1000\n", "Mean acceptance fraction: 0.540000\n", "Mean lnprob and Max lnprob values: -302.280630 -300.899564\n", "Time to run previous set of walkers (seconds): 0.002779\n", "Step: 136 / 1000\n", "Mean acceptance fraction: 0.540809\n", "Mean lnprob and Max lnprob values: -302.302119 -300.874526\n", "Time to run previous set of walkers (seconds): 0.001954\n", "Step: 137 / 1000\n", "Mean acceptance fraction: 0.541679\n", "Mean lnprob and Max lnprob values: -302.325982 -300.896505\n", "Time to run previous set of walkers (seconds): 0.001900\n", "Step: 138 / 1000\n", "Mean acceptance fraction: 0.542609\n", "Mean lnprob and Max lnprob values: -302.452612 -300.886381\n", "Time to run previous set of walkers (seconds): 0.002254\n", "Step: 139 / 1000\n", "Mean acceptance fraction: 0.542374\n", "Mean lnprob and Max lnprob values: -302.477438 -300.886381\n", "Time to run previous set of walkers (seconds): 0.001893\n", "Step: 140 / 1000\n", "Mean acceptance fraction: 0.542643\n", "Mean lnprob and Max lnprob values: -302.505397 -300.886381\n", "Time to run previous set of walkers (seconds): 0.001791\n", "Step: 141 / 1000\n", "Mean acceptance fraction: 0.543546\n", "Mean lnprob and Max lnprob values: -302.545571 -300.856378\n", "Time to run previous set of walkers (seconds): 0.001776\n", "Step: 142 / 1000\n", "Mean acceptance fraction: 0.544718\n", "Mean lnprob and Max lnprob values: -302.536770 -300.869314\n", "Time to run previous set of walkers (seconds): 0.001865\n", "Step: 143 / 1000\n", "Mean acceptance fraction: 0.545664\n", "Mean lnprob and Max lnprob values: -302.372962 -300.849420\n", "Time to run previous set of walkers (seconds): 0.001838\n", "Step: 144 / 1000\n", "Mean acceptance fraction: 0.545764\n", "Mean lnprob and Max lnprob values: -302.459867 -300.854181\n", "Time to run previous set of walkers (seconds): 0.002014\n", "Step: 145 / 1000\n", "Mean acceptance fraction: 0.546207\n", "Mean lnprob and Max lnprob values: -302.371571 -300.858110\n", "Time to run previous set of walkers (seconds): 0.001766\n", "Step: 146 / 1000\n", "Mean acceptance fraction: 0.546712\n", "Mean lnprob and Max lnprob values: -302.459085 -300.858110\n", "Time to run previous set of walkers (seconds): 0.001784\n", "Step: 147 / 1000\n", "Mean acceptance fraction: 0.548027\n", "Mean lnprob and Max lnprob values: -302.495006 -300.898525\n", "Time to run previous set of walkers (seconds): 0.001767\n", "Step: 148 / 1000\n", "Mean acceptance fraction: 0.548514\n", "Mean lnprob and Max lnprob values: -302.456841 -300.898277\n", "Time to run previous set of walkers (seconds): 0.001842\n", "Step: 149 / 1000\n", "Mean acceptance fraction: 0.548591\n", "Mean lnprob and Max lnprob values: -302.390435 -300.898277\n", "Time to run previous set of walkers (seconds): 0.001807\n", "Step: 150 / 1000\n", "Mean acceptance fraction: 0.549000\n", "Mean lnprob and Max lnprob values: -302.463359 -300.852158\n", "Time to run previous set of walkers (seconds): 0.002044\n", "Step: 151 / 1000\n", "Mean acceptance fraction: 0.549272\n", "Mean lnprob and Max lnprob values: -302.475390 -300.860996\n", "Time to run previous set of walkers (seconds): 0.001886\n", "Step: 152 / 1000\n", "Mean acceptance fraction: 0.549803\n", "Mean lnprob and Max lnprob values: -302.382671 -300.864338\n", "Time to run previous set of walkers (seconds): 0.002138\n", "Step: 153 / 1000\n", "Mean acceptance fraction: 0.550523\n", "Mean lnprob and Max lnprob values: -302.207351 -300.847397\n", "Time to run previous set of walkers (seconds): 0.001937\n", "Step: 154 / 1000\n", "Mean acceptance fraction: 0.551364\n", "Mean lnprob and Max lnprob values: -302.435580 -300.933867\n", "Time to run previous set of walkers (seconds): 0.001773\n", "Step: 155 / 1000\n", "Mean acceptance fraction: 0.552258\n", "Mean lnprob and Max lnprob values: -302.404425 -300.865104\n", "Time to run previous set of walkers (seconds): 0.001946\n", "Step: 156 / 1000\n", "Mean acceptance fraction: 0.552436\n", "Mean lnprob and Max lnprob values: -302.453676 -300.875034\n", "Time to run previous set of walkers (seconds): 0.002225\n", "Step: 157 / 1000\n", "Mean acceptance fraction: 0.552866\n", "Mean lnprob and Max lnprob values: -302.401667 -300.891364\n", "Time to run previous set of walkers (seconds): 0.001796\n", "Step: 158 / 1000\n", "Mean acceptance fraction: 0.553544\n", "Mean lnprob and Max lnprob values: -302.223016 -300.868133\n", "Time to run previous set of walkers (seconds): 0.002177\n", "Step: 159 / 1000\n", "Mean acceptance fraction: 0.554654\n", "Mean lnprob and Max lnprob values: -302.214440 -300.877635\n", "Time to run previous set of walkers (seconds): 0.002508\n", "Step: 160 / 1000\n", "Mean acceptance fraction: 0.555500\n", "Mean lnprob and Max lnprob values: -302.283457 -300.901608\n", "Time to run previous set of walkers (seconds): 0.002609\n", "Step: 161 / 1000\n", "Mean acceptance fraction: 0.556708\n", "Mean lnprob and Max lnprob values: -302.232735 -300.919126\n", "Time to run previous set of walkers (seconds): 0.001850\n", "Step: 162 / 1000\n", "Mean acceptance fraction: 0.557593\n", "Mean lnprob and Max lnprob values: -302.315891 -300.877137\n", "Time to run previous set of walkers (seconds): 0.001889\n", "Step: 163 / 1000\n", "Mean acceptance fraction: 0.558098\n", "Mean lnprob and Max lnprob values: -302.389620 -300.875757\n", "Time to run previous set of walkers (seconds): 0.002073\n", "Step: 164 / 1000\n", "Mean acceptance fraction: 0.558598\n", "Mean lnprob and Max lnprob values: -302.270422 -300.873478\n", "Time to run previous set of walkers (seconds): 0.002125\n", "Step: 165 / 1000\n", "Mean acceptance fraction: 0.558606\n", "Mean lnprob and Max lnprob values: -302.375388 -300.890228\n", "Time to run previous set of walkers (seconds): 0.001960\n", "Step: 166 / 1000\n", "Mean acceptance fraction: 0.559217\n", "Mean lnprob and Max lnprob values: -302.450923 -300.867057\n", "Time to run previous set of walkers (seconds): 0.002022\n", "Step: 167 / 1000\n", "Mean acceptance fraction: 0.559880\n", "Mean lnprob and Max lnprob values: -302.317818 -300.904886\n", "Time to run previous set of walkers (seconds): 0.002125\n", "Step: 168 / 1000\n", "Mean acceptance fraction: 0.560714\n", "Mean lnprob and Max lnprob values: -302.478552 -300.926159\n", "Time to run previous set of walkers (seconds): 0.002123\n", "Step: 169 / 1000\n", "Mean acceptance fraction: 0.561124\n", "Mean lnprob and Max lnprob values: -302.542762 -300.916513\n", "Time to run previous set of walkers (seconds): 0.001871\n", "Step: 170 / 1000\n", "Mean acceptance fraction: 0.561941\n", "Mean lnprob and Max lnprob values: -302.498562 -300.916513\n", "Time to run previous set of walkers (seconds): 0.002091\n", "Step: 171 / 1000\n", "Mean acceptance fraction: 0.561813\n", "Mean lnprob and Max lnprob values: -302.502227 -300.916513\n", "Time to run previous set of walkers (seconds): 0.001896\n", "Step: 172 / 1000\n", "Mean acceptance fraction: 0.562558\n", "Mean lnprob and Max lnprob values: -302.395527 -300.937829\n", "Time to run previous set of walkers (seconds): 0.002086\n", "Step: 173 / 1000\n", "Mean acceptance fraction: 0.562832\n", "Mean lnprob and Max lnprob values: -302.483682 -300.937829\n", "Time to run previous set of walkers (seconds): 0.002272\n", "Step: 174 / 1000\n", "Mean acceptance fraction: 0.562931\n", "Mean lnprob and Max lnprob values: -302.470327 -300.937297\n", "Time to run previous set of walkers (seconds): 0.001887\n", "Step: 175 / 1000\n", "Mean acceptance fraction: 0.563943\n", "Mean lnprob and Max lnprob values: -302.438455 -300.977240\n", "Time to run previous set of walkers (seconds): 0.001772\n", "Step: 176 / 1000\n", "Mean acceptance fraction: 0.564205\n", "Mean lnprob and Max lnprob values: -302.494195 -301.015286\n", "Time to run previous set of walkers (seconds): 0.002111\n", "Step: 177 / 1000\n", "Mean acceptance fraction: 0.564915\n", "Mean lnprob and Max lnprob values: -302.496213 -300.882615\n", "Time to run previous set of walkers (seconds): 0.001808\n", "Step: 178 / 1000\n", "Mean acceptance fraction: 0.565393\n", "Mean lnprob and Max lnprob values: -302.426449 -300.901297\n", "Time to run previous set of walkers (seconds): 0.002117\n", "Step: 179 / 1000\n", "Mean acceptance fraction: 0.565866\n", "Mean lnprob and Max lnprob values: -302.425937 -300.950777\n", "Time to run previous set of walkers (seconds): 0.001825\n", "Step: 180 / 1000\n", "Mean acceptance fraction: 0.566333\n", "Mean lnprob and Max lnprob values: -302.387745 -300.899568\n", "Time to run previous set of walkers (seconds): 0.001992\n", "Step: 181 / 1000\n", "Mean acceptance fraction: 0.567238\n", "Mean lnprob and Max lnprob values: -302.323123 -300.933762\n", "Time to run previous set of walkers (seconds): 0.002117\n", "Step: 182 / 1000\n", "Mean acceptance fraction: 0.568132\n", "Mean lnprob and Max lnprob values: -302.252860 -300.871933\n", "Time to run previous set of walkers (seconds): 0.001780\n", "Step: 183 / 1000\n", "Mean acceptance fraction: 0.568142\n", "Mean lnprob and Max lnprob values: -302.341630 -300.877841\n", "Time to run previous set of walkers (seconds): 0.001856\n", "Step: 184 / 1000\n", "Mean acceptance fraction: 0.568478\n", "Mean lnprob and Max lnprob values: -302.308297 -300.861836\n", "Time to run previous set of walkers (seconds): 0.002529\n", "Step: 185 / 1000\n", "Mean acceptance fraction: 0.568541\n", "Mean lnprob and Max lnprob values: -302.538648 -300.861836\n", "Time to run previous set of walkers (seconds): 0.003087\n", "Step: 186 / 1000\n", "Mean acceptance fraction: 0.569301\n", "Mean lnprob and Max lnprob values: -302.380398 -300.957919\n", "Time to run previous set of walkers (seconds): 0.002301\n", "Step: 187 / 1000\n", "Mean acceptance fraction: 0.569037\n", "Mean lnprob and Max lnprob values: -302.610043 -301.035435\n", "Time to run previous set of walkers (seconds): 0.001820\n", "Step: 188 / 1000\n", "Mean acceptance fraction: 0.569894\n", "Mean lnprob and Max lnprob values: -302.580526 -300.909933\n", "Time to run previous set of walkers (seconds): 0.001765\n", "Step: 189 / 1000\n", "Mean acceptance fraction: 0.570370\n", "Mean lnprob and Max lnprob values: -302.431987 -300.899018\n", "Time to run previous set of walkers (seconds): 0.002562\n", "Step: 190 / 1000\n", "Mean acceptance fraction: 0.570947\n", "Mean lnprob and Max lnprob values: -302.331809 -300.914103\n", "Time to run previous set of walkers (seconds): 0.002456\n", "Step: 191 / 1000\n", "Mean acceptance fraction: 0.571361\n", "Mean lnprob and Max lnprob values: -302.454046 -300.907786\n", "Time to run previous set of walkers (seconds): 0.002543\n", "Step: 192 / 1000\n", "Mean acceptance fraction: 0.571875\n", "Mean lnprob and Max lnprob values: -302.377681 -300.856407\n", "Time to run previous set of walkers (seconds): 0.002769\n", "Step: 193 / 1000\n", "Mean acceptance fraction: 0.572332\n", "Mean lnprob and Max lnprob values: -302.478159 -300.856407\n", "Time to run previous set of walkers (seconds): 0.002550\n", "Step: 194 / 1000\n", "Mean acceptance fraction: 0.572732\n", "Mean lnprob and Max lnprob values: -302.383675 -300.854689\n", "Time to run previous set of walkers (seconds): 0.001909\n", "Step: 195 / 1000\n", "Mean acceptance fraction: 0.572821\n", "Mean lnprob and Max lnprob values: -302.399547 -300.885839\n", "Time to run previous set of walkers (seconds): 0.003007\n", "Step: 196 / 1000\n", "Mean acceptance fraction: 0.572704\n", "Mean lnprob and Max lnprob values: -302.344634 -300.885839\n", "Time to run previous set of walkers (seconds): 0.002446\n", "Step: 197 / 1000\n", "Mean acceptance fraction: 0.573452\n", "Mean lnprob and Max lnprob values: -302.363560 -300.944387\n", "Time to run previous set of walkers (seconds): 0.002505\n", "Step: 198 / 1000\n", "Mean acceptance fraction: 0.573788\n", "Mean lnprob and Max lnprob values: -302.343653 -300.893522\n", "Time to run previous set of walkers (seconds): 0.003269\n", "Step: 199 / 1000\n", "Mean acceptance fraction: 0.574121\n", "Mean lnprob and Max lnprob values: -302.295866 -301.000400\n", "Time to run previous set of walkers (seconds): 0.001918\n", "Step: 200 / 1000\n", "Mean acceptance fraction: 0.574700\n", "Mean lnprob and Max lnprob values: -302.417605 -301.041964\n", "Time to run previous set of walkers (seconds): 0.002460\n", "Step: 201 / 1000\n", "Mean acceptance fraction: 0.574776\n", "Mean lnprob and Max lnprob values: -302.594043 -301.039992\n", "Time to run previous set of walkers (seconds): 0.002082\n", "Step: 202 / 1000\n", "Mean acceptance fraction: 0.575297\n", "Mean lnprob and Max lnprob values: -302.546412 -300.895424\n", "Time to run previous set of walkers (seconds): 0.001925\n", "Step: 203 / 1000\n", "Mean acceptance fraction: 0.575468\n", "Mean lnprob and Max lnprob values: -302.453883 -300.896501\n", "Time to run previous set of walkers (seconds): 0.002079\n", "Step: 204 / 1000\n", "Mean acceptance fraction: 0.575637\n", "Mean lnprob and Max lnprob values: -302.618218 -300.896382\n", "Time to run previous set of walkers (seconds): 0.002358\n", "Step: 205 / 1000\n", "Mean acceptance fraction: 0.575951\n", "Mean lnprob and Max lnprob values: -302.554152 -300.928324\n", "Time to run previous set of walkers (seconds): 0.003426\n", "Step: 206 / 1000\n", "Mean acceptance fraction: 0.576408\n", "Mean lnprob and Max lnprob values: -302.542266 -300.906276\n", "Time to run previous set of walkers (seconds): 0.003070\n", "Step: 207 / 1000\n", "Mean acceptance fraction: 0.576473\n", "Mean lnprob and Max lnprob values: -302.432956 -300.891904\n", "Time to run previous set of walkers (seconds): 0.001974\n", "Step: 208 / 1000\n", "Mean acceptance fraction: 0.576875\n", "Mean lnprob and Max lnprob values: -302.468666 -300.886907\n", "Time to run previous set of walkers (seconds): 0.002589\n", "Step: 209 / 1000\n", "Mean acceptance fraction: 0.577512\n", "Mean lnprob and Max lnprob values: -302.437287 -300.953598\n", "Time to run previous set of walkers (seconds): 0.003540\n", "Step: 210 / 1000\n", "Mean acceptance fraction: 0.578143\n", "Mean lnprob and Max lnprob values: -302.474067 -300.847084\n", "Time to run previous set of walkers (seconds): 0.002639\n", "Step: 211 / 1000\n", "Mean acceptance fraction: 0.578578\n", "Mean lnprob and Max lnprob values: -302.370474 -300.889404\n", "Time to run previous set of walkers (seconds): 0.002484\n", "Step: 212 / 1000\n", "Mean acceptance fraction: 0.578868\n", "Mean lnprob and Max lnprob values: -302.380930 -300.952479\n", "Time to run previous set of walkers (seconds): 0.002669\n", "Step: 213 / 1000\n", "Mean acceptance fraction: 0.579108\n", "Mean lnprob and Max lnprob values: -302.427667 -300.919092\n", "Time to run previous set of walkers (seconds): 0.002208\n", "Step: 214 / 1000\n", "Mean acceptance fraction: 0.579393\n", "Mean lnprob and Max lnprob values: -302.449032 -300.845255\n", "Time to run previous set of walkers (seconds): 0.002413\n", "Step: 215 / 1000\n", "Mean acceptance fraction: 0.579628\n", "Mean lnprob and Max lnprob values: -302.373572 -300.868175\n", "Time to run previous set of walkers (seconds): 0.002804\n", "Step: 216 / 1000\n", "Mean acceptance fraction: 0.580093\n", "Mean lnprob and Max lnprob values: -302.482510 -300.933141\n", "Time to run previous set of walkers (seconds): 0.002501\n", "Step: 217 / 1000\n", "Mean acceptance fraction: 0.579862\n", "Mean lnprob and Max lnprob values: -302.420161 -300.933141\n", "Time to run previous set of walkers (seconds): 0.002393\n", "Step: 218 / 1000\n", "Mean acceptance fraction: 0.579541\n", "Mean lnprob and Max lnprob values: -302.597696 -300.966567\n", "Time to run previous set of walkers (seconds): 0.001986\n", "Step: 219 / 1000\n", "Mean acceptance fraction: 0.579635\n", "Mean lnprob and Max lnprob values: -302.451586 -300.909944\n", "Time to run previous set of walkers (seconds): 0.002141\n", "Step: 220 / 1000\n", "Mean acceptance fraction: 0.579727\n", "Mean lnprob and Max lnprob values: -302.485505 -300.912049\n", "Time to run previous set of walkers (seconds): 0.002460\n", "Step: 221 / 1000\n", "Mean acceptance fraction: 0.580452\n", "Mean lnprob and Max lnprob values: -302.358347 -300.928750\n", "Time to run previous set of walkers (seconds): 0.001873\n", "Step: 222 / 1000\n", "Mean acceptance fraction: 0.580811\n", "Mean lnprob and Max lnprob values: -302.313185 -300.891737\n", "Time to run previous set of walkers (seconds): 0.001767\n", "Step: 223 / 1000\n", "Mean acceptance fraction: 0.580762\n", "Mean lnprob and Max lnprob values: -302.284712 -300.849416\n", "Time to run previous set of walkers (seconds): 0.002115\n", "Step: 224 / 1000\n", "Mean acceptance fraction: 0.581071\n", "Mean lnprob and Max lnprob values: -302.357683 -300.865255\n", "Time to run previous set of walkers (seconds): 0.001923\n", "Step: 225 / 1000\n", "Mean acceptance fraction: 0.581200\n", "Mean lnprob and Max lnprob values: -302.559902 -300.959684\n", "Time to run previous set of walkers (seconds): 0.001760\n", "Step: 226 / 1000\n", "Mean acceptance fraction: 0.581681\n", "Mean lnprob and Max lnprob values: -302.413776 -300.890547\n", "Time to run previous set of walkers (seconds): 0.001827\n", "Step: 227 / 1000\n", "Mean acceptance fraction: 0.581762\n", "Mean lnprob and Max lnprob values: -302.490864 -300.938185\n", "Time to run previous set of walkers (seconds): 0.002426\n", "Step: 228 / 1000\n", "Mean acceptance fraction: 0.581667\n", "Mean lnprob and Max lnprob values: -302.521164 -300.971265\n", "Time to run previous set of walkers (seconds): 0.001875\n", "Step: 229 / 1000\n", "Mean acceptance fraction: 0.581834\n", "Mean lnprob and Max lnprob values: -302.521242 -300.867445\n", "Time to run previous set of walkers (seconds): 0.001796\n", "Step: 230 / 1000\n", "Mean acceptance fraction: 0.582087\n", "Mean lnprob and Max lnprob values: -302.550672 -300.931419\n", "Time to run previous set of walkers (seconds): 0.001848\n", "Step: 231 / 1000\n", "Mean acceptance fraction: 0.582511\n", "Mean lnprob and Max lnprob values: -302.460365 -300.887572\n", "Time to run previous set of walkers (seconds): 0.002365\n", "Step: 232 / 1000\n", "Mean acceptance fraction: 0.583017\n", "Mean lnprob and Max lnprob values: -302.392847 -300.901337\n", "Time to run previous set of walkers (seconds): 0.001869\n", "Step: 233 / 1000\n", "Mean acceptance fraction: 0.583433\n", "Mean lnprob and Max lnprob values: -302.423396 -300.887033\n", "Time to run previous set of walkers (seconds): 0.001786\n", "Step: 234 / 1000\n", "Mean acceptance fraction: 0.583718\n", "Mean lnprob and Max lnprob values: -302.319029 -300.906582\n", "Time to run previous set of walkers (seconds): 0.002380\n", "Step: 235 / 1000\n", "Mean acceptance fraction: 0.584043\n", "Mean lnprob and Max lnprob values: -302.244327 -300.906582\n", "Time to run previous set of walkers (seconds): 0.002221\n", "Step: 236 / 1000\n", "Mean acceptance fraction: 0.584534\n", "Mean lnprob and Max lnprob values: -302.227743 -300.912849\n", "Time to run previous set of walkers (seconds): 0.001901\n", "Step: 237 / 1000\n", "Mean acceptance fraction: 0.584979\n", "Mean lnprob and Max lnprob values: -302.267433 -300.898497\n", "Time to run previous set of walkers (seconds): 0.002167\n", "Step: 238 / 1000\n", "Mean acceptance fraction: 0.585252\n", "Mean lnprob and Max lnprob values: -302.285274 -300.847858\n", "Time to run previous set of walkers (seconds): 0.002151\n", "Step: 239 / 1000\n", "Mean acceptance fraction: 0.585523\n", "Mean lnprob and Max lnprob values: -302.266784 -300.847604\n", "Time to run previous set of walkers (seconds): 0.002305\n", "Step: 240 / 1000\n", "Mean acceptance fraction: 0.585375\n", "Mean lnprob and Max lnprob values: -302.159944 -300.847604\n", "Time to run previous set of walkers (seconds): 0.002667\n", "Step: 241 / 1000\n", "Mean acceptance fraction: 0.585726\n", "Mean lnprob and Max lnprob values: -302.137532 -300.847488\n", "Time to run previous set of walkers (seconds): 0.002455\n", "Step: 242 / 1000\n", "Mean acceptance fraction: 0.585950\n", "Mean lnprob and Max lnprob values: -302.161799 -300.880006\n", "Time to run previous set of walkers (seconds): 0.001877\n", "Step: 243 / 1000\n", "Mean acceptance fraction: 0.586296\n", "Mean lnprob and Max lnprob values: -302.275620 -300.851042\n", "Time to run previous set of walkers (seconds): 0.001870\n", "Step: 244 / 1000\n", "Mean acceptance fraction: 0.586393\n", "Mean lnprob and Max lnprob values: -302.448520 -300.851042\n", "Time to run previous set of walkers (seconds): 0.002287\n", "Step: 245 / 1000\n", "Mean acceptance fraction: 0.586571\n", "Mean lnprob and Max lnprob values: -302.395195 -300.856411\n", "Time to run previous set of walkers (seconds): 0.001805\n", "Step: 246 / 1000\n", "Mean acceptance fraction: 0.586463\n", "Mean lnprob and Max lnprob values: -302.329226 -300.858199\n", "Time to run previous set of walkers (seconds): 0.001896\n", "Step: 247 / 1000\n", "Mean acceptance fraction: 0.586802\n", "Mean lnprob and Max lnprob values: -302.421614 -300.888949\n", "Time to run previous set of walkers (seconds): 0.002317\n", "Step: 248 / 1000\n", "Mean acceptance fraction: 0.587258\n", "Mean lnprob and Max lnprob values: -302.276270 -300.877394\n", "Time to run previous set of walkers (seconds): 0.001921\n", "Step: 249 / 1000\n", "Mean acceptance fraction: 0.587390\n", "Mean lnprob and Max lnprob values: -302.350327 -300.893691\n", "Time to run previous set of walkers (seconds): 0.001747\n", "Step: 250 / 1000\n", "Mean acceptance fraction: 0.587680\n", "Mean lnprob and Max lnprob values: -302.391591 -300.972781\n", "Time to run previous set of walkers (seconds): 0.001812\n", "Step: 251 / 1000\n", "Mean acceptance fraction: 0.587849\n", "Mean lnprob and Max lnprob values: -302.342248 -300.850020\n", "Time to run previous set of walkers (seconds): 0.002340\n", "Step: 252 / 1000\n", "Mean acceptance fraction: 0.587897\n", "Mean lnprob and Max lnprob values: -302.315968 -300.850167\n", "Time to run previous set of walkers (seconds): 0.001824\n", "Step: 253 / 1000\n", "Mean acceptance fraction: 0.588103\n", "Mean lnprob and Max lnprob values: -302.332044 -300.850167\n", "Time to run previous set of walkers (seconds): 0.001763\n", "Step: 254 / 1000\n", "Mean acceptance fraction: 0.588425\n", "Mean lnprob and Max lnprob values: -302.377799 -300.860766\n", "Time to run previous set of walkers (seconds): 0.002367\n", "Step: 255 / 1000\n", "Mean acceptance fraction: 0.588588\n", "Mean lnprob and Max lnprob values: -302.274576 -300.884849\n", "Time to run previous set of walkers (seconds): 0.002905\n", "Step: 256 / 1000\n", "Mean acceptance fraction: 0.588828\n", "Mean lnprob and Max lnprob values: -302.335029 -300.878994\n", "Time to run previous set of walkers (seconds): 0.002164\n", "Step: 257 / 1000\n", "Mean acceptance fraction: 0.589183\n", "Mean lnprob and Max lnprob values: -302.337817 -300.855839\n", "Time to run previous set of walkers (seconds): 0.002262\n", "Step: 258 / 1000\n", "Mean acceptance fraction: 0.589457\n", "Mean lnprob and Max lnprob values: -302.318377 -300.884202\n", "Time to run previous set of walkers (seconds): 0.001929\n", "Step: 259 / 1000\n", "Mean acceptance fraction: 0.589653\n", "Mean lnprob and Max lnprob values: -302.255771 -300.884202\n", "Time to run previous set of walkers (seconds): 0.001852\n", "Step: 260 / 1000\n", "Mean acceptance fraction: 0.590038\n", "Mean lnprob and Max lnprob values: -302.332575 -300.925680\n", "Time to run previous set of walkers (seconds): 0.002059\n", "Step: 261 / 1000\n", "Mean acceptance fraction: 0.590000\n", "Mean lnprob and Max lnprob values: -302.131894 -300.920013\n", "Time to run previous set of walkers (seconds): 0.002225\n", "Step: 262 / 1000\n", "Mean acceptance fraction: 0.590573\n", "Mean lnprob and Max lnprob values: -302.288836 -300.886119\n", "Time to run previous set of walkers (seconds): 0.001822\n", "Step: 263 / 1000\n", "Mean acceptance fraction: 0.590646\n", "Mean lnprob and Max lnprob values: -302.279136 -300.891100\n", "Time to run previous set of walkers (seconds): 0.001814\n", "Step: 264 / 1000\n", "Mean acceptance fraction: 0.591023\n", "Mean lnprob and Max lnprob values: -302.259485 -300.889925\n", "Time to run previous set of walkers (seconds): 0.002301\n", "Step: 265 / 1000\n", "Mean acceptance fraction: 0.591170\n", "Mean lnprob and Max lnprob values: -302.180135 -300.884129\n", "Time to run previous set of walkers (seconds): 0.002099\n", "Step: 266 / 1000\n", "Mean acceptance fraction: 0.591053\n", "Mean lnprob and Max lnprob values: -302.188136 -300.884129\n", "Time to run previous set of walkers (seconds): 0.002415\n", "Step: 267 / 1000\n", "Mean acceptance fraction: 0.591386\n", "Mean lnprob and Max lnprob values: -302.265891 -300.871241\n", "Time to run previous set of walkers (seconds): 0.001928\n", "Step: 268 / 1000\n", "Mean acceptance fraction: 0.591940\n", "Mean lnprob and Max lnprob values: -302.199253 -300.851523\n", "Time to run previous set of walkers (seconds): 0.001798\n", "Step: 269 / 1000\n", "Mean acceptance fraction: 0.592491\n", "Mean lnprob and Max lnprob values: -302.184694 -300.865281\n", "Time to run previous set of walkers (seconds): 0.002030\n", "Step: 270 / 1000\n", "Mean acceptance fraction: 0.592889\n", "Mean lnprob and Max lnprob values: -302.192013 -300.934485\n", "Time to run previous set of walkers (seconds): 0.002817\n", "Step: 271 / 1000\n", "Mean acceptance fraction: 0.593063\n", "Mean lnprob and Max lnprob values: -302.202330 -300.863573\n", "Time to run previous set of walkers (seconds): 0.002568\n", "Step: 272 / 1000\n", "Mean acceptance fraction: 0.593162\n", "Mean lnprob and Max lnprob values: -302.187357 -300.890273\n", "Time to run previous set of walkers (seconds): 0.002064\n", "Step: 273 / 1000\n", "Mean acceptance fraction: 0.593626\n", "Mean lnprob and Max lnprob values: -302.272888 -300.900504\n", "Time to run previous set of walkers (seconds): 0.002105\n", "Step: 274 / 1000\n", "Mean acceptance fraction: 0.593832\n", "Mean lnprob and Max lnprob values: -302.182625 -300.866501\n", "Time to run previous set of walkers (seconds): 0.002022\n", "Step: 275 / 1000\n", "Mean acceptance fraction: 0.594218\n", "Mean lnprob and Max lnprob values: -302.340189 -300.888368\n", "Time to run previous set of walkers (seconds): 0.002457\n", "Step: 276 / 1000\n", "Mean acceptance fraction: 0.594420\n", "Mean lnprob and Max lnprob values: -302.307734 -300.898420\n", "Time to run previous set of walkers (seconds): 0.003059\n", "Step: 277 / 1000\n", "Mean acceptance fraction: 0.594729\n", "Mean lnprob and Max lnprob values: -302.291008 -300.949981\n", "Time to run previous set of walkers (seconds): 0.002252\n", "Step: 278 / 1000\n", "Mean acceptance fraction: 0.594640\n", "Mean lnprob and Max lnprob values: -302.278142 -300.949981\n", "Time to run previous set of walkers (seconds): 0.002266\n", "Step: 279 / 1000\n", "Mean acceptance fraction: 0.594946\n", "Mean lnprob and Max lnprob values: -302.360016 -300.935791\n", "Time to run previous set of walkers (seconds): 0.002502\n", "Step: 280 / 1000\n", "Mean acceptance fraction: 0.595179\n", "Mean lnprob and Max lnprob values: -302.393221 -300.936480\n", "Time to run previous set of walkers (seconds): 0.001899\n", "Step: 281 / 1000\n", "Mean acceptance fraction: 0.594840\n", "Mean lnprob and Max lnprob values: -302.337711 -300.934080\n", "Time to run previous set of walkers (seconds): 0.001866\n", "Step: 282 / 1000\n", "Mean acceptance fraction: 0.594894\n", "Mean lnprob and Max lnprob values: -302.364634 -300.880795\n", "Time to run previous set of walkers (seconds): 0.002117\n", "Step: 283 / 1000\n", "Mean acceptance fraction: 0.595230\n", "Mean lnprob and Max lnprob values: -302.394909 -300.895624\n", "Time to run previous set of walkers (seconds): 0.001928\n", "Step: 284 / 1000\n", "Mean acceptance fraction: 0.595352\n", "Mean lnprob and Max lnprob values: -302.402285 -300.917272\n", "Time to run previous set of walkers (seconds): 0.002129\n", "Step: 285 / 1000\n", "Mean acceptance fraction: 0.595123\n", "Mean lnprob and Max lnprob values: -302.455431 -300.883243\n", "Time to run previous set of walkers (seconds): 0.002651\n", "Step: 286 / 1000\n", "Mean acceptance fraction: 0.595245\n", "Mean lnprob and Max lnprob values: -302.578831 -300.863627\n", "Time to run previous set of walkers (seconds): 0.002351\n", "Step: 287 / 1000\n", "Mean acceptance fraction: 0.595436\n", "Mean lnprob and Max lnprob values: -302.657592 -300.878311\n", "Time to run previous set of walkers (seconds): 0.002580\n", "Step: 288 / 1000\n", "Mean acceptance fraction: 0.595382\n", "Mean lnprob and Max lnprob values: -302.514666 -300.878311\n", "Time to run previous set of walkers (seconds): 0.003327\n", "Step: 289 / 1000\n", "Mean acceptance fraction: 0.595571\n", "Mean lnprob and Max lnprob values: -302.500862 -300.878311\n", "Time to run previous set of walkers (seconds): 0.002928\n", "Step: 290 / 1000\n", "Mean acceptance fraction: 0.595483\n", "Mean lnprob and Max lnprob values: -302.490641 -300.874739\n", "Time to run previous set of walkers (seconds): 0.002572\n", "Step: 291 / 1000\n", "Mean acceptance fraction: 0.595395\n", "Mean lnprob and Max lnprob values: -302.593704 -300.912920\n", "Time to run previous set of walkers (seconds): 0.002387\n", "Step: 292 / 1000\n", "Mean acceptance fraction: 0.595445\n", "Mean lnprob and Max lnprob values: -302.679815 -300.917349\n", "Time to run previous set of walkers (seconds): 0.002064\n", "Step: 293 / 1000\n", "Mean acceptance fraction: 0.595427\n", "Mean lnprob and Max lnprob values: -302.539964 -300.993638\n", "Time to run previous set of walkers (seconds): 0.002200\n", "Step: 294 / 1000\n", "Mean acceptance fraction: 0.595544\n", "Mean lnprob and Max lnprob values: -302.456633 -300.955542\n", "Time to run previous set of walkers (seconds): 0.001963\n", "Step: 295 / 1000\n", "Mean acceptance fraction: 0.595559\n", "Mean lnprob and Max lnprob values: -302.484955 -300.920836\n", "Time to run previous set of walkers (seconds): 0.002543\n", "Step: 296 / 1000\n", "Mean acceptance fraction: 0.595608\n", "Mean lnprob and Max lnprob values: -302.502732 -300.852756\n", "Time to run previous set of walkers (seconds): 0.002166\n", "Step: 297 / 1000\n", "Mean acceptance fraction: 0.595791\n", "Mean lnprob and Max lnprob values: -302.560698 -300.854020\n", "Time to run previous set of walkers (seconds): 0.002030\n", "Step: 298 / 1000\n", "Mean acceptance fraction: 0.596007\n", "Mean lnprob and Max lnprob values: -302.443655 -300.881208\n", "Time to run previous set of walkers (seconds): 0.001864\n", "Step: 299 / 1000\n", "Mean acceptance fraction: 0.596154\n", "Mean lnprob and Max lnprob values: -302.454003 -300.892614\n", "Time to run previous set of walkers (seconds): 0.001775\n", "Step: 300 / 1000\n", "Mean acceptance fraction: 0.596100\n", "Mean lnprob and Max lnprob values: -302.404222 -300.902160\n", "Time to run previous set of walkers (seconds): 0.001805\n", "Step: 301 / 1000\n", "Mean acceptance fraction: 0.596445\n", "Mean lnprob and Max lnprob values: -302.443937 -300.906846\n", "Time to run previous set of walkers (seconds): 0.001802\n", "Step: 302 / 1000\n", "Mean acceptance fraction: 0.596490\n", "Mean lnprob and Max lnprob values: -302.410514 -300.927699\n", "Time to run previous set of walkers (seconds): 0.001775\n", "Step: 303 / 1000\n", "Mean acceptance fraction: 0.596766\n", "Mean lnprob and Max lnprob values: -302.420770 -300.947152\n", "Time to run previous set of walkers (seconds): 0.001949\n", "Step: 304 / 1000\n", "Mean acceptance fraction: 0.596908\n", "Mean lnprob and Max lnprob values: -302.468594 -300.913308\n", "Time to run previous set of walkers (seconds): 0.001883\n", "Step: 305 / 1000\n", "Mean acceptance fraction: 0.597148\n", "Mean lnprob and Max lnprob values: -302.411152 -300.861793\n", "Time to run previous set of walkers (seconds): 0.001857\n", "Step: 306 / 1000\n", "Mean acceptance fraction: 0.597092\n", "Mean lnprob and Max lnprob values: -302.435088 -300.894735\n", "Time to run previous set of walkers (seconds): 0.001791\n", "Step: 307 / 1000\n", "Mean acceptance fraction: 0.597134\n", "Mean lnprob and Max lnprob values: -302.554918 -300.918865\n", "Time to run previous set of walkers (seconds): 0.001900\n", "Step: 308 / 1000\n", "Mean acceptance fraction: 0.597338\n", "Mean lnprob and Max lnprob values: -302.470246 -300.973303\n", "Time to run previous set of walkers (seconds): 0.002386\n", "Step: 309 / 1000\n", "Mean acceptance fraction: 0.597476\n", "Mean lnprob and Max lnprob values: -302.542300 -300.898423\n", "Time to run previous set of walkers (seconds): 0.003080\n", "Step: 310 / 1000\n", "Mean acceptance fraction: 0.597903\n", "Mean lnprob and Max lnprob values: -302.464964 -300.951061\n", "Time to run previous set of walkers (seconds): 0.001814\n", "Step: 311 / 1000\n", "Mean acceptance fraction: 0.597781\n", "Mean lnprob and Max lnprob values: -302.476564 -300.927853\n", "Time to run previous set of walkers (seconds): 0.002075\n", "Step: 312 / 1000\n", "Mean acceptance fraction: 0.597596\n", "Mean lnprob and Max lnprob values: -302.443921 -300.926559\n", "Time to run previous set of walkers (seconds): 0.001823\n", "Step: 313 / 1000\n", "Mean acceptance fraction: 0.597700\n", "Mean lnprob and Max lnprob values: -302.385230 -300.847155\n", "Time to run previous set of walkers (seconds): 0.001740\n", "Step: 314 / 1000\n", "Mean acceptance fraction: 0.597675\n", "Mean lnprob and Max lnprob values: -302.379403 -300.860358\n", "Time to run previous set of walkers (seconds): 0.001832\n", "Step: 315 / 1000\n", "Mean acceptance fraction: 0.597873\n", "Mean lnprob and Max lnprob values: -302.412228 -300.865043\n", "Time to run previous set of walkers (seconds): 0.001816\n", "Step: 316 / 1000\n", "Mean acceptance fraction: 0.598133\n", "Mean lnprob and Max lnprob values: -302.356025 -300.865043\n", "Time to run previous set of walkers (seconds): 0.002060\n", "Step: 317 / 1000\n", "Mean acceptance fraction: 0.598391\n", "Mean lnprob and Max lnprob values: -302.454196 -300.907527\n", "Time to run previous set of walkers (seconds): 0.001768\n", "Step: 318 / 1000\n", "Mean acceptance fraction: 0.598553\n", "Mean lnprob and Max lnprob values: -302.594733 -300.850339\n", "Time to run previous set of walkers (seconds): 0.001850\n", "Step: 319 / 1000\n", "Mean acceptance fraction: 0.598652\n", "Mean lnprob and Max lnprob values: -302.675279 -300.856046\n", "Time to run previous set of walkers (seconds): 0.001833\n", "Step: 320 / 1000\n", "Mean acceptance fraction: 0.598719\n", "Mean lnprob and Max lnprob values: -302.568268 -300.856046\n", "Time to run previous set of walkers (seconds): 0.001799\n", "Step: 321 / 1000\n", "Mean acceptance fraction: 0.598567\n", "Mean lnprob and Max lnprob values: -302.491800 -300.858345\n", "Time to run previous set of walkers (seconds): 0.001987\n", "Step: 322 / 1000\n", "Mean acceptance fraction: 0.598758\n", "Mean lnprob and Max lnprob values: -302.569811 -300.867104\n", "Time to run previous set of walkers (seconds): 0.002037\n", "Step: 323 / 1000\n", "Mean acceptance fraction: 0.598793\n", "Mean lnprob and Max lnprob values: -302.592306 -301.022926\n", "Time to run previous set of walkers (seconds): 0.002362\n", "Step: 324 / 1000\n", "Mean acceptance fraction: 0.598858\n", "Mean lnprob and Max lnprob values: -302.515496 -300.932443\n", "Time to run previous set of walkers (seconds): 0.002077\n", "Step: 325 / 1000\n", "Mean acceptance fraction: 0.599108\n", "Mean lnprob and Max lnprob values: -302.451247 -300.934386\n", "Time to run previous set of walkers (seconds): 0.001979\n", "Step: 326 / 1000\n", "Mean acceptance fraction: 0.599325\n", "Mean lnprob and Max lnprob values: -302.384238 -300.934386\n", "Time to run previous set of walkers (seconds): 0.001867\n", "Step: 327 / 1000\n", "Mean acceptance fraction: 0.599725\n", "Mean lnprob and Max lnprob values: -302.158294 -300.893050\n", "Time to run previous set of walkers (seconds): 0.001773\n", "Step: 328 / 1000\n", "Mean acceptance fraction: 0.599878\n", "Mean lnprob and Max lnprob values: -302.298934 -300.893050\n", "Time to run previous set of walkers (seconds): 0.001812\n", "Step: 329 / 1000\n", "Mean acceptance fraction: 0.599878\n", "Mean lnprob and Max lnprob values: -302.305658 -300.893050\n", "Time to run previous set of walkers (seconds): 0.001877\n", "Step: 330 / 1000\n", "Mean acceptance fraction: 0.600182\n", "Mean lnprob and Max lnprob values: -302.390426 -301.035856\n", "Time to run previous set of walkers (seconds): 0.001799\n", "Step: 331 / 1000\n", "Mean acceptance fraction: 0.600151\n", "Mean lnprob and Max lnprob values: -302.403972 -300.894145\n", "Time to run previous set of walkers (seconds): 0.001774\n", "Step: 332 / 1000\n", "Mean acceptance fraction: 0.600331\n", "Mean lnprob and Max lnprob values: -302.443062 -300.908017\n", "Time to run previous set of walkers (seconds): 0.001915\n", "Step: 333 / 1000\n", "Mean acceptance fraction: 0.600601\n", "Mean lnprob and Max lnprob values: -302.340583 -300.866748\n", "Time to run previous set of walkers (seconds): 0.001873\n", "Step: 334 / 1000\n", "Mean acceptance fraction: 0.600988\n", "Mean lnprob and Max lnprob values: -302.359560 -300.865492\n", "Time to run previous set of walkers (seconds): 0.001872\n", "Step: 335 / 1000\n", "Mean acceptance fraction: 0.601134\n", "Mean lnprob and Max lnprob values: -302.286706 -300.964368\n", "Time to run previous set of walkers (seconds): 0.001889\n", "Step: 336 / 1000\n", "Mean acceptance fraction: 0.601280\n", "Mean lnprob and Max lnprob values: -302.339334 -301.039309\n", "Time to run previous set of walkers (seconds): 0.001896\n", "Step: 337 / 1000\n", "Mean acceptance fraction: 0.601454\n", "Mean lnprob and Max lnprob values: -302.319171 -300.982947\n", "Time to run previous set of walkers (seconds): 0.001814\n", "Step: 338 / 1000\n", "Mean acceptance fraction: 0.601479\n", "Mean lnprob and Max lnprob values: -302.305442 -300.942319\n", "Time to run previous set of walkers (seconds): 0.001810\n", "Step: 339 / 1000\n", "Mean acceptance fraction: 0.601652\n", "Mean lnprob and Max lnprob values: -302.257020 -300.892503\n", "Time to run previous set of walkers (seconds): 0.001726\n", "Step: 340 / 1000\n", "Mean acceptance fraction: 0.601882\n", "Mean lnprob and Max lnprob values: -302.345752 -300.901760\n", "Time to run previous set of walkers (seconds): 0.001874\n", "Step: 341 / 1000\n", "Mean acceptance fraction: 0.601906\n", "Mean lnprob and Max lnprob values: -302.433820 -300.901760\n", "Time to run previous set of walkers (seconds): 0.001901\n", "Step: 342 / 1000\n", "Mean acceptance fraction: 0.601930\n", "Mean lnprob and Max lnprob values: -302.445408 -300.929016\n", "Time to run previous set of walkers (seconds): 0.001821\n", "Step: 343 / 1000\n", "Mean acceptance fraction: 0.601983\n", "Mean lnprob and Max lnprob values: -302.565105 -300.870791\n", "Time to run previous set of walkers (seconds): 0.001865\n", "Step: 344 / 1000\n", "Mean acceptance fraction: 0.602122\n", "Mean lnprob and Max lnprob values: -302.427222 -300.858409\n", "Time to run previous set of walkers (seconds): 0.001816\n", "Step: 345 / 1000\n", "Mean acceptance fraction: 0.602348\n", "Mean lnprob and Max lnprob values: -302.331755 -300.929029\n", "Time to run previous set of walkers (seconds): 0.001770\n", "Step: 346 / 1000\n", "Mean acceptance fraction: 0.602601\n", "Mean lnprob and Max lnprob values: -302.242976 -300.862824\n", "Time to run previous set of walkers (seconds): 0.001737\n", "Step: 347 / 1000\n", "Mean acceptance fraction: 0.602767\n", "Mean lnprob and Max lnprob values: -302.263638 -300.862824\n", "Time to run previous set of walkers (seconds): 0.001850\n", "Step: 348 / 1000\n", "Mean acceptance fraction: 0.602902\n", "Mean lnprob and Max lnprob values: -302.229451 -300.845281\n", "Time to run previous set of walkers (seconds): 0.001926\n", "Step: 349 / 1000\n", "Mean acceptance fraction: 0.603152\n", "Mean lnprob and Max lnprob values: -302.051327 -300.844455\n", "Time to run previous set of walkers (seconds): 0.001830\n", "Step: 350 / 1000\n", "Mean acceptance fraction: 0.603371\n", "Mean lnprob and Max lnprob values: -302.055105 -300.844455\n", "Time to run previous set of walkers (seconds): 0.001845\n", "Step: 351 / 1000\n", "Mean acceptance fraction: 0.603476\n", "Mean lnprob and Max lnprob values: -302.108470 -300.844455\n", "Time to run previous set of walkers (seconds): 0.001906\n", "Step: 352 / 1000\n", "Mean acceptance fraction: 0.603608\n", "Mean lnprob and Max lnprob values: -302.151909 -300.844455\n", "Time to run previous set of walkers (seconds): 0.001912\n", "Step: 353 / 1000\n", "Mean acceptance fraction: 0.603824\n", "Mean lnprob and Max lnprob values: -302.338816 -300.844455\n", "Time to run previous set of walkers (seconds): 0.001767\n", "Step: 354 / 1000\n", "Mean acceptance fraction: 0.603644\n", "Mean lnprob and Max lnprob values: -302.325041 -300.853416\n", "Time to run previous set of walkers (seconds): 0.001772\n", "Step: 355 / 1000\n", "Mean acceptance fraction: 0.603718\n", "Mean lnprob and Max lnprob values: -302.436562 -300.853416\n", "Time to run previous set of walkers (seconds): 0.001842\n", "Step: 356 / 1000\n", "Mean acceptance fraction: 0.603989\n", "Mean lnprob and Max lnprob values: -302.419707 -300.848090\n", "Time to run previous set of walkers (seconds): 0.001920\n", "Step: 357 / 1000\n", "Mean acceptance fraction: 0.604062\n", "Mean lnprob and Max lnprob values: -302.398303 -300.848090\n", "Time to run previous set of walkers (seconds): 0.001852\n", "Step: 358 / 1000\n", "Mean acceptance fraction: 0.603966\n", "Mean lnprob and Max lnprob values: -302.307953 -300.857864\n", "Time to run previous set of walkers (seconds): 0.002044\n", "Step: 359 / 1000\n", "Mean acceptance fraction: 0.604318\n", "Mean lnprob and Max lnprob values: -302.278723 -300.867465\n", "Time to run previous set of walkers (seconds): 0.002383\n", "Step: 360 / 1000\n", "Mean acceptance fraction: 0.604417\n", "Mean lnprob and Max lnprob values: -302.397989 -300.867899\n", "Time to run previous set of walkers (seconds): 0.002183\n", "Step: 361 / 1000\n", "Mean acceptance fraction: 0.604404\n", "Mean lnprob and Max lnprob values: -302.404834 -300.877012\n", "Time to run previous set of walkers (seconds): 0.001795\n", "Step: 362 / 1000\n", "Mean acceptance fraction: 0.604392\n", "Mean lnprob and Max lnprob values: -302.412052 -300.859061\n", "Time to run previous set of walkers (seconds): 0.001995\n", "Step: 363 / 1000\n", "Mean acceptance fraction: 0.604628\n", "Mean lnprob and Max lnprob values: -302.417655 -300.893360\n", "Time to run previous set of walkers (seconds): 0.001857\n", "Step: 364 / 1000\n", "Mean acceptance fraction: 0.604780\n", "Mean lnprob and Max lnprob values: -302.455022 -300.881225\n", "Time to run previous set of walkers (seconds): 0.002028\n", "Step: 365 / 1000\n", "Mean acceptance fraction: 0.604822\n", "Mean lnprob and Max lnprob values: -302.495033 -300.967231\n", "Time to run previous set of walkers (seconds): 0.001934\n", "Step: 366 / 1000\n", "Mean acceptance fraction: 0.604945\n", "Mean lnprob and Max lnprob values: -302.344849 -300.872315\n", "Time to run previous set of walkers (seconds): 0.002209\n", "Step: 367 / 1000\n", "Mean acceptance fraction: 0.604714\n", "Mean lnprob and Max lnprob values: -302.389695 -300.872315\n", "Time to run previous set of walkers (seconds): 0.002059\n", "Step: 368 / 1000\n", "Mean acceptance fraction: 0.604810\n", "Mean lnprob and Max lnprob values: -302.548853 -300.848641\n", "Time to run previous set of walkers (seconds): 0.002568\n", "Step: 369 / 1000\n", "Mean acceptance fraction: 0.604878\n", "Mean lnprob and Max lnprob values: -302.608003 -300.848641\n", "Time to run previous set of walkers (seconds): 0.001989\n", "Step: 370 / 1000\n", "Mean acceptance fraction: 0.604838\n", "Mean lnprob and Max lnprob values: -302.578721 -300.881331\n", "Time to run previous set of walkers (seconds): 0.001912\n", "Step: 371 / 1000\n", "Mean acceptance fraction: 0.604933\n", "Mean lnprob and Max lnprob values: -302.593075 -300.881331\n", "Time to run previous set of walkers (seconds): 0.001807\n", "Step: 372 / 1000\n", "Mean acceptance fraction: 0.604919\n", "Mean lnprob and Max lnprob values: -302.598526 -300.860563\n", "Time to run previous set of walkers (seconds): 0.002095\n", "Step: 373 / 1000\n", "Mean acceptance fraction: 0.604933\n", "Mean lnprob and Max lnprob values: -302.493990 -300.889504\n", "Time to run previous set of walkers (seconds): 0.002779\n", "Step: 374 / 1000\n", "Mean acceptance fraction: 0.605000\n", "Mean lnprob and Max lnprob values: -302.525106 -300.910594\n", "Time to run previous set of walkers (seconds): 0.002039\n", "Step: 375 / 1000\n", "Mean acceptance fraction: 0.605120\n", "Mean lnprob and Max lnprob values: -302.429492 -300.919570\n", "Time to run previous set of walkers (seconds): 0.002077\n", "Step: 376 / 1000\n", "Mean acceptance fraction: 0.605293\n", "Mean lnprob and Max lnprob values: -302.393821 -300.923439\n", "Time to run previous set of walkers (seconds): 0.001933\n", "Step: 377 / 1000\n", "Mean acceptance fraction: 0.605491\n", "Mean lnprob and Max lnprob values: -302.405361 -300.908617\n", "Time to run previous set of walkers (seconds): 0.001828\n", "Step: 378 / 1000\n", "Mean acceptance fraction: 0.605767\n", "Mean lnprob and Max lnprob values: -302.287193 -300.887602\n", "Time to run previous set of walkers (seconds): 0.001826\n", "Step: 379 / 1000\n", "Mean acceptance fraction: 0.605963\n", "Mean lnprob and Max lnprob values: -302.314499 -300.887926\n", "Time to run previous set of walkers (seconds): 0.001874\n", "Step: 380 / 1000\n", "Mean acceptance fraction: 0.606000\n", "Mean lnprob and Max lnprob values: -302.369602 -300.864067\n", "Time to run previous set of walkers (seconds): 0.002058\n", "Step: 381 / 1000\n", "Mean acceptance fraction: 0.605984\n", "Mean lnprob and Max lnprob values: -302.416678 -300.900838\n", "Time to run previous set of walkers (seconds): 0.001897\n", "Step: 382 / 1000\n", "Mean acceptance fraction: 0.606073\n", "Mean lnprob and Max lnprob values: -302.335972 -300.870424\n", "Time to run previous set of walkers (seconds): 0.001787\n", "Step: 383 / 1000\n", "Mean acceptance fraction: 0.606005\n", "Mean lnprob and Max lnprob values: -302.448760 -300.870424\n", "Time to run previous set of walkers (seconds): 0.001775\n", "Step: 384 / 1000\n", "Mean acceptance fraction: 0.606042\n", "Mean lnprob and Max lnprob values: -302.489853 -300.858200\n", "Time to run previous set of walkers (seconds): 0.001827\n", "Step: 385 / 1000\n", "Mean acceptance fraction: 0.606130\n", "Mean lnprob and Max lnprob values: -302.342952 -300.858200\n", "Time to run previous set of walkers (seconds): 0.001833\n", "Step: 386 / 1000\n", "Mean acceptance fraction: 0.606425\n", "Mean lnprob and Max lnprob values: -302.326166 -300.877904\n", "Time to run previous set of walkers (seconds): 0.001774\n", "Step: 387 / 1000\n", "Mean acceptance fraction: 0.606434\n", "Mean lnprob and Max lnprob values: -302.318327 -300.883146\n", "Time to run previous set of walkers (seconds): 0.001802\n", "Step: 388 / 1000\n", "Mean acceptance fraction: 0.606856\n", "Mean lnprob and Max lnprob values: -302.241478 -300.853105\n", "Time to run previous set of walkers (seconds): 0.002585\n", "Step: 389 / 1000\n", "Mean acceptance fraction: 0.606889\n", "Mean lnprob and Max lnprob values: -302.279389 -300.877330\n", "Time to run previous set of walkers (seconds): 0.002057\n", "Step: 390 / 1000\n", "Mean acceptance fraction: 0.606846\n", "Mean lnprob and Max lnprob values: -302.250091 -300.989029\n", "Time to run previous set of walkers (seconds): 0.001979\n", "Step: 391 / 1000\n", "Mean acceptance fraction: 0.607084\n", "Mean lnprob and Max lnprob values: -302.235926 -300.927384\n", "Time to run previous set of walkers (seconds): 0.002062\n", "Step: 392 / 1000\n", "Mean acceptance fraction: 0.606990\n", "Mean lnprob and Max lnprob values: -302.340972 -300.868056\n", "Time to run previous set of walkers (seconds): 0.001768\n", "Step: 393 / 1000\n", "Mean acceptance fraction: 0.607125\n", "Mean lnprob and Max lnprob values: -302.343882 -300.877274\n", "Time to run previous set of walkers (seconds): 0.002026\n", "Step: 394 / 1000\n", "Mean acceptance fraction: 0.607107\n", "Mean lnprob and Max lnprob values: -302.271923 -300.871879\n", "Time to run previous set of walkers (seconds): 0.002005\n", "Step: 395 / 1000\n", "Mean acceptance fraction: 0.607139\n", "Mean lnprob and Max lnprob values: -302.239020 -300.916726\n", "Time to run previous set of walkers (seconds): 0.001872\n", "Step: 396 / 1000\n", "Mean acceptance fraction: 0.607247\n", "Mean lnprob and Max lnprob values: -302.346486 -300.892882\n", "Time to run previous set of walkers (seconds): 0.001878\n", "Step: 397 / 1000\n", "Mean acceptance fraction: 0.607380\n", "Mean lnprob and Max lnprob values: -302.477795 -300.892882\n", "Time to run previous set of walkers (seconds): 0.001875\n", "Step: 398 / 1000\n", "Mean acceptance fraction: 0.607387\n", "Mean lnprob and Max lnprob values: -302.539787 -300.877613\n", "Time to run previous set of walkers (seconds): 0.001947\n", "Step: 399 / 1000\n", "Mean acceptance fraction: 0.607469\n", "Mean lnprob and Max lnprob values: -302.468791 -300.877613\n", "Time to run previous set of walkers (seconds): 0.001755\n", "Step: 400 / 1000\n", "Mean acceptance fraction: 0.607525\n", "Mean lnprob and Max lnprob values: -302.575019 -300.863426\n", "Time to run previous set of walkers (seconds): 0.001801\n", "Step: 401 / 1000\n", "Mean acceptance fraction: 0.607581\n", "Mean lnprob and Max lnprob values: -302.599161 -300.863672\n", "Time to run previous set of walkers (seconds): 0.001799\n", "Step: 402 / 1000\n", "Mean acceptance fraction: 0.607587\n", "Mean lnprob and Max lnprob values: -302.559589 -300.914600\n", "Time to run previous set of walkers (seconds): 0.002447\n", "Step: 403 / 1000\n", "Mean acceptance fraction: 0.607667\n", "Mean lnprob and Max lnprob values: -302.556711 -300.856552\n", "Time to run previous set of walkers (seconds): 0.002101\n", "Step: 404 / 1000\n", "Mean acceptance fraction: 0.607896\n", "Mean lnprob and Max lnprob values: -302.515784 -300.856851\n", "Time to run previous set of walkers (seconds): 0.001793\n", "Step: 405 / 1000\n", "Mean acceptance fraction: 0.608000\n", "Mean lnprob and Max lnprob values: -302.274780 -300.866390\n", "Time to run previous set of walkers (seconds): 0.001851\n", "Step: 406 / 1000\n", "Mean acceptance fraction: 0.608153\n", "Mean lnprob and Max lnprob values: -302.351636 -300.866502\n", "Time to run previous set of walkers (seconds): 0.001820\n", "Step: 407 / 1000\n", "Mean acceptance fraction: 0.608280\n", "Mean lnprob and Max lnprob values: -302.429367 -300.926409\n", "Time to run previous set of walkers (seconds): 0.001778\n", "Step: 408 / 1000\n", "Mean acceptance fraction: 0.608529\n", "Mean lnprob and Max lnprob values: -302.429005 -300.856273\n", "Time to run previous set of walkers (seconds): 0.001745\n", "Step: 409 / 1000\n", "Mean acceptance fraction: 0.608631\n", "Mean lnprob and Max lnprob values: -302.481103 -300.869033\n", "Time to run previous set of walkers (seconds): 0.001780\n", "Step: 410 / 1000\n", "Mean acceptance fraction: 0.608683\n", "Mean lnprob and Max lnprob values: -302.471843 -300.895869\n", "Time to run previous set of walkers (seconds): 0.001738\n", "Step: 411 / 1000\n", "Mean acceptance fraction: 0.608978\n", "Mean lnprob and Max lnprob values: -302.417631 -300.892031\n", "Time to run previous set of walkers (seconds): 0.001885\n", "Step: 412 / 1000\n", "Mean acceptance fraction: 0.609102\n", "Mean lnprob and Max lnprob values: -302.402763 -300.971722\n", "Time to run previous set of walkers (seconds): 0.001969\n", "Step: 413 / 1000\n", "Mean acceptance fraction: 0.608959\n", "Mean lnprob and Max lnprob values: -302.387623 -301.026660\n", "Time to run previous set of walkers (seconds): 0.001783\n", "Step: 414 / 1000\n", "Mean acceptance fraction: 0.608986\n", "Mean lnprob and Max lnprob values: -302.545367 -300.894561\n", "Time to run previous set of walkers (seconds): 0.001755\n", "Step: 415 / 1000\n", "Mean acceptance fraction: 0.609084\n", "Mean lnprob and Max lnprob values: -302.393948 -300.890653\n", "Time to run previous set of walkers (seconds): 0.001918\n", "Step: 416 / 1000\n", "Mean acceptance fraction: 0.608966\n", "Mean lnprob and Max lnprob values: -302.371319 -300.868351\n", "Time to run previous set of walkers (seconds): 0.002159\n", "Step: 417 / 1000\n", "Mean acceptance fraction: 0.608993\n", "Mean lnprob and Max lnprob values: -302.276615 -300.852927\n", "Time to run previous set of walkers (seconds): 0.001907\n", "Step: 418 / 1000\n", "Mean acceptance fraction: 0.608947\n", "Mean lnprob and Max lnprob values: -302.277285 -300.858533\n", "Time to run previous set of walkers (seconds): 0.001860\n", "Step: 419 / 1000\n", "Mean acceptance fraction: 0.609069\n", "Mean lnprob and Max lnprob values: -302.292231 -300.858844\n", "Time to run previous set of walkers (seconds): 0.002053\n", "Step: 420 / 1000\n", "Mean acceptance fraction: 0.609143\n", "Mean lnprob and Max lnprob values: -302.317793 -300.914561\n", "Time to run previous set of walkers (seconds): 0.002217\n", "Step: 421 / 1000\n", "Mean acceptance fraction: 0.609240\n", "Mean lnprob and Max lnprob values: -302.251422 -300.854113\n", "Time to run previous set of walkers (seconds): 0.001782\n", "Step: 422 / 1000\n", "Mean acceptance fraction: 0.609265\n", "Mean lnprob and Max lnprob values: -302.256642 -300.854113\n", "Time to run previous set of walkers (seconds): 0.001863\n", "Step: 423 / 1000\n", "Mean acceptance fraction: 0.609291\n", "Mean lnprob and Max lnprob values: -302.509710 -300.858321\n", "Time to run previous set of walkers (seconds): 0.002173\n", "Step: 424 / 1000\n", "Mean acceptance fraction: 0.609481\n", "Mean lnprob and Max lnprob values: -302.510511 -300.959171\n", "Time to run previous set of walkers (seconds): 0.002092\n", "Step: 425 / 1000\n", "Mean acceptance fraction: 0.609412\n", "Mean lnprob and Max lnprob values: -302.418920 -300.894193\n", "Time to run previous set of walkers (seconds): 0.002081\n", "Step: 426 / 1000\n", "Mean acceptance fraction: 0.609390\n", "Mean lnprob and Max lnprob values: -302.429150 -300.982352\n", "Time to run previous set of walkers (seconds): 0.002429\n", "Step: 427 / 1000\n", "Mean acceptance fraction: 0.609766\n", "Mean lnprob and Max lnprob values: -302.572988 -301.035365\n", "Time to run previous set of walkers (seconds): 0.002440\n", "Step: 428 / 1000\n", "Mean acceptance fraction: 0.609860\n", "Mean lnprob and Max lnprob values: -302.523188 -300.875297\n", "Time to run previous set of walkers (seconds): 0.001848\n", "Step: 429 / 1000\n", "Mean acceptance fraction: 0.609930\n", "Mean lnprob and Max lnprob values: -302.488327 -300.859587\n", "Time to run previous set of walkers (seconds): 0.002181\n", "Step: 430 / 1000\n", "Mean acceptance fraction: 0.609977\n", "Mean lnprob and Max lnprob values: -302.491085 -300.846145\n", "Time to run previous set of walkers (seconds): 0.001944\n", "Step: 431 / 1000\n", "Mean acceptance fraction: 0.610023\n", "Mean lnprob and Max lnprob values: -302.412329 -300.846145\n", "Time to run previous set of walkers (seconds): 0.001805\n", "Step: 432 / 1000\n", "Mean acceptance fraction: 0.610162\n", "Mean lnprob and Max lnprob values: -302.509268 -300.848531\n", "Time to run previous set of walkers (seconds): 0.002519\n", "Step: 433 / 1000\n", "Mean acceptance fraction: 0.610092\n", "Mean lnprob and Max lnprob values: -302.474832 -300.860123\n", "Time to run previous set of walkers (seconds): 0.001866\n", "Step: 434 / 1000\n", "Mean acceptance fraction: 0.609977\n", "Mean lnprob and Max lnprob values: -302.392766 -300.867619\n", "Time to run previous set of walkers (seconds): 0.001842\n", "Step: 435 / 1000\n", "Mean acceptance fraction: 0.610000\n", "Mean lnprob and Max lnprob values: -302.241359 -300.875009\n", "Time to run previous set of walkers (seconds): 0.001830\n", "Step: 436 / 1000\n", "Mean acceptance fraction: 0.610069\n", "Mean lnprob and Max lnprob values: -302.363620 -300.972434\n", "Time to run previous set of walkers (seconds): 0.002378\n", "Step: 437 / 1000\n", "Mean acceptance fraction: 0.610114\n", "Mean lnprob and Max lnprob values: -302.359989 -300.995024\n", "Time to run previous set of walkers (seconds): 0.001784\n", "Step: 438 / 1000\n", "Mean acceptance fraction: 0.610114\n", "Mean lnprob and Max lnprob values: -302.453307 -300.918902\n", "Time to run previous set of walkers (seconds): 0.001839\n", "Step: 439 / 1000\n", "Mean acceptance fraction: 0.610182\n", "Mean lnprob and Max lnprob values: -302.372920 -300.885803\n", "Time to run previous set of walkers (seconds): 0.002289\n", "Step: 440 / 1000\n", "Mean acceptance fraction: 0.610341\n", "Mean lnprob and Max lnprob values: -302.333077 -300.903070\n", "Time to run previous set of walkers (seconds): 0.001969\n", "Step: 441 / 1000\n", "Mean acceptance fraction: 0.610181\n", "Mean lnprob and Max lnprob values: -302.402594 -300.887900\n", "Time to run previous set of walkers (seconds): 0.001804\n", "Step: 442 / 1000\n", "Mean acceptance fraction: 0.610407\n", "Mean lnprob and Max lnprob values: -302.367617 -300.907332\n", "Time to run previous set of walkers (seconds): 0.001964\n", "Step: 443 / 1000\n", "Mean acceptance fraction: 0.610587\n", "Mean lnprob and Max lnprob values: -302.299126 -300.862378\n", "Time to run previous set of walkers (seconds): 0.001875\n", "Step: 444 / 1000\n", "Mean acceptance fraction: 0.610586\n", "Mean lnprob and Max lnprob values: -302.311613 -300.858624\n", "Time to run previous set of walkers (seconds): 0.002037\n", "Step: 445 / 1000\n", "Mean acceptance fraction: 0.610787\n", "Mean lnprob and Max lnprob values: -302.280350 -300.897612\n", "Time to run previous set of walkers (seconds): 0.001776\n", "Step: 446 / 1000\n", "Mean acceptance fraction: 0.610942\n", "Mean lnprob and Max lnprob values: -302.298049 -300.866201\n", "Time to run previous set of walkers (seconds): 0.001857\n", "Step: 447 / 1000\n", "Mean acceptance fraction: 0.611029\n", "Mean lnprob and Max lnprob values: -302.185831 -300.848851\n", "Time to run previous set of walkers (seconds): 0.001804\n", "Step: 448 / 1000\n", "Mean acceptance fraction: 0.611272\n", "Mean lnprob and Max lnprob values: -302.279872 -300.894567\n", "Time to run previous set of walkers (seconds): 0.001700\n", "Step: 449 / 1000\n", "Mean acceptance fraction: 0.611537\n", "Mean lnprob and Max lnprob values: -302.378527 -300.899024\n", "Time to run previous set of walkers (seconds): 0.001813\n", "Step: 450 / 1000\n", "Mean acceptance fraction: 0.611444\n", "Mean lnprob and Max lnprob values: -302.413188 -300.867644\n", "Time to run previous set of walkers (seconds): 0.001960\n", "Step: 451 / 1000\n", "Mean acceptance fraction: 0.611508\n", "Mean lnprob and Max lnprob values: -302.324529 -300.867644\n", "Time to run previous set of walkers (seconds): 0.001766\n", "Step: 452 / 1000\n", "Mean acceptance fraction: 0.611681\n", "Mean lnprob and Max lnprob values: -302.340113 -300.870043\n", "Time to run previous set of walkers (seconds): 0.001764\n", "Step: 453 / 1000\n", "Mean acceptance fraction: 0.611611\n", "Mean lnprob and Max lnprob values: -302.272997 -300.898720\n", "Time to run previous set of walkers (seconds): 0.001803\n", "Step: 454 / 1000\n", "Mean acceptance fraction: 0.611806\n", "Mean lnprob and Max lnprob values: -302.305125 -300.874799\n", "Time to run previous set of walkers (seconds): 0.001929\n", "Step: 455 / 1000\n", "Mean acceptance fraction: 0.612066\n", "Mean lnprob and Max lnprob values: -302.301923 -300.958559\n", "Time to run previous set of walkers (seconds): 0.002027\n", "Step: 456 / 1000\n", "Mean acceptance fraction: 0.611952\n", "Mean lnprob and Max lnprob values: -302.273607 -300.897316\n", "Time to run previous set of walkers (seconds): 0.001794\n", "Step: 457 / 1000\n", "Mean acceptance fraction: 0.612254\n", "Mean lnprob and Max lnprob values: -302.292651 -300.891926\n", "Time to run previous set of walkers (seconds): 0.001824\n", "Step: 458 / 1000\n", "Mean acceptance fraction: 0.612445\n", "Mean lnprob and Max lnprob values: -302.225839 -300.879615\n", "Time to run previous set of walkers (seconds): 0.001935\n", "Step: 459 / 1000\n", "Mean acceptance fraction: 0.612375\n", "Mean lnprob and Max lnprob values: -302.302596 -300.879615\n", "Time to run previous set of walkers (seconds): 0.001776\n", "Step: 460 / 1000\n", "Mean acceptance fraction: 0.612630\n", "Mean lnprob and Max lnprob values: -302.431848 -300.870263\n", "Time to run previous set of walkers (seconds): 0.001734\n", "Step: 461 / 1000\n", "Mean acceptance fraction: 0.612777\n", "Mean lnprob and Max lnprob values: -302.499337 -300.855135\n", "Time to run previous set of walkers (seconds): 0.001728\n", "Step: 462 / 1000\n", "Mean acceptance fraction: 0.612944\n", "Mean lnprob and Max lnprob values: -302.536158 -300.855135\n", "Time to run previous set of walkers (seconds): 0.001874\n", "Step: 463 / 1000\n", "Mean acceptance fraction: 0.612959\n", "Mean lnprob and Max lnprob values: -302.383744 -300.887516\n", "Time to run previous set of walkers (seconds): 0.002024\n", "Step: 464 / 1000\n", "Mean acceptance fraction: 0.612996\n", "Mean lnprob and Max lnprob values: -302.261100 -300.879705\n", "Time to run previous set of walkers (seconds): 0.002679\n", "Step: 465 / 1000\n", "Mean acceptance fraction: 0.613226\n", "Mean lnprob and Max lnprob values: -302.244993 -300.879705\n", "Time to run previous set of walkers (seconds): 0.003205\n", "Step: 466 / 1000\n", "Mean acceptance fraction: 0.613433\n", "Mean lnprob and Max lnprob values: -302.301396 -300.893821\n", "Time to run previous set of walkers (seconds): 0.003740\n", "Step: 467 / 1000\n", "Mean acceptance fraction: 0.613640\n", "Mean lnprob and Max lnprob values: -302.388491 -300.954200\n", "Time to run previous set of walkers (seconds): 0.003793\n", "Step: 468 / 1000\n", "Mean acceptance fraction: 0.613611\n", "Mean lnprob and Max lnprob values: -302.461163 -300.959698\n", "Time to run previous set of walkers (seconds): 0.003556\n", "Step: 469 / 1000\n", "Mean acceptance fraction: 0.613454\n", "Mean lnprob and Max lnprob values: -302.503416 -300.948698\n", "Time to run previous set of walkers (seconds): 0.003587\n", "Step: 470 / 1000\n", "Mean acceptance fraction: 0.613574\n", "Mean lnprob and Max lnprob values: -302.431004 -300.864687\n", "Time to run previous set of walkers (seconds): 0.003921\n", "Step: 471 / 1000\n", "Mean acceptance fraction: 0.613737\n", "Mean lnprob and Max lnprob values: -302.482506 -300.854302\n", "Time to run previous set of walkers (seconds): 0.003704\n", "Step: 472 / 1000\n", "Mean acceptance fraction: 0.613771\n", "Mean lnprob and Max lnprob values: -302.408656 -300.854302\n", "Time to run previous set of walkers (seconds): 0.003299\n", "Step: 473 / 1000\n", "Mean acceptance fraction: 0.614038\n", "Mean lnprob and Max lnprob values: -302.459568 -300.917925\n", "Time to run previous set of walkers (seconds): 0.003378\n", "Step: 474 / 1000\n", "Mean acceptance fraction: 0.614135\n", "Mean lnprob and Max lnprob values: -302.441030 -300.879528\n", "Time to run previous set of walkers (seconds): 0.004037\n", "Step: 475 / 1000\n", "Mean acceptance fraction: 0.614253\n", "Mean lnprob and Max lnprob values: -302.379593 -300.855101\n", "Time to run previous set of walkers (seconds): 0.003972\n", "Step: 476 / 1000\n", "Mean acceptance fraction: 0.614328\n", "Mean lnprob and Max lnprob values: -302.329497 -300.879893\n", "Time to run previous set of walkers (seconds): 0.003985\n", "Step: 477 / 1000\n", "Mean acceptance fraction: 0.614319\n", "Mean lnprob and Max lnprob values: -302.281371 -300.879893\n", "Time to run previous set of walkers (seconds): 0.004214\n", "Step: 478 / 1000\n", "Mean acceptance fraction: 0.614310\n", "Mean lnprob and Max lnprob values: -302.329222 -300.868659\n", "Time to run previous set of walkers (seconds): 0.003643\n", "Step: 479 / 1000\n", "Mean acceptance fraction: 0.614196\n", "Mean lnprob and Max lnprob values: -302.387656 -300.865182\n", "Time to run previous set of walkers (seconds): 0.003701\n", "Step: 480 / 1000\n", "Mean acceptance fraction: 0.614271\n", "Mean lnprob and Max lnprob values: -302.445047 -300.866799\n", "Time to run previous set of walkers (seconds): 0.005014\n", "Step: 481 / 1000\n", "Mean acceptance fraction: 0.614304\n", "Mean lnprob and Max lnprob values: -302.331914 -300.888966\n", "Time to run previous set of walkers (seconds): 0.003049\n", "Step: 482 / 1000\n", "Mean acceptance fraction: 0.614398\n", "Mean lnprob and Max lnprob values: -302.224942 -300.865064\n", "Time to run previous set of walkers (seconds): 0.002310\n", "Step: 483 / 1000\n", "Mean acceptance fraction: 0.614431\n", "Mean lnprob and Max lnprob values: -302.173294 -300.865064\n", "Time to run previous set of walkers (seconds): 0.002079\n", "Step: 484 / 1000\n", "Mean acceptance fraction: 0.614442\n", "Mean lnprob and Max lnprob values: -302.220469 -300.879633\n", "Time to run previous set of walkers (seconds): 0.003320\n", "Step: 485 / 1000\n", "Mean acceptance fraction: 0.614412\n", "Mean lnprob and Max lnprob values: -302.268326 -300.872144\n", "Time to run previous set of walkers (seconds): 0.003575\n", "Step: 486 / 1000\n", "Mean acceptance fraction: 0.614444\n", "Mean lnprob and Max lnprob values: -302.240173 -300.872144\n", "Time to run previous set of walkers (seconds): 0.003352\n", "Step: 487 / 1000\n", "Mean acceptance fraction: 0.614476\n", "Mean lnprob and Max lnprob values: -302.331085 -300.856284\n", "Time to run previous set of walkers (seconds): 0.002482\n", "Step: 488 / 1000\n", "Mean acceptance fraction: 0.614652\n", "Mean lnprob and Max lnprob values: -302.392847 -300.902763\n", "Time to run previous set of walkers (seconds): 0.002256\n", "Step: 489 / 1000\n", "Mean acceptance fraction: 0.614826\n", "Mean lnprob and Max lnprob values: -302.518959 -300.849527\n", "Time to run previous set of walkers (seconds): 0.002486\n", "Step: 490 / 1000\n", "Mean acceptance fraction: 0.615102\n", "Mean lnprob and Max lnprob values: -302.269865 -300.851200\n", "Time to run previous set of walkers (seconds): 0.001852\n", "Step: 491 / 1000\n", "Mean acceptance fraction: 0.615132\n", "Mean lnprob and Max lnprob values: -302.305137 -300.854215\n", "Time to run previous set of walkers (seconds): 0.001763\n", "Step: 492 / 1000\n", "Mean acceptance fraction: 0.615285\n", "Mean lnprob and Max lnprob values: -302.230480 -300.910794\n", "Time to run previous set of walkers (seconds): 0.001957\n", "Step: 493 / 1000\n", "Mean acceptance fraction: 0.615416\n", "Mean lnprob and Max lnprob values: -302.200444 -300.910794\n", "Time to run previous set of walkers (seconds): 0.001886\n", "Step: 494 / 1000\n", "Mean acceptance fraction: 0.615466\n", "Mean lnprob and Max lnprob values: -302.292306 -300.900547\n", "Time to run previous set of walkers (seconds): 0.001800\n", "Step: 495 / 1000\n", "Mean acceptance fraction: 0.615495\n", "Mean lnprob and Max lnprob values: -302.367106 -300.860757\n", "Time to run previous set of walkers (seconds): 0.001753\n", "Step: 496 / 1000\n", "Mean acceptance fraction: 0.615706\n", "Mean lnprob and Max lnprob values: -302.410518 -300.865898\n", "Time to run previous set of walkers (seconds): 0.002149\n", "Step: 497 / 1000\n", "Mean acceptance fraction: 0.615915\n", "Mean lnprob and Max lnprob values: -302.406707 -300.872563\n", "Time to run previous set of walkers (seconds): 0.001868\n", "Step: 498 / 1000\n", "Mean acceptance fraction: 0.615924\n", "Mean lnprob and Max lnprob values: -302.342326 -300.872563\n", "Time to run previous set of walkers (seconds): 0.001812\n", "Step: 499 / 1000\n", "Mean acceptance fraction: 0.615932\n", "Mean lnprob and Max lnprob values: -302.288909 -300.872563\n", "Time to run previous set of walkers (seconds): 0.002465\n", "Step: 500 / 1000\n", "Mean acceptance fraction: 0.615880\n", "Mean lnprob and Max lnprob values: -302.350237 -300.875627\n", "Time to run previous set of walkers (seconds): 0.002182\n", "Step: 501 / 1000\n", "Mean acceptance fraction: 0.615948\n", "Mean lnprob and Max lnprob values: -302.214720 -300.866564\n", "Time to run previous set of walkers (seconds): 0.001836\n", "Step: 502 / 1000\n", "Mean acceptance fraction: 0.616056\n", "Mean lnprob and Max lnprob values: -302.344110 -300.864462\n", "Time to run previous set of walkers (seconds): 0.001965\n", "Step: 503 / 1000\n", "Mean acceptance fraction: 0.616183\n", "Mean lnprob and Max lnprob values: -302.395000 -300.866564\n", "Time to run previous set of walkers (seconds): 0.002042\n", "Step: 504 / 1000\n", "Mean acceptance fraction: 0.616329\n", "Mean lnprob and Max lnprob values: -302.359389 -300.890805\n", "Time to run previous set of walkers (seconds): 0.001820\n", "Step: 505 / 1000\n", "Mean acceptance fraction: 0.616337\n", "Mean lnprob and Max lnprob values: -302.383479 -300.914544\n", "Time to run previous set of walkers (seconds): 0.001833\n", "Step: 506 / 1000\n", "Mean acceptance fraction: 0.616304\n", "Mean lnprob and Max lnprob values: -302.350750 -300.918162\n", "Time to run previous set of walkers (seconds): 0.002140\n", "Step: 507 / 1000\n", "Mean acceptance fraction: 0.616272\n", "Mean lnprob and Max lnprob values: -302.231949 -300.879250\n", "Time to run previous set of walkers (seconds): 0.001822\n", "Step: 508 / 1000\n", "Mean acceptance fraction: 0.616280\n", "Mean lnprob and Max lnprob values: -302.348150 -300.914105\n", "Time to run previous set of walkers (seconds): 0.001932\n", "Step: 509 / 1000\n", "Mean acceptance fraction: 0.616405\n", "Mean lnprob and Max lnprob values: -302.269475 -300.963432\n", "Time to run previous set of walkers (seconds): 0.002013\n", "Step: 510 / 1000\n", "Mean acceptance fraction: 0.616529\n", "Mean lnprob and Max lnprob values: -302.199677 -300.976217\n", "Time to run previous set of walkers (seconds): 0.002252\n", "Step: 511 / 1000\n", "Mean acceptance fraction: 0.616536\n", "Mean lnprob and Max lnprob values: -302.298379 -300.909758\n", "Time to run previous set of walkers (seconds): 0.001972\n", "Step: 512 / 1000\n", "Mean acceptance fraction: 0.616680\n", "Mean lnprob and Max lnprob values: -302.348399 -300.961053\n", "Time to run previous set of walkers (seconds): 0.001866\n", "Step: 513 / 1000\n", "Mean acceptance fraction: 0.616686\n", "Mean lnprob and Max lnprob values: -302.400015 -300.969317\n", "Time to run previous set of walkers (seconds): 0.002169\n", "Step: 514 / 1000\n", "Mean acceptance fraction: 0.616732\n", "Mean lnprob and Max lnprob values: -302.470515 -300.906573\n", "Time to run previous set of walkers (seconds): 0.001919\n", "Step: 515 / 1000\n", "Mean acceptance fraction: 0.616757\n", "Mean lnprob and Max lnprob values: -302.504312 -300.959345\n", "Time to run previous set of walkers (seconds): 0.001843\n", "Step: 516 / 1000\n", "Mean acceptance fraction: 0.616764\n", "Mean lnprob and Max lnprob values: -302.458326 -300.959613\n", "Time to run previous set of walkers (seconds): 0.001857\n", "Step: 517 / 1000\n", "Mean acceptance fraction: 0.616983\n", "Mean lnprob and Max lnprob values: -302.378804 -300.959613\n", "Time to run previous set of walkers (seconds): 0.002051\n", "Step: 518 / 1000\n", "Mean acceptance fraction: 0.617124\n", "Mean lnprob and Max lnprob values: -302.455533 -300.940006\n", "Time to run previous set of walkers (seconds): 0.001841\n", "Step: 519 / 1000\n", "Mean acceptance fraction: 0.617360\n", "Mean lnprob and Max lnprob values: -302.282473 -300.942176\n", "Time to run previous set of walkers (seconds): 0.001799\n", "Step: 520 / 1000\n", "Mean acceptance fraction: 0.617423\n", "Mean lnprob and Max lnprob values: -302.253686 -300.905917\n", "Time to run previous set of walkers (seconds): 0.002264\n", "Step: 521 / 1000\n", "Mean acceptance fraction: 0.617582\n", "Mean lnprob and Max lnprob values: -302.384186 -300.888212\n", "Time to run previous set of walkers (seconds): 0.001840\n", "Step: 522 / 1000\n", "Mean acceptance fraction: 0.617586\n", "Mean lnprob and Max lnprob values: -302.399979 -300.888212\n", "Time to run previous set of walkers (seconds): 0.001787\n", "Step: 523 / 1000\n", "Mean acceptance fraction: 0.617763\n", "Mean lnprob and Max lnprob values: -302.332578 -300.862562\n", "Time to run previous set of walkers (seconds): 0.001834\n", "Step: 524 / 1000\n", "Mean acceptance fraction: 0.617672\n", "Mean lnprob and Max lnprob values: -302.348358 -300.851444\n", "Time to run previous set of walkers (seconds): 0.002051\n", "Step: 525 / 1000\n", "Mean acceptance fraction: 0.617829\n", "Mean lnprob and Max lnprob values: -302.525026 -300.854793\n", "Time to run previous set of walkers (seconds): 0.001885\n", "Step: 526 / 1000\n", "Mean acceptance fraction: 0.617738\n", "Mean lnprob and Max lnprob values: -302.512924 -300.883554\n", "Time to run previous set of walkers (seconds): 0.001786\n", "Step: 527 / 1000\n", "Mean acceptance fraction: 0.617761\n", "Mean lnprob and Max lnprob values: -302.507575 -300.883554\n", "Time to run previous set of walkers (seconds): 0.001822\n", "Step: 528 / 1000\n", "Mean acceptance fraction: 0.617955\n", "Mean lnprob and Max lnprob values: -302.308604 -300.978530\n", "Time to run previous set of walkers (seconds): 0.002109\n", "Step: 529 / 1000\n", "Mean acceptance fraction: 0.617883\n", "Mean lnprob and Max lnprob values: -302.432078 -300.847220\n", "Time to run previous set of walkers (seconds): 0.001801\n", "Step: 530 / 1000\n", "Mean acceptance fraction: 0.617849\n", "Mean lnprob and Max lnprob values: -302.345581 -300.849251\n", "Time to run previous set of walkers (seconds): 0.001755\n", "Step: 531 / 1000\n", "Mean acceptance fraction: 0.617928\n", "Mean lnprob and Max lnprob values: -302.303374 -300.873526\n", "Time to run previous set of walkers (seconds): 0.002095\n", "Step: 532 / 1000\n", "Mean acceptance fraction: 0.617932\n", "Mean lnprob and Max lnprob values: -302.295492 -300.933224\n", "Time to run previous set of walkers (seconds): 0.001798\n", "Step: 533 / 1000\n", "Mean acceptance fraction: 0.617955\n", "Mean lnprob and Max lnprob values: -302.331176 -300.861964\n", "Time to run previous set of walkers (seconds): 0.001756\n", "Step: 534 / 1000\n", "Mean acceptance fraction: 0.618090\n", "Mean lnprob and Max lnprob values: -302.331348 -300.865553\n", "Time to run previous set of walkers (seconds): 0.001788\n", "Step: 535 / 1000\n", "Mean acceptance fraction: 0.618093\n", "Mean lnprob and Max lnprob values: -302.361533 -300.884909\n", "Time to run previous set of walkers (seconds): 0.002072\n", "Step: 536 / 1000\n", "Mean acceptance fraction: 0.618097\n", "Mean lnprob and Max lnprob values: -302.363482 -300.890684\n", "Time to run previous set of walkers (seconds): 0.001850\n", "Step: 537 / 1000\n", "Mean acceptance fraction: 0.618063\n", "Mean lnprob and Max lnprob values: -302.519808 -300.896907\n", "Time to run previous set of walkers (seconds): 0.001798\n", "Step: 538 / 1000\n", "Mean acceptance fraction: 0.618086\n", "Mean lnprob and Max lnprob values: -302.574028 -300.863789\n", "Time to run previous set of walkers (seconds): 0.001792\n", "Step: 539 / 1000\n", "Mean acceptance fraction: 0.618033\n", "Mean lnprob and Max lnprob values: -302.424332 -300.867996\n", "Time to run previous set of walkers (seconds): 0.002043\n", "Step: 540 / 1000\n", "Mean acceptance fraction: 0.617907\n", "Mean lnprob and Max lnprob values: -302.480380 -300.867996\n", "Time to run previous set of walkers (seconds): 0.001796\n", "Step: 541 / 1000\n", "Mean acceptance fraction: 0.617893\n", "Mean lnprob and Max lnprob values: -302.434842 -300.911312\n", "Time to run previous set of walkers (seconds): 0.001782\n", "Step: 542 / 1000\n", "Mean acceptance fraction: 0.617934\n", "Mean lnprob and Max lnprob values: -302.446631 -300.934075\n", "Time to run previous set of walkers (seconds): 0.002077\n", "Step: 543 / 1000\n", "Mean acceptance fraction: 0.617827\n", "Mean lnprob and Max lnprob values: -302.399355 -300.963322\n", "Time to run previous set of walkers (seconds): 0.001839\n", "Step: 544 / 1000\n", "Mean acceptance fraction: 0.617996\n", "Mean lnprob and Max lnprob values: -302.364875 -300.963322\n", "Time to run previous set of walkers (seconds): 0.001870\n", "Step: 545 / 1000\n", "Mean acceptance fraction: 0.618073\n", "Mean lnprob and Max lnprob values: -302.297419 -300.898348\n", "Time to run previous set of walkers (seconds): 0.001757\n", "Step: 546 / 1000\n", "Mean acceptance fraction: 0.618077\n", "Mean lnprob and Max lnprob values: -302.263599 -300.899215\n", "Time to run previous set of walkers (seconds): 0.002295\n", "Step: 547 / 1000\n", "Mean acceptance fraction: 0.618135\n", "Mean lnprob and Max lnprob values: -302.232933 -300.987317\n", "Time to run previous set of walkers (seconds): 0.001940\n", "Step: 548 / 1000\n", "Mean acceptance fraction: 0.618230\n", "Mean lnprob and Max lnprob values: -302.100130 -300.851928\n", "Time to run previous set of walkers (seconds): 0.001833\n", "Step: 549 / 1000\n", "Mean acceptance fraction: 0.618142\n", "Mean lnprob and Max lnprob values: -302.105242 -300.861516\n", "Time to run previous set of walkers (seconds): 0.001912\n", "Step: 550 / 1000\n", "Mean acceptance fraction: 0.618127\n", "Mean lnprob and Max lnprob values: -302.196625 -300.864279\n", "Time to run previous set of walkers (seconds): 0.002281\n", "Step: 551 / 1000\n", "Mean acceptance fraction: 0.618149\n", "Mean lnprob and Max lnprob values: -302.222561 -300.883360\n", "Time to run previous set of walkers (seconds): 0.002188\n", "Step: 552 / 1000\n", "Mean acceptance fraction: 0.618207\n", "Mean lnprob and Max lnprob values: -302.143440 -300.884542\n", "Time to run previous set of walkers (seconds): 0.002641\n", "Step: 553 / 1000\n", "Mean acceptance fraction: 0.618192\n", "Mean lnprob and Max lnprob values: -302.200513 -300.857288\n", "Time to run previous set of walkers (seconds): 0.002112\n", "Step: 554 / 1000\n", "Mean acceptance fraction: 0.618213\n", "Mean lnprob and Max lnprob values: -302.337555 -300.856958\n", "Time to run previous set of walkers (seconds): 0.002936\n", "Step: 555 / 1000\n", "Mean acceptance fraction: 0.618216\n", "Mean lnprob and Max lnprob values: -302.282561 -300.900974\n", "Time to run previous set of walkers (seconds): 0.002213\n", "Step: 556 / 1000\n", "Mean acceptance fraction: 0.618147\n", "Mean lnprob and Max lnprob values: -302.417798 -300.875788\n", "Time to run previous set of walkers (seconds): 0.001866\n", "Step: 557 / 1000\n", "Mean acceptance fraction: 0.618205\n", "Mean lnprob and Max lnprob values: -302.585162 -300.879657\n", "Time to run previous set of walkers (seconds): 0.001983\n", "Step: 558 / 1000\n", "Mean acceptance fraction: 0.618280\n", "Mean lnprob and Max lnprob values: -302.402975 -300.848823\n", "Time to run previous set of walkers (seconds): 0.002127\n", "Step: 559 / 1000\n", "Mean acceptance fraction: 0.618301\n", "Mean lnprob and Max lnprob values: -302.376258 -300.848823\n", "Time to run previous set of walkers (seconds): 0.002389\n", "Step: 560 / 1000\n", "Mean acceptance fraction: 0.618304\n", "Mean lnprob and Max lnprob values: -302.446904 -300.899332\n", "Time to run previous set of walkers (seconds): 0.001902\n", "Step: 561 / 1000\n", "Mean acceptance fraction: 0.618289\n", "Mean lnprob and Max lnprob values: -302.448681 -300.869179\n", "Time to run previous set of walkers (seconds): 0.002064\n", "Step: 562 / 1000\n", "Mean acceptance fraction: 0.618399\n", "Mean lnprob and Max lnprob values: -302.391884 -300.855447\n", "Time to run previous set of walkers (seconds): 0.001928\n", "Step: 563 / 1000\n", "Mean acceptance fraction: 0.618561\n", "Mean lnprob and Max lnprob values: -302.339570 -300.906196\n", "Time to run previous set of walkers (seconds): 0.001869\n", "Step: 564 / 1000\n", "Mean acceptance fraction: 0.618564\n", "Mean lnprob and Max lnprob values: -302.272797 -300.883734\n", "Time to run previous set of walkers (seconds): 0.001786\n", "Step: 565 / 1000\n", "Mean acceptance fraction: 0.618602\n", "Mean lnprob and Max lnprob values: -302.262148 -300.893393\n", "Time to run previous set of walkers (seconds): 0.002202\n", "Step: 566 / 1000\n", "Mean acceptance fraction: 0.618622\n", "Mean lnprob and Max lnprob values: -302.358514 -300.893393\n", "Time to run previous set of walkers (seconds): 0.001862\n", "Step: 567 / 1000\n", "Mean acceptance fraction: 0.618765\n", "Mean lnprob and Max lnprob values: -302.236116 -300.849296\n", "Time to run previous set of walkers (seconds): 0.003141\n", "Step: 568 / 1000\n", "Mean acceptance fraction: 0.618856\n", "Mean lnprob and Max lnprob values: -302.222517 -300.931576\n", "Time to run previous set of walkers (seconds): 0.002136\n", "Step: 569 / 1000\n", "Mean acceptance fraction: 0.619016\n", "Mean lnprob and Max lnprob values: -302.257947 -300.868737\n", "Time to run previous set of walkers (seconds): 0.002350\n", "Step: 570 / 1000\n", "Mean acceptance fraction: 0.619088\n", "Mean lnprob and Max lnprob values: -302.248532 -300.861502\n", "Time to run previous set of walkers (seconds): 0.002156\n", "Step: 571 / 1000\n", "Mean acceptance fraction: 0.619089\n", "Mean lnprob and Max lnprob values: -302.320291 -300.890088\n", "Time to run previous set of walkers (seconds): 0.002516\n", "Step: 572 / 1000\n", "Mean acceptance fraction: 0.619266\n", "Mean lnprob and Max lnprob values: -302.330257 -300.912995\n", "Time to run previous set of walkers (seconds): 0.003953\n", "Step: 573 / 1000\n", "Mean acceptance fraction: 0.619250\n", "Mean lnprob and Max lnprob values: -302.239092 -300.862977\n", "Time to run previous set of walkers (seconds): 0.002704\n", "Step: 574 / 1000\n", "Mean acceptance fraction: 0.619251\n", "Mean lnprob and Max lnprob values: -302.306654 -300.891184\n", "Time to run previous set of walkers (seconds): 0.003104\n", "Step: 575 / 1000\n", "Mean acceptance fraction: 0.619409\n", "Mean lnprob and Max lnprob values: -302.275076 -300.976821\n", "Time to run previous set of walkers (seconds): 0.005214\n", "Step: 576 / 1000\n", "Mean acceptance fraction: 0.619427\n", "Mean lnprob and Max lnprob values: -302.251146 -300.943401\n", "Time to run previous set of walkers (seconds): 0.002415\n", "Step: 577 / 1000\n", "Mean acceptance fraction: 0.619497\n", "Mean lnprob and Max lnprob values: -302.407664 -300.907176\n", "Time to run previous set of walkers (seconds): 0.001986\n", "Step: 578 / 1000\n", "Mean acceptance fraction: 0.619585\n", "Mean lnprob and Max lnprob values: -302.438214 -300.908156\n", "Time to run previous set of walkers (seconds): 0.002078\n", "Step: 579 / 1000\n", "Mean acceptance fraction: 0.619482\n", "Mean lnprob and Max lnprob values: -302.464188 -300.877437\n", "Time to run previous set of walkers (seconds): 0.002046\n", "Step: 580 / 1000\n", "Mean acceptance fraction: 0.619517\n", "Mean lnprob and Max lnprob values: -302.281530 -300.922728\n", "Time to run previous set of walkers (seconds): 0.002671\n", "Step: 581 / 1000\n", "Mean acceptance fraction: 0.619621\n", "Mean lnprob and Max lnprob values: -302.304048 -300.922728\n", "Time to run previous set of walkers (seconds): 0.002426\n", "Step: 582 / 1000\n", "Mean acceptance fraction: 0.619845\n", "Mean lnprob and Max lnprob values: -302.239353 -300.860191\n", "Time to run previous set of walkers (seconds): 0.002288\n", "Step: 583 / 1000\n", "Mean acceptance fraction: 0.620000\n", "Mean lnprob and Max lnprob values: -302.199864 -300.870218\n", "Time to run previous set of walkers (seconds): 0.001957\n", "Step: 584 / 1000\n", "Mean acceptance fraction: 0.620137\n", "Mean lnprob and Max lnprob values: -302.249545 -300.869013\n", "Time to run previous set of walkers (seconds): 0.002252\n", "Step: 585 / 1000\n", "Mean acceptance fraction: 0.620256\n", "Mean lnprob and Max lnprob values: -302.278683 -300.877285\n", "Time to run previous set of walkers (seconds): 0.002062\n", "Step: 586 / 1000\n", "Mean acceptance fraction: 0.620256\n", "Mean lnprob and Max lnprob values: -302.392242 -300.982439\n", "Time to run previous set of walkers (seconds): 0.001813\n", "Step: 587 / 1000\n", "Mean acceptance fraction: 0.620256\n", "Mean lnprob and Max lnprob values: -302.400318 -300.885637\n", "Time to run previous set of walkers (seconds): 0.001797\n", "Step: 588 / 1000\n", "Mean acceptance fraction: 0.620238\n", "Mean lnprob and Max lnprob values: -302.254357 -300.894746\n", "Time to run previous set of walkers (seconds): 0.002075\n", "Step: 589 / 1000\n", "Mean acceptance fraction: 0.620170\n", "Mean lnprob and Max lnprob values: -302.237395 -300.882644\n", "Time to run previous set of walkers (seconds): 0.001769\n", "Step: 590 / 1000\n", "Mean acceptance fraction: 0.620169\n", "Mean lnprob and Max lnprob values: -302.269302 -300.894746\n", "Time to run previous set of walkers (seconds): 0.001759\n", "Step: 591 / 1000\n", "Mean acceptance fraction: 0.620169\n", "Mean lnprob and Max lnprob values: -302.203408 -300.881939\n", "Time to run previous set of walkers (seconds): 0.001964\n", "Step: 592 / 1000\n", "Mean acceptance fraction: 0.620169\n", "Mean lnprob and Max lnprob values: -302.330442 -300.891513\n", "Time to run previous set of walkers (seconds): 0.002129\n", "Step: 593 / 1000\n", "Mean acceptance fraction: 0.620236\n", "Mean lnprob and Max lnprob values: -302.318941 -300.890348\n", "Time to run previous set of walkers (seconds): 0.002111\n", "Step: 594 / 1000\n", "Mean acceptance fraction: 0.620253\n", "Mean lnprob and Max lnprob values: -302.240137 -300.890348\n", "Time to run previous set of walkers (seconds): 0.002206\n", "Step: 595 / 1000\n", "Mean acceptance fraction: 0.620252\n", "Mean lnprob and Max lnprob values: -302.329931 -300.877038\n", "Time to run previous set of walkers (seconds): 0.003010\n", "Step: 596 / 1000\n", "Mean acceptance fraction: 0.620185\n", "Mean lnprob and Max lnprob values: -302.380022 -300.877038\n", "Time to run previous set of walkers (seconds): 0.001911\n", "Step: 597 / 1000\n", "Mean acceptance fraction: 0.620034\n", "Mean lnprob and Max lnprob values: -302.324666 -300.897096\n", "Time to run previous set of walkers (seconds): 0.001794\n", "Step: 598 / 1000\n", "Mean acceptance fraction: 0.620033\n", "Mean lnprob and Max lnprob values: -302.226223 -300.905493\n", "Time to run previous set of walkers (seconds): 0.001912\n", "Step: 599 / 1000\n", "Mean acceptance fraction: 0.620117\n", "Mean lnprob and Max lnprob values: -302.213892 -300.905493\n", "Time to run previous set of walkers (seconds): 0.001863\n", "Step: 600 / 1000\n", "Mean acceptance fraction: 0.620267\n", "Mean lnprob and Max lnprob values: -302.117636 -300.932201\n", "Time to run previous set of walkers (seconds): 0.001758\n", "Step: 601 / 1000\n", "Mean acceptance fraction: 0.620316\n", "Mean lnprob and Max lnprob values: -302.109985 -300.900083\n", "Time to run previous set of walkers (seconds): 0.001742\n", "Step: 602 / 1000\n", "Mean acceptance fraction: 0.620415\n", "Mean lnprob and Max lnprob values: -302.075515 -300.872619\n", "Time to run previous set of walkers (seconds): 0.001865\n", "Step: 603 / 1000\n", "Mean acceptance fraction: 0.620448\n", "Mean lnprob and Max lnprob values: -302.004448 -300.873076\n", "Time to run previous set of walkers (seconds): 0.001916\n", "Step: 604 / 1000\n", "Mean acceptance fraction: 0.620579\n", "Mean lnprob and Max lnprob values: -302.075448 -300.881396\n", "Time to run previous set of walkers (seconds): 0.002637\n", "Step: 605 / 1000\n", "Mean acceptance fraction: 0.620645\n", "Mean lnprob and Max lnprob values: -302.190736 -300.868496\n", "Time to run previous set of walkers (seconds): 0.002051\n", "Step: 606 / 1000\n", "Mean acceptance fraction: 0.620660\n", "Mean lnprob and Max lnprob values: -302.278606 -300.886657\n", "Time to run previous set of walkers (seconds): 0.001873\n", "Step: 607 / 1000\n", "Mean acceptance fraction: 0.620857\n", "Mean lnprob and Max lnprob values: -302.298203 -300.881822\n", "Time to run previous set of walkers (seconds): 0.001790\n", "Step: 608 / 1000\n", "Mean acceptance fraction: 0.620921\n", "Mean lnprob and Max lnprob values: -302.251238 -300.875620\n", "Time to run previous set of walkers (seconds): 0.001794\n", "Step: 609 / 1000\n", "Mean acceptance fraction: 0.620920\n", "Mean lnprob and Max lnprob values: -302.279076 -300.897253\n", "Time to run previous set of walkers (seconds): 0.001875\n", "Step: 610 / 1000\n", "Mean acceptance fraction: 0.620967\n", "Mean lnprob and Max lnprob values: -302.218910 -300.897253\n", "Time to run previous set of walkers (seconds): 0.001844\n", "Step: 611 / 1000\n", "Mean acceptance fraction: 0.621015\n", "Mean lnprob and Max lnprob values: -302.192364 -300.871469\n", "Time to run previous set of walkers (seconds): 0.001913\n", "Step: 612 / 1000\n", "Mean acceptance fraction: 0.621176\n", "Mean lnprob and Max lnprob values: -302.104496 -300.935958\n", "Time to run previous set of walkers (seconds): 0.001811\n", "Step: 613 / 1000\n", "Mean acceptance fraction: 0.621387\n", "Mean lnprob and Max lnprob values: -302.226158 -300.894538\n", "Time to run previous set of walkers (seconds): 0.001885\n", "Step: 614 / 1000\n", "Mean acceptance fraction: 0.621417\n", "Mean lnprob and Max lnprob values: -302.174214 -300.895923\n", "Time to run previous set of walkers (seconds): 0.001776\n", "Step: 615 / 1000\n", "Mean acceptance fraction: 0.621480\n", "Mean lnprob and Max lnprob values: -302.196819 -300.863011\n", "Time to run previous set of walkers (seconds): 0.001776\n", "Step: 616 / 1000\n", "Mean acceptance fraction: 0.621591\n", "Mean lnprob and Max lnprob values: -302.394455 -300.894046\n", "Time to run previous set of walkers (seconds): 0.001851\n", "Step: 617 / 1000\n", "Mean acceptance fraction: 0.621686\n", "Mean lnprob and Max lnprob values: -302.379041 -300.898446\n", "Time to run previous set of walkers (seconds): 0.001780\n", "Step: 618 / 1000\n", "Mean acceptance fraction: 0.621634\n", "Mean lnprob and Max lnprob values: -302.151974 -300.877287\n", "Time to run previous set of walkers (seconds): 0.001738\n", "Step: 619 / 1000\n", "Mean acceptance fraction: 0.621761\n", "Mean lnprob and Max lnprob values: -302.218671 -300.877287\n", "Time to run previous set of walkers (seconds): 0.001770\n", "Step: 620 / 1000\n", "Mean acceptance fraction: 0.621855\n", "Mean lnprob and Max lnprob values: -302.191421 -300.878025\n", "Time to run previous set of walkers (seconds): 0.002028\n", "Step: 621 / 1000\n", "Mean acceptance fraction: 0.621965\n", "Mean lnprob and Max lnprob values: -302.138379 -300.854580\n", "Time to run previous set of walkers (seconds): 0.001790\n", "Step: 622 / 1000\n", "Mean acceptance fraction: 0.622042\n", "Mean lnprob and Max lnprob values: -302.023498 -300.854580\n", "Time to run previous set of walkers (seconds): 0.001784\n", "Step: 623 / 1000\n", "Mean acceptance fraction: 0.622087\n", "Mean lnprob and Max lnprob values: -302.136599 -300.893213\n", "Time to run previous set of walkers (seconds): 0.001797\n", "Step: 624 / 1000\n", "Mean acceptance fraction: 0.622163\n", "Mean lnprob and Max lnprob values: -302.271231 -300.923619\n", "Time to run previous set of walkers (seconds): 0.001902\n", "Step: 625 / 1000\n", "Mean acceptance fraction: 0.622208\n", "Mean lnprob and Max lnprob values: -302.267242 -300.916464\n", "Time to run previous set of walkers (seconds): 0.001770\n", "Step: 626 / 1000\n", "Mean acceptance fraction: 0.622173\n", "Mean lnprob and Max lnprob values: -302.334222 -300.847705\n", "Time to run previous set of walkers (seconds): 0.001802\n", "Step: 627 / 1000\n", "Mean acceptance fraction: 0.622153\n", "Mean lnprob and Max lnprob values: -302.271133 -300.918092\n", "Time to run previous set of walkers (seconds): 0.001808\n", "Step: 628 / 1000\n", "Mean acceptance fraction: 0.622038\n", "Mean lnprob and Max lnprob values: -302.353041 -300.918092\n", "Time to run previous set of walkers (seconds): 0.001873\n", "Step: 629 / 1000\n", "Mean acceptance fraction: 0.622067\n", "Mean lnprob and Max lnprob values: -302.364802 -300.948033\n", "Time to run previous set of walkers (seconds): 0.001765\n", "Step: 630 / 1000\n", "Mean acceptance fraction: 0.622063\n", "Mean lnprob and Max lnprob values: -302.401433 -300.880924\n", "Time to run previous set of walkers (seconds): 0.001772\n", "Step: 631 / 1000\n", "Mean acceptance fraction: 0.622076\n", "Mean lnprob and Max lnprob values: -302.359417 -300.880924\n", "Time to run previous set of walkers (seconds): 0.001894\n", "Step: 632 / 1000\n", "Mean acceptance fraction: 0.622041\n", "Mean lnprob and Max lnprob values: -302.466630 -300.937793\n", "Time to run previous set of walkers (seconds): 0.001858\n", "Step: 633 / 1000\n", "Mean acceptance fraction: 0.622243\n", "Mean lnprob and Max lnprob values: -302.397686 -300.901843\n", "Time to run previous set of walkers (seconds): 0.001775\n", "Step: 634 / 1000\n", "Mean acceptance fraction: 0.622240\n", "Mean lnprob and Max lnprob values: -302.322449 -300.900588\n", "Time to run previous set of walkers (seconds): 0.001785\n", "Step: 635 / 1000\n", "Mean acceptance fraction: 0.622346\n", "Mean lnprob and Max lnprob values: -302.293845 -300.896996\n", "Time to run previous set of walkers (seconds): 0.001883\n", "Step: 636 / 1000\n", "Mean acceptance fraction: 0.622453\n", "Mean lnprob and Max lnprob values: -302.238323 -300.845592\n", "Time to run previous set of walkers (seconds): 0.001776\n", "Step: 637 / 1000\n", "Mean acceptance fraction: 0.622418\n", "Mean lnprob and Max lnprob values: -302.171026 -300.899673\n", "Time to run previous set of walkers (seconds): 0.001778\n", "Step: 638 / 1000\n", "Mean acceptance fraction: 0.622461\n", "Mean lnprob and Max lnprob values: -302.069637 -300.899673\n", "Time to run previous set of walkers (seconds): 0.001754\n", "Step: 639 / 1000\n", "Mean acceptance fraction: 0.622567\n", "Mean lnprob and Max lnprob values: -302.098918 -300.858797\n", "Time to run previous set of walkers (seconds): 0.001916\n", "Step: 640 / 1000\n", "Mean acceptance fraction: 0.622516\n", "Mean lnprob and Max lnprob values: -302.063298 -300.858797\n", "Time to run previous set of walkers (seconds): 0.001806\n", "Step: 641 / 1000\n", "Mean acceptance fraction: 0.622543\n", "Mean lnprob and Max lnprob values: -302.163044 -300.872556\n", "Time to run previous set of walkers (seconds): 0.001793\n", "Step: 642 / 1000\n", "Mean acceptance fraction: 0.622523\n", "Mean lnprob and Max lnprob values: -302.104146 -300.868649\n", "Time to run previous set of walkers (seconds): 0.001833\n", "Step: 643 / 1000\n", "Mean acceptance fraction: 0.622659\n", "Mean lnprob and Max lnprob values: -302.201633 -300.905421\n", "Time to run previous set of walkers (seconds): 0.001916\n", "Step: 644 / 1000\n", "Mean acceptance fraction: 0.622826\n", "Mean lnprob and Max lnprob values: -302.245531 -300.900691\n", "Time to run previous set of walkers (seconds): 0.001783\n", "Step: 645 / 1000\n", "Mean acceptance fraction: 0.622930\n", "Mean lnprob and Max lnprob values: -302.345002 -300.920065\n", "Time to run previous set of walkers (seconds): 0.001830\n", "Step: 646 / 1000\n", "Mean acceptance fraction: 0.622988\n", "Mean lnprob and Max lnprob values: -302.326007 -300.960361\n", "Time to run previous set of walkers (seconds): 0.001838\n", "Step: 647 / 1000\n", "Mean acceptance fraction: 0.622983\n", "Mean lnprob and Max lnprob values: -302.151165 -300.888028\n", "Time to run previous set of walkers (seconds): 0.001833\n", "Step: 648 / 1000\n", "Mean acceptance fraction: 0.623117\n", "Mean lnprob and Max lnprob values: -302.112571 -300.853027\n", "Time to run previous set of walkers (seconds): 0.001794\n", "Step: 649 / 1000\n", "Mean acceptance fraction: 0.623128\n", "Mean lnprob and Max lnprob values: -302.149548 -300.920957\n", "Time to run previous set of walkers (seconds): 0.001784\n", "Step: 650 / 1000\n", "Mean acceptance fraction: 0.623123\n", "Mean lnprob and Max lnprob values: -302.279138 -300.866968\n", "Time to run previous set of walkers (seconds): 0.001959\n", "Step: 651 / 1000\n", "Mean acceptance fraction: 0.623118\n", "Mean lnprob and Max lnprob values: -302.309100 -300.890645\n", "Time to run previous set of walkers (seconds): 0.001896\n", "Step: 652 / 1000\n", "Mean acceptance fraction: 0.623190\n", "Mean lnprob and Max lnprob values: -302.337699 -300.891402\n", "Time to run previous set of walkers (seconds): 0.001803\n", "Step: 653 / 1000\n", "Mean acceptance fraction: 0.623247\n", "Mean lnprob and Max lnprob values: -302.386429 -300.856399\n", "Time to run previous set of walkers (seconds): 0.001789\n", "Step: 654 / 1000\n", "Mean acceptance fraction: 0.623517\n", "Mean lnprob and Max lnprob values: -302.379192 -300.903276\n", "Time to run previous set of walkers (seconds): 0.001909\n", "Step: 655 / 1000\n", "Mean acceptance fraction: 0.623542\n", "Mean lnprob and Max lnprob values: -302.293845 -300.897653\n", "Time to run previous set of walkers (seconds): 0.001800\n", "Step: 656 / 1000\n", "Mean acceptance fraction: 0.623598\n", "Mean lnprob and Max lnprob values: -302.399189 -300.860767\n", "Time to run previous set of walkers (seconds): 0.002066\n", "Step: 657 / 1000\n", "Mean acceptance fraction: 0.623501\n", "Mean lnprob and Max lnprob values: -302.318853 -300.856241\n", "Time to run previous set of walkers (seconds): 0.002023\n", "Step: 658 / 1000\n", "Mean acceptance fraction: 0.623495\n", "Mean lnprob and Max lnprob values: -302.385910 -300.877788\n", "Time to run previous set of walkers (seconds): 0.002641\n", "Step: 659 / 1000\n", "Mean acceptance fraction: 0.623612\n", "Mean lnprob and Max lnprob values: -302.350131 -300.892857\n", "Time to run previous set of walkers (seconds): 0.002085\n", "Step: 660 / 1000\n", "Mean acceptance fraction: 0.623697\n", "Mean lnprob and Max lnprob values: -302.281354 -300.866912\n", "Time to run previous set of walkers (seconds): 0.001915\n", "Step: 661 / 1000\n", "Mean acceptance fraction: 0.623707\n", "Mean lnprob and Max lnprob values: -302.311922 -300.950929\n", "Time to run previous set of walkers (seconds): 0.002740\n", "Step: 662 / 1000\n", "Mean acceptance fraction: 0.623746\n", "Mean lnprob and Max lnprob values: -302.350485 -300.950929\n", "Time to run previous set of walkers (seconds): 0.001940\n", "Step: 663 / 1000\n", "Mean acceptance fraction: 0.623680\n", "Mean lnprob and Max lnprob values: -302.335901 -300.959169\n", "Time to run previous set of walkers (seconds): 0.001803\n", "Step: 664 / 1000\n", "Mean acceptance fraction: 0.623675\n", "Mean lnprob and Max lnprob values: -302.318194 -300.904879\n", "Time to run previous set of walkers (seconds): 0.002269\n", "Step: 665 / 1000\n", "Mean acceptance fraction: 0.623789\n", "Mean lnprob and Max lnprob values: -302.346201 -300.904703\n", "Time to run previous set of walkers (seconds): 0.001885\n", "Step: 666 / 1000\n", "Mean acceptance fraction: 0.623769\n", "Mean lnprob and Max lnprob values: -302.336502 -300.881501\n", "Time to run previous set of walkers (seconds): 0.001780\n", "Step: 667 / 1000\n", "Mean acceptance fraction: 0.623703\n", "Mean lnprob and Max lnprob values: -302.554798 -300.890207\n", "Time to run previous set of walkers (seconds): 0.001803\n", "Step: 668 / 1000\n", "Mean acceptance fraction: 0.623802\n", "Mean lnprob and Max lnprob values: -302.494244 -300.873772\n", "Time to run previous set of walkers (seconds): 0.002515\n", "Step: 669 / 1000\n", "Mean acceptance fraction: 0.623752\n", "Mean lnprob and Max lnprob values: -302.466587 -300.890309\n", "Time to run previous set of walkers (seconds): 0.001829\n", "Step: 670 / 1000\n", "Mean acceptance fraction: 0.623731\n", "Mean lnprob and Max lnprob values: -302.388356 -300.884945\n", "Time to run previous set of walkers (seconds): 0.001761\n", "Step: 671 / 1000\n", "Mean acceptance fraction: 0.623741\n", "Mean lnprob and Max lnprob values: -302.368574 -300.882911\n", "Time to run previous set of walkers (seconds): 0.002160\n", "Step: 672 / 1000\n", "Mean acceptance fraction: 0.623795\n", "Mean lnprob and Max lnprob values: -302.384963 -300.950971\n", "Time to run previous set of walkers (seconds): 0.001923\n", "Step: 673 / 1000\n", "Mean acceptance fraction: 0.623834\n", "Mean lnprob and Max lnprob values: -302.400803 -300.845996\n", "Time to run previous set of walkers (seconds): 0.001785\n", "Step: 674 / 1000\n", "Mean acceptance fraction: 0.623724\n", "Mean lnprob and Max lnprob values: -302.374648 -300.967266\n", "Time to run previous set of walkers (seconds): 0.001806\n", "Step: 675 / 1000\n", "Mean acceptance fraction: 0.623763\n", "Mean lnprob and Max lnprob values: -302.373166 -300.919992\n", "Time to run previous set of walkers (seconds): 0.002466\n", "Step: 676 / 1000\n", "Mean acceptance fraction: 0.623787\n", "Mean lnprob and Max lnprob values: -302.307266 -300.847359\n", "Time to run previous set of walkers (seconds): 0.001910\n", "Step: 677 / 1000\n", "Mean acceptance fraction: 0.623929\n", "Mean lnprob and Max lnprob values: -302.396662 -300.860226\n", "Time to run previous set of walkers (seconds): 0.001787\n", "Step: 678 / 1000\n", "Mean acceptance fraction: 0.623968\n", "Mean lnprob and Max lnprob values: -302.455038 -300.847515\n", "Time to run previous set of walkers (seconds): 0.002059\n", "Step: 679 / 1000\n", "Mean acceptance fraction: 0.624006\n", "Mean lnprob and Max lnprob values: -302.558823 -300.847515\n", "Time to run previous set of walkers (seconds): 0.004570\n", "Step: 680 / 1000\n", "Mean acceptance fraction: 0.623971\n", "Mean lnprob and Max lnprob values: -302.516908 -300.884261\n", "Time to run previous set of walkers (seconds): 0.002926\n", "Step: 681 / 1000\n", "Mean acceptance fraction: 0.623950\n", "Mean lnprob and Max lnprob values: -302.450039 -300.972334\n", "Time to run previous set of walkers (seconds): 0.002015\n", "Step: 682 / 1000\n", "Mean acceptance fraction: 0.623930\n", "Mean lnprob and Max lnprob values: -302.426388 -300.856573\n", "Time to run previous set of walkers (seconds): 0.001863\n", "Step: 683 / 1000\n", "Mean acceptance fraction: 0.623939\n", "Mean lnprob and Max lnprob values: -302.514938 -300.860982\n", "Time to run previous set of walkers (seconds): 0.002100\n", "Step: 684 / 1000\n", "Mean acceptance fraction: 0.623904\n", "Mean lnprob and Max lnprob values: -302.606833 -300.932299\n", "Time to run previous set of walkers (seconds): 0.002177\n", "Step: 685 / 1000\n", "Mean acceptance fraction: 0.623869\n", "Mean lnprob and Max lnprob values: -302.538464 -300.900164\n", "Time to run previous set of walkers (seconds): 0.001844\n", "Step: 686 / 1000\n", "Mean acceptance fraction: 0.623878\n", "Mean lnprob and Max lnprob values: -302.542996 -300.920299\n", "Time to run previous set of walkers (seconds): 0.001777\n", "Step: 687 / 1000\n", "Mean acceptance fraction: 0.623901\n", "Mean lnprob and Max lnprob values: -302.476546 -300.928689\n", "Time to run previous set of walkers (seconds): 0.002023\n", "Step: 688 / 1000\n", "Mean acceptance fraction: 0.623881\n", "Mean lnprob and Max lnprob values: -302.319733 -300.947915\n", "Time to run previous set of walkers (seconds): 0.002116\n", "Step: 689 / 1000\n", "Mean acceptance fraction: 0.623861\n", "Mean lnprob and Max lnprob values: -302.369166 -300.924018\n", "Time to run previous set of walkers (seconds): 0.001782\n", "Step: 690 / 1000\n", "Mean acceptance fraction: 0.623855\n", "Mean lnprob and Max lnprob values: -302.310457 -300.922694\n", "Time to run previous set of walkers (seconds): 0.001772\n", "Step: 691 / 1000\n", "Mean acceptance fraction: 0.623864\n", "Mean lnprob and Max lnprob values: -302.218544 -300.880387\n", "Time to run previous set of walkers (seconds): 0.002163\n", "Step: 692 / 1000\n", "Mean acceptance fraction: 0.623844\n", "Mean lnprob and Max lnprob values: -302.212804 -300.899723\n", "Time to run previous set of walkers (seconds): 0.001834\n", "Step: 693 / 1000\n", "Mean acceptance fraction: 0.623882\n", "Mean lnprob and Max lnprob values: -302.186561 -300.849762\n", "Time to run previous set of walkers (seconds): 0.001814\n", "Step: 694 / 1000\n", "Mean acceptance fraction: 0.623934\n", "Mean lnprob and Max lnprob values: -302.341306 -300.899909\n", "Time to run previous set of walkers (seconds): 0.002037\n", "Step: 695 / 1000\n", "Mean acceptance fraction: 0.624043\n", "Mean lnprob and Max lnprob values: -302.258172 -300.927277\n", "Time to run previous set of walkers (seconds): 0.001791\n", "Step: 696 / 1000\n", "Mean acceptance fraction: 0.624095\n", "Mean lnprob and Max lnprob values: -302.288526 -300.871944\n", "Time to run previous set of walkers (seconds): 0.001918\n", "Step: 697 / 1000\n", "Mean acceptance fraction: 0.624175\n", "Mean lnprob and Max lnprob values: -302.368822 -300.868943\n", "Time to run previous set of walkers (seconds): 0.001794\n", "Step: 698 / 1000\n", "Mean acceptance fraction: 0.624226\n", "Mean lnprob and Max lnprob values: -302.458813 -300.868943\n", "Time to run previous set of walkers (seconds): 0.002121\n", "Step: 699 / 1000\n", "Mean acceptance fraction: 0.624249\n", "Mean lnprob and Max lnprob values: -302.444984 -300.868943\n", "Time to run previous set of walkers (seconds): 0.001803\n", "Step: 700 / 1000\n", "Mean acceptance fraction: 0.624300\n", "Mean lnprob and Max lnprob values: -302.411963 -300.898640\n", "Time to run previous set of walkers (seconds): 0.001823\n", "Step: 701 / 1000\n", "Mean acceptance fraction: 0.624351\n", "Mean lnprob and Max lnprob values: -302.373759 -300.904206\n", "Time to run previous set of walkers (seconds): 0.001815\n", "Step: 702 / 1000\n", "Mean acceptance fraction: 0.624359\n", "Mean lnprob and Max lnprob values: -302.360836 -300.866251\n", "Time to run previous set of walkers (seconds): 0.001997\n", "Step: 703 / 1000\n", "Mean acceptance fraction: 0.624452\n", "Mean lnprob and Max lnprob values: -302.246962 -300.852838\n", "Time to run previous set of walkers (seconds): 0.001811\n", "Step: 704 / 1000\n", "Mean acceptance fraction: 0.624489\n", "Mean lnprob and Max lnprob values: -302.234356 -300.852838\n", "Time to run previous set of walkers (seconds): 0.001800\n", "Step: 705 / 1000\n", "Mean acceptance fraction: 0.624511\n", "Mean lnprob and Max lnprob values: -302.258124 -300.844657\n", "Time to run previous set of walkers (seconds): 0.002083\n", "Step: 706 / 1000\n", "Mean acceptance fraction: 0.624405\n", "Mean lnprob and Max lnprob values: -302.296132 -300.844657\n", "Time to run previous set of walkers (seconds): 0.001778\n", "Step: 707 / 1000\n", "Mean acceptance fraction: 0.624470\n", "Mean lnprob and Max lnprob values: -302.246035 -300.853191\n", "Time to run previous set of walkers (seconds): 0.001765\n", "Step: 708 / 1000\n", "Mean acceptance fraction: 0.624407\n", "Mean lnprob and Max lnprob values: -302.331250 -300.892159\n", "Time to run previous set of walkers (seconds): 0.001750\n", "Step: 709 / 1000\n", "Mean acceptance fraction: 0.624485\n", "Mean lnprob and Max lnprob values: -302.347514 -300.930137\n", "Time to run previous set of walkers (seconds): 0.002089\n", "Step: 710 / 1000\n", "Mean acceptance fraction: 0.624479\n", "Mean lnprob and Max lnprob values: -302.314694 -300.957331\n", "Time to run previous set of walkers (seconds): 0.001834\n", "Step: 711 / 1000\n", "Mean acceptance fraction: 0.624515\n", "Mean lnprob and Max lnprob values: -302.428763 -300.957331\n", "Time to run previous set of walkers (seconds): 0.001827\n", "Step: 712 / 1000\n", "Mean acceptance fraction: 0.624410\n", "Mean lnprob and Max lnprob values: -302.376712 -300.897119\n", "Time to run previous set of walkers (seconds): 0.001853\n", "Step: 713 / 1000\n", "Mean acceptance fraction: 0.624362\n", "Mean lnprob and Max lnprob values: -302.493985 -300.897488\n", "Time to run previous set of walkers (seconds): 0.002096\n", "Step: 714 / 1000\n", "Mean acceptance fraction: 0.624384\n", "Mean lnprob and Max lnprob values: -302.500169 -300.897488\n", "Time to run previous set of walkers (seconds): 0.001980\n", "Step: 715 / 1000\n", "Mean acceptance fraction: 0.624364\n", "Mean lnprob and Max lnprob values: -302.498499 -300.919524\n", "Time to run previous set of walkers (seconds): 0.002080\n", "Step: 716 / 1000\n", "Mean acceptance fraction: 0.624302\n", "Mean lnprob and Max lnprob values: -302.584985 -300.883110\n", "Time to run previous set of walkers (seconds): 0.002309\n", "Step: 717 / 1000\n", "Mean acceptance fraction: 0.624310\n", "Mean lnprob and Max lnprob values: -302.524336 -300.947267\n", "Time to run previous set of walkers (seconds): 0.002120\n", "Step: 718 / 1000\n", "Mean acceptance fraction: 0.624304\n", "Mean lnprob and Max lnprob values: -302.611729 -300.995857\n", "Time to run previous set of walkers (seconds): 0.002597\n", "Step: 719 / 1000\n", "Mean acceptance fraction: 0.624228\n", "Mean lnprob and Max lnprob values: -302.533825 -300.913694\n", "Time to run previous set of walkers (seconds): 0.002061\n", "Step: 720 / 1000\n", "Mean acceptance fraction: 0.624208\n", "Mean lnprob and Max lnprob values: -302.631620 -300.931360\n", "Time to run previous set of walkers (seconds): 0.002113\n", "Step: 721 / 1000\n", "Mean acceptance fraction: 0.624327\n", "Mean lnprob and Max lnprob values: -302.445020 -300.922481\n", "Time to run previous set of walkers (seconds): 0.001853\n", "Step: 722 / 1000\n", "Mean acceptance fraction: 0.624224\n", "Mean lnprob and Max lnprob values: -302.416130 -300.860546\n", "Time to run previous set of walkers (seconds): 0.001852\n", "Step: 723 / 1000\n", "Mean acceptance fraction: 0.624177\n", "Mean lnprob and Max lnprob values: -302.415402 -300.864572\n", "Time to run previous set of walkers (seconds): 0.002272\n", "Step: 724 / 1000\n", "Mean acceptance fraction: 0.624227\n", "Mean lnprob and Max lnprob values: -302.407154 -300.864572\n", "Time to run previous set of walkers (seconds): 0.001825\n", "Step: 725 / 1000\n", "Mean acceptance fraction: 0.624331\n", "Mean lnprob and Max lnprob values: -302.377589 -300.910003\n", "Time to run previous set of walkers (seconds): 0.001817\n", "Step: 726 / 1000\n", "Mean acceptance fraction: 0.624229\n", "Mean lnprob and Max lnprob values: -302.479274 -300.897210\n", "Time to run previous set of walkers (seconds): 0.001933\n", "Step: 727 / 1000\n", "Mean acceptance fraction: 0.624182\n", "Mean lnprob and Max lnprob values: -302.417065 -300.963342\n", "Time to run previous set of walkers (seconds): 0.002205\n", "Step: 728 / 1000\n", "Mean acceptance fraction: 0.624148\n", "Mean lnprob and Max lnprob values: -302.402935 -300.952681\n", "Time to run previous set of walkers (seconds): 0.002138\n", "Step: 729 / 1000\n", "Mean acceptance fraction: 0.624266\n", "Mean lnprob and Max lnprob values: -302.338393 -300.895005\n", "Time to run previous set of walkers (seconds): 0.002025\n", "Step: 730 / 1000\n", "Mean acceptance fraction: 0.624411\n", "Mean lnprob and Max lnprob values: -302.399418 -300.852897\n", "Time to run previous set of walkers (seconds): 0.002164\n", "Step: 731 / 1000\n", "Mean acceptance fraction: 0.624528\n", "Mean lnprob and Max lnprob values: -302.446454 -300.852897\n", "Time to run previous set of walkers (seconds): 0.001850\n", "Step: 732 / 1000\n", "Mean acceptance fraction: 0.624495\n", "Mean lnprob and Max lnprob values: -302.444554 -300.850826\n", "Time to run previous set of walkers (seconds): 0.001809\n", "Step: 733 / 1000\n", "Mean acceptance fraction: 0.624475\n", "Mean lnprob and Max lnprob values: -302.472410 -300.892583\n", "Time to run previous set of walkers (seconds): 0.002249\n", "Step: 734 / 1000\n", "Mean acceptance fraction: 0.624455\n", "Mean lnprob and Max lnprob values: -302.414956 -300.930695\n", "Time to run previous set of walkers (seconds): 0.001799\n", "Step: 735 / 1000\n", "Mean acceptance fraction: 0.624612\n", "Mean lnprob and Max lnprob values: -302.460649 -300.883572\n", "Time to run previous set of walkers (seconds): 0.001781\n", "Step: 736 / 1000\n", "Mean acceptance fraction: 0.624565\n", "Mean lnprob and Max lnprob values: -302.319234 -300.883572\n", "Time to run previous set of walkers (seconds): 0.001790\n", "Step: 737 / 1000\n", "Mean acceptance fraction: 0.624518\n", "Mean lnprob and Max lnprob values: -302.501007 -300.870444\n", "Time to run previous set of walkers (seconds): 0.002703\n", "Step: 738 / 1000\n", "Mean acceptance fraction: 0.624593\n", "Mean lnprob and Max lnprob values: -302.377768 -300.920464\n", "Time to run previous set of walkers (seconds): 0.001797\n", "Step: 739 / 1000\n", "Mean acceptance fraction: 0.624628\n", "Mean lnprob and Max lnprob values: -302.283622 -300.920464\n", "Time to run previous set of walkers (seconds): 0.001774\n", "Step: 740 / 1000\n", "Mean acceptance fraction: 0.624703\n", "Mean lnprob and Max lnprob values: -302.213772 -300.984769\n", "Time to run previous set of walkers (seconds): 0.002512\n", "Step: 741 / 1000\n", "Mean acceptance fraction: 0.624669\n", "Mean lnprob and Max lnprob values: -302.299214 -300.936549\n", "Time to run previous set of walkers (seconds): 0.001820\n", "Step: 742 / 1000\n", "Mean acceptance fraction: 0.624717\n", "Mean lnprob and Max lnprob values: -302.246924 -300.950878\n", "Time to run previous set of walkers (seconds): 0.001782\n", "Step: 743 / 1000\n", "Mean acceptance fraction: 0.624805\n", "Mean lnprob and Max lnprob values: -302.304206 -300.950878\n", "Time to run previous set of walkers (seconds): 0.002106\n", "Step: 744 / 1000\n", "Mean acceptance fraction: 0.624919\n", "Mean lnprob and Max lnprob values: -302.259999 -300.972841\n", "Time to run previous set of walkers (seconds): 0.002239\n", "Step: 745 / 1000\n", "Mean acceptance fraction: 0.624953\n", "Mean lnprob and Max lnprob values: -302.295871 -300.895683\n", "Time to run previous set of walkers (seconds): 0.001813\n", "Step: 746 / 1000\n", "Mean acceptance fraction: 0.624946\n", "Mean lnprob and Max lnprob values: -302.215240 -300.883651\n", "Time to run previous set of walkers (seconds): 0.001751\n", "Step: 747 / 1000\n", "Mean acceptance fraction: 0.624993\n", "Mean lnprob and Max lnprob values: -302.295968 -300.878500\n", "Time to run previous set of walkers (seconds): 0.002350\n", "Step: 748 / 1000\n", "Mean acceptance fraction: 0.624960\n", "Mean lnprob and Max lnprob values: -302.296111 -300.878500\n", "Time to run previous set of walkers (seconds): 0.001853\n", "Step: 749 / 1000\n", "Mean acceptance fraction: 0.625007\n", "Mean lnprob and Max lnprob values: -302.387768 -300.884505\n", "Time to run previous set of walkers (seconds): 0.001769\n", "Step: 750 / 1000\n", "Mean acceptance fraction: 0.625067\n", "Mean lnprob and Max lnprob values: -302.439120 -300.932309\n", "Time to run previous set of walkers (seconds): 0.001966\n", "Step: 751 / 1000\n", "Mean acceptance fraction: 0.625087\n", "Mean lnprob and Max lnprob values: -302.335933 -300.905673\n", "Time to run previous set of walkers (seconds): 0.002155\n", "Step: 752 / 1000\n", "Mean acceptance fraction: 0.625173\n", "Mean lnprob and Max lnprob values: -302.341484 -300.905673\n", "Time to run previous set of walkers (seconds): 0.001806\n", "Step: 753 / 1000\n", "Mean acceptance fraction: 0.625166\n", "Mean lnprob and Max lnprob values: -302.424456 -300.852474\n", "Time to run previous set of walkers (seconds): 0.001782\n", "Step: 754 / 1000\n", "Mean acceptance fraction: 0.625186\n", "Mean lnprob and Max lnprob values: -302.508105 -300.913966\n", "Time to run previous set of walkers (seconds): 0.002261\n", "Step: 755 / 1000\n", "Mean acceptance fraction: 0.625245\n", "Mean lnprob and Max lnprob values: -302.624425 -300.900407\n", "Time to run previous set of walkers (seconds): 0.001815\n", "Step: 756 / 1000\n", "Mean acceptance fraction: 0.625198\n", "Mean lnprob and Max lnprob values: -302.512153 -300.937487\n", "Time to run previous set of walkers (seconds): 0.001744\n", "Step: 757 / 1000\n", "Mean acceptance fraction: 0.625324\n", "Mean lnprob and Max lnprob values: -302.532662 -300.866321\n", "Time to run previous set of walkers (seconds): 0.002063\n", "Step: 758 / 1000\n", "Mean acceptance fraction: 0.625264\n", "Mean lnprob and Max lnprob values: -302.486438 -300.862847\n", "Time to run previous set of walkers (seconds): 0.002680\n", "Step: 759 / 1000\n", "Mean acceptance fraction: 0.625323\n", "Mean lnprob and Max lnprob values: -302.467119 -300.941377\n", "Time to run previous set of walkers (seconds): 0.001999\n", "Step: 760 / 1000\n", "Mean acceptance fraction: 0.625132\n", "Mean lnprob and Max lnprob values: -302.470672 -300.868080\n", "Time to run previous set of walkers (seconds): 0.002000\n", "Step: 761 / 1000\n", "Mean acceptance fraction: 0.625243\n", "Mean lnprob and Max lnprob values: -302.449677 -300.871956\n", "Time to run previous set of walkers (seconds): 0.002324\n", "Step: 762 / 1000\n", "Mean acceptance fraction: 0.625289\n", "Mean lnprob and Max lnprob values: -302.473737 -300.875219\n", "Time to run previous set of walkers (seconds): 0.001833\n", "Step: 763 / 1000\n", "Mean acceptance fraction: 0.625216\n", "Mean lnprob and Max lnprob values: -302.496922 -300.875219\n", "Time to run previous set of walkers (seconds): 0.001803\n", "Step: 764 / 1000\n", "Mean acceptance fraction: 0.625118\n", "Mean lnprob and Max lnprob values: -302.449574 -300.899021\n", "Time to run previous set of walkers (seconds): 0.002243\n", "Step: 765 / 1000\n", "Mean acceptance fraction: 0.625163\n", "Mean lnprob and Max lnprob values: -302.462847 -300.874320\n", "Time to run previous set of walkers (seconds): 0.001877\n", "Step: 766 / 1000\n", "Mean acceptance fraction: 0.625157\n", "Mean lnprob and Max lnprob values: -302.414894 -300.888745\n", "Time to run previous set of walkers (seconds): 0.001759\n", "Step: 767 / 1000\n", "Mean acceptance fraction: 0.625215\n", "Mean lnprob and Max lnprob values: -302.614536 -300.889828\n", "Time to run previous set of walkers (seconds): 0.001891\n", "Step: 768 / 1000\n", "Mean acceptance fraction: 0.625234\n", "Mean lnprob and Max lnprob values: -302.672293 -300.868457\n", "Time to run previous set of walkers (seconds): 0.002278\n", "Step: 769 / 1000\n", "Mean acceptance fraction: 0.625267\n", "Mean lnprob and Max lnprob values: -302.524916 -300.913685\n", "Time to run previous set of walkers (seconds): 0.001837\n", "Step: 770 / 1000\n", "Mean acceptance fraction: 0.625364\n", "Mean lnprob and Max lnprob values: -302.439550 -300.898920\n", "Time to run previous set of walkers (seconds): 0.001769\n", "Step: 771 / 1000\n", "Mean acceptance fraction: 0.625396\n", "Mean lnprob and Max lnprob values: -302.411324 -300.869564\n", "Time to run previous set of walkers (seconds): 0.002571\n", "Step: 772 / 1000\n", "Mean acceptance fraction: 0.625389\n", "Mean lnprob and Max lnprob values: -302.460982 -300.857993\n", "Time to run previous set of walkers (seconds): 0.001851\n", "Step: 773 / 1000\n", "Mean acceptance fraction: 0.625446\n", "Mean lnprob and Max lnprob values: -302.359058 -300.857993\n", "Time to run previous set of walkers (seconds): 0.001770\n", "Step: 774 / 1000\n", "Mean acceptance fraction: 0.625517\n", "Mean lnprob and Max lnprob values: -302.232980 -300.856574\n", "Time to run previous set of walkers (seconds): 0.001817\n", "Step: 775 / 1000\n", "Mean acceptance fraction: 0.625587\n", "Mean lnprob and Max lnprob values: -302.297261 -300.856574\n", "Time to run previous set of walkers (seconds): 0.002247\n", "Step: 776 / 1000\n", "Mean acceptance fraction: 0.625606\n", "Mean lnprob and Max lnprob values: -302.102168 -300.873250\n", "Time to run previous set of walkers (seconds): 0.001785\n", "Step: 777 / 1000\n", "Mean acceptance fraction: 0.625663\n", "Mean lnprob and Max lnprob values: -302.139624 -300.885499\n", "Time to run previous set of walkers (seconds): 0.001796\n", "Step: 778 / 1000\n", "Mean acceptance fraction: 0.625707\n", "Mean lnprob and Max lnprob values: -302.277842 -300.875857\n", "Time to run previous set of walkers (seconds): 0.002163\n", "Step: 779 / 1000\n", "Mean acceptance fraction: 0.625802\n", "Mean lnprob and Max lnprob values: -302.229791 -300.888949\n", "Time to run previous set of walkers (seconds): 0.001822\n", "Step: 780 / 1000\n", "Mean acceptance fraction: 0.625833\n", "Mean lnprob and Max lnprob values: -302.275644 -300.893467\n", "Time to run previous set of walkers (seconds): 0.001763\n", "Step: 781 / 1000\n", "Mean acceptance fraction: 0.625787\n", "Mean lnprob and Max lnprob values: -302.211561 -300.893467\n", "Time to run previous set of walkers (seconds): 0.002227\n", "Step: 782 / 1000\n", "Mean acceptance fraction: 0.625895\n", "Mean lnprob and Max lnprob values: -302.090133 -300.895178\n", "Time to run previous set of walkers (seconds): 0.002600\n", "Step: 783 / 1000\n", "Mean acceptance fraction: 0.625964\n", "Mean lnprob and Max lnprob values: -302.026355 -300.888228\n", "Time to run previous set of walkers (seconds): 0.002118\n", "Step: 784 / 1000\n", "Mean acceptance fraction: 0.626071\n", "Mean lnprob and Max lnprob values: -302.237952 -300.919622\n", "Time to run previous set of walkers (seconds): 0.002182\n", "Step: 785 / 1000\n", "Mean acceptance fraction: 0.626102\n", "Mean lnprob and Max lnprob values: -302.281317 -300.853233\n", "Time to run previous set of walkers (seconds): 0.002318\n", "Step: 786 / 1000\n", "Mean acceptance fraction: 0.626158\n", "Mean lnprob and Max lnprob values: -302.251903 -300.889029\n", "Time to run previous set of walkers (seconds): 0.001832\n", "Step: 787 / 1000\n", "Mean acceptance fraction: 0.626213\n", "Mean lnprob and Max lnprob values: -302.398873 -300.902519\n", "Time to run previous set of walkers (seconds): 0.001807\n", "Step: 788 / 1000\n", "Mean acceptance fraction: 0.626269\n", "Mean lnprob and Max lnprob values: -302.341576 -300.908424\n", "Time to run previous set of walkers (seconds): 0.002150\n", "Step: 789 / 1000\n", "Mean acceptance fraction: 0.626375\n", "Mean lnprob and Max lnprob values: -302.192580 -300.910545\n", "Time to run previous set of walkers (seconds): 0.001995\n", "Step: 790 / 1000\n", "Mean acceptance fraction: 0.626392\n", "Mean lnprob and Max lnprob values: -302.271903 -300.966931\n", "Time to run previous set of walkers (seconds): 0.001793\n", "Step: 791 / 1000\n", "Mean acceptance fraction: 0.626397\n", "Mean lnprob and Max lnprob values: -302.266302 -300.872313\n", "Time to run previous set of walkers (seconds): 0.001842\n", "Step: 792 / 1000\n", "Mean acceptance fraction: 0.626477\n", "Mean lnprob and Max lnprob values: -302.219915 -300.892480\n", "Time to run previous set of walkers (seconds): 0.002385\n", "Step: 793 / 1000\n", "Mean acceptance fraction: 0.626482\n", "Mean lnprob and Max lnprob values: -302.340891 -300.941264\n", "Time to run previous set of walkers (seconds): 0.001850\n", "Step: 794 / 1000\n", "Mean acceptance fraction: 0.626486\n", "Mean lnprob and Max lnprob values: -302.352524 -300.939899\n", "Time to run previous set of walkers (seconds): 0.001761\n", "Step: 795 / 1000\n", "Mean acceptance fraction: 0.626503\n", "Mean lnprob and Max lnprob values: -302.228908 -300.982480\n", "Time to run previous set of walkers (seconds): 0.002342\n", "Step: 796 / 1000\n", "Mean acceptance fraction: 0.626520\n", "Mean lnprob and Max lnprob values: -302.203798 -300.938817\n", "Time to run previous set of walkers (seconds): 0.001828\n", "Step: 797 / 1000\n", "Mean acceptance fraction: 0.626487\n", "Mean lnprob and Max lnprob values: -302.102347 -300.908404\n", "Time to run previous set of walkers (seconds): 0.001738\n", "Step: 798 / 1000\n", "Mean acceptance fraction: 0.626529\n", "Mean lnprob and Max lnprob values: -302.182411 -300.908404\n", "Time to run previous set of walkers (seconds): 0.001803\n", "Step: 799 / 1000\n", "Mean acceptance fraction: 0.626583\n", "Mean lnprob and Max lnprob values: -302.140686 -300.913569\n", "Time to run previous set of walkers (seconds): 0.002250\n", "Step: 800 / 1000\n", "Mean acceptance fraction: 0.626625\n", "Mean lnprob and Max lnprob values: -302.123422 -300.897793\n", "Time to run previous set of walkers (seconds): 0.001826\n", "Step: 801 / 1000\n", "Mean acceptance fraction: 0.626654\n", "Mean lnprob and Max lnprob values: -302.212400 -300.857260\n", "Time to run previous set of walkers (seconds): 0.001812\n", "Step: 802 / 1000\n", "Mean acceptance fraction: 0.626608\n", "Mean lnprob and Max lnprob values: -302.310559 -300.848775\n", "Time to run previous set of walkers (seconds): 0.002007\n", "Step: 803 / 1000\n", "Mean acceptance fraction: 0.626625\n", "Mean lnprob and Max lnprob values: -302.175826 -300.983446\n", "Time to run previous set of walkers (seconds): 0.002287\n", "Step: 804 / 1000\n", "Mean acceptance fraction: 0.626592\n", "Mean lnprob and Max lnprob values: -302.148056 -300.950998\n", "Time to run previous set of walkers (seconds): 0.001993\n", "Step: 805 / 1000\n", "Mean acceptance fraction: 0.626621\n", "Mean lnprob and Max lnprob values: -302.191196 -300.857375\n", "Time to run previous set of walkers (seconds): 0.001816\n", "Step: 806 / 1000\n", "Mean acceptance fraction: 0.626613\n", "Mean lnprob and Max lnprob values: -302.272465 -300.921081\n", "Time to run previous set of walkers (seconds): 0.001755\n", "Step: 807 / 1000\n", "Mean acceptance fraction: 0.626642\n", "Mean lnprob and Max lnprob values: -302.195137 -300.898781\n", "Time to run previous set of walkers (seconds): 0.001760\n", "Step: 808 / 1000\n", "Mean acceptance fraction: 0.626708\n", "Mean lnprob and Max lnprob values: -302.131368 -300.890155\n", "Time to run previous set of walkers (seconds): 0.001816\n", "Step: 809 / 1000\n", "Mean acceptance fraction: 0.626737\n", "Mean lnprob and Max lnprob values: -302.192039 -300.877390\n", "Time to run previous set of walkers (seconds): 0.002046\n", "Step: 810 / 1000\n", "Mean acceptance fraction: 0.626753\n", "Mean lnprob and Max lnprob values: -302.095318 -300.917836\n", "Time to run previous set of walkers (seconds): 0.001914\n", "Step: 811 / 1000\n", "Mean acceptance fraction: 0.626757\n", "Mean lnprob and Max lnprob values: -302.162149 -300.857118\n", "Time to run previous set of walkers (seconds): 0.001834\n", "Step: 812 / 1000\n", "Mean acceptance fraction: 0.626761\n", "Mean lnprob and Max lnprob values: -302.239104 -300.872242\n", "Time to run previous set of walkers (seconds): 0.001783\n", "Step: 813 / 1000\n", "Mean acceptance fraction: 0.626876\n", "Mean lnprob and Max lnprob values: -302.293934 -300.907194\n", "Time to run previous set of walkers (seconds): 0.001830\n", "Step: 814 / 1000\n", "Mean acceptance fraction: 0.626916\n", "Mean lnprob and Max lnprob values: -302.236621 -300.856351\n", "Time to run previous set of walkers (seconds): 0.001984\n", "Step: 815 / 1000\n", "Mean acceptance fraction: 0.627006\n", "Mean lnprob and Max lnprob values: -302.255295 -300.888658\n", "Time to run previous set of walkers (seconds): 0.002004\n", "Step: 816 / 1000\n", "Mean acceptance fraction: 0.626998\n", "Mean lnprob and Max lnprob values: -302.226685 -300.943100\n", "Time to run previous set of walkers (seconds): 0.001960\n", "Step: 817 / 1000\n", "Mean acceptance fraction: 0.626965\n", "Mean lnprob and Max lnprob values: -302.321305 -300.872813\n", "Time to run previous set of walkers (seconds): 0.001901\n", "Step: 818 / 1000\n", "Mean acceptance fraction: 0.626944\n", "Mean lnprob and Max lnprob values: -302.342486 -300.884509\n", "Time to run previous set of walkers (seconds): 0.002160\n", "Step: 819 / 1000\n", "Mean acceptance fraction: 0.626984\n", "Mean lnprob and Max lnprob values: -302.376939 -300.855596\n", "Time to run previous set of walkers (seconds): 0.002266\n", "Step: 820 / 1000\n", "Mean acceptance fraction: 0.626829\n", "Mean lnprob and Max lnprob values: -302.363266 -300.855596\n", "Time to run previous set of walkers (seconds): 0.002347\n", "Step: 821 / 1000\n", "Mean acceptance fraction: 0.626724\n", "Mean lnprob and Max lnprob values: -302.356815 -300.917222\n", "Time to run previous set of walkers (seconds): 0.002109\n", "Step: 822 / 1000\n", "Mean acceptance fraction: 0.626740\n", "Mean lnprob and Max lnprob values: -302.334427 -300.924558\n", "Time to run previous set of walkers (seconds): 0.001837\n", "Step: 823 / 1000\n", "Mean acceptance fraction: 0.626792\n", "Mean lnprob and Max lnprob values: -302.280024 -300.844922\n", "Time to run previous set of walkers (seconds): 0.002226\n", "Step: 824 / 1000\n", "Mean acceptance fraction: 0.626784\n", "Mean lnprob and Max lnprob values: -302.350087 -300.953716\n", "Time to run previous set of walkers (seconds): 0.001897\n", "Step: 825 / 1000\n", "Mean acceptance fraction: 0.626848\n", "Mean lnprob and Max lnprob values: -302.246463 -300.935060\n", "Time to run previous set of walkers (seconds): 0.001926\n", "Step: 826 / 1000\n", "Mean acceptance fraction: 0.626864\n", "Mean lnprob and Max lnprob values: -302.449438 -300.935060\n", "Time to run previous set of walkers (seconds): 0.001974\n", "Step: 827 / 1000\n", "Mean acceptance fraction: 0.626892\n", "Mean lnprob and Max lnprob values: -302.422708 -300.887405\n", "Time to run previous set of walkers (seconds): 0.001908\n", "Step: 828 / 1000\n", "Mean acceptance fraction: 0.626872\n", "Mean lnprob and Max lnprob values: -302.326096 -300.866175\n", "Time to run previous set of walkers (seconds): 0.001901\n", "Step: 829 / 1000\n", "Mean acceptance fraction: 0.626876\n", "Mean lnprob and Max lnprob values: -302.284549 -300.866175\n", "Time to run previous set of walkers (seconds): 0.002060\n", "Step: 830 / 1000\n", "Mean acceptance fraction: 0.626964\n", "Mean lnprob and Max lnprob values: -302.297590 -300.864133\n", "Time to run previous set of walkers (seconds): 0.002785\n", "Step: 831 / 1000\n", "Mean acceptance fraction: 0.627064\n", "Mean lnprob and Max lnprob values: -302.188737 -300.921029\n", "Time to run previous set of walkers (seconds): 0.001982\n", "Step: 832 / 1000\n", "Mean acceptance fraction: 0.627043\n", "Mean lnprob and Max lnprob values: -302.253703 -300.902982\n", "Time to run previous set of walkers (seconds): 0.001890\n", "Step: 833 / 1000\n", "Mean acceptance fraction: 0.627119\n", "Mean lnprob and Max lnprob values: -302.192787 -300.912627\n", "Time to run previous set of walkers (seconds): 0.002667\n", "Step: 834 / 1000\n", "Mean acceptance fraction: 0.626990\n", "Mean lnprob and Max lnprob values: -302.279830 -300.912627\n", "Time to run previous set of walkers (seconds): 0.001985\n", "Step: 835 / 1000\n", "Mean acceptance fraction: 0.627042\n", "Mean lnprob and Max lnprob values: -302.251118 -300.896247\n", "Time to run previous set of walkers (seconds): 0.001926\n", "Step: 836 / 1000\n", "Mean acceptance fraction: 0.627057\n", "Mean lnprob and Max lnprob values: -302.362952 -300.955394\n", "Time to run previous set of walkers (seconds): 0.003631\n", "Step: 837 / 1000\n", "Mean acceptance fraction: 0.627073\n", "Mean lnprob and Max lnprob values: -302.372301 -300.937640\n", "Time to run previous set of walkers (seconds): 0.004165\n", "Step: 838 / 1000\n", "Mean acceptance fraction: 0.627196\n", "Mean lnprob and Max lnprob values: -302.413194 -300.889394\n", "Time to run previous set of walkers (seconds): 0.003058\n", "Step: 839 / 1000\n", "Mean acceptance fraction: 0.627151\n", "Mean lnprob and Max lnprob values: -302.450749 -300.889394\n", "Time to run previous set of walkers (seconds): 0.002608\n", "Step: 840 / 1000\n", "Mean acceptance fraction: 0.627202\n", "Mean lnprob and Max lnprob values: -302.289187 -300.868326\n", "Time to run previous set of walkers (seconds): 0.001872\n", "Step: 841 / 1000\n", "Mean acceptance fraction: 0.627277\n", "Mean lnprob and Max lnprob values: -302.317390 -300.873694\n", "Time to run previous set of walkers (seconds): 0.002065\n", "Step: 842 / 1000\n", "Mean acceptance fraction: 0.627316\n", "Mean lnprob and Max lnprob values: -302.500190 -300.865029\n", "Time to run previous set of walkers (seconds): 0.001858\n", "Step: 843 / 1000\n", "Mean acceptance fraction: 0.627307\n", "Mean lnprob and Max lnprob values: -302.407933 -300.873694\n", "Time to run previous set of walkers (seconds): 0.002113\n", "Step: 844 / 1000\n", "Mean acceptance fraction: 0.627464\n", "Mean lnprob and Max lnprob values: -302.390362 -300.871313\n", "Time to run previous set of walkers (seconds): 0.002593\n", "Step: 845 / 1000\n", "Mean acceptance fraction: 0.627373\n", "Mean lnprob and Max lnprob values: -302.433265 -300.869699\n", "Time to run previous set of walkers (seconds): 0.002126\n", "Step: 846 / 1000\n", "Mean acceptance fraction: 0.627376\n", "Mean lnprob and Max lnprob values: -302.431260 -300.862933\n", "Time to run previous set of walkers (seconds): 0.001800\n", "Step: 847 / 1000\n", "Mean acceptance fraction: 0.627414\n", "Mean lnprob and Max lnprob values: -302.485595 -300.955213\n", "Time to run previous set of walkers (seconds): 0.002216\n", "Step: 848 / 1000\n", "Mean acceptance fraction: 0.627453\n", "Mean lnprob and Max lnprob values: -302.364969 -300.876552\n", "Time to run previous set of walkers (seconds): 0.001813\n", "Step: 849 / 1000\n", "Mean acceptance fraction: 0.627468\n", "Mean lnprob and Max lnprob values: -302.337443 -300.971293\n", "Time to run previous set of walkers (seconds): 0.001777\n", "Step: 850 / 1000\n", "Mean acceptance fraction: 0.627518\n", "Mean lnprob and Max lnprob values: -302.269794 -300.959602\n", "Time to run previous set of walkers (seconds): 0.001827\n", "Step: 851 / 1000\n", "Mean acceptance fraction: 0.627685\n", "Mean lnprob and Max lnprob values: -302.241488 -300.853090\n", "Time to run previous set of walkers (seconds): 0.002208\n", "Step: 852 / 1000\n", "Mean acceptance fraction: 0.627805\n", "Mean lnprob and Max lnprob values: -302.328057 -300.853090\n", "Time to run previous set of walkers (seconds): 0.001773\n", "Step: 853 / 1000\n", "Mean acceptance fraction: 0.627855\n", "Mean lnprob and Max lnprob values: -302.437500 -300.856915\n", "Time to run previous set of walkers (seconds): 0.001738\n", "Step: 854 / 1000\n", "Mean acceptance fraction: 0.627775\n", "Mean lnprob and Max lnprob values: -302.436578 -300.868522\n", "Time to run previous set of walkers (seconds): 0.001990\n", "Step: 855 / 1000\n", "Mean acceptance fraction: 0.627789\n", "Mean lnprob and Max lnprob values: -302.585254 -300.971310\n", "Time to run previous set of walkers (seconds): 0.002419\n", "Step: 856 / 1000\n", "Mean acceptance fraction: 0.627722\n", "Mean lnprob and Max lnprob values: -302.421351 -300.955490\n", "Time to run previous set of walkers (seconds): 0.001898\n", "Step: 857 / 1000\n", "Mean acceptance fraction: 0.627853\n", "Mean lnprob and Max lnprob values: -302.482948 -300.860483\n", "Time to run previous set of walkers (seconds): 0.001889\n", "Step: 858 / 1000\n", "Mean acceptance fraction: 0.627855\n", "Mean lnprob and Max lnprob values: -302.575540 -300.895101\n", "Time to run previous set of walkers (seconds): 0.002554\n", "Step: 859 / 1000\n", "Mean acceptance fraction: 0.627800\n", "Mean lnprob and Max lnprob values: -302.600410 -300.895101\n", "Time to run previous set of walkers (seconds): 0.002316\n", "Step: 860 / 1000\n", "Mean acceptance fraction: 0.627802\n", "Mean lnprob and Max lnprob values: -302.654322 -300.899970\n", "Time to run previous set of walkers (seconds): 0.002071\n", "Step: 861 / 1000\n", "Mean acceptance fraction: 0.627782\n", "Mean lnprob and Max lnprob values: -302.480245 -300.884793\n", "Time to run previous set of walkers (seconds): 0.001996\n", "Step: 862 / 1000\n", "Mean acceptance fraction: 0.627854\n", "Mean lnprob and Max lnprob values: -302.461139 -300.884793\n", "Time to run previous set of walkers (seconds): 0.002207\n", "Step: 863 / 1000\n", "Mean acceptance fraction: 0.627752\n", "Mean lnprob and Max lnprob values: -302.532249 -300.884793\n", "Time to run previous set of walkers (seconds): 0.002010\n", "Step: 864 / 1000\n", "Mean acceptance fraction: 0.627859\n", "Mean lnprob and Max lnprob values: -302.356015 -300.884793\n", "Time to run previous set of walkers (seconds): 0.001951\n", "Step: 865 / 1000\n", "Mean acceptance fraction: 0.627908\n", "Mean lnprob and Max lnprob values: -302.290143 -300.930767\n", "Time to run previous set of walkers (seconds): 0.001969\n", "Step: 866 / 1000\n", "Mean acceptance fraction: 0.627910\n", "Mean lnprob and Max lnprob values: -302.430182 -300.925037\n", "Time to run previous set of walkers (seconds): 0.001915\n", "Step: 867 / 1000\n", "Mean acceptance fraction: 0.627797\n", "Mean lnprob and Max lnprob values: -302.392146 -300.925037\n", "Time to run previous set of walkers (seconds): 0.001796\n", "Step: 868 / 1000\n", "Mean acceptance fraction: 0.627846\n", "Mean lnprob and Max lnprob values: -302.153221 -300.913550\n", "Time to run previous set of walkers (seconds): 0.001822\n", "Step: 869 / 1000\n", "Mean acceptance fraction: 0.627791\n", "Mean lnprob and Max lnprob values: -302.118835 -300.885310\n", "Time to run previous set of walkers (seconds): 0.001941\n", "Step: 870 / 1000\n", "Mean acceptance fraction: 0.627782\n", "Mean lnprob and Max lnprob values: -302.038069 -300.880803\n", "Time to run previous set of walkers (seconds): 0.001849\n", "Step: 871 / 1000\n", "Mean acceptance fraction: 0.627819\n", "Mean lnprob and Max lnprob values: -302.173262 -300.880803\n", "Time to run previous set of walkers (seconds): 0.001770\n", "Step: 872 / 1000\n", "Mean acceptance fraction: 0.627775\n", "Mean lnprob and Max lnprob values: -302.138537 -300.875596\n", "Time to run previous set of walkers (seconds): 0.002037\n", "Step: 873 / 1000\n", "Mean acceptance fraction: 0.627824\n", "Mean lnprob and Max lnprob values: -302.137457 -300.875596\n", "Time to run previous set of walkers (seconds): 0.001820\n", "Step: 874 / 1000\n", "Mean acceptance fraction: 0.627826\n", "Mean lnprob and Max lnprob values: -302.191096 -300.903845\n", "Time to run previous set of walkers (seconds): 0.001765\n", "Step: 875 / 1000\n", "Mean acceptance fraction: 0.627920\n", "Mean lnprob and Max lnprob values: -302.156673 -300.903845\n", "Time to run previous set of walkers (seconds): 0.002019\n", "Step: 876 / 1000\n", "Mean acceptance fraction: 0.627877\n", "Mean lnprob and Max lnprob values: -302.182851 -300.971392\n", "Time to run previous set of walkers (seconds): 0.002249\n", "Step: 877 / 1000\n", "Mean acceptance fraction: 0.627925\n", "Mean lnprob and Max lnprob values: -302.211725 -300.861185\n", "Time to run previous set of walkers (seconds): 0.002007\n", "Step: 878 / 1000\n", "Mean acceptance fraction: 0.627950\n", "Mean lnprob and Max lnprob values: -302.218250 -300.972436\n", "Time to run previous set of walkers (seconds): 0.001802\n", "Step: 879 / 1000\n", "Mean acceptance fraction: 0.628089\n", "Mean lnprob and Max lnprob values: -302.211245 -300.879012\n", "Time to run previous set of walkers (seconds): 0.003530\n", "Step: 880 / 1000\n", "Mean acceptance fraction: 0.628114\n", "Mean lnprob and Max lnprob values: -302.237171 -300.885670\n", "Time to run previous set of walkers (seconds): 0.002386\n", "Step: 881 / 1000\n", "Mean acceptance fraction: 0.628161\n", "Mean lnprob and Max lnprob values: -302.158696 -300.890012\n", "Time to run previous set of walkers (seconds): 0.001910\n", "Step: 882 / 1000\n", "Mean acceptance fraction: 0.628152\n", "Mean lnprob and Max lnprob values: -302.114460 -300.866864\n", "Time to run previous set of walkers (seconds): 0.002384\n", "Step: 883 / 1000\n", "Mean acceptance fraction: 0.628245\n", "Mean lnprob and Max lnprob values: -302.115581 -300.876086\n", "Time to run previous set of walkers (seconds): 0.001865\n", "Step: 884 / 1000\n", "Mean acceptance fraction: 0.628235\n", "Mean lnprob and Max lnprob values: -302.031422 -300.938316\n", "Time to run previous set of walkers (seconds): 0.001818\n", "Step: 885 / 1000\n", "Mean acceptance fraction: 0.628362\n", "Mean lnprob and Max lnprob values: -302.216297 -300.919453\n", "Time to run previous set of walkers (seconds): 0.001835\n", "Step: 886 / 1000\n", "Mean acceptance fraction: 0.628375\n", "Mean lnprob and Max lnprob values: -302.321498 -300.916633\n", "Time to run previous set of walkers (seconds): 0.002214\n", "Step: 887 / 1000\n", "Mean acceptance fraction: 0.628377\n", "Mean lnprob and Max lnprob values: -302.264795 -300.960672\n", "Time to run previous set of walkers (seconds): 0.001783\n", "Step: 888 / 1000\n", "Mean acceptance fraction: 0.628468\n", "Mean lnprob and Max lnprob values: -302.288892 -300.892631\n", "Time to run previous set of walkers (seconds): 0.001880\n", "Step: 889 / 1000\n", "Mean acceptance fraction: 0.628470\n", "Mean lnprob and Max lnprob values: -302.216593 -300.868693\n", "Time to run previous set of walkers (seconds): 0.001951\n", "Step: 890 / 1000\n", "Mean acceptance fraction: 0.628539\n", "Mean lnprob and Max lnprob values: -302.237565 -300.884056\n", "Time to run previous set of walkers (seconds): 0.001761\n", "Step: 891 / 1000\n", "Mean acceptance fraction: 0.628608\n", "Mean lnprob and Max lnprob values: -302.221344 -300.886457\n", "Time to run previous set of walkers (seconds): 0.001856\n", "Step: 892 / 1000\n", "Mean acceptance fraction: 0.628621\n", "Mean lnprob and Max lnprob values: -302.088053 -300.861252\n", "Time to run previous set of walkers (seconds): 0.001883\n", "Step: 893 / 1000\n", "Mean acceptance fraction: 0.628690\n", "Mean lnprob and Max lnprob values: -301.995604 -300.864619\n", "Time to run previous set of walkers (seconds): 0.001907\n", "Step: 894 / 1000\n", "Mean acceptance fraction: 0.628770\n", "Mean lnprob and Max lnprob values: -301.910519 -300.864724\n", "Time to run previous set of walkers (seconds): 0.001757\n", "Step: 895 / 1000\n", "Mean acceptance fraction: 0.628838\n", "Mean lnprob and Max lnprob values: -302.049310 -300.882865\n", "Time to run previous set of walkers (seconds): 0.001941\n", "Step: 896 / 1000\n", "Mean acceptance fraction: 0.628884\n", "Mean lnprob and Max lnprob values: -302.010732 -300.875498\n", "Time to run previous set of walkers (seconds): 0.001860\n", "Step: 897 / 1000\n", "Mean acceptance fraction: 0.628863\n", "Mean lnprob and Max lnprob values: -302.037431 -300.880004\n", "Time to run previous set of walkers (seconds): 0.002149\n", "Step: 898 / 1000\n", "Mean acceptance fraction: 0.628920\n", "Mean lnprob and Max lnprob values: -302.167050 -300.857169\n", "Time to run previous set of walkers (seconds): 0.001883\n", "Step: 899 / 1000\n", "Mean acceptance fraction: 0.628988\n", "Mean lnprob and Max lnprob values: -302.189761 -300.885818\n", "Time to run previous set of walkers (seconds): 0.001766\n", "Step: 900 / 1000\n", "Mean acceptance fraction: 0.628967\n", "Mean lnprob and Max lnprob values: -302.224843 -300.844635\n", "Time to run previous set of walkers (seconds): 0.001745\n", "Step: 901 / 1000\n", "Mean acceptance fraction: 0.628968\n", "Mean lnprob and Max lnprob values: -302.367959 -300.869044\n", "Time to run previous set of walkers (seconds): 0.001820\n", "Step: 902 / 1000\n", "Mean acceptance fraction: 0.628969\n", "Mean lnprob and Max lnprob values: -302.356938 -301.011524\n", "Time to run previous set of walkers (seconds): 0.001965\n", "Step: 903 / 1000\n", "Mean acceptance fraction: 0.629048\n", "Mean lnprob and Max lnprob values: -302.308150 -301.011524\n", "Time to run previous set of walkers (seconds): 0.001780\n", "Step: 904 / 1000\n", "Mean acceptance fraction: 0.629049\n", "Mean lnprob and Max lnprob values: -302.354957 -300.963114\n", "Time to run previous set of walkers (seconds): 0.002156\n", "Step: 905 / 1000\n", "Mean acceptance fraction: 0.629017\n", "Mean lnprob and Max lnprob values: -302.387623 -300.963114\n", "Time to run previous set of walkers (seconds): 0.001904\n", "Step: 906 / 1000\n", "Mean acceptance fraction: 0.629062\n", "Mean lnprob and Max lnprob values: -302.336641 -300.942601\n", "Time to run previous set of walkers (seconds): 0.001877\n", "Step: 907 / 1000\n", "Mean acceptance fraction: 0.629074\n", "Mean lnprob and Max lnprob values: -302.308202 -300.951625\n", "Time to run previous set of walkers (seconds): 0.002114\n", "Step: 908 / 1000\n", "Mean acceptance fraction: 0.629174\n", "Mean lnprob and Max lnprob values: -302.225518 -300.951625\n", "Time to run previous set of walkers (seconds): 0.002214\n", "Step: 909 / 1000\n", "Mean acceptance fraction: 0.629142\n", "Mean lnprob and Max lnprob values: -302.334287 -300.943572\n", "Time to run previous set of walkers (seconds): 0.001860\n", "Step: 910 / 1000\n", "Mean acceptance fraction: 0.629121\n", "Mean lnprob and Max lnprob values: -302.373225 -300.910011\n", "Time to run previous set of walkers (seconds): 0.001877\n", "Step: 911 / 1000\n", "Mean acceptance fraction: 0.629100\n", "Mean lnprob and Max lnprob values: -302.437166 -300.884885\n", "Time to run previous set of walkers (seconds): 0.002096\n", "Step: 912 / 1000\n", "Mean acceptance fraction: 0.629002\n", "Mean lnprob and Max lnprob values: -302.344444 -300.884885\n", "Time to run previous set of walkers (seconds): 0.001796\n", "Step: 913 / 1000\n", "Mean acceptance fraction: 0.629091\n", "Mean lnprob and Max lnprob values: -302.495759 -300.887897\n", "Time to run previous set of walkers (seconds): 0.001834\n", "Step: 914 / 1000\n", "Mean acceptance fraction: 0.629147\n", "Mean lnprob and Max lnprob values: -302.430580 -300.923201\n", "Time to run previous set of walkers (seconds): 0.001947\n", "Step: 915 / 1000\n", "Mean acceptance fraction: 0.629071\n", "Mean lnprob and Max lnprob values: -302.396610 -300.917426\n", "Time to run previous set of walkers (seconds): 0.001791\n", "Step: 916 / 1000\n", "Mean acceptance fraction: 0.629061\n", "Mean lnprob and Max lnprob values: -302.305831 -300.917426\n", "Time to run previous set of walkers (seconds): 0.002105\n", "Step: 917 / 1000\n", "Mean acceptance fraction: 0.629062\n", "Mean lnprob and Max lnprob values: -302.249513 -301.012947\n", "Time to run previous set of walkers (seconds): 0.002043\n", "Step: 918 / 1000\n", "Mean acceptance fraction: 0.629063\n", "Mean lnprob and Max lnprob values: -302.282871 -300.896468\n", "Time to run previous set of walkers (seconds): 0.002016\n", "Step: 919 / 1000\n", "Mean acceptance fraction: 0.629173\n", "Mean lnprob and Max lnprob values: -302.259968 -300.935424\n", "Time to run previous set of walkers (seconds): 0.001917\n", "Step: 920 / 1000\n", "Mean acceptance fraction: 0.629141\n", "Mean lnprob and Max lnprob values: -302.318360 -300.957361\n", "Time to run previous set of walkers (seconds): 0.002007\n", "Step: 921 / 1000\n", "Mean acceptance fraction: 0.629055\n", "Mean lnprob and Max lnprob values: -302.379884 -300.947058\n", "Time to run previous set of walkers (seconds): 0.001942\n", "Step: 922 / 1000\n", "Mean acceptance fraction: 0.629132\n", "Mean lnprob and Max lnprob values: -302.440931 -300.867922\n", "Time to run previous set of walkers (seconds): 0.001964\n", "Step: 923 / 1000\n", "Mean acceptance fraction: 0.629079\n", "Mean lnprob and Max lnprob values: -302.477670 -300.858404\n", "Time to run previous set of walkers (seconds): 0.001773\n", "Step: 924 / 1000\n", "Mean acceptance fraction: 0.629102\n", "Mean lnprob and Max lnprob values: -302.545841 -300.964070\n", "Time to run previous set of walkers (seconds): 0.001843\n", "Step: 925 / 1000\n", "Mean acceptance fraction: 0.629114\n", "Mean lnprob and Max lnprob values: -302.587337 -300.889818\n", "Time to run previous set of walkers (seconds): 0.002143\n", "Step: 926 / 1000\n", "Mean acceptance fraction: 0.629082\n", "Mean lnprob and Max lnprob values: -302.465235 -300.905168\n", "Time to run previous set of walkers (seconds): 0.001951\n", "Step: 927 / 1000\n", "Mean acceptance fraction: 0.629083\n", "Mean lnprob and Max lnprob values: -302.359158 -300.880591\n", "Time to run previous set of walkers (seconds): 0.001830\n", "Step: 928 / 1000\n", "Mean acceptance fraction: 0.629084\n", "Mean lnprob and Max lnprob values: -302.402644 -300.891051\n", "Time to run previous set of walkers (seconds): 0.001870\n", "Step: 929 / 1000\n", "Mean acceptance fraction: 0.629171\n", "Mean lnprob and Max lnprob values: -302.492632 -300.869640\n", "Time to run previous set of walkers (seconds): 0.002094\n", "Step: 930 / 1000\n", "Mean acceptance fraction: 0.629204\n", "Mean lnprob and Max lnprob values: -302.443101 -300.869640\n", "Time to run previous set of walkers (seconds): 0.001867\n", "Step: 931 / 1000\n", "Mean acceptance fraction: 0.629151\n", "Mean lnprob and Max lnprob values: -302.380294 -300.963728\n", "Time to run previous set of walkers (seconds): 0.001841\n", "Step: 932 / 1000\n", "Mean acceptance fraction: 0.629120\n", "Mean lnprob and Max lnprob values: -302.433418 -300.936140\n", "Time to run previous set of walkers (seconds): 0.002013\n", "Step: 933 / 1000\n", "Mean acceptance fraction: 0.629164\n", "Mean lnprob and Max lnprob values: -302.440642 -300.909549\n", "Time to run previous set of walkers (seconds): 0.001772\n", "Step: 934 / 1000\n", "Mean acceptance fraction: 0.629197\n", "Mean lnprob and Max lnprob values: -302.404948 -300.885268\n", "Time to run previous set of walkers (seconds): 0.001887\n", "Step: 935 / 1000\n", "Mean acceptance fraction: 0.629262\n", "Mean lnprob and Max lnprob values: -302.406448 -300.867835\n", "Time to run previous set of walkers (seconds): 0.001818\n", "Step: 936 / 1000\n", "Mean acceptance fraction: 0.629263\n", "Mean lnprob and Max lnprob values: -302.487047 -300.886308\n", "Time to run previous set of walkers (seconds): 0.001849\n", "Step: 937 / 1000\n", "Mean acceptance fraction: 0.629242\n", "Mean lnprob and Max lnprob values: -302.370896 -300.879468\n", "Time to run previous set of walkers (seconds): 0.001902\n", "Step: 938 / 1000\n", "Mean acceptance fraction: 0.629232\n", "Mean lnprob and Max lnprob values: -302.326263 -300.896042\n", "Time to run previous set of walkers (seconds): 0.001775\n", "Step: 939 / 1000\n", "Mean acceptance fraction: 0.629159\n", "Mean lnprob and Max lnprob values: -302.246918 -300.941728\n", "Time to run previous set of walkers (seconds): 0.001958\n", "Step: 940 / 1000\n", "Mean acceptance fraction: 0.629149\n", "Mean lnprob and Max lnprob values: -302.324819 -300.901323\n", "Time to run previous set of walkers (seconds): 0.001982\n", "Step: 941 / 1000\n", "Mean acceptance fraction: 0.629182\n", "Mean lnprob and Max lnprob values: -302.140180 -300.895113\n", "Time to run previous set of walkers (seconds): 0.001765\n", "Step: 942 / 1000\n", "Mean acceptance fraction: 0.629214\n", "Mean lnprob and Max lnprob values: -302.154473 -300.861569\n", "Time to run previous set of walkers (seconds): 0.001733\n", "Step: 943 / 1000\n", "Mean acceptance fraction: 0.629236\n", "Mean lnprob and Max lnprob values: -302.139838 -300.899930\n", "Time to run previous set of walkers (seconds): 0.002049\n", "Step: 944 / 1000\n", "Mean acceptance fraction: 0.629280\n", "Mean lnprob and Max lnprob values: -302.190753 -300.944610\n", "Time to run previous set of walkers (seconds): 0.002369\n", "Step: 945 / 1000\n", "Mean acceptance fraction: 0.629259\n", "Mean lnprob and Max lnprob values: -302.272718 -300.892278\n", "Time to run previous set of walkers (seconds): 0.002007\n", "Step: 946 / 1000\n", "Mean acceptance fraction: 0.629281\n", "Mean lnprob and Max lnprob values: -302.295416 -300.885437\n", "Time to run previous set of walkers (seconds): 0.001853\n", "Step: 947 / 1000\n", "Mean acceptance fraction: 0.629324\n", "Mean lnprob and Max lnprob values: -302.447404 -300.894928\n", "Time to run previous set of walkers (seconds): 0.002228\n", "Step: 948 / 1000\n", "Mean acceptance fraction: 0.629325\n", "Mean lnprob and Max lnprob values: -302.419402 -300.918733\n", "Time to run previous set of walkers (seconds): 0.001937\n", "Step: 949 / 1000\n", "Mean acceptance fraction: 0.629347\n", "Mean lnprob and Max lnprob values: -302.355948 -300.874778\n", "Time to run previous set of walkers (seconds): 0.001825\n", "Step: 950 / 1000\n", "Mean acceptance fraction: 0.629358\n", "Mean lnprob and Max lnprob values: -302.297280 -300.901924\n", "Time to run previous set of walkers (seconds): 0.001978\n", "Step: 951 / 1000\n", "Mean acceptance fraction: 0.629443\n", "Mean lnprob and Max lnprob values: -302.308002 -300.861185\n", "Time to run previous set of walkers (seconds): 0.002503\n", "Step: 952 / 1000\n", "Mean acceptance fraction: 0.629569\n", "Mean lnprob and Max lnprob values: -302.322906 -300.863455\n", "Time to run previous set of walkers (seconds): 0.002324\n", "Step: 953 / 1000\n", "Mean acceptance fraction: 0.629601\n", "Mean lnprob and Max lnprob values: -302.216590 -300.867570\n", "Time to run previous set of walkers (seconds): 0.002314\n", "Step: 954 / 1000\n", "Mean acceptance fraction: 0.629665\n", "Mean lnprob and Max lnprob values: -302.245853 -300.867570\n", "Time to run previous set of walkers (seconds): 0.001886\n", "Step: 955 / 1000\n", "Mean acceptance fraction: 0.629749\n", "Mean lnprob and Max lnprob values: -302.202527 -300.864802\n", "Time to run previous set of walkers (seconds): 0.001919\n", "Step: 956 / 1000\n", "Mean acceptance fraction: 0.629780\n", "Mean lnprob and Max lnprob values: -302.253296 -300.915560\n", "Time to run previous set of walkers (seconds): 0.001863\n", "Step: 957 / 1000\n", "Mean acceptance fraction: 0.629843\n", "Mean lnprob and Max lnprob values: -302.366546 -300.882547\n", "Time to run previous set of walkers (seconds): 0.001987\n", "Step: 958 / 1000\n", "Mean acceptance fraction: 0.629843\n", "Mean lnprob and Max lnprob values: -302.371398 -300.906405\n", "Time to run previous set of walkers (seconds): 0.001899\n", "Step: 959 / 1000\n", "Mean acceptance fraction: 0.629771\n", "Mean lnprob and Max lnprob values: -302.430225 -300.903294\n", "Time to run previous set of walkers (seconds): 0.001763\n", "Step: 960 / 1000\n", "Mean acceptance fraction: 0.629812\n", "Mean lnprob and Max lnprob values: -302.484945 -300.903273\n", "Time to run previous set of walkers (seconds): 0.001819\n", "Step: 961 / 1000\n", "Mean acceptance fraction: 0.629740\n", "Mean lnprob and Max lnprob values: -302.595170 -300.881940\n", "Time to run previous set of walkers (seconds): 0.001798\n", "Step: 962 / 1000\n", "Mean acceptance fraction: 0.629709\n", "Mean lnprob and Max lnprob values: -302.544805 -300.882480\n", "Time to run previous set of walkers (seconds): 0.001765\n", "Step: 963 / 1000\n", "Mean acceptance fraction: 0.629688\n", "Mean lnprob and Max lnprob values: -302.483725 -300.882480\n", "Time to run previous set of walkers (seconds): 0.001858\n", "Step: 964 / 1000\n", "Mean acceptance fraction: 0.629699\n", "Mean lnprob and Max lnprob values: -302.350619 -300.922558\n", "Time to run previous set of walkers (seconds): 0.002133\n", "Step: 965 / 1000\n", "Mean acceptance fraction: 0.629699\n", "Mean lnprob and Max lnprob values: -302.335491 -300.923756\n", "Time to run previous set of walkers (seconds): 0.001872\n", "Step: 966 / 1000\n", "Mean acceptance fraction: 0.629607\n", "Mean lnprob and Max lnprob values: -302.204683 -300.853399\n", "Time to run previous set of walkers (seconds): 0.001894\n", "Step: 967 / 1000\n", "Mean acceptance fraction: 0.629628\n", "Mean lnprob and Max lnprob values: -302.223527 -300.853399\n", "Time to run previous set of walkers (seconds): 0.001830\n", "Step: 968 / 1000\n", "Mean acceptance fraction: 0.629680\n", "Mean lnprob and Max lnprob values: -302.219743 -300.857471\n", "Time to run previous set of walkers (seconds): 0.002145\n", "Step: 969 / 1000\n", "Mean acceptance fraction: 0.629680\n", "Mean lnprob and Max lnprob values: -302.248742 -300.887592\n", "Time to run previous set of walkers (seconds): 0.002459\n", "Step: 970 / 1000\n", "Mean acceptance fraction: 0.629794\n", "Mean lnprob and Max lnprob values: -302.270840 -300.958244\n", "Time to run previous set of walkers (seconds): 0.001904\n", "Step: 971 / 1000\n", "Mean acceptance fraction: 0.629876\n", "Mean lnprob and Max lnprob values: -302.364550 -300.987109\n", "Time to run previous set of walkers (seconds): 0.002017\n", "Step: 972 / 1000\n", "Mean acceptance fraction: 0.629815\n", "Mean lnprob and Max lnprob values: -302.420595 -300.966817\n", "Time to run previous set of walkers (seconds): 0.001789\n", "Step: 973 / 1000\n", "Mean acceptance fraction: 0.629815\n", "Mean lnprob and Max lnprob values: -302.350958 -300.905586\n", "Time to run previous set of walkers (seconds): 0.001827\n", "Step: 974 / 1000\n", "Mean acceptance fraction: 0.629836\n", "Mean lnprob and Max lnprob values: -302.260169 -300.896331\n", "Time to run previous set of walkers (seconds): 0.001811\n", "Step: 975 / 1000\n", "Mean acceptance fraction: 0.629815\n", "Mean lnprob and Max lnprob values: -302.282851 -300.909235\n", "Time to run previous set of walkers (seconds): 0.002240\n", "Step: 976 / 1000\n", "Mean acceptance fraction: 0.629867\n", "Mean lnprob and Max lnprob values: -302.364023 -300.870473\n", "Time to run previous set of walkers (seconds): 0.001825\n", "Step: 977 / 1000\n", "Mean acceptance fraction: 0.629887\n", "Mean lnprob and Max lnprob values: -302.249280 -300.949901\n", "Time to run previous set of walkers (seconds): 0.001759\n", "Step: 978 / 1000\n", "Mean acceptance fraction: 0.629857\n", "Mean lnprob and Max lnprob values: -302.165621 -300.874364\n", "Time to run previous set of walkers (seconds): 0.002338\n", "Step: 979 / 1000\n", "Mean acceptance fraction: 0.629877\n", "Mean lnprob and Max lnprob values: -302.213289 -300.878891\n", "Time to run previous set of walkers (seconds): 0.001880\n", "Step: 980 / 1000\n", "Mean acceptance fraction: 0.629827\n", "Mean lnprob and Max lnprob values: -302.292772 -300.890174\n", "Time to run previous set of walkers (seconds): 0.001780\n", "Step: 981 / 1000\n", "Mean acceptance fraction: 0.629888\n", "Mean lnprob and Max lnprob values: -302.333553 -300.890174\n", "Time to run previous set of walkers (seconds): 0.001812\n", "Step: 982 / 1000\n", "Mean acceptance fraction: 0.629929\n", "Mean lnprob and Max lnprob values: -302.260463 -300.872313\n", "Time to run previous set of walkers (seconds): 0.002709\n", "Step: 983 / 1000\n", "Mean acceptance fraction: 0.630010\n", "Mean lnprob and Max lnprob values: -302.364109 -300.875059\n", "Time to run previous set of walkers (seconds): 0.001986\n", "Step: 984 / 1000\n", "Mean acceptance fraction: 0.629929\n", "Mean lnprob and Max lnprob values: -302.376321 -300.918472\n", "Time to run previous set of walkers (seconds): 0.002320\n", "Step: 985 / 1000\n", "Mean acceptance fraction: 0.629939\n", "Mean lnprob and Max lnprob values: -302.271141 -300.980578\n", "Time to run previous set of walkers (seconds): 0.002415\n", "Step: 986 / 1000\n", "Mean acceptance fraction: 0.629939\n", "Mean lnprob and Max lnprob values: -302.262286 -300.873160\n", "Time to run previous set of walkers (seconds): 0.001945\n", "Step: 987 / 1000\n", "Mean acceptance fraction: 0.629899\n", "Mean lnprob and Max lnprob values: -302.236551 -300.869947\n", "Time to run previous set of walkers (seconds): 0.001812\n", "Step: 988 / 1000\n", "Mean acceptance fraction: 0.629949\n", "Mean lnprob and Max lnprob values: -302.300054 -300.918242\n", "Time to run previous set of walkers (seconds): 0.001892\n", "Step: 989 / 1000\n", "Mean acceptance fraction: 0.629929\n", "Mean lnprob and Max lnprob values: -302.323666 -300.915258\n", "Time to run previous set of walkers (seconds): 0.001888\n", "Step: 990 / 1000\n", "Mean acceptance fraction: 0.629949\n", "Mean lnprob and Max lnprob values: -302.417948 -300.896443\n", "Time to run previous set of walkers (seconds): 0.001861\n", "Step: 991 / 1000\n", "Mean acceptance fraction: 0.629990\n", "Mean lnprob and Max lnprob values: -302.417189 -300.868979\n", "Time to run previous set of walkers (seconds): 0.001823\n", "Step: 992 / 1000\n", "Mean acceptance fraction: 0.630020\n", "Mean lnprob and Max lnprob values: -302.337477 -300.859739\n", "Time to run previous set of walkers (seconds): 0.001913\n", "Step: 993 / 1000\n", "Mean acceptance fraction: 0.630010\n", "Mean lnprob and Max lnprob values: -302.259316 -300.893361\n", "Time to run previous set of walkers (seconds): 0.002082\n", "Step: 994 / 1000\n", "Mean acceptance fraction: 0.629990\n", "Mean lnprob and Max lnprob values: -302.233038 -300.878619\n", "Time to run previous set of walkers (seconds): 0.001935\n", "Step: 995 / 1000\n", "Mean acceptance fraction: 0.629970\n", "Mean lnprob and Max lnprob values: -302.221722 -300.906305\n", "Time to run previous set of walkers (seconds): 0.002406\n", "Step: 996 / 1000\n", "Mean acceptance fraction: 0.629920\n", "Mean lnprob and Max lnprob values: -302.248309 -300.856796\n", "Time to run previous set of walkers (seconds): 0.002219\n", "Step: 997 / 1000\n", "Mean acceptance fraction: 0.629980\n", "Mean lnprob and Max lnprob values: -302.240812 -300.870479\n", "Time to run previous set of walkers (seconds): 0.001986\n", "Step: 998 / 1000\n", "Mean acceptance fraction: 0.630050\n", "Mean lnprob and Max lnprob values: -302.301180 -300.866969\n", "Time to run previous set of walkers (seconds): 0.002027\n", "Step: 999 / 1000\n", "Mean acceptance fraction: 0.630050\n", "Mean lnprob and Max lnprob values: -302.365545 -300.898235\n", "Time to run previous set of walkers (seconds): 0.002125\n", "Step: 1000 / 1000\n", "Mean acceptance fraction: 0.630150\n", "Mean lnprob and Max lnprob values: -302.384315 -300.898467\n", "Time to run previous set of walkers (seconds): 0.001817\n" ] } ], "source": [ "currenttime = time.time()\n", "Step = 1\n", "for pos, prob, state in sampler.sample(\n", " starting_guesses, iterations=nsteps):\n", " print('Step:', Step, '/', nsteps)\n", " print(\"Mean acceptance fraction: %f\" % (np.mean(\n", " sampler.acceptance_fraction\n", " )))\n", " print(\"Mean lnprob and Max lnprob values: %f %f\" % (\n", " np.mean(prob), np.max(prob)\n", " ))\n", " print(\n", " \"Time to run previous set of walkers (seconds): %f\" %\n", " (time.time() - currenttime))\n", " currenttime = time.time()\n", " Step += 1" ] }, { "cell_type": "code", "execution_count": 146, "id": "aebf4d73", "metadata": {}, "outputs": [], "source": [ "emcee_trace = sampler.chain[:, :, :].reshape(\n", " (-1, ndim))\n", "lnprob = sampler.lnprobability" ] }, { "cell_type": "code", "execution_count": null, "id": "7b8f5947", "metadata": {}, "outputs": [], "source": [ "theta = emcee_trace[np.argmax(lnprob)] # best fit parameters" ] }, { "cell_type": "code", "execution_count": 148, "id": "85572e2f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_86016/792421300.py:21: UserWarning: Attempt to set non-positive ylim on a log-scaled axis will be ignored.\n", " plt.ylim(min(y), max(y))\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.array([])\n", "y = np.array([])\n", "maxlnprob = np.max(lnprob)\n", "for i in range(len(lnprob)):\n", " x = np.append(x, range(len(lnprob[i])))\n", " y = np.append(y, maxlnprob - lnprob[i])\n", "plt.figure()\n", "plt.hexbin(\n", " x[y > 0],\n", " y[y > 0],\n", " gridsize=[70, 30],\n", " cmap='inferno',\n", " bins='log',\n", " mincnt=1,\n", " yscale='log',\n", " linewidths=0)\n", "plt.ylabel('maxlnprob -lnprob')\n", "plt.xlabel('iteration')\n", "try:\n", " plt.xlim(min(x), max(x))\n", " plt.ylim(min(y), max(y))\n", "except Exception:\n", " print('Negative values in Convergence....')" ] }, { "cell_type": "code", "execution_count": 149, "id": "4e3c7fb9", "metadata": {}, "outputs": [], "source": [ "ll = ['a', 'b', 'c']" ] }, { "cell_type": "code", "execution_count": 150, "id": "e48448b6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for ID in range(ndim):\n", " plt.figure()\n", " x = np.array([])\n", " y = np.array([])\n", " for i in sampler.chain:\n", " x = np.append(x, range(len(i.T[ID])))\n", " y = np.append(y, i.T[ID])\n", "\n", " plt.figure()\n", " if (max(y) / min(y)) > 50 and len(y[y < 0]) < 1:\n", " plt.hexbin(\n", " x,\n", " y,\n", " gridsize=[70, 30],\n", " cmap='inferno',\n", " bins='log',\n", " mincnt=1,\n", " yscale='log',\n", " linewidths=0\n", " )\n", " else:\n", " plt.hexbin(\n", " x,\n", " y,\n", " gridsize=[70, 30],\n", " cmap='inferno',\n", " bins='log',\n", " mincnt=1,\n", " linewidths=0\n", " )\n", " plt.ylabel(ll[ID])\n", " plt.xlabel('iteration')\n", " plt.xlim(min(x), max(x))\n", " plt.ylim(min(y), max(y))" ] }, { "cell_type": "code", "execution_count": 157, "id": "e018f167", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "samples = sampler.chain[:, nburn:, :].reshape(\n", " (-1, ndim))\n", "\n", "fig = corner.corner(\n", " samples,\n", " labels=ll,\n", " title_kwargs={'y': 1.05},\n", " title_fmt=\".2f\",\n", " use_math_text=True,\n", " bins=15,\n", " quantiles=[0.16, 0.5, 0.84],\n", " show_titles=True,\n", " color='DarkOrange',\n", " hist_kwargs={'color': 'black', 'linewidth': 1.5},\n", " contour_kwargs={'linewidths': 1, 'colors': 'black'}\n", ")" ] }, { "cell_type": "code", "execution_count": 154, "id": "c31040d7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 154, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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0199.788509197.508949199.737729201.801712198.4861298.67193552.02084251.96194952.0197352.07966352.0266210.1722894.9184224.847624.9123244.9721864.8826470.19456
\n", "
" ], "text/plain": [ " a_bestfit a_16 a_50 a_84 a_mean a_err \\\n", "0 199.788509 197.508949 199.737729 201.801712 198.486129 8.671935 \n", "\n", " b_bestfit b_16 b_50 b_84 b_mean b_err c_bestfit \\\n", "0 52.020842 51.961949 52.01973 52.079663 52.026621 0.172289 4.918422 \n", "\n", " c_16 c_50 c_84 c_mean c_err \n", "0 4.84762 4.912324 4.972186 4.882647 0.19456 " ] }, "execution_count": 156, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_results = pd.DataFrame()\n", "\n", "new_row = {}\n", "\n", "for i in range(len(ll)):\n", " new_row[ll[i] + '_bestfit'] = emcee_trace[np.argmax(lnprob)][i]\n", " new_row[ll[i] + '_16'] = np.percentile(emcee_trace.T[i], 16)\n", " new_row[ll[i] + '_50'] = np.percentile(emcee_trace.T[i], 50)\n", " new_row[ll[i] + '_84'] = np.percentile(emcee_trace.T[i], 84)\n", " new_row[ll[i] + '_mean'] = np.mean(emcee_trace.T[i])\n", " new_row[ll[i] + '_err'] = np.std(emcee_trace.T[i])\n", "\n", "df_results = pd.concat([df_results, pd.DataFrame([new_row])], ignore_index=True)\n", "df_results\n" ] } ], "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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 5 }