Bilby-MCMC-I.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bilby-MCMC I: introduction\n",
    "\n",
    "In this tutorial, we will get started with the [Bilby-MCMC](https://arxiv.org/abs/2106.08730) sampler. Unlike the other samples available through Bilby, Bilby-MCMC is part of the Bilby package itself, yuo can find the [implementation here](https://git.ligo.org/lscsoft/bilby/-/tree/master/bilby/bilby_mcmc).\n",
    "\n",
    "## Setup\n",
    "\n",
    "As usual, we will begin by importing the modules we need, setting up inline plots in `jupyter`, and clearing any existing output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bilby\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "outdir = \"outdir_Bilby-MCMC_I\"\n",
    "! rm -rf outdir_Bilby-MCMC_I/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulate the data\n",
    "Okay, now let's create some example problem. We'll use a sinusoid in white Gaussian noise with a known variance for this problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First define the signal model\n",
    "def signal(time, amplitude, frequency):\n",
    "    return amplitude * np.sin(2 * np.pi * frequency * time)\n",
    "\n",
    "\n",
    "# Now we define the injection parameters which we make simulated data with\n",
    "simulation_parameters = dict(amplitude=3, frequency=1)\n",
    "\n",
    "# For this example, we'll use standard Gaussian noise\n",
    "\n",
    "# These lines of code generate the fake data. Note the ** just unpacks the\n",
    "# contents of the simulation_parameters when calling the model function.\n",
    "sampling_frequency = 16\n",
    "time_duration = 10\n",
    "time = np.arange(0, time_duration, 1 / sampling_frequency)\n",
    "N = len(time)\n",
    "sigma = np.random.normal(1, 0.01, N)\n",
    "data = signal(time, **simulation_parameters) + np.random.normal(0, sigma, N)\n",
    "\n",
    "# We quickly plot the data to check it looks sensible\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(time, data, 'o', label='data')\n",
    "ax.plot(time, signal(time, **simulation_parameters), '--r', label='signal')\n",
    "ax.set_xlabel('time')\n",
    "ax.set_ylabel('y')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the likelihood and priors\n",
    "\n",
    "Okay, the final setup step is to create the `likelihood` and `prior` objects. Here, we using a Gaussian likelihood with a known standard deviation `sigma` and uniform priors over the three model parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "likelihood = bilby.likelihood.GaussianLikelihood(time, data, signal, sigma=sigma)\n",
    "\n",
    "priors = dict()\n",
    "priors['amplitude'] = bilby.core.prior.Uniform(0, 5, 'amplitude')\n",
    "priors['frequency'] = bilby.core.prior.Uniform(0, 2, 'frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Bilby-MCMC: initial example\n",
    "\n",
    "To use the `bilby_mcmc` sampler, we pass `bilby_mcmc` as the `sampler` argument to `run_sampler`. We have also set a few others options and provided comments about why:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13:04 bilby INFO    : Running for label 'runA', output will be saved to 'outdir_Bilby-MCMC_I'\n",
      "13:04 bilby INFO    : Search parameters:\n",
      "13:04 bilby INFO    :   amplitude = Uniform(minimum=0, maximum=5, name='amplitude', latex_label='amplitude', unit=None, boundary=None)\n",
      "13:04 bilby INFO    :   frequency = Uniform(minimum=0, maximum=2, name='frequency', latex_label='frequency', unit=None, boundary=None)\n",
      "13:04 bilby INFO    : Single likelihood evaluation took 2.594e-04 s\n",
      "13:04 bilby INFO    : Using sampler Bilby_MCMC with kwargs {'nsamples': 500, 'nensemble': 1, 'pt_ensemble': False, 'ntemps': 6, 'Tmax': None, 'Tmax_from_SNR': 20, 'initial_betas': None, 'adapt': False, 'adapt_t0': 100, 'adapt_nu': 10, 'pt_rejection_sample': False, 'burn_in_nact': 10, 'thin_by_nact': 1, 'fixed_discard': 0, 'autocorr_c': 5, 'L1steps': 100, 'L2steps': 3, 'npool': 1, 'printdt': 5, 'min_tau': 1, 'proposal_cycle': 'default', 'stop_after_convergence': False, 'fixed_tau': None, 'tau_window': None, 'evidence_method': 'stepping_stone'}\n",
      "13:04 bilby INFO    : Initializing BilbyPTMCMCSampler with:\n",
      "  Convergence settings: ConvergenceInputs(autocorr_c=5, burn_in_nact=10, thin_by_nact=1, fixed_discard=0, target_nsamples=500, stop_after_convergence=False, L1steps=100, L2steps=3, min_tau=1, fixed_tau=None, tau_window=None)\n",
      "  Parallel-tempering settings: ParallelTemperingInputs(ntemps=6, nensemble=1, Tmax=None, Tmax_from_SNR=20, initial_betas=None, adapt=False, adapt_t0=100, adapt_nu=10, pt_ensemble=False)\n",
      "  proposal_cycle: default\n",
      "  pt_rejection_sample: False\n",
      "13:04 bilby INFO    : Setting parallel tempering inputs=ParallelTemperingInputs(ntemps=6, nensemble=1, Tmax=None, Tmax_from_SNR=20, initial_betas=None, adapt=False, adapt_t0=100, adapt_nu=10, pt_ensemble=False)\n",
      "13:04 bilby INFO    : Initializing BilbyPTMCMCSampler with:ntemps=6,nensemble=1,pt_ensemble=False,initial_betas=[1.         0.34657242 0.12011244 0.04162766 0.014427   0.005     ]\n",
      "\n",
      "13:04 bilby INFO    : Using ProposalCycle:\n",
      "  AdaptiveGaussianProposal(acceptance_ratio:nan,n:0,scale:1,)\n",
      "  DifferentialEvolutionProposal(acceptance_ratio:nan,n:0,)\n",
      "  UniformProposal(acceptance_ratio:nan,n:0,)\n",
      "  KDEProposal(acceptance_ratio:nan,n:0,trained:0,)\n",
      "  GMMProposal(acceptance_ratio:nan,n:0,trained:0,)\n",
      "  NormalizingFlowProposal(acceptance_ratio:nan,n:0,trained:0,)\n",
      "\n",
      "13:04 bilby INFO    : Setting convergence_inputs=ConvergenceInputs(autocorr_c=5, burn_in_nact=10, thin_by_nact=1, fixed_discard=0, target_nsamples=500, stop_after_convergence=False, L1steps=100, L2steps=3, min_tau=1, fixed_tau=None, tau_window=None)\n",
      "13:04 bilby INFO    : Drawing 500 samples\n",
      "13:04 bilby INFO    : Checkpoint every 1800s\n",
      "13:04 bilby INFO    : Print update every 5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.40e+01|0:00:05|5.40e+01(10tau)|t=1|n=20|a=0.10|e=4.5e-02%|0.68ms/ev|maxl=-230.91|ETF=0:00:32\n",
      "1.47e+02|0:00:10|1.27e+02(z1)|t=1|n=20|a=0.12|e=2.3e-02%|0.69ms/ev|maxl=-230.91|ETF=0:00:32\n",
      "2.20e+02|0:00:15|1.27e+02(z1)|t=1|n=93|a=0.13|e=7.0e-02%|0.69ms/ev|maxl=-230.91|ETF=0:00:28\n",
      "2.91e+02|0:00:20|1.27e+02(z1)|t=1|n=164|a=0.13|e=9.4e-02%|0.71ms/ev|maxl=-230.91|ETF=0:00:23\n",
      "3.62e+02|0:00:25|1.27e+02(z1)|t=1|n=235|a=0.14|e=1.1e-01%|0.71ms/ev|maxl=-230.91|ETF=0:00:18\n",
      "4.31e+02|0:00:30|1.27e+02(z1)|t=1|n=304|a=0.14|e=1.2e-01%|0.73ms/ev|maxl=-230.91|ETF=0:00:14\n",
      "5.04e+02|0:00:35|1.27e+02(z1)|t=1|n=377|a=0.14|e=1.2e-01%|0.69ms/ev|maxl=-230.91|ETF=0:00:08\n",
      "5.75e+02|0:00:40|1.27e+02(z1)|t=1|n=448|a=0.14|e=1.3e-01%|0.71ms/ev|maxl=-230.91|ETF=0:00:03\n",
      "6.47e+02|0:00:45|1.27e+02(z1)|t=1|n=520|a=0.14|e=1.3e-01%|0.70ms/ev|maxl=-230.91|ETF=0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13:05 bilby INFO    : Reached convergence: exiting sampling\n",
      "13:05 bilby INFO    : Checkpoint start\n",
      "13:05 bilby INFO    : Written checkpoint file outdir_Bilby-MCMC_I/runA_resume.pickle\n",
      "13:05 bilby INFO    : Zero-temperature proposals:\n",
      "13:05 bilby INFO    : AdaptiveGaussianProposal(acceptance_ratio:0.23,n:1.3e+04,scale:0.0027,)\n",
      "13:05 bilby INFO    : DifferentialEvolutionProposal(acceptance_ratio:0.47,n:1.3e+04,)\n",
      "13:05 bilby INFO    : UniformProposal(acceptance_ratio:1,n:91,)\n",
      "13:05 bilby INFO    : KDEProposal(acceptance_ratio:7.6e-05,n:1.3e+04,trained:0,)\n",
      "13:05 bilby INFO    : GMMProposal(acceptance_ratio:0,n:1.3e+04,trained:0,)\n",
      "13:05 bilby INFO    : NormalizingFlowProposal(acceptance_ratio:0,n:1.3e+04,trained:0,)\n",
      "13:05 bilby INFO    : Current taus={'amplitude': 1, 'frequency': 1}\n",
      "13:05 bilby INFO    : Temperature swaps = 215\n",
      "13:05 bilby INFO    : Temp:0<->1|beta=1|hot-samp=520|swap=0.52|conv=True|\n",
      "13:05 bilby INFO    : Temp:1<->2|beta=0.3466|hot-samp=611|swap=0.52|conv=True|\n",
      "13:05 bilby INFO    : Temp:2<->3|beta=0.1201|hot-samp=635|swap=0.52|conv=True|\n",
      "13:05 bilby INFO    : Temp:3<->4|beta=0.04163|hot-samp=634|swap=0.44|conv=True|\n",
      "13:05 bilby INFO    : Temp:4<->5|beta=0.01443|hot-samp=328|swap=0.30|conv=False|\n",
      "13:05 bilby INFO    : Temp:5<->6|beta=0.005|hot-samp=637|swap=-|conv=True|\n",
      "13:05 bilby INFO    : Creating diagnostic plots\n",
      "13:05 bilby INFO    : Log-evidence of -238.15+/-0.57 calculated using thermodynamic method\n",
      "13:05 bilby INFO    : Log-evidence of -236.83+/-0.11 calculated using stepping_stone method\n",
      "13:05 bilby INFO    : Checkpoint finished\n",
      "13:05 bilby INFO    : Log-evidence of -238.15+/-0.57 calculated using thermodynamic method\n",
      "13:05 bilby INFO    : Log-evidence of -236.83+/-0.12 calculated using stepping_stone method\n",
      "13:05 bilby INFO    : Sampling time: 0:00:45.251680\n",
      "13:05 bilby INFO    : Summary of results:\n",
      "nsamples: 520\n",
      "ln_noise_evidence:    nan\n",
      "ln_evidence: -236.829 +/-  0.121\n",
      "ln_bayes_factor:    nan +/-  0.121\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result = bilby.run_sampler(\n",
    "    likelihood=likelihood,\n",
    "    priors=priors,\n",
    "    injection_parameters=simulation_parameters,  # Used in plots to show the \"true\" values\n",
    "    outdir=outdir,  # The directory to store the results\n",
    "    label=\"runA\",  # A label to name results/plots with\n",
    "    sampler='bilby_mcmc',  # Use the bilby_mcmc sampler\n",
    "    printdt=5,  # Print an update on the progress every 5 seconds\n",
    "    nsamples=500,  # The number of effective samples to draw before stopping\n",
    "    ntemps=6,  # The number of parallel-temperatures to use\n",
    "    resume=False,  # Force the sampler to start from scratch\n",
    "    adapt=False  # Switch off dynamic adaptation of the temperatures, this is not recommended in general\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, sampling has now finished. Let's take a look at what is stored in the `outdir`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "runA_E0_T0_checkpoint_trace.png  runA_E0_stepping_stone.png  runA_resume.pickle\r\n",
      "runA_E0_beta_lnl.png\t\t runA_result.json\r\n"
     ]
    }
   ],
   "source": [
    "! ls outdir_Bilby-MCMC_I/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have some images, the stored result file `runA_result.json`, and the resume file `runA_resume.pickle`. Let's go through each of these to understand what they do.\n",
    "* `runA_result.json` this is a copy of `result` object returned by `run_sampler` (see above). You can load it up with `bilby.core.result.read_in_result(FILENAME)` if you want to use the results elsewhere.\n",
    "* `runA_resume.pickle` this is a file that stores the progress of the run at each checkpoint. By default, `bilby_mcmc` writes a checkpoint every 30 mins. You can change this by passing `check_point_delta_t=X` to run sampler, where `X` is the time in seconds at which you want to checkpoint. If you stop a run, an attempt will be made to write a checkpoint file before the program exits. If you restart a run and a checkpoint file exists (and `resume=True` is passed), the analysis will start from you it left off. \n",
    "* `runA_E0_beta_lnl.png` and `runA_E0_stepping_stone.png` are graphical representations of the thermodynamic integration and stepping stone methods to estimate the evidence.\n",
    "* `runA_E0_T0_checkpoint_trace.png` this is a graphical illustration of the sampler progress at each checkpoint (or, if the run is finished, at the end). Let's take a look at the trace for our analysis above (see next code block). There are five rows of images. The first 2 rows contain the sample evolution and a histogram of the posterior for the `frequency` and `amplitude`. Next, there are evolution and histogram plots for the sample log-likelihood and log-prior values. These are useful to debug an analysis: watch out for points where a larger log-likelihood is suddenly found! Finally, there is a plot showing the evolution of the estimated ACT `tau`. Again, this is useful for debugging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lV69e6dSpUz766KNcfvnlmTp16jYf3wUAAAAAAGt4A6QGGDp0aAYPHrzBoCFJateunTvuuCN169ZNkjz66KNbtU/Lli3z7LPPbjD8WKNLly659NJLk3xxLNbzzz+/wXG33HJLkqRJkyaF9toOOOCA/Od//meSZMaMGRkzZsxW1QwAAAAAABsiACkSe+65Zzp27Jjkizs2tqcePXoU2hvaa8aMGZk6dWqS5Kyzzsquu+66wXUuuOCCQvu3v/1t5RYJAAAAAMBOTQBSRFatWpUkqVVr+/61rtlnY3u99NJLhXb37t03uk6LFi1y0EEHJUlefvnlSqwQAAAAAICdnQCkSCxevDjTpk1LkrRv33677jVu3LhCe0N7raljc2pZ0z9v3rx8/PHHlVQhAAAAAAA7O7dOF4lbbrklZWVlSZIzzzxzu+2zYMGC3HfffUmSpk2brnMc1hrz5s0rtFu1alXheq1bt06SlJeXZ/78+WnXrt1m1zJ//vxN1goAAAAAwM5JAFIEXnvttdx6661Jvggc1lxSXtnKy8vzne98J8uWLUuSXHvttWnQoMF649b0J0mjRo0qXLNhw4aF9vLly7eonjXhCQAAAAAA/DNHYNVwixYtyhlnnJGysrKUlJTk/vvv3+il49vqpptuyhNPPJHki4vQL7vssg2OW7lyZaFdt27dCtesV69eob1ixYpKqBIAAAAAALwBUqnKysqyyy67bPM69913Xy644IJNjlu2bFlOOeWUwlFQN910U44//vht3n9DHnzwwVx77bVJkjZt2uShhx7a6GXr9evXL7RXr169zu//bO0L1Tf0NklF1j5qa0MWLFiQo446aovWBAAAAACgOAhAaqiVK1emT58++ctf/pIk+f73v5//+I//2C57PfXUUxkwYEDKy8vTvHnzPPfcc2nRosVGxzdu3LjQXr58eYUByNoXn2/quKx/tqn7RQAAAAAA2HkJQCpRnTp1Mm3atG1eZ++9966wv6ysLGeeeWZefPHFJMlFF12Un/zkJ9u874aMHTs2Z5xxRj799NM0adIkzz77bA444IAK56wdTMyfPz9Nmzbd6Ng1b3GUlJQINAAAAAAAqDQCkErWvn377br+559/nvPOOy9PPvlkkuSss87KL3/5y+2y14QJE3Laaadl5cqVadSoUZ5++ul07Nhxk/M6dOhQaE+fPj2HHXbYRsdOnz49yRcXmq99IToAAAAAAGwLl6DXMAMHDsyvfvWrJMmpp56aBx54YKN3cWyLyZMn52tf+1rhCKsnn3wyXbp02ay5xx57bKE9bty4jY5buHBh/va3vyVJunXrtm0FAwAAAADAWgQgNcj3v//93H333UmSE044IY8++milXLr+z/72t7/lpJNOygcffJBddtklo0ePznHHHbfZ8w866KAcfPDBSZJHHnkkn3zyyQbHjRo1qtA+/fTTt6VkAAAAAABYhwCkhrj++uszfPjwJMkxxxyTMWPGpF69elu0xpw5c1JSUpKSkpKNBhpz585Nz549s2jRotSuXTsPPfRQevfuvcX1XnXVVUmSv//97xk8ePB6/bNmzcp///d/J0natm0rAAEAAAAAoFK5A6QGGDFiRG644YYkScuWLTNs2LDMnj27wjnt2rXb4rdDli5dmp49exYuJr/yyivTvn37vP322xud06RJk7Rs2XK95+eff37uvffe/OlPf8rPf/7zLFy4MBdffHGaNGmSCRMm5Mc//nE++uij1KpVKyNGjEidOv5RBAAAAACg8vivzjXA6NGjC+3S0tJ17tjYmNmzZ6dNmzZbtM+UKVMyY8aMwu/Dhg3LsGHDKpxz/vnnr3OU1Rq1a9fO448/nt69e+f111/P6NGj1/kcSVK3bt3cfvvt6dWr1xbVCQAAAAAAm+IILLabpk2b5s9//nPuuOOOHHvssdlzzz1Tv3797L///rn44ovzxhtv5OKLL67qMgEAAAAAKELeAKkBxo4dWynrtGnTJuXl5RvtP+644yrs3xp16tTJoEGDMmjQoEpdFwAAAAAAKuINEAAAAAAAoOgIQAAAAAAAgKIjAAEAAAAAAIqOAAQAAAAAACg6AhAAAAAAAKDoCEAAAAAAAICiIwABAAAAAACKjgAEAAAAAAAoOgIQAAAAAACg6AhAAAAAAACAoiMAAQAAAAAAio4ABAAAAAAAKDoCEAAAAAAAoOgIQAAAAAAAgKIjAAEAAAAAAIqOAAQAAAAAACg6AhAAAAAAAKDoCEAAAAAAAICiIwABAAAAAACKjgAEAAAAAAAoOgIQAAAAAACg6AhAAAAAAACAoiMAAQAAAAAAio4ABAAAAAAAKDoCEAAAAAAAoOgIQAAAAAAAgKIjAAEAAAAAAIqOAAQAAAAAACg6AhAAAAAAAKDoCEAAAAAAAICiIwABAAAAAACKjgAEAAAAAAAoOgIQAAAAAACg6AhAAAAAAACAoiMAAQAAAAAAio4ABAAAAAAAKDoCEAAAAAAAoOgIQAAAAAAAgKIjAAEAAAAAAIqOAAQAAAAAACg6AhAAAAAAAKDoCEAAAAAAAICiIwABAAAAAACKjgAEAAAAAAAoOgIQAACAGmDw4MEpKSkp/IwdO3aTc5555pn07ds3rVq1Sr169dKqVav07ds3zzzzzPYvGAAAqpgABAAAoJp76623Mnz48M0eX15enoEDB6ZXr1557LHHUlpamtWrV6e0tDSPPfZYevXqlYEDB6a8vHw7Vg0AAFVLAAIAAFCNff7557n44otTVlaWvfbaa7PmXHPNNRk5cmSS5PDDD8/DDz+cCRMm5OGHH87hhx+eJBk5cmSuvfba7VY3AABUNQEIAABANXbbbbfl9ddfT/v27XPhhRducvzMmTMzbNiwJMmRRx6ZP/3pT+nfv386d+6c/v375+WXX86RRx6ZJBk6dGhmzZq1XesHAICqIgABAACopubNm1d4S+MXv/hF6tatu8k5w4cPT1lZWZJkxIgRadCgwTr9u+66a0aMGJEkKSsry6233lrJVQMAQPUgAAEAAKimLr300ixfvjznn39+jjvuuE2OLy8vz5gxY5Ik7du3T9euXTc4rmvXrmnXrl2S5PHHH3cXCAAARUkAAgAAUA098sgj+d3vfpc99tgjt9xyy2bNmT17dkpLS5Mk3bt3r3Dsmv758+dnzpw521QrAABURwIQAACAauYf//hHvve97yX54p6OZs2abda8adOmFdrt27evcOza/WvPAwCAYlGnqgsAAABgXYMHD87ChQtzzDHHbNbF52vMmzev0G7VqlWFY1u3br3BeZtj/vz5FfYvWLBgi9YDAIDtQQACAABQjbz88su5++67U6dOndx5550pKSnZ7LnLli0rtBs1alTh2IYNGxbay5cv36Ia1w5PAACgunIEFgAAQDWxevXqXHLJJSkvL8+///u/59BDD92i+StXriy069atW+HYevXqFdorVqzYskIBAKAG8AYIAABANXHTTTdl2rRp2XffffPDH/5wi+fXr1+/0F69enWFY1etWlVoN2jQYIv22dSRWQsWLMhRRx21RWsCAEBlE4AAAABUA9OnT89///d/J0lGjBixzhFVm6tx48aF9qaOtfr4448L7U0dl/XPNnW/CAAAVAcCEAAAgGpg+PDhWb16dfbff/988skn+dWvfrXemLfffrvQ/uMf/5iFCxcmSU477bQ0bNhwnWBiUxeVr/0Whzs9AAAoRgIQAACAamDNkVTvvPNOzj777E2O//GPf1xoz549Ow0bNkyHDh0Kz6ZPn17h/LX7Dz744C0tFwAAqj2XoAMAABSJ/fbbL/vss0+SZNy4cRWOHT9+fJKkZcuWadOmzfYuDQAAdjgBCAAAQDUwatSolJeXV/iz9sXoL774YuH5mgCjpKQkffr0SfLFGx6vvvrqBvd69dVXC2+A9OnTJyUlJdv3wwEAQBUQgAAAABSRK664InXqfHHa8eWXX54VK1as079ixYpcfvnlSZI6derkiiuu2OE1AgDAjiAAAQAAKCIHHXRQrrrqqiTJxIkT061bt/z617/OxIkT8+tf/zrdunXLxIkTkyRXX311DjzwwKosFwAAthuXoAMAABSZG2+8MYsXL869996bSZMmpX///uuNufDCCzNkyJAqqA4AAHYMb4AAAAAUmVq1auWee+7JU089lT59+mSfffZJ3bp1s88++6RPnz75/e9/n7vvvju1avlXQgAAipc3QAAAAGqI66+/Ptdff/1mj+/du3d69+69/QoCAIBqzP/uAwAAAAAAFB0BCAAAAAAAUHQEIAAAAAAAQNERgNQQc+fOzS9+8YucddZZadeuXRo2bJj69eunVatW6dOnTx5++OGUlZVt8z5lZWV57rnncvXVV+erX/1qmjVrll122SW77757jjjiiFx11VWZNWvWJte54IILUlJSslk/c+bM2ea6AQAAAABgbS5BrwGuu+66DBkyJOXl5ev1lZaWprS0NE888UR++tOfZvTo0dl33323ap8lS5bk4IMPztKlS9fr+/DDDzNp0qRMmjQpI0aMyLBhw/K9731vq/YBAAAAAIDtTQBSA7z33nspLy9Pw4YNc/rpp+eEE07IgQcemPr162fatGm57bbb8vrrr2fixInp2bNn3njjjTRq1GiL91m1alUh/DjssMPSp0+fdOnSJc2bN8+HH36Yp59+OiNGjMjKlStzxRVXpEGDBrnkkksqXHOfffbJH/7whwrHtGzZcotrBQAAAACAighAaoA999wzQ4cOzaBBg9K4ceN1+jp16pSzzz4755xzTh555JHMmDEjw4cPz7XXXrvF+5SUlOTEE0/Mj370o3Tt2nW9/h49eqRfv37p0aNHVqxYkcGDB+fss89er6a17bLLLjnkkEO2uBYAAAAAANgW7gCpAYYOHZrBgwdvNGioXbt27rjjjtStWzdJ8uijj27VPi1btsyzzz67wfBjjS5duuTSSy9N8sWxWM8///xW7QUAAAAAANuTAKRI7LnnnunYsWOSbNYl5duiR48ehfb23gsAAAAAALaGAKSIrFq1KklSq9b2/Wtds8+O2AsAAAAAALaG/3pdJBYvXpxp06YlSdq3b79d9xo3blyhvam9li5dmq9+9avZfffdU69evey99945+eSTc/vtt+eTTz7ZrnUCAAAAALDzcgl6kbjllltSVlaWJDnzzDO32z4LFizIfffdlyRp2rTpOsdhbcjy5cvz8ssvF35fuHBhFi5cmGeffTY333xzHnnkkRxzzDFbVcv8+fM3WSsAAAAAADsnAUgReO2113LrrbcmSVq1alW4pLyylZeX5zvf+U6WLVuWJLn22mvToEGDDY4tKSlJ165dc9ppp+WII45I8+bNs3LlykyZMiX33HNPJkyYkNLS0px00kl56aWXcvjhh29xPa1bt96mzwMAAAAAQPESgNRwixYtyhlnnJGysrKUlJTk/vvvz6677rpd9rrpppvyxBNPJPniIvTLLrtso2OHDx+e3Xfffb3nRx99dC6++OJcc801uemmm/Lxxx/noosuysSJE1NSUrJd6gYAAAAAYOfjDpBKtCaE2NafUaNGbdZ+y5YtyymnnFI4Cuqmm27K8ccfv10+24MPPphrr702SdKmTZs89NBDFV6AvqHwY42SkpLceOON6dmzZ5LkjTfeyJ///OctrmnevHkV/kyYMGGL1wQAAAAAoDh4A6SGWrlyZfr06ZO//OUvSZLvf//7+Y//+I/tstdTTz2VAQMGpLy8PM2bN89zzz2XFi1abPO6AwcOzPPPP5/ki4vVu3XrtkXzW7Vqtc01AAAAAABQnAQglahOnTqZNm3aNq+z9957V9hfVlaWM888My+++GKS5KKLLspPfvKTbd53Q8aOHZszzjgjn376aZo0aZJnn302BxxwQKWs3aFDh0K7tLS0UtYEAAAAAIBEAFLp2rdvv13X//zzz3PeeeflySefTJKcddZZ+eUvf7ld9powYUJOO+20rFy5Mo0aNcrTTz+djh07Vtr65eXllbYWAAAAAACszR0gNczAgQPzq1/9Kkly6qmn5oEHHqjwLo6tNXny5Hzta1/L8uXLU79+/Tz55JPp0qVLpe4xderUQnufffap1LUBAAAAANi5CUBqkO9///u5++67kyQnnHBCHn300eyyyy6Vvs/f/va3nHTSSfnggw+yyy67ZPTo0TnuuOMqfZ+131zp3r17pa8PAAAAAMDOSwBSQ1x//fUZPnx4kuSYY47JmDFjUq9evS1aY86cOSkpKUlJSclGA425c+emZ8+eWbRoUWrXrp2HHnoovXv33qJ9Xn311SxYsGCj/eXl5bnmmmvywgsvJEm+8pWvbPEF6AAAAAAAUBF3gNQAI0aMyA033JAkadmyZYYNG5bZs2dXOKddu3Zb/HbI0qVL07Nnz8ybNy9JcuWVV6Z9+/Z5++23NzqnSZMmadmy5TrPnnnmmdx888352te+lhNPPDEdOnTI7rvvnlWrVmXy5Mm55557MmHChCTJrrvumrvuuislJSVbVCsAAAAAAFREAFIDjB49utAuLS3Nscceu8k5s2fPTps2bbZonylTpmTGjBmF34cNG5Zhw4ZVOOf888/PqFGj1nu+atWqjBkzJmPGjNno3H333TcPPfRQOnfuvEV1AgAAAADApghAqHQDBgxI8+bN88orr2Ty5MlZvHhxli5dmjp16qRp06Y54ogjctppp+Wcc85J/fr1q7pcAAAAAACKkACkBhg7dmylrNOmTZuUl5dvtP+4446rsH9zffnLX86gQYMyaNCgbV4LAAAAAAC2hkvQAQAAAACAoiMAAQAAAAAAio4ABAAAAAAAKDoCEAAAAAAAoOgIQAAAAAAAgKIjAAEAAAAAAIqOAAQAAAAAACg6AhAAAAAAAKDoCEAAAAAAAICiIwABAAAAAACKjgAEAAAAAAAoOgIQAAAAAACg6AhAAAAAAACAoiMAAQAAAAAAio4ABAAAAAAAKDoCEAAAAAAAoOgIQAAAAAAAgKIjAAEAAAAAAIqOAAQAAAAAACg6AhAAAAAAAKDoCEAAAAAAAICiIwABAAAAAACKjgAEAAAAAAAoOgIQAAAAAACg6AhAAAAAAACAoiMAAQAAAAAAio4ABAAAAAAAKDoCEAAAAAAAoOgIQAAAAAAAgKIjAAEAAAAAAIqOAAQAAAAAACg6AhAAAAAAAKDoCEAAAAAAAICiIwABAAAAAACKjgAEAAAAAAAoOgIQAAAAAACg6AhAAAAAAACAoiMAAQAAAAAAio4ABAAAAAAAKDoCEAAAAAAAoOgIQAAAAKqBjz76KL/61a9y5ZVXpnv37jnggAOy2267pW7dutlrr71y3HHHZdiwYVm6dOlmrffMM8+kb9++adWqVerVq5dWrVqlb9++eeaZZ7bzJwEAgOqhTlUXAAAAQDJhwoScffbZG+xbsmRJxo0bl3HjxuWWW27J//3f/+Xkk0/e4Njy8vJ85zvfyciRI9d5XlpamsceeyyPPfZYLrnkktx5550pKSmp9M8BAADVhQAEAACgmmjdunV69OiRTp06pXXr1tl7773z+eefZ/78+Xn00Ufz29/+Nu+//36+/vWv5/XXX0/Hjh3XW+Oaa64phB+HH354Bg8enLZt22bWrFkZNmxYJk2alJEjR6ZZs2YZMmTIjv6IAACwwwhAAAAAqoEePXpk7ty5G+0/88wz8/jjj+f000/P6tWrc8MNN2T06NHrjJk5c2aGDRuWJDnyyCMzfvz4NGjQIEnSuXPnfP3rX0/37t0zceLEDB06NAMGDEjbtm2334cCAIAq5A4QAACAaqB27dqbHPONb3wj7du3T5KMHz9+vf7hw4enrKwsSTJixIhC+LHGrrvumhEjRiRJysrKcuutt25r2QAAUG0JQAAAAGqQhg0bJklWrly5zvPy8vKMGTMmSdK+fft07dp1g/O7du2adu3aJUkef/zxlJeXb8dqAQCg6ghAAAAAaohp06blzTffTJLCmyBrzJ49O6WlpUmS7t27V7jOmv758+dnzpw5lV8oAABUAwIQAACAauyTTz7JjBkz8tOf/jQ9evTIZ599liT53ve+t864adOmFdr/HI78s7X7154HAADFxCXoAAAA1cyoUaMyYMCAjfZfddVVOffcc9d5Nm/evEK7VatWFa7funXrDc7bXPPnz6+wf8GCBVu8JgAAVDYBCAAAQA1x2GGH5c4770yXLl3W61u2bFmh3ahRowrXWXOPSJIsX758i+tYO0ABAIDqyhFYAAAA1cw3vvGNTJkyJVOmTMmECRPy8MMP5/TTT8+bb76Zc889N7/73e/Wm7P2peh169atcP169eoV2itWrKi8wgEAoBrxBggAAEA1s/vuu2f33Xcv/N65c+f0798/DzzwQM4///z06dMn99xzTy644ILCmPr16xfaq1evrnD9VatWFdoNGjTY4vo2dWzWggULctRRR23xugAAUJkEIAAAADXEeeedl9/97nd55JFHctlll6VPnz5p0qRJkqRx48aFcZs61urjjz8utDd1XNaGbOqOEQAAqA5q3BFYn3/+eVWXAAAAUGX69OmT5IsQ4+mnny48XzuU2NQl5Wu/weE+DwAAilWNC0C+9KUv5fjjj8+CBQuquhQAAIAdrlmzZoX2u+++W2h36NCh0J4+fXqFa6zdf/DBB1didQAAUH3UuADkk08+ybhx4/LBBx9UdSkAAAA7XGlpaaG99vFV++23X/bZZ58kybhx4ypcY/z48UmSli1bpk2bNpVfJAAAVAM1LgBZ47rrrsvw4cPzwgsvZMmSJZscf80112TEiBE7oDIAAIDt5ze/+U2hfeihhxbaJSUlheOxpk+fnldffXWD81999dXCGyB9+vRJSUnJdqwWAACqTo0NQB577LFcddVVOemkk9KiRYu0aNEiJ510Uq688srcf//9mTRpUlatWlUY/8ILL+Tmm2+uwooBAAA2btSoUVm5cmWFY4YPH57f//73SZI2bdrk2GOPXaf/iiuuSJ06dZIkl19+eVasWLFO/4oVK3L55ZcnSerUqZMrrriissoHAIBqp05VF7C1GjVqlGXLlhV+X7x4cV544YW88MILhWe1a9fOl7/85STJrFmzsssuu+zwOgEAADbH9ddfnyuvvDL9+vXLsccem7Zt2xb+vWfKlCl58MEH86c//SlJUrdu3dx1112FsGONgw46KFdddVVuvvnmTJw4Md26dcsPfvCDtG3bNrNmzcrQoUMzadKkJMnVV1+dAw88cId/TgAA2FFqbADyyiuvZLfddsubb76Zt956K2+++WbefPPNzJo1K+Xl5UmSsrKyzJo1qzCnZcuWVVUuAADAJv3973/PXXfdlbvuumujY1q1apV77703PXv23GD/jTfemMWLF+fee+/NpEmT0r9///XGXHjhhRkyZEil1Q0AANVRjQ1Aki8CjZYtW+aUU04pPPv4448zefLkvPnmm5k8eXKmT5+exYsXZ7fddst1111XhdUCAABs3AsvvJDnn38+L774YqZNm5ZFixZl6dKlqV+/fpo3b57DDjssp556as4888zsuuuuG12nVq1aueeee9KvX7+MHDkyr7/+et5///00bdo0nTt3zsCBA9OrV68d+MkAAKBq1OgAZEMaNmyYo48+OkcffXRVlwIAALDZ2rZtm7Zt22bgwIGVsl7v3r3Tu3fvSlkLAABqohp7CToAAAAAAMDGCEAAAAAAAICiU2MDkFGjRuXpp59OaWlpVZcCAAAAAABUMzX2DpCf/OQn+clPfpIk2X333XPooYemY8eOhZ9DDjmkwosBAQAAAACA4lVjA5Dy8vJC+4MPPsj48ePz0ksvFZ6VlJRkv/32S8eOHdcJRw444ICqKBcAAAAAANi1q8poAAAgAElEQVSBamwAMnbs2Hz++eeZPHlypkyZksmTJ+evf/1rPvnkkyRfBCSzZs3KO++8k8cffzzJF6FIWVlZVZYNAAAAAADsADUuABk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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython import display\n",
    "display.Image(\"outdir_Bilby-MCMC_I/runA_E0_T0_checkpoint_trace.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Bilby-MCMC: why is the autocorrelation time always 1?\n",
    "\n",
    "In teh figure above, you may be surprised that the autocorrelation time `tau` is always 1. The reason for this is that, by default, `bilby_mcmc` take a number of *internal steps* before adding to the MCMC chain. Each step individually respects the Markovian nature, in effect we are pre-thinning the chain. This is done to reduce the computational overheads associated with storing many samples which will ultimately be discarded and to improve the scaling efficiency when we parallelise with multiple threads on a CPU. \n",
    "\n",
    "Let's re-run the analysis above, but set the number of internal steps `L1steps` to 1 i.e. make `bilby_mcmc` behave like a regular sampler. (FYI `L1steps=100` by default).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13:23 bilby INFO    : Running for label 'runB', output will be saved to 'outdir_Bilby-MCMC_I'\n",
      "13:23 bilby WARNING : Parameter logp has no default prior and is set to -2.3025850929940455, this will not be sampled and may cause an error.\n",
      "13:23 bilby WARNING : Parameter logl has no default prior and is set to -501.69780664137784, this will not be sampled and may cause an error.\n",
      "13:23 bilby INFO    : Search parameters:\n",
      "13:23 bilby INFO    :   amplitude = Uniform(minimum=0, maximum=5, name='amplitude', latex_label='amplitude', unit=None, boundary=None)\n",
      "13:23 bilby INFO    :   frequency = Uniform(minimum=0, maximum=2, name='frequency', latex_label='frequency', unit=None, boundary=None)\n",
      "13:23 bilby INFO    : Single likelihood evaluation took 2.810e-04 s\n",
      "13:23 bilby INFO    : Using sampler Bilby_MCMC with kwargs {'nsamples': 500, 'nensemble': 1, 'pt_ensemble': False, 'ntemps': 6, 'Tmax': None, 'Tmax_from_SNR': 20, 'initial_betas': None, 'adapt': False, 'adapt_t0': 100, 'adapt_nu': 10, 'pt_rejection_sample': False, 'burn_in_nact': 10, 'thin_by_nact': 1, 'fixed_discard': 0, 'autocorr_c': 5, 'L1steps': 1, 'L2steps': 3, 'npool': 1, 'printdt': 5, 'min_tau': 1, 'proposal_cycle': 'default', 'stop_after_convergence': False, 'fixed_tau': None, 'tau_window': None, 'evidence_method': 'stepping_stone'}\n",
      "13:23 bilby INFO    : Initializing BilbyPTMCMCSampler with:\n",
      "  Convergence settings: ConvergenceInputs(autocorr_c=5, burn_in_nact=10, thin_by_nact=1, fixed_discard=0, target_nsamples=500, stop_after_convergence=False, L1steps=1, L2steps=3, min_tau=1, fixed_tau=None, tau_window=None)\n",
      "  Parallel-tempering settings: ParallelTemperingInputs(ntemps=6, nensemble=1, Tmax=None, Tmax_from_SNR=20, initial_betas=None, adapt=False, adapt_t0=100, adapt_nu=10, pt_ensemble=False)\n",
      "  proposal_cycle: default\n",
      "  pt_rejection_sample: False\n",
      "13:23 bilby INFO    : Setting parallel tempering inputs=ParallelTemperingInputs(ntemps=6, nensemble=1, Tmax=None, Tmax_from_SNR=20, initial_betas=None, adapt=False, adapt_t0=100, adapt_nu=10, pt_ensemble=False)\n",
      "13:23 bilby INFO    : Initializing BilbyPTMCMCSampler with:ntemps=6,nensemble=1,pt_ensemble=False,initial_betas=[1.         0.34657242 0.12011244 0.04162766 0.014427   0.005     ]\n",
      "\n",
      "13:23 bilby INFO    : Using ProposalCycle:\n",
      "  AdaptiveGaussianProposal(acceptance_ratio:nan,n:0,scale:1,)\n",
      "  DifferentialEvolutionProposal(acceptance_ratio:nan,n:0,)\n",
      "  UniformProposal(acceptance_ratio:nan,n:0,)\n",
      "  KDEProposal(acceptance_ratio:nan,n:0,trained:0,)\n",
      "  GMMProposal(acceptance_ratio:nan,n:0,trained:0,)\n",
      "  NormalizingFlowProposal(acceptance_ratio:nan,n:0,trained:0,)\n",
      "\n",
      "13:23 bilby INFO    : Setting convergence_inputs=ConvergenceInputs(autocorr_c=5, burn_in_nact=10, thin_by_nact=1, fixed_discard=0, target_nsamples=500, stop_after_convergence=False, L1steps=1, L2steps=3, min_tau=1, fixed_tau=None, tau_window=None)\n",
      "13:23 bilby INFO    : Drawing 500 samples\n",
      "13:23 bilby INFO    : Checkpoint every 1800s\n",
      "13:23 bilby INFO    : Print update every 5s\n",
      "13:23 bilby INFO    : Gaussian KDE construction at 1000 finished for length 4633 chain, took 0.03s. Current accept-ratio=0.00\n",
      "13:23 bilby INFO    : Gaussian KDE construction at 1000 finished for length 4835 chain, took 0.04s. Current accept-ratio=0.00\n",
      "13:23 bilby INFO    : Gaussian Mixture Model construction at 1000 finished for length 4859 chain, took 0.53s. Current accept-ratio=0.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.86e+03|0:00:05|1.99e+03(10tau)|t=56|n=51|a=0.12|e=1.7e-01%|1.06ms/ev|maxl=-230.91|ETF=0:00:26\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13:23 bilby INFO    : Starting NF training\n",
      "/home/greg/programs/anaconda3/envs/bilby/lib/python3.7/site-packages/torch/autograd/__init__.py:132: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at  /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\n",
      "  allow_unreachable=True)  # allow_unreachable flag\n",
      "13:24 bilby INFO    : Flow training step (1000) finished for length 4917 chain, took 7.32s. Current accept-ratio=0.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.92e+03|0:00:12|5.55e+02(10tau)|t=56|n=78|a=0.12|e=2.6e-01%|128.52ms/ev|maxl=-230.91|ETF=0:50:10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13:24 bilby INFO    : Gaussian KDE construction at 1000 finished for length 4924 chain, took 0.03s. Current accept-ratio=0.01\n",
      "13:24 bilby INFO    : Gaussian KDE construction at 1000 finished for length 4928 chain, took 0.02s. Current accept-ratio=0.05\n",
      "13:24 bilby INFO    : Gaussian Mixture Model construction at 1000 finished for length 4959 chain, took 0.10s. Current accept-ratio=0.06\n",
      "13:24 bilby INFO    : Starting NF training\n",
      "13:24 bilby INFO    : Flow training step (1000) finished for length 4962 chain, took 6.58s. Current accept-ratio=0.01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.96e+03|0:00:19|5.55e+02(10tau)|t=56|n=79|a=0.12|e=2.7e-01%|153.91ms/ev|maxl=-230.91|ETF=0:59:56\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13:24 bilby INFO    : Starting NF training\n",
      "13:24 bilby INFO    : Flow training step (1000) finished for length 4986 chain, took 6.58s. Current accept-ratio=0.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.99e+03|0:00:26|5.55e+02(10tau)|t=56|n=79|a=0.12|e=2.6e-01%|278.14ms/ev|maxl=-230.91|ETF=1:48:18\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13:24 bilby INFO    : Gaussian KDE construction at 1000 finished for length 4994 chain, took 0.02s. Current accept-ratio=0.00\n",
      "13:24 bilby INFO    : Gaussian Mixture Model construction at 1000 finished for length 5039 chain, took 0.06s. Current accept-ratio=0.00\n",
      "13:24 bilby INFO    : Starting NF training\n",
      "13:24 bilby INFO    : Flow training step (1000) finished for length 5050 chain, took 6.65s. Current accept-ratio=0.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.05e+03|0:00:33|5.55e+02(10tau)|t=56|n=81|a=0.12|e=2.7e-01%|111.64ms/ev|maxl=-230.91|ETF=0:43:16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13:24 bilby INFO    : Gaussian KDE construction at 1000 finished for length 5065 chain, took 0.03s. Current accept-ratio=0.00\n",
      "13:24 bilby INFO    : Gaussian Mixture Model construction at 1000 finished for length 5069 chain, took 0.05s. Current accept-ratio=0.00\n",
      "13:24 bilby INFO    : Starting NF training\n",
      "13:24 bilby INFO    : Flow training step (1000) finished for length 5113 chain, took 6.79s. Current accept-ratio=0.07\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.11e+03|0:00:40|5.55e+02(10tau)|t=56|n=82|a=0.12|e=2.7e-01%|116.71ms/ev|maxl=-230.91|ETF=0:45:07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13:24 bilby INFO    : Gaussian Mixture Model construction at 1000 finished for length 5180 chain, took 0.10s. Current accept-ratio=0.00\n",
      "13:24 bilby INFO    : Starting NF training\n",
      "13:24 bilby INFO    : Flow training step (1000) finished for length 5242 chain, took 6.99s. Current accept-ratio=0.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.24e+03|0:00:48|5.55e+02(10tau)|t=56|n=84|a=0.13|e=2.7e-01%|63.54ms/ev|maxl=-230.91|ETF=0:24:26\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13:24 bilby INFO    : Gaussian Mixture Model construction at 1000 finished for length 5271 chain, took 0.27s. Current accept-ratio=0.01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.70e+03|0:00:53|5.55e+02(10tau)|t=56|n=92|a=0.16|e=2.7e-01%|11.08ms/ev|maxl=-230.91|ETF=0:04:10\n",
      "6.02e+03|0:00:58|5.55e+02(10tau)|t=56|n=98|a=0.17|e=2.7e-01%|15.21ms/ev|maxl=-230.91|ETF=0:05:39\n",
      "6.47e+03|0:01:03|5.55e+02(10tau)|t=56|n=106|a=0.19|e=2.7e-01%|11.30ms/ev|maxl=-230.91|ETF=0:04:07\n",
      "6.93e+03|0:01:08|5.55e+02(10tau)|t=56|n=114|a=0.21|e=2.7e-01%|10.85ms/ev|maxl=-230.91|ETF=0:03:52\n",
      "7.34e+03|0:01:13|5.55e+02(10tau)|t=56|n=122|a=0.23|e=2.8e-01%|12.08ms/ev|maxl=-230.91|ETF=0:04:13\n",
      "7.88e+03|0:01:18|5.55e+02(10tau)|t=56|n=132|a=0.24|e=2.8e-01%|9.33ms/ev|maxl=-230.91|ETF=0:03:10\n",
      "8.29e+03|0:01:23|5.55e+02(10tau)|t=56|n=139|a=0.25|e=2.8e-01%|12.17ms/ev|maxl=-230.91|ETF=0:04:03\n",
      "8.69e+03|0:01:28|5.55e+02(10tau)|t=56|n=146|a=0.26|e=2.8e-01%|12.66ms/ev|maxl=-230.91|ETF=0:04:08\n",
      "8.98e+03|0:01:33|5.55e+02(10tau)|t=56|n=151|a=0.26|e=2.8e-01%|17.17ms/ev|maxl=-230.91|ETF=0:05:32\n",
      "9.24e+03|0:01:38|5.55e+02(10tau)|t=56|n=156|a=0.27|e=2.8e-01%|18.82ms/ev|maxl=-230.91|ETF=0:05:59\n",
      "9.52e+03|0:01:43|5.55e+02(10tau)|t=56|n=161|a=0.27|e=2.8e-01%|18.06ms/ev|maxl=-230.91|ETF=0:05:39\n",
      "9.80e+03|0:01:48|5.55e+02(10tau)|t=56|n=166|a=0.28|e=2.8e-01%|17.80ms/ev|maxl=-230.91|ETF=0:05:29\n",
      "1.01e+04|0:01:53|5.55e+02(10tau)|t=56|n=171|a=0.28|e=2.8e-01%|20.01ms/ev|maxl=-230.91|ETF=0:06:05\n",
      "1.03e+04|0:01:58|5.55e+02(10tau)|t=56|n=176|a=0.29|e=2.8e-01%|17.67ms/ev|maxl=-230.91|ETF=0:05:17\n",
      "1.06e+04|0:02:03|5.55e+02(10tau)|t=56|n=181|a=0.29|e=2.8e-01%|17.63ms/ev|maxl=-230.91|ETF=0:05:12\n",
      "1.09e+04|0:02:08|5.55e+02(10tau)|t=56|n=185|a=0.29|e=2.8e-01%|19.93ms/ev|maxl=-230.91|ETF=0:05:48\n",
      "1.11e+04|0:02:14|5.55e+02(10tau)|t=56|n=190|a=0.30|e=2.8e-01%|19.00ms/ev|maxl=-230.91|ETF=0:05:26\n",
      "1.15e+04|0:02:19|5.55e+02(10tau)|t=56|n=196|a=0.30|e=2.9e-01%|15.41ms/ev|maxl=-230.91|ETF=0:04:20\n",
      "1.17e+04|0:02:24|5.55e+02(10tau)|t=56|n=201|a=0.31|e=2.9e-01%|17.67ms/ev|maxl=-230.91|ETF=0:04:53\n",
      "1.21e+04|0:02:29|5.55e+02(10tau)|t=22|n=536|a=0.31|e=7.4e-01%|12.87ms/ev|maxl=-230.91|ETF=0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13:26 bilby INFO    : Reached convergence: exiting sampling\n",
      "13:26 bilby INFO    : Checkpoint start\n",
      "13:26 bilby INFO    : Written checkpoint file outdir_Bilby-MCMC_I/runB_resume.pickle\n",
      "13:26 bilby INFO    : Zero-temperature proposals:\n",
      "13:26 bilby INFO    : AdaptiveGaussianProposal(acceptance_ratio:0.21,n:2.3e+03,scale:0.001,)\n",
      "13:26 bilby INFO    : DifferentialEvolutionProposal(acceptance_ratio:0.44,n:2.6e+03,)\n",
      "13:26 bilby INFO    : UniformProposal(acceptance_ratio:1,n:21,)\n",
      "13:26 bilby INFO    : KDEProposal(acceptance_ratio:0.47,n:2.4e+03,trained:1,)\n",
      "13:26 bilby INFO    : GMMProposal(acceptance_ratio:0.41,n:2.4e+03,trained:1,)\n",
      "13:26 bilby INFO    : NormalizingFlowProposal(acceptance_ratio:0.017,n:2.5e+03,trained:1,)\n",
      "13:26 bilby INFO    : Current taus={'amplitude': 21.6, 'frequency': 10.4}\n",
      "13:26 bilby INFO    : Temperature swaps = 4044\n",
      "13:26 bilby INFO    : Temp:0<->1|beta=1|hot-samp=536|swap=0.52|conv=True|\n",
      "13:26 bilby INFO    : Temp:1<->2|beta=0.3466|hot-samp=612|swap=0.56|conv=True|\n",
      "13:26 bilby INFO    : Temp:2<->3|beta=0.1201|hot-samp=547|swap=0.51|conv=True|\n",
      "13:26 bilby INFO    : Temp:3<->4|beta=0.04163|hot-samp=420|swap=0.42|conv=False|\n",
      "13:26 bilby INFO    : Temp:4<->5|beta=0.01443|hot-samp=241|swap=0.25|conv=False|\n",
      "13:26 bilby INFO    : Temp:5<->6|beta=0.005|hot-samp=565|swap=-|conv=True|\n",
      "13:26 bilby INFO    : Creating diagnostic plots\n",
      "13:26 bilby INFO    : Log-evidence of -238.08+/-0.52 calculated using thermodynamic method\n",
      "13:26 bilby INFO    : Log-evidence of -236.55+/-0.14 calculated using stepping_stone method\n",
      "13:26 bilby INFO    : Checkpoint finished\n",
      "13:26 bilby INFO    : Log-evidence of -238.08+/-0.52 calculated using thermodynamic method\n",
      "13:26 bilby INFO    : Log-evidence of -236.55+/-0.13 calculated using stepping_stone method\n",
      "13:26 bilby INFO    : Sampling time: 0:02:29.038780\n",
      "13:26 bilby INFO    : Summary of results:\n",
      "nsamples: 552\n",
      "ln_noise_evidence:    nan\n",
      "ln_evidence: -236.551 +/-  0.131\n",
      "ln_bayes_factor:    nan +/-  0.131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result = bilby.run_sampler(\n",
    "    likelihood=likelihood,\n",
    "    priors=priors,\n",
    "    injection_parameters=simulation_parameters,\n",
    "    outdir=outdir,\n",
    "    label=\"runB\",\n",
    "    sampler='bilby_mcmc',\n",
    "    printdt=5,\n",
    "    nsamples=500,\n",
    "    ntemps=6,\n",
    "    resume=False,\n",
    "    adapt=False,\n",
    "    L1steps=1,  # Set L1steps to 1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython import display\n",
    "display.Image(\"outdir_Bilby-MCMC_I/runB_E0_T0_checkpoint_trace.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, that is better. Now we can see that the ACT is something like 20 (on a simple analysis like this, it is typically massively overestimated). Looking now at the traceplots, you will see gray points in the background. These are the full set of steps while the blue points are only those taken as posterior samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Bilby-MCMC: how do I use an ensemble sampler like `emcee`?\n",
    "\n",
    "`bilby_mcmc` comes equiped with the option to use ensemble sampling. A \"cloud\" of points are evolved together with steps for a give \"walker\" in the cloud proposed based on the walkers complement. To use `bilby_mcmc` in this mode, simply add `nensemble` to the list of arguments.\n",
    "\n",
    "**Note**: we can also use parallel-tempering (below we do not). You can choose if you want the whole ensemble to be parallel-tempered or just the primary chain with `pt_ensemble`. \n",
    "\n",
    "In my experience, the benefit of ensemble sampling come predominantly when you have a difficult-to-sample space without good custom proposals. In practise, `bilby_mcmc` is poorly optimized for ensemble sampling and if you need it, you'd be better off using `emcee` or it's parallel-tempered spin off `ptemcee`. But, the feature is here in case it is useful. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13:41 bilby INFO    : Running for label 'runB', output will be saved to 'outdir_Bilby-MCMC_I'\n",
      "13:41 bilby WARNING : Parameter logp has no default prior and is set to -2.3025850929940455, this will not be sampled and may cause an error.\n",
      "13:41 bilby WARNING : Parameter logl has no default prior and is set to -671.8593275529283, this will not be sampled and may cause an error.\n",
      "13:41 bilby INFO    : Search parameters:\n",
      "13:41 bilby INFO    :   amplitude = Uniform(minimum=0, maximum=5, name='amplitude', latex_label='amplitude', unit=None, boundary=None)\n",
      "13:41 bilby INFO    :   frequency = Uniform(minimum=0, maximum=2, name='frequency', latex_label='frequency', unit=None, boundary=None)\n",
      "13:41 bilby INFO    : Single likelihood evaluation took 3.320e-04 s\n",
      "13:41 bilby INFO    : Using sampler Bilby_MCMC with kwargs {'nsamples': 500, 'nensemble': 10, 'pt_ensemble': False, 'ntemps': 1, 'Tmax': None, 'Tmax_from_SNR': 20, 'initial_betas': None, 'adapt': False, 'adapt_t0': 100, 'adapt_nu': 10, 'pt_rejection_sample': False, 'burn_in_nact': 10, 'thin_by_nact': 1, 'fixed_discard': 0, 'autocorr_c': 5, 'L1steps': 1, 'L2steps': 3, 'npool': 1, 'printdt': 5, 'min_tau': 1, 'proposal_cycle': 'default', 'stop_after_convergence': False, 'fixed_tau': None, 'tau_window': None, 'evidence_method': 'stepping_stone'}\n",
      "13:41 bilby INFO    : Initializing BilbyPTMCMCSampler with:\n",
      "  Convergence settings: ConvergenceInputs(autocorr_c=5, burn_in_nact=10, thin_by_nact=1, fixed_discard=0, target_nsamples=500, stop_after_convergence=False, L1steps=1, L2steps=3, min_tau=1, fixed_tau=None, tau_window=None)\n",
      "  Parallel-tempering settings: ParallelTemperingInputs(ntemps=1, nensemble=10, Tmax=None, Tmax_from_SNR=20, initial_betas=None, adapt=False, adapt_t0=100, adapt_nu=10, pt_ensemble=False)\n",
      "  proposal_cycle: default\n",
      "  pt_rejection_sample: False\n",
      "13:41 bilby INFO    : Setting parallel tempering inputs=ParallelTemperingInputs(ntemps=1, nensemble=10, Tmax=None, Tmax_from_SNR=20, initial_betas=None, adapt=False, adapt_t0=100, adapt_nu=10, pt_ensemble=False)\n",
      "13:41 bilby INFO    : Initializing BilbyPTMCMCSampler with:ntemps=1,nensemble=10,pt_ensemble=False,initial_betas=[1]\n",
      "\n",
      "13:41 bilby INFO    : Using ProposalCycle:\n",
      "  AdaptiveGaussianProposal(acceptance_ratio:nan,n:0,scale:1,)\n",
      "  DifferentialEvolutionProposal(acceptance_ratio:nan,n:0,)\n",
      "  UniformProposal(acceptance_ratio:nan,n:0,)\n",
      "  KDEProposal(acceptance_ratio:nan,n:0,trained:0,)\n",
      "  GMMProposal(acceptance_ratio:nan,n:0,trained:0,)\n",
      "  NormalizingFlowProposal(acceptance_ratio:nan,n:0,trained:0,)\n",
      "\n",
      "13:41 bilby INFO    : Setting convergence_inputs=ConvergenceInputs(autocorr_c=5, burn_in_nact=10, thin_by_nact=1, fixed_discard=0, target_nsamples=500, stop_after_convergence=False, L1steps=1, L2steps=3, min_tau=1, fixed_tau=None, tau_window=None)\n",
      "13:41 bilby INFO    : Drawing 500 samples\n",
      "13:41 bilby INFO    : Checkpoint every 1800s\n",
      "13:41 bilby INFO    : Print update every 5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.52e+03|0:00:05|1.58e+03(10tau)|t=38|n=288|a=0.24|e=8.2e-01%|1.89ms/ev|maxl=-230.91|ETF=0:00:15\n",
      "6.25e+03|0:00:10|3.84e+02(10tau)|t=38|n=1248|a=0.26|e=2.0e+00%|2.45ms/ev|maxl=-230.91|ETF=0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13:41 bilby INFO    : Reached convergence: exiting sampling\n",
      "13:41 bilby INFO    : Checkpoint start\n",
      "13:41 bilby INFO    : Written checkpoint file outdir_Bilby-MCMC_I/runB_resume.pickle\n",
      "13:41 bilby INFO    : Zero-temperature proposals:\n",
      "13:41 bilby INFO    : AdaptiveGaussianProposal(acceptance_ratio:0.2,n:9.2e+02,scale:1e-05,)\n",
      "13:41 bilby INFO    : DifferentialEvolutionProposal(acceptance_ratio:0.41,n:9.4e+02,)\n",
      "13:41 bilby INFO    : UniformProposal(acceptance_ratio:1,n:9,)\n",
      "13:41 bilby INFO    : KDEProposal(acceptance_ratio:0,n:9.4e+02,trained:0,)\n",
      "13:41 bilby INFO    : GMMProposal(acceptance_ratio:0.0011,n:9.1e+02,trained:0,)\n",
      "13:41 bilby INFO    : NormalizingFlowProposal(acceptance_ratio:0,n:9.7e+02,trained:0,)\n",
      "13:41 bilby INFO    : Current taus={'amplitude': 41.0, 'frequency': 27.3}\n",
      "13:41 bilby INFO    : Ensemble swaps = 1562\n",
      "13:41 bilby INFO    : ProposalCycle:\n",
      "  EnsembleStretch(acceptance_ratio:0.68,n:1.6e+04,)\n",
      "\n",
      "13:41 bilby INFO    : Creating diagnostic plots\n",
      "13:41 bilby INFO    : Checkpoint finished\n",
      "13:41 bilby INFO    : Sampling time: 0:00:10.001817\n",
      "13:41 bilby INFO    : Summary of results:\n",
      "nsamples: 1096\n",
      "ln_noise_evidence:    nan\n",
      "ln_evidence:    nan +/-    nan\n",
      "ln_bayes_factor:    nan +/-    nan\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result = bilby.run_sampler(\n",
    "    likelihood=likelihood,\n",
    "    priors=priors,\n",
    "    injection_parameters=simulation_parameters,\n",
    "    outdir=outdir,\n",
    "    label=\"runC\",\n",
    "    sampler='bilby_mcmc',\n",
    "    printdt=5,\n",
    "    nsamples=500,\n",
    "    ntemps=1,\n",
    "    nensemble=10,\n",
    "    resume=False,\n",
    "    adapt=False,\n",
    "    L1steps=1,  # Set L1steps to 1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you run an ensemble, multiple trace plots will be produced: one for each walker. These are labelled by `En` where `n` is the walker number:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ls: cannot access 'outdir_Bilby-MCMC_I/runC*': No such file or directory\r\n"
     ]
    }
   ],
   "source": [
    "! ls outdir_Bilby-MCMC_I/runC*"
   ]
  }
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