Gravitational-wave transient inference with Bilby

gravitational-wave-transient-inference-with-bilby.ipynb

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      "name": "Gravitational-wave transient inference with Bilby",
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        "<a href=\"https://colab.research.google.com/gist/ColmTalbot/58dacd07a820d96ed0e1c43e2bf824c1/gravitational-wave-transient-inference-with-bilby.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Astrophysical inference for gravitational-wave transients\n",
        "\n",
        "In this notebook we will go through some of the useful tools `Bilby` has for doing Bayesian inference on gravitational-wave transients.\n",
        "\n",
        "Out of necessity it will not be an exhaustive example of all the built in options and of course users are encouraged to augment `Bilby` themselves.\n",
        "\n",
        "There are far more [examples](https://git.ligo.org/lscsoft/bilby/tree/master/examples/gw_examples) than this in the `Bilby` git repository."
      ]
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      "cell_type": "markdown",
      "metadata": {
        "id": "gXe5czb3jXVo",
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      "source": [
        "### Install packages\n",
        "\n",
        "Since this notebook was written for [colab](colab.research.google.com) we have to install the relevant packages in this case everything is available from `pip`.\n",
        "\n",
        "Copy and paste the following into a code cell below.\n",
        "\n",
        "```python\n",
        "!pip install bilby lalsuite gwpy\n",
        "```\n",
        "\n",
        "We install\n",
        "- `Bilby`: this is the library used for Bayesian inference, it also has a bunch of built in tools for gravitational-wave transients.\n",
        "- `LALSuite`: the LIGO Algorithms Library, `Bilby` (generally) uses `lalsimulation` to generate waveforms.\n",
        "- `gwpy`: a package for interfacing with gravitational-wave data, it is the most convenient way to access open or proprietary data."
      ]
    },
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "source": [
        "!pip install bilby lalsuite gwpy"
      ],
      "execution_count": 0,
      "outputs": [
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ie_ycPGek-CN",
        "colab_type": "text"
      },
      "source": [
        "## Imports\n",
        "\n",
        "I'll put all (or at least most of) the imports here.\n",
        "Explanations of some of the specific elements will be where they're first used."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UlY2Uc54jNnm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import bilby\n",
        "\n",
        "from bilby import run_sampler\n",
        "\n",
        "from bilby.core.prior import Constraint, Uniform\n",
        "\n",
        "from bilby.gw.conversion import (\n",
        "    convert_to_lal_binary_black_hole_parameters,\n",
        "    generate_all_bbh_parameters\n",
        ")\n",
        "from bilby.gw.detector.networks import InterferometerList\n",
        "from bilby.gw.detector.psd import PowerSpectralDensity\n",
        "from bilby.gw.likelihood import GravitationalWaveTransient\n",
        "from bilby.gw.prior import BBHPriorDict\n",
        "from bilby.gw.result import CBCResult\n",
        "from bilby.gw.source import lal_binary_black_hole\n",
        "from bilby.gw.utils import get_event_time\n",
        "from bilby.gw.waveform_generator import WaveformGenerator\n",
        "\n",
        "from gwpy.timeseries import TimeSeries"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r4kYgF6JleY4",
        "colab_type": "text"
      },
      "source": [
        "## Accessing data\n",
        "\n",
        "In this example we're going to grab the data to analyse the end of the inspiral for GW170814.\n",
        "\n",
        "To make the analysis run in a reasonable time we're going to just look at the final ~3s of the inspiral.\n",
        "\n",
        "We ask for 128s of data after the signal in order to estimate the power spectral density.\n",
        "\n",
        "The methods below are adapted from [`bilby_pipe.data_generation`](https://git.ligo.org/lscsoft/bilby_pipe/blob/master/bilby_pipe/data_generation.py)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-7tFpOB0mY3w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "interferometers = InterferometerList([\"H1\", \"L1\", \"V1\"])\n",
        "trigger_time = get_event_time(\"170814\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B50pVUSJsbpB",
        "colab_type": "text"
      },
      "source": [
        "### First we need to specify various bits of information about how much data to get and how it should be processed.\n",
        "\n",
        "We fetch a 4s stretch of data and put the trigger time `3`s through the\n",
        "segment.\n",
        "\n",
        "We also specify the \"roll_off\", this tells use how quickly the [tukey window]() applied to the data before performing the fast fourier transform of the data should fall off.\n",
        "This is in units of seconds.\n",
        "A fall off of `0` is a rectangular window and leads to issues due to the \"lines\" (sharp features in the power spectrum).\n",
        "We should always be careful to not apply the window to part of the signal being analysed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "02lrbi2isYkx",
        "colab_type": "code",
        "outputId": "3e132dc3-fb7a-401f-951e-81ab2948bd6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "start_time = trigger_time - 3\n",
        "duration = 4\n",
        "end_time = start_time + duration\n",
        "roll_off = 0.2\n",
        "\n",
        "for interferometer in interferometers:\n",
        "    print(\n",
        "        \"Getting analysis segment data for {}\".format(interferometer.name)\n",
        "    )\n",
        "    analysis_data = TimeSeries.fetch_open_data(\n",
        "        interferometer.name, start_time, end_time\n",
        "    )\n",
        "    interferometer.strain_data.roll_off = roll_off\n",
        "    interferometer.strain_data.set_from_gwpy_timeseries(analysis_data)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Getting analysis segment data for H1\n",
            "Getting analysis segment data for L1\n",
            "Getting analysis segment data for V1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MC1rlu8ZwZV8",
        "colab_type": "text"
      },
      "source": [
        "### Now we need to get the data to compute the power spectral density (PSD).\n",
        "\n",
        "We average over multiple segments.\n",
        "We take the median average."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R800b5Nvtois",
        "colab_type": "code",
        "outputId": "c6988685-e54b-4100-85c0-76a4b5291a8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "actual_psd_start_time = start_time + duration\n",
        "psd_duration = 128\n",
        "actual_psd_end_time = actual_psd_start_time + psd_duration\n",
        "psd_tukey_alpha = 2 * roll_off / duration\n",
        "overlap = duration / 2\n",
        "\n",
        "for interferometer in interferometers:\n",
        "    print(\"Getting psd segment data for {}\".format(interferometer.name))\n",
        "    psd_data = TimeSeries.fetch_open_data(\n",
        "        interferometer.name, actual_psd_start_time, actual_psd_end_time\n",
        "    )\n",
        "    psd = psd_data.psd(\n",
        "        fftlength=duration, overlap=overlap, window=(\"tukey\", psd_tukey_alpha),\n",
        "        method=\"median\"\n",
        "    )\n",
        "    interferometer.power_spectral_density = PowerSpectralDensity(\n",
        "        frequency_array=psd.frequencies.value, psd_array=psd.value\n",
        "    )\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Getting psd segment data for H1\n",
            "Getting psd segment data for L1\n",
            "Getting psd segment data for V1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ri3suek-weSv",
        "colab_type": "text"
      },
      "source": [
        "### Finally let's plot the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Im0skix1vldL",
        "colab_type": "code",
        "outputId": "3e6901e6-651b-4888-8323-4c2421c0ba0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        }
      },
      "source": [
        "for interferometer in interferometers:\n",
        "    plt.loglog(interferometer.frequency_array, abs(interferometer.frequency_domain_strain))\n",
        "    plt.loglog(interferometer.frequency_array, abs(interferometer.amplitude_spectral_density_array))\n",
        "    plt.xlim(interferometer.minimum_frequency, interferometer.maximum_frequency)\n",
        "    plt.show()\n",
        "    plt.close()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "20:52 bilby INFO    : Generating frequency domain strain from given time domain strain.\n",
            "20:52 bilby INFO    : Applying a tukey window with alpha=0.1, roll off=0.2\n",
            "20:52 bilby INFO    : Generating frequency domain strain from given time domain strain.\n",
            "20:52 bilby INFO    : Applying a tukey window with alpha=0.1, roll off=0.2\n",
            "20:52 bilby INFO    : Generating frequency domain strain from given time domain strain.\n",
            "20:52 bilby INFO    : Applying a tukey window with alpha=0.1, roll off=0.2\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lJCFxg-Plm8K",
        "colab_type": "text"
      },
      "source": [
        "## Setting up our model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RsaAi7IewpxR",
        "colab_type": "text"
      },
      "source": [
        "### Now that we have data we need a model to construct out likelihood\n",
        "\n",
        "In this case our model will be a gravitational-wave template for a binary black hole merger.\n",
        "\n",
        "This is accessed using a `waveform_generator`.\n",
        "\n",
        "This is given:\n",
        "- the duration and sampling frequency of the data that we want to compare the model to.\n",
        "- a `frequency_domain_source_model`, this is a python function which reutrns the waveform template given the parameters. For details of how to define such a model see [here](https://git.ligo.org/lscsoft/bilby/blob/master/bilby/gw/source.py).\n",
        "- a `parameter_conversion` function, this enables the user to sample in parameters that aren't directly used by the source model, e.g., sampling in chirp mass and mass ratio instead of component masses.\n",
        "- `waveform_arguments`, this is a dictionary of arguments which are passed to the source model which should not be sampled, e.g., the waveform approximant/reference frequency.\n",
        "\n",
        "This source model calls down to `lalsimulation` and should be able to use any waveform model implemented in that pakage.\n",
        "\n",
        "Other options:\n",
        "- a `time_domain_source_model` can be passed as an alternative to the `frequency_domain_source_model`, this will then be FFT'd if used in the likelihood below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xlN1uDiFwpW9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "waveform_arguments = dict(\n",
        "    waveform_approximant=\"IMRPhenomPv2\",\n",
        "    reference_frequency=20\n",
        ")\n",
        "\n",
        "waveform_generator = WaveformGenerator(\n",
        "    duration=interferometers.duration,\n",
        "    sampling_frequency=interferometers.sampling_frequency,\n",
        "    frequency_domain_source_model=lal_binary_black_hole,\n",
        "    parameter_conversion=convert_to_lal_binary_black_hole_parameters,\n",
        "    waveform_arguments=waveform_arguments\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3GU0ErXPlxqC",
        "colab_type": "text"
      },
      "source": [
        "## Prior choice\n",
        "\n",
        "`Bilby` has a built in `BBHPriorDict` class which has all the necessary parameters for sampling a binary black hole system.\n",
        "\n",
        "We'll use that as a base but then make a few adjustments.\n",
        "\n",
        "- we specify to use aligned spins rather than the full six-dimensional spins.\n",
        "- we need to add a prior for time as that is difficult to define a default prior for.\n",
        "- we remove the priors on the component masses and repace them with priors on the chirp mass and mass ratio as those are parameters which are easier to sample in."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ycRlKeJrx_22",
        "colab_type": "code",
        "outputId": "6820d6a8-b360-41f7-97ed-3c3024357904",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "source": [
        "priors = BBHPriorDict(aligned_spin=True)\n",
        "priors[\"chirp_mass\"] = Uniform(\n",
        "    minimum=24, maximum=30,\n",
        "    latex_label=\"$\\\\mathcal{M}$\", unit=\"$M_{\\\\odot}$\", boundary=\"reflective\"\n",
        ")\n",
        "priors[\"mass_ratio\"] = Uniform(\n",
        "    minimum=0.4, maximum=1, latex_label=\"$q$\", boundary=\"reflective\"\n",
        ")\n",
        "del priors[\"mass_1\"], priors[\"mass_2\"]\n",
        "\n",
        "priors[\"geocent_time\"] = Uniform(\n",
        "    minimum=trigger_time - 0.1, maximum=trigger_time + 0.1,\n",
        "    latex_label=\"$t_c$\", unit=\"$s$\", boundary=\"reflective\"\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "20:52 bilby INFO    : Using aligned spin prior\n",
            "20:52 bilby INFO    : No prior given, using default BBH priors in /usr/local/lib/python3.6/dist-packages/bilby/gw/prior_files/aligned_spin_binary_black_holes.prior.\n",
            "/usr/local/lib/python3.6/dist-packages/bilby/gw/prior.py:234: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  z_prior.prob(x / aas)), aas) for x in xx]\n",
            "/usr/local/lib/python3.6/dist-packages/bilby/gw/prior.py:234: RuntimeWarning: invalid value encountered in multiply\n",
            "  z_prior.prob(x / aas)), aas) for x in xx]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gPAVxPdvvIjK",
        "colab_type": "code",
        "outputId": "a3deac02-5426-4b52-a845-b95566873d37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        }
      },
      "source": [
        "for key in priors:\n",
        "    print(f\"{key}: {priors[key]}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mass_ratio: Uniform(minimum=0.4, maximum=1, name=None, latex_label='$q$', unit=None, boundary='reflective')\n",
            "chi_1: AlignedSpin(a_prior=Uniform(minimum=0, maximum=0.8, name=None, latex_label=None, unit=None, boundary=None), z_prior=Uniform(minimum=-1, maximum=1, name=None, latex_label=None, unit=None, boundary=None), name='chi_1', latex_label='chi_1', unit=None, boundary='reflective')\n",
            "chi_2: AlignedSpin(a_prior=Uniform(minimum=0, maximum=0.8, name=None, latex_label=None, unit=None, boundary=None), z_prior=Uniform(minimum=-1, maximum=1, name=None, latex_label=None, unit=None, boundary=None), name='chi_2', latex_label='chi_2', unit=None, boundary='reflective')\n",
            "luminosity_distance: UniformSourceFrame(minimum=100.0, maximum=5000.0, cosmology=FlatLambdaCDM(name=\"Planck15\", H0=67.7 km / (Mpc s), Om0=0.307, Tcmb0=2.725 K, Neff=3.05, m_nu=[0.   0.   0.06] eV, Ob0=0.0486), name='luminosity_distance', latex_label='$d_L$', unit=Unit(\"Mpc\"), boundary=None)\n",
            "dec: Cosine(name='dec', latex_label='$\\\\mathrm{DEC}$', unit=None, minimum=-1.5707963267948966, maximum=1.5707963267948966, boundary='reflective')\n",
            "ra: Uniform(minimum=0, maximum=6.283185307179586, name='ra', latex_label='$\\\\mathrm{RA}$', unit=None, boundary='periodic')\n",
            "theta_jn: Sine(name='theta_jn', latex_label='$\\\\theta_{JN}$', unit=None, minimum=0, maximum=3.141592653589793, boundary='reflective')\n",
            "psi: Uniform(minimum=0, maximum=3.141592653589793, name='psi', latex_label='$\\\\psi$', unit=None, boundary='periodic')\n",
            "phase: Uniform(minimum=0, maximum=6.283185307179586, name='phase', latex_label='$\\\\phi$', unit=None, boundary='periodic')\n",
            "chirp_mass: Uniform(minimum=24, maximum=30, name=None, latex_label='$\\\\mathcal{M}$', unit='$M_{\\\\odot}$', boundary='reflective')\n",
            "geocent_time: Uniform(minimum=1186741861.4, maximum=1186741861.6, name=None, latex_label='$t_c$', unit='$s$', boundary='reflective')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SaZN6wTtvJTl",
        "colab_type": "text"
      },
      "source": [
        "Let's take a look at what priors we have.\n",
        "\n",
        "These are mostly uniform with just a few exceptions.\n",
        "\n",
        "- Since we're only considering aligned spins we are using the `bilby.gw.prior.AlignedSpin` class.\n",
        "This takes a prior on spin magnitude $a$ and _cosine_ spin tilt $z$ and calculates the implied prior on the aligned component of the spin.\n",
        "- For luminosity distance we use the `bilby.gw.prior.UniformSourceFrame` prior. This ensures that our prior belief on distance is that events happen uniformly throughout the universe. The `cosmology` argument can either be an astropy `Cosmology` object or the name of a known cosmology, e.g., `WMAP9`.\n",
        "- `dec` and `theta_jn` have `Cosine` and `Sine` priors respectively, these are saying that the prior distribution is proportional to the appropriate trignometric function.\n",
        "These are chose so that the prior is uniform over the sphere."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYmn_DbXjAwE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prior_samples = priors.sample(10000)\n",
        "\n",
        "plt.hist(prior_samples[\"chi_1\"], bins=100)\n",
        "plt.show()\n",
        "plt.close()\n",
        "\n",
        "plt.hist(prior_samples[\"luminosity_distance\"], bins=100)\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NE0DKfntlpc5",
        "colab_type": "text"
      },
      "source": [
        "## Let's make a likelihood!\n",
        "\n",
        "The standard likelihood used in gravitational-wave transient inference is the [Whittle Likelihood](https://en.wikipedia.org/wiki/Whittle_likelihood)\n",
        "$$\\mathcal{L}(d | \\theta) = \\prod_{i} \\prod_{j} \\frac{1}{2 \\pi S_{i,j} } \\exp\\left( -\\frac{|\\tilde{d}_{i,j} - \\tilde{h}_{i,j}(\\theta)|^2}{2S_{i,j} } \\right).$$\n",
        "The product here is over detectors $i$ and frequency bins $j$.\n",
        "$\\tilde{d}$ is the frequency-domain strain data, $\\tilde{h}$ is the template waveform, and $S$ is the noise power spectral density.\n",
        "\n",
        "In practice we are only interested in the (logarithm of) the terms which depend on the binary parameters.\n",
        "In this case we define the \"log likelihood ratio\" as follows\n",
        "$$\\ln \\mathcal{L} = \\sum_{j} \\sum_{j} - \\frac{1}{2} |\\tilde{h}_{i,j}|^2 + \\mathbb{R}\\left( \\tilde{d}_{i,j}^{*} \\tilde{h}_{i,j} \\right).$$\n",
        "\n",
        "_Note_: There are exceptions, e.g., when the power spectral density is modeled.\n",
        "\n",
        "There are some parameters which alter the template (and by extension the likelihood) in simple ways. The `GravitationalWaveTransient` likelihood in `Bilby` has implementation of marginalisation over the distance, time, and phase.\n",
        "\n",
        "The likelihood takes the following arguments\n",
        "- `interferometers`: this is the data for our likelihood\n",
        "- `waveform_generator`: this is our model for the signal\n",
        "- `priors`: if we use any of the marginalisations we need to provide the prior. The priors for the marginalised parameters are modified in place to fiducial values\n",
        "- `distance_marginalization`: whether to marginalize the likelihood over distance. I generally recommend using this as it removes the distance/inclination degeneracy and the posterior can be generated in post-processing. I've disabled it in this example as it takes a few minutes to set up the required lookup table.\n",
        "- `phase_marginalization`: whether to marginalize the likelihood over phase. Marginalising over phase analytically removes the degeneracy with polarisation. Note that this is only valid when there are no higher-order modes in the waveform.\n",
        "- `time_marginalization`: whether to marginalize the likelihood over time. This uses a FFT to marginalise over time.\n",
        "- `jitter_time`: when using the `time_marginalization` option the likelihood is evaluated on a fixed grid of times. To avoid coarse-graining issues on this grid we have the ablililty to sample in the position of the grid.\n",
        "\n",
        "_Note_: If you're using `bilby<0.5.6` there is a message `Time jittering requested with non-time-marginalised likelihood, ignoring.`. Ignore this."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aOb4fmu7xtxm",
        "colab_type": "code",
        "outputId": "ccb7b604-4758-4424-cb98-39b6f4c7f832",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "likelihood = GravitationalWaveTransient(\n",
        "    interferometers=interferometers, waveform_generator=waveform_generator,\n",
        "    priors=priors, time_marginalization=False, distance_marginalization=False,\n",
        "    phase_marginalization=True\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "20:52 bilby WARNING : The waveform_generator start_time is not equal to that of the provided interferometers. Overwriting the waveform_generator.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n6qOe8Vbl_g9",
        "colab_type": "text"
      },
      "source": [
        "## It's samplin' time\n",
        "\n",
        "To make this run in just a few minutes the sampler setting are woefully inadequate to have faith in the results.\n",
        "\n",
        "The do a better run increase `nlive` and `walks`, for the review we are using nlive $= O(1000)$ and walks $= O(100)$.\n",
        "For details on the specific sampler and others, see the documentation either in [`Bilby`](https://docs.ligo.org/lscsoft/bilby/samplers.html) or the individual samplers, e.g., [`dynesty`](https://dynesty.readthedocs.io/en/latest/api.html).\n",
        "\n",
        "We pass a `conversion_function` which is applied after the sampling has finished. In this case we tell it to `generate_all_bbh_parameters`, this generates all the parameters that `Bilby` knows how to produce for binary black hole systems. This includes constructing the posterior for the marginalised parameters."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LdnFZAQEz77b",
        "colab_type": "code",
        "outputId": "6ba0b112-7099-4df4-ae9b-1bdcada1f2ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        }
      },
      "source": [
        "result = run_sampler(\n",
        "    likelihood=likelihood, priors=priors, save=False,\n",
        "    nlive=50, walks=25,\n",
        "    conversion_function=generate_all_bbh_parameters,\n",
        "    result_class=CBCResult)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "20:52 bilby INFO    : Running for label 'label', output will be saved to 'outdir'\n",
            "20:52 bilby INFO    : Using LAL version Branch: None;Tag: lalsuite-v6.62;Id: a75e6c243c5695e1abdcc4e5d91f623cf0db1d22;;Builder: Unknown User <>;Repository status: UNCLEAN: Modified working tree\n",
            "20:52 bilby INFO    : Search parameters:\n",
            "20:52 bilby INFO    :   mass_ratio = Uniform(minimum=0.4, maximum=1, name=None, latex_label='$q$', unit=None, boundary='reflective')\n",
            "20:52 bilby INFO    :   chi_1 = AlignedSpin(a_prior=Uniform(minimum=0, maximum=0.8, name=None, latex_label=None, unit=None, boundary=None), z_prior=Uniform(minimum=-1, maximum=1, name=None, latex_label=None, unit=None, boundary=None), name='chi_1', latex_label='chi_1', unit=None, boundary='reflective')\n",
            "20:52 bilby INFO    :   chi_2 = AlignedSpin(a_prior=Uniform(minimum=0, maximum=0.8, name=None, latex_label=None, unit=None, boundary=None), z_prior=Uniform(minimum=-1, maximum=1, name=None, latex_label=None, unit=None, boundary=None), name='chi_2', latex_label='chi_2', unit=None, boundary='reflective')\n",
            "20:52 bilby INFO    :   luminosity_distance = UniformSourceFrame(minimum=100.0, maximum=5000.0, cosmology=FlatLambdaCDM(name=\"Planck15\", H0=67.7 km / (Mpc s), Om0=0.307, Tcmb0=2.725 K, Neff=3.05, m_nu=[0.   0.   0.06] eV, Ob0=0.0486), name='luminosity_distance', latex_label='$d_L$', unit=Unit(\"Mpc\"), boundary=None)\n",
            "20:52 bilby INFO    :   dec = Cosine(name='dec', latex_label='$\\\\mathrm{DEC}$', unit=None, minimum=-1.5707963267948966, maximum=1.5707963267948966, boundary='reflective')\n",
            "20:52 bilby INFO    :   ra = Uniform(minimum=0, maximum=6.283185307179586, name='ra', latex_label='$\\\\mathrm{RA}$', unit=None, boundary='periodic')\n",
            "20:52 bilby INFO    :   theta_jn = Sine(name='theta_jn', latex_label='$\\\\theta_{JN}$', unit=None, minimum=0, maximum=3.141592653589793, boundary='reflective')\n",
            "20:52 bilby INFO    :   psi = Uniform(minimum=0, maximum=3.141592653589793, name='psi', latex_label='$\\\\psi$', unit=None, boundary='periodic')\n",
            "20:52 bilby INFO    :   chirp_mass = Uniform(minimum=24, maximum=30, name=None, latex_label='$\\\\mathcal{M}$', unit='$M_{\\\\odot}$', boundary='reflective')\n",
            "20:52 bilby INFO    :   geocent_time = Uniform(minimum=1186741861.4, maximum=1186741861.6, name=None, latex_label='$t_c$', unit='$s$', boundary='reflective')\n",
            "20:52 bilby INFO    :   phase = 0.0\n",
            "20:52 bilby INFO    : Single likelihood evaluation took 1.338e-02 s\n",
            "20:52 bilby INFO    : Using sampler Dynesty with kwargs {'bound': 'multi', 'sample': 'rwalk', 'verbose': True, 'periodic': None, 'reflective': None, 'check_point_delta_t': 600, 'nlive': 50, 'first_update': None, 'walks': 25, 'npdim': None, 'rstate': None, 'queue_size': None, 'pool': None, 'use_pool': None, 'live_points': None, 'logl_args': None, 'logl_kwargs': None, 'ptform_args': None, 'ptform_kwargs': None, 'enlarge': None, 'bootstrap': None, 'vol_dec': 0.5, 'vol_check': 2.0, 'facc': 0.5, 'slices': 5, 'update_interval': 30, 'print_func': <bound method Dynesty._print_func of <bilby.core.sampler.dynesty.Dynesty object at 0x7f8a4a836c18>>, 'dlogz': 0.1, 'maxiter': None, 'maxcall': None, 'logl_max': inf, 'add_live': True, 'print_progress': True, 'save_bounds': False, 'n_effective': None}\n",
            "20:52 bilby INFO    : Checkpoint every n_check_point = 40000\n",
            "20:52 bilby INFO    : Using dynesty version 1.0.0\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            " 11| logz ratio=-11.912 +/-  0.367 | dlogz: 53.555 >  0.100"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/dynesty/dynesty.py:298: UserWarning: A note of caution: having `nlive < ndim * (ndim + 1) // 2` may result in unconstrained bounding distributions.\n",
            "  warnings.warn(\"A note of caution: \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            " 1533| logz ratio=135.882 +/-  0.963 | dlogz:  0.002 >  0.100"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "21:01 bilby INFO    : Writing checkpoint file outdir/label_resume.pickle\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            " 1533| logz ratio=135.882 +/-  0.963 | dlogz:  0.002 >  0.100\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "21:01 bilby INFO    : Sampling time: 0:08:18.667136\n",
            "21:01 bilby INFO    : Reconstructing marginalised parameters.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1583/1583 [00:14<00:00, 107.17it/s]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "21:01 bilby INFO    : Computing SNRs for every sample.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1583/1583 [00:22<00:00, 68.89it/s]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "21:02 bilby INFO    : Summary of results:\n",
            "nsamples: 1583\n",
            "log_noise_evidence: -24870.652\n",
            "log_evidence: -24734.770 +/-  0.963\n",
            "log_bayes_factor: 135.882 +/-  0.963\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sC7I-6qomJtN",
        "colab_type": "text"
      },
      "source": [
        "## and finally some plotting.\n",
        "\n",
        "First we'll make plots of the inferred waveform. Hopefully this will line up with a feature in the whitened time-domain strain."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R6wxfsXA0cBt",
        "colab_type": "code",
        "outputId": "aba90ccd-96b5-4559-e629-14dcea24d33d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "for interferometer in interferometers:\n",
        "    fig = result.plot_interferometer_waveform_posterior(\n",
        "        interferometer=interferometer\n",
        "    )\n",
        "    plt.show()\n",
        "    plt.close()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "21:02 bilby INFO    : Generating waveform figure for H1\n",
            "21:02 bilby INFO    : Generating waveform figure for L1\n",
            "21:02 bilby INFO    : Generating waveform figure for V1\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SfVw1zb0x9Lz",
        "colab_type": "text"
      },
      "source": [
        "Now let's make a couple of corner plots on same parameters.\n",
        "\n",
        "In this first one we plot the source-frame masses and effective aligned spin, then we'll look at the distance and right ascension/declination."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1NUyDG77szh",
        "colab_type": "code",
        "outputId": "ee7b2385-cb5e-4336-ed1d-c53aafea75c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        }
      },
      "source": [
        "result.plot_corner(parameters=[\"mass_1_source\", \"mass_2_source\", \"chi_eff\"])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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P++XannbPPfd8YwbA77F+6Zd+6Zc3vnTWg1JKKaVc0kRBKaWUUi4N2TEKyrn02OAOUxSV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\n4GzOeH2xt5r//FjCf9eXcsOE6BOSJW5JeaOTpftrSDBpeOXKDP/1YHP5g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KEAACAASURB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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 547.2x547.2 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYA463zT-OeN",
        "colab_type": "code",
        "outputId": "9e074a3d-5298-49e0-9d6f-b6d604e3d324",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        }
      },
      "source": [
        "result.plot_corner(parameters=[\"luminosity_distance\", \"ra\", \"dec\"])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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DvY+jr1uLy1U/y4ZtdbW5c+euNN3g71g/9EM/9MMVH17ds+BKW7LLABjaM9jNkTQeMrl+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HZ8PFAWOlpKWdtfHVMURVEUJTT0OLIghKgVQkxp83chhHhXCJHToel0oGogOqgoiqIoyuDqbRoi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\nCWMhCIIgCIKGhLEQBEEQBEFD/h8x+2U2ondg4gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 547.2x547.2 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nQtY18Vgl1m0",
        "colab_type": "text"
      },
      "source": [
        "## Bonus cells\n",
        "\n",
        "Here's a small set of examples showing other things which are possible."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PbSMBFnbyR6H",
        "colab_type": "text"
      },
      "source": [
        "## Calibration\n",
        "\n",
        "It is possible to include a model for the detector calibration.\n",
        "This is an attribute of the `Interferometer` object.\n",
        "Currently the calibration model is limited to be a cubic spline as implemented in `LALInference` however the user can provide their own model, or contribute one to `Bilby`!\n",
        "\n",
        "```python\n",
        "from bilby.gw.detector.calibration import CubicSpline\n",
        "interferometer.calibration_model = CubicSpline(\n",
        "    prefix=interferometer.name,\n",
        "    minimum_frequency=interferometer.minimum_frequency,\n",
        "    maximum_frequency=interferometer.maximum_frequency,\n",
        "    n_points=9\n",
        ")\n",
        "```\n",
        "\n",
        "There are also tools for creating the required prior for the spline calibration.\n",
        "\n",
        "```python\n",
        "from bilby.gw.prior import CalibrationPriorDict\n",
        "priors.update(CalibrationPriorDict.constant_uncertainty_spline(\n",
        "    amplitude_sigma=0.1,\n",
        "    phase_sigma0.03,\n",
        "    minimum_frequency=interferometer.minimum_frequency,\n",
        "    maximum_frequency=interferoemter.maximum_frequency,\n",
        "    n_nodes=9,\n",
        "    label=interferometer.name):\n",
        ")\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvCfDgXf0Nf5",
        "colab_type": "text"
      },
      "source": [
        "## Generating fake data\n",
        "\n",
        "A standard use-case for `Bilby` is analysing signals injected into fake data.\n",
        "The standard example for this is [here](https://git.ligo.org/lscsoft/bilby/blob/master/examples/gw_examples/injection_examples/fast_tutorial.py)\n",
        "\n",
        "Creating the data and injecting the signal in this case is by doing the following.\n",
        "\n",
        "```python\n",
        "injection_parameters = dict(\n",
        "    mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0,\n",
        "    phi_12=1.7, phi_jl=0.3, luminosity_distance=2000., theta_jn=0.4, psi=2.659,\n",
        "    phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108)\n",
        "\n",
        "ifos = bilby.gw.detector.InterferometerList(['H1', 'L1', 'V1', 'K1'])\n",
        "ifos.set_strain_data_from_power_spectral_densities(\n",
        "    sampling_frequency=sampling_frequency, duration=duration,\n",
        "    start_time=injection_parameters['geocent_time'] - 3)\n",
        "ifos.inject_signal(waveform_generator=waveform_generator,\n",
        "                   parameters=injection_parameters)\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FkxhgCFdGQKp",
        "colab_type": "text"
      },
      "source": [
        "## Defining a new source model\n",
        "\n",
        "Defining a new source model in `Bilby` just requires writing a python function.\n",
        "There are a few conventions that must be followed.\n",
        "\n",
        "```python\n",
        "def time_domain_sine_gaussian(\n",
        "        time_array, amplitude, phase, frequency, sigma\n",
        "):\n",
        "    \"\"\"\n",
        "    A time-domain sine-gaussian source model.\n",
        "\n",
        "    This assumes the waveform is circularly polarised so we are free to set the\n",
        "    cross term to be zero.\n",
        "\n",
        "    This is based on https://lscsoft.docs.ligo.org/lalsuite/lalinference/group___l_a_l_inference_template__h.html#ga61ea5a03b8aeffaeb3155df1866cbdcf\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    time_array: array-like\n",
        "        This is the array of times that the waveform will be evaluated at.\n",
        "        This will be determined automatically by the WaveformGenerator that\n",
        "        calls this function.\n",
        "    amplitude: float\n",
        "        Peak amplitude of the sine-gaussian burst.\n",
        "    phase: float\n",
        "        Phase of the burst at the time of peak amplitude.\n",
        "    frequency: float\n",
        "        Frequency of the sine wave.\n",
        "    Sigma: float\n",
        "        Width of the Gaussian in seconds.\n",
        "    \n",
        "    Returns\n",
        "    -------\n",
        "    dict: Dictionary containing array of plus polarisation.\n",
        "    \"\"\"\n",
        "    if 20 * sigma >= time_array:\n",
        "        raise ValueError(\"Sine-gaussian isn't contained in segment.\")\n",
        "    h_plus = np.zeros_like(time_array)\n",
        "    \n",
        "    idxs_after = time_array <= 10 * sigma\n",
        "    time_after = time_array[idxs_after]\n",
        "    scaled_time_after = time_after * sigma\n",
        "    gaussian_term = np.exp(- scaled_time_after)\n",
        "    sine_term = np.sin(2 * np.pi * frequency * time_after + phi)\n",
        "    h_plus[times_after] = gaussian_term * sine_term\n",
        "\n",
        "    idxs_before = time_array <= time_array[-1] - 10 * sigma\n",
        "    time_before = time_array[idxs_before] - time_array[-1]\n",
        "    scaled_time_before = time_before * sigma\n",
        "    gaussian_term = np.exp(- scaled_time_before)\n",
        "    sine_term = np.sin(2 * np.pi * frequency * time_before + phi)\n",
        "    h_plus[times_after] = gaussian_term * sine_term\n",
        "\n",
        "    return{'plus': h_plus}\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fzdT-TSBPhid",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}