sgvb_lvk.ipynb

sgvb_lvk.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/avivajpeyi/687b7ac854fc8005506276e7f121b9b0/sgvb_lvk.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lIYdn1woOS1n",
        "outputId": "4853a186-5a3c-40f8-fa15-84a3431256f5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/983.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m563.2/983.9 kB\u001b[0m \u001b[31m17.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m983.9/983.9 kB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/108.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m108.2/108.2 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m41.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.4/47.4 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m41.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.3/40.3 MB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m84.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.7/295.7 kB\u001b[0m \u001b[31m22.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m128.6/128.6 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.1/50.1 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.5/4.5 MB\u001b[0m \u001b[31m98.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "pydrive2 1.21.3 requires cryptography<44, but you have cryptography 45.0.2 which is incompatible.\n",
            "pyopenssl 24.2.1 requires cryptography<44,>=41.0.5, but you have cryptography 45.0.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "! pip install bilby[gw] sgvb_psd -q"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SGVB for LVK"
      ],
      "metadata": {
        "id": "KHjmhs4HMIhG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Compare against Welch"
      ],
      "metadata": {
        "id": "-yU36PN9MYjH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"GWPY_RCPARAMS\"] = \"FALSE\"\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", \"Wswiglal-redir-stdio\")\n",
        "\n",
        "import bilby\n",
        "from gwpy.timeseries import TimeSeries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy.special\n",
        "import scipy.interpolate\n",
        "import scipy.signal\n",
        "\n",
        "print(bilby.__version__)\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "\n",
        "#### HELPER FUNCTIONS\n",
        "\n",
        "def empirical_cdf(data):\n",
        "    \"\"\" Compute the empirical cumulative distribution function (ECDF). \"\"\"\n",
        "    sorted_data = np.sort(data)\n",
        "    n = len(data)\n",
        "\n",
        "    def ecdf(x):\n",
        "        return np.searchsorted(sorted_data, x, side='right') / n\n",
        "\n",
        "    return ecdf, sorted_data\n",
        "\n",
        "def anderson_darling_statistic(data):\n",
        "    \"\"\" Compute the Anderson-Darling test statistic for normality. \"\"\"\n",
        "    n = len(data)\n",
        "    ecdf, sorted_data = empirical_cdf(data)\n",
        "\n",
        "    # Transform data to standard normal quantiles\n",
        "    mean = np.mean(data)\n",
        "    std = np.std(data, ddof=1)  # Sample standard deviation\n",
        "    standardized = (sorted_data - mean) / std\n",
        "\n",
        "    # Compute theoretical normal CDF values\n",
        "    normal_cdf = 0.5 * (1 + scipy.special.erf(standardized / np.sqrt(2)))  # Standard normal CDF\n",
        "\n",
        "    # Compute Anderson-Darling test statistic\n",
        "    s = np.sum((2 * np.arange(1, n + 1) - 1) * (np.log(normal_cdf) + np.log(1 - normal_cdf[::-1])))\n",
        "    A2 = -n - s / n\n",
        "\n",
        "    return A2\n",
        "\n",
        "def anderson_p_value(data, freqs=None, fmin=0, fmax=np.inf):\n",
        "    \"\"\" Approximate the p-value for the Anderson-Darling test for normality. \"\"\"\n",
        "\n",
        "    # If provided, cut the frequencies to a min/max value\n",
        "    if freqs is not None:\n",
        "        idxs = (freqs > fmin) & (freqs < fmax)\n",
        "        data = data[idxs]\n",
        "\n",
        "    # Concatenate the real and imaginary parts together\n",
        "    data = np.concatenate([data.real, data.imag])\n",
        "\n",
        "    if len(data) == 0:\n",
        "        return np.nan\n",
        "\n",
        "    A2 = anderson_darling_statistic(data)\n",
        "\n",
        "    critical_values = [\n",
        "        0.200, 0.300, 0.400, 0.500, 0.576, 0.656, 0.787, 0.918,\n",
        "        1.092, 1.250, 1.500, 1.750, 2.000, 2.500, 3.000, 3.500,\n",
        "        4.000, 4.500, 5.000, 6.000, 7.000, 8.000, 10.000\n",
        "    ]\n",
        "\n",
        "    significance_levels = [\n",
        "        0.90, 0.85, 0.80, 0.75, 0.70, 0.60, 0.50, 0.40,\n",
        "        0.30, 0.25, 0.20, 0.15, 0.10, 0.05, 0.01, 0.005,\n",
        "        0.0025, 0.001, 0.0005, 0.0002, 0.0001, 0.00005, 0.00001\n",
        "    ]\n",
        "\n",
        "    # Approximate p-value using interpolation\n",
        "    if A2 < critical_values[0]:\n",
        "        pval = significance_levels[0]\n",
        "    elif A2 > critical_values[-1]:\n",
        "        pval = significance_levels[-1]\n",
        "    else:\n",
        "        pval = np.interp(A2, critical_values, significance_levels)\n",
        "\n",
        "    return float(pval)\n",
        "\n",
        "def fbins_anderson_p_value(freq, data, bin_width_Hz=8, fmin=0, fmax=np.inf):\n",
        "    \"\"\"\n",
        "    Compute Anderson-Darling p-values for frequency bins.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    freq : array-like\n",
        "        Frequency values.\n",
        "    data : array-like\n",
        "        Data corresponding to frequencies.\n",
        "    bin_width_Hz : float, optional\n",
        "        Width of frequency bins in Hz. Default is 8.\n",
        "    fmin : float, optional\n",
        "        Minimum frequency for analysis. Default is 0.\n",
        "    fmax : float, optional\n",
        "        Maximum frequency for analysis. Default is np.inf.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    tuple\n",
        "        A tuple containing frequency bins and corresponding p-values.\n",
        "    \"\"\"\n",
        "    bin_width = int(bin_width_Hz * duration)\n",
        "    idxs = np.arange(0, len(data), bin_width)[:-1]\n",
        "    pvals = [anderson_p_value(data[ii:ii+bin_width], freq[ii:ii+bin_width], fmin=fmin, fmax=fmax) for ii in idxs]\n",
        "    fbins = [freq[ii + bin_width // 2] for ii in idxs]\n",
        "    return fbins, pvals\n",
        "\n",
        "def get_window_like(data, roll_off=0.1):\n",
        "    \"\"\"\n",
        "    Generate a Tukey window with a specified roll-off.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    data : array-like\n",
        "        Input data to determine window size.\n",
        "    roll_off : float, optional\n",
        "        Roll-off factor for the Tukey window. Default is 0.1.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    array\n",
        "        A Tukey window of the same length as the input data.\n",
        "    \"\"\"\n",
        "    psd_alpha = 2 * roll_off / duration\n",
        "    return scipy.signal.get_window((\"tukey\", psd_alpha), len(data))\n",
        "\n",
        "def whiten(timeseries, asd, asd_frequencies, roll_off=0.1):\n",
        "    \"\"\"\n",
        "    Whiten a time series by dividing by the amplitude spectral density (ASD).\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    timeseries : array-like\n",
        "        The input time series data.\n",
        "    asd : array-like\n",
        "        The amplitude spectral density.\n",
        "    asd_frequencies : array-like\n",
        "        Frequencies corresponding to ASD values.\n",
        "    roll_off : float, optional\n",
        "        Roll-off factor for windowing. Default is 0.1.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    tuple\n",
        "        The original frequency series and the whitened frequency series.\n",
        "    \"\"\"\n",
        "    duration = timeseries.duration.value\n",
        "    timeseries_windowed = timeseries * get_window_like(timeseries, roll_off)\n",
        "    frequency_series = timeseries_windowed.fft()\n",
        "    whitened_frequencyseries = frequency_series * np.sqrt(4 / duration) / asd\n",
        "    return frequency_series, whitened_frequencyseries\n",
        "\n",
        "def plot_whitening(frequencies, asd, h_f, wh_f, label=None, bin_width_Hz=8):\n",
        "    \"\"\"\n",
        "    Plot whitening analysis, including the frequency domain signal, ASD, p-values, and histogram.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    frequencies : array-like\n",
        "        Frequency values.\n",
        "    asd : array-like\n",
        "        Amplitude spectral density.\n",
        "    h_f : array-like\n",
        "        Frequency-domain representation of the original signal.\n",
        "    wh_f : array-like\n",
        "        Frequency-domain representation of the whitened signal.\n",
        "    label : str, optional\n",
        "        Label for the ASD plot. Default is None.\n",
        "    bin_width_Hz : float, optional\n",
        "        Width of frequency bins for p-value computation. Default is 8.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    None\n",
        "    \"\"\"\n",
        "    fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True, figsize=(8, 5))\n",
        "    ax1, ax2 = axes[:, 0]\n",
        "    gs = axes[1, 0].get_gridspec()\n",
        "    axes[0, 1].remove()\n",
        "    axes[1, 1].remove()\n",
        "    ax3 = fig.add_subplot(gs[0:, 1])\n",
        "\n",
        "    ax1.semilogy(frequencies, np.abs(h_f), label=r\"|htilde(f)|\")\n",
        "    ax1.semilogy(frequencies, asd, label=label)\n",
        "    ax1.set(ylabel=\"Strain data [Hz-1]\")\n",
        "    ax1.legend()\n",
        "\n",
        "    fbins, pvals = fbins_anderson_p_value(frequencies, wh_f, bin_width_Hz=bin_width_Hz)\n",
        "    ax2.scatter(fbins, pvals, s=2)\n",
        "    ax2.axhline(1e-2, color=\"r\")\n",
        "    ax2.set(xlabel=\"Frequency [Hz]\", ylabel=\"p-value\", yscale=\"log\")\n",
        "\n",
        "    bin_width = 0.025\n",
        "    bins = np.arange(0, 1 + bin_width, bin_width)\n",
        "    ax3.hist(pvals, bins=bins)\n",
        "    ax3.set(xlabel=\"p-values\", ylabel=\"Density\", xlim=(0, 1))\n",
        "    fig.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "\n",
        "### DEFINE FS AND DURATION\n",
        "\n",
        "sampling_frequency = 4096\n",
        "duration = 4\n",
        "frequencies = np.fft.rfftfreq(sampling_frequency * duration, d=1/sampling_frequency)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X87blJoGMRFQ",
        "outputId": "e05ffe65-0a75-41b3-ef0e-e811227f5ca4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.5.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Starting with known PSD....\n",
        "\n",
        "\n",
        "And then, we construct another bilby psd object, but this time from the extrapolated PSD array. This ensures that when we simulate the data it will contain low-frequency noise.\n",
        "\n"
      ],
      "metadata": {
        "id": "0G3Y_pH1M74O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Create a bilby PSD object: the argument points to a named ASD file packaged as part of bilby\n",
        "# This file can be replaced by a path to a specific ASD on file\n",
        "psd = bilby.gw.detector.PowerSpectralDensity.from_amplitude_spectral_density_file(\"aLIGO_O4_high_asd.txt\")\n",
        "\n",
        "psd_array = psd.power_spectral_density_interpolated(frequencies)\n",
        "psd_array[np.isinf(psd_array)] = psd.psd_array[0]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.loglog(frequencies, psd_array)\n",
        "ax.set(xlabel=\"Frequency [Hz]\", ylabel=\"PSD [Hz-1]\")\n",
        "plt.show()\n",
        "\n",
        "#### Extrapolating for low noise\n",
        "\n",
        "psd = bilby.gw.detector.PowerSpectralDensity.from_power_spectral_density_array(frequencies, psd_array)\n",
        "\n",
        "# We generate a random noise realization in the frequency domain h_f\n",
        "h_f_sim, _ = psd.get_noise_realisation(sampling_frequency, duration)\n",
        "\n",
        "# Inverse FFT to get the timeseries\n",
        "h_t_sim = np.fft.irfft(h_f_sim)\n",
        "time = np.arange(0, duration, 1 / sampling_frequency)\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 4))\n",
        "ax1.loglog(frequencies, np.abs(h_f_sim), label=r\"|htilde(f|\")\n",
        "ax1.loglog(frequencies, psd.asd_array, label=r\"sqrt(S(f))\")\n",
        "ax1.set(xlabel=\"Frequency [Hz]\", ylabel=\"Strain data [Hz-1]\")\n",
        "ax1.legend()\n",
        "\n",
        "ax2.plot(time, h_t_sim)\n",
        "ax2.set(xlabel=\"Time [s]\", ylabel=\"Strain []\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        },
        "id": "mmPTWgs5M6Xj",
        "outputId": "e92ff17d-c624-42e9-a0a4-91a84bbd5a97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check if whitened datta is normal"
      ],
      "metadata": {
        "id": "UdY0VmvFNTLy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "wh_f_sim = h_f_sim *  np.sqrt(4 / duration) / np.sqrt(psd.psd_array)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "kwargs = dict(bins=\"auto\", alpha=0.5, density=True)\n",
        "ax.hist(wh_f_sim.real, label=r\"real\", **kwargs)\n",
        "ax.hist(wh_f_sim.imag, label=r\"imag\", **kwargs)\n",
        "\n",
        "xs = np.linspace(-5, 5, 1000)\n",
        "ax.plot(xs, np.exp(-xs**2 / 2) / np.sqrt(2 * np.pi), label=\"Standard normal PDF\")\n",
        "ax.set(xlabel=\"Whitened strain\")\n",
        "ax.legend()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "### QUANTIFY\n",
        "print(\"anderson_p_value:\")\n",
        "print(anderson_p_value(wh_f_sim))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "hM5PJx8TNV0y",
        "outputId": "91a50ada-0eb1-4c24-efdc-cbf53c850a87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "anderson_p_value:\n",
            "0.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Freq dep test of normality (Gupta + Cornish)"
      ],
      "metadata": {
        "id": "oWQl9mTBNXE_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_whitening(psd.frequency_array, psd.asd_array, h_f_sim, wh_f_sim, label=\"True ASD\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "K1DWc9_SNkHG",
        "outputId": "69d17244-9241-40fd-9d1a-7564a1cc65c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Estimate PSD using off-source welch Median\n"
      ],
      "metadata": {
        "id": "CxpVM8dzNpnL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "t0 = 1256790000   # A time when the Hanford is online during O3b\n",
        "ifo = \"H1\"  # The detector name as used by `gwpy`\n",
        "duration = 4   # The duration of data used for analysis\n",
        "post_trigger_duration = 2  # The end time of the analysis data relative to the trigger time\n",
        "\n",
        "# We download a total of 132 seconds of data around the signal (128s before and then 4s of analysis data)\n",
        "start = t0 + post_trigger_duration\n",
        "end = t0 + post_trigger_duration - duration - 128\n",
        "data = TimeSeries.fetch_open_data(ifo, start, end, cache=True)\n",
        "\n",
        "# We then create two sets of data\n",
        "data_psd = data.crop(end=t0 + post_trigger_duration - duration)\n",
        "data_analysis = data.crop(start=t0 + post_trigger_duration - duration, end=t0 + post_trigger_duration)\n",
        "\n",
        "\n",
        "def estimate_psd_offsource_averaging(\n",
        "    psd_data,\n",
        "    duration,\n",
        "    psd_fractional_overlap,\n",
        "    psd_method,\n",
        "    roll_off,\n",
        "):\n",
        "    \"\"\" Estimate a Power Spectral Density (PSD) from averaging off-source strain data\n",
        "\n",
        "    Note: this function utilises [gwpy.timeseries.TimeSeries.psd](https://gwpy.github.io/docs/stable/api/gwpy.timeseries.TimeSeries/#gwpy.timeseries.TimeSeries.psd). It is recommended you read the documentation for that function for a detailed understanding of the implementation and options.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    psd_data: gwpy.timeseries.TimeSeries\n",
        "        A timeseries of the strain data to use for off-source averaging. Note: this method assumes\n",
        "        the data has been truncated and does not include the signal.\n",
        "    duration: float\n",
        "        The duration (in seconds) to use for the PSD estimation (assumed to be identical to the\n",
        "        duration of the analysis data).\n",
        "    psd_fractional_overlap: float [0, 1]\n",
        "        The fractional amount of overlap between neigbourning FFT estimates.\n",
        "    psd_method: str\n",
        "        See gwpy documentation\n",
        "    roll_off: float [0, 1]\n",
        "        MATCH WITH BILBY_PIPE\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    frequencies, psd: array_like\n",
        "        The frequencies and estimated PSD\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    psd_alpha = 2 * roll_off / duration\n",
        "    overlap = psd_fractional_overlap * duration\n",
        "    window = (\"tukey\", psd_alpha)\n",
        "\n",
        "    psd = psd_data.psd(\n",
        "        fftlength=duration,\n",
        "        overlap=overlap,\n",
        "        window=window,\n",
        "        method=psd_method,\n",
        "    )\n",
        "    return psd.frequencies.value, psd.value\n",
        "\n",
        "\n",
        "kwargs = dict(\n",
        "    duration=4,\n",
        "    psd_fractional_overlap=0.5,\n",
        "    roll_off=0.1,\n",
        "    psd_method=\"median\"\n",
        ")\n",
        "\n",
        "_, median_psd = estimate_psd_offsource_averaging(\n",
        "    data_psd, **kwargs\n",
        ")\n",
        "median_asd = np.sqrt(median_psd)\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "kwargs = dict(lw=1, alpha=0.8)\n",
        "ax.loglog(frequencies, median_asd, label=\"Median: 128s\", **kwargs)\n",
        "ax.legend()\n",
        "ax.set(ylim=(1e-24, 1e-19), xlabel='Frequency [Hz]', ylabel=\"PSD\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "OEP4XQVPNt0L",
        "outputId": "a5a94381-3343-4bae-9a88-5d11318cd949"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we test our Welch ASD to see how good it is"
      ],
      "metadata": {
        "id": "RZa7Z7tHN4B1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kwargs = dict(bins=\"auto\", alpha=0.5, density=True)\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "h_f_median, wh_f_median = whiten(data_analysis, median_asd, frequencies)\n",
        "\n",
        "\n",
        "ax.hist(wh_f_median.real, label=r\"real\", **kwargs)\n",
        "ax.hist(wh_f_median.imag, label=r\"imag\", **kwargs)\n",
        "ax.set_title(f\"p-value={anderson_p_value(wh_f_median):0.5f}\")\n",
        "xs = np.linspace(-5, 5, 1000)\n",
        "ax.plot(xs, np.exp(-xs**2 / 2) / np.sqrt(2 * np.pi))\n",
        "ax.set(xlim=(-5, 5))\n",
        "ax.legend()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "plot_whitening(frequencies, median_asd, h_f_median, wh_f_median, bin_width_Hz=8)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 941
        },
        "id": "VdUGrWkbN83N",
        "outputId": "6dce3695-26c6-425b-95eb-85cb19183fe0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Now** estimate the SGVB PSD for this data"
      ],
      "metadata": {
        "id": "0F80lGczN7te"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# SGVB PSD estimate and test here..."
      ],
      "metadata": {
        "id": "84O-9eo8Oq6D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Parameter estimation"
      ],
      "metadata": {
        "id": "oxiwPOdCMRpM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# DATA GENERATION\n",
        "\n",
        "import bilby\n",
        "import numpy as np\n",
        "from sgvb_psd.psd_estimator import PSDEstimator\n",
        "\n",
        "duration = 4.0\n",
        "sampling_frequency = 2048.0\n",
        "minimum_frequency = 20\n",
        "\n",
        "\n",
        "def setup_logger_and_seed(outdir, label, seed=0, level=\"DEBUG\"):\n",
        "    bilby.core.utils.setup_logger(outdir=outdir, label=label, log_level=level)\n",
        "    bilby.core.utils.random.seed(seed)\n",
        "\n",
        "\n",
        "def make_waveform_generator(duration, sampling_frequency, minimum_frequency):\n",
        "    waveform_args = dict(\n",
        "        waveform_approximant=\"IMRPhenomPv2\",\n",
        "        reference_frequency=50.0,\n",
        "        minimum_frequency=minimum_frequency,\n",
        "    )\n",
        "    return bilby.gw.WaveformGenerator(\n",
        "        duration=duration,\n",
        "        sampling_frequency=sampling_frequency,\n",
        "        frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,\n",
        "        parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters,\n",
        "        waveform_arguments=waveform_args,\n",
        "    )\n",
        "\n",
        "\n",
        "def simulate_ifo_data(ifos, waveform_generator, injection_parameters, sampling_frequency, duration):\n",
        "    ifos.set_strain_data_from_power_spectral_densities(\n",
        "        sampling_frequency=sampling_frequency,\n",
        "        duration=duration,\n",
        "        start_time=injection_parameters[\"geocent_time\"] - 2,\n",
        "    )\n",
        "    ifos.inject_signal(waveform_generator=waveform_generator, parameters=injection_parameters)\n",
        "    return ifos\n",
        "\n",
        "\n",
        "def run_analysis(ifos, waveform_generator, injection_parameters, priors, outdir, label, sampler=\"dynesty\", npoints=1000):\n",
        "    likelihood = bilby.gw.GravitationalWaveTransient(interferometers=ifos, waveform_generator=waveform_generator)\n",
        "    return bilby.run_sampler(\n",
        "        likelihood=likelihood,\n",
        "        priors=priors,\n",
        "        sampler=sampler,\n",
        "        npoints=npoints,\n",
        "        injection_parameters=injection_parameters,\n",
        "        outdir=outdir,\n",
        "        label=label,\n",
        "        result_class=bilby.gw.result.CBCResult,\n",
        "    )\n",
        "\n",
        "\n",
        "# Save the data"
      ],
      "metadata": {
        "id": "Dn6WdhYpD5QE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# DEFAAULT ANALYSIS\n",
        "\n",
        "outdir = \"outdir\"\n",
        "label = 'default'\n",
        "setup_logger_and_seed(outdir, label)\n",
        "\n",
        "injection_parameters = dict(\n",
        "    mass_1=36.0, mass_2=29.0, a_1=0.4, a_2=0.3,\n",
        "    tilt_1=0.5, tilt_2=1.0, phi_12=1.7, phi_jl=0.3,\n",
        "    luminosity_distance=2000.0, theta_jn=0.4, psi=2.659,\n",
        "    phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108,\n",
        ")\n",
        "\n",
        "waveform_generator = make_waveform_generator(duration, sampling_frequency, minimum_frequency)\n",
        "ifos = bilby.gw.detector.InterferometerList([\"H1\", \"L1\"])\n",
        "ifos = simulate_ifo_data(ifos, waveform_generator, injection_parameters, sampling_frequency, duration)\n",
        "\n",
        "priors = bilby.gw.prior.BBHPriorDict()\n",
        "for key in [\n",
        "    \"a_1\", \"a_2\", \"tilt_1\", \"tilt_2\",\n",
        "    \"phi_12\", \"phi_jl\", \"psi\", \"ra\", \"dec\",\n",
        "    \"geocent_time\", \"phase\",\n",
        "]:\n",
        "    priors[key] = injection_parameters[key]\n",
        "priors.validate_prior(duration, minimum_frequency)\n",
        "\n",
        "# Default PSD analysis\n",
        "result_default = run_analysis(\n",
        "    ifos, waveform_generator, injection_parameters,\n",
        "    priors, outdir, label\n",
        ")\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "fh5nOzMVGska",
        "outputId": "5bc8475c-0e29-4618-a334-b13680448bfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "58118a6e7dc14a4c9550002f490ab969",
            "2e7b2b7b509246e885aa979f72a05f2a",
            "ce4128ac68664d03bb8e8f55b60815f6",
            "7d0c55d208624907ba7373c6dc5d20f1",
            "b68ec47a184d4a6296fe743a0d75e151",
            "0b110fc639584102bfb56b54add5330a",
            "b23fadf1f2f94d3689982d29b78346bb",
            "41c7c714ecd244b6885e1e0b628e6671",
            "ea152e31745a468f98533b39ac655edd",
            "03ff9b3d6e3c4b1faec239aa3033f385",
            "107a6e6baa22411f898e644e77ceacf4"
          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "03:49 bilby DEBUG   : Overwriting meta data key rng with value Generator(PCG64). Old value was Generator(PCG64)\n",
            "03:49 bilby DEBUG   : Overwriting meta data key seed with value 0. Old value was 0\n",
            "03:49 bilby INFO    : Waveform generator initiated with\n",
            "  frequency_domain_source_model: bilby.gw.source.lal_binary_black_hole\n",
            "  time_domain_source_model: None\n",
            "  parameter_conversion: bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters\n",
            "03:49 bilby DEBUG   : PSD file set to None\n",
            "03:49 bilby DEBUG   : PSD file aLIGO_O4_high_asd.txt exists in default dir.\n",
            "03:49 bilby DEBUG   : Assuming L shape for name\n",
            "03:49 bilby DEBUG   : PSD file set to None\n",
            "03:49 bilby DEBUG   : PSD file aLIGO_O4_high_asd.txt exists in default dir.\n",
            "03:49 bilby DEBUG   : Assuming L shape for name\n",
            "03:49 bilby DEBUG   : Setting data using noise realization from providedpower_spectal_density\n",
            "03:49 bilby DEBUG   : Setting data using noise realization from providedpower_spectal_density\n",
            "/usr/local/lib/python3.11/dist-packages/lalsimulation/lalsimulation.py:8: UserWarning: Wswiglal-redir-stdio:\n",
            "\n",
            "SWIGLAL standard output/error redirection is enabled in IPython.\n",
            "This may lead to performance penalties. To disable locally, use:\n",
            "\n",
            "with lal.no_swig_redirect_standard_output_error():\n",
            "    ...\n",
            "\n",
            "To disable globally, use:\n",
            "\n",
            "lal.swig_redirect_standard_output_error(False)\n",
            "\n",
            "Note however that this will likely lead to error messages from\n",
            "LAL functions being either misdirected or lost when called from\n",
            "Jupyter notebooks.\n",
            "\n",
            "To suppress this warning, use:\n",
            "\n",
            "import warnings\n",
            "warnings.filterwarnings(\"ignore\", \"Wswiglal-redir-stdio\")\n",
            "import lal\n",
            "\n",
            "  import lal\n",
            "03:49 bilby DEBUG   : Assuming reference_frequency = 50.0\n",
            "03:49 bilby DEBUG   : Assuming waveform_approximant = IMRPhenomPv2\n",
            "03:49 bilby DEBUG   : Assuming minimum_frequency = 20.0\n",
            "03:49 bilby DEBUG   : Setting meta data key cosmology with value FlatLambdaCDM(name=\"Planck15\", H0=67.74 km / (Mpc s), Om0=0.3075, Tcmb0=2.7255 K, Neff=3.046, m_nu=[0.   0.   0.06] eV, Ob0=0.0486)\n",
            "03:49 bilby INFO    : Injected signal in H1:\n",
            "03:49 bilby INFO    :   optimal SNR = 11.78\n",
            "03:49 bilby INFO    :   matched filter SNR = 12.87+1.30j\n",
            "03:49 bilby INFO    :   mass_1 = 36.0\n",
            "03:49 bilby INFO    :   mass_2 = 29.0\n",
            "03:49 bilby INFO    :   a_1 = 0.4\n",
            "03:49 bilby INFO    :   a_2 = 0.3\n",
            "03:49 bilby INFO    :   tilt_1 = 0.5\n",
            "03:49 bilby INFO    :   tilt_2 = 1.0\n",
            "03:49 bilby INFO    :   phi_12 = 1.7\n",
            "03:49 bilby INFO    :   phi_jl = 0.3\n",
            "03:49 bilby INFO    :   luminosity_distance = 2000.0\n",
            "03:49 bilby INFO    :   theta_jn = 0.4\n",
            "03:49 bilby INFO    :   psi = 2.659\n",
            "03:49 bilby INFO    :   phase = 1.3\n",
            "03:49 bilby INFO    :   geocent_time = 1126259642.413\n",
            "03:49 bilby INFO    :   ra = 1.375\n",
            "03:49 bilby INFO    :   dec = -1.2108\n",
            "03:49 bilby DEBUG   : Assuming reference_frequency = 50.0\n",
            "03:49 bilby DEBUG   : Assuming waveform_approximant = IMRPhenomPv2\n",
            "03:49 bilby DEBUG   : Assuming minimum_frequency = 20.0\n",
            "03:49 bilby INFO    : Injected signal in L1:\n",
            "03:49 bilby INFO    :   optimal SNR = 9.53\n",
            "03:49 bilby INFO    :   matched filter SNR = 8.54-0.31j\n",
            "03:49 bilby INFO    :   mass_1 = 36.0\n",
            "03:49 bilby INFO    :   mass_2 = 29.0\n",
            "03:49 bilby INFO    :   a_1 = 0.4\n",
            "03:49 bilby INFO    :   a_2 = 0.3\n",
            "03:49 bilby INFO    :   tilt_1 = 0.5\n",
            "03:49 bilby INFO    :   tilt_2 = 1.0\n",
            "03:49 bilby INFO    :   phi_12 = 1.7\n",
            "03:49 bilby INFO    :   phi_jl = 0.3\n",
            "03:49 bilby INFO    :   luminosity_distance = 2000.0\n",
            "03:49 bilby INFO    :   theta_jn = 0.4\n",
            "03:49 bilby INFO    :   psi = 2.659\n",
            "03:49 bilby INFO    :   phase = 1.3\n",
            "03:49 bilby INFO    :   geocent_time = 1126259642.413\n",
            "03:49 bilby INFO    :   ra = 1.375\n",
            "03:49 bilby INFO    :   dec = -1.2108\n",
            "03:49 bilby INFO    : No prior given, using default BBH priors in /usr/local/lib/python3.11/dist-packages/bilby/gw/prior_files/precessing_spins_bbh.prior.\n",
            "03:49 bilby DEBUG   : Supplied PDF for luminosity_distance is not normalised, normalising.\n",
            "03:49 bilby DEBUG   : Supplied PDF for luminosity_distance is not normalised, normalising.\n",
            "03:49 bilby DEBUG   : Supplied PDF for luminosity_distance is not normalised, normalising.\n",
            "03:49 bilby DEBUG   : dec converted to delta function prior.\n",
            "03:49 bilby DEBUG   : ra converted to delta function prior.\n",
            "03:49 bilby DEBUG   : psi converted to delta function prior.\n",
            "03:49 bilby DEBUG   : phase converted to delta function prior.\n",
            "03:49 bilby DEBUG   : a_1 converted to delta function prior.\n",
            "03:49 bilby DEBUG   : a_2 converted to delta function prior.\n",
            "03:49 bilby DEBUG   : tilt_1 converted to delta function prior.\n",
            "03:49 bilby DEBUG   : tilt_2 converted to delta function prior.\n",
            "03:49 bilby DEBUG   : phi_12 converted to delta function prior.\n",
            "03:49 bilby DEBUG   : phi_jl converted to delta function prior.\n",
            "03:49 bilby DEBUG   : geocent_time converted to delta function prior.\n",
            "03:49 bilby DEBUG   : Assuming reference_frequency = 50.0\n",
            "03:49 bilby DEBUG   : Assuming waveform_approximant = IMRPhenomPv2\n",
            "03:49 bilby DEBUG   : Assuming minimum_frequency = 20.0\n",
            "03:50 bilby DEBUG   : The waveform_generator start_time is not equal to that of the provided interferometers. Overwriting the waveform_generator.\n",
            "03:50 bilby DEBUG   : Time jittering requested with non-time-marginalised likelihood, ignoring.\n",
            "03:50 bilby INFO    : Running for label 'default', output will be saved to 'outdir'\n",
            "03:50 bilby DEBUG   : mass_1 already in prior\n",
            "03:50 bilby DEBUG   : mass_2 already in prior\n",
            "03:50 bilby DEBUG   : mass_ratio already in prior\n",
            "03:50 bilby DEBUG   : chirp_mass already in prior\n",
            "03:50 bilby DEBUG   : luminosity_distance already in prior\n",
            "03:50 bilby DEBUG   : dec already in prior\n",
            "03:50 bilby DEBUG   : ra already in prior\n",
            "03:50 bilby DEBUG   : theta_jn already in prior\n",
            "03:50 bilby DEBUG   : psi already in prior\n",
            "03:50 bilby DEBUG   : phase already in prior\n",
            "03:50 bilby DEBUG   : a_1 already in prior\n",
            "03:50 bilby DEBUG   : a_2 already in prior\n",
            "03:50 bilby DEBUG   : tilt_1 already in prior\n",
            "03:50 bilby DEBUG   : tilt_2 already in prior\n",
            "03:50 bilby DEBUG   : phi_12 already in prior\n",
            "03:50 bilby DEBUG   : phi_jl already in prior\n",
            "03:50 bilby DEBUG   : geocent_time already in prior\n",
            "03:50 bilby INFO    : Using lal version 7.7.0\n",
            "03:50 bilby INFO    : Using lal git version Branch: None;Tag: lalsuite-v7.26;Id: 736838ae5f43f532121c0a5627305e276d482254;;Builder: Unknown User <>;Repository status: UNCLEAN: Modified working tree\n",
            "03:50 bilby INFO    : Using lalsimulation version 6.2.0\n",
            "03:50 bilby INFO    : Using lalsimulation git version Branch: None;Tag: lalsuite-v7.26;Id: 736838ae5f43f532121c0a5627305e276d482254;;Builder: Unknown User <>;Repository status: UNCLEAN: Modified working tree\n",
            "/usr/local/lib/python3.11/dist-packages/_distutils_hack/__init__.py:31: UserWarning: Setuptools is replacing distutils. Support for replacing an already imported distutils is deprecated. In the future, this condition will fail. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml\n",
            "  warnings.warn(\n",
            "03:50 bilby INFO    : Analysis priors:\n",
            "03:50 bilby INFO    : mass_ratio=bilby.gw.prior.UniformInComponentsMassRatio(minimum=0.125, maximum=1, name='mass_ratio', latex_label='$q$', unit=None, boundary=None, equal_mass=False)\n",
            "03:50 bilby INFO    : chirp_mass=bilby.gw.prior.UniformInComponentsChirpMass(minimum=25, maximum=100, name='chirp_mass', latex_label='$\\\\mathcal{M}$', unit=None, boundary=None)\n",
            "03:50 bilby INFO    : luminosity_distance=bilby.gw.prior.UniformSourceFrame(minimum=100.0, maximum=5000.0, cosmology='Planck15', name='luminosity_distance', latex_label='$d_L$', unit='Mpc', boundary=None)\n",
            "03:50 bilby INFO    : theta_jn=Sine(minimum=0, maximum=3.141592653589793, name='theta_jn', latex_label='$\\\\theta_{JN}$', unit=None, boundary=None)\n",
            "03:50 bilby INFO    : mass_1=Constraint(minimum=5, maximum=100, name='mass_1', latex_label='$m_1$', unit=None)\n",
            "03:50 bilby INFO    : mass_2=Constraint(minimum=5, maximum=100, name='mass_2', latex_label='$m_2$', unit=None)\n",
            "03:50 bilby INFO    : dec=-1.2108\n",
            "03:50 bilby INFO    : ra=1.375\n",
            "03:50 bilby INFO    : psi=2.659\n",
            "03:50 bilby INFO    : phase=1.3\n",
            "03:50 bilby INFO    : a_1=0.4\n",
            "03:50 bilby INFO    : a_2=0.3\n",
            "03:50 bilby INFO    : tilt_1=0.5\n",
            "03:50 bilby INFO    : tilt_2=1.0\n",
            "03:50 bilby INFO    : phi_12=1.7\n",
            "03:50 bilby INFO    : phi_jl=0.3\n",
            "03:50 bilby INFO    : geocent_time=1126259642.413\n",
            "03:50 bilby INFO    : Analysis likelihood class: <class 'bilby.gw.likelihood.base.GravitationalWaveTransient'>\n",
            "03:50 bilby INFO    : Analysis likelihood noise evidence: -8098.88635429227\n",
            "03:50 bilby INFO    : Single likelihood evaluation took 9.545e-02 s\n",
            "03:50 bilby DEBUG   : use_ratio not spec. but gives valid answer, setting True\n",
            "03:50 bilby DEBUG   : Checking cached data\n",
            "03:50 bilby INFO    : Using sampler Dynesty with kwargs {'nlive': 1000, 'bound': 'live', 'sample': 'act-walk', 'periodic': None, 'reflective': None, 'update_interval': 600, 'first_update': None, 'npdim': None, 'rstate': None, 'queue_size': 1, 'pool': None, 'use_pool': None, 'live_points': None, 'logl_args': None, 'logl_kwargs': None, 'ptform_args': None, 'ptform_kwargs': None, 'gradient': None, 'grad_args': None, 'grad_kwargs': None, 'compute_jac': False, 'enlarge': None, 'bootstrap': None, 'walks': 100, 'facc': 0.2, 'slices': None, 'fmove': 0.9, 'max_move': 100, 'update_func': None, 'ncdim': None, 'blob': False, 'save_history': False, 'history_filename': None, 'maxiter': None, 'maxcall': None, 'dlogz': 0.1, 'logl_max': inf, 'n_effective': None, 'add_live': True, 'print_progress': True, 'print_func': <bound method Dynesty._print_func of <bilby.core.sampler.dynesty.Dynesty object at 0x7a0b1df5c0d0>>, 'save_bounds': False, 'checkpoint_file': None, 'checkpoint_every': 60, 'resume': False, 'seed': None}\n",
            "03:50 bilby INFO    : Global meta data was removed from the result object for compatibility. Use the `BILBY_INCLUDE_GLOBAL_METADATA` environment variable to include it. This behaviour will be removed in a future release. For more details see: https://bilby-dev.github.io/bilby/faq.html#global-meta-data\n",
            "03:50 bilby INFO    : Checkpoint every check_point_delta_t = 600s\n",
            "03:50 bilby INFO    : Using dynesty version 2.1.5\n",
            "03:50 bilby INFO    : Using the bilby-implemented act-walk sampling tracking the autocorrelation function and thinning by 2 with maximum length 10000\n",
            "03:50 bilby INFO    : Resume file outdir/default_resume.pickle does not exist.\n",
            "03:50 bilby INFO    : Generating initial points from the prior\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "58118a6e7dc14a4c9550002f490ab969"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "03:51 bilby DEBUG   : Running sampler with checkpointing\n",
            "03:57 bilby DEBUG   : it: 415, accept: 51, reject: 0, fail: 364, act: 52.00, nact: 7.98 \n",
            "03:57 bilby DEBUG   : it: 1507, accept: 203, reject: 0, fail: 1304, act: 52.00, nact: 28.98 \n",
            "03:58 bilby DEBUG   : it: 2053, accept: 265, reject: 0, fail: 1788, act: 52.00, nact: 39.48 \n",
            "03:58 bilby DEBUG   : it: 2326, accept: 295, reject: 0, fail: 2031, act: 52.00, nact: 44.73 \n",
            "03:58 bilby DEBUG   : it: 2463, accept: 315, reject: 0, fail: 2148, act: 52.00, nact: 47.37 \n",
            "03:58 bilby DEBUG   : it: 2531, accept: 324, reject: 0, fail: 2207, act: 52.00, nact: 48.67 \n",
            "03:58 bilby DEBUG   : it: 2565, accept: 326, reject: 0, fail: 2239, act: 52.00, nact: 49.33 \n",
            "03:58 bilby DEBUG   : it: 2590, accept: 329, reject: 0, fail: 2261, act: 52.00, nact: 49.81 \n",
            "03:58 bilby DEBUG   : act: 52.00, max failures: 52, thin: 104, iteration: 2600, n_found: 24\n",
            "03:58 bilby DEBUG   : Finished building cache with length 24 after 2600 iterations with 945 likelihood calls and ACT=52.00\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "03:58 bilby DEBUG   : Not rebuilding cache, remaining size 1\n",
            "03:58 bilby DEBUG   : Force rebuilding cache\n",
            "03:58 bilby DEBUG   : it: 417, accept: 58, reject: 0, fail: 359, act: 33.00, nact: 12.64 \n",
            "03:58 bilby DEBUG   : it: 1033, accept: 145, reject: 0, fail: 888, act: 44.00, nact: 23.48 \n",
            "03:59 bilby DEBUG   : it: 1616, accept: 213, reject: 0, fail: 1403, act: 44.00, nact: 36.73 \n",
            "03:59 bilby DEBUG   : it: 1908, accept: 247, reject: 0, fail: 1661, act: 44.00, nact: 43.36 \n",
            "03:59 bilby DEBUG   : it: 2054, accept: 254, reject: 0, fail: 1800, act: 76.00, nact: 27.03 \n",
            "03:59 bilby DEBUG   : it: 2927, accept: 358, reject: 0, fail: 2569, act: 76.00, nact: 38.51 \n",
            "03:59 bilby DEBUG   : it: 3363, accept: 413, reject: 0, fail: 2950, act: 76.00, nact: 44.25 \n",
            "03:59 bilby DEBUG   : it: 3581, accept: 434, reject: 0, fail: 3147, act: 76.00, nact: 47.12 \n",
            "03:59 bilby DEBUG   : it: 3690, accept: 440, reject: 0, fail: 3250, act: 76.00, nact: 48.55 \n",
            "03:59 bilby DEBUG   : it: 3745, accept: 444, reject: 0, fail: 3301, act: 76.00, nact: 49.28 \n",
            "04:00 bilby DEBUG   : it: 3782, accept: 448, reject: 0, fail: 3334, act: 76.00, nact: 49.76 \n",
            "04:00 bilby DEBUG   : act: 76.00, max failures: 76, thin: 152, iteration: 3800, n_found: 24\n",
            "04:00 bilby DEBUG   : Finished building cache with length 25 after 3800 iterations with 1333 likelihood calls and ACT=76.00\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 25\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 25\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:00 bilby DEBUG   : Not rebuilding cache, remaining size 1\n",
            "04:00 bilby DEBUG   : Force rebuilding cache\n",
            "04:00 bilby DEBUG   : it: 457, accept: 58, reject: 0, fail: 399, act: 25.00, nact: 18.28 \n",
            "04:00 bilby DEBUG   : it: 853, accept: 96, reject: 0, fail: 757, act: 77.00, nact: 11.08 \n",
            "04:01 bilby DEBUG   : it: 2351, accept: 273, reject: 0, fail: 2078, act: 77.00, nact: 30.53 \n",
            "04:01 bilby DEBUG   : it: 3100, accept: 355, reject: 0, fail: 2745, act: 77.00, nact: 40.26 \n",
            "04:01 bilby DEBUG   : it: 3475, accept: 404, reject: 0, fail: 3071, act: 77.00, nact: 45.13 \n",
            "04:01 bilby DEBUG   : it: 3662, accept: 421, reject: 0, fail: 3241, act: 77.00, nact: 47.56 \n",
            "04:01 bilby DEBUG   : it: 3756, accept: 430, reject: 0, fail: 3326, act: 77.00, nact: 48.78 \n",
            "04:01 bilby DEBUG   : it: 3803, accept: 432, reject: 0, fail: 3371, act: 77.00, nact: 49.39 \n",
            "04:01 bilby DEBUG   : it: 3841, accept: 439, reject: 0, fail: 3402, act: 77.00, nact: 49.88 \n",
            "04:01 bilby DEBUG   : act: 77.00, max failures: 77, thin: 154, iteration: 3850, n_found: 24\n",
            "04:01 bilby DEBUG   : Finished building cache with length 25 after 3850 iterations with 1368 likelihood calls and ACT=77.00\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 25\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 25\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:01 bilby DEBUG   : Not rebuilding cache, remaining size 1\n",
            "04:01 bilby DEBUG   : Force rebuilding cache\n",
            "04:01 bilby DEBUG   : it: 540, accept: 52, reject: 0, fail: 488, act: 35.00, nact: 15.43 \n",
            "04:02 bilby DEBUG   : it: 1145, accept: 112, reject: 0, fail: 1033, act: 45.90, nact: 24.95 \n",
            "04:02 bilby DEBUG   : it: 1719, accept: 169, reject: 0, fail: 1550, act: 48.00, nact: 35.81 \n",
            "04:02 bilby DEBUG   : it: 2059, accept: 213, reject: 0, fail: 1846, act: 48.00, nact: 42.90 \n",
            "04:02 bilby DEBUG   : it: 2229, accept: 232, reject: 0, fail: 1997, act: 48.00, nact: 46.44 \n",
            "04:02 bilby DEBUG   : it: 2314, accept: 243, reject: 0, fail: 2071, act: 48.00, nact: 48.21 \n",
            "04:02 bilby DEBUG   : it: 2357, accept: 248, reject: 0, fail: 2109, act: 48.00, nact: 49.10 \n",
            "04:02 bilby DEBUG   : it: 2380, accept: 251, reject: 0, fail: 2129, act: 48.00, nact: 49.58 \n",
            "04:02 bilby DEBUG   : act: 48.00, max failures: 48, thin: 96, iteration: 2400, n_found: 24\n",
            "04:02 bilby DEBUG   : Finished building cache with length 25 after 2400 iterations with 819 likelihood calls and ACT=48.00\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 25\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 25\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:02 bilby INFO    : Written checkpoint file outdir/default_resume.pickle\n",
            "04:02 bilby DEBUG   : Using BILBY_STYLE=default\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:02 bilby DEBUG   : Not rebuilding cache, remaining size 1\n",
            "04:02 bilby DEBUG   : Force rebuilding cache\n",
            "04:02 bilby DEBUG   : it: 433, accept: 55, reject: 0, fail: 378, act: 31.00, nact: 13.97 \n",
            "04:03 bilby DEBUG   : it: 991, accept: 108, reject: 0, fail: 883, act: 32.00, nact: 30.97 \n",
            "04:03 bilby DEBUG   : it: 1295, accept: 140, reject: 0, fail: 1155, act: 47.00, nact: 27.55 \n",
            "04:03 bilby DEBUG   : it: 1822, accept: 203, reject: 0, fail: 1619, act: 90.00, nact: 20.24 \n",
            "04:04 bilby DEBUG   : it: 3161, accept: 363, reject: 0, fail: 2798, act: 90.00, nact: 35.12 \n",
            "04:04 bilby DEBUG   : it: 3830, accept: 431, reject: 0, fail: 3399, act: 90.00, nact: 42.56 \n",
            "04:04 bilby DEBUG   : it: 4165, accept: 490, reject: 0, fail: 3675, act: 90.00, nact: 46.28 \n",
            "04:04 bilby DEBUG   : it: 4332, accept: 505, reject: 0, fail: 3827, act: 90.00, nact: 48.13 \n",
            "04:04 bilby DEBUG   : it: 4416, accept: 512, reject: 0, fail: 3904, act: 90.00, nact: 49.07 \n",
            "04:04 bilby DEBUG   : it: 4460, accept: 514, reject: 0, fail: 3946, act: 90.00, nact: 49.56 \n",
            "04:04 bilby DEBUG   : act: 90.00, max failures: 90, thin: 180, iteration: 4500, n_found: 24\n",
            "04:04 bilby DEBUG   : Finished building cache with length 25 after 4500 iterations with 1557 likelihood calls and ACT=90.00\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 25\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 25\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:04 bilby DEBUG   : Not rebuilding cache, remaining size 1\n",
            "04:04 bilby DEBUG   : Force rebuilding cache\n",
            "04:04 bilby DEBUG   : it: 541, accept: 64, reject: 0, fail: 477, act: 41.00, nact: 13.20 \n",
            "04:05 bilby DEBUG   : it: 1295, accept: 147, reject: 0, fail: 1148, act: 62.00, nact: 20.89 \n",
            "04:05 bilby DEBUG   : it: 2197, accept: 250, reject: 0, fail: 1947, act: 62.00, nact: 35.44 \n",
            "04:05 bilby DEBUG   : it: 2648, accept: 304, reject: 0, fail: 2344, act: 62.00, nact: 42.71 \n",
            "04:05 bilby DEBUG   : it: 2874, accept: 327, reject: 0, fail: 2547, act: 62.00, nact: 46.35 \n",
            "04:05 bilby DEBUG   : it: 2987, accept: 334, reject: 0, fail: 2653, act: 64.00, nact: 46.67 \n",
            "04:05 bilby DEBUG   : it: 3093, accept: 339, reject: 0, fail: 2754, act: 71.00, nact: 43.56 \n",
            "04:06 bilby DEBUG   : it: 3321, accept: 362, reject: 0, fail: 2959, act: 71.00, nact: 46.77 \n",
            "04:06 bilby DEBUG   : it: 3435, accept: 375, reject: 0, fail: 3060, act: 71.00, nact: 48.38 \n",
            "04:06 bilby DEBUG   : it: 3492, accept: 380, reject: 0, fail: 3112, act: 71.00, nact: 49.18 \n",
            "04:06 bilby DEBUG   : it: 3526, accept: 385, reject: 0, fail: 3141, act: 71.00, nact: 49.66 \n",
            "04:06 bilby DEBUG   : act: 71.00, max failures: 71, thin: 142, iteration: 3550, n_found: 24\n",
            "04:06 bilby DEBUG   : Finished building cache with length 25 after 3550 iterations with 1276 likelihood calls and ACT=71.00\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 25\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 25\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:06 bilby DEBUG   : Not rebuilding cache, remaining size 1\n",
            "04:06 bilby DEBUG   : Force rebuilding cache\n",
            "04:06 bilby DEBUG   : it: 427, accept: 51, reject: 0, fail: 376, act: 32.00, nact: 13.34 \n",
            "04:06 bilby DEBUG   : it: 1013, accept: 121, reject: 0, fail: 892, act: 50.72, nact: 19.97 \n",
            "04:06 bilby DEBUG   : it: 1774, accept: 190, reject: 0, fail: 1584, act: 57.84, nact: 30.67 \n",
            "04:07 bilby DEBUG   : it: 2333, accept: 247, reject: 0, fail: 2086, act: 76.48, nact: 30.50 \n",
            "04:07 bilby DEBUG   : it: 3078, accept: 341, reject: 0, fail: 2737, act: 68.65, nact: 44.84 \n",
            "04:07 bilby DEBUG   : it: 3255, accept: 360, reject: 0, fail: 2895, act: 64.79, nact: 50.24 \n",
            "04:07 bilby DEBUG   : act: 64.79, max failures: 60, thin: 130, iteration: 3255, n_found: 25\n",
            "04:07 bilby DEBUG   : Finished building cache with length 26 after 3255 iterations with 1118 likelihood calls and ACT=64.79\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 26\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 26\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 25\n",
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            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 25\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
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            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
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            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
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            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
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            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
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            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
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            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:07 bilby DEBUG   : Not rebuilding cache, remaining size 1\n",
            "04:07 bilby DEBUG   : Force rebuilding cache\n",
            "04:07 bilby DEBUG   : it: 586, accept: 51, reject: 0, fail: 535, act: 49.00, nact: 11.96 \n",
            "04:08 bilby DEBUG   : it: 1518, accept: 149, reject: 0, fail: 1369, act: 58.00, nact: 26.17 \n",
            "04:08 bilby DEBUG   : it: 2209, accept: 235, reject: 0, fail: 1974, act: 66.00, nact: 33.47 \n",
            "04:08 bilby DEBUG   : it: 2754, accept: 294, reject: 0, fail: 2460, act: 66.00, nact: 41.73 \n",
            "04:08 bilby DEBUG   : it: 3027, accept: 330, reject: 0, fail: 2697, act: 66.00, nact: 45.86 \n",
            "04:08 bilby DEBUG   : it: 3163, accept: 341, reject: 0, fail: 2822, act: 66.00, nact: 47.92 \n",
            "04:08 bilby DEBUG   : it: 3231, accept: 351, reject: 0, fail: 2880, act: 66.00, nact: 48.95 \n",
            "04:08 bilby DEBUG   : it: 3265, accept: 351, reject: 0, fail: 2914, act: 66.00, nact: 49.47 \n",
            "04:08 bilby DEBUG   : it: 3297, accept: 353, reject: 0, fail: 2944, act: 66.00, nact: 49.95 \n",
            "04:08 bilby DEBUG   : act: 66.00, max failures: 66, thin: 132, iteration: 3300, n_found: 24\n",
            "04:08 bilby DEBUG   : Finished building cache with length 25 after 3300 iterations with 1114 likelihood calls and ACT=66.00\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 25\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 25\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 24\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 23\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 22\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 21\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 20\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 19\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 18\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 17\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 16\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 15\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 14\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 13\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 12\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 11\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 10\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 9\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 8\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 7\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 6\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 5\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 4\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 3\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 2\n",
            "04:08 bilby DEBUG   : Not rebuilding cache, remaining size 1\n",
            "04:08 bilby DEBUG   : Force rebuilding cache\n",
            "04:09 bilby DEBUG   : it: 463, accept: 56, reject: 0, fail: 407, act: 31.00, nact: 14.94 \n",
            "04:09 bilby DEBUG   : it: 1006, accept: 110, reject: 0, fail: 896, act: 45.95, nact: 21.90 \n",
            "04:09 bilby DEBUG   : it: 1651, accept: 191, reject: 0, fail: 1460, act: 49.62, nact: 33.27 \n",
            "04:09 bilby DEBUG   : it: 2065, accept: 236, reject: 0, fail: 1829, act: 58.62, nact: 35.22 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SGVB analysis\n",
        "\n",
        "\n",
        "def estimate_sgvb_psd(strain, sampling_frequency):\n",
        "    model = PSDEstimator(\n",
        "        x=strain,\n",
        "        N_theta=50,\n",
        "        nchunks=4,\n",
        "        ntrain_map=1000,\n",
        "        max_hyperparm_eval=1,\n",
        "        fs=sampling_frequency,\n",
        "    )\n",
        "    _ = optim.run(lr=0.003);\n",
        "    return np.median(psd_estimates, axis=0)\n",
        "\n",
        "\n",
        "def set_custom_psd(ifos, psd_array, sampling_frequency, duration):\n",
        "    freqs = np.fft.rfftfreq(int(duration * sampling_frequency), 1 / sampling_frequency)\n",
        "    for ifo in ifos:\n",
        "        ifo.frequency_array = freqs\n",
        "        ifo.power_spectral_density_array = psd_array\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# SGVB PSD analysis\n",
        "residual_strain = {ifo.name: ifo.strain_data.frequency_domain_strain \\\n",
        "                    - waveform_generator.frequency_domain_strain(injection_parameters)[ifo.name]\n",
        "                    for ifo in ifos}\n",
        "# Estimate median PSD from residuals\n",
        "median_psd = estimate_sgvb_psd(residual_strain, sampling_frequency)\n",
        "set_custom_psd(ifos, median_psd, sampling_frequency, duration)\n",
        "\n",
        "result_sgvb = run_analysis(\n",
        "    ifos, waveform_generator, injection_parameters,\n",
        "    priors, outdir, \"sgvb\"\n",
        ")\n"
      ],
      "metadata": {
        "id": "7uHimEQkGvwq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2wSnksH1LyMA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compare_posteriors(result_default, result_sgvb):\n",
        "    result_default.plot_corner(label=\"default_psd\")\n",
        "    result_sgvb.plot_corner(label=\"sgvb_psd\")\n",
        "\n",
        "\n",
        "compare_posteriors(result_default, result_sgvb)\n",
        "\n",
        "# print bayes factor betweenn results\n",
        "\n",
        "print(result_default.log_evidence - result_sgvb.log_evidence)"
      ],
      "metadata": {
        "id": "SxkqVPQHHOXe"
      },
      "execution_count": null,
      "outputs": []
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