.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "binary_examples/PyFstat_example_semi_coherent_binary_search_using_MCMC.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_binary_examples_PyFstat_example_semi_coherent_binary_search_using_MCMC.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_binary_examples_PyFstat_example_semi_coherent_binary_search_using_MCMC.py:


Binary CW example: Semicoherent MCMC search
==========================================================

MCMC search of a CW signal produced by a source in a binary
system using the semicoherent F-statistic.

.. GENERATED FROM PYTHON SOURCE LINES 8-145

.. code-block:: Python
   :lineno-start: 9


    import os

    import numpy as np

    import pyfstat

    # If False, sky priors are used
    directed_search = True
    # If False, ecc and argp priors are used
    known_eccentricity = True

    label = "PyFstatExampleSemiCoherentBinarySearchUsingMCMC"
    outdir = os.path.join("PyFstat_example_data", label)
    logger = pyfstat.set_up_logger(label=label, outdir=outdir)

    # Properties of the GW data
    data_parameters = {
        "sqrtSX": 1e-23,
        "tstart": 1000000000,
        "duration": 10 * 86400,
        "detectors": "H1",
    }
    tend = data_parameters["tstart"] + data_parameters["duration"]
    mid_time = 0.5 * (data_parameters["tstart"] + tend)

    # Properties of the signal
    depth = 0.1
    signal_parameters = {
        "F0": 30.0,
        "F1": 0,
        "F2": 0,
        "Alpha": 0.15,
        "Delta": 0.45,
        "tp": mid_time,
        "argp": 0.3,
        "asini": 10.0,
        "ecc": 0.1,
        "period": 45 * 24 * 3600.0,
        "tref": mid_time,
        "h0": data_parameters["sqrtSX"] / depth,
        "cosi": 1.0,
    }

    data = pyfstat.BinaryModulatedWriter(
        label=label, outdir=outdir, **data_parameters, **signal_parameters
    )
    data.make_data()

    theta_prior = {
        "F0": signal_parameters["F0"],
        "F1": signal_parameters["F1"],
        "F2": signal_parameters["F2"],
        "asini": {
            "type": "unif",
            "lower": 0.9 * signal_parameters["asini"],
            "upper": 1.1 * signal_parameters["asini"],
        },
        "period": {
            "type": "unif",
            "lower": 0.9 * signal_parameters["period"],
            "upper": 1.1 * signal_parameters["period"],
        },
        "tp": {
            "type": "unif",
            "lower": mid_time - signal_parameters["period"] / 2.0,
            "upper": mid_time + signal_parameters["period"] / 2.0,
        },
    }

    if directed_search:
        for key in "Alpha", "Delta":
            theta_prior[key] = signal_parameters[key]
    else:
        theta_prior.update(
            {
                "Alpha": {
                    "type": "unif",
                    "lower": signal_parameters["Alpha"] - 0.01,
                    "upper": signal_parameters["Alpha"] + 0.01,
                },
                "Delta": {
                    "type": "unif",
                    "lower": signal_parameters["Delta"] - 0.01,
                    "upper": signal_parameters["Delta"] + 0.01,
                },
            }
        )


    if known_eccentricity:
        for key in "ecc", "argp":
            theta_prior[key] = signal_parameters[key]
    else:
        theta_prior.update(
            {
                "ecc": {
                    "type": "unif",
                    "lower": signal_parameters["ecc"] - 5e-2,
                    "upper": signal_parameters["ecc"] + 5e-2,
                },
                "argp": {
                    "type": "unif",
                    "lower": signal_parameters["argp"] - np.pi / 2,
                    "upper": signal_parameters["argp"] + np.pi / 2,
                },
            }
        )

    ntemps = 3
    log10beta_min = -1
    nwalkers = 150
    nsteps = [100, 200]

    mcmc = pyfstat.MCMCSemiCoherentSearch(
        label=label,
        outdir=outdir,
        nsegs=10,
        sftfilepattern=data.sftfilepath,
        theta_prior=theta_prior,
        tref=signal_parameters["tref"],
        minStartTime=data_parameters["tstart"],
        maxStartTime=tend,
        nsteps=nsteps,
        nwalkers=nwalkers,
        ntemps=ntemps,
        log10beta_min=log10beta_min,
        binary=True,
    )

    mcmc.run(
        plot_walkers=True,
        walker_plot_args={"plot_det_stat": True, "injection_parameters": signal_parameters},
    )
    mcmc.plot_corner(add_prior=True, truths=signal_parameters)
    mcmc.plot_prior_posterior(injection_parameters=signal_parameters)
    mcmc.print_summary()


.. _sphx_glr_download_binary_examples_PyFstat_example_semi_coherent_binary_search_using_MCMC.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: PyFstat_example_semi_coherent_binary_search_using_MCMC.ipynb <PyFstat_example_semi_coherent_binary_search_using_MCMC.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: PyFstat_example_semi_coherent_binary_search_using_MCMC.py <PyFstat_example_semi_coherent_binary_search_using_MCMC.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: PyFstat_example_semi_coherent_binary_search_using_MCMC.zip <PyFstat_example_semi_coherent_binary_search_using_MCMC.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_