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.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "mcmc_examples/PyFstat_example_semi_coherent_MCMC_search.py"
.. LINE NUMBERS ARE GIVEN BELOW.

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.. _sphx_glr_mcmc_examples_PyFstat_example_semi_coherent_MCMC_search.py:


MCMC search: Semicoherent F-statistic
=======================================

Directed MCMC search for an isolated CW signal using the
semicoherent F-statistic.

.. GENERATED FROM PYTHON SOURCE LINES 8-108

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


    import os

    import numpy as np

    import pyfstat

    label = "PyFstatExampleSemiCoherentMCMCSearch"
    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": 100 * 86400,
        "detectors": "H1",
    }
    tend = data_parameters["tstart"] + data_parameters["duration"]
    mid_time = 0.5 * (data_parameters["tstart"] + tend)

    # Properties of the signal
    depth = 10
    signal_parameters = {
        "F0": 30.0,
        "F1": -1e-10,
        "F2": 0,
        "Alpha": np.radians(83.6292),
        "Delta": np.radians(22.0144),
        "tref": mid_time,
        "h0": data_parameters["sqrtSX"] / depth,
        "cosi": 1.0,
    }

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

    # The predicted twoF (expectation over noise realizations) can be accessed by
    twoF = data.predict_fstat()
    logger.info("Predicted twoF value: {}\n".format(twoF))

    DeltaF0 = 1e-7
    DeltaF1 = 1e-13
    VF0 = (np.pi * data_parameters["duration"] * DeltaF0) ** 2 / 3.0
    VF1 = (np.pi * data_parameters["duration"] ** 2 * DeltaF1) ** 2 * 4 / 45.0
    logger.info("\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n".format(VF0 * VF1, VF0, VF1))

    theta_prior = {
        "F0": {
            "type": "unif",
            "lower": signal_parameters["F0"] - DeltaF0 / 2.0,
            "upper": signal_parameters["F0"] + DeltaF0 / 2.0,
        },
        "F1": {
            "type": "unif",
            "lower": signal_parameters["F1"] - DeltaF1 / 2.0,
            "upper": signal_parameters["F1"] + DeltaF1 / 2.0,
        },
    }
    for key in "F2", "Alpha", "Delta":
        theta_prior[key] = signal_parameters[key]

    ntemps = 1
    log10beta_min = -1
    nwalkers = 100
    nsteps = [300, 300]

    mcmc = pyfstat.MCMCSemiCoherentSearch(
        label=label,
        outdir=outdir,
        nsegs=10,
        sftfilepattern=data.sftfilepath,
        theta_prior=theta_prior,
        tref=mid_time,
        minStartTime=data_parameters["tstart"],
        maxStartTime=tend,
        nsteps=nsteps,
        nwalkers=nwalkers,
        ntemps=ntemps,
        log10beta_min=log10beta_min,
    )
    mcmc.transform_dictionary = dict(
        F0=dict(subtractor=signal_parameters["F0"], symbol="$f-f^\\mathrm{s}$"),
        F1=dict(
            subtractor=signal_parameters["F1"], symbol="$\\dot{f}-\\dot{f}^\\mathrm{s}$"
        ),
    )
    mcmc.run(
        walker_plot_args={"plot_det_stat": True, "injection_parameters": signal_parameters}
    )
    mcmc.print_summary()
    mcmc.plot_corner(add_prior=True, truths=signal_parameters)
    mcmc.plot_prior_posterior(injection_parameters=signal_parameters)
    mcmc.plot_chainconsumer(truth=signal_parameters)
    mcmc.plot_cumulative_max(
        savefig=True,
        custom_ax_kwargs={"title": "Cumulative 2F for the best MCMC candidate"},
    )


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