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.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "transient_examples/PyFstat_example_long_transient_MCMC_search.py"
.. LINE NUMBERS ARE GIVEN BELOW.

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        :ref:`Go to the end <sphx_glr_download_transient_examples_PyFstat_example_long_transient_MCMC_search.py>`
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.. rst-class:: sphx-glr-example-title

.. _sphx_glr_transient_examples_PyFstat_example_long_transient_MCMC_search.py:


Long transient MCMC search
==========================

MCMC search for a long transient CW signal.

By default, the standard persistent-CW 2F-statistic
and the transient max2F statistic are compared.

You can turn on either `BSGL = True` or `BtSG = True` (not both!)
to test alternative statistics.

.. GENERATED FROM PYTHON SOURCE LINES 13-113

.. code-block:: Python
   :lineno-start: 14


    import os

    import numpy as np
    import PyFstat_example_make_data_for_long_transient_search as data

    import pyfstat
    from pyfstat.utils import get_predict_fstat_parameters_from_dict

    if not os.path.isdir(data.outdir) or not np.any(
        [f.endswith(".sft") for f in os.listdir(data.outdir)]
    ):
        raise RuntimeError(
            "Please first run PyFstat_example_make_data_for_long_transient_search.py !"
        )

    label = "PyFstatExampleLongTransientMCMCSearch"
    logger = pyfstat.set_up_logger(label=label, outdir=data.outdir)

    tstart = data.tstart
    duration = data.duration

    inj = {
        "tref": data.tstart,
        "F0": data.F0,
        "F1": data.F1,
        "F2": data.F2,
        "Alpha": data.Alpha,
        "Delta": data.Delta,
        "transient_tstart": data.transient_tstart,
        "transient_duration": data.transient_duration,
    }

    DeltaF0 = 6e-7
    DeltaF1 = 1e-13

    # to make the search cheaper, we exactly target the transientStartTime
    # to the injected value and only search over TransientTau
    theta_prior = {
        "F0": {
            "type": "unif",
            "lower": inj["F0"] - DeltaF0 / 2.0,
            "upper": inj["F0"] + DeltaF0 / 2.0,
        },
        "F1": {
            "type": "unif",
            "lower": inj["F1"] - DeltaF1 / 2.0,
            "upper": inj["F1"] + DeltaF1 / 2.0,
        },
        "F2": inj["F2"],
        "Alpha": inj["Alpha"],
        "Delta": inj["Delta"],
        "transient_tstart": tstart + 0.25 * duration,
        "transient_duration": {
            "type": "halfnorm",
            "loc": 0.001 * duration,
            "scale": 0.5 * duration,
        },
    }

    ntemps = 2
    log10beta_min = -1
    nwalkers = 100
    nsteps = [100, 100]

    transientWindowType = "rect"
    BSGL = False
    BtSG = False

    mcmc = pyfstat.MCMCTransientSearch(
        label=label + ("BSGL" if BSGL else "") + ("BtSG" if BtSG else ""),
        outdir=data.outdir,
        sftfilepattern=os.path.join(data.outdir, f"*{data.label}*sft"),
        theta_prior=theta_prior,
        tref=inj["tref"],
        nsteps=nsteps,
        nwalkers=nwalkers,
        ntemps=ntemps,
        log10beta_min=log10beta_min,
        transientWindowType=transientWindowType,
        BSGL=BSGL,
        BtSG=BtSG,
    )
    mcmc.run(walker_plot_args={"plot_det_stat": True, "injection_parameters": inj})
    mcmc.print_summary()
    mcmc.plot_corner(add_prior=True, truths=inj)
    mcmc.plot_prior_posterior(injection_parameters=inj)

    # plot cumulative 2F, first building a dict as required for PredictFStat
    d, maxtwoF = mcmc.get_max_twoF()
    for key, val in mcmc.theta_prior.items():
        if key not in d:
            d[key] = val
    d["h0"] = data.h0
    d["cosi"] = data.cosi
    d["psi"] = data.psi
    PFS_input = get_predict_fstat_parameters_from_dict(
        d, transientWindowType=transientWindowType
    )
    mcmc.plot_cumulative_max(PFS_input=PFS_input)


.. _sphx_glr_download_transient_examples_PyFstat_example_long_transient_MCMC_search.py:

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

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