Wavelet analysis 
===============================================

This notebook provide an example of analysis replacing the Lomb-Scargle
periodogram by a wavelet analysis of the time series. The wavelet
analysis is not a part of the PLATO MSAP4 rotation & activity baseline
algorithms but it represents an interesting alternative in order to
assess the performances of the framework.

.. code:: ipython3

    import numpy as np

.. code:: ipython3

    import star_privateer as sp

.. code:: ipython3

    sp.__version__




.. parsed-literal::

    '1.1.2'



A simple example
----------------

Our working case is KIC 3733735.

.. code:: ipython3

    filename = sp.get_target_filename (sp.timeseries, '003733735')
    t, s, dt = sp.load_resource (filename)

In order to save computing time, we rebin the data in 4-hour bins.

.. code:: ipython3

    dt *= 4
    t = np.mean (t.reshape (-1,4), axis=1)
    s = np.mean (s.reshape (-1,4), axis=1)

We now run the analysis pipeline. In particular, we can take a look at
the plots made from the different analysis methods.

.. code:: ipython3

    (p_wps, p_acf, gwps, wps, acf, 
     cs, coi, features, feature_names, _) = sp.analysis_pipeline (t, s, figsize=(8,12),
                                                                 wavelet_analysis=True, plot=True,
                                                                 xlim=(0,50), normscale='log', ylogscale=True,
                                                                 add_periodogram=True)



.. image:: wavelet_analysis_files/wavelet_analysis_10_0.png


It is also possible to compute the wavelet power spectrum and plot it
independently from the other methods.

.. code:: ipython3

    dt = (t[1]-t[0])*86400
    (periods, wps, gwps, 
     coi, scales) = sp.compute_wps(s, dt, normalise=True, mother=None)

The GWPS peaks can be fitted with a set of Gaussian profile.

.. code:: ipython3

    (prot_ps, E_prot_ps, 
     param_gauss) = sp.compute_prot_err_gaussian_fit (periods, gwps, n_profile=5, 
                                                           threshold=0.1)

.. code:: ipython3

    fig = sp.plot_wps(t-t[0], periods, wps, gwps, coi, 
                         scales, shading='auto', color_coi='darkgrey', 
                         ylogscale=True, lw=1, normscale='log', 
                         vmin=None, vmax=None, filename=None, dpi=300,
                         figsize=(8,4), ylim=(1, 100), show_contour=False,
                         param_gauss=param_gauss) 



.. image:: wavelet_analysis_files/wavelet_analysis_15_0.png