.. _preprocessing:

Feature Extraction
==================

The first stage in consists in transforming the raw data into a uniform data matrix which will subsequently be given
as input to the learning algorithm.

The current implementation of ``actsnclass`` text-like data from the SuperNova Photometric Classification Challenge
(SNPCC) which is described in `Kessler et al., 2010 <https://arxiv.org/abs/1008.1024>`_.

Processing 1 Light curve
------------------------

The raw data looks like this:

.. literalinclude:: images/DES_SN848233.DAT
 :lines: 1-61

You can load this data using:

.. code-block:: python
   :linenos:

   >>> from actsnclass.fit_lightcurves import LightCurve

   >>> path_to_lc = 'data/SIMGEN_PUBLIC_DES/DES_SN848233.DAT'

   >>> lc = LightCurve()                        # create light curve instance
   >>> lc.load_snpcc_lc(path_to_lc)             # read data
   >>> lc.photometry                            # check structure of photometry
             mjd band     flux  fluxerr   SNR
    0    56194.145    g   7.600    4.680   1.62
    1    56194.156    r   3.875    2.752   1.41
    ...        ...  ...      ...      ...   ...
    106  56348.008    z  70.690    6.706  10.54
    107  56348.996    g  26.000    5.581   4.66
    [108 rows x 5 columns]

Once the data is loaded, you can fit each individual filter to the parametric function proposed by
`Bazin et al., 2009 <https://arxiv.org/abs/0904.1066>`_ in one specific filter.

.. code-block:: python
   :linenos:

   >>> rband_features = lc.fit_bazin('r')
   >>> print(rband_features)
   [159.25796385, -13.39398527,  55.16210333, 111.81204143, -20.13492354]

The designation for each parameter are stored in:

.. code-block::python
   :linenos:

   >>> lc.bazin_features_names
   ['a', 'b', 't0', 'tfall', 'trsise']

It is possible to perform the fit in all filters at once and visualize the result using:

.. code-block:: python
   :linenos:

   >>> lc.fit_bazin_all()                            # perform Bazin fit in all filters
   >>> lc.plot_bazin_fit(save=True, show=True,
                         output_file='plots/SN' + str(lc.id) + '.png')   # save to file

.. image:: images/SN848233.png
   :align: center
   :height: 480 px
   :width: 640 px
   :alt: Bazing fit to light curve.


Processing all light curves in the data set
-------------------------------------------

There are 2 way to perform the Bazin fits for the entire SNPCC data set. Using a python interpreter,

.. code-block:: python
   :linenos:

   >>> from actsnclass import fit_snpcc_bazin

   >>> path_to_data_dir = 'data/SIMGEN_PUBLIC_DES/'            # raw data directory
   >>> output_file = 'results/Bazin.dat'                              # output file
   >>> fit_snpcc_bazin(path_to_data_dir=path_to_data_dir, features_file=output_file)

The above will produce a file called ``Bazin.dat`` in the `results` directory.

The same result can be achieved using the command line:

.. code-block:: bash

   >> fit_dataset.py -dd <path_to_data_dir> -o <output_file>