Injection Files
============================

Lets look at the different ways we can use injection files in GlitchPop.

Writing a Random Injection File w/ Glitches
--------------

To generate a random injection file and the accompanying time series, we can do the following. Lets generate an example with only glitches to save some computing time.

.. code-block:: python

    import GlitchPop as gp
    import GlitchPop.write

    #Generating time series and injection file
    #file = True gives us the name of the .json file
    ts, filename = gp.write.random_glitch('L1', 'O2', duration = 350, gps = 11e8, mu = 0.25, file = True)

    #Plotting time series
    plot = ts.plot()

.. image:: images/inj_write.png
   :alt: coloured time series from injection writer
   :width: 500px
   :height: 400px
   :align: center

This time series is the object that is create upon writing injection files. There is some rather loud glitches (presumably koi fish) in this time series!

Reading an Injection File w/ Glitches
--------------

Continuing on from the last example, lets now read that same injection file and ensure it produces the same time series.

.. code-block:: python

    import GlitchPop.read
    import matplotlib.pyplot as plt

    #Reading injection file
    td = gp.read.single_ifo(filename)

    #Plotting time series
    plot = td.plot()
    plt.show()

    #Plotting Residuals
    plt.title('Residuals')
    plt.plot(td-ts)
    plt.show()

.. image:: images/inj_write.png
   :alt: coloured time series from injection reader
   :width: 500px
   :height: 400px
   :align: center

.. image:: images/inj_res.png
   :alt: residuals of injection reader/writer
   :width: 500px
   :height: 400px
   :align: center

As expected, the residuals between the two time series are a horizontal line at zero. Since this used random (unseeded) generation, users will obtain different plots to this example each time the first code block is ran.

Example Injection File
--------------

In GlitchPop, injection files are in the form of .json files containing 7 glitch related fields, 20 GW related fields, 6 noise fields and a descriptive string that is used as the file name. The descriptive string (and file name) follows the following format:

IFO abberviation - observing run - duration in seconds - 'S' - starting GPS time - '_' - 'B' - number of blips - 'L' - number of low frequency blips - 'T' - number of tomtes - 'K' - number of koi fish

The injection file produced from the previous example is shown below (as seen in JupyerLab). Note, although there is many fields, a lot of this fields remain empty, such as x and y spins, depending on the type of simulation one is running.

.. image:: images/inj_file.png
   :alt: image of .json injection file
   :width: 500px
   :height: 700px
   :align: center