.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_plot_interferogram.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_interferogram.py:


Plot an interferogram
=====================


.. code-block:: default


    import os
    if "SPHINX_DOC_BUILD" in os.environ:
        os.chdir(r"C:\tmp\msnoise_doc_project")

    import matplotlib
    matplotlib.use("agg")

    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    from pandas.plotting import register_matplotlib_converters
    register_matplotlib_converters()

    plt.style.use("ggplot")

    from msnoise.api import connect, get_results, build_movstack_datelist, get_params, get_t_axis

    # connect to the database
    db = connect()

    # Obtain a list of dates between ``start_date`` and ``enddate``
    start, end, datelist = build_movstack_datelist(db)

    # Get the list of parameters from the DB:
    params = get_params(db)

    # Get the time axis for plotting the CCF:
    taxis = get_t_axis(db)

    # Get the results for two station, filter id=1, ZZ component, mov_stack=1 and the results as a 2D array:
    n, ccfs = get_results(db, "YA_UV05", "YA_UV12", 1, "ZZ", datelist, 1, format="matrix", params=params)

    # Convert to a pandas DataFrame object for convenience, and drop empty rows:
    df = pd.DataFrame(ccfs, index=pd.DatetimeIndex(datelist), columns=taxis)
    df = df.dropna()

    # Define the 99% percentile of the data, for visualisation purposes:
    clim = df.mean(axis="index").quantile(0.99)

    fig, ax = plt.subplots()
    plt.pcolormesh(df.columns, df.index.to_pydatetime(), df.values,
                   vmin=-clim, vmax=clim, rasterized=True)
    plt.colorbar()
    plt.title("Interferogram")
    plt.xlabel("Lag Time (s)")
    plt.ylim(df.index[0],df.index[-1])
    plt.xlim(df.columns[0], df.columns[-1])
    plt.subplots_adjust(left=0.15)





.. image:: /auto_examples/images/sphx_glr_plot_interferogram_001.png
    :class: sphx-glr-single-img




Running a simple moving window average can be done with pandas's functions:


.. code-block:: default



    smooth = df.rolling(5).mean()

    fig, ax = plt.subplots()
    plt.pcolormesh(smooth.columns, smooth.index.to_pydatetime(), smooth.values,
                   vmin=-clim, vmax=clim, rasterized=True)
    plt.colorbar()
    plt.title("Interferogram (smoothed over 5 days)")
    plt.xlabel("Lag Time (s)")
    plt.ylim(smooth.index[0],smooth.index[-1])
    plt.xlim(smooth.columns[0], smooth.columns[-1])
    plt.subplots_adjust(left=0.15)


    plt.show()  
    #EOF


.. image:: /auto_examples/images/sphx_glr_plot_interferogram_002.png
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    D:\PythonForSource\MSNoise_Stack\MSNoise\examples\plot_interferogram.py:75: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
      plt.show()




.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  1.870 seconds)


.. _sphx_glr_download_auto_examples_plot_interferogram.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_interferogram.py <plot_interferogram.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_interferogram.ipynb <plot_interferogram.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_