.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "generated/gallery/skip_umbral_flashes.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_generated_gallery_skip_umbral_flashes.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_generated_gallery_skip_umbral_flashes.py:


=======================
Umbral flashes analysis
=======================

In this example, we are going to work with spectra and slit-jaw
images to study an example of dynamical phenomena called umbral flashes.

.. GENERATED FROM PYTHON SOURCE LINES 9-22

.. code-block:: Python


    import astropy.units as u
    import matplotlib.dates as mdates
    import matplotlib.pyplot as plt
    import pooch
    from astropy.coordinates import SpectralCoord
    from astropy.visualization import time_support

    from irispy.io import read_files
    from irispy.utils import image_clipping

    time_support()


.. GENERATED FROM PYTHON SOURCE LINES 23-30

We start with getting the data. This is done by downloading the data from the IRIS archive.

In this case, we will use ``pooch`` as to keep this example self contained
but using your browser will also work.

Using the url: http://www.lmsal.com/solarsoft/irisa/data/level2_compressed/2013/09/02/20130902_163935_4000255147/
we are after the 1400 slit-jaw file and the raster file (~900 MB).

.. GENERATED FROM PYTHON SOURCE LINES 30-40

.. code-block:: Python


    raster_filename = pooch.retrieve(
        "http://www.lmsal.com/solarsoft/irisa/data/level2_compressed/2013/09/02/20130902_163935_4000255147/iris_l2_20130902_163935_4000255147_raster.tar.gz",
        known_hash="e0fdbe63b6ff88856d97dff1fd4c3f62bf0bb4a85c025930c36fd69086ed0533",
    )
    sji_filename = pooch.retrieve(
        "http://www.lmsal.com/solarsoft/irisa/data/level2_compressed/2013/09/02/20130902_163935_4000255147/iris_l2_20130902_163935_4000255147_SJI_1400_t000.fits.gz",
        known_hash="e04eedbe7fd7e5e173b6a9308db3e45991634a4f8d9b245a334d5c492d85d0ed",
    )


.. GENERATED FROM PYTHON SOURCE LINES 41-42

Now to open the files using ``irispy-lmsal``.

.. GENERATED FROM PYTHON SOURCE LINES 42-49

.. code-block:: Python


    # Note that when ``memmap=True``, the data values are read from the FITS file
    # directly without the scaling to Float32, the data values are no longer in DN,
    # but in scaled integer units that start at -2$^{16}$/2.
    raster = read_files(raster_filename, memmap=True, uncertainty=False)
    sji_1400 = read_files(sji_filename, memmap=True, uncertainty=False)


.. GENERATED FROM PYTHON SOURCE LINES 50-52

We are after the Mg II k and C II lines, which we can select using a key.
We will produce a space-time image of the Mg II k3 wavelength.

.. GENERATED FROM PYTHON SOURCE LINES 52-69

.. code-block:: Python


    mg_ii = raster["Mg II k 2796"][0]
    c_ii = raster["C II 1336"][0]

    # Instead of using the pixel index, we can crop the data in wavelength space.
    lower_corner = [SpectralCoord(279.63, unit=u.nm), None]
    upper_corner = [SpectralCoord(279.63, unit=u.nm), None]
    mg_crop = mg_ii.crop(lower_corner, upper_corner)

    # The Mg II k3 core is located around wavelength pixel 103.
    # Which we will use to clip the data.
    vmin, vmax = image_clipping(mg_ii.data[..., 103])
    mg_crop.plot(vmin=vmin, vmax=vmax)
    plt.xlabel("Solar Y")

    plt.show()


.. GENERATED FROM PYTHON SOURCE LINES 70-75

The middle section between 60"-75" is on the umbra of a sunspot, even though
it is not obvious from this image. One can see very clearly the umbral oscillations,
with a clear regular pattern of dark/bright streaks.

Let us now load the 1400 SJI for context.

.. GENERATED FROM PYTHON SOURCE LINES 75-81

.. code-block:: Python


    vmin, vmax = image_clipping(sji_1400[0].data)
    sji_1400[0].plot(vmin=vmin, vmax=vmax)

    plt.show()


.. GENERATED FROM PYTHON SOURCE LINES 82-86

The slit pixel 220 is a location on the sunspot's umbra.
Let us plot the k3 intensity (spectral pixel 103 of ``mg_ii``) and the
core of the brightest C II line (spectral pixel 90 of ``c_ii``) vs
time in minutes (showing first ~10 minutes only)

.. GENERATED FROM PYTHON SOURCE LINES 86-103

.. code-block:: Python


    mg_ii_times = mg_ii.axis_world_coords("time", wcs=mg_ii.extra_coords)[0][:200]
    c_ii_times = c_ii.axis_world_coords("time", wcs=c_ii.extra_coords)[0][:200]

    plt.figure()
    plt.plot(mg_ii_times, mg_ii.data[:200, 220, 103], label="Mg II k3")
    (ax,) = plt.plot(c_ii_times, c_ii.data[:200, 220, 90], label="C II")
    plt.legend()
    plt.ylabel("DN (Memory Mapped Value)")
    plt.xlabel("Time (UTC)")
    ax.axes.xaxis.set_major_formatter(mdates.ConciseDateFormatter(ax.axes.xaxis.get_major_locator()))
    # Rotates and right-aligns the x labels so they don't crowd each other.
    for label in ax.axes.get_xticklabels(which="major"):
        label.set(rotation=30, horizontalalignment="right")

    plt.show()


.. GENERATED FROM PYTHON SOURCE LINES 104-108

Imagine now you wanted to compare these oscillations with
the intensity from the SJI. The SJI images are typically
taken at a different cadence, so you will need to load the corresponding
time array for the 1400 SJI.

.. GENERATED FROM PYTHON SOURCE LINES 108-113

.. code-block:: Python


    # We will take the first 50 to cut down on the size of the data for this example.
    # This is memory intensive and that an upstream bug needs to be fixed.
    times_sji = sji_1400.time[:50]


.. GENERATED FROM PYTHON SOURCE LINES 114-116

Now we can plot both spectral lines and SJI for a pixel close to the slit
at the same Y position (pre-worked out to be at index 220).

.. GENERATED FROM PYTHON SOURCE LINES 116-131

.. code-block:: Python


    plt.figure()
    plt.plot(mg_ii_times, mg_ii.data[:200, 220, 103], label="Mg II k3")
    plt.plot(c_ii_times, c_ii.data[:200, 220, 90], label="C II")
    (ax,) = plt.plot(times_sji, sji_1400.data[:50, 190, 220], label="1400 SJI")
    plt.legend()
    plt.ylabel("Counts (Memory Mapped Value)")
    plt.xlabel("Time (UTC)")
    ax.axes.xaxis.set_major_formatter(mdates.ConciseDateFormatter(ax.axes.xaxis.get_major_locator()))
    # Rotates and right-aligns the x labels so they don't crowd each other.
    for label in ax.axes.get_xticklabels(which="major"):
        label.set(rotation=30, horizontalalignment="right")

    plt.show()


.. GENERATED FROM PYTHON SOURCE LINES 132-134

You are now ready to explore all the correlations, anti-correlations,
and phase differences.


.. _sphx_glr_download_generated_gallery_skip_umbral_flashes.py:

.. only:: html

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

    .. container:: sphx-glr-download sphx-glr-download-jupyter

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

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

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


.. only:: html

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

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