.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "generated/gallery/paper_figures.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_paper_figures.py>`
        to download the full example code.

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

.. _sphx_glr_generated_gallery_paper_figures.py:


=============
Paper Figures
=============

Recreation of figures from the original paper [Garton2018]_.

.. [Garton2018] https://www.swsc-journal.org/articles/swsc/pdf/2018/01/swsc170041.pdf

Start of with imports

.. GENERATED FROM PYTHON SOURCE LINES 12-32

.. code-block:: Python


    import copy

    import numpy as np
    from matplotlib import pyplot as plt
    from matplotlib.colors import LogNorm
    from matplotlib.patches import Rectangle
    from sunpy.map import (
        Map,
        all_coordinates_from_map,
        coordinate_is_on_solar_disk,
        pixelate_coord_path,
        sample_at_coords,
    )

    import astropy.units as u
    from astropy.coordinates import SkyCoord
    from astropy.units import UnitsError
    from astropy.visualization import make_lupton_rgb








.. GENERATED FROM PYTHON SOURCE LINES 33-43

Figure 2
--------

A summary plot showing SDO/AIA 131Å, 171Å, 193Å, 211Å, 335Å and 94Å observations from 2016-09-22 at 12:00 UT.
A small equatorial coronal hole (CH) is visible close to disk center, it is more visible in some wavelengths than
others.

.. note::
    Figures 2 and 3 use data from a different date compared to later figures.


.. GENERATED FROM PYTHON SOURCE LINES 44-82

.. code-block:: Python


    m94 = Map("https://jsoc1.stanford.edu/data/aia/synoptic/2016/09/22/H1200/AIA20160922_1200_0094.fits")
    m131 = Map("https://jsoc1.stanford.edu/data/aia/synoptic/2016/09/22/H1200/AIA20160922_1200_0131.fits")
    m171 = Map("https://jsoc1.stanford.edu/data/aia/synoptic/2016/09/22/H1200/AIA20160922_1200_0171.fits")
    m193 = Map("https://jsoc1.stanford.edu/data/aia/synoptic/2016/09/22/H1200/AIA20160922_1200_0193.fits")
    m211 = Map("https://jsoc1.stanford.edu/data/aia/synoptic/2016/09/22/H1200/AIA20160922_1200_0211.fits")
    m335 = Map("https://jsoc1.stanford.edu/data/aia/synoptic/2016/09/22/H1200/AIA20160922_1200_0335.fits")


    fig, axes = plt.subplot_mosaic(
        [["131", "171"], ["193", "211"], ["335", "94"]],
        layout="constrained",
        figsize=(6, 9),
        per_subplot_kw={
            "131": {"projection": m131},
            "171": {"projection": m171},
            "193": {"projection": m193},
            "211": {"projection": m211},
            "335": {"projection": m335},
            "94": {"projection": m94},
        },
    )
    temps = {
        "131": 0.4,
        "171": 0.7,
        "193": 1.2,
        "211": 2.0,
        "335": 2.5,
        "94": 6.3,
    }
    for m in [m131, m171, m193, m211, m335, m94]:
        wave_str = f"{m.wavelength.value:0.0f}"
        line_coords = SkyCoord([-200, 0], [-100, -100], unit=(u.arcsec, u.arcsec), frame=m.coordinate_frame)
        m.plot(axes=axes[wave_str], clip_interval=(0.1, 99.9) * u.percent)
        axes[wave_str].set_title(f"{wave_str} Å {temps[wave_str]} MK")
        axes[wave_str].plot_coord(line_coords, "w", linewidth=1.5)
        axes[wave_str].set_facecolor("k")




.. image-sg:: /generated/gallery/images/sphx_glr_paper_figures_001.png
   :alt: 131 Å 0.4 MK, 171 Å 0.7 MK, 193 Å 1.2 MK, 211 Å 2.0 MK, 335 Å 2.5 MK, 94 Å 6.3 MK
   :srcset: /generated/gallery/images/sphx_glr_paper_figures_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


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.. GENERATED FROM PYTHON SOURCE LINES 83-90

Figure 3
--------

Intensity cuts across the CH along the path indicated in Figure 1 for each wavelength.
The Michelson contrast for each intensity cut is calculated via :math:`C_{M} \frac{I_{Max}-I_{Min}}{I_{Max}+I_{Min}}`
and display in the plot title.


.. GENERATED FROM PYTHON SOURCE LINES 91-109

.. code-block:: Python


    fig, axes = plt.subplot_mosaic(
        [["131"], ["171"], ["193"], ["211"], ["335"], ["94"]], layout="constrained", figsize=(6, 9), sharex=True
    )
    for m in [m131, m171, m193, m211, m335, m94]:
        line_coords = SkyCoord([-200, 0], [-100, -100], unit=(u.arcsec, u.arcsec), frame=m.coordinate_frame)
        x, y = m.world_to_pixel(line_coords)
        xmin, xmax = np.round(x).value.astype(int)
        ymin, ymax = np.round(y).value.astype(int)
        intensity_coords = pixelate_coord_path(m, line_coords, bresenham=True)
        intensity1 = sample_at_coords(m, intensity_coords)
        contrast = (intensity1.max() - intensity1.min()) / (intensity1.max() + intensity1.min())
        axes[format(m.wavelength.value, "0.0f")].plot(intensity_coords.Tx, intensity1)
        axes[format(m.wavelength.value, "0.0f")].set_title(
            f"AIA {m.wavelength.value: 0.0f} Å, Contrast: {contrast:0.2f}"
        )





.. image-sg:: /generated/gallery/images/sphx_glr_paper_figures_002.png
   :alt: AIA  131 Å, Contrast: 0.69, AIA  171 Å, Contrast: 0.59, AIA  193 Å, Contrast: 0.87, AIA  211 Å, Contrast: 0.84, AIA  335 Å, Contrast: 1.04, AIA  94 Å, Contrast: 1.26
   :srcset: /generated/gallery/images/sphx_glr_paper_figures_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 110-117

Figure 4
--------

Figure 4 shows intensity ratio plots for three wavelength combinations 171Å vs 193Å, 171Å vs 211Å,
and finally 211Å vs 193Å. The plots in the left column are simple scatter plots of the intensity of one wavelength
against the other. The plots on the right are 2D histograms constructed using the same data focusing on the lower
intensity regions marked by the red rectangles.

.. GENERATED FROM PYTHON SOURCE LINES 118-207

.. code-block:: Python


    m171 = Map("https://jsoc1.stanford.edu/data/aia/synoptic/2016/10/31/H0200/AIA20161031_0232_0171.fits")
    m193 = Map("https://jsoc1.stanford.edu/data/aia/synoptic/2016/10/31/H0200/AIA20161031_0232_0193.fits")
    m211 = Map("https://jsoc1.stanford.edu/data/aia/synoptic/2016/10/31/H0200/AIA20161031_0232_0211.fits")

    m171_orig = copy.deepcopy(m171)
    m193_orig = copy.deepcopy(m193)
    m211_orig = copy.deepcopy(m211)

    # Since the data are taken at similar times neglect any coordinate changes so just use 171 maps coordinates
    coords = all_coordinates_from_map(m171)
    disk_mask = coordinate_is_on_solar_disk(coords)

    m171 = m171 / m171.exposure_time
    m193 = m193 / m193.exposure_time
    m211 = m211 / m211.exposure_time

    # # Smooth the data with 4x4 median filter (not need on NRT data)
    # m171.data[:, :] = medfilt2d(m171.data, 5)
    # m193.data[:, :] = medfilt2d(m193.data, 5)
    # m211.data[:, :] = medfilt2d(m211.data, 5)


    fig, axes = plt.subplot_mosaic(
        [["cool_scat", "cool_hist"], ["warm_scat", "warm_hist"], ["hot_scat", "hot_hist"]],
        layout="constrained",
        figsize=(6, 9),
    )

    axes["cool_scat"].scatter(m171.data[disk_mask], m193.data[disk_mask], alpha=0.2, edgecolor="none", s=2.5)
    axes["cool_scat"].set_xlim(0, 2000)
    axes["cool_scat"].set_ylim(0, 2000)
    axes["cool_scat"].set_xlabel(171)
    axes["cool_scat"].set_ylabel(193)
    axes["cool_scat"].add_patch(Rectangle((0, 0), 300, 300, ec="r", fill=None))
    cool_counts, *cool_bins = axes["cool_hist"].hist2d(
        m171.data[disk_mask].flatten(),
        m193.data[disk_mask].flatten(),
        bins=150,
        range=[[0, 300], [0, 300]],
        norm=LogNorm(),
        density=True,
    )
    axes["cool_hist"].set_facecolor("k")
    axes["cool_hist"].contour(cool_counts.T, np.linspace(1e-6, 1e-4, 5), cmap="Greys", extent=(0, 300, 0, 300))
    axes["cool_hist"].set_xlabel(171)
    axes["cool_hist"].set_ylabel(193)

    axes["warm_scat"].scatter(m171.data[disk_mask], m211.data[disk_mask], alpha=0.2, edgecolor="none", s=2.5)
    axes["warm_scat"].set_xlim(0, 2000)
    axes["warm_scat"].set_ylim(0, 2000)
    axes["warm_scat"].set_xlabel(171)
    axes["warm_scat"].set_ylabel(211)
    axes["warm_scat"].add_patch(Rectangle((0, 0), 300, 100, ec="r", fill=None))
    warm_counts, *warm_bins = axes["warm_hist"].hist2d(
        m171.data[disk_mask].flatten(),
        m211.data[disk_mask].flatten(),
        bins=250,
        range=[[0, 300], [0, 300]],
        norm=LogNorm(),
        density=True,
    )
    axes["warm_hist"].set_ylim(0, 100)
    axes["warm_hist"].set_facecolor("k")
    axes["warm_hist"].contour(warm_counts.T, np.linspace(1e-5, 5e-4, 5), cmap="Greys", extent=(0, 300, 0, 300))
    axes["warm_hist"].set_xlabel(171)
    axes["warm_hist"].set_ylabel(211)

    axes["hot_scat"].scatter(m193.data[disk_mask], m211.data[disk_mask], alpha=0.2, edgecolor="none", s=2.5)
    axes["hot_scat"].set_xlim(0, 2000)
    axes["hot_scat"].set_ylim(0, 2000)
    axes["hot_scat"].set_xlabel(193)
    axes["hot_scat"].set_ylabel(211)
    axes["hot_scat"].add_patch(Rectangle((0, 0), 300, 100, ec="r", fill=None))
    hot_counts, *hot_bins = axes["hot_hist"].hist2d(
        m193.data[disk_mask].flatten(),
        m211.data[disk_mask].flatten(),
        bins=250,
        range=[[0, 300], [0, 300]],
        norm=LogNorm(),
        density=True,
    )
    axes["hot_hist"].set_ylim(0, 100)
    axes["hot_hist"].set_facecolor("k")
    axes["hot_hist"].contour(hot_counts.T, np.linspace(1e-5, 1e-3, 5), cmap="Greys", extent=(0, 300, 0, 300))
    axes["hot_hist"].set_xlabel(211)
    axes["hot_hist"].set_ylabel(193)





.. image-sg:: /generated/gallery/images/sphx_glr_paper_figures_003.png
   :alt: paper figures
   :srcset: /generated/gallery/images/sphx_glr_paper_figures_003.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


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    Text(324.7295, 0.5, '193')



.. GENERATED FROM PYTHON SOURCE LINES 208-217

Figure 5
--------

Show a smaller region around the low intensities in both log (left) and linear (right) scale. Also show are the
delineation between candidate CH and non-CH intensities.

.. note::
    The delineations are currently :math:`\chi` by eye as the constants/intercepts are not stated in the paper.


.. GENERATED FROM PYTHON SOURCE LINES 218-334

.. code-block:: Python


    # From old IDL code
    threshold_171v193 = 1.5102
    threshold_171v211 = 0.6357
    threshold_193v211 = 0.7
    # Equations of the form I_y = c * I_x**thres c by eye
    xx = np.linspace(0, 300, 500)
    fit171v193 = 2.25 * xx**threshold_171v193
    fit171v211 = 0.5 * xx**threshold_171v211
    fit193v211 = 2600 * xx**-threshold_193v211

    fig, axes = plt.subplot_mosaic(
        [["cool_log", "cool_hist"], ["warm_log", "warm_hist"], ["hot_log", "hot_hist"]],
        layout="constrained",
        figsize=(6, 9),
    )

    # 171 v 193
    axes["cool_log"].hist2d(
        m171.data[disk_mask].flatten(),
        m193.data[disk_mask].flatten(),
        bins=150,
        range=[[0, 300], [0, 300]],
        norm=LogNorm(),
        density=True,
    )
    axes["cool_log"].set_xlim(10, 300)
    axes["cool_log"].set_xscale("log")
    axes["cool_log"].set_ylim(10, 300)
    axes["cool_log"].set_yscale("log")
    axes["cool_log"].set_facecolor("k")
    axes["cool_log"].set_xlabel(171)
    axes["cool_log"].set_ylabel(193)
    axes["cool_log"].plot(xx, fit171v193, "w")  # I_193 = 2.25 * I_171^0.7

    cool_counts, *cool_bins = axes["cool_hist"].hist2d(
        m171.data[disk_mask].flatten(),
        m193.data[disk_mask].flatten(),
        bins=150,
        range=[[0, 300], [0, 300]],
        norm=LogNorm(),
        density=True,
    )
    axes["cool_hist"].set_facecolor("k")
    axes["cool_hist"].set_xlabel(171)
    axes["cool_hist"].set_ylabel(193)
    axes["cool_hist"].plot(xx, fit171v193, "w")


    # 171 v 211
    axes["warm_log"].hist2d(
        m171.data[disk_mask].flatten(),
        m211.data[disk_mask].flatten(),
        bins=250,
        range=[[0, 300], [0, 300]],
        norm=LogNorm(),
        density=True,
    )
    axes["warm_log"].set_xlim(10, 300)
    axes["warm_log"].set_xscale("log")
    axes["warm_log"].set_ylim(3, 100)
    axes["warm_log"].set_yscale("log")
    axes["warm_log"].set_facecolor("k")
    axes["warm_log"].set_xlabel(171)
    axes["warm_log"].set_ylabel(211)
    axes["warm_log"].plot(xx, fit171v211, "w")

    warm_counts, *warm_bins = axes["warm_hist"].hist2d(
        m171.data[disk_mask].flatten(),
        m211.data[disk_mask].flatten(),
        bins=250,
        range=[[0, 300], [0, 300]],
        norm=LogNorm(),
        density=True,
    )
    axes["warm_hist"].set_ylim(0, 100)
    axes["warm_hist"].set_facecolor("k")
    axes["warm_hist"].set_xlabel(171)
    axes["warm_hist"].set_ylabel(211)
    axes["warm_hist"].plot(xx, fit171v211, "w")

    # 193 v 211
    axes["hot_log"].hist2d(
        m193.data[disk_mask].flatten(),
        m211.data[disk_mask].flatten(),
        bins=250,
        range=[[0, 300], [0, 300]],
        norm=LogNorm(),
        density=True,
    )
    axes["hot_log"].set_xlim(10, 300)
    axes["hot_log"].set_xscale("log")
    axes["hot_log"].set_ylim(2, 100)
    axes["hot_log"].set_yscale("log")
    axes["hot_log"].set_xlabel(193)
    axes["hot_log"].set_ylabel(211)
    axes["hot_log"].set_facecolor("k")
    axes["hot_log"].plot(xx, 0.3 * xx, "r")
    axes["hot_log"].plot(xx, fit193v211, "w")

    hot_counts, *hot_bins = axes["hot_hist"].hist2d(
        m193.data[disk_mask].flatten(),
        m211.data[disk_mask].flatten(),
        bins=250,
        range=[[0, 300], [0, 300]],
        norm=LogNorm(),
        density=True,
    )
    axes["hot_hist"].set_ylim(0, 100)
    axes["hot_hist"].set_facecolor("k")
    axes["hot_hist"].set_xlabel(193)
    axes["hot_hist"].set_ylabel(211)
    axes["hot_hist"].plot(xx, 0.3 * xx, "r")
    axes["hot_hist"].plot(xx, fit193v211, "w")





.. image-sg:: /generated/gallery/images/sphx_glr_paper_figures_004.png
   :alt: paper figures
   :srcset: /generated/gallery/images/sphx_glr_paper_figures_004.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/chimera-py/checkouts/latest/examples/paper_figures.py:227: RuntimeWarning: divide by zero encountered in power
      fit193v211 = 2600 * xx**-threshold_193v211

    [<matplotlib.lines.Line2D object at 0x7aa4743c5120>]



.. GENERATED FROM PYTHON SOURCE LINES 335-342

Figure 6
--------

Figure 6 shows the log transformed and clipped 171, 193, and 211 as a tri-color image (top left). Candidate CH masks
for each intensity ratio pair are also shown 193Å - 171Å (top right), 211Å - 171Å (bottom left), and
211Å - 193Å (bottom right).
These were obtained by thresholding ratios of the log transformed and clipped observations.

.. GENERATED FROM PYTHON SOURCE LINES 343-386

.. code-block:: Python


    # From IDL code how they were obtained unclear maybe fitting of intensity valley?
    d171_min, d171_max = 1.2, 3.9
    d193_min, d193_max = 1.4, 3.0
    d211_min, d211_max = 0.8, 2.7

    d171_clipped = np.clip(np.log10(m171.data), d171_min, d171_max)
    d171_clipped_scaled = ((d171_clipped - d171_min) / (d171_max - d171_min)) * 255

    d193_clipped = np.clip(np.log10(m193.data), d193_min, d193_max)
    d193_clipped_scaled = ((d193_clipped - d193_min) / (d193_max - d193_min)) * 255

    d211_clipped = np.clip(np.log10(m211.data), d211_min, d211_max)
    d211_clipped_scaled = ((d211_clipped - d211_min) / (d211_max - d211_min)) * 255

    tri_color_img = make_lupton_rgb(
        d171_clipped_scaled, d193_clipped_scaled, d211_clipped_scaled, Q=10, stretch=50
    )

    mask_171_211 = (d171_clipped_scaled / d211_clipped_scaled) >= (
        (np.mean(m171.data[disk_mask]) * 0.6357) / np.mean(m211.data[disk_mask])
    )

    mask_211_193 = (d211_clipped_scaled + d193_clipped_scaled) < (
        0.7 * (np.mean(m193.data[disk_mask]) + np.mean(m211.data[disk_mask]))
    )

    mask_171_193 = (d171_clipped_scaled / d193_clipped_scaled) >= (
        (np.mean(m171.data[disk_mask]) * 1.5102) / np.mean(m193.data[disk_mask])
    )

    fig, axes = plt.subplot_mosaic(
        [["tri", "193_171"], ["211_171", "211_193"]], layout="constrained", figsize=(6, 6)
    )

    axes["tri"].imshow(tri_color_img, origin="lower")
    axes["193_171"].imshow(mask_171_193, origin="lower")
    axes["193_171"].set_title("171 / 193")
    axes["211_171"].imshow(mask_171_211, origin="lower")
    axes["211_171"].set_title("171 / 211")
    axes["211_193"].imshow(mask_211_193, origin="lower")
    axes["211_193"].set_title("211 / 193")




.. image-sg:: /generated/gallery/images/sphx_glr_paper_figures_005.png
   :alt: 171 / 193, 171 / 211, 211 / 193
   :srcset: /generated/gallery/images/sphx_glr_paper_figures_005.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/chimera-py/checkouts/latest/examples/paper_figures.py:349: RuntimeWarning: divide by zero encountered in log10
      d171_clipped = np.clip(np.log10(m171.data), d171_min, d171_max)
    /home/docs/checkouts/readthedocs.org/user_builds/chimera-py/checkouts/latest/examples/paper_figures.py:349: RuntimeWarning: invalid value encountered in log10
      d171_clipped = np.clip(np.log10(m171.data), d171_min, d171_max)
    /home/docs/checkouts/readthedocs.org/user_builds/chimera-py/checkouts/latest/examples/paper_figures.py:352: RuntimeWarning: divide by zero encountered in log10
      d193_clipped = np.clip(np.log10(m193.data), d193_min, d193_max)
    /home/docs/checkouts/readthedocs.org/user_builds/chimera-py/checkouts/latest/examples/paper_figures.py:352: RuntimeWarning: invalid value encountered in log10
      d193_clipped = np.clip(np.log10(m193.data), d193_min, d193_max)
    /home/docs/checkouts/readthedocs.org/user_builds/chimera-py/checkouts/latest/examples/paper_figures.py:355: RuntimeWarning: divide by zero encountered in log10
      d211_clipped = np.clip(np.log10(m211.data), d211_min, d211_max)
    /home/docs/checkouts/readthedocs.org/user_builds/chimera-py/checkouts/latest/examples/paper_figures.py:355: RuntimeWarning: invalid value encountered in log10
      d211_clipped = np.clip(np.log10(m211.data), d211_min, d211_max)
    /home/docs/checkouts/readthedocs.org/user_builds/chimera-py/checkouts/latest/examples/paper_figures.py:362: RuntimeWarning: divide by zero encountered in divide
      mask_171_211 = (d171_clipped_scaled / d211_clipped_scaled) >= (
    /home/docs/checkouts/readthedocs.org/user_builds/chimera-py/checkouts/latest/examples/paper_figures.py:362: RuntimeWarning: invalid value encountered in divide
      mask_171_211 = (d171_clipped_scaled / d211_clipped_scaled) >= (
    /home/docs/checkouts/readthedocs.org/user_builds/chimera-py/checkouts/latest/examples/paper_figures.py:370: RuntimeWarning: divide by zero encountered in divide
      mask_171_193 = (d171_clipped_scaled / d193_clipped_scaled) >= (
    /home/docs/checkouts/readthedocs.org/user_builds/chimera-py/checkouts/latest/examples/paper_figures.py:370: RuntimeWarning: invalid value encountered in divide
      mask_171_193 = (d171_clipped_scaled / d193_clipped_scaled) >= (

    Text(0.5, 1.0, '211 / 193')



.. GENERATED FROM PYTHON SOURCE LINES 387-392

Figure 7
--------

Figure 7 shows the log transformed and clipped 171, 193, and 211 as a tri-color image (left) and the final CH mask
obtained by taking the product of the individual candidate masks.

.. GENERATED FROM PYTHON SOURCE LINES 393-399

.. code-block:: Python


    fig, axes = plt.subplot_mosaic([["tri", "comb_mask"]], layout="constrained", figsize=(6, 3))

    axes["tri"].imshow(tri_color_img, origin="lower")
    axes["comb_mask"].imshow(mask_171_193 * mask_171_211 * mask_211_193, origin="lower")




.. image-sg:: /generated/gallery/images/sphx_glr_paper_figures_006.png
   :alt: paper figures
   :srcset: /generated/gallery/images/sphx_glr_paper_figures_006.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <matplotlib.image.AxesImage object at 0x7aa4746f2f50>



.. GENERATED FROM PYTHON SOURCE LINES 400-405

Figure 8
--------

Figure 8 shows the top 5 (by area) contours created from the CH mask over plotted on the same tri-color image as in
Figures 6 and 7.

.. GENERATED FROM PYTHON SOURCE LINES 406-424

.. code-block:: Python


    mask_map = Map(((mask_171_193 * mask_171_211 * mask_211_193).astype(int), m171.meta))
    try:
        contours = mask_map.contour(0.5 / u.s)
    except UnitsError:
        contours = mask_map.contour(50 * u.percent)

    contours = sorted(contours, key=lambda x: x.size, reverse=True)

    fig, axes = plt.subplot_mosaic(
        [["seg"]], layout="constrained", figsize=(6, 3), subplot_kw={"projection": m171}
    )
    m171.plot(axes=axes["seg"])
    axes["seg"].imshow(tri_color_img)

    # For the moment just plot to top 5 contours based on "size" for contour
    for contour in contours[:6]:
        axes["seg"].plot_coord(contour, color="w", linewidth=0.5)



.. image-sg:: /generated/gallery/images/sphx_glr_paper_figures_007.png
   :alt: AIA $171 \; \mathrm{\mathring{A}}$ 2016-10-31 02:32:10
   :srcset: /generated/gallery/images/sphx_glr_paper_figures_007.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 22.121 seconds)


.. _sphx_glr_download_generated_gallery_paper_figures.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: paper_figures.zip <paper_figures.zip>`


.. only:: html

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

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