.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/suvi/plot_suvi_l2_brght.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_suvi_plot_suvi_l2_brght.py>`
        to download the full example code.

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

.. _sphx_glr_examples_suvi_plot_suvi_l2_brght.py:


Plot SUVI L2 Bright Regions
===========================
Purpose:
  Use Python to plot SUVI L2 bright region (brght) product in each coordinate system
  (Heliographic Stonyhurst, Carrington, Pixel, and R-Theta).

.. GENERATED FROM PYTHON SOURCE LINES 11-13

Overview
------------

.. GENERATED FROM PYTHON SOURCE LINES 15-27

In this example, we will be plotting the bright region boundaries (bnd_loc), center location (center_loc),
peak location (peak_loc), and bright region extent (brght_extent) for the bright region report (brght) product.
Bright region boundaries outline the bright region, which are akin to coronal active regions. Center location is
determined by center-of-mass calculations, whereas peak location is determined by the region with the highest
irradiance. The bright region extent is defined by the min/max lat/lon of all vertices of the bright region boundary
polygon. The extent is only available in the Heliographic Stonyhurst coordinate system.

We will plot these variables in each the 4 coordinate systems:
 * Heliographic Stonyhurst `(longitude, latitude)`
 * Heliographic Carrington `(longitude, latitude)`
 * Pixel `(x pixels, y pixels)`
 * R-Theta `(solar radii, degrees)`

.. GENERATED FROM PYTHON SOURCE LINES 29-31

Imports
-----------

.. GENERATED FROM PYTHON SOURCE LINES 33-34

First, we will import the necessary libraries:

.. GENERATED FROM PYTHON SOURCE LINES 34-49

.. code-block:: Python

    __authors__ = "elucas, ajarvis"

    import matplotlib.pyplot as plt
    import matplotlib.cm as cm
    import numpy as np
    from datetime import datetime, timedelta
    import netCDF4 as nc
    from astropy.coordinates import SkyCoord
    import sunpy
    import sunpy.map
    import astropy.units as u
    from sunpy.coordinates import frames
    from sunpy.net import Fido, attrs as a
    from astropy.io import fits








.. GENERATED FROM PYTHON SOURCE LINES 50-53

Helper Functions
-----------------------------------------
We need to define a few helper functions for use throughout plotting:

.. GENERATED FROM PYTHON SOURCE LINES 53-69

.. code-block:: Python


    # Fetch the SUVI SunPy map to use as a base for plotting.
    def create_suvi_sunpymap(date, goes=16, wavelength=131, rng=2):
        ds0 = (date - timedelta(minutes=rng)).strftime("%Y/%m/%d %H:%M:%S")
        ds1 = (date + timedelta(minutes=rng)).strftime("%Y/%m/%d %H:%M:%S")
        q = Fido.search(a.Time(ds0, ds1), a.Instrument.suvi, a.Wavelength(wavelength * u.angstrom))
        tmp_files = Fido.fetch(q)
        # Select files for level 2 composites only
        for tmp_file in tmp_files:
            if (f'g{goes}' in tmp_file) and ('l2' in tmp_file):
                data, header = fits.getdata(tmp_file), fits.getheader(tmp_file, 1)
                suvi_map = sunpy.map.Map(data, header)
                return suvi_map, data, header
        print('No SUVI map available')
        return None








.. GENERATED FROM PYTHON SOURCE LINES 70-78

.. code-block:: Python



    # Convert time given in the .nc file to datetime.
    def convert_time(time_nc):
        date_2000 = datetime(2000, 1, 1, 12, 0)
        date = date_2000 + timedelta(seconds=time_nc)
        return date








.. GENERATED FROM PYTHON SOURCE LINES 79-104

.. code-block:: Python



    # Define legend for this product.
    def legend_handles(colors, coord='hg'):
        if coord == 'hg':
            markers = ['o', '*', 'o', '_']
            labels = ['boundaries', 'peak', 'center', 'extent']
            linestyles = ['-', 'none', 'none', '-']
            fillstyles = ['full', 'full', 'none', 'full']
        elif coord == 'car' or coord == 'pix':
            markers = ['o', '*', 'o']
            labels = ['boundaries', 'peak', 'center']
            linestyles = ['-', 'none', 'none']
            fillstyles = ['full', 'full', 'none']
        elif coord == 'rt':
            markers = ['*', 'o']
            labels = ['peak', 'center']
            linestyles = ['none', 'none']
            fillstyles = ['full', 'none']
        else:
            return None, None
        f = lambda m, c, ls, fs: plt.plot([], [], marker=m, color=c, ls=ls, fillstyle=fs)[0]
        handles = [f(markers[i], colors[i], linestyles[i], fillstyles[i]) for i in range(len(colors))]
        return handles, labels








.. GENERATED FROM PYTHON SOURCE LINES 105-107

Retrieving the SUVI SunPy Map
-------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 109-112

| Let's use some of the helper functions above to retrieve a SUVI image.
| We'll grab an image of the sun in the 284Å wavelength for datetime 2023-01-09 18:51.
| Example data file: `dr_suvi-l2-brght_g16_s20230109T184800Z_e20230109T185200Z_v1-0-5.nc <../../_static/suvi/dr_suvi-l2-brght_g16_s20230109T184800Z_e20230109T185200Z_v1-0-5.nc>`__

.. GENERATED FROM PYTHON SOURCE LINES 112-119

.. code-block:: Python


    date = datetime(2023, 1, 9, 18, 51)
    goes = 16
    wavelength = 284
    suvi_brght_path = f'../../data/suvi/dr_suvi-l2-brght_g16_s20230109T184800Z_e20230109T185200Z_v1-0-5.nc'
    brght_nc = nc.Dataset(suvi_brght_path)








.. GENERATED FROM PYTHON SOURCE LINES 120-121

Let's look at the variables contained in the bright region files.

.. GENERATED FROM PYTHON SOURCE LINES 121-124

.. code-block:: Python

    for k in brght_nc.variables.keys():
        print(k)





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

 .. code-block:: none

    time
    wavelength
    degraded_status
    num_brght_regions
    brght_area
    srs_status
    xrs_status
    euv_status
    brght_extent_hg
    tot_flux
    peak_flux
    peak_loc_pix
    peak_loc_hg
    peak_loc_car
    peak_loc_rtheta
    center_loc_pix
    center_loc_hg
    center_loc_car
    center_loc_rtheta
    bnd_loc_pix
    bnd_loc_hg
    bnd_loc_car




.. GENERATED FROM PYTHON SOURCE LINES 125-128

For our examples, we will be looking at the bnd_loc, peak_loc, center_loc, and brght_extent variables.
We can see the general structure by inspecting the variables in any coordinate system, for example,
in Heliographic Stonyhurst.

.. GENERATED FROM PYTHON SOURCE LINES 128-131

.. code-block:: Python


    print(brght_nc['bnd_loc_hg'])





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

 .. code-block:: none

    <class 'netCDF4._netCDF4.Variable'>
    float32 bnd_loc_hg(time, feature_number, vertex, location)
        long_name: Bright region boundary in Stonyhurst/heliographic coordinates (lon, lat)
        comments: Values provided for on-disk flares only.
        units: degrees, degrees
        _FillValue: -9999.0
    unlimited dimensions: time, feature_number, vertex
    current shape = (1, 7, 16, 2)
    filling on




.. GENERATED FROM PYTHON SOURCE LINES 132-137

| The structure for bnd_loc in this example is `(1, 7, 16, 2)`:
|  `1` : The first dimension of the structure is the time dimension, which will always have 1 value for this product.
|  `7` : The second dimension is the number of separate bright regions within this image.
|  `16`: The third dimension is each vertex of the bright region boundary polygon. We are limited to 16 vertices for legacy operational reasons.
|  `2` : The fourth dimension is the coordinate pair, with units as described above.

.. GENERATED FROM PYTHON SOURCE LINES 137-142

.. code-block:: Python


    print(brght_nc['peak_loc_hg'])
    print('')
    print(brght_nc['center_loc_hg'])





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

 .. code-block:: none

    <class 'netCDF4._netCDF4.Variable'>
    float32 peak_loc_hg(time, feature_number, wavelength, location)
        long_name: Peak bright region flux location in Stonyhurst/heliographic coordinates (lon, lat)
        comments: Values provided for on-disk flares only.
        units: degrees, degrees
        _FillValue: -9999.0
    unlimited dimensions: time, feature_number, wavelength
    current shape = (1, 7, 6, 2)
    filling on

    <class 'netCDF4._netCDF4.Variable'>
    float32 center_loc_hg(time, feature_number, wavelength, location)
        long_name: Centroid bright region flux location in Stonyhurst/heliographic coordinates (lon, lat)
        comments: Values provided for on-disk flares only.
        units: degrees, degrees
        _FillValue: -9999.0
    unlimited dimensions: time, feature_number, wavelength
    current shape = (1, 7, 6, 2)
    filling on




.. GENERATED FROM PYTHON SOURCE LINES 143-148

| The structures for peak_loc and center_loc in this example are `(1, 7, 6, 2)`:
|  `1`: The first dimension of the structure is the time dimension, which will always have 1 value for this product.
|  `7`: The second dimension is the number of separate bright regions within this image.
|  `6`: The third dimension is each wavelength, in the order [094, 131, 171, 195, 284, 304].
|  `2`: The fourth dimension is the coordinate pair, with units as described above.

.. GENERATED FROM PYTHON SOURCE LINES 148-151

.. code-block:: Python


    print(brght_nc['brght_extent_hg'])





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

 .. code-block:: none

    <class 'netCDF4._netCDF4.Variable'>
    float32 brght_extent_hg(time, feature_number, cardinal_directions)
        long_name: Maximum extent of bright region in the [North (lat),  South (lat), East (lon) and West (lon)] in Stonyhurst/heliographic coordinates
        units: degrees
        _FillValue: -9999.0
    unlimited dimensions: time, feature_number
    current shape = (1, 7, 4)
    filling on




.. GENERATED FROM PYTHON SOURCE LINES 152-156

| The structure for brght_extent in this example is `(1, 7, 4)`:
|  `1`: The first dimension of the structure is the time dimension, which will always have 1 value for this product.
|  `7`: The second dimension is the number of separate bright regions within this image.
|  `4`: The third dimension is an array of the N/S Latitudes [0, 1] and the E/W Longitudes [2, 3] of the bright region bounding box, in that order.

.. GENERATED FROM PYTHON SOURCE LINES 159-161

We need to extract the time (found in the 'time' variable), and convert to python datetime in order to fetch a
corresponding SUVI SunPy map at the closest time possible.

.. GENERATED FROM PYTHON SOURCE LINES 161-167

.. code-block:: Python


    # Get file time
    brght_time = convert_time(brght_nc['time'][:][0])
    # Access SUVI SunPy map for that time and defined wavelength
    suvi_map, suvi_data, suvi_header = create_suvi_sunpymap(brght_time, goes=goes, wavelength=wavelength)





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

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    /usr/share/miniconda/envs/goesr-spwx-examples/lib/python3.12/site-packages/sunpy/net/vso/vso.py:238: SunpyUserWarning: VSO-D400 Bad Request - Invalid wavelength, wavetype of filter specification
      warn_user(resp["error"])

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.. GENERATED FROM PYTHON SOURCE LINES 168-174

Plotting the Flare Locations
----------------------------
Now that the SUVI SunPy map has been retrieved, we can overplot the coordinates from the .nc file onto that image.

`Note: Heliographic Stonyhurst and Heliographic Carrington are only defined on-disk, so any features
(or pieces of features) that extend off-disk will not be plotted.`

.. GENERATED FROM PYTHON SOURCE LINES 176-178

Heliographic Stonyhurst Coordinates
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 178-268

.. code-block:: Python


    # Plot the SUVI SunPy composite image
    fig_hg = plt.figure(figsize=(10, 10))
    fig_hg.tight_layout(h_pad=-50)
    fig_hg.suptitle(f'Heliographic Stonyhurst Coordinates, GOES-{goes}')
    ax_hg = fig_hg.add_subplot(projection=suvi_map)
    suvi_map.plot(axes=ax_hg, clip_interval=(0.1, 99.9) * u.percent)
    num_points = 100

    # Extract feature locations from netCDF
    bnd_hg = brght_nc['bnd_loc_hg'][:].data[0]
    peak_hg = brght_nc['peak_loc_hg'][:].data[0]
    center_hg = brght_nc['center_loc_hg'][:].data[0]
    extent_hg = brght_nc['brght_extent_hg'][:].data[0]

    colors = cm.Reds_r(np.linspace(0.1, 0.7, 4))


    # Plot bright region boundaries
    for br in bnd_hg:
        br[br == -9999.] = np.nan
        # Close boundary polygon (on-disk only)
        br = [*br, br[0]]

        bnd_hgs_sc = SkyCoord([(float(v[0]), float(v[1])) * u.degree for v in br], obstime=date,
                              observer=suvi_map.observer_coordinate, frame=frames.HeliographicStonyhurst)

        # Transform SkyCoord object to the SUVI SunPy map's coordinate frame
        bnd_hgs_sc = bnd_hgs_sc.transform_to(suvi_map.coordinate_frame)

        # Plot boundary
        ax_hg.plot_coord(bnd_hgs_sc, color=colors[0], marker='o', linestyle='-', markersize=2, alpha=0.8)

    # Plot peak bright region locations
    for br in peak_hg:
        br[br == -9999.] = np.nan
        wavelength_i = list(brght_nc['wavelength'][:]).index(wavelength)

        x = br[wavelength_i][0]
        y = br[wavelength_i][1]
        peak_hgs_sc = SkyCoord(x * u.degree, y * u.degree, obstime=date, observer=suvi_map.observer_coordinate,
                               frame=frames.HeliographicStonyhurst)

        # Transform SkyCoord object to the SUVI SunPy map's coordinate frame
        peak_hgs_sc = peak_hgs_sc.transform_to(suvi_map.coordinate_frame)

        # Plot bright region
        ax_hg.plot_coord(peak_hgs_sc, color=colors[1], marker='*', markersize=5, alpha=0.8)

    # Plot bright region 'center of mass' locations
    for br in center_hg:
        br[br == -9999.] = np.nan
        wavelength_i = list(brght_nc['wavelength'][:]).index(wavelength)

        x = br[wavelength_i][0]
        y = br[wavelength_i][1]
        center_hgs_sc = SkyCoord(x * u.degree, y * u.degree, obstime=date, observer=suvi_map.observer_coordinate,
                                 frame=frames.HeliographicStonyhurst)

        # Transform SkyCoord object to the SUVI SunPy map's coordinate frame
        center_hgs_sc = center_hgs_sc.transform_to(suvi_map.coordinate_frame)

        # Plot bright region
        ax_hg.plot_coord(center_hgs_sc, color=colors[2], marker='o', fillstyle='none', markersize=8, alpha=0.8)

    # Plot bright extent bounding boxes
    for br in extent_hg:
        br[br == -9999.] = np.nan
        if np.isnan(br).all():
            continue

        # coordinates are in N(0) lat, S(1) lat, E(2) lon, W(3) lon
        # (lon, lat) because SkyCoord is in (lon, lat)
        bl_coord = [br[2], br[1]]
        tr_coord = [br[3], br[0]]

        bottom_left = SkyCoord(bl_coord[0] * u.degree, bl_coord[1] * u.degree, obstime=date,
                               observer=suvi_map.observer_coordinate, frame=frames.HeliographicStonyhurst)
        top_right = SkyCoord(tr_coord[0] * u.degree, tr_coord[1] * u.degree, obstime=date,
                             observer=suvi_map.observer_coordinate, frame=frames.HeliographicStonyhurst)

        suvi_map.draw_quadrangle(bottom_left, top_right=top_right, axes=ax_hg,
                                 edgecolor=colors[3], linewidth=2, alpha=0.8)

    # Add legend for features
    handles, labels = legend_handles(colors, coord='hg')
    plt.legend(handles, labels, loc=1, framealpha=1)

    plt.show()




.. image-sg:: /examples/suvi/images/sphx_glr_plot_suvi_l2_brght_001.png
   :alt: Heliographic Stonyhurst Coordinates, GOES-16, SUVI $284 \; \mathrm{\mathring{A}}$ 2023-01-09 18:51:29
   :srcset: /examples/suvi/images/sphx_glr_plot_suvi_l2_brght_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    INFO: Missing metadata for solar radius: assuming the standard radius of the photosphere. [sunpy.map.mapbase]
    INFO: Missing metadata for solar radius: assuming the standard radius of the photosphere. [sunpy.map.mapbase]




.. GENERATED FROM PYTHON SOURCE LINES 269-271

Heliographic Carrington Coordinates
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 271-340

.. code-block:: Python


    # Plot the SUVI SunPy composite image
    fig_car = plt.figure(figsize=(10, 10))
    fig_car.tight_layout(h_pad=-50)
    fig_car.suptitle(f'Carrington Coordinates, GOES-{goes}')
    ax_car = fig_car.add_subplot(projection=suvi_map)
    suvi_map.plot(axes=ax_car, clip_interval=(0.1, 99.9) * u.percent)
    num_points = 100

    # Extract feature locations from netCDF
    bnd_car = brght_nc['bnd_loc_car'][:].data[0]
    peak_car = brght_nc['peak_loc_car'][:].data[0]
    center_car = brght_nc['center_loc_car'][:].data[0]

    colors = cm.YlOrBr_r(np.linspace(0.3, 0.55, 3))

    # Plot bright region boundaries
    for br in bnd_car:
        br[br == -9999.] = np.nan
        # Close boundary polygon (on-disk only)
        br = [*br, br[0]]

        bnd_car_sc = SkyCoord([(float(v[0]), float(v[1])) * u.degree for v in br], obstime=date,
                              observer=suvi_map.observer_coordinate, frame=frames.HeliographicCarrington)

        # Transform SkyCoord object to the SUVI SunPy map's coordinate frame
        bnd_car_sc = bnd_car_sc.transform_to(suvi_map.coordinate_frame)

        # Plot boundary
        ax_car.plot_coord(bnd_car_sc, color=colors[0], marker='o', linestyle='-', markersize=2, alpha=0.8)

    # Plot peak bright region locations
    for br in peak_car:
        br[br == -9999.] = np.nan
        wavelength_i = list(brght_nc['wavelength'][:]).index(wavelength)

        x = br[wavelength_i][0]
        y = br[wavelength_i][1]
        peak_car_sc = SkyCoord(x * u.degree, y * u.degree, obstime=date, observer=suvi_map.observer_coordinate,
                               frame=frames.HeliographicCarrington)

        # Transform SkyCoord object to the SUVI SunPy map's coordinate frame
        peak_car_sc = peak_car_sc.transform_to(suvi_map.coordinate_frame)

        # Plot bright region
        ax_car.plot_coord(peak_car_sc, color=colors[1], marker='*', markersize=5, alpha=0.8)

    # Plot bright region 'center of mass' locations
    for br in center_car:
        br[br == -9999.] = np.nan
        wavelength_i = list(brght_nc['wavelength'][:]).index(wavelength)

        x = br[wavelength_i][0]
        y = br[wavelength_i][1]
        center_car_sc = SkyCoord(x * u.degree, y * u.degree, obstime=date, observer=suvi_map.observer_coordinate,
                                 frame=frames.HeliographicCarrington)

        # Transform SkyCoord object to the SUVI SunPy map's coordinate frame
        center_car_sc = center_car_sc.transform_to(suvi_map.coordinate_frame)

        # Plot bright region
        ax_car.plot_coord(center_car_sc, color=colors[2], marker='o', markersize=8, fillstyle='none', alpha=0.8)

    # Add legend for features
    handles, labels = legend_handles(colors, coord='car')
    plt.legend(handles, labels, loc=1, framealpha=1)

    plt.show()




.. image-sg:: /examples/suvi/images/sphx_glr_plot_suvi_l2_brght_002.png
   :alt: Carrington Coordinates, GOES-16, SUVI $284 \; \mathrm{\mathring{A}}$ 2023-01-09 18:51:29
   :srcset: /examples/suvi/images/sphx_glr_plot_suvi_l2_brght_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 341-343

Pixel Coordinates
^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 343-398

.. code-block:: Python


    # Plot the SUVI SunPy composite image
    fig_px = plt.figure(figsize=(10, 10))
    fig_px.tight_layout()
    ax_px = fig_px.add_subplot(projection=suvi_map)
    suvi_map.plot(axes=ax_px, clip_interval=(0.1, 99.9) * u.percent)
    fig_px.suptitle(f'Pixel Coordinates, GOES-{goes}')
    num_points = 100


    # Extract feature locations from netCDF
    bnd_pix = brght_nc['bnd_loc_pix'][:].data[0]
    peak_pix = brght_nc['peak_loc_pix'][:].data[0]
    center_pix = brght_nc['center_loc_pix'][:].data[0]

    colors = cm.Greens_r(np.linspace(0., 0.3, 3))

    # Plot bright region boundaries
    for br in bnd_pix:
        br[br == -9999.] = np.nan
        # Close boundary polygon (on-disk only)
        br = [*br, br[0]]
        x = [a[0] for a in br]
        y = [a[1] for a in br]
        # Plot boundary
        ax_px.plot_coord(x, y, color=colors[0], marker='o', markersize=2, linestyle='-', alpha=0.8)

    # Plot peak bright region locations
    for br in peak_pix:
        br[br == -9999.] = np.nan
        wavelength_i = list(brght_nc['wavelength'][:]).index(wavelength)

        x = br[wavelength_i][0]
        y = br[wavelength_i][1]

        # Plotbright region
        ax_px.plot_coord(x, y, color=colors[1], marker='*', markersize=5, alpha=0.8)

    # Plot bright region 'center of mass' locations
    for br in center_pix:
        br[br == -9999.] = np.nan
        wavelength_i = list(brght_nc['wavelength'][:]).index(wavelength)

        x = br[wavelength_i][0]
        y = br[wavelength_i][1]

        # Plot bright region
        ax_px.plot_coord(x, y, color=colors[2], marker='o', fillstyle='none', markersize=8, alpha=0.8)

    # Add legend for features
    handles, labels = legend_handles(colors, coord='pix')
    plt.legend(handles, labels, loc=1, framealpha=1)

    plt.show()




.. image-sg:: /examples/suvi/images/sphx_glr_plot_suvi_l2_brght_003.png
   :alt: Pixel Coordinates, GOES-16, SUVI $284 \; \mathrm{\mathring{A}}$ 2023-01-09 18:51:29
   :srcset: /examples/suvi/images/sphx_glr_plot_suvi_l2_brght_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 399-401

R-Theta Coordinates
^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 401-475

.. code-block:: Python


    # Plot the SUVI SunPy map
    fig_rt = plt.figure(figsize=(10, 10))
    fig_rt.suptitle(f'R-Theta Coordinates, GOES-{goes}')
    fig_rt.tight_layout(h_pad=-50)
    ax_rt = fig_rt.add_subplot(projection=suvi_map)
    suvi_map.plot(axes=ax_rt, clip_interval=(0.1, 99.9) * u.percent)
    num_points = 100

    # Extract features from netCDF
    peak_rt = brght_nc['peak_loc_rtheta'][:].data[0]
    center_rt = brght_nc['center_loc_rtheta'][:].data[0]

    colors = cm.Purples_r(np.linspace(0., 0.3, 2))

    # Plot peak bright region locations
    for br in peak_rt:
        br[br == -9999.] = np.nan
        wavelength_i = list(brght_nc['wavelength'][:]).index(wavelength)

        # Need to rotate psi by 90 degrees, according to SkyCoord cylindrical specifications
        rho = br[wavelength_i][0]
        psi = br[wavelength_i][1] + 90.

        # R-theta trigonometry to find correct z for initializing SkyCoord object
        # z is assumed to be 0 if feature is off-disk
        solar_radius = 1.
        if -1. <= rho <= 1.:
            angle = np.arcsin(rho / solar_radius)
            z = solar_radius * np.cos(angle)
        else:
            z = 0.

        # Create SkyCoord object in heliocentric frame
        peak_rt_sc = SkyCoord(rho=rho * u.solRad, psi=psi * u.deg, z=z * u.solRad, representation_type='cylindrical',
                              obstime=date, observer=suvi_map.observer_coordinate, frame=frames.Heliocentric)

        # Transform SkyCoord object to the SUVI SunPy map's coordinate frame
        peak_rt_sc = peak_rt_sc.transform_to(suvi_map.coordinate_frame)

        ax_rt.plot_coord(peak_rt_sc, color=colors[0], marker='*', markersize=5, alpha=0.8)

    # Plot bright region 'center of mass' locations
    for br in center_rt:
        br[br == -9999.] = np.nan
        wavelength_i = list(brght_nc['wavelength'][:]).index(wavelength)

        # Need to rotate psi by 90 degrees, according to SkyCoord cylindrical specifications
        rho = br[wavelength_i][0]
        psi = br[wavelength_i][1] + 90.

        # R-theta trigonometry to find correct z for initializing SkyCoord object
        # z is assumed to be 0 if feature is off-disk
        solar_radius = 1.
        if -1. <= rho <= 1.:
            angle = np.arcsin(rho / solar_radius)
            z = solar_radius * np.cos(angle)
        else:
            z = 0.

        # Create SkyCoord object in heliocentric frame
        center_rt_sc = SkyCoord(rho=rho * u.solRad, psi=psi * u.deg, z=z * u.solRad, representation_type='cylindrical',
                                obstime=date, observer=suvi_map.observer_coordinate, frame=frames.Heliocentric)

       # Transform SkyCoord object to the SUVI SunPy map's coordinate frame
        center_rt_sc = center_rt_sc.transform_to(suvi_map.coordinate_frame)

        ax_rt.plot_coord(center_rt_sc, color=colors[1], marker='o', fillstyle='none', markersize=8, alpha=0.8)

    # Add legend for features
    handles, labels = legend_handles(colors, coord='rt')
    plt.legend(handles, labels, loc=1, framealpha=1)

    plt.show()



.. image-sg:: /examples/suvi/images/sphx_glr_plot_suvi_l2_brght_004.png
   :alt: R-Theta Coordinates, GOES-16, SUVI $284 \; \mathrm{\mathring{A}}$ 2023-01-09 18:51:29
   :srcset: /examples/suvi/images/sphx_glr_plot_suvi_l2_brght_004.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 16.708 seconds)


.. _sphx_glr_download_examples_suvi_plot_suvi_l2_brght.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_suvi_l2_brght.ipynb <plot_suvi_l2_brght.ipynb>`

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

      :download:`Download Python source code: plot_suvi_l2_brght.py <plot_suvi_l2_brght.py>`


.. only:: html

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

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