.. 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 various coordinate systems.

.. GENERATED FROM PYTHON SOURCE LINES 10-12

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

.. GENERATED FROM PYTHON SOURCE LINES 14-32

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.

`Notes for this product: The extent is only available in the Heliographic Stonyhurst coordinate system. R-Theta is a
legacy operational coordinate system that does not contain boundary vertices.`

We will plot these variables for each of the following coordinate systems:
 * Heliographic Stonyhurst `(longitude, latitude)`
 * Heliographic Carrington `(longitude, latitude)`
 * Pixel `(x pixels, y pixels)`
 * R-Theta `(solar radii, degrees)`
 * Helioprojective Cartesian `(x arcsec, y arcsec)`
 * Heliocentric Cartesian `(x AU, y AU, z AU)`
 * Heliocentric Radial `(solar radii, degrees, z solar radii)`

.. GENERATED FROM PYTHON SOURCE LINES 34-36

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

.. GENERATED FROM PYTHON SOURCE LINES 38-39

First, we will import the necessary libraries:

.. GENERATED FROM PYTHON SOURCE LINES 39-54

.. 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 55-58

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

.. GENERATED FROM PYTHON SOURCE LINES 58-74

.. 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 only files for level 2 composites from G16
        for tmp_file in tmp_files:
            if (f'g{goes}' in tmp_file) and ('l2' in tmp_file):
                data, header = fits.getdata(tmp_file, ext=1), fits.getheader(tmp_file, ext=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 75-83

.. 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 84-109

.. 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 in ['car', 'pix', 'hpc', 'hcc', 'hcr']:
            markers = ['o', '*', 'o']
            labels = ['boundaries', 'peak', 'center']
            linestyles = ['-', 'none', 'none']
            fillstyles = ['full', 'full', 'none']
        elif coord in ['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 110-112

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

.. GENERATED FROM PYTHON SOURCE LINES 114-117

| 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 117-124

.. 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 125-126

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

.. GENERATED FROM PYTHON SOURCE LINES 126-129

.. 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
    peak_loc_hpc
    peak_loc_hcc
    peak_loc_hcr
    center_loc_pix
    center_loc_hg
    center_loc_car
    center_loc_rtheta
    center_loc_hpc
    center_loc_hcc
    center_loc_hcr
    bnd_loc_pix
    bnd_loc_hg
    bnd_loc_car
    bnd_loc_hpc
    bnd_loc_hcc
    bnd_loc_hcr




.. GENERATED FROM PYTHON SOURCE LINES 130-133

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 133-136

.. code-block:: Python


    print(brght_nc['bnd_loc_hg'])





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

 .. code-block:: none

    <class '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, 12, 16, 2)
    filling on




.. GENERATED FROM PYTHON SOURCE LINES 137-142

| 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 142-147

.. 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.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, 12, 6, 2)
    filling on

    <class '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, 12, 6, 2)
    filling on




.. GENERATED FROM PYTHON SOURCE LINES 148-153

| 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 (3 for HCC and HCR), with units as described above.

.. GENERATED FROM PYTHON SOURCE LINES 153-156

.. code-block:: Python


    print(brght_nc['brght_extent_hg'])





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

 .. code-block:: none

    <class '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, 12, 4)
    filling on




.. GENERATED FROM PYTHON SOURCE LINES 157-161

| 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 164-166

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 166-172

.. 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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    /home/runner/micromamba/envs/goesr-spwx-examples/lib/python3.12/site-packages/sunpy/net/vso/vso.py:222: SunpyUserWarning: VSO-D400 Bad Request - Invalid wavelength, wavetype of filter specification
      warn_user(resp["error"])

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.. GENERATED FROM PYTHON SOURCE LINES 173-179

Plotting the Bright Regions
----------------------------
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 181-183

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

.. GENERATED FROM PYTHON SOURCE LINES 183-276

.. 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

    # Ignore INFO message about 'Missing metadata for solar radius: assuming the standard radius of the photosphere.'
    # RSUN_REF will be included in future data products.

    # 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 277-279

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

.. GENERATED FROM PYTHON SOURCE LINES 279-348

.. 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'Heliographic 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: Heliographic 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 349-351

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

.. GENERATED FROM PYTHON SOURCE LINES 351-406

.. 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 407-409

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

.. GENERATED FROM PYTHON SOURCE LINES 409-484

.. 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





.. GENERATED FROM PYTHON SOURCE LINES 485-487

Helioprojective Cartesian Coordinates
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 487-557

.. code-block:: Python


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

    # Extract feature locations from netCDF
    bnd_hpc = brght_nc['bnd_loc_hpc'][:].data[0]
    peak_hpc = brght_nc['peak_loc_hpc'][:].data[0]
    center_hpc = brght_nc['center_loc_hpc'][:].data[0]

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

    # Plot bright region boundaries
    for br in bnd_hpc:
        br[br == -9999.] = np.nan
        # Close boundary polygon (on-disk only)
        br = [*br, br[0]]
        bnd_hpc_sc = SkyCoord([(float(v[0]), float(v[1])) * u.arcsec for v in br], obstime=date,
                              observer=suvi_map.observer_coordinate, frame=frames.Helioprojective)

        # Transform SkyCoord object to the SUVI SunPy map's coordinate frame
        with frames.Helioprojective.assume_spherical_screen(suvi_map.observer_coordinate):
            bnd_hpc_sc = bnd_hpc_sc.transform_to(suvi_map.coordinate_frame)

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

    # Plot peak bright region locations
    for br in peak_hpc:
        br[br == -9999.] = np.nan
        wavelength_i = list(brght_nc['wavelength'][:]).index(wavelength)
        x = float(br[wavelength_i][0])
        y = float(br[wavelength_i][1])
        peak_hpc_sc = SkyCoord(x * u.arcsec, y * u.arcsec, obstime=date, observer=suvi_map.observer_coordinate,
                               frame=frames.Helioprojective)

        # Transform SkyCoord object to the SUVI SunPy map's coordinate frame
        with frames.Helioprojective.assume_spherical_screen(suvi_map.observer_coordinate):
            peak_hpc_sc = peak_hpc_sc.transform_to(suvi_map.coordinate_frame)

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

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

        x = float(br[wavelength_i][0])
        y = float(br[wavelength_i][1])
        center_hpc_sc = SkyCoord(x * u.arcsec, y * u.arcsec, obstime=date, observer=suvi_map.observer_coordinate,
                                 frame=frames.Helioprojective)

        # Transform SkyCoord object to the SUVI SunPy map's coordinate frame
        with frames.Helioprojective.assume_spherical_screen(suvi_map.observer_coordinate):
            center_hpc_sc = center_hpc_sc.transform_to(suvi_map.coordinate_frame)

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

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

    plt.show()




.. image-sg:: /examples/suvi/images/sphx_glr_plot_suvi_l2_brght_005.png
   :alt: Helioprojective Cartesian Coordinates, GOES-16, SUVI $284 \; \mathrm{\mathring{A}}$ 2023-01-09 18:51:29
   :srcset: /examples/suvi/images/sphx_glr_plot_suvi_l2_brght_005.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /home/runner/micromamba/envs/goesr-spwx-examples/lib/python3.12/site-packages/sunpy/util/decorators.py:321: SunpyDeprecationWarning: The assume_spherical_screen function is deprecated and may be removed in a future version.
            Use sunpy.coordinates.screens.SphericalScreen instead.
      gen = func(*args, **kwargs)




.. GENERATED FROM PYTHON SOURCE LINES 558-560

Heliocentric Cartesian Coordinates
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 560-630

.. code-block:: Python


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

    # Extract feature locations from netCDF
    bnd_hcc = brght_nc['bnd_loc_hcc'][:].data[0]
    peak_hcc = brght_nc['peak_loc_hcc'][:].data[0]
    center_hcc = brght_nc['center_loc_hcc'][:].data[0]

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


    # Plot bright region boundaries
    for br in bnd_hcc:
        br[br == -9999.] = np.nan
        # Close boundary polygon (on-disk only)
        br = [*br, br[0]]
        bnd_hcc_sc = SkyCoord([(float(v[0]), float(v[1]), float(v[2])) * u.AU for v in br], obstime=date,
                              observer=suvi_map.observer_coordinate, frame=frames.Heliocentric)

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

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

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

        x = br[wavelength_i][0]
        y = br[wavelength_i][1]
        z = br[wavelength_i][2]
        peak_hcc_sc = SkyCoord(x * u.AU, y * u.AU, z * u.AU, obstime=date, observer=suvi_map.observer_coordinate,
                               frame=frames.Heliocentric)

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

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

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

        x = br[wavelength_i][0]
        y = br[wavelength_i][1]
        z = br[wavelength_i][2]
        center_hcc_sc = SkyCoord(x * u.AU, y * u.AU, z * u.AU, obstime=date, observer=suvi_map.observer_coordinate,
                                 frame=frames.Heliocentric)

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

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

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




.. image-sg:: /examples/suvi/images/sphx_glr_plot_suvi_l2_brght_006.png
   :alt: Heliocentric Cartesian Coordinates, GOES-16, SUVI $284 \; \mathrm{\mathring{A}}$ 2023-01-09 18:51:29
   :srcset: /examples/suvi/images/sphx_glr_plot_suvi_l2_brght_006.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 631-633

Heliocentric Radial Coordinates
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 633-706

.. code-block:: Python


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

    # Extract features from netCDF
    peak_hcr = brght_nc['peak_loc_hcr'][:].data[0]
    center_hcr = brght_nc['center_loc_hcr'][:].data[0]
    bnd_hcr = brght_nc['bnd_loc_hcr'][:].data[0]

    colors = cm.BuPu(np.linspace(0.7, 1., 3))

    # Plot bright region boundaries
    for br in bnd_hcr:
        br[br == -9999.] = np.nan
        # Close boundary polygon (on-disk only)
        br = [*br, br[0]]
        bnd_hcr_sc = SkyCoord([(float(v[0]) * u.solRad, float(v[1] + 90.) * u.deg, float(v[2]) * u.solRad) for v in br],
                              representation_type='cylindrical', obstime=date,
                              observer=suvi_map.observer_coordinate, frame=frames.Heliocentric)

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

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

    # Plot peak bright region locations
    for br in peak_hcr:
        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.
        z = br[wavelength_i][2]

        # Create SkyCoord object in heliocentric frame
        peak_hcr_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_hcr_sc = peak_hcr_sc.transform_to(suvi_map.coordinate_frame)

        ax_hcr.plot_coord(peak_hcr_sc, color=colors[1], marker='*', markersize=5, alpha=0.8)

    # Plot bright region 'center of mass' locations
    for br in center_hcr:
        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.
        z = br[wavelength_i][2]

        # Create SkyCoord object in heliocentric frame
        center_hcr_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_hcr_sc = center_hcr_sc.transform_to(suvi_map.coordinate_frame)

        ax_hcr.plot_coord(center_hcr_sc, color=colors[2], marker='o', fillstyle='none', markersize=8, alpha=0.8)

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



.. image-sg:: /examples/suvi/images/sphx_glr_plot_suvi_l2_brght_007.png
   :alt: Heliocentric Radial Coordinates, GOES-16, SUVI $284 \; \mathrm{\mathring{A}}$ 2023-01-09 18:51:29
   :srcset: /examples/suvi/images/sphx_glr_plot_suvi_l2_brght_007.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 20.501 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>`

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

      :download:`Download zipped: plot_suvi_l2_brght.zip <plot_suvi_l2_brght.zip>`


.. only:: html

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

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