Module `pyaurorax.tools`

Data analysis toolkit for working with all-sky imager data available within the AuroraX platform.

This portion of the PyAuroraX library allows you to easily generate basic plots for ASI data, and common manipulations. These include things like displaying single images, making keograms, projecting ASI data onto maps, and extracting metrics for a given lat/lon bounding box.

Example

For shorter function calls, you can initialize the tools submodule using like so:

import pyaurorax
aurorax = pyaurorax.PyAuroraX()
at = aurorax.tools

Sub-modules

pyaurorax.tools.bounding_box: Methods for working with data in a specific bounding box.
pyaurorax.tools.calibration: Perform various calibration procedures on image data.
pyaurorax.tools.ccd_contour: Obtain contours in pixel coordinates from a skymap for plotting over CCD images.
pyaurorax.tools.classes: Class definitions for data analysis objects.
pyaurorax.tools.grid_files: Prepare grid data for plotting.
pyaurorax.tools.keogram: Generate keograms.
pyaurorax.tools.montage: Create montages.
pyaurorax.tools.mosaic: Prepare data and create mosaics.
pyaurorax.tools.spectra: Work with spectrograph data.

Functions

Expand source code

def display(image: np.ndarray,
            cmap: Optional[str] = None,
            figsize: Optional[Tuple[int, int]] = None,
            aspect: Optional[Union[Literal["equal", "auto"], float]] = None,
            colorbar: bool = False,
            title: Optional[str] = None,
            returnfig: bool = False,
            savefig: bool = False,
            savefig_filename: Optional[str] = None,
            savefig_quality: Optional[int] = None) -> Any:
    """
    Render a visualization of a single image.

    Either display it (default behaviour), save it to disk (using the `savefig` parameter), or 
    return the matplotlib plot object for further usage (using the `returnfig` parameter).

    Args:
        image (numpy.ndarray): 
            The image to display, represented as a numpy array.

        cmap (str): 
            The matplotlib colormap to use.

            Commonly used colormaps are:

            - REGO: `gist_heat`
            - THEMIS ASI: `gray`
            - TREx Blue: `Blues_r`
            - TREx NIR: `gray`
            - TREx RGB: `None`

            A list of all available colormaps can be found on the 
            [matplotlib documentation](https://matplotlib.org/stable/gallery/color/colormap_reference.html).

        figsize (tuple): 
            The matplotlib figure size to use when displaying, tuple of two integers (ie. `figsize=(14, 4)`)
    
        aspect (str or float): 
            The matplotlib imshow aspect ration to use. A common value for this is `auto`. All valid values 
            can be found on the [matplotlib documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html).

        colorbar (bool): 
            Display a colorbar. Default is `False`.

        title (str): 
            A title to display above the rendered image. Defaults to no title.

        returnfig (bool): 
            Instead of displaying the image, return the matplotlib figure object. This allows for further plot 
            manipulation, for example, adding labels or a title in a different location than the default. 
            
            Remember - if this parameter is supplied, be sure that you close your plot after finishing work 
            with it. This can be achieved by doing `plt.close(fig)`. 
            
            Note that this method cannot be used in combination with `savefig`.

        savefig (bool): 
            Save the displayed image to disk instead of displaying it. The parameter savefig_filename is required if 
            this parameter is set to True. Defaults to `False`.

        savefig_filename (str): 
            Filename to save the image to. Must be specified if the savefig parameter is set to True.

        savefig_quality (int): 
            Quality level of the saved image. This can be specified if the savefig_filename is a JPG image. If it
            is a PNG, quality is ignored. Default quality level for JPGs is matplotlib/Pillow's default of 75%.

    Returns:
        The displayed image, by default. If `savefig` is set to True, nothing will be returned. If `returnfig` is 
        set to True, the plotting variables `(fig, ax)` will be returned.

    Raises:
        ValueError: issues with supplied parameters.
    """
    # check return mode
    if (returnfig is True and savefig is True):
        raise ValueError("Only one of returnfig or savefig can be set to True")
    if (returnfig is True and (savefig_filename is not None or savefig_quality is not None)):
        warnings.warn("The figure will be returned, but a savefig option parameter was supplied. Consider " +
                      "removing the savefig option parameter(s) as they will be ignored.",
                      stacklevel=1)
    elif (savefig is False and (savefig_filename is not None or savefig_quality is not None)):
        warnings.warn("A savefig option parameter was supplied, but the savefig parameter is False. The " +
                      "savefig option parameters will be ignored.",
                      stacklevel=1)

    # init figure
    fig = plt.figure(figsize=figsize)
    ax = fig.add_axes((0, 0, 1, 1))
    ax.set_axis_off()

    # plot data
    im = ax.imshow(image, cmap=cmap, origin="lower", aspect=aspect)

    # show colour bar
    if (colorbar is True):
        fig.colorbar(im, ax=ax)

    # show title
    if (title is not None):
        ax.set_title(title)

    # save figure or show it
    if (savefig is True):
        # check that filename has been set
        if (savefig_filename is None):
            raise ValueError("The savefig_filename parameter is missing, but required since savefig was set to True.")

        # save the figure
        f_extension = os.path.splitext(savefig_filename)[-1].lower()
        if (".jpg" == f_extension or ".jpeg" == f_extension):
            # check quality setting
            if (savefig_quality is not None):
                plt.savefig(savefig_filename, quality=savefig_quality, bbox_inches="tight")
            else:
                plt.savefig(savefig_filename, bbox_inches="tight")
        else:
            if (savefig_quality is not None):
                # quality specified, but output filename is not a JPG, so show a warning
                warnings.warn("The savefig_quality parameter was specified, but is only used for saving JPG files. The " +
                              "savefig_filename parameter was determined to not be a JPG file, so the quality will be ignored",
                              stacklevel=1)
            plt.savefig(savefig_filename, bbox_inches="tight")

        # clean up by closing the figure
        plt.close(fig)
    elif (returnfig is True):
        # return the figure and axis objects
        return (fig, ax)
    else:
        # show the figure
        plt.show(fig)

        # cleanup by closing the figure
        plt.close(fig)

    # return
    return None

Render a visualization of a single image.

Either display it (default behaviour), save it to disk (using the savefig parameter), or return the matplotlib plot object for further usage (using the returnfig parameter).

Args

image : numpy.ndarray

The image to display, represented as a numpy array.

cmap : str

The matplotlib colormap to use.

Commonly used colormaps are:

REGO: gist_heat
THEMIS ASI: gray
TREx Blue: Blues_r
TREx NIR: gray
TREx RGB: None

A list of all available colormaps can be found on the matplotlib documentation.

figsize : tuple

The matplotlib figure size to use when displaying, tuple of two integers (ie. figsize=(14, 4))

aspect : str or float

The matplotlib imshow aspect ration to use. A common value for this is auto. All valid values can be found on the matplotlib documentation.

colorbar : bool

Display a colorbar. Default is False.

title : str

A title to display above the rendered image. Defaults to no title.

returnfig : bool

Instead of displaying the image, return the matplotlib figure object. This allows for further plot manipulation, for example, adding labels or a title in a different location than the default.

Remember - if this parameter is supplied, be sure that you close your plot after finishing work with it. This can be achieved by doing plt.close(fig).

Note that this method cannot be used in combination with savefig.

savefig : bool

Save the displayed image to disk instead of displaying it. The parameter savefig_filename is required if this parameter is set to True. Defaults to False.

savefig_filename : str

Filename to save the image to. Must be specified if the savefig parameter is set to True.

savefig_quality : int

Quality level of the saved image. This can be specified if the savefig_filename is a JPG image. If it is a PNG, quality is ignored. Default quality level for JPGs is matplotlib/Pillow's default of 75%.

Returns

The displayed image, by default. If savefig is set to True, nothing will be returned. If returnfig is set to True, the plotting variables (fig, ax) will be returned.

Raises

ValueError: issues with supplied parameters.

def movie(input_filenames: List[str], output_filename: str, n_parallel: int = 1, fps: int = 25, progress_bar_disable: bool = False) ‑> None

Expand source code

def movie(
    input_filenames: List[str],
    output_filename: str,
    n_parallel: int = 1,
    fps: int = 25,
    progress_bar_disable: bool = False,
) -> None:
    """
    Generate a movie file from a list of filenames. Note that the codec used is "mp4v".

    Args:
        input_filenames (List[str]): 
            Filenames of frames to use for movie generation. No sorting is applied, so it is 
            assumed the list is in the desired order. This parameter is required.
        
        output_filename (str): 
            Filename for the created movie file. This parameter is required.

        n_parallel (int): 
            Number of multiprocessing workers to use. Default is `1`, which does not use
            multiprocessing.

        fps (int): 
            Frames per second (FPS) for the movie file. Default is `25`.

        progress_bar_disable (bool): 
            Toggle the progress bars off. Default is `False`.        

    Raises:
        IOError: I/O related issue while generating movie
    """
    # read in all frames
    #
    # NOTE: we do with using multiprocessing, but we need to be very
    # careful to ensure that the frames are being added to the list
    # in order. This preserves the order of the file list that was
    # supplied.
    frame_list = []
    if (n_parallel == 1):
        # don't do anything special, just a basic loop
        if (progress_bar_disable is True):
            # no progress bar
            for fname in input_filenames:
                img = cv2.imread(fname)
                frame_list.append(img)
        else:
            # with progress bar
            for fname in tqdm(input_filenames, desc="Reading files: ", unit="files"):
                img = cv2.imread(fname)
                frame_list.append(img)
    else:
        # multiple workers, do it in a multiprocessing loop
        if (progress_bar_disable is True):
            with ProcessPoolExecutor(max_workers=n_parallel) as executor:
                for result in executor.map(__process_frame, input_filenames):
                    frame_list.append(result["img"])
        else:
            results = tqdm_process_map(
                __process_frame,
                input_filenames,
                max_workers=n_parallel,
                chunksize=1,
                desc="Reading files: ",
                unit="files",
                tqdm_class=tqdm,
            )
            for result in results:
                frame_list.append(result["img"])

    # get dimensions of images so we can initialize the video writer
    #
    # NOTE: we assume that all images are the same size. Perhaps we can
    # put a check in while we read all the images in?
    if (len(frame_list) == 0):
        raise IOError("No images read in. Check that filenames were indeed supplied.")
    elif (len(frame_list[0].shape) == 2 or len(frame_list[0].shape) == 3):
        width = frame_list[0].shape[0]
        height = frame_list[0].shape[1]
    else:
        # some unexpected image shape
        raise IOError("Unexpected shape of image data. Found shape %s, but was expecting only 2 or 3 dimensions." % (frame_list[0].shape))

    # initialize videowriter object
    fourcc = cv2.VideoWriter_fourcc(*"mp4v")  # type: ignore
    frame_size = (height, width)
    writer = cv2.VideoWriter(output_filename, fourcc, fps, frame_size)

    # write the frames
    if (progress_bar_disable is True):
        # no progress bar
        for i in range(0, len(frame_list)):
            writer.write(frame_list[i])
    else:
        # with progress bar
        for i in tqdm(range(0, len(frame_list)), desc="Encoding frames: ", unit="frames"):
            writer.write(frame_list[i])

    # close the writer
    writer.release()

Generate a movie file from a list of filenames. Note that the codec used is "mp4v".

Args

input_filenames : List[str]: Filenames of frames to use for movie generation. No sorting is applied, so it is assumed the list is in the desired order. This parameter is required.
output_filename : str: Filename for the created movie file. This parameter is required.
n_parallel : int: Number of multiprocessing workers to use. Default is 1, which does not use multiprocessing.
fps : int: Frames per second (FPS) for the movie file. Default is 25.
progress_bar_disable : bool: Toggle the progress bars off. Default is False.

Raises

IOError: I/O related issue while generating movie

def scale_intensity(data: numpy.ndarray, min: float | None = None, max: float | None = None, top: float | None = None, memory_saver: bool = True) ‑> numpy.ndarray

Expand source code

def scale_intensity(
    data: np.ndarray,
    min: Optional[float] = None,
    max: Optional[float] = None,
    top: Optional[float] = None,
    memory_saver: bool = True,
) -> np.ndarray:
    """
    Scale all values of an array that lie in the range min<=x<=max in to 
    the range 0<=x<=high.
    
    Args:
        data (numpy.ndarray): 
            Data array, can be 2, 3, or 4-dimensional. Assumed to be an image, or array of 
            images. Also assumed that the first 2 dimensions are the image's x and y 
            coordinates, and the following dimensions are some combination of the number of 
            images, and/or the colour channel.

        min (float): 
            Minimum value of array to be considered

        max (float): 
            Maximum value of array to be considered

        top (float): 
            Maximum value of the scaled result. If not supplied, the max value
            of the data array's dtype is used.

        memory_saver (bool): 
            Utilize less RAM when scaling a set of images. Defaults to `True`. If set to `False` then
            the scaling routine will be faster, but will utilize significantly more RAM.

    Returns:
        A new `numpy.ndarray` that is the same dimensions as the inputted data array, 
        with the scaling applied.

    Raises:
        ValueError: Issues with the supplied min, max, or top
    """

    if (memory_saver is True):
        # Save original data shape
        input_shape = data.shape

        # determine if we are single or 3 channel
        if (len(data.shape) == 2):
            n_channels = 1
            data = data[:, :, np.newaxis]
        elif (len(data.shape) == 3):
            # single channel
            n_channels = 1
        elif (len(data.shape) == 4):
            # three channel
            n_channels = 3
        else:
            raise ValueError(
                "Unable to determine number of channels based on the supplied images. Make sure you are supplying a "
                +
                "[rows,cols,images] or [rows,cols,channels,images] sized array."
            )

        # init destination array
        images_scaled = np.empty((data.shape), dtype=data.dtype)

        # cycle through each image
        for i in range(0, data.shape[-1]):
            if (n_channels == 1):
                images_scaled[:, :, i] = __scale_data(data[:, :, i], min, max,
                                                      top)
            else:
                images_scaled[:, :, :,
                              i] = __scale_data(data[:, :, :, i], min, max,
                                                top)

        # Reshape to confirm output is the same shape as input
        images_scaled = np.reshape(images_scaled, input_shape)

        # return
        return images_scaled
    else:
        # scale and return
        return __scale_data(data, min, max, top)

Scale all values of an array that lie in the range min<=x<=max in to the range 0<=x<=high.

Args

data : numpy.ndarray: Data array, can be 2, 3, or 4-dimensional. Assumed to be an image, or array of images. Also assumed that the first 2 dimensions are the image's x and y coordinates, and the following dimensions are some combination of the number of images, and/or the colour channel.
min : float: Minimum value of array to be considered
max : float: Maximum value of array to be considered
top : float: Maximum value of the scaled result. If not supplied, the max value of the data array's dtype is used.
memory_saver : bool: Utilize less RAM when scaling a set of images. Defaults to True. If set to False then the scaling routine will be faster, but will utilize significantly more RAM.

Returns

A new numpy.ndarray that is the same dimensions as the inputted data array, with the scaling applied.

Raises

ValueError: Issues with the supplied min, max, or top

def set_theme(theme: str) ‑> None

Expand source code

def set_theme(theme: str) -> None:
    """
    A handy wrapper for setting the matplotlib global theme. Common choices are `light`, 
    `dark`, or `default`.

    Args:
        theme (str): 
            Theme name. Common choices are `light`, `dark`, or `default`. If default, then
            matplotlib theme settings will be fully reset to their defaults.

            Additional themes can be found on the 
            [matplotlib documentation](https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html)
    """
    if ("theme" == "default"):
        mpl.rcParams.update(mpl.rcParamsDefault)
    elif (theme == "light"):
        plt.style.use("default")
    elif (theme == "dark"):
        plt.style.use("dark_background")
    else:
        plt.style.use(theme)

A handy wrapper for setting the matplotlib global theme. Common choices are light, dark, or default.

Args

theme : str

Theme name. Common choices are light, dark, or default. If default, then matplotlib theme settings will be fully reset to their defaults.

Additional themes can be found on the matplotlib documentation

Classes

class Keogram (data: numpy.ndarray, timestamp: List[datetime.datetime], instrument_type: str, slice_idx: int | None = None, ccd_y: numpy.ndarray | None = None, mag_y: numpy.ndarray | None = None, geo_y: numpy.ndarray | None = None)

Expand source code

@dataclass
class Keogram:
    """
    Class representation for a keogram

    Attributes:
        data (numpy.ndarray): 
            The derived keogram data.
        timestamp (List[datetime.datetime]): 
            Timestamps corresponding to each keogram slice.
        instrument_type (str):
            String giving instrument type, either 'asi' or 'spectrograph'.
        ccd_y (numpy.ndarray): 
            The y-axis representing CCD Y coordinates for the keogram.
        mag_y (numpy.ndarray): 
            The y-axis representing magnetic latitude for the keogram.
        geo_y (numpy.ndarray): 
            The y-axis representing geographic latitude for the keogram.
    """

    def __init__(self,
                 data: np.ndarray,
                 timestamp: List[datetime.datetime],
                 instrument_type: str,
                 slice_idx: Optional[int] = None,
                 ccd_y: Optional[np.ndarray] = None,
                 mag_y: Optional[np.ndarray] = None,
                 geo_y: Optional[np.ndarray] = None):
        # public vars
        self.data = data
        self.timestamp = timestamp
        self.instrument_type = instrument_type
        self.ccd_y = ccd_y
        self.mag_y = mag_y
        self.geo_y = geo_y

        # private vars
        self.__slice_idx = slice_idx

    def __str__(self) -> str:
        return self.__repr__()

    def __repr__(self) -> str:
        data_str = "array(dims=%s, dtype=%s)" % (self.data.shape, self.data.dtype)
        timestamp_str = "[%d datetime objects]" % (len(self.timestamp))
        ccd_y_str = "None" if self.ccd_y is None else "array(%d values)" % (self.ccd_y.shape[0])
        mag_y_str = "None" if self.mag_y is None else "array(%d values)" % (self.mag_y.shape[0])
        geo_y_str = "None" if self.geo_y is None else "array(%d values)" % (self.geo_y.shape[0])

        return "Keogram(data=%s, timestamp=%s, ccd_y=%s, mag_y=%s, geo_y=%s)" % (data_str, timestamp_str, ccd_y_str, mag_y_str, geo_y_str)

    def set_geographic_latitudes(self, skymap: Skymap, altitude_km: Optional[Union[int, float]] = None) -> None:
        """
        Set the geographic latitude values for this keogram, using the specified skymap 
        data. The data will be set to the geo_y attribute of this Keogram object, which
        can then be used for plotting and/or further analysis.

        Args:
            skymap (pyaurorax.data.ucalgary.Skymap): 
                The skymap object to use. This parameter is required.

            altitude_km (int): 
                The altitude to use, in kilometers. If not specified, it will use the default in the 
                skymap object. If the specified altitude is not valid, a ValueError will be raised.
        
        Returns:
            None. The Keogram object's `geo_y` attribute will be updated.

        Raises:
            ValueError: Issues with specified altitude.
        """
        # check for slice idx
        if (self.__slice_idx is None):
            raise ValueError("Unable to set the geographic latitudes since the slice_idx is None. If this keogram " +
                             "object was created as part of the custom_keogram routines or is a spectrogaph keogram, " +
                             "this is expected and performing this action is not supported at this time.")

        # determine altitude index to use
        if (altitude_km is not None):
            # Obtain lat/lon arrays from skymap
            if (altitude_km * 1000.0 in skymap.full_map_altitude):
                altitude_idx = np.where(altitude_km * 1000.0 == skymap.full_map_altitude)

                self.geo_y = np.squeeze(skymap.full_map_latitude[altitude_idx, :, self.__slice_idx]).copy()
            else:
                # Make sure altitude is in range that can be interpolated
                if (altitude_km * 1000.0 < skymap.full_map_altitude[0]) or (altitude_km * 1000.0 > skymap.full_map_altitude[2]):
                    raise ValueError("Altitude " + str(altitude_km) + " outside valid range of " +
                                     str((skymap.full_map_altitude[0] / 1000.0, skymap.full_map_altitude[2] / 1000.0)))

                # Initialze empty lat/lon arrays
                lats = np.full(np.squeeze(skymap.full_map_latitude[0, :, :]).shape, np.nan, dtype=skymap.full_map_latitude[0, :, :].dtype)

                # Interpolate lats and lons at desired altitude
                for i in range(skymap.full_map_latitude.shape[1]):
                    for j in range(skymap.full_map_latitude.shape[2]):
                        lats[i, j] = np.interp(altitude_km * 1000.0, skymap.full_map_altitude, skymap.full_map_latitude[:, i, j])

                self.geo_y = lats[:, self.__slice_idx].copy()
        else:
            # use default middle altitude
            self.geo_y = np.squeeze(skymap.full_map_latitude[1, :, self.__slice_idx]).copy()

    def set_magnetic_latitudes(self, skymap: Skymap, timestamp: datetime.datetime, altitude_km: Optional[Union[int, float]] = None) -> None:
        """
        Set the magnetic latitude values for this keogram, using the specified skymap 
        data. AACGMv2 will be utilized to perform the calculations. The resulting data
        will be set to the mag_y attribute of this Keogram object, which can then be
        used for plotting and/or further analysis.

        Args:
            skymap (pyaurorax.data.ucalgary.Skymap): 
                The skymap object to use. This parameter is required.

            timestamp (datetime.datetime): 
                The timestamp to use when converting skymap data to magnetic coordinates. Utilizes
                AACGMv2 to do the conversion.

            altitude_km (int): 
                The altitude to use. If not specified, it will use the default in the skymap
                object. If the specified altitude is not valid, a ValueError will be raised.
        
        Returns:
            None. The Keogram object's `mag_y` attribute will be updated.

        Raises:
            ValueError: Issues with specified altitude.
        """
        # check for slice idx
        if (self.__slice_idx is None):
            raise ValueError("Unable to set the geographic latitudes since the slice_idx is None. If this keogram " +
                             "object was created as part of the custom_keogram routines or is a spectrogaph keogram, " +
                             "this is expected and performing this action is not supported at this time.")

        # determine altitude index to use
        if (altitude_km is not None):
            # Obtain lat/lon arrays from skymap
            if (altitude_km * 1000.0 in skymap.full_map_altitude):
                altitude_idx = np.where(altitude_km * 1000.0 == skymap.full_map_altitude)

                lats = np.squeeze(skymap.full_map_latitude[altitude_idx, :, :])
                lons = np.squeeze(skymap.full_map_longitude[altitude_idx, :, :])
                lons[np.where(lons > 180)] -= 360.0

            else:
                # Make sure altitude is in range that can be interpolated
                if (altitude_km * 1000.0 < skymap.full_map_altitude[0]) or (altitude_km * 1000.0 > skymap.full_map_altitude[2]):
                    raise ValueError("Altitude " + str(altitude_km) + " outside valid range of " +
                                     str((skymap.full_map_altitude[0] / 1000.0, skymap.full_map_altitude[2] / 1000.0)))

                # Initialze empty lat/lon arrays
                lats = np.full(np.squeeze(skymap.full_map_latitude[0, :, :]).shape, np.nan, dtype=skymap.full_map_latitude[0, :, :].dtype)
                lons = lats.copy()

                # Interpolate lats and lons at desired altitude
                for i in range(skymap.full_map_latitude.shape[1]):
                    for j in range(skymap.full_map_latitude.shape[2]):
                        lats[i, j] = np.interp(altitude_km * 1000.0, skymap.full_map_altitude, skymap.full_map_latitude[:, i, j])
                        lons[i, j] = np.interp(altitude_km * 1000.0, skymap.full_map_altitude, skymap.full_map_longitude[:, i, j])

                lons[np.where(lons > 180)] -= 360.0

            # Convert lats and lons to geomagnetic coordinates
            mag_lats, mag_lons, mag_alts = aacgmv2.convert_latlon_arr(lats.flatten(),
                                                                      lons.flatten(), (lons * 0.0).flatten(),
                                                                      timestamp,
                                                                      method_code='G2A')
            mag_lats = np.reshape(mag_lats, lats.shape)
            mag_lons = np.reshape(mag_lons, lons.shape)

            # Set the y axis to the desired slice index of the magnetic latitudes
            self.mag_y = mag_lats[:, self.__slice_idx].copy()
        else:
            # Convert middle altitude lats and lons to geomagnetic coordinates
            mag_lats, mag_lons, mag_alts = aacgmv2.convert_latlon_arr(np.squeeze(skymap.full_map_latitude[1, :, :]).flatten(),
                                                                      np.squeeze(skymap.full_map_longitude[1, :, :]).flatten(),
                                                                      (skymap.full_map_longitude[1, :, :] * 0.0).flatten(),
                                                                      timestamp,
                                                                      method_code='G2A')
            mag_lats = np.reshape(mag_lats, np.squeeze(skymap.full_map_latitude[1, :, :]).shape)
            mag_lons = np.reshape(mag_lons, np.squeeze(skymap.full_map_longitude[1, :, :]).shape)

            # Set the y axis to the desired slice index of the magnetic latitudes
            self.mag_y = mag_lats[:, self.__slice_idx].copy()

    def plot(self,
             y_type: Literal["ccd", "mag", "geo"] = "ccd",
             title: Optional[str] = None,
             figsize: Optional[Tuple[int, int]] = None,
             cmap: Optional[str] = None,
             aspect: Optional[Union[Literal["equal", "auto"], float]] = None,
             axes_visible: bool = True,
             xlabel: str = "Time (UTC)",
             ylabel: Optional[str] = None,
             xtick_increment: Optional[int] = None,
             ytick_increment: Optional[int] = None,
             returnfig: bool = False,
             savefig: bool = False,
             savefig_filename: Optional[str] = None,
             savefig_quality: Optional[int] = None) -> Any:
        """
        Generate a plot of the keogram data. 
        
        Either display it (default behaviour), save it to disk (using the `savefig` parameter), or 
        return the matplotlib plot object for further usage (using the `returnfig` parameter).

        Args:
            y_type (str): 
                Type of y-axis to use when plotting. Options are `ccd`, `mag`, or `geo`. The
                default is `ccd`. This parameter is required.

            title (str): 
                The title to display above the plotted keogram.

            figsize (tuple): 
                The matplotlib figure size to use when plotting. For example `figsize=(14,4)`.

            cmap (str): 
                The matplotlib colormap to use.

                Commonly used colormaps are:

                - REGO: `gist_heat`
                - THEMIS ASI: `gray`
                - TREx Blue: `Blues_r`
                - TREx NIR: `gray`
                - TREx RGB: `None`

                A list of all available colormaps can be found on the 
                [matplotlib documentation](https://matplotlib.org/stable/gallery/color/colormap_reference.html).
            
            aspect (str or float): 
                The matplotlib imshow aspect ration to use. A common value for this is `auto`. All valid values 
                can be found on the [matplotlib documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html).

            axes_visible (bool): 
                Display the axes. Default is `True`.

            xlabel (str): 
                The x-axis label to use. Default is `Time (UTC)`.

            ylabel (str): 
                The y-axis label to use. Default is based on y_type.

            xtick_increment (int): 
                The x-axis tick increment to use. Default is 100.

            ytick_increment (int): 
                The y-axis tick increment to use. Default is 50.

            returnfig (bool): 
                Instead of displaying the image, return the matplotlib figure object. This allows for further plot 
                manipulation, for example, adding labels or a title in a different location than the default. 
                
                Remember - if this parameter is supplied, be sure that you close your plot after finishing work 
                with it. This can be achieved by doing `plt.close(fig)`. 
                
                Note that this method cannot be used in combination with `savefig`.

            savefig (bool): 
                Save the displayed image to disk instead of displaying it. The parameter savefig_filename is required if 
                this parameter is set to True. Defaults to `False`.

            savefig_filename (str): 
                Filename to save the image to. Must be specified if the savefig parameter is set to True.

            savefig_quality (int): 
                Quality level of the saved image. This can be specified if the savefig_filename is a JPG image. If it
                is a PNG, quality is ignored. Default quality level for JPGs is matplotlib/Pillow's default of 75%.

        Returns:
            The displayed keogram, by default. If `savefig` is set to True, nothing will be returned. If `returnfig` is 
            set to True, the plotting variables `(fig, ax)` will be returned.

        Raises:
            ValueError: Issues with the y-axis choice.
        """
        # check return mode
        if (returnfig is True and savefig is True):
            raise ValueError("Only one of returnfig or savefig can be set to True")
        if returnfig is True and (savefig_filename is not None or savefig_quality is not None):
            warnings.warn("The figure will be returned, but a savefig option parameter was supplied. Consider " +
                          "removing the savefig option parameter(s) as they will be ignored.",
                          stacklevel=1)
        elif savefig is False and (savefig_filename is not None or savefig_quality is not None):
            warnings.warn("A savefig option parameter was supplied, but the savefig parameter is False. The " +
                          "savefig option parameters will be ignored.",
                          stacklevel=1)

        # init figure and plot data
        fig = plt.figure(figsize=figsize)
        ax = fig.add_axes((0, 0, 1, 1))
        ax.imshow(self.data, origin="lower", cmap=cmap, aspect=aspect)

        # set title
        if (title is not None):
            ax.set_title(title)

        # set axes
        if (axes_visible is True):
            # do checks for y-axis that was chosen
            if (y_type == "geo" and self.geo_y is None):
                raise ValueError("Unable to plot using geo_y data. The geo_y attribute is currently None, so either populate "
                                 "it with data using the set_geographic_latitudes() function, or choose a different y_type")
            elif (y_type == "mag" and self.mag_y is None):
                raise ValueError("Unable to plot using mag_y data. The mag_y attribute is currently None, so either populate "
                                 "it with data using the set_magnetic_latitudes() function, or choose a different y_type")

            # set y axis data, and y label
            y_axis_data = self.ccd_y
            if (y_type == "mag"):
                y_axis_data = self.mag_y
                if (ylabel is None):
                    ylabel = "Magnetic latitude"
            elif (y_type == "geo"):
                y_axis_data = self.geo_y
                if (ylabel is None):
                    ylabel = "Geographic latitude"
            else:
                if (ylabel is None):
                    ylabel = "CCD Y"

            # print labels
            ax.set_xlabel(xlabel, fontsize=14)
            ax.set_ylabel(ylabel, fontsize=14)

            # generate x ticks and labels
            #
            # TODO: make this more dynamic
            if (xtick_increment is None):
                xtick_increment = 100  # assume data is 3 second cadence; good enough for now
            x_ticks = np.arange(0, self.data.shape[1], xtick_increment)
            x_labels = self.timestamp[::xtick_increment]
            for i in range(0, len(x_labels)):
                x_labels[i] = x_labels[i].strftime("%H:%M")  # type: ignore
            ax.set_xticks(x_ticks, x_labels)  # type: ignore

            # do check for ccd_y
            if (self.ccd_y is None):
                warnings.warn(
                    "Unable to plot y-axis. If this keogram object was create as part of the custom_keogram " +
                    "routines, this is expected and plotting a custom keogram with axes is not supported at this time.",
                    stacklevel=1,
                )
            else:
                # generate y ticks and labels
                if (y_type == "ccd"):
                    # TODO: make this more dynamic
                    if (ytick_increment is None):
                        ytick_increment = 50

                    # generate y ticks and labels
                    y_ticks = y_axis_data[::ytick_increment]  # type: ignore
                    y_labels = y_axis_data[::ytick_increment]  # type: ignore

                    # apply yticks
                    ax.set_yticks(y_ticks, y_labels)  # type: ignore
                elif (y_type == "geo" and self.geo_y is not None) or (y_type == "mag" and self.mag_y is not None):
                    # set tick increments
                    if (ytick_increment is None):
                        ytick_increment = 50

                    # generate y ticks and labels
                    y_ticks = self.ccd_y[25::ytick_increment]
                    y_labels = np.round(
                        y_axis_data,  # type: ignore
                        1).astype(str)[25::ytick_increment]
                    y_labels[np.where(y_labels == 'nan')] = ''

                    # apply yticks
                    ax.set_yticks(y_ticks, y_labels)
        else:
            # disable axes
            ax.set_axis_off()

        # save figure or show it
        if (savefig is True):
            # check that filename has been set
            if (savefig_filename is None):
                raise ValueError("The savefig_filename parameter is missing, but required since savefig was set to True.")

            # save the figure
            f_extension = os.path.splitext(savefig_filename)[-1].lower()
            if (".jpg" == f_extension or ".jpeg" == f_extension):
                # check quality setting
                if (savefig_quality is not None):
                    plt.savefig(savefig_filename, quality=savefig_quality, bbox_inches="tight")
                else:
                    plt.savefig(savefig_filename, bbox_inches="tight")
            else:
                if (savefig_quality is not None):
                    # quality specified, but output filename is not a JPG, so show a warning
                    warnings.warn("The savefig_quality parameter was specified, but is only used for saving JPG files. The " +
                                  "savefig_filename parameter was determined to not be a JPG file, so the quality will be ignored",
                                  stacklevel=1)
                plt.savefig(savefig_filename, bbox_inches="tight")

            # clean up by closing the figure
            plt.close(fig)
        elif (returnfig is True):
            # return the figure and axis objects
            return (fig, ax)
        else:
            # show the figure
            plt.show(fig)

            # cleanup by closing the figure
            plt.close(fig)

        # return
        return None

Class representation for a keogram

Attributes

data : numpy.ndarray: The derived keogram data.
timestamp : List[datetime.datetime]: Timestamps corresponding to each keogram slice.
instrument_type (str):
String giving instrument type, either 'asi' or 'spectrograph'.
ccd_y : numpy.ndarray: The y-axis representing CCD Y coordinates for the keogram.
mag_y : numpy.ndarray: The y-axis representing magnetic latitude for the keogram.
geo_y : numpy.ndarray: The y-axis representing geographic latitude for the keogram.

Methods

def plot(self, y_type: Literal['ccd', 'mag', 'geo'] = 'ccd', title: str | None = None, figsize: Tuple[int, int] | None = None, cmap: str | None = None, aspect: Literal['equal', 'auto'] | float | None = None, axes_visible: bool = True, xlabel: str = 'Time (UTC)', ylabel: str | None = None, xtick_increment: int | None = None, ytick_increment: int | None = None, returnfig: bool = False, savefig: bool = False, savefig_filename: str | None = None, savefig_quality: int | None = None) ‑> Any

Expand source code

def plot(self,
         y_type: Literal["ccd", "mag", "geo"] = "ccd",
         title: Optional[str] = None,
         figsize: Optional[Tuple[int, int]] = None,
         cmap: Optional[str] = None,
         aspect: Optional[Union[Literal["equal", "auto"], float]] = None,
         axes_visible: bool = True,
         xlabel: str = "Time (UTC)",
         ylabel: Optional[str] = None,
         xtick_increment: Optional[int] = None,
         ytick_increment: Optional[int] = None,
         returnfig: bool = False,
         savefig: bool = False,
         savefig_filename: Optional[str] = None,
         savefig_quality: Optional[int] = None) -> Any:
    """
    Generate a plot of the keogram data. 
    
    Either display it (default behaviour), save it to disk (using the `savefig` parameter), or 
    return the matplotlib plot object for further usage (using the `returnfig` parameter).

    Args:
        y_type (str): 
            Type of y-axis to use when plotting. Options are `ccd`, `mag`, or `geo`. The
            default is `ccd`. This parameter is required.

        title (str): 
            The title to display above the plotted keogram.

        figsize (tuple): 
            The matplotlib figure size to use when plotting. For example `figsize=(14,4)`.

        cmap (str): 
            The matplotlib colormap to use.

            Commonly used colormaps are:

            - REGO: `gist_heat`
            - THEMIS ASI: `gray`
            - TREx Blue: `Blues_r`
            - TREx NIR: `gray`
            - TREx RGB: `None`

            A list of all available colormaps can be found on the 
            [matplotlib documentation](https://matplotlib.org/stable/gallery/color/colormap_reference.html).
        
        aspect (str or float): 
            The matplotlib imshow aspect ration to use. A common value for this is `auto`. All valid values 
            can be found on the [matplotlib documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html).

        axes_visible (bool): 
            Display the axes. Default is `True`.

        xlabel (str): 
            The x-axis label to use. Default is `Time (UTC)`.

        ylabel (str): 
            The y-axis label to use. Default is based on y_type.

        xtick_increment (int): 
            The x-axis tick increment to use. Default is 100.

        ytick_increment (int): 
            The y-axis tick increment to use. Default is 50.

        returnfig (bool): 
            Instead of displaying the image, return the matplotlib figure object. This allows for further plot 
            manipulation, for example, adding labels or a title in a different location than the default. 
            
            Remember - if this parameter is supplied, be sure that you close your plot after finishing work 
            with it. This can be achieved by doing `plt.close(fig)`. 
            
            Note that this method cannot be used in combination with `savefig`.

        savefig (bool): 
            Save the displayed image to disk instead of displaying it. The parameter savefig_filename is required if 
            this parameter is set to True. Defaults to `False`.

        savefig_filename (str): 
            Filename to save the image to. Must be specified if the savefig parameter is set to True.

        savefig_quality (int): 
            Quality level of the saved image. This can be specified if the savefig_filename is a JPG image. If it
            is a PNG, quality is ignored. Default quality level for JPGs is matplotlib/Pillow's default of 75%.

    Returns:
        The displayed keogram, by default. If `savefig` is set to True, nothing will be returned. If `returnfig` is 
        set to True, the plotting variables `(fig, ax)` will be returned.

    Raises:
        ValueError: Issues with the y-axis choice.
    """
    # check return mode
    if (returnfig is True and savefig is True):
        raise ValueError("Only one of returnfig or savefig can be set to True")
    if returnfig is True and (savefig_filename is not None or savefig_quality is not None):
        warnings.warn("The figure will be returned, but a savefig option parameter was supplied. Consider " +
                      "removing the savefig option parameter(s) as they will be ignored.",
                      stacklevel=1)
    elif savefig is False and (savefig_filename is not None or savefig_quality is not None):
        warnings.warn("A savefig option parameter was supplied, but the savefig parameter is False. The " +
                      "savefig option parameters will be ignored.",
                      stacklevel=1)

    # init figure and plot data
    fig = plt.figure(figsize=figsize)
    ax = fig.add_axes((0, 0, 1, 1))
    ax.imshow(self.data, origin="lower", cmap=cmap, aspect=aspect)

    # set title
    if (title is not None):
        ax.set_title(title)

    # set axes
    if (axes_visible is True):
        # do checks for y-axis that was chosen
        if (y_type == "geo" and self.geo_y is None):
            raise ValueError("Unable to plot using geo_y data. The geo_y attribute is currently None, so either populate "
                             "it with data using the set_geographic_latitudes() function, or choose a different y_type")
        elif (y_type == "mag" and self.mag_y is None):
            raise ValueError("Unable to plot using mag_y data. The mag_y attribute is currently None, so either populate "
                             "it with data using the set_magnetic_latitudes() function, or choose a different y_type")

        # set y axis data, and y label
        y_axis_data = self.ccd_y
        if (y_type == "mag"):
            y_axis_data = self.mag_y
            if (ylabel is None):
                ylabel = "Magnetic latitude"
        elif (y_type == "geo"):
            y_axis_data = self.geo_y
            if (ylabel is None):
                ylabel = "Geographic latitude"
        else:
            if (ylabel is None):
                ylabel = "CCD Y"

        # print labels
        ax.set_xlabel(xlabel, fontsize=14)
        ax.set_ylabel(ylabel, fontsize=14)

        # generate x ticks and labels
        #
        # TODO: make this more dynamic
        if (xtick_increment is None):
            xtick_increment = 100  # assume data is 3 second cadence; good enough for now
        x_ticks = np.arange(0, self.data.shape[1], xtick_increment)
        x_labels = self.timestamp[::xtick_increment]
        for i in range(0, len(x_labels)):
            x_labels[i] = x_labels[i].strftime("%H:%M")  # type: ignore
        ax.set_xticks(x_ticks, x_labels)  # type: ignore

        # do check for ccd_y
        if (self.ccd_y is None):
            warnings.warn(
                "Unable to plot y-axis. If this keogram object was create as part of the custom_keogram " +
                "routines, this is expected and plotting a custom keogram with axes is not supported at this time.",
                stacklevel=1,
            )
        else:
            # generate y ticks and labels
            if (y_type == "ccd"):
                # TODO: make this more dynamic
                if (ytick_increment is None):
                    ytick_increment = 50

                # generate y ticks and labels
                y_ticks = y_axis_data[::ytick_increment]  # type: ignore
                y_labels = y_axis_data[::ytick_increment]  # type: ignore

                # apply yticks
                ax.set_yticks(y_ticks, y_labels)  # type: ignore
            elif (y_type == "geo" and self.geo_y is not None) or (y_type == "mag" and self.mag_y is not None):
                # set tick increments
                if (ytick_increment is None):
                    ytick_increment = 50

                # generate y ticks and labels
                y_ticks = self.ccd_y[25::ytick_increment]
                y_labels = np.round(
                    y_axis_data,  # type: ignore
                    1).astype(str)[25::ytick_increment]
                y_labels[np.where(y_labels == 'nan')] = ''

                # apply yticks
                ax.set_yticks(y_ticks, y_labels)
    else:
        # disable axes
        ax.set_axis_off()

    # save figure or show it
    if (savefig is True):
        # check that filename has been set
        if (savefig_filename is None):
            raise ValueError("The savefig_filename parameter is missing, but required since savefig was set to True.")

        # save the figure
        f_extension = os.path.splitext(savefig_filename)[-1].lower()
        if (".jpg" == f_extension or ".jpeg" == f_extension):
            # check quality setting
            if (savefig_quality is not None):
                plt.savefig(savefig_filename, quality=savefig_quality, bbox_inches="tight")
            else:
                plt.savefig(savefig_filename, bbox_inches="tight")
        else:
            if (savefig_quality is not None):
                # quality specified, but output filename is not a JPG, so show a warning
                warnings.warn("The savefig_quality parameter was specified, but is only used for saving JPG files. The " +
                              "savefig_filename parameter was determined to not be a JPG file, so the quality will be ignored",
                              stacklevel=1)
            plt.savefig(savefig_filename, bbox_inches="tight")

        # clean up by closing the figure
        plt.close(fig)
    elif (returnfig is True):
        # return the figure and axis objects
        return (fig, ax)
    else:
        # show the figure
        plt.show(fig)

        # cleanup by closing the figure
        plt.close(fig)

    # return
    return None

Generate a plot of the keogram data.

Either display it (default behaviour), save it to disk (using the savefig parameter), or return the matplotlib plot object for further usage (using the returnfig parameter).

Args

y_type : str

Type of y-axis to use when plotting. Options are ccd, mag, or geo. The default is ccd. This parameter is required.

title : str

The title to display above the plotted keogram.

figsize : tuple

The matplotlib figure size to use when plotting. For example figsize=(14,4).

cmap : str

The matplotlib colormap to use.

Commonly used colormaps are:

REGO: gist_heat
THEMIS ASI: gray
TREx Blue: Blues_r
TREx NIR: gray
TREx RGB: None

A list of all available colormaps can be found on the matplotlib documentation.

aspect : str or float

The matplotlib imshow aspect ration to use. A common value for this is auto. All valid values can be found on the matplotlib documentation.

axes_visible : bool

Display the axes. Default is True.

xlabel : str

The x-axis label to use. Default is Time (UTC).

ylabel : str

The y-axis label to use. Default is based on y_type.

xtick_increment : int

The x-axis tick increment to use. Default is 100.

ytick_increment : int

The y-axis tick increment to use. Default is 50.

returnfig : bool

Instead of displaying the image, return the matplotlib figure object. This allows for further plot manipulation, for example, adding labels or a title in a different location than the default.

Remember - if this parameter is supplied, be sure that you close your plot after finishing work with it. This can be achieved by doing plt.close(fig).

Note that this method cannot be used in combination with savefig.

savefig : bool

Save the displayed image to disk instead of displaying it. The parameter savefig_filename is required if this parameter is set to True. Defaults to False.

savefig_filename : str

Filename to save the image to. Must be specified if the savefig parameter is set to True.

savefig_quality : int

Returns

The displayed keogram, by default. If savefig is set to True, nothing will be returned. If returnfig is set to True, the plotting variables (fig, ax) will be returned.

Raises

ValueError: Issues with the y-axis choice.

def set_geographic_latitudes(self, skymap: pyucalgarysrs.data.classes.Skymap, altitude_km: int | float | None = None) ‑> None

Expand source code

def set_geographic_latitudes(self, skymap: Skymap, altitude_km: Optional[Union[int, float]] = None) -> None:
    """
    Set the geographic latitude values for this keogram, using the specified skymap 
    data. The data will be set to the geo_y attribute of this Keogram object, which
    can then be used for plotting and/or further analysis.

    Args:
        skymap (pyaurorax.data.ucalgary.Skymap): 
            The skymap object to use. This parameter is required.

        altitude_km (int): 
            The altitude to use, in kilometers. If not specified, it will use the default in the 
            skymap object. If the specified altitude is not valid, a ValueError will be raised.
    
    Returns:
        None. The Keogram object's `geo_y` attribute will be updated.

    Raises:
        ValueError: Issues with specified altitude.
    """
    # check for slice idx
    if (self.__slice_idx is None):
        raise ValueError("Unable to set the geographic latitudes since the slice_idx is None. If this keogram " +
                         "object was created as part of the custom_keogram routines or is a spectrogaph keogram, " +
                         "this is expected and performing this action is not supported at this time.")

    # determine altitude index to use
    if (altitude_km is not None):
        # Obtain lat/lon arrays from skymap
        if (altitude_km * 1000.0 in skymap.full_map_altitude):
            altitude_idx = np.where(altitude_km * 1000.0 == skymap.full_map_altitude)

            self.geo_y = np.squeeze(skymap.full_map_latitude[altitude_idx, :, self.__slice_idx]).copy()
        else:
            # Make sure altitude is in range that can be interpolated
            if (altitude_km * 1000.0 < skymap.full_map_altitude[0]) or (altitude_km * 1000.0 > skymap.full_map_altitude[2]):
                raise ValueError("Altitude " + str(altitude_km) + " outside valid range of " +
                                 str((skymap.full_map_altitude[0] / 1000.0, skymap.full_map_altitude[2] / 1000.0)))

            # Initialze empty lat/lon arrays
            lats = np.full(np.squeeze(skymap.full_map_latitude[0, :, :]).shape, np.nan, dtype=skymap.full_map_latitude[0, :, :].dtype)

            # Interpolate lats and lons at desired altitude
            for i in range(skymap.full_map_latitude.shape[1]):
                for j in range(skymap.full_map_latitude.shape[2]):
                    lats[i, j] = np.interp(altitude_km * 1000.0, skymap.full_map_altitude, skymap.full_map_latitude[:, i, j])

            self.geo_y = lats[:, self.__slice_idx].copy()
    else:
        # use default middle altitude
        self.geo_y = np.squeeze(skymap.full_map_latitude[1, :, self.__slice_idx]).copy()

Set the geographic latitude values for this keogram, using the specified skymap data. The data will be set to the geo_y attribute of this Keogram object, which can then be used for plotting and/or further analysis.

Args

skymap : Skymap: The skymap object to use. This parameter is required.
altitude_km : int: The altitude to use, in kilometers. If not specified, it will use the default in the skymap object. If the specified altitude is not valid, a ValueError will be raised.

Returns

None. The Keogram object's geo_y attribute will be updated.

Raises

ValueError: Issues with specified altitude.

def set_magnetic_latitudes(self, skymap: pyucalgarysrs.data.classes.Skymap, timestamp: datetime.datetime, altitude_km: int | float | None = None) ‑> None

Expand source code

def set_magnetic_latitudes(self, skymap: Skymap, timestamp: datetime.datetime, altitude_km: Optional[Union[int, float]] = None) -> None:
    """
    Set the magnetic latitude values for this keogram, using the specified skymap 
    data. AACGMv2 will be utilized to perform the calculations. The resulting data
    will be set to the mag_y attribute of this Keogram object, which can then be
    used for plotting and/or further analysis.

    Args:
        skymap (pyaurorax.data.ucalgary.Skymap): 
            The skymap object to use. This parameter is required.

        timestamp (datetime.datetime): 
            The timestamp to use when converting skymap data to magnetic coordinates. Utilizes
            AACGMv2 to do the conversion.

        altitude_km (int): 
            The altitude to use. If not specified, it will use the default in the skymap
            object. If the specified altitude is not valid, a ValueError will be raised.
    
    Returns:
        None. The Keogram object's `mag_y` attribute will be updated.

    Raises:
        ValueError: Issues with specified altitude.
    """
    # check for slice idx
    if (self.__slice_idx is None):
        raise ValueError("Unable to set the geographic latitudes since the slice_idx is None. If this keogram " +
                         "object was created as part of the custom_keogram routines or is a spectrogaph keogram, " +
                         "this is expected and performing this action is not supported at this time.")

    # determine altitude index to use
    if (altitude_km is not None):
        # Obtain lat/lon arrays from skymap
        if (altitude_km * 1000.0 in skymap.full_map_altitude):
            altitude_idx = np.where(altitude_km * 1000.0 == skymap.full_map_altitude)

            lats = np.squeeze(skymap.full_map_latitude[altitude_idx, :, :])
            lons = np.squeeze(skymap.full_map_longitude[altitude_idx, :, :])
            lons[np.where(lons > 180)] -= 360.0

        else:
            # Make sure altitude is in range that can be interpolated
            if (altitude_km * 1000.0 < skymap.full_map_altitude[0]) or (altitude_km * 1000.0 > skymap.full_map_altitude[2]):
                raise ValueError("Altitude " + str(altitude_km) + " outside valid range of " +
                                 str((skymap.full_map_altitude[0] / 1000.0, skymap.full_map_altitude[2] / 1000.0)))

            # Initialze empty lat/lon arrays
            lats = np.full(np.squeeze(skymap.full_map_latitude[0, :, :]).shape, np.nan, dtype=skymap.full_map_latitude[0, :, :].dtype)
            lons = lats.copy()

            # Interpolate lats and lons at desired altitude
            for i in range(skymap.full_map_latitude.shape[1]):
                for j in range(skymap.full_map_latitude.shape[2]):
                    lats[i, j] = np.interp(altitude_km * 1000.0, skymap.full_map_altitude, skymap.full_map_latitude[:, i, j])
                    lons[i, j] = np.interp(altitude_km * 1000.0, skymap.full_map_altitude, skymap.full_map_longitude[:, i, j])

            lons[np.where(lons > 180)] -= 360.0

        # Convert lats and lons to geomagnetic coordinates
        mag_lats, mag_lons, mag_alts = aacgmv2.convert_latlon_arr(lats.flatten(),
                                                                  lons.flatten(), (lons * 0.0).flatten(),
                                                                  timestamp,
                                                                  method_code='G2A')
        mag_lats = np.reshape(mag_lats, lats.shape)
        mag_lons = np.reshape(mag_lons, lons.shape)

        # Set the y axis to the desired slice index of the magnetic latitudes
        self.mag_y = mag_lats[:, self.__slice_idx].copy()
    else:
        # Convert middle altitude lats and lons to geomagnetic coordinates
        mag_lats, mag_lons, mag_alts = aacgmv2.convert_latlon_arr(np.squeeze(skymap.full_map_latitude[1, :, :]).flatten(),
                                                                  np.squeeze(skymap.full_map_longitude[1, :, :]).flatten(),
                                                                  (skymap.full_map_longitude[1, :, :] * 0.0).flatten(),
                                                                  timestamp,
                                                                  method_code='G2A')
        mag_lats = np.reshape(mag_lats, np.squeeze(skymap.full_map_latitude[1, :, :]).shape)
        mag_lons = np.reshape(mag_lons, np.squeeze(skymap.full_map_longitude[1, :, :]).shape)

        # Set the y axis to the desired slice index of the magnetic latitudes
        self.mag_y = mag_lats[:, self.__slice_idx].copy()

Set the magnetic latitude values for this keogram, using the specified skymap data. AACGMv2 will be utilized to perform the calculations. The resulting data will be set to the mag_y attribute of this Keogram object, which can then be used for plotting and/or further analysis.

Args

skymap : Skymap: The skymap object to use. This parameter is required.
timestamp : datetime.datetime: The timestamp to use when converting skymap data to magnetic coordinates. Utilizes AACGMv2 to do the conversion.
altitude_km : int: The altitude to use. If not specified, it will use the default in the skymap object. If the specified altitude is not valid, a ValueError will be raised.

Returns

None. The Keogram object's mag_y attribute will be updated.

Raises

ValueError: Issues with specified altitude.

class Montage (data: numpy.ndarray, timestamp: List[datetime.datetime], n_channels: int)

Expand source code

@dataclass
class Montage:
    """
    Class representation for a montage

    Attributes:
        data (numpy.ndarray): 
            The derived montage data.

        timestamp (List[datetime.datetime]): 
            Timestamps corresponding to each montage image.
    """

    def __init__(self, data: np.ndarray, timestamp: List[datetime.datetime], n_channels: int):
        # public vars
        self.data = data
        self.timestamp = timestamp

        # private vars
        self.__n_channels = n_channels

    def __str__(self) -> str:
        return self.__repr__()

    def __repr__(self) -> str:
        data_str = "array(dims=%s, dtype=%s)" % (self.data.shape, self.data.dtype)
        timestamp_str = "[%d datetime objects]" % (len(self.timestamp))

        return "Montage(data=%s, timestamp=%s)" % (data_str, timestamp_str)

    def plot(self,
             rows: int,
             cols: int,
             figsize: Optional[Tuple[int, int]] = None,
             cmap: Optional[str] = None,
             timestamps_display: bool = True,
             timestamps_format: str = "%Y-%m-%d %H:%M:%S",
             timestamps_fontsize: int = 11,
             returnfig: bool = False,
             savefig: bool = False,
             savefig_filename: Optional[str] = None,
             savefig_quality: Optional[int] = None) -> Any:
        """
        Generate a plot of the montage data. 
        
        Either display it (default behaviour), save it to disk (using the `savefig` parameter), or 
        return the matplotlib plot object for further usage (using the `returnfig` parameter).

        Args:
            figsize (tuple): 
                The matplotlib figure size to use when plotting. For example `figsize=(14,4)`.

            cmap (str): 
                The matplotlib colormap to use.

                Commonly used colormaps are:

                - REGO: `gist_heat`
                - THEMIS ASI: `gray`
                - TREx Blue: `Blues_r`
                - TREx NIR: `gray`
                - TREx RGB: `None`

                A list of all available colormaps can be found on the 
                [matplotlib documentation](https://matplotlib.org/stable/gallery/color/colormap_reference.html).
            
            returnfig (bool): 
                Instead of displaying the image, return the matplotlib figure object. This allows for further plot 
                manipulation, for example, adding labels or a title in a different location than the default. 
                
                Remember - if this parameter is supplied, be sure that you close your plot after finishing work 
                with it. This can be achieved by doing `plt.close(fig)`. 
                
                Note that this method cannot be used in combination with `savefig`.

            savefig (bool): 
                Save the displayed image to disk instead of displaying it. The parameter savefig_filename is required if 
                this parameter is set to True. Defaults to `False`.

            savefig_filename (str): 
                Filename to save the image to. Must be specified if the savefig parameter is set to True.

            savefig_quality (int): 
                Quality level of the saved image. This can be specified if the savefig_filename is a JPG image. If it
                is a PNG, quality is ignored. Default quality level for JPGs is matplotlib/Pillow's default of 75%.

        Returns:
            The displayed montage, by default. If `savefig` is set to True, nothing will be returned. If `returnfig` is 
            set to True, the plotting variables `(fig, ax)` will be returned.

        Raises:
            ValueError: Issues with the y-axis choice.
        """
        # check return mode
        if (returnfig is True and savefig is True):
            raise ValueError("Only one of returnfig or savefig can be set to True")
        if (returnfig is True and (savefig_filename is not None or savefig_quality is not None)):
            warnings.warn("The figure will be returned, but a savefig option parameter was supplied. Consider " +
                          "removing the savefig option parameter(s) as they will be ignored.",
                          stacklevel=1)
        elif (savefig is False and (savefig_filename is not None or savefig_quality is not None)):
            warnings.warn("A savefig option parameter was supplied, but the savefig parameter is False. The " +
                          "savefig option parameters will be ignored.",
                          stacklevel=1)

        # init figure
        fig, axs = plt.subplots(nrows=rows, ncols=cols, figsize=figsize)

        # for each image
        for ax, i in zip(axs.flat, range(0, len(self.timestamp))):  # type: ignore
            if (self.__n_channels == 1):
                # single channel
                ax.imshow(self.data[:, :, i], cmap=cmap, origin="lower", interpolation="nearest")
            elif (self.__n_channels == 3):
                # single channel
                ax.imshow(self.data[:, :, :, i], cmap=cmap, origin="lower", interpolation="nearest")
            else:
                raise ValueError("Can only plot 3 or 4 dimensional data (series of single-channel or RGB mages), but found data of shape %s" %
                                 (self.data.shape))

            ax.set_axis_off()

            # show timestamp
            if (timestamps_display is True):
                ax.text(
                    int(np.floor(self.data.shape[1] / 2.)),
                    5,
                    self.timestamp[i].strftime(timestamps_format),
                    ha="center",
                    fontsize=timestamps_fontsize,
                )
        plt.tight_layout(h_pad=0, w_pad=0)

        # save figure or show it
        if (savefig is True):
            # check that filename has been set
            if (savefig_filename is None):
                raise ValueError("The savefig_filename parameter is missing, but required since savefig was set to True.")

            # save the figure
            f_extension = os.path.splitext(savefig_filename)[-1].lower()
            if (".jpg" == f_extension or ".jpeg" == f_extension):
                # check quality setting
                if (savefig_quality is not None):
                    plt.savefig(savefig_filename, quality=savefig_quality, bbox_inches="tight")
                else:
                    plt.savefig(savefig_filename, bbox_inches="tight")
            else:
                if (savefig_quality is not None):
                    # quality specified, but output filename is not a JPG, so show a warning
                    warnings.warn("The savefig_quality parameter was specified, but is only used for saving JPG files. The " +
                                  "savefig_filename parameter was determined to not be a JPG file, so the quality will be ignored",
                                  stacklevel=1)
                plt.savefig(savefig_filename, bbox_inches="tight")

            # clean up by closing the figure
            plt.close(fig)
        elif (returnfig is True):
            # return the figure and axis objects
            return (fig, axs)
        else:
            # show the figure
            plt.show(fig)

            # cleanup by closing the figure
            plt.close(fig)

        # return
        return None

Class representation for a montage

Attributes

data : numpy.ndarray: The derived montage data.
timestamp : List[datetime.datetime]: Timestamps corresponding to each montage image.

Methods

def plot(self, rows: int, cols: int, figsize: Tuple[int, int] | None = None, cmap: str | None = None, timestamps_display: bool = True, timestamps_format: str = '%Y-%m-%d %H:%M:%S', timestamps_fontsize: int = 11, returnfig: bool = False, savefig: bool = False, savefig_filename: str | None = None, savefig_quality: int | None = None) ‑> Any

Expand source code

def plot(self,
         rows: int,
         cols: int,
         figsize: Optional[Tuple[int, int]] = None,
         cmap: Optional[str] = None,
         timestamps_display: bool = True,
         timestamps_format: str = "%Y-%m-%d %H:%M:%S",
         timestamps_fontsize: int = 11,
         returnfig: bool = False,
         savefig: bool = False,
         savefig_filename: Optional[str] = None,
         savefig_quality: Optional[int] = None) -> Any:
    """
    Generate a plot of the montage data. 
    
    Either display it (default behaviour), save it to disk (using the `savefig` parameter), or 
    return the matplotlib plot object for further usage (using the `returnfig` parameter).

    Args:
        figsize (tuple): 
            The matplotlib figure size to use when plotting. For example `figsize=(14,4)`.

        cmap (str): 
            The matplotlib colormap to use.

            Commonly used colormaps are:

            - REGO: `gist_heat`
            - THEMIS ASI: `gray`
            - TREx Blue: `Blues_r`
            - TREx NIR: `gray`
            - TREx RGB: `None`

            A list of all available colormaps can be found on the 
            [matplotlib documentation](https://matplotlib.org/stable/gallery/color/colormap_reference.html).
        
        returnfig (bool): 
            Instead of displaying the image, return the matplotlib figure object. This allows for further plot 
            manipulation, for example, adding labels or a title in a different location than the default. 
            
            Remember - if this parameter is supplied, be sure that you close your plot after finishing work 
            with it. This can be achieved by doing `plt.close(fig)`. 
            
            Note that this method cannot be used in combination with `savefig`.

        savefig (bool): 
            Save the displayed image to disk instead of displaying it. The parameter savefig_filename is required if 
            this parameter is set to True. Defaults to `False`.

        savefig_filename (str): 
            Filename to save the image to. Must be specified if the savefig parameter is set to True.

        savefig_quality (int): 
            Quality level of the saved image. This can be specified if the savefig_filename is a JPG image. If it
            is a PNG, quality is ignored. Default quality level for JPGs is matplotlib/Pillow's default of 75%.

    Returns:
        The displayed montage, by default. If `savefig` is set to True, nothing will be returned. If `returnfig` is 
        set to True, the plotting variables `(fig, ax)` will be returned.

    Raises:
        ValueError: Issues with the y-axis choice.
    """
    # check return mode
    if (returnfig is True and savefig is True):
        raise ValueError("Only one of returnfig or savefig can be set to True")
    if (returnfig is True and (savefig_filename is not None or savefig_quality is not None)):
        warnings.warn("The figure will be returned, but a savefig option parameter was supplied. Consider " +
                      "removing the savefig option parameter(s) as they will be ignored.",
                      stacklevel=1)
    elif (savefig is False and (savefig_filename is not None or savefig_quality is not None)):
        warnings.warn("A savefig option parameter was supplied, but the savefig parameter is False. The " +
                      "savefig option parameters will be ignored.",
                      stacklevel=1)

    # init figure
    fig, axs = plt.subplots(nrows=rows, ncols=cols, figsize=figsize)

    # for each image
    for ax, i in zip(axs.flat, range(0, len(self.timestamp))):  # type: ignore
        if (self.__n_channels == 1):
            # single channel
            ax.imshow(self.data[:, :, i], cmap=cmap, origin="lower", interpolation="nearest")
        elif (self.__n_channels == 3):
            # single channel
            ax.imshow(self.data[:, :, :, i], cmap=cmap, origin="lower", interpolation="nearest")
        else:
            raise ValueError("Can only plot 3 or 4 dimensional data (series of single-channel or RGB mages), but found data of shape %s" %
                             (self.data.shape))

        ax.set_axis_off()

        # show timestamp
        if (timestamps_display is True):
            ax.text(
                int(np.floor(self.data.shape[1] / 2.)),
                5,
                self.timestamp[i].strftime(timestamps_format),
                ha="center",
                fontsize=timestamps_fontsize,
            )
    plt.tight_layout(h_pad=0, w_pad=0)

    # save figure or show it
    if (savefig is True):
        # check that filename has been set
        if (savefig_filename is None):
            raise ValueError("The savefig_filename parameter is missing, but required since savefig was set to True.")

        # save the figure
        f_extension = os.path.splitext(savefig_filename)[-1].lower()
        if (".jpg" == f_extension or ".jpeg" == f_extension):
            # check quality setting
            if (savefig_quality is not None):
                plt.savefig(savefig_filename, quality=savefig_quality, bbox_inches="tight")
            else:
                plt.savefig(savefig_filename, bbox_inches="tight")
        else:
            if (savefig_quality is not None):
                # quality specified, but output filename is not a JPG, so show a warning
                warnings.warn("The savefig_quality parameter was specified, but is only used for saving JPG files. The " +
                              "savefig_filename parameter was determined to not be a JPG file, so the quality will be ignored",
                              stacklevel=1)
            plt.savefig(savefig_filename, bbox_inches="tight")

        # clean up by closing the figure
        plt.close(fig)
    elif (returnfig is True):
        # return the figure and axis objects
        return (fig, axs)
    else:
        # show the figure
        plt.show(fig)

        # cleanup by closing the figure
        plt.close(fig)

    # return
    return None

Generate a plot of the montage data.

Either display it (default behaviour), save it to disk (using the savefig parameter), or return the matplotlib plot object for further usage (using the returnfig parameter).

Args

figsize : tuple

The matplotlib figure size to use when plotting. For example figsize=(14,4).

cmap : str

The matplotlib colormap to use.

Commonly used colormaps are:

REGO: gist_heat
THEMIS ASI: gray
TREx Blue: Blues_r
TREx NIR: gray
TREx RGB: None

A list of all available colormaps can be found on the matplotlib documentation.

returnfig : bool

Instead of displaying the image, return the matplotlib figure object. This allows for further plot manipulation, for example, adding labels or a title in a different location than the default.

Remember - if this parameter is supplied, be sure that you close your plot after finishing work with it. This can be achieved by doing plt.close(fig).

Note that this method cannot be used in combination with savefig.

savefig : bool

Save the displayed image to disk instead of displaying it. The parameter savefig_filename is required if this parameter is set to True. Defaults to False.

savefig_filename : str

Filename to save the image to. Must be specified if the savefig parameter is set to True.

savefig_quality : int

Returns

The displayed montage, by default. If savefig is set to True, nothing will be returned. If returnfig is set to True, the plotting variables (fig, ax) will be returned.

Raises

ValueError: Issues with the y-axis choice.

class Mosaic (polygon_data: matplotlib.collections.PolyCollection | List[matplotlib.collections.PolyCollection], cartopy_projection: cartopy.crs.Projection, contour_data: Dict[str, List[Any]] | None = None, spect_cmap: str | None = None, spect_intensity_scale: Tuple[int, int] | None = None)

Expand source code

@dataclass
class Mosaic:
    """
    Class representation for a generated mosaic.

    Attributes:
        polygon_data (matplotlib.collections.PolyCollection): 
            Generated polygons containing rendered data.

        cartopy_projection (cartopy.crs.Projection): 
            Cartopy projection to utilize.

        contour_data (Dict[str, List[Any]]): 
            Generated contour data.

        spect_cmap (str): 
            String giving the cmap to use for spect legend.
            
        spect_intensity_scale (Tuple[int]): 
            The min and max values that spectrograph data
            is scaled to in the mosaic, if any is present.
    """
    polygon_data: Union[PolyCollection, List[PolyCollection]]
    cartopy_projection: Projection
    contour_data: Optional[Dict[str, List[Any]]] = None
    spect_cmap: Optional[str] = None
    spect_intensity_scale: Optional[Tuple[int, int]] = None

    def __str__(self) -> str:
        return self.__repr__()

    def __repr__(self) -> str:
        if isinstance(self.polygon_data, list):
            polycollection_str = "[PolyCollection(...), ...]"
        else:
            polycollection_str = "PolyCollection(...)"

        if self.contour_data is not None:
            return "Mosaic(polygon_data=PolyCollection(...), cartopy_projection=Projection(%s), %s Contours)" % (
                self.cartopy_projection.to_string(),
                len(self.contour_data.get("x", [])),
            )
        else:
            return "Mosaic(polygon_data=" + polycollection_str + ", cartopy_projection=Projection(%s))" % (self.cartopy_projection.to_string())

    def plot(self,
             map_extent: Sequence[Union[float, int]],
             figsize: Optional[Tuple[int, int]] = None,
             rayleighs: bool = False,
             max_rayleighs: int = 20000,
             title: Optional[str] = None,
             colorbar_title: Optional[str] = None,
             ocean_color: Optional[str] = None,
             land_color: str = "gray",
             land_edgecolor: str = "#8A8A8A",
             borders_color: str = "#AEAEAE",
             borders_disable: bool = False,
             cbar_colormap: str = "",
             returnfig: bool = False,
             savefig: bool = False,
             savefig_filename: Optional[str] = None,
             savefig_quality: Optional[int] = None) -> Any:
        """
        Generate a plot of the mosaic data. 
        
        Either display it (default behaviour), save it to disk (using the `savefig` parameter), or 
        return the matplotlib plot object for further usage (using the `returnfig` parameter).

        Args:
            map_extent (List[int]): 
                Latitude/longitude range to be visible on the rendered map. This is a list of 4 integers 
                and/or floats, in the order of [min_lon, max_lon, min_lat, max_lat].

            figsize (tuple): 
                The matplotlib figure size to use when plotting. For example `figsize=(14,4)`.

            rayleighs (bool): 
                Set to `True` if the data being plotted is in Rayleighs. Defaults to `False`.

            max_rayleighs (int): 
                Max intensity scale for Rayleighs. Defaults to `20000`.

            ocean_color (str): 
                Colour of the ocean. Default is cartopy's default shade of blue. Colours can be supplied
                as a word, or hexcode prefixed with a '#' character (ie. `#55AADD`).
            
            land_color (str): 
                Colour of the land. Default is `gray`. Colours can be supplied as a word, or hexcode 
                prefixed with a '#' character (ie. `#41BB87`).

            land_edgecolor (str): 
                Color of the land edges. Default is `#8A8A8A`. Colours can be supplied as a word, or
                hexcode prefixed with a '#' character.

            borders_color (str): 
                Color of the country borders. Default is `AEAEAE`. Colours can be supplied as a word, or
                hexcode prefixed with a '#' character.
            
            borders_disable (bool): 
                Disbale rendering of the borders. Default is `False`.

            cbar_colorcmap (str): 
                The matplotlib colormap to use for the plotted color bar. Default is `gray`, unless
                mosaic was created with spectrograph data, in which case defaults to the colormap
                used for spectrograph data..

                Commonly used colormaps are:

                - REGO: `gist_heat`
                - THEMIS ASI: `gray`
                - TREx Blue: `Blues_r`
                - TREx NIR: `gray`
                - TREx RGB: `None`

                A list of all available colormaps can be found on the 
                [matplotlib documentation](https://matplotlib.org/stable/gallery/color/colormap_reference.html).

            returnfig (bool): 
                Instead of displaying the image, return the matplotlib figure object. This allows for further plot 
                manipulation, for example, adding labels or a title in a different location than the default. 
                
                Remember - if this parameter is supplied, be sure that you close your plot after finishing work 
                with it. This can be achieved by doing `plt.close(fig)`. 
                
                Note that this method cannot be used in combination with `savefig`.

            savefig (bool): 
                Save the displayed image to disk instead of displaying it. The parameter savefig_filename is required if 
                this parameter is set to True. Defaults to `False`.

            savefig_filename (str): 
                Filename to save the image to. Must be specified if the savefig parameter is set to True.

            savefig_quality (int): 
                Quality level of the saved image. This can be specified if the savefig_filename is a JPG image. If it
                is a PNG, quality is ignored. Default quality level for JPGs is matplotlib/Pillow's default of 75%.

        Returns:
            The displayed montage, by default. If `savefig` is set to True, nothing will be returned. If `returnfig` is 
            set to True, the plotting variables `(fig, ax)` will be returned.

        Raises:
        """
        # check return mode
        if (returnfig is True and savefig is True):
            raise ValueError("Only one of returnfig or savefig can be set to True")
        if returnfig is True and (savefig_filename is not None or savefig_quality is not None):
            warnings.warn("The figure will be returned, but a savefig option parameter was supplied. Consider " +
                          "removing the savefig option parameter(s) as they will be ignored.",
                          stacklevel=1)
        elif (savefig is False and (savefig_filename is not None or savefig_quality is not None)):
            warnings.warn("A savefig option parameter was supplied, but the savefig parameter is False. The " +
                          "savefig option parameters will be ignored.",
                          stacklevel=1)

        # Get colormap if there is spectrograph data
        if self.spect_cmap is not None:
            cbar_colormap = self.spect_cmap

        # initialize figure
        fig = plt.figure(figsize=figsize)
        ax = fig.add_axes((0, 0, 1, 1), projection=self.cartopy_projection)
        ax.set_extent(map_extent, crs=cartopy.crs.Geodetic())  # type: ignore

        # add ocean
        #
        # NOTE: we use the default ocean color
        if (ocean_color is not None):
            ax.add_feature(  # type: ignore
                cartopy.feature.OCEAN, facecolor=ocean_color, zorder=0)
        else:
            ax.add_feature(cartopy.feature.OCEAN, zorder=0)  # type: ignore

        # add land
        ax.add_feature(  # type: ignore
            cartopy.feature.LAND, facecolor=land_color, edgecolor=land_edgecolor, zorder=0)

        # add borders
        if (borders_disable is False):
            ax.add_feature(  # type: ignore
                cartopy.feature.BORDERS, edgecolor=borders_color, zorder=0)

        # add polygon data
        #
        # NOTE: it seems that when running this function a second time, the polygon
        # data is not too happy. So to handle this, we plot a copy of the polygon data
        if isinstance(self.polygon_data, list):
            for polygon_data in self.polygon_data:
                ax.add_collection(copy(polygon_data))
        else:
            ax.add_collection(copy(self.polygon_data))

        if self.contour_data is not None:
            for i in range(len(self.contour_data["x"])):
                ax.plot(self.contour_data["x"][i],
                        self.contour_data["y"][i],
                        color=self.contour_data["color"][i],
                        linewidth=self.contour_data["linewidth"][i],
                        linestyle=self.contour_data["linestyle"][i],
                        marker=self.contour_data["marker"][i],
                        zorder=self.contour_data["zorder"][i])

        # set title
        if (title is not None):
            ax.set_title(title)

        # add text
        if (rayleighs is True):
            if isinstance(self.polygon_data, list):
                raise ValueError("Rayleighs Keyword is currently not available for mosaics with multiple sets of data.")

            # Create a colorbar, in Rayleighs, that accounts for the scaling limit we applied
            cbar_ticks = [float(j) / 5. for j in range(0, 6)]
            cbar_ticknames = [str(int(max_rayleighs / 5) * j) for j in range(0, 6)]

            # Any pixels with the max Rayleigh value could be greater than it, so we include the plus sign
            cbar_ticknames[-1] += "+"
            self.polygon_data.set_cmap(cbar_colormap)
            cbar = plt.colorbar(self.polygon_data, shrink=0.5, ticks=cbar_ticks, ax=ax)
            cbar.ax.set_yticklabels(cbar_ticknames)
            plt.text(1.025,
                     0.5,
                     "Intensity (Rayleighs)",
                     fontsize=14,
                     transform=ax.transAxes,
                     va="center",
                     rotation="vertical",
                     weight="bold",
                     style="oblique")

        if (self.spect_cmap) is not None:

            if (self.spect_intensity_scale) is None:
                intensity_max = np.nan
                intensity_min = np.nan
            else:
                intensity_max = self.spect_intensity_scale[1]
                intensity_min = self.spect_intensity_scale[0]

            # Create a colorbar, in Rayleighs, that accounts for the scaling limit we applied
            cbar_ticks = [float(j) / 5. for j in range(0, 6)]
            cbar_ticknames = [str(int((intensity_max / 5) + intensity_min) * j) for j in range(0, 6)]

            # Any specrograph bins with the max intensity value could be greater than it, so we include the plus sign
            cbar_ticknames[-1] += "+"
            if isinstance(self.polygon_data, list):
                self.polygon_data[0].set_cmap(cbar_colormap)
            else:
                self.polygon_data.set_cmap(cbar_colormap)
            if isinstance(self.polygon_data, list):
                cbar = plt.colorbar(self.polygon_data[0], shrink=0.5, ticks=cbar_ticks, ax=ax)
            else:
                cbar = plt.colorbar(self.polygon_data, shrink=0.5, ticks=cbar_ticks, ax=ax)
            cbar.ax.set_yticklabels(cbar_ticknames)
            if colorbar_title is None:
                plt.text(1.025,
                         0.5,
                         "Spectrograph Intensity (Rayleighs)",
                         fontsize=10,
                         transform=ax.transAxes,
                         va="center",
                         rotation="vertical",
                         weight="bold",
                         style="oblique")
            else:
                plt.text(1.025,
                         0.5,
                         colorbar_title,
                         fontsize=10,
                         transform=ax.transAxes,
                         va="center",
                         rotation="vertical",
                         weight="bold",
                         style="oblique")

        # save figure or show it
        if (savefig is True):
            # check that filename has been set
            if (savefig_filename is None):
                raise ValueError("The savefig_filename parameter is missing, but required since savefig was set to True.")

            # save the figure
            f_extension = os.path.splitext(savefig_filename)[-1].lower()
            if (".jpg" == f_extension or ".jpeg" == f_extension):
                # check quality setting
                if (savefig_quality is not None):
                    plt.savefig(savefig_filename, quality=savefig_quality, bbox_inches="tight")
                else:
                    plt.savefig(savefig_filename, bbox_inches="tight")
            else:
                if (savefig_quality is not None):
                    # quality specified, but output filename is not a JPG, so show a warning
                    warnings.warn("The savefig_quality parameter was specified, but is only used for saving JPG files. The " +
                                  "savefig_filename parameter was determined to not be a JPG file, so the quality will be ignored",
                                  stacklevel=1)
                plt.savefig(savefig_filename, bbox_inches="tight")

            # clean up by closing the figure
            plt.close(fig)
        elif (returnfig is True):
            # return the figure and axis objects
            return (fig, ax)
        else:
            # show the figure
            plt.show(fig)

            # cleanup by closing the figure
            plt.close(fig)

        # return
        return None

    def add_geo_contours(self,
                         lats: Optional[Union[ndarray, list]] = None,
                         lons: Optional[Union[ndarray, list]] = None,
                         constant_lats: Optional[Union[float, int, Sequence[Union[float, int]], ndarray]] = None,
                         constant_lons: Optional[Union[float, int, Sequence[Union[float, int]], ndarray]] = None,
                         color: str = "black",
                         linewidth: Union[float, int] = 1,
                         linestyle: str = "solid",
                         marker: str = "",
                         bring_to_front: bool = False):
        """
        Add geographic contours to a mosaic.

        Args:
            lats (ndarray or list):
                Sequence of geographic latitudes defining a contour.
            
            lons (ndarray or list):
                Sequence of geographic longitudes defining a contour.

            constant_lats (float, int, or Sequence):
                Geographic Latitude(s) at which to add line(s) of constant latitude.
            
            constant_lons (float, int, or Sequence):
                Geographic Longitude(s) at which to add line(s) of constant longitude.

            color (str):
                The matplotlib color used for the contour(s).

            linewidth (float or int):
                The contour thickness.
            
            linestyle (str):
                The matplotlib linestyle used for the contour(s).

            marker (str):
                The matplotlib marker used for the contour(s).

        Returns:
            The object's contour_data parameter is populated appropriately.

        Raises:
            ValueError: issues encountered with supplied parameters.
        """
        # Make sure some form of lat/lon is provided
        if (constant_lats is None) and (constant_lons is None) and (lats is None) and (lons is None):
            raise ValueError("No latitudes or longitudes provided.")

        # If manually passing in lats & lons, make sure both are provided
        if (lats is not None or lons is not None) and (lats is None or lons is None):
            raise (ValueError("Manually supplying contour requires both lats and lons."))

        # Check that color exists in matplotlib
        if color not in matplotlib.colors.CSS4_COLORS:
            raise ValueError(f"Color '{color}' not recognized by matplotlib.")

        # Check that linestyle is valid
        if linestyle not in ["-", "--", "-.", ":", "solid", "dashed", "dashdot", "dotted"]:
            raise ValueError(f"Linestyle '{linestyle}' not recognized by matplotlib.")

        # Check that linewidth is valid
        if linewidth <= 0:
            raise ValueError("Linewidth must be greater than zero.")

        # Check that marker is valid
        if marker not in ["", "o", ".", "p", "*", "x", "+", "X"]:
            raise ValueError(f"Marker '{marker}' is not currently supported.")

        # Convert numerics to lists if necessary
        if constant_lats is not None:
            if isinstance(constant_lats, (float, int)):
                constant_lats = [constant_lats]
        if constant_lons is not None:
            if isinstance(constant_lons, (float, int)):
                constant_lons = [constant_lons]

        # Initialize contour data dict if it doesn't exist yet
        if self.contour_data is None:
            self.contour_data = {"x": [], "y": [], "color": [], "linewidth": [], "linestyle": [], "marker": [], "zorder": []}

        # Obtain the mosaic's projection
        source_proj = pyproj.CRS.from_user_input(cartopy.crs.Geodetic())
        mosaic_proj = pyproj.CRS.from_user_input(self.cartopy_projection)
        transformer = pyproj.Transformer.from_crs(source_proj, mosaic_proj, always_xy=True)

        # First handling manually supplied lat/lon arrays
        if (lats is not None) and (lons is not None):
            # Convert lists to ndarrays if necessary
            if isinstance(lats, list):
                lats = np.array(lats)
            if isinstance(lons, list):
                lons = np.array(lons)

            if len(lats) != len(lons):
                raise ValueError("Lat/Lon data must be of the same size.")

            # Create specified contour from geographic coords
            x, y = transformer.transform(lons, lats)
            # Add contour to dict, along with color and linewidth
            self.contour_data["x"].append(x)
            self.contour_data["y"].append(y)
            self.contour_data["color"].append(color)
            self.contour_data["linewidth"].append(linewidth)
            self.contour_data["linestyle"].append(linestyle)
            self.contour_data["marker"].append(marker)
            self.contour_data["zorder"].append(int(bring_to_front))

        # Next handling lines of constant latitude
        if constant_lats is not None:
            # Generate longitudinal domain of the lat line (full globe)
            lon_domain = np.arange(-180, 180 + 0.2, 0.2)

            # Iterate through all lines of constant lat requested
            for lat in constant_lats:
                # Create line of constant lat
                const_lat_x, const_lat_y = (lon_domain, lon_domain * 0 + lat)
                sort_idx = np.argsort(const_lat_x)
                const_lat_y = const_lat_y[sort_idx]
                const_lat_x = const_lat_x[sort_idx]
                const_lat_x, const_lat_y = transformer.transform(const_lat_x, const_lat_y)
                # Add contour to dict, along with color and linewidth
                self.contour_data["x"].append(const_lat_x)
                self.contour_data["y"].append(const_lat_y)
                self.contour_data["color"].append(color)
                self.contour_data["linewidth"].append(linewidth)
                self.contour_data["linestyle"].append(linestyle)
                self.contour_data["marker"].append(marker)
                self.contour_data["zorder"].append(int(bring_to_front))

        # Now handling lines of constant longitude
        if constant_lons is not None:
            # Generate latitudinal domain of the lon line (full globe)
            lat_domain = np.arange(-90, 90 + 0.1, 0.1)

            # Iterate through all lines of constant lon requested
            for lon in constant_lons:
                # Create line of constant lon and add to dict
                const_lon_x, const_lon_y = (lat_domain * 0 + lon, lat_domain)
                sort_idx = np.argsort(const_lon_y)
                const_lon_x = const_lon_x[sort_idx]
                const_lon_y = const_lon_y[sort_idx]
                const_lon_x, const_lon_y = transformer.transform(const_lon_x, const_lon_y)

                # Add contour to dict, along with color and linewidth
                self.contour_data["x"].append(const_lon_x)
                self.contour_data["y"].append(const_lon_y)
                self.contour_data["color"].append(color)
                self.contour_data["linewidth"].append(linewidth)
                self.contour_data["linestyle"].append(linestyle)
                self.contour_data["marker"].append(marker)
                self.contour_data["zorder"].append(int(bring_to_front))

    def add_mag_contours(self,
                         timestamp: datetime.datetime,
                         constant_lats: Optional[Union[float, int, Sequence[Union[float, int]], ndarray]] = None,
                         constant_lons: Optional[Union[float, int, Sequence[Union[float, int]], ndarray]] = None,
                         lats: Optional[Union[ndarray, list]] = None,
                         lons: Optional[Union[ndarray, list]] = None,
                         color: str = "black",
                         linewidth: Union[float, int] = 1,
                         linestyle: str = "solid",
                         marker: str = "",
                         bring_to_front: bool = False):
        """
        Add geomagnetic contours to a mosaic.

        Args:
            timestamp (datetime.datetime):
                The timestamp used in computing AACGM coordinates.

            lats (ndarray or list):
                Sequence of geomagnetic latitudes defining a contour.
            
            lons (ndarray or list):
                Sequence of geomagnetic longitudes defining a contour.

            constant_lats (float, int, Sequence):
                Geomagnetic latitude(s) at which to add contour(s) of constant latitude.
            
            constant_lons (float, int, Sequence):
                Geomagnetic longitude(s) at which to add contours(s) of constant longitude.

            color (str):
                The matplotlib color used for the contour(s).

            linewidth (float or int):
                The contour thickness.

            linestyle (str):
                The matplotlib linestyle used for the contour(s).

            marker (str):
                The matplotlib marker used for the contour(s).

        Returns:
            The object's contour_data parameter is populated appropriately.

        Raises:
            ValueError: issues encountered with supplied parameters.
        """
        # Make sure some form of lat/lon is provided
        if (constant_lats is None) and (constant_lons is None) and (lats is None) and (lons is None):
            raise ValueError("No latitudes or longitudes provided.")

        # If manually passing in lats & lons, make sure both are provided
        if (lats is not None or lons is not None) and (lats is None or lons is None):
            raise (ValueError("Manually supplying contour requires both lats and lons."))

        # Check that color exists in matplotlib
        if color not in matplotlib.colors.CSS4_COLORS:
            raise ValueError(f"Color '{color}' not recognized by matplotlib.")

        # Check that linestyle is valid
        if linestyle not in ["-", "--", "-.", ":", "solid", "dashed", "dashdot", "dotted"]:
            raise ValueError(f"Linestyle '{linestyle}' not recognized by matplotlib.")

        # Check that linewidth is valid
        if linewidth <= 0:
            raise ValueError("linewidth must be greater than zero.")

        # Convert numerics to lists if necessary
        if constant_lats is not None:
            if isinstance(constant_lats, (float, int)):
                constant_lats = [constant_lats]
        if constant_lons is not None:
            if isinstance(constant_lons, (float, int)):
                constant_lons = [constant_lons]

        # Initialize contour data dict if it doesn't exist yet
        if self.contour_data is None:
            self.contour_data = {"x": [], "y": [], "color": [], "linewidth": [], "linestyle": [], "marker": [], "zorder": []}

        # Obtain the mosaic's projection
        source_proj = pyproj.CRS.from_user_input(cartopy.crs.Geodetic())
        mosaic_proj = pyproj.CRS.from_user_input(self.cartopy_projection)
        transformer = pyproj.Transformer.from_crs(source_proj, mosaic_proj, always_xy=True)

        # First handling manually supplied lat/lon arrays
        if (lats is not None) and (lons is not None):
            # Convert lists to ndarrays if necessary
            if isinstance(lats, list):
                lats = np.array(lats)
            if isinstance(lons, list):
                lons = np.array(lons)

            if len(lats) != len(lons):
                raise ValueError("Lat/Lon data must be of the same size.")

            # Create specified contour from magnetic coords
            y, x, alt = aacgmv2.convert_latlon_arr(lats, lons, lats * 0.0, timestamp, method_code="A2G")
            x, y = transformer.transform(x, y)
            # Add contour to dict, along with color and linewidth
            self.contour_data["x"].append(x)
            self.contour_data["y"].append(y)
            self.contour_data["color"].append(color)
            self.contour_data["linewidth"].append(linewidth)
            self.contour_data["linestyle"].append(linestyle)
            self.contour_data["marker"].append(marker)
            self.contour_data["zorder"].append(int(bring_to_front))

        # Next handling lines of constant latitude
        if constant_lats is not None:
            # Generate longitudinal domain of the lat line (full globe)
            lon_domain = np.arange(-180, 180 + 0.2, 0.2)

            # iterate through all lines of constant lat requested
            for lat in constant_lats:
                # Create line of constant lat from magnetic coords
                const_lat_x, const_lat_y = (lon_domain, lon_domain * 0 + lat)
                const_lat_y, const_lat_x, alt = aacgmv2.convert_latlon_arr(const_lat_y, const_lat_x, const_lat_x * 0.0, timestamp, method_code="A2G")
                sort_idx = np.argsort(const_lat_x)
                const_lat_y = const_lat_y[sort_idx]
                const_lat_x = const_lat_x[sort_idx]
                const_lat_x, const_lat_y = transformer.transform(const_lat_x, const_lat_y)

                # Add contour to dict, along with color and linewidth
                self.contour_data["x"].append(const_lat_x)
                self.contour_data["y"].append(const_lat_y)
                self.contour_data["color"].append(color)
                self.contour_data["linewidth"].append(linewidth)
                self.contour_data["linestyle"].append(linestyle)
                self.contour_data["marker"].append(marker)
                self.contour_data["zorder"].append(int(bring_to_front))

        # Now handling lines of constant longitude
        if constant_lons is not None:
            # Generate latitudinal domain of the lon line (full globe)
            lat_domain = np.arange(-90, 90 + 0.1, 0.1)

            # iterate through all lines of constant lon requested
            for lon in constant_lons:
                # Create line of constant lon from magnetic coords
                const_lon_x, const_lon_y = (lat_domain * 0 + lon, lat_domain)
                const_lon_y, const_lon_x, alt = aacgmv2.convert_latlon_arr(const_lon_y, const_lon_x, const_lon_x * 0.0, timestamp, method_code="A2G")
                sort_idx = np.argsort(const_lon_y)
                const_lon_x = const_lon_x[sort_idx]
                const_lon_y = const_lon_y[sort_idx]
                const_lon_x, const_lon_y = transformer.transform(const_lon_x, const_lon_y)

                # Add contour to dict, along with color and linewidth
                self.contour_data["x"].append(const_lon_x)
                self.contour_data["y"].append(const_lon_y)
                self.contour_data["color"].append(color)
                self.contour_data["linewidth"].append(linewidth)
                self.contour_data["linestyle"].append(linestyle)
                self.contour_data["marker"].append(marker)
                self.contour_data["zorder"].append(int(bring_to_front))

Class representation for a generated mosaic.

Attributes

polygon_data : matplotlib.collections.PolyCollection: Generated polygons containing rendered data.
cartopy_projection : cartopy.crs.Projection: Cartopy projection to utilize.
contour_data : Dict[str, List[Any]]: Generated contour data.
spect_cmap : str: String giving the cmap to use for spect legend.
spect_intensity_scale : Tuple[int]: The min and max values that spectrograph data is scaled to in the mosaic, if any is present.

Class variables

var cartopy_projection : cartopy.crs.Projection
var contour_data : Dict[str, List[Any]] | None
var polygon_data : matplotlib.collections.PolyCollection | List[matplotlib.collections.PolyCollection]
var spect_cmap : str | None
var spect_intensity_scale : Tuple[int, int] | None

Methods

Expand source code

def add_geo_contours(self,
                     lats: Optional[Union[ndarray, list]] = None,
                     lons: Optional[Union[ndarray, list]] = None,
                     constant_lats: Optional[Union[float, int, Sequence[Union[float, int]], ndarray]] = None,
                     constant_lons: Optional[Union[float, int, Sequence[Union[float, int]], ndarray]] = None,
                     color: str = "black",
                     linewidth: Union[float, int] = 1,
                     linestyle: str = "solid",
                     marker: str = "",
                     bring_to_front: bool = False):
    """
    Add geographic contours to a mosaic.

    Args:
        lats (ndarray or list):
            Sequence of geographic latitudes defining a contour.
        
        lons (ndarray or list):
            Sequence of geographic longitudes defining a contour.

        constant_lats (float, int, or Sequence):
            Geographic Latitude(s) at which to add line(s) of constant latitude.
        
        constant_lons (float, int, or Sequence):
            Geographic Longitude(s) at which to add line(s) of constant longitude.

        color (str):
            The matplotlib color used for the contour(s).

        linewidth (float or int):
            The contour thickness.
        
        linestyle (str):
            The matplotlib linestyle used for the contour(s).

        marker (str):
            The matplotlib marker used for the contour(s).

    Returns:
        The object's contour_data parameter is populated appropriately.

    Raises:
        ValueError: issues encountered with supplied parameters.
    """
    # Make sure some form of lat/lon is provided
    if (constant_lats is None) and (constant_lons is None) and (lats is None) and (lons is None):
        raise ValueError("No latitudes or longitudes provided.")

    # If manually passing in lats & lons, make sure both are provided
    if (lats is not None or lons is not None) and (lats is None or lons is None):
        raise (ValueError("Manually supplying contour requires both lats and lons."))

    # Check that color exists in matplotlib
    if color not in matplotlib.colors.CSS4_COLORS:
        raise ValueError(f"Color '{color}' not recognized by matplotlib.")

    # Check that linestyle is valid
    if linestyle not in ["-", "--", "-.", ":", "solid", "dashed", "dashdot", "dotted"]:
        raise ValueError(f"Linestyle '{linestyle}' not recognized by matplotlib.")

    # Check that linewidth is valid
    if linewidth <= 0:
        raise ValueError("Linewidth must be greater than zero.")

    # Check that marker is valid
    if marker not in ["", "o", ".", "p", "*", "x", "+", "X"]:
        raise ValueError(f"Marker '{marker}' is not currently supported.")

    # Convert numerics to lists if necessary
    if constant_lats is not None:
        if isinstance(constant_lats, (float, int)):
            constant_lats = [constant_lats]
    if constant_lons is not None:
        if isinstance(constant_lons, (float, int)):
            constant_lons = [constant_lons]

    # Initialize contour data dict if it doesn't exist yet
    if self.contour_data is None:
        self.contour_data = {"x": [], "y": [], "color": [], "linewidth": [], "linestyle": [], "marker": [], "zorder": []}

    # Obtain the mosaic's projection
    source_proj = pyproj.CRS.from_user_input(cartopy.crs.Geodetic())
    mosaic_proj = pyproj.CRS.from_user_input(self.cartopy_projection)
    transformer = pyproj.Transformer.from_crs(source_proj, mosaic_proj, always_xy=True)

    # First handling manually supplied lat/lon arrays
    if (lats is not None) and (lons is not None):
        # Convert lists to ndarrays if necessary
        if isinstance(lats, list):
            lats = np.array(lats)
        if isinstance(lons, list):
            lons = np.array(lons)

        if len(lats) != len(lons):
            raise ValueError("Lat/Lon data must be of the same size.")

        # Create specified contour from geographic coords
        x, y = transformer.transform(lons, lats)
        # Add contour to dict, along with color and linewidth
        self.contour_data["x"].append(x)
        self.contour_data["y"].append(y)
        self.contour_data["color"].append(color)
        self.contour_data["linewidth"].append(linewidth)
        self.contour_data["linestyle"].append(linestyle)
        self.contour_data["marker"].append(marker)
        self.contour_data["zorder"].append(int(bring_to_front))

    # Next handling lines of constant latitude
    if constant_lats is not None:
        # Generate longitudinal domain of the lat line (full globe)
        lon_domain = np.arange(-180, 180 + 0.2, 0.2)

        # Iterate through all lines of constant lat requested
        for lat in constant_lats:
            # Create line of constant lat
            const_lat_x, const_lat_y = (lon_domain, lon_domain * 0 + lat)
            sort_idx = np.argsort(const_lat_x)
            const_lat_y = const_lat_y[sort_idx]
            const_lat_x = const_lat_x[sort_idx]
            const_lat_x, const_lat_y = transformer.transform(const_lat_x, const_lat_y)
            # Add contour to dict, along with color and linewidth
            self.contour_data["x"].append(const_lat_x)
            self.contour_data["y"].append(const_lat_y)
            self.contour_data["color"].append(color)
            self.contour_data["linewidth"].append(linewidth)
            self.contour_data["linestyle"].append(linestyle)
            self.contour_data["marker"].append(marker)
            self.contour_data["zorder"].append(int(bring_to_front))

    # Now handling lines of constant longitude
    if constant_lons is not None:
        # Generate latitudinal domain of the lon line (full globe)
        lat_domain = np.arange(-90, 90 + 0.1, 0.1)

        # Iterate through all lines of constant lon requested
        for lon in constant_lons:
            # Create line of constant lon and add to dict
            const_lon_x, const_lon_y = (lat_domain * 0 + lon, lat_domain)
            sort_idx = np.argsort(const_lon_y)
            const_lon_x = const_lon_x[sort_idx]
            const_lon_y = const_lon_y[sort_idx]
            const_lon_x, const_lon_y = transformer.transform(const_lon_x, const_lon_y)

            # Add contour to dict, along with color and linewidth
            self.contour_data["x"].append(const_lon_x)
            self.contour_data["y"].append(const_lon_y)
            self.contour_data["color"].append(color)
            self.contour_data["linewidth"].append(linewidth)
            self.contour_data["linestyle"].append(linestyle)
            self.contour_data["marker"].append(marker)
            self.contour_data["zorder"].append(int(bring_to_front))

Add geographic contours to a mosaic.

Args

lats (ndarray or list): Sequence of geographic latitudes defining a contour.

lons (ndarray or list): Sequence of geographic longitudes defining a contour.

constant_lats (float, int, or Sequence): Geographic Latitude(s) at which to add line(s) of constant latitude.

constant_lons (float, int, or Sequence): Geographic Longitude(s) at which to add line(s) of constant longitude.

color (str): The matplotlib color used for the contour(s).

linewidth (float or int): The contour thickness.

linestyle (str): The matplotlib linestyle used for the contour(s).

marker (str): The matplotlib marker used for the contour(s).

Returns

The object's contour_data parameter is populated appropriately.

Raises

ValueError: issues encountered with supplied parameters.

Expand source code

def add_mag_contours(self,
                     timestamp: datetime.datetime,
                     constant_lats: Optional[Union[float, int, Sequence[Union[float, int]], ndarray]] = None,
                     constant_lons: Optional[Union[float, int, Sequence[Union[float, int]], ndarray]] = None,
                     lats: Optional[Union[ndarray, list]] = None,
                     lons: Optional[Union[ndarray, list]] = None,
                     color: str = "black",
                     linewidth: Union[float, int] = 1,
                     linestyle: str = "solid",
                     marker: str = "",
                     bring_to_front: bool = False):
    """
    Add geomagnetic contours to a mosaic.

    Args:
        timestamp (datetime.datetime):
            The timestamp used in computing AACGM coordinates.

        lats (ndarray or list):
            Sequence of geomagnetic latitudes defining a contour.
        
        lons (ndarray or list):
            Sequence of geomagnetic longitudes defining a contour.

        constant_lats (float, int, Sequence):
            Geomagnetic latitude(s) at which to add contour(s) of constant latitude.
        
        constant_lons (float, int, Sequence):
            Geomagnetic longitude(s) at which to add contours(s) of constant longitude.

        color (str):
            The matplotlib color used for the contour(s).

        linewidth (float or int):
            The contour thickness.

        linestyle (str):
            The matplotlib linestyle used for the contour(s).

        marker (str):
            The matplotlib marker used for the contour(s).

    Returns:
        The object's contour_data parameter is populated appropriately.

    Raises:
        ValueError: issues encountered with supplied parameters.
    """
    # Make sure some form of lat/lon is provided
    if (constant_lats is None) and (constant_lons is None) and (lats is None) and (lons is None):
        raise ValueError("No latitudes or longitudes provided.")

    # If manually passing in lats & lons, make sure both are provided
    if (lats is not None or lons is not None) and (lats is None or lons is None):
        raise (ValueError("Manually supplying contour requires both lats and lons."))

    # Check that color exists in matplotlib
    if color not in matplotlib.colors.CSS4_COLORS:
        raise ValueError(f"Color '{color}' not recognized by matplotlib.")

    # Check that linestyle is valid
    if linestyle not in ["-", "--", "-.", ":", "solid", "dashed", "dashdot", "dotted"]:
        raise ValueError(f"Linestyle '{linestyle}' not recognized by matplotlib.")

    # Check that linewidth is valid
    if linewidth <= 0:
        raise ValueError("linewidth must be greater than zero.")

    # Convert numerics to lists if necessary
    if constant_lats is not None:
        if isinstance(constant_lats, (float, int)):
            constant_lats = [constant_lats]
    if constant_lons is not None:
        if isinstance(constant_lons, (float, int)):
            constant_lons = [constant_lons]

    # Initialize contour data dict if it doesn't exist yet
    if self.contour_data is None:
        self.contour_data = {"x": [], "y": [], "color": [], "linewidth": [], "linestyle": [], "marker": [], "zorder": []}

    # Obtain the mosaic's projection
    source_proj = pyproj.CRS.from_user_input(cartopy.crs.Geodetic())
    mosaic_proj = pyproj.CRS.from_user_input(self.cartopy_projection)
    transformer = pyproj.Transformer.from_crs(source_proj, mosaic_proj, always_xy=True)

    # First handling manually supplied lat/lon arrays
    if (lats is not None) and (lons is not None):
        # Convert lists to ndarrays if necessary
        if isinstance(lats, list):
            lats = np.array(lats)
        if isinstance(lons, list):
            lons = np.array(lons)

        if len(lats) != len(lons):
            raise ValueError("Lat/Lon data must be of the same size.")

        # Create specified contour from magnetic coords
        y, x, alt = aacgmv2.convert_latlon_arr(lats, lons, lats * 0.0, timestamp, method_code="A2G")
        x, y = transformer.transform(x, y)
        # Add contour to dict, along with color and linewidth
        self.contour_data["x"].append(x)
        self.contour_data["y"].append(y)
        self.contour_data["color"].append(color)
        self.contour_data["linewidth"].append(linewidth)
        self.contour_data["linestyle"].append(linestyle)
        self.contour_data["marker"].append(marker)
        self.contour_data["zorder"].append(int(bring_to_front))

    # Next handling lines of constant latitude
    if constant_lats is not None:
        # Generate longitudinal domain of the lat line (full globe)
        lon_domain = np.arange(-180, 180 + 0.2, 0.2)

        # iterate through all lines of constant lat requested
        for lat in constant_lats:
            # Create line of constant lat from magnetic coords
            const_lat_x, const_lat_y = (lon_domain, lon_domain * 0 + lat)
            const_lat_y, const_lat_x, alt = aacgmv2.convert_latlon_arr(const_lat_y, const_lat_x, const_lat_x * 0.0, timestamp, method_code="A2G")
            sort_idx = np.argsort(const_lat_x)
            const_lat_y = const_lat_y[sort_idx]
            const_lat_x = const_lat_x[sort_idx]
            const_lat_x, const_lat_y = transformer.transform(const_lat_x, const_lat_y)

            # Add contour to dict, along with color and linewidth
            self.contour_data["x"].append(const_lat_x)
            self.contour_data["y"].append(const_lat_y)
            self.contour_data["color"].append(color)
            self.contour_data["linewidth"].append(linewidth)
            self.contour_data["linestyle"].append(linestyle)
            self.contour_data["marker"].append(marker)
            self.contour_data["zorder"].append(int(bring_to_front))

    # Now handling lines of constant longitude
    if constant_lons is not None:
        # Generate latitudinal domain of the lon line (full globe)
        lat_domain = np.arange(-90, 90 + 0.1, 0.1)

        # iterate through all lines of constant lon requested
        for lon in constant_lons:
            # Create line of constant lon from magnetic coords
            const_lon_x, const_lon_y = (lat_domain * 0 + lon, lat_domain)
            const_lon_y, const_lon_x, alt = aacgmv2.convert_latlon_arr(const_lon_y, const_lon_x, const_lon_x * 0.0, timestamp, method_code="A2G")
            sort_idx = np.argsort(const_lon_y)
            const_lon_x = const_lon_x[sort_idx]
            const_lon_y = const_lon_y[sort_idx]
            const_lon_x, const_lon_y = transformer.transform(const_lon_x, const_lon_y)

            # Add contour to dict, along with color and linewidth
            self.contour_data["x"].append(const_lon_x)
            self.contour_data["y"].append(const_lon_y)
            self.contour_data["color"].append(color)
            self.contour_data["linewidth"].append(linewidth)
            self.contour_data["linestyle"].append(linestyle)
            self.contour_data["marker"].append(marker)
            self.contour_data["zorder"].append(int(bring_to_front))

Add geomagnetic contours to a mosaic.

Args

timestamp (datetime.datetime): The timestamp used in computing AACGM coordinates.

lats (ndarray or list): Sequence of geomagnetic latitudes defining a contour.

lons (ndarray or list): Sequence of geomagnetic longitudes defining a contour.

constant_lats (float, int, Sequence): Geomagnetic latitude(s) at which to add contour(s) of constant latitude.

constant_lons (float, int, Sequence): Geomagnetic longitude(s) at which to add contours(s) of constant longitude.

color (str): The matplotlib color used for the contour(s).

linewidth (float or int): The contour thickness.

linestyle (str): The matplotlib linestyle used for the contour(s).

marker (str): The matplotlib marker used for the contour(s).

Returns

The object's contour_data parameter is populated appropriately.

Raises

ValueError: issues encountered with supplied parameters.

def plot(self, map_extent: Sequence[float | int], figsize: Tuple[int, int] | None = None, rayleighs: bool = False, max_rayleighs: int = 20000, title: str | None = None, colorbar_title: str | None = None, ocean_color: str | None = None, land_color: str = 'gray', land_edgecolor: str = '#8A8A8A', borders_color: str = '#AEAEAE', borders_disable: bool = False, cbar_colormap: str = '', returnfig: bool = False, savefig: bool = False, savefig_filename: str | None = None, savefig_quality: int | None = None) ‑> Any

Expand source code

def plot(self,
         map_extent: Sequence[Union[float, int]],
         figsize: Optional[Tuple[int, int]] = None,
         rayleighs: bool = False,
         max_rayleighs: int = 20000,
         title: Optional[str] = None,
         colorbar_title: Optional[str] = None,
         ocean_color: Optional[str] = None,
         land_color: str = "gray",
         land_edgecolor: str = "#8A8A8A",
         borders_color: str = "#AEAEAE",
         borders_disable: bool = False,
         cbar_colormap: str = "",
         returnfig: bool = False,
         savefig: bool = False,
         savefig_filename: Optional[str] = None,
         savefig_quality: Optional[int] = None) -> Any:
    """
    Generate a plot of the mosaic data. 
    
    Either display it (default behaviour), save it to disk (using the `savefig` parameter), or 
    return the matplotlib plot object for further usage (using the `returnfig` parameter).

    Args:
        map_extent (List[int]): 
            Latitude/longitude range to be visible on the rendered map. This is a list of 4 integers 
            and/or floats, in the order of [min_lon, max_lon, min_lat, max_lat].

        figsize (tuple): 
            The matplotlib figure size to use when plotting. For example `figsize=(14,4)`.

        rayleighs (bool): 
            Set to `True` if the data being plotted is in Rayleighs. Defaults to `False`.

        max_rayleighs (int): 
            Max intensity scale for Rayleighs. Defaults to `20000`.

        ocean_color (str): 
            Colour of the ocean. Default is cartopy's default shade of blue. Colours can be supplied
            as a word, or hexcode prefixed with a '#' character (ie. `#55AADD`).
        
        land_color (str): 
            Colour of the land. Default is `gray`. Colours can be supplied as a word, or hexcode 
            prefixed with a '#' character (ie. `#41BB87`).

        land_edgecolor (str): 
            Color of the land edges. Default is `#8A8A8A`. Colours can be supplied as a word, or
            hexcode prefixed with a '#' character.

        borders_color (str): 
            Color of the country borders. Default is `AEAEAE`. Colours can be supplied as a word, or
            hexcode prefixed with a '#' character.
        
        borders_disable (bool): 
            Disbale rendering of the borders. Default is `False`.

        cbar_colorcmap (str): 
            The matplotlib colormap to use for the plotted color bar. Default is `gray`, unless
            mosaic was created with spectrograph data, in which case defaults to the colormap
            used for spectrograph data..

            Commonly used colormaps are:

            - REGO: `gist_heat`
            - THEMIS ASI: `gray`
            - TREx Blue: `Blues_r`
            - TREx NIR: `gray`
            - TREx RGB: `None`

            A list of all available colormaps can be found on the 
            [matplotlib documentation](https://matplotlib.org/stable/gallery/color/colormap_reference.html).

        returnfig (bool): 
            Instead of displaying the image, return the matplotlib figure object. This allows for further plot 
            manipulation, for example, adding labels or a title in a different location than the default. 
            
            Remember - if this parameter is supplied, be sure that you close your plot after finishing work 
            with it. This can be achieved by doing `plt.close(fig)`. 
            
            Note that this method cannot be used in combination with `savefig`.

        savefig (bool): 
            Save the displayed image to disk instead of displaying it. The parameter savefig_filename is required if 
            this parameter is set to True. Defaults to `False`.

        savefig_filename (str): 
            Filename to save the image to. Must be specified if the savefig parameter is set to True.

        savefig_quality (int): 
            Quality level of the saved image. This can be specified if the savefig_filename is a JPG image. If it
            is a PNG, quality is ignored. Default quality level for JPGs is matplotlib/Pillow's default of 75%.

    Returns:
        The displayed montage, by default. If `savefig` is set to True, nothing will be returned. If `returnfig` is 
        set to True, the plotting variables `(fig, ax)` will be returned.

    Raises:
    """
    # check return mode
    if (returnfig is True and savefig is True):
        raise ValueError("Only one of returnfig or savefig can be set to True")
    if returnfig is True and (savefig_filename is not None or savefig_quality is not None):
        warnings.warn("The figure will be returned, but a savefig option parameter was supplied. Consider " +
                      "removing the savefig option parameter(s) as they will be ignored.",
                      stacklevel=1)
    elif (savefig is False and (savefig_filename is not None or savefig_quality is not None)):
        warnings.warn("A savefig option parameter was supplied, but the savefig parameter is False. The " +
                      "savefig option parameters will be ignored.",
                      stacklevel=1)

    # Get colormap if there is spectrograph data
    if self.spect_cmap is not None:
        cbar_colormap = self.spect_cmap

    # initialize figure
    fig = plt.figure(figsize=figsize)
    ax = fig.add_axes((0, 0, 1, 1), projection=self.cartopy_projection)
    ax.set_extent(map_extent, crs=cartopy.crs.Geodetic())  # type: ignore

    # add ocean
    #
    # NOTE: we use the default ocean color
    if (ocean_color is not None):
        ax.add_feature(  # type: ignore
            cartopy.feature.OCEAN, facecolor=ocean_color, zorder=0)
    else:
        ax.add_feature(cartopy.feature.OCEAN, zorder=0)  # type: ignore

    # add land
    ax.add_feature(  # type: ignore
        cartopy.feature.LAND, facecolor=land_color, edgecolor=land_edgecolor, zorder=0)

    # add borders
    if (borders_disable is False):
        ax.add_feature(  # type: ignore
            cartopy.feature.BORDERS, edgecolor=borders_color, zorder=0)

    # add polygon data
    #
    # NOTE: it seems that when running this function a second time, the polygon
    # data is not too happy. So to handle this, we plot a copy of the polygon data
    if isinstance(self.polygon_data, list):
        for polygon_data in self.polygon_data:
            ax.add_collection(copy(polygon_data))
    else:
        ax.add_collection(copy(self.polygon_data))

    if self.contour_data is not None:
        for i in range(len(self.contour_data["x"])):
            ax.plot(self.contour_data["x"][i],
                    self.contour_data["y"][i],
                    color=self.contour_data["color"][i],
                    linewidth=self.contour_data["linewidth"][i],
                    linestyle=self.contour_data["linestyle"][i],
                    marker=self.contour_data["marker"][i],
                    zorder=self.contour_data["zorder"][i])

    # set title
    if (title is not None):
        ax.set_title(title)

    # add text
    if (rayleighs is True):
        if isinstance(self.polygon_data, list):
            raise ValueError("Rayleighs Keyword is currently not available for mosaics with multiple sets of data.")

        # Create a colorbar, in Rayleighs, that accounts for the scaling limit we applied
        cbar_ticks = [float(j) / 5. for j in range(0, 6)]
        cbar_ticknames = [str(int(max_rayleighs / 5) * j) for j in range(0, 6)]

        # Any pixels with the max Rayleigh value could be greater than it, so we include the plus sign
        cbar_ticknames[-1] += "+"
        self.polygon_data.set_cmap(cbar_colormap)
        cbar = plt.colorbar(self.polygon_data, shrink=0.5, ticks=cbar_ticks, ax=ax)
        cbar.ax.set_yticklabels(cbar_ticknames)
        plt.text(1.025,
                 0.5,
                 "Intensity (Rayleighs)",
                 fontsize=14,
                 transform=ax.transAxes,
                 va="center",
                 rotation="vertical",
                 weight="bold",
                 style="oblique")

    if (self.spect_cmap) is not None:

        if (self.spect_intensity_scale) is None:
            intensity_max = np.nan
            intensity_min = np.nan
        else:
            intensity_max = self.spect_intensity_scale[1]
            intensity_min = self.spect_intensity_scale[0]

        # Create a colorbar, in Rayleighs, that accounts for the scaling limit we applied
        cbar_ticks = [float(j) / 5. for j in range(0, 6)]
        cbar_ticknames = [str(int((intensity_max / 5) + intensity_min) * j) for j in range(0, 6)]

        # Any specrograph bins with the max intensity value could be greater than it, so we include the plus sign
        cbar_ticknames[-1] += "+"
        if isinstance(self.polygon_data, list):
            self.polygon_data[0].set_cmap(cbar_colormap)
        else:
            self.polygon_data.set_cmap(cbar_colormap)
        if isinstance(self.polygon_data, list):
            cbar = plt.colorbar(self.polygon_data[0], shrink=0.5, ticks=cbar_ticks, ax=ax)
        else:
            cbar = plt.colorbar(self.polygon_data, shrink=0.5, ticks=cbar_ticks, ax=ax)
        cbar.ax.set_yticklabels(cbar_ticknames)
        if colorbar_title is None:
            plt.text(1.025,
                     0.5,
                     "Spectrograph Intensity (Rayleighs)",
                     fontsize=10,
                     transform=ax.transAxes,
                     va="center",
                     rotation="vertical",
                     weight="bold",
                     style="oblique")
        else:
            plt.text(1.025,
                     0.5,
                     colorbar_title,
                     fontsize=10,
                     transform=ax.transAxes,
                     va="center",
                     rotation="vertical",
                     weight="bold",
                     style="oblique")

    # save figure or show it
    if (savefig is True):
        # check that filename has been set
        if (savefig_filename is None):
            raise ValueError("The savefig_filename parameter is missing, but required since savefig was set to True.")

        # save the figure
        f_extension = os.path.splitext(savefig_filename)[-1].lower()
        if (".jpg" == f_extension or ".jpeg" == f_extension):
            # check quality setting
            if (savefig_quality is not None):
                plt.savefig(savefig_filename, quality=savefig_quality, bbox_inches="tight")
            else:
                plt.savefig(savefig_filename, bbox_inches="tight")
        else:
            if (savefig_quality is not None):
                # quality specified, but output filename is not a JPG, so show a warning
                warnings.warn("The savefig_quality parameter was specified, but is only used for saving JPG files. The " +
                              "savefig_filename parameter was determined to not be a JPG file, so the quality will be ignored",
                              stacklevel=1)
            plt.savefig(savefig_filename, bbox_inches="tight")

        # clean up by closing the figure
        plt.close(fig)
    elif (returnfig is True):
        # return the figure and axis objects
        return (fig, ax)
    else:
        # show the figure
        plt.show(fig)

        # cleanup by closing the figure
        plt.close(fig)

    # return
    return None

Generate a plot of the mosaic data.

Either display it (default behaviour), save it to disk (using the savefig parameter), or return the matplotlib plot object for further usage (using the returnfig parameter).

Args

map_extent : List[int]

Latitude/longitude range to be visible on the rendered map. This is a list of 4 integers and/or floats, in the order of [min_lon, max_lon, min_lat, max_lat].

figsize : tuple

The matplotlib figure size to use when plotting. For example figsize=(14,4).

rayleighs : bool

Set to True if the data being plotted is in Rayleighs. Defaults to False.

max_rayleighs : int

Max intensity scale for Rayleighs. Defaults to 20000.

ocean_color : str

Colour of the ocean. Default is cartopy's default shade of blue. Colours can be supplied as a word, or hexcode prefixed with a '#' character (ie. #55AADD).

land_color : str

Colour of the land. Default is gray. Colours can be supplied as a word, or hexcode prefixed with a '#' character (ie. #41BB87).

land_edgecolor : str

Color of the land edges. Default is #8A8A8A. Colours can be supplied as a word, or hexcode prefixed with a '#' character.

borders_color : str

Color of the country borders. Default is AEAEAE. Colours can be supplied as a word, or hexcode prefixed with a '#' character.

borders_disable : bool

Disbale rendering of the borders. Default is False.

cbar_colorcmap : str

The matplotlib colormap to use for the plotted color bar. Default is gray, unless mosaic was created with spectrograph data, in which case defaults to the colormap used for spectrograph data..

Commonly used colormaps are:

REGO: gist_heat
THEMIS ASI: gray
TREx Blue: Blues_r
TREx NIR: gray
TREx RGB: None

A list of all available colormaps can be found on the matplotlib documentation.

returnfig : bool

Instead of displaying the image, return the matplotlib figure object. This allows for further plot manipulation, for example, adding labels or a title in a different location than the default.

Remember - if this parameter is supplied, be sure that you close your plot after finishing work with it. This can be achieved by doing plt.close(fig).

Note that this method cannot be used in combination with savefig.

savefig : bool

Save the displayed image to disk instead of displaying it. The parameter savefig_filename is required if this parameter is set to True. Defaults to False.

savefig_filename : str

Filename to save the image to. Must be specified if the savefig parameter is set to True.

savefig_quality : int

Returns

The displayed montage, by default. If savefig is set to True, nothing will be returned. If returnfig is set to True, the plotting variables (fig, ax) will be returned. Raises:

class MosaicData (site_uid_list: List[str], timestamps: List[datetime.datetime], images: Dict[str, numpy.ndarray], images_dimensions: Dict[str, Tuple], data_types: List[str])

Expand source code

@dataclass
class MosaicData:
    """
    Prepared image data for use by mosaic routines.

    Attributes:
        site_uid_list (List[str]): 
            List of site unique identifiers contained within this object.
        timestamps (List[datetime.datetime]): 
            Timestamps of corresponding images.
        images (Dict[str, numpy.ndarray]): 
            Image data prepared into the necessary format; a dictionary. Keys are the site UID, 
            ndarray is the prepared data.
        images_dimensions (Dict[str, Tuple]): 
            The image dimensions.
        data_types (List[str]): 
            The data types for each data object.    
    """

    site_uid_list: List[str]
    timestamps: List[datetime.datetime]
    images: Dict[str, ndarray]
    images_dimensions: Dict[str, Tuple]
    data_types: List[str]

    def __str__(self) -> str:
        return self.__repr__()

    def __repr__(self) -> str:
        unique_dimensions = str(list(dict.fromkeys(self.images_dimensions.values()))).replace("[", "").replace("]", "").replace("), (", "),(")
        images_str = "Dict[%d sites of array(dims=%s)]" % (len(self.images.keys()), unique_dimensions)
        timestamps_str = "[%d timestamps]" % (len(self.timestamps))

        return "MosaicData(images=%s, timestamps=%s, site_uid_list=%s)" % (images_str, timestamps_str, self.site_uid_list.__repr__())

Prepared image data for use by mosaic routines.

Attributes

site_uid_list : List[str]: List of site unique identifiers contained within this object.
timestamps : List[datetime.datetime]: Timestamps of corresponding images.
images : Dict[str, numpy.ndarray]: Image data prepared into the necessary format; a dictionary. Keys are the site UID, ndarray is the prepared data.
images_dimensions : Dict[str, Tuple]: The image dimensions.
data_types : List[str]: The data types for each data object.

Class variables

var data_types : List[str]
var images : Dict[str, numpy.ndarray]
var images_dimensions : Dict[str, Tuple]
var site_uid_list : List[str]
var timestamps : List[datetime.datetime]

class MosaicSkymap (site_uid_list: List[str], elevation: List[numpy.ndarray], polyfill_lat: List[numpy.ndarray], polyfill_lon: List[numpy.ndarray])

Expand source code

@dataclass
class MosaicSkymap:
    """
    Prepared skymap data for use by mosaic routines.

    Attributes:
        site_uid_list (List[str]): 
            List of site unique identifiers contained within this object.
        elevation (List[numpy.ndarray]): 
            List of elevation data, with each element corresponding to each site. Order 
            matches that of the `site_uid_list` attribute.
        polyfoll_lat (List[numpy.ndarray]): 
            List of latitude polygon data, with each element corresponding to each site. 
            Order matches that of the `site_uid_list` attribute. 
        polyfoll_lon (List[numpy.ndarray]): 
            List of longitude polygon data, with each element corresponding to each site. 
            Order matches that of the `site_uid_list` attribute. 
    """

    site_uid_list: List[str]
    elevation: List[ndarray]
    polyfill_lat: List[ndarray]
    polyfill_lon: List[ndarray]

    def __str__(self) -> str:
        return self.__repr__()

    def __repr__(self) -> str:
        unique_polyfill_lat_dims = str(list(dict.fromkeys(fill_arr.shape
                                                          for fill_arr in self.polyfill_lat))).replace("[", "").replace("]",
                                                                                                                        "").replace("), (", "),(")
        unique_polyfill_lon_dims = str(list(dict.fromkeys(fill_arr.shape
                                                          for fill_arr in self.polyfill_lon))).replace("[", "").replace("]",
                                                                                                                        "").replace("), (", "),(")
        unique_elevation_dims = str(list(dict.fromkeys(el.shape for el in self.elevation))).replace("[", "").replace("]", "").replace("), (", "),(")

        polyfill_lat_str = "array(dims=%s, dtype=%s)" % (unique_polyfill_lat_dims, self.polyfill_lat[0].dtype)
        polyfill_lon_str = "array(dims=%s, dtype=%s)" % (unique_polyfill_lon_dims, self.polyfill_lon[0].dtype)
        elevation_str = "array(dims=%s, dtype=%s)" % (unique_elevation_dims, self.elevation[0].dtype)

        return "MosaicSkymap(polyfill_lat=%s, polyfill_lon=%s, elevation=%s, site_uid_list=%s)" % (
            polyfill_lat_str,
            polyfill_lon_str,
            elevation_str,
            self.site_uid_list.__repr__(),
        )

Prepared skymap data for use by mosaic routines.

Attributes

site_uid_list : List[str]: List of site unique identifiers contained within this object.
elevation : List[numpy.ndarray]: List of elevation data, with each element corresponding to each site. Order matches that of the site_uid_list attribute.
polyfoll_lat : List[numpy.ndarray]: List of latitude polygon data, with each element corresponding to each site. Order matches that of the site_uid_list attribute.
polyfoll_lon : List[numpy.ndarray]: List of longitude polygon data, with each element corresponding to each site. Order matches that of the site_uid_list attribute.

Class variables

var elevation : List[numpy.ndarray]
var polyfill_lat : List[numpy.ndarray]
var polyfill_lon : List[numpy.ndarray]
var site_uid_list : List[str]