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
Expand source code
# Copyright 2024 University of Calgary
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
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
```
"""
# pull in top-level functions
from ._display import display
from ._movie import movie
from ._scale_intensity import scale_intensity
from ._util import set_theme
# pull in classes
from .classes.keogram import Keogram
from .classes.montage import Montage
from .classes.mosaic import Mosaic, MosaicData, MosaicSkymap
# pull in submodules
from . import keogram
from . import montage
from . import calibration
from . import bounding_box
from . import mosaic
from . import ccd_contour
from . import grid_files
__all__ = [
# sub-modules
"keogram",
"montage",
"calibration",
"bounding_box",
"mosaic",
"ccd_contour",
"grid_files",
# top level functions
"display",
"movie",
"scale_intensity",
"set_theme",
# classses
"Keogram",
"Montage",
"Mosaic",
"MosaicData",
"MosaicSkymap",
]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.
Functions
def display(image: numpy.ndarray, cmap: Optional[str] = None, figsize: Optional[Tuple[int, int]] = None, aspect: Union[Literal['equal', 'auto'], float, ForwardRef(None)] = 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
savefigparameter), or return the matplotlib plot object for further usage (using thereturnfigparameter).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.
- REGO:
figsize:tuple- The matplotlib figure size to use when displaying, tuple of two integers (ie.
figsize=(14, 4)) aspect:strorfloat- 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
savefigis set to True, nothing will be returned. Ifreturnfigis set to True, the plotting variables(fig, ax)will be returned.Raises
ValueError- issues with supplied parameters.
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 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
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() def scale_intensity(data: numpy.ndarray, min: Optional[float] = None, max: Optional[float] = None, top: Optional[float] = None, memory_saver: bool = True) ‑> numpy.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 toFalsethen the scaling routine will be faster, but will utilize significantly more RAM.
Returns
A new
numpy.ndarraythat is the same dimensions as the inputted data array, with the scaling applied.Raises
ValueError- Issues with the supplied min, max, or top
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): # determine if we are single or 3 channel n_channels = 1 if (len(data.shape) == 3): # single channel n_channels = 1 elif (len(data.shape) == 4): # three channel n_channels = 3 else: 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) # return return images_scaled else: # scale and return return __scale_data(data, min, max, top) def set_theme(theme: str) ‑> None-
A handy wrapper for setting the matplotlib global theme. Common choices are
light,dark, ordefault.Args
theme:str-
Theme name. Common choices are
light,dark, ordefault. If default, then matplotlib theme settings will be fully reset to their defaults.Additional themes can be found on the matplotlib documentation
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)
Classes
class Keogram (data: numpy.ndarray, timestamp: List[datetime.datetime], slice_idx: Optional[int] = None, ccd_y: Optional[numpy.ndarray] = None, mag_y: Optional[numpy.ndarray] = None, geo_y: Optional[numpy.ndarray] = None)-
Class representation for a keogram
Attributes
data:numpy.ndarray- The derived keogram data.
timestamp:List[datetime.datetime]- Timestamps corresponding to each keogram slice.
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.
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. 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], 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.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 create as part of the custom_keogram routines, this is expected and performing " + "this action 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 create as part of the custom_keogram routines, this is expected and performing " + "this action 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, 1).astype(str)[25::ytick_increment] # type: ignore 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 NoneMethods
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: Union[Literal['equal', 'auto'], float, ForwardRef(None)] = 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
savefigparameter), or return the matplotlib plot object for further usage (using thereturnfigparameter).Args
y_type:str- Type of y-axis to use when plotting. Options are
ccd,mag, orgeo. The default isccd. 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.
- REGO:
aspect:strorfloat- 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- 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
savefigis set to True, nothing will be returned. Ifreturnfigis set to True, the plotting variables(fig, ax)will be returned.Raises
ValueError- Issues with the y-axis choice.
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, 1).astype(str)[25::ytick_increment] # type: ignore 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 def set_geographic_latitudes(self, skymap: pyucalgarysrs.data.classes.Skymap, altitude_km: Union[int, float, ForwardRef(None)] = 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: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_yattribute will be updated.Raises
ValueError- Issues with specified altitude.
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 create as part of the custom_keogram routines, this is expected and performing " + "this action 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: pyucalgarysrs.data.classes.Skymap, timestamp: datetime.datetime, altitude_km: Union[int, float, ForwardRef(None)] = 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: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_yattribute will be updated.Raises
ValueError- Issues with specified altitude.
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 create as part of the custom_keogram routines, this is expected and performing " + "this action 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()
class Montage (data: numpy.ndarray, timestamp: List[datetime.datetime], n_channels: int)-
Class representation for a montage
Attributes
data:numpy.ndarray- The derived montage data.
timestamp:List[datetime.datetime]- Timestamps corresponding to each montage image.
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 NoneMethods
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
savefigparameter), or return the matplotlib plot object for further usage (using thereturnfigparameter).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.
- REGO:
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
savefigis set to True, nothing will be returned. Ifreturnfigis set to True, the plotting variables(fig, ax)will be returned.Raises
ValueError- Issues with the y-axis choice.
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
class Mosaic (polygon_data: Union[matplotlib.collections.PolyCollection, List[matplotlib.collections.PolyCollection]], cartopy_projection: cartopy.crs.Projection, contour_data: Optional[Dict[str, List[Any]]] = None)-
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.
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. """ polygon_data: Union[PolyCollection, List[PolyCollection]] cartopy_projection: Projection contour_data: Optional[Dict[str, List[Any]]] = 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, ocean_color: Optional[str] = None, land_color: str = "gray", land_edgecolor: str = "#8A8A8A", borders_color: str = "#AEAEAE", borders_disable: bool = False, cbar_colormap: str = "gray", 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`. 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) # 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(cartopy.feature.OCEAN, facecolor=ocean_color, zorder=0) # type: ignore else: ax.add_feature(cartopy.feature.OCEAN, zorder=0) # type: ignore # add land ax.add_feature(cartopy.feature.LAND, facecolor=land_color, edgecolor=land_edgecolor, zorder=0) # type: ignore # add borders if (borders_disable is False): ax.add_feature(cartopy.feature.BORDERS, edgecolor=borders_color, zorder=0) # type: ignore # 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") # 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 variables
var cartopy_projection : cartopy.crs.Projectionvar contour_data : Optional[Dict[str, List[Any]]]var polygon_data : Union[matplotlib.collections.PolyCollection, List[matplotlib.collections.PolyCollection]]
Methods
def add_geo_contours(self, lats: Union[numpy.ndarray, list, ForwardRef(None)] = None, lons: Union[numpy.ndarray, list, ForwardRef(None)] = None, constant_lats: Union[float, int, Sequence[Union[float, int]], numpy.ndarray, ForwardRef(None)] = None, constant_lons: Union[float, int, Sequence[Union[float, int]], numpy.ndarray, ForwardRef(None)] = 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.
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)) def add_mag_contours(self, timestamp: datetime.datetime, constant_lats: Union[float, int, Sequence[Union[float, int]], numpy.ndarray, ForwardRef(None)] = None, constant_lons: Union[float, int, Sequence[Union[float, int]], numpy.ndarray, ForwardRef(None)] = None, lats: Union[numpy.ndarray, list, ForwardRef(None)] = None, lons: Union[numpy.ndarray, list, ForwardRef(None)] = 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.
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)) 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, ocean_color: Optional[str] = None, land_color: str = 'gray', land_edgecolor: str = '#8A8A8A', borders_color: str = '#AEAEAE', borders_disable: bool = False, cbar_colormap: str = 'gray', 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
savefigparameter), or return the matplotlib plot object for further usage (using thereturnfigparameter).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
Trueif the data being plotted is in Rayleighs. Defaults toFalse. 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.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.
- REGO:
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
savefigis set to True, nothing will be returned. Ifreturnfigis set to True, the plotting variables(fig, ax)will be returned. Raises: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, ocean_color: Optional[str] = None, land_color: str = "gray", land_edgecolor: str = "#8A8A8A", borders_color: str = "#AEAEAE", borders_disable: bool = False, cbar_colormap: str = "gray", 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`. 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) # 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(cartopy.feature.OCEAN, facecolor=ocean_color, zorder=0) # type: ignore else: ax.add_feature(cartopy.feature.OCEAN, zorder=0) # type: ignore # add land ax.add_feature(cartopy.feature.LAND, facecolor=land_color, edgecolor=land_edgecolor, zorder=0) # type: ignore # add borders if (borders_disable is False): ax.add_feature(cartopy.feature.BORDERS, edgecolor=borders_color, zorder=0) # type: ignore # 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") # 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 MosaicData (site_uid_list: List[str], timestamps: List[datetime.datetime], images: Dict[str, numpy.ndarray], images_dimensions: Dict[str, Tuple])-
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.
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. """ site_uid_list: List[str] timestamps: List[datetime.datetime] images: Dict[str, ndarray] images_dimensions: Dict[str, Tuple] 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__())Class variables
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])-
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_listattribute. polyfoll_lat:List[numpy.ndarray]- List of latitude polygon data, with each element corresponding to each site.
Order matches that of the
site_uid_listattribute. polyfoll_lon:List[numpy.ndarray]- List of longitude polygon data, with each element corresponding to each site.
Order matches that of the
site_uid_listattribute.
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__(), )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]