Plotting#

class Plotting#

Bases: object

Collection of standardised plotting utilities for Scarabaeus output data.

Provides methods to visualise OD solutions, trajectories, residuals, covariance ellipses, B-plane maps, and more. All methods apply a consistent style defined in set_custom_styling().

Notes

Scarabaeus’ plotting library contains multiple color palettes. See below for the colors contained within each:

Methods

apply_scale_to_axes([scale])

Apply logarithmic or linear scaling to a Matplotlib axes object.

compare_state_errors_norm(solution2, ...[, ...])

Compares the state errors and sigma values of two solutions by plotting them together.

get_fig_handle(fig_handle[, show, save_dir, ...])

Get the figure handle from another class

plot_asteroid_3d([obj_file, scale_factor, ...])

Plot a 3D representation of an asteroid, either from an object file or a simple spherical mesh.

plot_compare_state_error_norm(...[, title, ...])

Plots state errors norm for multiple iterations on the same figure for comparison.

plot_covariance(P_bplane_parameters[, title])

Given a B-plane covariance, plot a graphical representation.

plot_covariance_est(n_sigma[, title, scale, ...])

Plots the covariance history.

plot_covariance_json(json_path[, iteration, ...])

Plot the 1σ (=√diag) of every state component over time, from JSON.

plot_estimate_json(json_path[, iteration, ...])

Plot the estimated state deviation per component over time, from JSON.

plot_fogm_parameters_estimate(epochs, n_sigma)

Plot only stochastic Gauss-Markov parameters over time.

plot_gs_visibility(visibility_windows[, ...])

Method to plot when the spacecraft can be seen by a set of ground stations

plot_measurement_availability(...[, scale, ...])

Plots the availability of each measurement over time with x-axis labels as 'time - t_0' in integer days, where t_0 is the last time in combined_times.

plot_measurements_dataset(measurement_data)

Method to plot the radiometric measurements as function of time.

plot_media_coverage([type])

Plot DSN coverage intervals (in Ephemeris Time) from a media correction JSON file.

plot_multiple_covariance(sc_states, ...[, ...])

Plot multiple covariances corresponding to different spacecraft positions and their covariances in the B-plane.

plot_multiple_orbits_3d(positions_list[, ...])

Method for plotting multiple 3D trajectories, with default view as XY-plane.

plot_opnav_measurements(opnav_measurements)

Method to plot opnav measurements as function of time.

plot_orbit_3d(positions[, title, origin, ...])

Method for plotting a single 3D trajectory

plot_parameter_uncertainty_json(json_path[, ...])

Plot the 1σ uncertainty time history of estimated parameters from JSON.

plot_parameters_errors(true_trajectory, ...)

Plots the state errors and sigma bounds for each component of parameters.

plot_parameters_errors_norm(true_trajectory, ...)

Plots the state errors and sigma bounds for each component of parameters.

plot_parameters_estimate(epochs, n_sigma[, ...])

Plots the estimated parameters over time with error bars and shaded uncertainty bounds (±nσ).

plot_position_velocity(data[, times, title, ...])

Method to plot the position-velocity as function of time.

plot_postfits_asynchronous(dataset_names, ...)

Plots the postfit residuals for various measurements (e.g., range, range rate, OpNav u, OpNav v) dynamically based on dataset names.

plot_postfits_asynchronous_with_hist(...[, ...])

Plots postfit residuals with histograms for various measurement types.

plot_postfits_histogram(epochs, dataset_names)

Plots the postfit residuals in a histogram for various measurements (e.g., range, range rate, OpNav u, OpNav v) dynamically based on dataset names.

plot_postfits_scatter(epochs, dataset_names)

Plots the postfit residuals in a scatter plot for various measurements (e.g., range, range rate, OpNav u, OpNav v) dynamically based on dataset names.

plot_prefit_residuals(n_sigma[, title, ...])

Plots the prefit residuals for range and range rate measurements.

plot_radiometric_measurements(...[, title, ...])

Method to plot the radiometric measurements as function of time.

plot_residuals_json(json_path[, iteration, ...])

Plot prefit/postfit residuals directly from a solution JSON file.

plot_scenario_param([scale, x_offset_flag])

Plots the scenario paramters

plot_state_errors(true_trajectory, epochs, ...)

Plots the state errors and sigma bounds for each component of position and velocity.

plot_state_errors_norm(true_trajectory, ...)

Plots the state errors and sigma values over time.

plot_uv_measurements_over_time([scale, ...])

Plot u vs v measurements, colored by time, and add subplots of u and v pixels over time with a semilog y-axis for the time.

plot_with_x_offset([x_offset_flag])

Subtract the first value from all elements to create a zero-based x-axis.

save_plot([save_dir, save_name, save_format])

Save a Matplotlib figure to disk.

set_custom_styling()

Apply the Scarabaeus default rcParams style to all subsequent plots.

time_series_basic_line(time, data[, title, ...])

Method for plotting time series data on a line plot.

time_series_basic_scatter(time, data[, ...])

Method for plotting time series data on a scatter plot.
static get_fig_handle(fig_handle, show: bool = False, save_dir: str = None, save_name: str = None, save_format: str = None)#

Get the figure handle from another class

Parameters:

  • fig_handle (matplotlib plt.figure() object) – Figure handle generated outside the Plotting class

  • show (bool) – Option to show plot. Defaults to False.

  • save_dir (str) – Save directory. Defaults to None.

  • save_name (str) – Figure saving name. Defaults to None.

  • save_format (str) – Figure saving format. Can be “pickle” or “png”. Defaults to None.

static plot_asteroid_3d(obj_file=None, scale_factor=1.0, ax=None, radius=None, name='Asteroid')#

Plot a 3D representation of an asteroid, either from an object file or a simple spherical mesh.

Parameters: - obj_file (str, optional): Path to the asteroid 3D model file (e.g., .obj). If not provided, a simple spherical mesh is plotted. - scale_factor (float): Scaling factor to adjust the size of the asteroid. Default is 1.0. - ax (matplotlib.axes._subplots.Axes3DSubplot, optional): 3D axes to plot the asteroid on. If not provided, a new figure is created. - radius (float, optional): Radius of the asteroid in kilometers if a simple spherical mesh is used. Default is None. - name (str, optional): Name of the asteroid to display in the legend. Default is “Asteroid”.

Returns: - ax: The 3D axes containing the plot.

Raises: - ValueError: If both obj_file and radius are None.

static plot_covariance_json(json_path, iteration: str = 'final', use_smoothed: bool = True, time_unit: str = 'hr', log_scale: bool = True, save_dir: str = None, save_name: str = None, show: bool = False)#

Plot the 1σ (=√diag) of every state component over time, from JSON.

One line per state-covariance column, drawn against timeline_epochs (the deviation/covariance grid), labelled from the leg metadata where available. Sequence leg boundaries are marked. Note the components carry mixed units (km, km/s, parameter units), so a log y-axis is used by default to show their relative evolution on one panel.

Parameters:

  • json_path (str or path-like) – Path to a SolutionOD JSON file.

  • iteration (str, optional) – Which iteration to plot for multi-iteration files. Defaults to "final".

  • use_smoothed (bool, optional) – Prefer covariance_smooth_diagonals when present and non-empty, else the filter covariance_diagonals. Defaults to True.

  • time_unit ({"s", "min", "hr", "day"}, optional) – Units for the elapsed-time x-axis. Defaults to "hr".

  • log_scale (bool, optional) – Use a logarithmic y-axis (recommended for mixed-unit sigmas). Defaults to True.

  • save_dir (str, optional) – If both given, the figure is written via save_plot().

  • save_name (str, optional) – If both given, the figure is written via save_plot().

  • show (bool, optional) – Display the figure. Defaults to False.

Return type:

matplotlib.figure.Figure

static plot_estimate_json(json_path, iteration: str = 'final', use_smoothed: bool = False, time_unit: str = 'hr', save_dir: str = None, save_name: str = None, show: bool = False)#

Plot the estimated state deviation per component over time, from JSON.

One stacked subplot per state component showing deviation_estimated (or the smoothed deviation) against timeline_epochs, with a ±1σ band from the covariance diagonal. Sequence leg boundaries are marked.

Parameters:

  • json_path (str or path-like) – Path to a SolutionOD JSON file.

  • iteration (str, optional) – Which iteration to plot for multi-iteration files. Defaults to "final".

  • use_smoothed (bool, optional) – Plot deviation_smoothed when present and non-empty instead of the filter deviation_estimated. Defaults to False.

  • time_unit ({"s", "min", "hr", "day"}, optional) – Units for the elapsed-time x-axis. Defaults to "hr".

  • save_dir (str, optional) – If both given, the figure is written via save_plot().

  • save_name (str, optional) – If both given, the figure is written via save_plot().

  • show (bool, optional) – Display the figure. Defaults to False.

Return type:

matplotlib.figure.Figure

static plot_gs_visibility(visibility_windows: dict, title: str = 'Ground Station Visibility Windows', epoch_start: str = None, show: bool = False, save_dir: str = None, save_name: str = None, save_format: str = None, scale: str = 'linxy')#

Method to plot when the spacecraft can be seen by a set of ground stations

Parameters:

  • visibility_windows (dict) – dictionary of the GS visibilities

  • epoch_start (str) – Starting epoch

  • show (bool) – Option to show plot. Defaults to False.

  • save_dir (str) – Save directory. Defaults to None.

  • save_name (str) – Figure saving name. Defaults to None.

  • save_format (str) – Figure saving format. Can be “pickle” or “png”. Defaults to None.

  • scale (str) – Scaling option [‘logx’, ‘logy’, ‘logxy’, ‘linx’, ‘liny’, ‘linxy’]

static plot_measurements_dataset(measurement_data, title: str = None, show: bool = False, save_dir: str = None, save_name: str = None, save_format: str = None)#

Method to plot the radiometric measurements as function of time.

static plot_multiple_orbits_3d(positions_list, labels=None, origin=None, title: str = '@Sun, J2000', units: str = 'km', show: bool = False, save_dir: str = None, save_name: str = None, save_format: str = None, line_styles=None, colors=None, markers=None, view_xy_plane: bool = True)#

Method for plotting multiple 3D trajectories, with default view as XY-plane.

Parameters:

  • positions_list (list) – list of several objects positions in X,Y,Z coordinates.

  • labels (list) – list of several objects labels. Defaults to None.

  • origin (np.array(,3)) – origin of the plot in X,Y,Z coordinates. Defaults to None.

  • title (str) – Title of plot. Defaults to “Reference Frame: J2000”.

  • units (str) – Units to use in the X,Y,Z labels. Defaults to “km”.

  • show (bool) – Option to show plot. Defaults to False.

  • save_dir (str) – Save directory. Defaults to None.

  • save_name (str) – Figure saving name. Defaults to None.

  • save_format (str) – Figure saving format. Defaults to None.

  • line_styles (list) – List of line styles for each orbit. Defaults to solid lines.

  • colors (list) – List of colors for each orbit. Defaults to default matplotlib colors.

  • markers (list) – List of markers for each orbit. Defaults to None (no markers).

  • view_xy_plane (bool) – Boolean to set the default view to XY-plane. Defaults to True.

Returns:

Matplotlib figure object.

static plot_opnav_measurements(opnav_measurements, title: str = 'Opnav Measurements', show: bool = False, save_dir: str = None, save_name: str = None, save_format: str = None, scale: str = 'linxy', x_offset_flag: bool = False)#

Method to plot opnav measurements as function of time.

Parameters:

  • opnav_measurements (list[EpochArray,np.array,ArrayWFrame]) – Dictionary of the measurement, generated from the MeasurementDataSet

  • title (str) – Title of plot. Defaults to None.

  • show (bool) – Option to show plot. Defaults to False.

  • save_dir (str) – Save directory. Defaults to None.

  • save_name (str) – Figure saving name. Defaults to None.

  • save_format (str) – Figure saving format. Can be “pickle” or “png”. Defaults to None.

  • scale (str) – Scaling option [‘logx’, ‘logy’, ‘logxy’, ‘linx’, ‘liny’, ‘linxy’]

  • x_offset_flag (bool) – Offset value for X axis

static plot_orbit_3d(positions, title: str = '3D Trajectory', origin=None, units: str = 'km', show: bool = False, save_dir: str = None, save_name: str = None, save_format: str = None)#

Method for plotting a single 3D trajectory

Parameters:

  • positions (np.array(N,3)) – object position in X,Y,Z coordinates (no frame assuemd).

  • origin (np.array(,3)) – origin of the plot in X,Y,Z coordinates (no frame assumed). Defaults to None.

  • title (str) – Title of plot. Defaults to “3D Trajectory”.

  • units (str) – Units to use in the X,Y,Z labels. Defaults to “km”.

  • show (bool) – Option to show plot. Defaults to False.

  • save_dir (str) – Save directory. Defaults to None.

  • save_name (str) – Figure saving name. Defaults to None.

  • save_format (str) – Figure saving format. Can be “pickle” or “png”. Defaults to None.

static plot_parameter_uncertainty_json(json_path, iteration: str = 'final', use_smoothed: bool = True, time_unit: str = 'hr', save_dir: str = None, save_name: str = None, show: bool = False)#

Plot the 1σ uncertainty time history of estimated parameters from JSON.

“Parameters” are the state components beyond position/velocity. For a sequence each parameter is drawn only over the leg(s) where it is estimated, using the legs layout and per-row leg_index written by SolutionOD; the covariance diagonals are per timeline row, so 1σ is sqrt(diag).

Parameters:

  • json_path (str or path-like) – Path to a SolutionOD JSON file.

  • iteration (str, optional) – Which iteration to plot for multi-iteration files. Defaults to "final".

  • use_smoothed (bool, optional) – Prefer the smoother covariance (covariance_smooth_diagonals) when present and non-empty, else the filter covariance. Defaults to True.

  • time_unit ({"s", "min", "hr", "day"}, optional) – Units for the elapsed-time x-axis. Defaults to "hr".

  • save_dir (str, optional) – If both given, the figure is written via save_plot().

  • save_name (str, optional) – If both given, the figure is written via save_plot().

  • show (bool, optional) – Display the figure. Defaults to False.

Return type:

matplotlib.figure.Figure

static plot_position_velocity(data, times=None, title: str = 'Pos-Vel', show: bool = False, save_dir: str = None, save_name: str = None, save_format: str = None, scale: str = 'linxy', x_offset_flag: bool = False)#

Method to plot the position-velocity as function of time.

Parameters:

  • data (np.array(n,6)) – Array of the state vector made by position and velocity

  • times (np.array()) – Time values

  • title (str) – Title of plot. Defaults to “Pos-Vel”.

  • show (bool) – Option to show plot. Defaults to False.

  • save_dir (str) – Save directory. Defaults to None.

  • save_name (str) – Figure saving name. Defaults to None.

  • save_format (str) – Figure saving format. Can be “pickle” or “png”. Defaults to None.

static plot_radiometric_measurements(radiometric_measurement, title: str = None, show: bool = False, save_dir: str = None, save_name: str = None, save_format: str = None, meas_name: str = 'meas_name', scale: str = 'linxy', x_offset_flag: bool = False)#

Method to plot the radiometric measurements as function of time.

Parameters:

  • radiometric_measurement (list[EpochArray,np.array,ArrayWFrame]) – Dictionary of the measurement, generated from the MeasurementDataSet

  • title (str) – Title of plot. Defaults to None.

  • show (bool) – Option to show plot. Defaults to False.

  • save_dir (str) – Save directory. Defaults to None.

  • save_name (str) – Figure saving name. Defaults to None.

  • save_format (str) – Figure saving format. Can be “pickle” or “png”. Defaults to None.

  • scale (str) – Scaling option [‘logx’, ‘logy’, ‘logxy’, ‘linx’, ‘liny’, ‘linxy’]

  • x_offset_flag (bool) – Offset value for X axis

static plot_residuals_json(json_path, iteration: str = 'final', kind: str = 'both', weighted: bool = False, n_sigma: float = 3.0, time_unit: str = 'hr', save_dir: str = None, save_name: str = None, show: bool = False)#

Plot prefit/postfit residuals directly from a solution JSON file.

One stacked subplot per dataset (measurement type). Each residual is plotted against its reception epoch from the per-entry time tag ([residual, sigma, epoch]) written by SolutionOD; older files without the tag fall back to the measurement index. Per-group RMS and count are shown in the legend, ±n_sigma reference lines are drawn, and sequence leg boundaries are marked.

Parameters:

  • json_path (str or path-like) – Path to a SolutionOD JSON file.

  • iteration (str, optional) – Which iteration to plot for multi-iteration files ("iter_1" is the raw first pass, "final" the converged solution). Defaults to "final".

  • kind ({"both", "prefit", "postfit"}, optional) – Which residual set(s) to draw. Defaults to "both".

  • weighted (bool, optional) – If True, plot residual / sigma (dimensionless); the ±n_sigma lines become flat at ±n_sigma. Otherwise plot raw residuals with a ±n_sigma * sigma envelope. Defaults to False.

  • n_sigma (float, optional) – Multiplier for the ±σ reference lines. Defaults to 3.

  • time_unit ({"s", "min", "hr", "day"}, optional) – Units for the elapsed-time x-axis. Defaults to "hr".

  • save_dir (str, optional) – If both given, the figure is written via save_plot().

  • save_name (str, optional) – If both given, the figure is written via save_plot().

  • show (bool, optional) – Display the figure. Defaults to False.

Return type:

matplotlib.figure.Figure

static set_custom_styling()#

Apply the Scarabaeus default rcParams style to all subsequent plots.

Sets font family, sizes, grid appearance, and the default color cycle to the matplot_default_palette. Called automatically by __init__(); can also be called standalone to reset styling after external rcParams changes.

static time_series_basic_line(time, data, title: str = 'Line Plot', x_axis_label: str = 'Time', y_axis_label: str = 'Value', show: bool = False, save_dir: str = None, save_name: str = None, save_format: str = None, scale: str = 'linxy', x_offset_flag: bool = False)#

Method for plotting time series data on a line plot.

Parameters:

  • time (np.array()) – timestamps values

  • data (np.array()) – data values

  • title (str) – Title of plot. Defaults to “Line Plot”.

  • x_axis_label (str) – X-axis label of plot. Defaults to “Time”.

  • y_axis_label (str) – Y-axis label of plot. Defaults to “Value”.

  • show (bool) – Option to show plot. Defaults to False.

  • save_dir (str) – Save directory. Defaults to None.

  • save_name (str) – Figure saving name. Defaults to None.

  • save_format (str) – Figure saving format. Can be “pickle” or “png”. Defaults to None.

  • scale (str) – Scaling option [‘logx’, ‘logy’, ‘logxy’, ‘linx’, ‘liny’, ‘linxy’]

  • x_offset_flag (bool) – Offset value for X axis

static time_series_basic_scatter(time, data, title: str = 'Scatter Plot', x_axis_label: str = 'Time', y_axis_label: str = 'Value', show: bool = False, save_dir: str = None, save_name: str = None, save_format: str = None, scale: str = 'linxy', x_offset_flag: bool = False)#

Method for plotting time series data on a scatter plot.

Parameters:

  • time (np.array()) – timestamps values

  • data (np.array()) – data values

  • title (str) – Title of plot. Defaults to “Scatter Plot”.

  • x_axis_label (str) – X-axis label of plot. Defaults to “Time”.

  • y_axis_label (str) – Y-axis label of plot. Defaults to “Value”.

  • show (bool) – Option to show plot. Defaults to False.

  • save_dir (str) – Save directory. Defaults to None.

  • save_name (str) – Figure saving name. Defaults to None.

  • save_format (str) – Figure saving format. Can be “pickle” or “png”. Defaults to None.

  • scale (str) – Scaling option [‘logx’, ‘logy’, ‘logxy’, ‘linx’, ‘liny’, ‘linxy’]

  • x_offset_flag (bool) – Offset value for X axis

apply_scale_to_axes(scale='linxy')#

Apply logarithmic or linear scaling to a Matplotlib axes object.

Parameters:

  • ax (matplotlib.axes.Axes) – The axes on which to apply the scaling.

  • scale (str, optional) –

    Scaling option. One of:

    • "logx" — log x-axis, linear y-axis.

    • "logy" — linear x-axis, log y-axis.

    • "logxy" — log on both axes.

    • "linx" — linear x-axis only.

    • "liny" — linear y-axis only.

    • "linxy" — linear on both axes (default).

Returns:

ax – The same axes object with the requested scale applied.

Return type:

matplotlib.axes.Axes

Raises:

ValueError – If scale is not one of the six recognised options.

compare_state_errors_norm(solution2, true_trajectory, epochs, n_sigma: int, labels: list[str] = ['w SNC', 'w/o SNC'], t_0: bool = True, use_smoothed: bool = True)#

Compares the state errors and sigma values of two solutions by plotting them together.

Parameters:

  • solution1 – The first SolutionOD object to compare.

  • solution2 – The second SolutionOD object to compare.

  • true_trajectory (Trajectory) – The true trajectory.

  • epochs (EpochArray) – The array of epochs.

  • n_sigma (int) – The number of standard deviations for sigma.

  • labels (list of str) – Labels for the solutions to be compared.

  • t_0 (bool) – If True, convert the x-axis to time in days relative to the initial epoch.

  • use_smoothed (bool) – If True and smoothed solution is available, uses smoothed covariance.

Returns:

The figure object.

Return type:

matplotlib.figure.Figure

plot_compare_state_error_norm(true_trajectory, epochs_list, n_sigma, title='State Error Comparison', labels=None, t_0=True, scale='linxy', x_offset_flag=False, use_smoothed=True)#

Plots state errors norm for multiple iterations on the same figure for comparison.

Parameters:

  • solutions (list) – List of SolutionOD objects.

  • true_trajectory (Trajectory) – The true trajectory.

  • epochs_list (list) – List of EpochArray objects, one for each solution.

  • n_sigma (int) – The number of standard deviations for sigma.

  • title (str) – Title of the plot.

  • labels (list, optional) – Labels for the solutions (e.g., [‘IT1’, ‘IT2’, ‘IT3’]).

  • t_0 (bool, optional) – If True, convert the x-axis to time in days relative to the initial epoch.

  • scale (str, optional) – Scale type for plots.

  • x_offset_flag (bool, optional) – If True, apply x-axis offset.

  • use_smoothed (bool) – If True and smoothed solution is available, uses smoothed covariance.

Returns:

Figure object.

Return type:

Figure

plot_covariance(P_bplane_parameters, title: str = 'B-plane Covariance')#

Given a B-plane covariance, plot a graphical representation. It assumes no correlation between position in the B-plane and linearized time of flight.

Parameters:

  • bplane (Bplane) – The Bplane Object

  • P_bplane_parameters (ndarray) – The B-plane covariance matrix.

plot_covariance_est(n_sigma, title: str = 'Covariance History', scale: str = 'linxy', x_offset_flag: bool = False, use_smoothed: bool = True)#

Plots the covariance history.

Parameters:

  • solution_it (SolutionOD) – The SolutionOD object.

  • n_sigma (int) – The number of standard deviations to plot.

  • title (str) – Title of the plot.

  • scale (str, optional) – Scale type for plots.

  • x_offset_flag (bool, optional) – If True, apply x-axis offset.

  • use_smoothed (bool) – If True and smoothed solution is available, uses smoothed covariance.

Returns:

Matplotlib figure object.

Return type:

Figure

plot_fogm_parameters_estimate(epochs: EpochArray, n_sigma: int, title: str = 'FOGM Parameter Estimates', sample_points: int = 40, t_0: bool = True, use_smoothed: bool = True)#

Plot only stochastic Gauss-Markov parameters over time.

Notes

Supported parameters: a_fogm, beta_fogm, a_pfogm, beta_pfogm.

For piecewise parameters (a_pfogm, beta_pfogm), the stored state may have size 3*n_batches, but only the active batch is plotted at each epoch. Therefore the output always has 3 rows (x, y, z) for each stochastic parameter.

Parameters:

  • solution_it (SolutionOD) – The orbit determination solution object.

  • epochs (EpochArray) – Epoch array for plotting.

  • n_sigma (int) – Number of sigma levels to show.

  • title (str, optional) – Figure title prefix.

  • sample_points (int, optional) – Number of sampled points to plot.

  • t_0 (bool, optional) – If True, plot time in days relative to initial epoch.

  • use_smoothed (bool, optional) – If True and available, use smoothed estimates/covariances.

Returns:

List of matplotlib figure objects.

Return type:

list

plot_measurement_availability(measurement_mask, measurement_names, scale: str = 'linxy', x_offset_flag: bool = False)#

Plots the availability of each measurement over time with x-axis labels as ‘time - t_0’ in integer days, where t_0 is the last time in combined_times.

Parameters:

  • combined_times (np.ndarray) – Array of combined unique epochs.

  • measurement_mask (np.ndarray) – Mask array showing the availability of each measurement at each time.

  • measurement_names (list of str) – List of names corresponding to each measurement type.

  • scale (str) – Scaling option [‘logx’, ‘logy’, ‘logxy’, ‘linx’, ‘liny’, ‘linxy’].

  • x_offset_flag (bool) – Offset value for X axis.

plot_media_coverage(type: str = 'tropo')#

Plot DSN coverage intervals (in Ephemeris Time) from a media correction JSON file.

This function is designed to handle both iono and tropo files, which share the same structure but differ in the top-level dictionary key (aux_iono or aux_tropo).

Parameters:

  • json_path (str) – Path to the JSON file containing the media corrections (iono or tropo).

  • type (str, optional) – Type of file to process, either ‘iono’ or ‘tropo’. The function will look for the corresponding key in the JSON (aux_iono or aux_tropo). Default is ‘tropo’.

Notes

The expected JSON has the following fields under the block aux_type: "start_day", "start_time", "end_day", "end_time" (all lists of strings), and "dsn" (list of DSN identifiers, e.g. "C10", "C40").

To compute the CSP UTC day-time to ET conversion, the _csp_time_to_et method is used internally and requires a leapsecond kernel loaded in the script.

Examples

>>> plot_media_coverage("orex_2018_iono.json", type="iono")
>>> plot_media_coverage("orex_2018_tropo.json", type="tropo")
plot_multiple_covariance(sc_states, sc_covariances, labels=None, zoom_flag=True, ast_covariances=None, scale: str = 'linxy', x_offset_flag: bool = False)#

Plot multiple covariances corresponding to different spacecraft positions and their covariances in the B-plane.

Parameters:

  • bplane (Bplane) – The Bplane Object

  • sc_states (list of ndarray) – List of relative spacecraft states (position and velocity in J2000) at different epochs.

  • sc_covariances (list of ndarray) – List of covariance matrices for the spacecraft states in J2000.

  • labels (list of str) – List of labels for each ellipse (optional).

  • zoom_flag (bool) – Flag to indicate whether to apply zoom on the last ellipse and hide axes when zoomed.

  • scale (str, optional) – Scale type for plots.

  • x_offset_flag (bool, optional) – If True, apply x-axis offset.

Returns:

None

Return type:

None

plot_parameters_errors(true_trajectory: Trajectory, epochs: EpochArray, n_sigma: int, title: str = 'Parameters Errors', t_f: bool = True, use_smoothed: bool = True)#

Plots the state errors and sigma bounds for each component of parameters. :returns: List of figures.

plot_parameters_errors_norm(true_trajectory: Trajectory, epochs: EpochArray, n_sigma: int, title: str = 'Parameters errors norm', t_0: bool = True, scale: str = 'linxy', x_offset_flag: bool = False, use_smoothed: bool = True)#

Plots the state errors and sigma bounds for each component of parameters.

Parameters:

  • solution_it (SolutionOD) – The SolutionOD object.

  • true_trajectory (Trajectory) – The true trajectory of the object.

  • epochs (EpochArray) – The array of epochs.

  • n_sigma (int) – The number of standard deviations for the sigma bounds.

  • title (str) – Title for the plot.

  • t_0 (bool) – If True, convert the x-axis to time in days relative to the initial epoch.

  • scale (str) – Scale type for plots.

  • x_offset_flag (bool) – If True, apply x-axis offset.

  • use_smoothed (bool) – If True and smoothed solution is available, uses smoothed covariance.

Returns:

List of figure objects.

Return type:

list of Figure

plot_parameters_estimate(epochs: EpochArray, n_sigma: int, title='Parameters Errors', sample_points: int = 40, t_0: bool = True, use_smoothed: bool = True)#

Plots the estimated parameters over time with error bars and shaded uncertainty bounds (±nσ). Only a sample of sample_points is plotted to reduce clutter.

Parameters:

  • solution_it (SolutionOD) – The SolutionOD object.

  • epochs (EpochArray) – The array of epochs.

  • n_sigma (int) – The number of standard deviations for uncertainty bounds.

  • title (str) – Title for the plot.

  • sample_points (int) – Number of data points to sample for plotting.

  • t_0 (bool) – If True, convert the x-axis to time in days relative to the initial epoch.

  • use_smoothed (bool) – If True and smoothed solution is available, uses smoothed covariance.

Returns:

List of figure objects.

Return type:

list of Figure

plot_postfits_asynchronous(dataset_names, n_sigma, title='Postfit Residuals Asynchronous', time_vectors=None, scale: str = 'linxy', x_offset_flag: bool = False, use_smoothed: bool = True)#

Plots the postfit residuals for various measurements (e.g., range, range rate, OpNav u, OpNav v) dynamically based on dataset names. It handles residuals from any ground station (e.g., GS1, GS2) and groups them by measurement type. Datasets can have different lengths, and the x-axis will represent either the time vector or the number of measurements if no time vector is provided.

Parameters:

  • solution_it (SolutionOD) – The SolutionOD object.

  • dataset_names (list) – List of dataset names.

  • n_sigma (float) – Number of standard deviations for the sigma lines.

  • title (str) – Title of the plot.

  • time_vectors (list, optional) – List of time vectors for each measurement type. If only one time vector is provided, it will be used for all measurement types. If not provided, the x-axis will represent the number of measurements.

  • scale (str, optional) – Scale type for plots.

  • x_offset_flag (bool, optional) – If True, apply x-axis offset.

  • use_smoothed (bool) – If True and smoothed solution is available, uses smoothed postfits.

plot_postfits_asynchronous_with_hist(dataset_names, n_sigma=3, title='', time_vectors=None, scale='linxy', x_offset_flag=False, plot_sigmas=False, iter_num=None, use_smoothed=True)#

Plots postfit residuals with histograms for various measurement types.

Parameters:

  • solution_it (SolutionOD) – The SolutionOD object.

  • dataset_names (list) – List of dataset names.

  • n_sigma (int) – Number of standard deviations for the sigma lines.

  • title (str) – Title of the plot.

  • time_vectors (list, optional) – List of time vectors for each measurement type.

  • scale (str) – Scale type for plots.

  • x_offset_flag (bool) – If True, apply x-axis offset.

  • plot_sigmas (bool) – If True, plot sigma bounds.

  • iter_num (int, optional) – Iteration number for file naming.

  • use_smoothed (bool) – If True and smoothed solution is available, uses smoothed postfits.

plot_postfits_histogram(epochs: EpochArray, dataset_names, n_sigma: int = 3, log_scale: bool = True, x_offset_flag: bool = False, use_smoothed: bool = True)#

Plots the postfit residuals in a histogram for various measurements (e.g., range, range rate, OpNav u, OpNav v) dynamically based on dataset names. It handles residuals from any ground station (e.g., GS1, GS2) and groups them by measurement type.

Parameters:

  • solution_it (SolutionOD) – The SolutionOD object.

  • epochs (EpochArray) – Array of epochs.

  • dataset_names (list) – List of dataset names.

  • n_sigma (int) – Number of standard deviations for the sigma lines.

  • log_scale (bool, optional) – If True, use absolute values for residuals.

  • x_offset_flag (bool, optional) – If True, apply x-axis offset.

  • use_smoothed (bool) – If True and smoothed solution is available, uses smoothed postfits.

Returns:

Tuple of (cleaned_dict, list of figures).

Return type:

tuple(dict, list of Figure)

plot_postfits_scatter(epochs: EpochArray, dataset_names, n_sigma=3, scale: str = 'linxy', x_offset_flag: bool = False, use_smoothed: bool = True)#

Plots the postfit residuals in a scatter plot for various measurements (e.g., range, range rate, OpNav u, OpNav v) dynamically based on dataset names. It handles residuals from any ground station (e.g., GS1, GS2) and groups them by measurement type.

Parameters:

  • solution_it (SolutionOD) – The SolutionOD object.

  • epochs (EpochArray) – Array of epochs.

  • dataset_names (list) – List of dataset names.

  • n_sigma (float) – Number of standard deviations for the sigma lines.

  • scale (str, optional) – Scale type for plots.

  • x_offset_flag (bool, optional) – If True, apply x-axis offset.

  • use_smoothed (bool) – If True and smoothed solution is available, uses smoothed postfits.

Returns:

Tuple of (cleaned_dict, list of figures).

Return type:

tuple(dict, list of Figure)

plot_prefit_residuals(n_sigma, title: str = 'Prefit Residuals', scale: str = 'linxy', x_offset_flag: bool = False)#

Plots the prefit residuals for range and range rate measurements.

Parameters:

  • filterOD (FilterOD) – The FilterOD object.

  • n_sigma (float) – The number of standard deviations to use for the sigma bounds.

  • scale (str, optional) – Scale type for plots.

  • x_offset_flag (bool, optional) – If True, apply x-axis offset.

Returns:

None

Return type:

None

plot_scenario_param(scale: str = 'linxy', x_offset_flag: bool = False)#

Plots the scenario paramters

Parameters:

  • scenario (ScenarioSetup) – The ScenarioSetup object.

  • title (str) – Title of the plot.

  • scale (str, optional) – Scale type for plots.

  • x_offset_flag (bool, optional) – If True, apply x-axis offset.

Returns:

None

Return type:

None

plot_state_errors(true_trajectory: Trajectory, epochs: EpochArray, n_sigma: int, title: str = 'State Errors', t_0: bool = True, scale: str = 'linxy', x_offset_flag: bool = False, use_smoothed: bool = True)#

Plots the state errors and sigma bounds for each component of position and velocity.

Parameters:

  • solution_it (SolutionOD) – Solution OD Object.

  • true_trajectory (Trajectory) – The true trajectory of the object.

  • epochs (EpochArray) – The array of epochs.

  • n_sigma (int) – The number of standard deviations for the sigma bounds.

  • title (str) – Title for the plot.

  • t_0 (bool) – If True, convert the x-axis to time in days relative to the initial epoch.

  • scale (str) – Scale type for axes (‘linxy’, ‘logxy’, etc.).

  • x_offset_flag (bool) – If True, apply x-axis offset.

  • use_smoothed (bool) – If True and smoothed solution is available, uses smoothed covariance.

Returns:

Figure object.

Return type:

fig

plot_state_errors_norm(true_trajectory: Trajectory, epochs: EpochArray, n_sigma: int, title: str = 'State Errors Norm', t_0: bool = True, scale: str = 'linxy', x_offset_flag: bool = False, use_smoothed: bool = True)#

Plots the state errors and sigma values over time.

Parameters:

  • solution_it (SolutionOD) – Solution OD Object.

  • true_trajectory (Trajectory) – The true trajectory.

  • epochs (EpochArray) – The array of epochs.

  • n_sigma (int) – The number of standard deviations for sigma.

  • title (str) – Title for the plot.

  • t_0 (bool) – If True, convert the x-axis to time in days relative to the initial epoch.

  • scale (str) – Scaling option [‘logx’, ‘logy’, ‘logxy’, ‘linx’, ‘liny’, ‘linxy’]

  • x_offset_flag (bool) – Offset value for X axis

  • use_smoothed (bool) – If True and smoothed solution is available, uses smoothed covariance.

Returns:

Figure object.

Return type:

fig

plot_uv_measurements_over_time(scale: str = 'linxy', x_offset_flag: bool = False)#

Plot u vs v measurements, colored by time, and add subplots of u and v pixels over time with a semilog y-axis for the time.

Parameters: measurements (MeasurementDataSet): A dataset containing opnav measurements.

Parameters:

  • scale (str) – Scaling option [‘logx’, ‘logy’, ‘logxy’, ‘linx’, ‘liny’, ‘linxy’]

  • x_offset_flag (bool) – Offset value for X axis

plot_with_x_offset(x_offset_flag: bool = False)#

Subtract the first value from all elements to create a zero-based x-axis.

Parameters:

  • values (list) – List of x-axis values to shift.

  • x_offset_flag (bool, optional) – When True, subtracts values[0] from every element. When False, returns values unchanged. Defaults to False.

Returns:

values – The (possibly offset) list of x-axis values.

Return type:

list

save_plot(save_dir=None, save_name: str = 'default', save_format: str = 'png')#

Save a Matplotlib figure to disk.

Parameters:

  • fig_handle (matplotlib.figure.Figure) – The figure object to save.

  • save_dir (str or path-like, optional) – Directory in which to write the file. Created automatically if it does not exist. Defaults to None (current working directory).

  • save_name (str, optional) – Base name for the output file (without extension for "png"). Defaults to "default".

  • save_format ({"png", "pickle"}, optional) – Output format. "png" saves a raster image; "pickle" serialises the figure object. Defaults to "png".

Return type:

None

Raises:

RuntimeError – If save_format is not "png" or "pickle".

color_palettes = {'colorblind_friendly_palette': ['#E69F00', '#56B4E9', '#009E73', '#F0E442', '#0072B2', '#D55E00', '#CC79A7'], 'cu_palette': ['#E4B542', '#000000'], 'cyanR_palette': ['#17BECF', '#D95F02'], 'matplot_default_palette': ['#1f77b4', '#ff7f0e', '#2ca02c', '#8c564b', '#9467bd', '#d62728', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'], 'scb_palette': ['#4067BE', '#15788c', '#E4B542', '#000000']}#