ADCS.helpers.plotting_mc.plot_controller_compare_mc module

ADCS.helpers.plotting_mc.plot_controller_compare_mc.plot_convergence_histogram_mc_compare(full_results_A, full_results_B, body_boresight=array([0., 0., 1.]), title='Final Tracking Error Distribution (Comparison)', bin_width_deg=5.0, under_thresh_deg=1.0, label_A='Case A', label_B='Case B')

Compare final-timestep tracking error distributions for two Monte Carlo cases.

This function computes the final angular tracking error for each Monte Carlo run and visualizes the resulting distributions using overlaid histograms. Summary statistics are displayed directly on the plot.

Final Error Definition

For each Monte Carlo run, the final tracking error is computed as

\[\theta_f = \cos^{-1}\!\left( \hat{\mathbf{b}}_f^{\text{ECI}} \cdot \hat{\mathbf{g}}_f \right)\]

where:

• \(\hat{\mathbf{b}}_f^{\text{ECI}}\) is the final boresight direction

• \(\hat{\mathbf{g}}_f\) is the final target direction

Histogram Construction

• Bin width is user-defined in degrees

• Overlapping histograms are shown with transparency

• A statistics box reports:

  • Sample count

  • Percentage below a threshold

  • Mean and maximum error

Return Values

The raw error arrays are returned to support further statistical analysis.

param full_results_A:

Monte Carlo result set for Case A.

type full_results_A:

list of dict

param full_results_B:

Monte Carlo result set for Case B.

type full_results_B:

list of dict

param body_boresight:

Fixed boresight direction expressed in the body frame.

type body_boresight:

numpy.ndarray

param title:

Plot title.

type title:

str

param bin_width_deg:

Histogram bin width in degrees.

type bin_width_deg:

float

param under_thresh_deg:

Threshold angle (degrees) used for convergence statistics.

type under_thresh_deg:

float

param label_A:

Legend label for Case A.

type label_A:

str

param label_B:

Legend label for Case B.

type label_B:

str

return:

Tuple containing final tracking error arrays for Case A and Case B.

rtype:

tuple of numpy.ndarray

Parameters:

• full_results_A (List[Dict[str, Any]])

• full_results_B (List[Dict[str, Any]])

• body_boresight (ndarray)

• title (str)

• bin_width_deg (float)

• under_thresh_deg (float)

• label_A (str)

• label_B (str)

Return type:

Tuple[ndarray, ndarray]

ADCS.helpers.plotting_mc.plot_controller_compare_mc.plot_h_tracking_mc_compare(full_results_A, full_results_B, title='Monte Carlo Stored Angular Momentum (Comparison)', label_A='Case A', label_B='Case B')

Compare stored reaction wheel angular momentum across two Monte Carlo cases.

This function overlays stored angular momentum time histories from two sets of Monte Carlo simulation results, enabling qualitative comparison of momentum buildup, dispersion, and actuator usage.

State Assumption

Each Monte Carlo result dictionary is expected to contain:

• "time" — time history

• "state" — state history with reaction wheel momentum stored from index 7 onward

The reaction wheel momentum vector is assumed to be

\[\mathbf{h}_{\text{rw}}(t) \in \mathbb{R}^{N_{\text{rw}}}\]

Visualization Strategy

• Each Monte Carlo run is plotted with low opacity

• Each wheel component uses a consistent color within a case

• Separate colormaps are used for Case A and Case B

param full_results_A:

Monte Carlo result set for Case A.

type full_results_A:

list of dict

param full_results_B:

Monte Carlo result set for Case B.

type full_results_B:

list of dict

param title:

Plot title.

type title:

str

param label_A:

Legend label for Case A.

type label_A:

str

param label_B:

Legend label for Case B.

type label_B:

str

return:

None. The function generates a Matplotlib figure.

rtype:

None

Parameters:

• full_results_A (List[Dict[str, Any]])

• full_results_B (List[Dict[str, Any]])

• title (str)

• label_A (str)

• label_B (str)

Return type:

None

ADCS.helpers.plotting_mc.plot_controller_compare_mc.plot_target_tracking_mc_compare(full_results_A, full_results_B, body_boresight=array([0., 0., 1.]), title='Monte Carlo Target Tracking Error (Comparison)', label_A='Case A', label_B='Case B')

Compare target tracking angular error histories for two Monte Carlo cases.

This function computes and overlays the instantaneous angular tracking error between a body-fixed boresight and an inertial target direction for two sets of Monte Carlo simulations.

Tracking Error Definition

Let:

• \(\hat{\mathbf{b}}\) be the normalized boresight direction in the body frame

• \(\mathbf{R}_{\mathcal{B}\rightarrow\mathcal{I}}\) be the attitude rotation matrix

• \(\hat{\mathbf{g}}_i\) be the normalized target direction in ECI

The boresight direction in ECI is

\[\hat{\mathbf{b}}_i^{\text{ECI}} = \mathbf{R}_{\mathcal{B}\rightarrow\mathcal{I}} \hat{\mathbf{b}}\]

The angular tracking error is

\[\theta_i = \cos^{-1}\!\left( \hat{\mathbf{b}}_i^{\text{ECI}} \cdot \hat{\mathbf{g}}_i \right)\]

Visualization Strategy

• Each Monte Carlo run is plotted with low opacity

• Case A and Case B are distinguished by color

param full_results_A:

Monte Carlo result set for Case A.

type full_results_A:

list of dict

param full_results_B:

Monte Carlo result set for Case B.

type full_results_B:

list of dict

param body_boresight:

Fixed boresight direction expressed in the body frame.

type body_boresight:

numpy.ndarray

param title:

Plot title.

type title:

str

param label_A:

Legend label for Case A.

type label_A:

str

param label_B:

Legend label for Case B.

type label_B:

str

return:

None. The function generates a Matplotlib figure.

rtype:

None

Parameters:

• full_results_A (List[Dict[str, Any]])

• full_results_B (List[Dict[str, Any]])

• body_boresight (ndarray)

• title (str)

• label_A (str)

• label_B (str)

Return type:

None