otaf.plotting package

Module contents

otaf.plotting.arrange_axes_in_grid(axes_list)[source]

Arrange a list of matplotlib axes in a 2D grid as close to a square as possible.

Parameters: axes_list (list): List of matplotlib axes to be arranged in the grid.

otaf.plotting.calculate_sample_in_deviation_domain(pos_u, theta, lambda_pos, lambda_theta)[source]

Calculates the sample data in the deviation domain.

Parameters: pos_u (array-like): Position data. theta (array-like): Theta data. lambda_pos (float): Lambda position scaling factor. lambda_theta (float): Lambda theta scaling factor.

Returns: np.ndarray: 2D sample data with scaled position and theta.

otaf.plotting.compare_jacobians(jacobian1, jacobian2, plot_type='side_by_side', class_labels=None)[source]

Compare two Jacobians visually using bar plots with optional class-based background coloring.

Parameters: jacobian1 (np.ndarray): First Jacobian array of shape (32,). jacobian2 (np.ndarray): Second Jacobian array of shape (32,). plot_type (str): Either ‘side_by_side’ or ‘overlay’ to choose plot style. class_labels (np.ndarray): Array of class labels to apply background coloring. Must be same length as Jacobians.

Returns: None: Displays a bar plot for visual comparison.

Parameters:

  • jacobian1 (ndarray)

  • jacobian2 (ndarray)

  • plot_type (str)

  • class_labels (ndarray | None)

otaf.plotting.create_open_cylinder(radius, segment, **kwargs)[source]

Creates a trimesh object containing a cylinder open on both ends

otaf.plotting.create_surface_from_planar_contour(vertices, segments=None)[source]
otaf.plotting.hex_to_rgba(hex_color, alpha=1.0, as_float=False)[source]

Converts a hexadecimal color code to RGBA values.

Parameters:

  • hex_color (str) – The hexadecimal color code (e.g., ‘#ffffff’).

  • alpha (float) – The alpha (transparency) value between 0.0 and 1.0 (default is 1.0).

  • as_float (bool) – If True, returns RGBA values as floats between 0 and 1. If False (default), returns integers.

Returns:

A tuple containing the RGBA values as floats (or integers if as_float is False)

and the alpha value as a float (e.g., (1.0, 1.0, 1.0, 1.0)).

Return type:

tuple

otaf.plotting.pair_plot(data, labels=None, subset_labels=None, plot_half='both', hide_diag=False, color_by=None)[source]

Creates a pair plot of all combinations of variables in the data array, with additional customization options.

Parameters: - data: 2D numpy array or similar where each column corresponds to a variable. - labels: List of variable names corresponding to the columns of data.

If None, default labels will be used as ‘Var1’, ‘Var2’, etc.

  • subset_labels: List of variable names to plot a specific subset of the pair plot.

    If None, all variables will be plotted.

  • plot_half: Whether to plot the full matrix (‘both’), only the lower triangular (‘lower’),

    or only the upper triangular (‘upper’). Defaults to ‘both’.

  • hide_diag: If True, the diagonal plots will be hidden.

  • color_by: If provided, the data points will be colored based on the values of this column (array-like).

Returns: - A customized pair plot of all variable combinations.

otaf.plotting.plot_best_worst_input_data(best_data, worst_data, variable_labels, figsize=(16, 8), labels=True, save_as_png=False, save_path='images', filename='best_worst_input_data.png', dpi=600)[source]

Plot the best and worst 5% input data.

Parameters:

  • best_data (list) – List of input data for the best 5%.

  • worst_data (list) – List of input data for the worst 5%.

  • variable_labels (list) – List of variable labels.

  • figsize (tuple, optional) – Size of the figure (width, height) in inches. Defaults to (16, 8).

  • labels (bool, optional) – Whether to label samples. Defaults to True.

  • save_as_png (bool, optional) – Whether to save the plot as a PNG. Defaults to False.

  • save_path (str, optional) – Path to save the plot. Defaults to ‘images’.

  • filename (str, optional) – Name of the file to save the plot. Defaults to ‘best_worst_input_data.png’.

  • dpi (int, optional) – Resolution of the saved plot. Defaults to 600 dpi.

otaf.plotting.plot_best_worst_results(best_results, worst_results, xlabel='Sample Index', ylabel='Failure rate', title='Best and Worst Results Plot', figsize=(10, 6), save_as_png=False, save_path='images', filename='best_worst_results.png', dpi=600)[source]

Plot the best and worst results.

Parameters:

  • best_results (array-like) – Array containing the best results.

  • worst_results (array-like) – Array containing the worst results.

  • xlabel (str, optional) – Label for the x-axis. Defaults to ‘Sample Index’.

  • ylabel (str, optional) – Label for the y-axis. Defaults to ‘Failure rate’.

  • title (str, optional) – Title of the plot. Defaults to ‘Best and Worst Results Plot’.

  • figsize (tuple, optional) – Size of the figure (width, height) in inches. Defaults to (10, 6).

  • save_as_png (bool, optional) – Whether to save the plot as a PNG. Defaults to False.

  • save_path (str, optional) – Path to save the plot. Defaults to ‘images’.

  • filename (str, optional) – Name of the file to save the plot. Defaults to ‘best_worst_results.png’.

  • dpi (int, optional) – Resolution of the saved plot. Defaults to 600 dpi.

otaf.plotting.plot_combined_CDF(distributions, x_min, x_max, color_list=None, legend_list=None)[source]

Plot combined CDFs for multiple distributions.

otaf.plotting.plot_deviated_surfs(ax, se_pos, se_rot, lambda_, scale_factor)[source]

Adds on the rectangular part a set of deviated lines to visualize the geometrical distribution of the defects and the impact of the allocations. The defects are multiplied by a scaleFactor.

Parameters: ax (matplotlib.axes.Axes): The axis on which to plot. se_pos (array-like): Positional deviations. se_rot (array-like): Rotational deviations. lambda_ (float): Scaling factor for the deviations. scale_factor (float): Factor to scale the deviations.

Returns: matplotlib.axes.Axes: The axis with the plot.

otaf.plotting.plot_deviation_domain(ax, sample, x_label='X', y_label='Y', x_bounds=(-0.15, 0.15), y_bounds=(-1, 1), ratio_bounds=1.5, r=None, tick_spacing=[0.25, 0.25])[source]

Plots a sample in the deviation domain on the provided matplotlib axis.

Parameters: ax (matplotlib.axes.Axes): The axis on which to plot. sample (array-like): 2D sample data with scaled position and theta. x_label (str, optional): Label for the x-axis. Defaults to “X”. y_label (str, optional): Label for the y-axis. Defaults to “Y”. x_bounds (tuple, optional): Bounds for the x-axis. Defaults to (-0.15, 0.15). y_bounds (tuple, optional): Bounds for the y-axis. Defaults to (-1, 1). Returns: matplotlib.axes.Axes: The axis with the plot.

otaf.plotting.plot_gld_pbox_cdf(gld_obj, lower_params, upper_params, x_values, xtol=1e-05, labels=None, colors=('tab:blue', 'tab:orange'), fill_color='gray', alpha=0.3, xlabel='X', ylabel='P', title='Probability-Box of the CDF')[source]

Plot a Probability-Box (P-Box) using two sets of GLD parameters representing the lower and upper bounds of the CDF.

Parameters:

  • gld_obj (object) – An instance of the GLD class from gldpy.

  • lower_params (array-like) – Parameters for the lower bound GLD.

  • upper_params (array-like) – Parameters for the upper bound GLD.

  • x_values (array-like) – X values where the P-box should be computed.

  • xtol (float, optional) – Tolerance for numerical CDF computation. Default is 1e-5.

  • labels (tuple of str, optional) – Labels for the lower and upper bound CDFs. Default is None.

  • colors (tuple of str, optional) – Colors for the lower and upper bound CDFs. Default is (‘tab:blue’, ‘tab:orange’).

  • fill_color (str, optional) – Color for the filled P-box region. Default is ‘gray’.

  • alpha (float, optional) – Transparency of the filled region. Default is 0.3.

otaf.plotting.plot_gld_pbox_cdf2(gld_obj, param_list, x_values, xtol=1e-05, labels=None, colors=('tab:blue', 'tab:orange'), fill_color='gray', alpha=0.3, xlabel='X', ylabel='P', title='Probability-Box of the CDF')[source]

Plot a Probability-Box (P-Box) using two sets of GLD parameters representing the lower and upper bounds of the CDF.

Parameters:

  • gld_obj (object) – An instance of the GLD class from gldpy.

  • lower_params (array-like) – Parameters for the lower bound GLD.

  • upper_params (array-like) – Parameters for the upper bound GLD.

  • x_values (array-like) – X values where the P-box should be computed.

  • xtol (float, optional) – Tolerance for numerical CDF computation. Default is 1e-5.

  • labels (tuple of str, optional) – Labels for the lower and upper bound CDFs. Default is None.

  • colors (tuple of str, optional) – Colors for the lower and upper bound CDFs. Default is (‘tab:blue’, ‘tab:orange’).

  • fill_color (str, optional) – Color for the filled P-box region. Default is ‘gray’.

  • alpha (float, optional) – Transparency of the filled region. Default is 0.3.

otaf.plotting.plot_points_3D(points_dict, color='r', marker='o', azim=0)[source]

Create a 3D scatter plot of points.

Parameters:

  • points_dict (dict) – A dictionary where keys are labels and values are 3D points (tuples of floats).

  • color (str, optional) – Color of the markers. Defaults to ‘r’ (red).

  • marker (str, optional) – Marker style. Defaults to ‘o’ (circle).

  • azim (float, optional) – Azimuthal viewing angle. Defaults to 0 (front view).

Return type:

None

otaf.plotting.plot_rect_part(ax, scale_factor=100)[source]

Plots the rectangular part of the example and changes the aspect ratio, so that it is 2 times higher and 2 times shorter.

Parameters: ax (matplotlib.axes.Axes): The axis on which to plot. scale_factor (float, optional): Scaling factor for the arrows. Defaults to 100.

Returns: matplotlib.axes.Axes: The axis with the plot.

otaf.plotting.plot_single_transform(transform, figsize=(8, 8), start_color='red', end_color='blue', azim=0)[source]

Plots a single transformation matrix in a 3D space.

Args: - transform (np.ndarray or similar): A 4x4 transformation matrix. - figsize (tuple): Size of the figure. - origin_color (str): Color of the origin point.

otaf.plotting.plot_transform_sequence(transforms, figsize=(8, 8), origin_color='blue', start_color='red', end_color='blue', azim=0)[source]

Plots a sequence of transformation matrices, showing the transformation of a point across multiple frames.

Args: - transforms (list of np.ndarray or similar): A list of 4x4 transformation matrices. - figsize (tuple): Size of the figure. - origin_color (str): Color of the origin point in each frame.

otaf.plotting.print_sample_in_deviation_domain(ax, pos_u, theta, lambda_pos, lambda_theta, t_max, theta_max, ratio=1, r=None, xlabel='U', ylabel='Theta', remove_ticks=False, fontsize=15)[source]

Plots a sample in the deviation domain on the provided matplotlib axis with customizable labels and tick options.

Parameters: ax (matplotlib.axes.Axes): The axis on which to plot. pos_u (array-like): Position data. theta (array-like): Theta data. lambda_pos (float): Lambda position scaling factor. lambda_theta (float): Lambda theta scaling factor. t_max (float): Maximum value for t (U domain). theta_max (float): Maximum value for theta (Theta domain). ratio (float, optional): Aspect ratio for the plot. Defaults to 1. r (float, optional): Additional parameter to display. Defaults to None. xlabel (str, optional): Label for the x-axis. Defaults to “U”. ylabel (str, optional): Label for the y-axis. Defaults to “Theta”. remove_ticks (bool, optional): If True, removes axis ticks. Defaults to False.

Returns: matplotlib.axes.Axes: The axis with the plot.

otaf.plotting.save_plot(fig=None, ax=None, filename='plot', folder='.', file_format='png', dpi=300, **kwargs)[source]

Saves a Matplotlib figure or axis to a specified location with customizable parameters.

Parameters:

  • fig (matplotlib.figure.Figure) – The figure to save. Default is None.

  • ax (matplotlib.axes.Axes) – The axis to save. Default is None.

  • filename (str) – The name of the file (with or without extension). Default is ‘plot’.

  • folder (str) – The folder where the file will be saved. Default is current directory ‘.’.

  • file_format (str) – The format to save the file (e.g., ‘png’, ‘pdf’, ‘svg’). Default is ‘png’.

  • dpi (int) – The dots per inch (DPI) for the output file. Default is 300.

  • **kwargs – Other optional keyword arguments for plt.savefig().

Returns:

The full path of the saved file.

Return type:

str

otaf.plotting.set_graph_legends(graph, x_title='', y_title='', title='', legends=None, colors=None)[source]

Sets titles, legends, and colors for the given graph.

Parameters: graph (object): The graph object to be modified. x_title (str, optional): Title for the x-axis. Defaults to an empty string. y_title (str, optional): Title for the y-axis. Defaults to an empty string. title (str, optional): Title for the graph. Defaults to an empty string. legends (list, optional): List of legend labels. Defaults to None. colors (list, optional): List of colors for the graph elements. Defaults to None.

Returns: object: The modified graph object.

otaf.plotting.spheres_from_point_cloud(pc, radius=1.0, color=[0.1, 0.1, 0.1, 1.0], global_translation=array([0, 0, 0]))[source]

Create a list of spheres from a point cloud.

Parameters:

  • pc (np.ndarray) – The point cloud as an Nx3 numpy array, where N is the number of points.

  • radius (float, optional) – The radius of the spheres. Defaults to 1.0.

  • color (List[float], optional) – The color of the spheres as an RGBA list. Defaults to [0.1, 0.1, 0.1, 1.0].

  • global_translation (np.ndarray, optional) – A translation vector to apply to all spheres. Defaults to [0, 0, 0].

Returns:

A list of tr Trimesh objects representing the spheres.

Return type:

List[tr Trimesh]

otaf.plotting.trimesh_scene_as_notebook_scene(scene, background_hex_color='e6e6e6')[source]