physped.visualization package

Submodules

physped.visualization.force_field module

physped.visualization.force_field.clip_fields(fields, clip=50)

Clip fields.

physped.visualization.force_field.plot_quiver(ax, params, fields)

Plot the force field.

physped.visualization.plot_discrete_grid module

physped.visualization.plot_discrete_grid.plot_discrete_grid(config: dict, slow_indices: tuple, trajectories: ~pandas.core.frame.DataFrame = Empty DataFrame Columns: [] Index: [])

physped.visualization.plot_fields module

physped.visualization.plot_fields.calculate_offsets(hist, axis: Tuple = (2, 3, 4))

Calculate offsets.

physped.visualization.plot_fields.clip_fields(fields, clip)

Clip fields.

physped.visualization.plot_fields.create_force_fields(grids: dict, sliced_histogram: ndarray) → dict

physped.visualization.plot_fields.infer_bounds_from_data(data: ndarray) → tuple

Infer bounds from data.

physped.visualization.plot_fields.plot_colorbar(ax: Axes, cs: QuadContourSet, label: str) → Axes

Plot the colorbar.

physped.visualization.plot_fields.plot_contourf_offset_field(ax: Axes, fields: dict, cmap='YlOrRd', bounds: tuple = (0, 0.36)) → Axes

Plot the offset field.

physped.visualization.plot_fields.plot_force_field_of_selection(grids, params, selection)

physped.visualization.plot_fields.plot_pcolormesh_offset_field(ax: Axes, fields: dict, cmap='YlOrRd', bounds: tuple = (0, 0.36)) → Axes

Plot the offset field.

physped.visualization.plot_fields.plot_quiver_force_vectors(ax: Axes, fields: dict, scale: int, sparseness: int) → Axes

Plot the force field.

physped.visualization.plot_fields.plot_quiverkey(ax: Axes, q: Quiver) → Axes

Plot the quiver key.

physped.visualization.plot_histograms module

physped.visualization.plot_histograms.compute_joint_kl_divergence(piecewise_potential: PiecewisePotential, simulated_paths: DataFrame) → float

physped.visualization.plot_histograms.compute_joint_kl_divergence_with_volume(piecewise_potential: PiecewisePotential, simulated_paths: DataFrame) → float

physped.visualization.plot_histograms.create_all_histograms(recorded_paths: DataFrame, simulated_paths: DataFrame, config: dict)

physped.visualization.plot_histograms.create_histogram(values: Series, bins: ndarray) → dict

Create a histogram of the input values.

Paramters: - values (pd.Series): A Pandas Series containing the values to bin. - bins (np.ndarray): An array of bin edges.

Returns: - dict: A dictionary containing the bin edges, bin width, bin centers, counts, and PDF of the histogram.

physped.visualization.plot_histograms.plot_histogram(ax: Axes, histograms: Dict[str, Any], observable: str, hist_type: str, config: dict) → Axes

Plot a histogram.

Parameters: - ax (plt.Axes): The axes to plot the histogram on. - histograms (Dict[str, Any]): The histograms to plot. - observable (str): The observable to plot the histogram for. - hist_type (str): The type of histogram to plot. - kl_div (float): The KL divergence value for the histogram.

Returns: - The axes object.

physped.visualization.plot_histograms.plot_multiple_histograms(observables: List, histograms: dict, histogram_type: str, config: dict)

Plot histograms for all observables.

Parameters: - ax (plt.Axes): The axes to plot the histogram on. - histograms (dict): The histograms to plot. - observable (str): The observable to plot the histogram for. - hist_type (str): The type of histogram to plot. - kl_div (float): The KL divergence value for the histogram.

Returns: - The axes object.

physped.visualization.plot_histograms.save_joint_kl_divergence_to_file(joint_kl_divergence: float, config: dict) → None

physped.visualization.plot_learn_piece_of_potential module

physped.visualization.plot_learn_piece_of_potential.learn_piece_of_potential_plot(config: dict, preprocessed_trajectories: DataFrame, piecewise_potential: PiecewisePotential)

physped.visualization.plot_potential_at_slow_index module

physped.visualization.plot_potential_at_slow_index.plot_potential_at_slow_index(config: DictConfig, slow_indices: List, piecewise_potential: PiecewisePotential)

physped.visualization.plot_trajectories module

Plot trajectories of particles in the metaforum dataset.

physped.visualization.plot_trajectories.intended_path_label_generator(label_count, i)

physped.visualization.plot_trajectories.plot_intended_path(ax: Axes, traj_plot_params: dict) → Axes

physped.visualization.plot_trajectories.plot_position_trajectories_in_cartesian_coordinates(ax: Axes, df: DataFrame, alpha: float = 1.0, traj_type: str = 'f') → Axes

Plot the trajectories of pedestrians in cartesian coordinates.

Parameters: - ax (plt.Axes): The matplotlib Axes object to plot on. - df (pd.DataFrame): The DataFrame containing the particle data.

Returns: - ax (plt.Axes): The modified matplotlib Axes object.

physped.visualization.plot_trajectories.plot_trajectories(trajs: DataFrame, config: dict, trajectory_type: str = None, traj_type='f')

Plot trajectories of pedestrians.

Parameters:

• trajs (pd.DataFrame) – DataFrame containing the trajectories of

• pedestrians.

• params (dict) – Dictionary containing the plot parameters.

• trajectory_type (str, optional) – Type of trajectory. Defaults to None.

Returns:

None

physped.visualization.plot_trajectories.plot_velocity_trajectories_in_cartesian_coordinates(ax: Axes, df: DataFrame) → Axes

Plot the trajectories of particles in the metaforum dataset.

physped.visualization.plot_trajectories.plot_velocity_trajectories_in_polar_coordinates(ax: Axes, df: DataFrame, alpha: float = 1.0, traj_type: str = 'f') → Axes

Plot the trajectories of particles in the metaforum dataset.

physped.visualization.plot_trajectories.plot_walls_in_environment(ax: Axes, traj_plot_params: dict) → Axes

physped.visualization.plot_utils module

physped.visualization.plot_utils.apply_polar_plot_style(ax: Axes, params: DictConfig) → Axes

Applies a polar plot style to the given axes object.

Parameters: - ax: The axes object to apply the polar plot style to. - params: A dictionary containing parameters for customizing the plot style.

Returns: - The modified axes object.

physped.visualization.plot_utils.apply_xy_plot_style(ax: Axes, params: DictConfig) → Axes

Apply XY plot style to the given Axes object.

Parameters:

• ax (plt.Axes) – The Axes object to apply the style to.

• params (dict) – A dictionary containing the plot parameters.

Returns:

The modified Axes object.

Return type:

plt.Axes

physped.visualization.plot_utils.convert_rad_to_deg(theta: float) → float

physped.visualization.plot_utils.highlight_grid_box(ax: Axes, limits: Tuple, c: str = 'k') → Axes

Highlight the selected grid box.

Parameters:

• ax (plt.Axes) – The matplotlib Axes object to plot on.

• limits (Tuple) – The limits of the grid box as a tuple (xlims, ylims).

• c (str) – The color of the highlight box. Default is “k” (black).

Returns:

The modified matplotlib Axes object.

Return type:

plt.Axes

physped.visualization.plot_utils.highlight_position_selection(ax: Axes, params: DictConfig) → Axes

physped.visualization.plot_utils.highlight_velocity_selection(ax: Axes, params: DictConfig) → Axes

physped.visualization.plot_utils.plot_cartesian_spatial_grid(ax: Axes, grid_params: DictConfig, alpha: float = 0.8) → Axes

physped.visualization.plot_utils.plot_polar_labels(ax: Axes, grid_params: DictConfig) → Axes

physped.visualization.plot_utils.plot_polar_velocity_grid(ax: Axes, grid_params: DictConfig) → Axes

physped.visualization.plot_utils.plot_station_background(ax: Axes, config: DictConfig) → Axes

Plot the background image of the station.

Parameters:

• ax (plt.Axes) – The matplotlib Axes object to plot on.

• params (dict) – A dictionary containing the parameters for plotting.

Returns:

The modified matplotlib Axes object.

Return type:

plt.Axes

Module contents