pylbo.visualisation.modes.mode_figure

Classes

Main class to hold the figure, axes and colorbar for eigenmode visualisations.

Module Contents

class pylbo.visualisation.modes.mode_figure.ModeFigure(figsize: tuple[int, int], data: pylbo.visualisation.modes.mode_data.ModeVisualisationData, show_ef_panel: bool)[source]

Bases: pylbo.visualisation.figure_window.FigureWindow

Main class to hold the figure, axes and colorbar for eigenmode visualisations.

Parameters:

figsize (tuple[int, int]) – The size of the figure.
data (ModeVisualisationData) – The data used for eigenmode visualisations.

fig[source]

The figure.

Type:: matplotlib.figure.Figure

axes[source]

The axes.

Type:: dict[str, matplotlib.axes.Axes]

cbar[source]

The colorbar.

Type:: matplotlib.colorbar.Colorbar

cbar_ax[source]

The axes for the colorbar.

Type:: matplotlib.axes.Axes

data[source]

Data object containing all data associated with the selected eigenmode.

Type:: ModeVisualisationData

u1_data

The data for the \(u_1\) coordinate.

Type:: np.ndarray

u2_data

The data for the \(u_2\) coordinate.

Type:: Union[float, np.ndarray]

u3_data

The data for the \(u_3\) coordinate.

Type:: Union[float, np.ndarray]

ef_data[source]

The data for the eigenfunction.

Type:: list[dict]

time_data

The data for the time.

Type:: Union[float, np.ndarray]

omega_txt

The text for the \(\omega\) label.

Type:: matplotlib.text.Text

k2k3_txt

The text for the \(k_2-k_3\) label.

Type:: matplotlib.text.Text

u2u3_txt

The text for the \(u_2-u_3\) label.

Type:: matplotlib.text.Text

t_txt

The text for the time label.

Type:: matplotlib.text.Text

cbar = None[source]

_cbar_hspace = 0.01[source]

_show_ef_panel[source]

_annotate = True[source]

axes[source]

cbar_ax[source]

data[source]

_view = None[source]

ef_data = [][source]

solution_shape = None[source]

_solutions = 0[source]

_check_if_number(val: float, attr_name: str) → float[source]

Checks if a given value is a number.

Parameters:

val (float) – The value to check.
attr_name (str) – The name of the value.

Raises:

ValueError – If the value is not a number.

_check_if_array(array: numpy.ndarray, attr_name: str) → numpy.ndarray[source]

Checks is a given value is a numpy array.

Parameters:

array (np.ndarray) – The value to check.
attr_name (str) – The name of the value.

Raises:

ValueError – If the value is not a numpy array.

abstract set_plot_arrays() → None[source]: Sets the arrays used for plotting. This should implement setting of u1_data, u2_data, u3_data, t_data and ef_data.

calculate_mode_solution(efdata: dict, u2: float | numpy.ndarray, u3: float | numpy.ndarray, t: float | numpy.ndarray) → numpy.ndarray[source]

Calculates the mode solution.

Parameters:

efdata (dict) – The data for the eigenfunction. This should be a dictionary with the keys 'ef' and 'omega', with 'ef'``containing the eigenfunction and ``'omega' the corresponding eigenvalue.
u2 (Union[float, np.ndarray]) – The data for the \(u_2\) coordinate.
u3 (Union[float, np.ndarray]) – The data for the \(u_3\) coordinate.
t (Union[float, np.ndarray]) – The data for the time.

Returns:

The mode solution.

Return type:

np.ndarray

property ax: matplotlib.axes.Axes[source]: returns: Alias for the axes containing the eigenmode solution view. :rtype: matplotlib.axes.Axes

property solutions: numpy.ndarray[source]: returns: The solutions for the eigenmode :rtype: np.ndarray

draw() → None[source]

abstract draw_solution() → None[source]

draw_textboxes() → None[source]

draw_eigenfunction() → None[source]: Draws the eigenfunction(s) to the figure.

add_axes_labels() → None[source]

_create_cbar_axes(width: float) → matplotlib.axes.Axes[source]

Creates the axes for the colorbar.

Parameters:: width (float) – The width of the colorbar axes.
Returns:: The axes for the colorbar.
Return type:: matplotlib.axes.Axes

get_view_xlabel() → str[source]

get_view_ylabel() → str[source]

get_view_cbar_label() → str[source]

add_omega_txt(ax, **kwargs) → None[source]

Creates a textbox on the axis with the value of the eigenfrequency.

Parameters:

ax (Axes) – The axes to use for the textbox.
**kwargs – Additional keyword arguments to pass to add_axis_label().

add_k2k3_txt(ax, **kwargs) → None[source]

Creates a textbox on the figure with the value of the k2 and k3 coordinates.

Parameters:

ax (Axes) – The axes to use for the textbox.
**kwargs – Additional keyword arguments to pass to add_axis_label().

add_u2u3_txt(ax, **kwargs) → None[source]

Creates a textbox on the figure with the value of the \(u_2-u_3\) coordinates.

Parameters:

ax (Axes) – The axes to use for the textbox.
**kwargs – Additional keyword arguments to pass to add_axis_label().

add_t_txt(ax, **kwargs) → None[source]

_create_figure_layout(figsize: tuple[int, int]) → tuple[matplotlib.figure.Figure, dict][source]

Create the figure layout for the visualisation. Two panels are created: the top one for the eigenfunction and the bottom one for the visualisation.

Parameters:

figsize (tuple[int, int]) – The size of the figure.

Returns:

fig (~matplotlib.figure.Figure) – The figure to use for the visualisation.
axes (dict) – The axes to use for the visualisation.

create_animation(times: numpy.ndarray, filename: str, fps: float = 10, dpi: int = 200) → None[source]

Creates an animation of the eigenmode solution over a given time interval.

Parameters:

times (np.ndarray) – The times at which to create the animation.
filename (str) – The filename of the animation.
fps (float) – The frames per second of the animation.
dpi (int) – The resolution of the animation.