pylbo.visualisation.modes.mode_data

Classes

ModeVisualisationData

Class that contains the data used for eigenmode visualisations.

Functions

`_handle_expected_input_value`(→ list[list[complex]])
`_check_grid_dataseries`(→ bool)	Check if all datasets in the dataseries have the same grid.

Module Contents

pylbo.visualisation.modes.mode_data._handle_expected_input_value(ds: pylbo.data_containers.LegolasDataSeries, value) → list[list[complex]]

pylbo.visualisation.modes.mode_data._check_grid_dataseries(ds: pylbo.data_containers.LegolasDataSeries) → bool

Check if all datasets in the dataseries have the same grid.

Parameters:: ds (LegolasDataSeries) – The dataseries to check.
Returns:: True if all datasets have the same grid, False otherwise.
Return type:: bool

class pylbo.visualisation.modes.mode_data.ModeVisualisationData(ds: pylbo.data_containers.LegolasDataSet | pylbo.data_containers.LegolasDataSeries, omega: complex | list[complex] | numpy.ndarray | list[list[complex]] | list[numpy.ndarray], ef_name: str = None, use_real_part: bool = True, complex_factor: complex | list[complex] | numpy.ndarray | list[list[complex]] | list[numpy.ndarray] = None, add_background: bool = False)

Class that contains the data used for eigenmode visualisations.

Parameters:

ds (LegolasDataSet/LegolasDataSeries) – The data set/series containing the eigenfunctions, having the same equilibria.
omega (list[list[complex]]) – The (approximate) eigenvalue(s) of the mode(s) to visualise.
ef_name (str) – The name of the eigenfunction to visualise.
use_real_part (bool) – Whether to use the real part of the eigenmode solution.
complex_factor (list[list[complex]]) – A complex factor to multiply the eigenmode solution with.
add_background (bool) – Whether to add the equilibrium background to the eigenmode solution.

ds

The dataseries containing the eigenfunctions and modes to visualise.

Type:: LegolasDataSeries

omega

The (approximate) eigenvalue(s) of the mode(s) to visualise.

Type:: list[list[complex]]

eigenfunction

The eigenfunction of the mode(s) to visualise.

Type:: list[list[np.ndarray]]

use_real_part

Whether to use the real part of the eigenmode solution.

Type:: bool

complex_factor

The complex factors to multiply the eigenmode solution with.

Type:: list[list[complex]]

add_background

Whether to add the equilibrium background to the eigenmode solution.

Type:: bool

ds

complex_factor = None

ds_bg

use_real_part = True

add_background = False

_print_bg_info = True

_ef_name = None

_ef_name_latex = None

_all_efs

omega = []

eigenfunction = []

property k2: float: The k2 wave number of the eigenmode solution.

property k3: float: The k3 wave number of the eigenmode solution.

property part_name: str: Returns the name of the part of the eigenmode solution to use, i.e. ‘real’ or ‘imag’.

get_ef_name_latex() → str: Returns the latex representation of the eigenfunction name.

_validate_complex_factor(complex_factor: list[list[complex]]) → list[list[complex]]

Validates the complex factors.

Parameters:: complex_factor (list[list[complex]]) – The complex factor to validate.
Returns:: The complex factor if it is valid, otherwise 1.
Return type:: complex

get_mode_solution(ef: numpy.ndarray, omega: complex, complex_factor: complex, u2: float | numpy.ndarray, u3: float | numpy.ndarray, t: float | numpy.ndarray, k2: float, k3: float) → numpy.ndarray

Calculates the full eigenmode solution for given coordinates and time. If a complex factor was given, the eigenmode solution is multiplied with the complex factor. If use_real_part is True the real part of the eigenmode solution is returned, otherwise the complex part.

Parameters:

ef (np.ndarray) – The eigenfunction to use.
omega (complex) – The eigenvalue to use.
complex_factor (complex,) – The complex factor to multiply with.
u2 (Union[float, np.ndarray]) – The y coordinate(s) of the eigenmode solution.
u3 (Union[float, np.ndarray]) – The z coordinate(s) of the eigenmode solution.
t (Union[float, np.ndarray]) – The time(s) of the eigenmode solution.
k2 (float) – The x2 wavenumber of the mode.
k3 (float) – The x3 wavenumber of the mode.

Returns:

The real or imaginary part of the eigenmode solution for the given set of coordinate(s) and time(s).

Return type:

np.ndarray

get_background(shape: tuple[int, Ellipsis], name=None) → numpy.ndarray

Returns the background of the eigenmode solution.

Parameters:

shape (tuple[int, ...]) – The shape of the eigenmode solution.
name (str) – The name of the background to use. If None, the background name will be inferred from the eigenfunction name.

Returns:

The background of the eigenmode solution, sampled on the eigenfunction grid and broadcasted to the same shape as the eigenmode solution.

Return type:

np.ndarray

_sample_background_on_ef_grid(bg: numpy.ndarray) → numpy.ndarray

Samples the background array on the eigenfunction grid.

Parameters:: bg (np.ndarray) – The background array with Gaussian grid spacing
Returns:: The background array with eigenfunction grid spacing
Return type:: np.ndarray

_get_background_name() → str

Returns the name of the background.

Returns:: The closest match between the eigenfunction name and the equilibrium name.
Return type:: str
Raises:: ValueError – If the eigenfunction name is a magnetic vector potential component or derived eigenfunction that is not the magnetic field.