pylbo.utilities.toolbox

Functions

timethis(func)

get_axis_geometry(ax)

Retrieves the geometry of a given matplotlib axis.

get_values(array, which_values)

Determines which values to retrieve from an array.

add_pickradius_to_item(item, pickradius)

Makes a matplotlib artist pickable and adds a pickradius.

custom_enumerate(iterable[, start, step])

Does a custom enumeration with a given stepsize.

transform_to_list(→ list)

Transforms a given input argument obj to a list. If obj

transform_to_numpy(→ numpy.ndarray)

Transforms a given input argument obj to a numpy array.

reduce_to_unique_array(→ numpy.ndarray)

Reduces a given array to its unique values, preserving the order.

get_all_eigenfunction_names(→ numpy.ndarray[str])

Merges the regular and derived eigenfunction names into a unique array,

get_maximum_eigenvalue(→ complex)

Calculates the maximum eigenvalue of a given array of eigenvalues.

solve_cubic_exact(a, b, c, d)

Solves a given cubic polynomial of the form

count_zeroes(eigfuncs[, real])

Counts the number of zeroes of an array of complex eigenfunctions by looking at

find_resonance_location(continuum, grid, sigma)

Finds the resonance location between sigma and the continuum. For example, if

Module Contents

pylbo.utilities.toolbox.timethis(func)[source]
pylbo.utilities.toolbox.get_axis_geometry(ax)[source]

Retrieves the geometry of a given matplotlib axis.

Parameters:

ax (Axes) – The axis to retrieve the geometry from.

Returns:

The geometry of the given matplotlib axis.

Return type:

tuple

pylbo.utilities.toolbox.get_values(array, which_values)[source]

Determines which values to retrieve from an array.

Parameters:
  • array (numpy.ndarray) – The array with values.

  • which_values (str) –

    Can be one of the following:

    • ”average”: returns the average of the array

    • ”minimum”: returns the minimum of the array

    • ”maximum”: returns the maximum of the array

    If not supplied or equal to None, simply returns the array.

Returns:

array – Numpy array with values depending on the argument provided.

Return type:

numpy.ndarray

pylbo.utilities.toolbox.add_pickradius_to_item(item, pickradius)[source]

Makes a matplotlib artist pickable and adds a pickradius. We have to handle this separately, because for line2D items the method set_picker() is deprecated from version 3.3 onwards.

Parameters:
  • item (Artist) – The artist which will be made pickable

  • pickradius (int, float) – Sets the pickradius, which determines if something is “on” the picked point.

pylbo.utilities.toolbox.custom_enumerate(iterable, start=0, step=1)[source]

Does a custom enumeration with a given stepsize.

Parameters:
  • iterable (Iterable) – The iterable to iterate over.

  • start (int) – The starting value for enumerate.

  • step (int) – The stepsize between enumerate values.

Yields:
  • start (int) – The current index in iterable, incremented with step.

  • itr (~typing.Iterable) – The corresponding entry of iterable.

pylbo.utilities.toolbox.transform_to_list(obj: any) list[source]

Transforms a given input argument obj to a list. If obj is a Numpy-array or tuple, a cast to list() is invoked.

Parameters:

obj (any) – The object to transform.

Returns:

The object converted to a list.

Return type:

list

pylbo.utilities.toolbox.transform_to_numpy(obj: any) numpy.ndarray[source]

Transforms a given input argument obj to a numpy array.

Parameters:

obj (any) – The object to transform.

Returns:

The object transformed to a numpy array.

Return type:

numpy.ndarray

pylbo.utilities.toolbox.reduce_to_unique_array(array: numpy.ndarray) numpy.ndarray[source]

Reduces a given array to its unique values, preserving the order.

Parameters:

array (numpy.ndarray) – The array to reduce.

Returns:

The array with unique values.

Return type:

numpy.ndarray

pylbo.utilities.toolbox.get_all_eigenfunction_names(data: pylbo.data_containers.LegolasDataContainer) numpy.ndarray[str][source]

Merges the regular and derived eigenfunction names into a unique array, preserving order.

Parameters:

data (LegolasDataContainer) – The data container containing the eigenfunction names.

Returns:

The array with unique eigenfunction names.

Return type:

numpy.ndarray

pylbo.utilities.toolbox.get_maximum_eigenvalue(eigenvalues: numpy.ndarray[complex], real: bool = True, re_range: Tuple[float, float] = None) complex[source]

Calculates the maximum eigenvalue of a given array of eigenvalues. The real or imaginary part is used, depending on the real argument. If a range is specified, the maximum eigenvalue is calculated within that range on the real axis.

Parameters:
  • eigenvalues (numpy.ndarray(dtype=complex)) – The array of eigenvalues.

  • real (bool) – If True, the real part of the eigenvalues is used. Imaginary part otherwise.

  • re_range (tuple(float, float)) – The range on the real axis to calculate the maximum eigenvalue. Defaults to None, which means all eigenvalues are considered.

Returns:

The maximum eigenvalue.

Return type:

complex

pylbo.utilities.toolbox.solve_cubic_exact(a, b, c, d)[source]

Solves a given cubic polynomial of the form \(ax^3 + bx^2 + cx + d = 0\) using the analytical cubic root formula instead of the general numpy.roots routine. From StackOverflow.

Parameters:
Returns:

roots – The three roots of the cubic polynomial as a Numpy array.

Return type:

np.ndarray(ndim=3, dtype=complex)

pylbo.utilities.toolbox.count_zeroes(eigfuncs, real=True)[source]

Counts the number of zeroes of an array of complex eigenfunctions by looking at sign changes of the real and imaginary part of the eigenfunctions. Excludes the eigenfunction boundaries.

Parameters:
  • eigfuncs (numpy.ndarray(dtype=complex)) – Array of eigenfunction arrays of complex numbers.

  • real (bool) – If True, counts the number of zeroes of the real part of the eigenfunctions. If False, counts the number of zeroes of the imaginary part.

Returns:

The number of zeroes of each eigenfunction.

Return type:

np.ndarray(dtype=int)

pylbo.utilities.toolbox.find_resonance_location(continuum, grid, sigma)[source]

Finds the resonance location between sigma and the continuum. For example, if the continuum is given by [5, 6, 7, 8, 9, 10] and the grid is equal to [0, 1, 2, 3, 4, 5], then for a sigma = 9 the resonance location is 4. For a sigma equal to 8.5 the resonance location is 3.5. For a sigma outside of the continuum the resonance location is None. If the continuum array is not monotone, then the resonance location is interpolated between the first matched interval.

Parameters:
  • continuum (numpy.ndarray(dtype=complex)) – Array containing the range of a specific continuum. Can be complex, but only the resonance with the real part is calculated.

  • grid (numpy.ndarray) – The grid on which the continuum is defined.

  • sigma (complex) – A given eigenvalue.

Returns:

The position where there is resonance between the eigenmode and the continuum. Returns None if there is no resonance with the specified continuum.

Return type:

None, np.ndarray(float)