:py:mod:`pylbo.visualisation.spectra.spectrum_multi`
====================================================

.. py:module:: pylbo.visualisation.spectra.spectrum_multi


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   pylbo.visualisation.spectra.spectrum_multi.MultiSpectrumPlot




.. py:class:: MultiSpectrumPlot(dataseries, xdata, use_squared_omega, use_real_parts, figsize, custom_figure, **kwargs)


   Bases: :py:obj:`pylbo.visualisation.spectra.spectrum_figure.SpectrumFigure`

   
   Subclass that draws the multispectra.

   :param dataseries: The dataseries that should be used.
   :type dataseries: ~pylbo.data_containers.LegolasDataSeries
   :param xdata: Data to use for the horizontal axis. This can either be a key from the
                 parameters dictionary, or a list/numpy array containing actual data.
   :type xdata: str, list, numpy.ndarray
   :param use_squared_omega: If `True`, this will square the eigenvalues when they are plotted on the
                             vertical axis. If `False` (default), either the real or imaginary part of the
                             eigenvalues will be plotted depending on the value of `use_real_parts`.
   :type use_squared_omega: bool
   :param use_real_parts: If `True` (default), this will plot the real part of the eigenvalues on the
                          vertical axis. If `False` the imaginary part will be used.
   :type use_real_parts: bool
   :param figsize: Optional figure size like the usual matplotlib (x, x) size.
   :type figsize: tuple
   :param custom_figure: Optional, in the form (fig, ax). If supplied no new figure will be created
                         but this one will be used instead. `fig` refers to the matplotlib figure and
                         `ax` to a (single) axes instance, meaning that you can pass a subplot as well.
   :type custom_figure: tuple















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   .. py:method:: _validate_xdata(xdata)

      
      Validates the xdata passed, does typechecking and necessary casting.
      If a string is passed, this will request the proper values based on the
      parameters.

      :param xdata: The xdata used as x values on the spectrum plot.
      :type xdata: str, list, numpy.ndarray

      :returns: **xdata_values** -- The xdata values of proper length and casted to a Numpy array.
      :rtype: numpy.ndarray















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   .. py:method:: _get_ydata()

      
      Gets the y data based on the value of :attr:`use_squared_omega`.

      :returns: **ydata** -- The y data values, either the real or imaginary parts based on
                :attr:`use_real_parts`. Every element is an array in itself corresponding
                to the various datasets, hence depending on the gridpoints in every dataset
                the elements themselves may be of different length.
      :rtype: numpy.ndarray















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   .. py:method:: set_x_scaling(x_scaling)

      
      Sets the x scaling, properly adjusted to the dataseries length.

      :param x_scaling: Values to use for the x-scaling.
      :type x_scaling: int, float, complex, numpy.ndarray















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   .. py:method:: set_y_scaling(y_scaling)

      
      Sets the y scaling, properly adjusted to the dataseries length.

      :param y_scaling: Values to use for the y-scaling.
      :type y_scaling: int, float, complex, numpy.ndarray















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   .. py:method:: add_spectrum()

      
      Draw method, creates the spectrum.
















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   .. py:method:: add_continua(interactive=True)

      
      Adds the continua to the plot, either interactive or not.

      :param interactive: If `True`, makes the legend interactive.
      :type interactive: bool















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   .. py:method:: add_eigenfunctions()

      
      Adds the eigenfunctions to the current figure.
















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