:py:mod:`aces.sefd.plotting`
============================

.. py:module:: aces.sefd.plotting

.. autoapi-nested-parse::

   Tooling to support the plotting of SEFD figures



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


Functions
~~~~~~~~~

.. autoapisummary::

   aces.sefd.plotting.fitfunc2
   aces.sefd.plotting.linfunc
   aces.sefd.plotting.fit_gauss
   aces.sefd.plotting.find_mode
   aces.sefd.plotting.make_sefd_cmap



Attributes
~~~~~~~~~~

.. autoapisummary::

   aces.sefd.plotting.logger


.. py:data:: logger

   

.. py:function:: fitfunc2(p, x)


.. py:function:: linfunc(p, x)


.. py:function:: fit_gauss(x: numpy.ndarray, y: numpy.ndarray, p0: list[float], fkey: str = 'gau') -> tuple[tuple[float, Ellipsis], float, float, float]

   Given the bin centers and counts for data that has been binned, an 
   attempt to fit a gaussian to said binned data will be performed.

   Args:
       x (np.ndarray): The bin centers of the binned data
       y (np.ndarray): The bin counts per bins of the binned data
       p0 (list[float]): Initial guess parameters of the desired model to fit
       fkey (str, optional): Model to fit to the data. Defaults to 'gau'.

   Raises:
       ValueError: Raised when an unkown key / fit function is suppliede

   Returns:
       tuple[tuple[float,...], float, float, float]: Contains the best fit parameters, the x and y of the model evaluated with best fit parameters, and the reduced chi2


.. py:function:: find_mode(data: numpy.ma.MaskedArray, parameters: dict[Any, Any]) -> float

   Estimates and returns the mode of those input data that lie within the limits set by the parameters.
   The mode is determined by fitting a guassian function to the peak of the histogram of the logarithms of
   the values.

   :param data: np.array of real values
   :param parameters: dictionary giving the data value bounds
   :return:


.. py:function:: make_sefd_cmap(ssl: float, ssu: float) -> tuple[matplotlib.colors.LinearSegmentedColormap, matplotlib.colors.Normalize]

   Construct a colour map that is appropriate (targeted) for SEFD plotting

   Args:
       ssl (float): Lower limit of the SEFD values to plot (Tsys)
       ssu (float): Upper limit of the SEFD values to plot (Tsys)

   Returns:
       tuple[mpl.colors.LinearSegmentedColormap, mpl.colors.Normalize]: The colours and normalise matplotlib instances to use