:py:mod:`fitlistreader.peak`
============================

.. py:module:: fitlistreader.peak


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

Classes
~~~~~~~

.. autoapisummary::

   fitlistreader.peak.BackgroundColl
   fitlistreader.peak.Peak




.. py:class:: BackgroundColl


   Container class to order attributes more nicely.

   .. py:attribute:: __slots__
      :value: ('regions', 'model', 'nparams', 'params', 'vol', 'dvol')

      


.. py:class:: Peak(fit, peak_index, spc, ID, use_val='cal', binwidth=1)


   Supply a sub-xml tree element <fit> and which peak in that fit you want to extract, ennumerated top-down via 0-indexing.
   use_val is an internal devision between "cal" and "uncal" for all  marker-values, which will be different if the spectrum was calibrated by HDTV.
           Usually you are interested in the "cal" version, which is the default.

   .. py:method:: build_bg(bg_xml)


   .. py:method:: _poly(coeff)

      returns polinomial function with coefficients as python function of signature f(x::float)=>poly(x)::float


   .. py:method:: _vol_from_poly(coeff, a, b)

      computes area under polinomial with coefficients coeff between x=a and x=b analytically


   .. py:method:: _better_vol_poly(coeff, a, b)

      more efficient implementation of `_vol_from_poly`. Does not require re-building of coefficients. Independant of `_poly`


   .. py:method:: _general_poly_vol_err_indep(coeff, dcoeff, a, da, b, db)

      general formula for the uncertainty in the volume determination of a polynomial with coefficients coeff between x=a and x=b.
      The single uncertainties dcoeff, da and db are assumed to be independant.
      dcoeff must have the same dimension as coeff.


   .. py:method:: build_empty()
      :classmethod:

      generates a valid Peak object with all filds containing None values or similar


   .. py:method:: dummy()