ijazz.alpha_tensors
===================

.. py:module:: ijazz.alpha_tensors


Attributes
----------

.. autoapisummary::

   ijazz.alpha_tensors.__doc__


Functions
---------

.. autoapisummary::

   ijazz.alpha_tensors.alpha_evt
   ijazz.alpha_tensors.alpha_2d
   ijazz.alpha_tensors.alpha_3d
   ijazz.alpha_tensors.alpha_3d_2g
   ijazz.alpha_tensors.alpha_3d_mmg


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

.. py:data:: __doc__
   :value: Multiline-String

   .. raw:: html

      <details><summary>Show Value</summary>

   .. code-block:: python

      """This Modules computes the alpha matrix which allows to smear with a gaussion function an histogram
      The input histogram is of dimension d_mc (MC reference) and the smeared 
      one of dimension d_dt (smeared to the dataspace).
      Author: fabrice.couderc@cea.fr"""

   .. raw:: html

      </details>



.. py:function:: alpha_evt(d_min, d_max, bin_m, r_ll, s_ll)

   Compute the probability migration from due to a Gaussian over-smearing.
   Per-event variation of the alpha matrix (dim(d_min) = dim(d_max) = dim(rll) = dim(sll)).
   Note the computation is done with a normalisation to the fitting region (win_z)

   :param - d_min: minimum value of the mass point for data (we return a bined probability)
   :param - d_max: maximum value of the mass point for data (we return a bined probability)
   :param - bin_m: MC binning in Mee
   :param - r_ll: Gaussian mean (relative, gaussian mean will be mu x r_ll)
   :param - s_ll: Gaussian resolution(relative as well)

   Returns a 2D tensor (dim(bining_mc), dim(r_ll))
   dim(r_ll) should be the number of events or categories

   NB: this can be used to compute the integral of pi !


.. py:function:: alpha_2d(bin_d, bin_m, r_ll, s_ll)

   Compute the probability migration from due to a Gaussian over-smearing.
   Note the computation is done with a normalisation to the fitting region

   :param - bin_d: data binning in Mee
   :param - bin_m: MC binning in Mee
   :param - r_ll: Gaussian mean (relative, gaussian mean, scalar number)
   :param - s_ll: Gaussian resolution(relative as well, , scalar number)

   Returns a 2D tensor (dim(bining_data), dim(bining_mc))


.. py:function:: alpha_3d(b_ic, b_jc, r_ll, s_ll)

   Compute the probability migration from due to a Gaussian over-smearing.
   Note the computation is done with a normalisation to the fitting region

   :param - b_ic: data binning in Mee (2D)
   :param - b_jc: MC binning in Mee (2D)
   :param - r_ll: Gaussian mean (relative, gaussian mean will be mu x r_ll)
   :param - s_ll: Gaussian resolution(relative as well)

   Returns a 3D tensor (dim(bining_data), dim(bining_mc), dim(r_ll))
   dim(r_ll) should be the number of events or categories


.. py:function:: alpha_3d_2g(b_ic, b_jc, rll_sll1, rll_sll2, rll_sll3, rll_sll4)

   Compute the probability migration from due to a Gaussian over-smearing.
   Note the computation is done with a normalisation to the fitting region

   :param - b_ic: data binning in Mee (2D)
   :param - b_jc: MC binning in Mee (2D)
   :param - r_ll: Gaussian mean (relative, gaussian mean will be mu x r_ll)
   :param - s_ll: Gaussian resolution(relative as well)

   Returns a 3D tensor (dim(bining_data), dim(bining_mc), dim(r_ll))
   dim(r_ll) should be the number of events or categories


.. py:function:: alpha_3d_mmg(b_ic, b_jc, r_ll, s_ll)

   Compute the probability migration from due to a Gaussian over-smearing.
   Note the computation is done with a normalisation to the fitting region

   :param - b_ic: data binning in Mee (2D)
   :param - b_jc: MC binning in Mee (2D)
   :param - r_ll: Gaussian mean (relative, gaussian mean will be mu x r_ll)
   :param - s_ll: Gaussian resolution(relative as well)

   Returns a 3D tensor (dim(bining_data), dim(bining_mc), dim(r_ll))
   dim(r_ll) should be the number of events or categories