:py:mod:`pypetb.Capability`
===========================

.. py:module:: pypetb.Capability


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

Classes
~~~~~~~

.. autoapisummary::

   pypetb.Capability.Capability




.. py:class:: Capability(mydf, mydict)


   Capability analysis works as a model.
   Once input parameter are specified, model is solved and available
    to print different reports depending on the user requirement

   Args:
   -----
   mydf : pandas dataframe
     At least the measures in a column. For LT analysis, batch column too.

   mydict : dictionary
     Value --> String. Measures column name | Batch --> Optional String.
     Batch ID column name | LSL --> Optional float. Lower specification
      limit | HSL --> Optional float. Higher specification limit |
      Goal --> Optional float. Goal. ONE OF LSL OR HSL MUST BE SPECIFIED

   Methods:
   ----------
   getLog: string
         printable string containing all individual calculations

   Normality_test: matplotlib figure
         report to check sample normality

   Report: matplotlib figure
         Capability report

   Raises:
   ---------
   TypeError

   Init_01
       mydict keys ar not correctly defined

   Init_02
       Specified measures column name is not found

   Init_03
       Dataframe contains null values

   Init_04
       Specified measures column a non numerical type column

   .. py:method:: getLog()

      Return a string which contain all important calculations.

      Returns:
      ---------
      log: String
          all step logged


   .. py:method:: Normality_test()

      Normality_test report is a figure that contain different chart and
      data description which helps to conclude if the measurement could be
      explained as a normal distribution so capability analysis could be
      done, or, inthe other hand, this analysis could not be take place.
      First, an histogram is showed, then, the probability plot. Third one
      is a time series plot and a descriptive data box.
      Finally, an advisement is showed based on p value.

      Returns:
      ---------
      Fig_NT : matplotlib figure
          Set of charts


   .. py:method:: __Cp_Report()

      Cp draws short term report

      Returns:
      ---------
      Fig_Cp : matplotlib figure
          Short Term Report


   .. py:method:: __Pp_Report()

      Pp draws long term report

      Returns:
      ---------
      Fig_Lp : matplotlib figure
          long Term Report


   .. py:method:: Report()

      Report is a figure that contain a chart where measures distirbution
      is showed with its tolerance. Then capability factor is determined
      and showed

      Returns:
      ---------
      myFigure : matplotlib figure
          Set of charts