detectda.hypo
=============

.. py:module:: detectda.hypo


Classes
-------

.. autoapisummary::

   detectda.hypo.VacuumSeries


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

.. py:class:: VacuumSeries(vacuum_video, observed_ImageSeries, parametric=True, div=1, n_jobs=None)

   Bases: :py:obj:`detectda.imgs.ImageSeries`


   Functionality to generate vacuum region videos for multiple hypothesis testing.


   .. py:method:: adjust_alpha(alpha, conservative=False)

      Adjust p-values based on a different alpha value.


      :param alpha: Statistical significance level.
      :type alpha: float
      :param conservative: Whether to use Benjamini-Yekutieli. The default is False.
      :type conservative: bool, optional

      :rtype: None.



   .. py:method:: fit(convert_to_int=False)

      Fits the Poisson mle for the vacuum region if parametric==True

      Else, it fits the empirical probability mass function.



   .. py:method:: gen_images(n)

      Generate and return a random image according to estimated null distribution



   .. py:method:: kolm_dist()

      Check how far the empirical distribution of vacuum values is from Poisson
      with parameter equal to mle, in terms of the Kolmogorov distance

      Uses the DKW inequality with the tight constant = 2 for Poisson testing.



   .. py:method:: plot_hypo()

      Plots hypothesis testing sequence.



   .. py:method:: transform(n, func='pers_entr', seed=0, alpha=0.05, conservative=False)

      Collects p-values and rejections for based off n Monte Carlo simulations...