hapsburg.hmm_inference
======================

.. py:module:: hapsburg.hmm_inference

.. autoapi-nested-parse::

   Main Inference Class for HMM. Wrapper for Inerence of Posterior.
   @ Author: Harald Ringbauer, 2019, All rights reserved



Classes
-------

.. autoapisummary::

   hapsburg.hmm_inference.HMM_Analyze


Functions
---------

.. autoapisummary::

   hapsburg.hmm_inference.prep_3x3matrix
   hapsburg.hmm_inference.exponentiate_r
   hapsburg.hmm_inference.print_memory_usage


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

.. py:class:: HMM_Analyze(folder='./Simulated/Example0/', t_model='model', e_model='haploid', p_model='SardHDF5', post_model='Standard', output=True, save=True, cython=True, manual_load=False, lowmem=False, save_fp=True, start=-np.inf, end=np.inf)

   Bases: :py:obj:`object`


   Analyze Class for HMMs.
   This is the main Class, all specific Inference schemes inherit from it
   and overwrite functions.
   Contains objects as field for pre-processing, transition as well as
   emission probabilities.
   Contains most Parameters (but some of them like the output folders are decided
   by pre-processing subclass)


   .. py:attribute:: folder
      :value: ''



   .. py:attribute:: output
      :value: True



   .. py:attribute:: save
      :value: True



   .. py:attribute:: save_fp
      :value: True



   .. py:attribute:: n_ref
      :value: 20



   .. py:attribute:: sanity_checks
      :value: True



   .. py:attribute:: ref_states
      :value: []



   .. py:attribute:: ob_stat
      :value: []



   .. py:attribute:: pCon
      :value: []



   .. py:attribute:: lowmem
      :value: False



   .. py:attribute:: overhang
      :value: 0



   .. py:attribute:: r_map
      :value: []



   .. py:attribute:: pos
      :value: []



   .. py:attribute:: v_path
      :value: []



   .. py:attribute:: posterior
      :value: []



   .. py:attribute:: iid
      :value: ''



   .. py:attribute:: ch
      :value: 0



   .. py:attribute:: start


   .. py:attribute:: end


   .. py:attribute:: fwd_bkwd
      :value: 0



   .. py:attribute:: e_model
      :value: 'haploid'



   .. py:attribute:: t_model
      :value: 'model'



   .. py:attribute:: p_model
      :value: 'SardHDF5'



   .. py:attribute:: post_model
      :value: 'Standard'



   .. py:method:: load_objects(iid='', ch=0, c=0.0)

      Load all the required Objects in right order



   .. py:method:: load_secondary_objects(c=0.0)

      Load all secondary objects
      (but not the pre-processing one)



   .. py:method:: load_data(iid='', ch=0)

      Load the External Data



   .. py:method:: load_emission_model(c=0.0, pCon=[])

      Method to load an Emission Model



   .. py:method:: load_transition_model()

      Load the Transition Model



   .. py:method:: load_preprocessing_model(conPop=[])


   .. py:method:: load_postprocessing_model()


   .. py:method:: prepare_rmap(cm=False, min_gap=1e-10, max_gap=0.05)

      Return the recombination map [in Morgan]
      Input: Map Positions [l]
      Return: Rec. Distance Array [l]
      cm: Whether input is in centimorgan or morgan
      min_cap: Minimum Gap between Loci



   .. py:method:: pre_compute_transition_matrix(t, r_vec, n_ref)

      Precompute and return the full transition Matrix
      t full Transition Matrix [k,k]. NO LOG STATE
      r_vec Map Length of Jumps [l]



   .. py:method:: calc_posterior(save=True, full=False, in_val=0.0001)

      Calculate the poserior for each path
      FULL: Wether to return fwd, bwd as well as tot_ll (Mode for postprocessing)
      in_val: The Initial Probability to copy from one Ind.



   .. py:method:: calc_posterior_lowmem(save=True, full=False, in_val=0.0001)

      same functionality as calc_posterior, but a low-memory implmentation.
      The emission matrix is not pre-computed, but calculated for each marker on the fly
      the reference panel is stored in the most compact format possible (aka one bit per allele)



   .. py:method:: compute_tot_neg_likelihood(c, in_val=0.0001)

      Calculate the poserior for each path
      in_val: The Initial Probability to copy from one Ind.



   .. py:method:: optimze_ll_transition_param(roh_trans_params)

      Calculate and return the log likelihoods for Transitions Parameters
      roh_trans_params [m]



   .. py:method:: optimize_ll_contamination_BFGS(init_c)

      Find MLE of contamination rate by L-BFGS-B.



   .. py:method:: optimize_ll_contamANDerr(init_c, init_err)


   .. py:method:: post_processing(save=True)

      Do the Postprocessing of ROH Blocks
      Parameters: See in Postprocessing



   .. py:method:: mmr_call(windowSize=0.001, save=True)

      Calculate Maximal Match Rate



.. py:function:: prep_3x3matrix(t, n_ref)

   Prepares the grouped 3x3 Matrix (3rd State: Everything in OTHER ROH State)


.. py:function:: exponentiate_r(rates, rec_v)

   Calculates exponentiation of the rates matrix with rec_v
   rates: 2D Matrix.
   rec_v: Array of length l


.. py:function:: print_memory_usage()

   Print the current Memory Usage in mB