hapsburg.preprocessing_lowmem
=============================

.. py:module:: hapsburg.preprocessing_lowmem


Classes
-------

.. autoapisummary::

   hapsburg.preprocessing_lowmem.PreProcessingHDF5_lowmem
   hapsburg.preprocessing_lowmem.PreProcessingEigenstrat_lowmem
   hapsburg.preprocessing_lowmem.PreProcessingEigenstratX_lowmem


Functions
---------

.. autoapisummary::

   hapsburg.preprocessing_lowmem.extract_snps_hdf5_lowmem
   hapsburg.preprocessing_lowmem.load_preprocessing_lowmem


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

.. py:function:: extract_snps_hdf5_lowmem(h5, ids_ref, markers, ch, meta_path_ref, verbose=True, diploid=True)

   Extract genotypes from h5 on ids and markers.
   If diploid, concatenate haplotypes along 0 axis.
   Extract indivuals first, and then subset to SNPs.
   Return 2D array [# haplotypes, # markers]


.. py:class:: PreProcessingHDF5_lowmem(conPop=[], save=True, output=True)

   Bases: :py:obj:`hapsburg.preprocessing.PreProcessingHDF5`


   Class for PreProcessing the Data.
   Standard: Intersect Reference Data with Individual Data
   Return the Intersection Dataset


   .. py:method:: load_data(iid='MA89', ch=6, start=-np.inf, end=np.inf)

      Return Matrix of reference [k,l], Matrix of Individual Data [2,l],
      as well as linkage Map [l]



   .. py:method:: optional_postprocessing(gts_ind, gts, r_map, pos, out_folder, pCon, read_counts=[])

      Postprocessing steps of gts_ind, gts, r_map, and the folder,
      based on boolean fields of the class.



.. py:class:: PreProcessingEigenstrat_lowmem(save=True, output=True, packed=1, sep='\\s+')

   Bases: :py:obj:`hapsburg.preprocessing.PreProcessingEigenstrat`


   Class for PreProcessing Eigenstrat Files
   Same as PreProcessingHDF5 for reference, but with Eigenstrat coe
   for target


   .. py:method:: optional_postprocessing(gts_ind, gts, r_map, pos, out_folder, read_counts=[])

      Postprocessing steps of gts_ind, gts, r_map, and the folder,
      based on boolean fields of the class.



   .. py:method:: load_data(iid='MA89', ch=6)

      Return Matrix of reference [k,l], Matrix of Individual Data [2,l],
      as well as linkage Map [l] and the output folder.
      Save the loaded data if self.save==True
      Various modifiers in class fields (check also PreProcessingHDF5)



.. py:class:: PreProcessingEigenstratX_lowmem(save=True, output=True, packed=1, sep='\\s+')

   Bases: :py:obj:`PreProcessingEigenstrat_lowmem`, :py:obj:`hapsburg.preprocessing.PreProcessingEigenstratX`


   Class for PreProcessing Eigenstrat Files
   Same as PreProcessingHDF5 for reference, but with Eigenstrat coe
   for target


   .. py:method:: set_output_folder(iid, ch='X')

      Set the output folder after folder_out.
      General Structure for HAPSBURG: folder_out/iid/chrX/



   .. py:method:: get_1000G_path(h5_path1000g, ch='X')

      Construct and return the path
      to the 1000 Genome reference panel



   .. py:method:: es_get_index_iid(es, iid)

      Get IID of Indices



   .. py:method:: extract_snps_es(es, id, markers)

      Use Eigenstrat object. Extract genotypes for individual index i
      (integer) for
      list of markers. Do conversion from Eigenstrat GT to format
      used here



   .. py:method:: load_data(iid='MA89', ch='X')

      Return Matrix of reference [k,l], Matrix of Individual Data [2,l],
      as well as linkage Map [l] and the output folder.
      Save the loaded data if self.save==True
      Various modifiers in class fields (check also PreProcessingHDF5)



.. py:function:: load_preprocessing_lowmem(p_model='Eigenstrat', conPop=[], save=True, output=True)

   Factory method to load the Transition Model.
   Return