deepbiop.fa
===========

.. py:module:: deepbiop.fa


Classes
-------

.. autoapisummary::

   deepbiop.fa.EncoderOption
   deepbiop.fa.ParquetEncoder
   deepbiop.fa.RecordData


Functions
---------

.. autoapisummary::

   deepbiop.fa.convert_multiple_fas_to_one_fa
   deepbiop.fa.encode_fa_path_to_parquet
   deepbiop.fa.encode_fa_path_to_parquet_chunk
   deepbiop.fa.encode_fa_paths_to_parquet
   deepbiop.fa.select_record_from_fa
   deepbiop.fa.select_record_from_fa_by_random
   deepbiop.fa.write_fa
   deepbiop.fa.write_fa_parallel


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

.. py:class:: EncoderOption

   Options for configuring the FASTA sequence encoder.

   This struct provides configuration options for encoding FASTA sequences,
   such as which bases to consider during encoding.

   # Fields

   * `bases` - A vector of valid bases (as bytes) to use for encoding. Defaults to "ATCGN".

   # Example

   ```
   use deepbiop_fa::encode::option::EncoderOption;

   let options = EncoderOption::default();
   ```


   .. py:property:: bases
      :type: list[int]



.. py:class:: ParquetEncoder

   An encoder for converting FASTA records to Parquet format.

   This struct provides functionality to encode FASTA sequence data into Parquet files,
   which are an efficient columnar storage format.

   # Fields

   * `option` - Configuration options for the encoder, including which bases to consider

   # Example

   ```
   use deepbiop_fa::encode::{option::EncoderOption, parquet::ParquetEncoder};

   let options = EncoderOption::default();
   let encoder = ParquetEncoder::new(options);
   ```


.. py:class:: RecordData

   .. py:property:: id
      :type: str



   .. py:property:: seq
      :type: str



.. py:function:: convert_multiple_fas_to_one_fa(paths, result_path, parallel)

.. py:function:: encode_fa_path_to_parquet(fa_path, bases, result_path = None)

.. py:function:: encode_fa_path_to_parquet_chunk(fa_path, chunk_size, parallel, bases)

.. py:function:: encode_fa_paths_to_parquet(fa_path, bases)

.. py:function:: select_record_from_fa(selected_reads, fq, output)

.. py:function:: select_record_from_fa_by_random(fq, number, output)

.. py:function:: write_fa(records_data, file_path = None)

.. py:function:: write_fa_parallel(records_data, file_path, threads)