loki2.encode_trans
==================

.. py:module:: loki2.encode_trans

.. autoapi-nested-parse::

   Transcriptomics encoding module.

   This module provides functionality to encode transcriptomics data
   using pre-trained models for cross-modal analysis.





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

.. py:function:: encode_transcriptomics(ad_path: pathlib.Path, output_path: pathlib.Path, model_path: pathlib.Path, housekeeping_path: pathlib.Path, batch_size: int = 100, num_threads: Optional[int] = None, device: str = 'cpu') -> None

   Encode transcriptomics data using a pre-trained model.

   This function processes AnnData objects containing single-cell RNA-seq data,
   generates gene expression prompts, and encodes them using a pre-trained
   CLIP model for cross-modal analysis.

   :param ad_path: Path to the input AnnData (.h5ad) file.
   :param output_path: Path where encoded embeddings will be saved (.pt file).
   :param model_path: Path to the pre-trained model checkpoint.
   :param housekeeping_path: Path to CSV file containing housekeeping genes.
   :param batch_size: Batch size for encoding. Defaults to 100.
   :param num_threads: Number of threads for PyTorch operations.
                       If None, uses default. Defaults to None.
   :param device: Device to use for encoding ('cpu' or 'cuda'). Defaults to 'cpu'.

   :raises ValueError: If observation names are not unique, if duplicate or
       missing cell identifiers are found, or if num_threads is invalid.


.. py:function:: load_model(model_path: pathlib.Path, device: str = 'cuda') -> Tuple[Any, Any, Any]

   Load a pre-trained CoCa model and tokenizer.

   :param model_path: Path to the pre-trained model checkpoint.
   :param device: Device to load the model on ('cpu' or 'cuda').
                  Defaults to 'cuda'.

   :returns:

             Tuple containing:
                 - model: Loaded CoCa model
                 - preprocess: Preprocessing function for images
                 - tokenizer: Text tokenizer
   :rtype: Tuple[Any, Any, Any]


.. py:function:: load_prompts_csv(csv_path: pathlib.Path) -> pandas.DataFrame

   Load gene prompts from a CSV file.

   :param csv_path: Path to the CSV file containing gene prompts.

   :returns: DataFrame with 'cell_id' and 'label' columns.
   :rtype: pd.DataFrame

   :raises ValueError: If required columns are missing.


.. py:function:: generate_gene_df(ad: Any, house_keeping_genes: pandas.DataFrame, todense: bool = True) -> pandas.DataFrame

   Generate a DataFrame with the top 50 genes for each observation.

   Removes genes containing '.' or '-' in their names, as well as genes
   listed in the housekeeping genes DataFrame.

   :param ad: AnnData object containing gene expression data.
   :param house_keeping_genes: DataFrame with a 'genesymbol' column listing
                               housekeeping genes to exclude.
   :param todense: Whether to convert the sparse matrix (ad.X) to a dense
                   matrix before creating a DataFrame. Defaults to True.

   :returns:

             DataFrame with two columns: 'cell_id' and 'label'.
                 Each label entry is a string with the top 50 gene names
                 (space-separated) for that observation.
   :rtype: pd.DataFrame


.. py:function:: encode_texts(model, tokenizer, texts, batch_size=256, device='cuda')

.. py:function:: build_parser() -> argparse.ArgumentParser

.. py:function:: main(argv: Iterable[str] = None) -> None