aigct.repository
================

.. py:module:: aigct.repository

.. autoapi-nested-parse::

   Data access layer methods for accessing variant repository.
   Classes here provide an encapsulation layer to hide the internal
   details of the repository structure.



Attributes
----------

.. autoapisummary::

   aigct.repository.MERGE_CHUNK_SIZE
   aigct.repository.TASK_SUBFOLDER
   aigct.repository.DATA_FOLDER
   aigct.repository.TASK_FOLDERS
   aigct.repository.VARIANT_PK_COLUMNS
   aigct.repository.VARIANT_NON_PK_COLUMNS
   aigct.repository.VARIANT_TABLE_DEF
   aigct.repository.VARIANT_LABEL_NON_PK_COLUMNS
   aigct.repository.VARIANT_EFFECT_LABEL_TABLE_DEF
   aigct.repository.VARIANT_EFFECT_SCORE_PK_COLUMNS
   aigct.repository.VARIANT_EFFECT_SCORE_NON_PK_COLUMNS
   aigct.repository.VARIANT_EFFECT_SCORE_TABLE_DEF
   aigct.repository.VARIANT_TASK_TABLE_DEF
   aigct.repository.VARIANT_EFFECT_SOURCE_TABLE_DEF
   aigct.repository.VARIANT_DATA_SOURCE_TABLE_DEF
   aigct.repository.VARIANT_FILTER_TABLE_DEF
   aigct.repository.VARIANT_FILTER_GENE_TABLE_DEF
   aigct.repository.VARIANT_FILTER_VARIANT_TABLE_DEF
   aigct.repository.TABLE_DEFS


Classes
-------

.. autoapisummary::

   aigct.repository.TableDef
   aigct.repository.RepoSessionContext
   aigct.repository.VariantEffectLabelCache
   aigct.repository.DataCache
   aigct.repository.TaskBasedDataCache
   aigct.repository.TaskDataCache
   aigct.repository.VariantEffectScoreCache
   aigct.repository.VariantCache
   aigct.repository.VariantTaskCache
   aigct.repository.VariantEffectSourceCache
   aigct.repository.VariantFilterCache
   aigct.repository.VariantEffectSourceRepository
   aigct.repository.VariantTaskRepository
   aigct.repository.VariantFilterRepository
   aigct.repository.VariantRepository
   aigct.repository.VariantEffectLabelRepository
   aigct.repository.VariantEffectScoreRepository


Functions
---------

.. autoapisummary::

   aigct.repository.read_repo_csv
   aigct.repository.query_by_filter
   aigct.repository.query_by_filters


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

.. py:data:: MERGE_CHUNK_SIZE
   :value: 500000


.. py:data:: TASK_SUBFOLDER

.. py:data:: DATA_FOLDER
   :value: 'data'


.. py:data:: TASK_FOLDERS

.. py:class:: TableDef

   .. py:attribute:: folder
      :type:  str


   .. py:attribute:: file_name
      :type:  str


   .. py:attribute:: pk_columns
      :type:  list[str]


   .. py:attribute:: non_pk_columns
      :type:  list[str]


   .. py:attribute:: columns
      :type:  list[str]


   .. py:attribute:: full_file_name
      :type:  str


   .. py:method:: __post_init__()


.. py:data:: VARIANT_PK_COLUMNS
   :value: ['GENOME_ASSEMBLY', 'CHROMOSOME', 'POSITION', 'REFERENCE_NUCLEOTIDE', 'ALTERNATE_NUCLEOTIDE']


.. py:data:: VARIANT_NON_PK_COLUMNS
   :value: ['PRIOR_GENOME_ASSEMBLY', 'PRIOR_CHROMOSOME', 'PRIOR_POSITION', 'PRIOR_PRIOR_GENOME_ASSEMBLY',...


.. py:data:: VARIANT_TABLE_DEF

.. py:data:: VARIANT_LABEL_NON_PK_COLUMNS
   :value: ['LABEL_SOURCE', 'RAW_LABEL', 'BINARY_LABEL']


.. py:data:: VARIANT_EFFECT_LABEL_TABLE_DEF

.. py:data:: VARIANT_EFFECT_SCORE_PK_COLUMNS
   :value: ['GENOME_ASSEMBLY', 'CHROMOSOME', 'POSITION', 'REFERENCE_NUCLEOTIDE', 'ALTERNATE_NUCLEOTIDE',...


.. py:data:: VARIANT_EFFECT_SCORE_NON_PK_COLUMNS
   :value: ['RAW_SCORE', 'RANK_SCORE']


.. py:data:: VARIANT_EFFECT_SCORE_TABLE_DEF

.. py:data:: VARIANT_TASK_TABLE_DEF

.. py:data:: VARIANT_EFFECT_SOURCE_TABLE_DEF

.. py:data:: VARIANT_DATA_SOURCE_TABLE_DEF

.. py:data:: VARIANT_FILTER_TABLE_DEF

.. py:data:: VARIANT_FILTER_GENE_TABLE_DEF

.. py:data:: VARIANT_FILTER_VARIANT_TABLE_DEF

.. py:data:: TABLE_DEFS

.. py:function:: read_repo_csv(file: str) -> pandas.DataFrame

.. py:class:: RepoSessionContext(data_folder_root: str, table_defs: dict[str, TableDef])

   .. py:attribute:: _data_folder_root


   .. py:attribute:: _table_defs


   .. py:property:: data_folder_root


   .. py:method:: table_def(table_name: str)


   .. py:method:: table_file(table_name: str, task: str = None)


.. py:class:: VariantEffectLabelCache

   Bases: :py:obj:`aigct.util.ParameterizedSingleton`


   Caches the variant csv file in a dataframe. Implements the singleton
   pattern to ensure there is only one instance of the cached dataframe.
   We use an _init_once method rather than the normal __init__ method
   as required by the ParameterizedSingleton class.


   .. py:method:: _init_once(data_folder_root: str)


   .. py:method:: get_data_frame(task_code: str)


.. py:class:: DataCache

   Bases: :py:obj:`aigct.util.ParameterizedSingleton`


   Caches a repository csv file in a dataframe. Implements the singleton
   pattern to ensure there is only one instance of the cached dataframe.
   We use an _init_once method rather than the normal __init__ method
   as required by the ParameterizedSingleton class.


   .. py:method:: _init_once(data_folder_root: str, table_def: TableDef)


   .. py:property:: data_frame


.. py:class:: TaskBasedDataCache

   Bases: :py:obj:`aigct.util.ParameterizedSingleton`


   Caches a repository csv file in a dataframe. Maintains a separate
   cache for each task in a dict. Implements the singleton
   pattern to ensure there is only one instance of the cached dataframe.
   We use an _init_once method rather than the normal __init__ method
   as required by the ParameterizedSingleton class.


   .. py:method:: _init_once(data_folder_root: str, table_def: TableDef, disable_cache: bool = False)


   .. py:method:: get_data_frame(task_code: str)


.. py:class:: TaskDataCache

   Bases: :py:obj:`DataCache`


   Caches the variant csv file in a dataframe. Implements the singleton
   pattern to ensure there is only one instance of the cached dataframe.
   We use an _init_once method rather than the normal __init__ method
   as required by the ParameterizedSingleton class.


   .. py:method:: _init_once(data_folder_root: str)


.. py:class:: VariantEffectScoreCache

   Bases: :py:obj:`TaskBasedDataCache`


   Caches the variant csv file in a dataframe. Implements the singleton
   pattern to ensure there is only one instance of the cached dataframe.
   We use an _init_once method rather than the normal __init__ method
   as required by the ParameterizedSingleton class.


   .. py:method:: _init_once(data_folder_root: str, disable_cache: bool = False)


.. py:class:: VariantCache

   Bases: :py:obj:`DataCache`


   Caches the variant csv file in a dataframe. Implements the singleton
   pattern to ensure there is only one instance of the cached dataframe.
   We use an _init_once method rather than the normal __init__ method
   as required by the ParameterizedSingleton class.


   .. py:method:: _init_once(data_folder_root: str)


.. py:class:: VariantTaskCache

   Bases: :py:obj:`DataCache`


   Caches the variant csv file in a dataframe. Implements the singleton
   pattern to ensure there is only one instance of the cached dataframe.
   We use an _init_once method rather than the normal __init__ method
   as required by the ParameterizedSingleton class.


   .. py:method:: _init_once(data_folder_root: str)


.. py:class:: VariantEffectSourceCache

   Bases: :py:obj:`DataCache`


   Caches a repository csv file in a dataframe. Implements the singleton
   pattern to ensure there is only one instance of the cached dataframe.
   We use an _init_once method rather than the normal __init__ method
   as required by the ParameterizedSingleton class.


   .. py:method:: _init_once(data_folder_root: str)


.. py:class:: VariantFilterCache

   Bases: :py:obj:`aigct.util.ParameterizedSingleton`


   Classes that wish to behave as threadsafe singletons can inherit from
   this class. To be used only by classes that have an initialization
   method that takes parameters. The class must implement an _init_once
   method instead of the normal __init__ method for initialization.
   It takes same parameters as __init__ method. By inheriting from this
   class all instantiations of the subclass will return the same instance.


   .. py:method:: _init_once(data_folder_root: str)


   .. py:method:: get_data_frames(task_code: str) -> dict


.. py:class:: VariantEffectSourceRepository(session_context: RepoSessionContext, variant_effect_score_repo)

   .. py:attribute:: _cache


   .. py:attribute:: _variant_effect_score_repo


   .. py:method:: get_all() -> pandas.DataFrame


   .. py:method:: get_by_task(task_code: str) -> pandas.DataFrame


   .. py:method:: get_by_code(codes: list[str]) -> pandas.DataFrame


.. py:class:: VariantTaskRepository(session_context: RepoSessionContext)

   .. py:attribute:: _cache


   .. py:method:: get_all() -> pandas.DataFrame


.. py:class:: VariantFilterRepository(session_context: RepoSessionContext)

   .. py:attribute:: _cache


   .. py:method:: get_by_task(task_code: str) -> dict[str, pandas.DataFrame]


   .. py:method:: get_by_task_filter_name(task_code: str, filter_name: str) -> aigct.model.VariantFilter


   .. py:method:: get_by_task_filter_names(task_code: str, filter_names: list[str]) -> list[aigct.model.VariantFilter]


.. py:function:: query_by_filter(query_df: pandas.DataFrame, filter: pandas.Series, filter_gene_df: pandas.DataFrame, filter_variant_df: pandas.DataFrame) -> pandas.DataFrame

.. py:function:: query_by_filters(query_df: pandas.DataFrame, filters: list[aigct.model.VariantFilter]) -> pandas.DataFrame

.. py:class:: VariantRepository(session_context: RepoSessionContext)

   .. py:attribute:: _cache


   .. py:method:: get_all() -> pandas.DataFrame


   .. py:method:: get(qry: aigct.model.VEQueryCriteria) -> pandas.DataFrame

      Fetches variants. The optional parameters are filter criteria used to
      limit the set of variants returned.



.. py:class:: VariantEffectLabelRepository(session_context: RepoSessionContext, variant_task_repo: VariantTaskRepository, variant_repo: VariantRepository, filter_repo: VariantFilterRepository)

   .. py:attribute:: _cache


   .. py:attribute:: _task_repo


   .. py:attribute:: _filter_repo


   .. py:attribute:: _variant_repo


   .. py:method:: get_all_by_task(task_code: str) -> pandas.DataFrame


   .. py:method:: get_all_for_all_tasks() -> pandas.DataFrame


   .. py:method:: get(task_code: str, qry: aigct.model.VEQueryCriteria = None) -> pandas.DataFrame

      Fetches variant effect labels.



.. py:class:: VariantEffectScoreRepository(session_context: RepoSessionContext, task_repo: VariantTaskRepository, variant_repo: VariantRepository, filter_repo: VariantFilterRepository)

   .. py:attribute:: _cache


   .. py:attribute:: _task_repo


   .. py:attribute:: _filter_repo


   .. py:attribute:: _variant_repo


   .. py:method:: get_all_by_task(task_code: str) -> pandas.DataFrame


   .. py:method:: get_all_by_task_slim(task_code: str) -> pandas.DataFrame


   .. py:method:: get(task_code: str, variant_effect_sources: list[str] | str = None, include_variant_effect_sources: bool = True, qry: aigct.model.VEQueryCriteria = None) -> pandas.DataFrame


   .. py:method:: get_all_for_all_tasks() -> pandas.DataFrame