:mod:`arche.tools.api`
======================

.. py:module:: arche.tools.api


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

.. data:: Filters
   

   

.. function:: get_job(key: str) -> Job


.. function:: get_jobs(keys: List[str]) -> List[Job]


.. function:: get_collection(key)


.. function:: get_errors_count(job)


.. function:: get_job_state(job)


.. function:: get_job_close_reason(job)


.. function:: get_items_count(job)


.. function:: get_counts(job: Job) -> Optional[Dict[str, int]]


.. function:: get_finish_time_difference_in_days(job1, job2)


.. function:: get_runtime(job)

   Returns the runtime in milliseconds or None if job is still running


.. function:: get_runtime_s(job)

   Returns job runtime in milliseconds.


.. function:: get_max_memusage(job)


.. function:: get_response_status_count(job)


.. function:: get_requests_count(job)


.. function:: get_crawlera_user(job)


.. function:: get_source(source_key)


.. function:: get_items_with_pool(source_key: str, count: int, start_index: int, workers: int = 4) -> np.ndarray

   Concurrently reads items from API using Pool

   :param source_key: a job or collection key, e.g. '112358/13/21'
   :param count: a number of items to retrieve
   :param start_index: an index to read from
   :param workers: the number of separate processors to get data in

   :returns: A numpy array of items


.. function:: get_items(key: str, count: int, start_index: int, start: Optional[str], filters: Optional[Filters] = None, p_bar: Union[tqdm, notebook.tqdm] = notebook.tqdm, desc: Optional[str] = None) -> np.ndarray