pyfracval.batch_runner
======================

.. py:module:: pyfracval.batch_runner

.. autoapi-nested-parse::

   Batch generation of multiple aggregates in parallel using Dask.

   This module provides functionality to generate multiple fractal aggregates
   in parallel via a Dask distributed scheduler — either a local cluster or a
   remote one (e.g. ``tcp://host:8786``).

   Each trial is submitted as an independent Dask task, so workers can be on
   any machine that is part of the Dask cluster.





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

.. py:function:: generate_aggregates_parallel(n_aggregates, config, output_base_dir = 'RESULTS', seed_start = 1000, n_workers = None, show_progress = True, scheduler_address = None)

   Generate multiple fractal aggregates in parallel via Dask.

   :param n_aggregates: Number of aggregates to generate.
   :param config: Simulation configuration dictionary (N, Df, kf, rp_g, rp_gstd, …).
   :param output_base_dir: Base directory for output files (default: ``"RESULTS"``).
   :param seed_start: Starting random seed.  Aggregate *i* uses ``seed_start + i``.
   :param n_workers: Workers for a local cluster.  Ignored when *scheduler_address* is set.
   :param show_progress: Show a ``tqdm`` progress bar while futures complete.
   :param scheduler_address: Remote Dask scheduler address (e.g. ``"tcp://host:8786"``).
                             ``None`` → start a ``LocalCluster``.

   :returns: One ``(success, coords, radii)`` tuple per aggregate, in submission
             order.
   :rtype: list[tuple[bool, np.ndarray | None, np.ndarray | None]]


.. py:function:: generate_aggregates_sequential(n_aggregates, config, output_base_dir = 'RESULTS', seed_start = 1000)

   Generate multiple aggregates sequentially (for comparison/debugging).

   :param n_aggregates: Number of aggregates to generate.
   :param config: Simulation configuration dictionary.
   :param output_base_dir: Base directory for output files (default: ``"RESULTS"``).
   :param seed_start: Starting random seed.

   :returns: One ``(success, coords, radii)`` tuple per aggregate.
   :rtype: list[tuple[bool, np.ndarray | None, np.ndarray | None]]