Parallel processing of sample folders
=====================================

The repository includes a screening script in
``examples/BactoScoop Parallel Processing.py`` and three drive-specific variants
at the repository root. They show how to apply the single-folder workflow to
many samples. Their acquisition drives, model paths, worker count, and channel
choices describe a specific experiment.

First validate one representative folder serially, then distribute
independent folders among workers. Keep each worker's outputs within its own
sample folder to avoid competing writes.

.. code-block:: python

   # Set these before importing NumPy or BactoScoop in worker processes.
   import os
   for variable in ("OMP_NUM_THREADS", "MKL_NUM_THREADS",
                    "OPENBLAS_NUM_THREADS", "NUMEXPR_NUM_THREADS"):
       os.environ.setdefault(variable, "1")
   os.environ.setdefault("BACTOSCOOP_LOG_LEVEL", "WARNING")

   from multiprocessing import get_context

   def process_folder(folder):
       from bactoscoop import ImageCollection
       ic = ImageCollection(folder, px=0.065)
       ic.create_image_objects(phase_channel="C1")
       ic.batch_process_mesh(phase_channel="C1", save_data=True)
       return folder, ic.processing_summary()

   if __name__ == "__main__":
       folders = [r"C:\data\sample_A", r"C:\data\sample_B"]
       with get_context("spawn").Pool(processes=2, maxtasksperchild=4) as pool:
           for folder, summary in pool.imap_unordered(process_folder, folders):
               print(folder, summary["status"])

This minimal example processes meshes from existing masks. Add curation,
detection, measurement, and export only after confirming the intended settings.
Use a script with a ``__main__`` guard on Windows, rather than pasting the worker
example directly into a notebook.

Memory and progress
-------------------

The screening scripts limit numerical-library threads, recycle workers with
``MAX_TASKS_PER_CHILD``, and record stage/progress information. Choose the worker
count for available memory and workload; the script's default 12 cores is not a
recommendation for every machine. Multiple simultaneous GPU segmentation jobs
can compete for GPU memory.

The full screening scripts were inspected, not executed for this documentation.
The documentation does not claim a newly measured throughput benchmark.