Multiprocessing Additions (#120)

* Allow specifiying MP init function

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix

* Update docstring

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

python/copclib/mp/transform.py

@@ -10,8 +10,10 @@ def _copy_points_transform(points, **kwargs):
 
 
 # Initialize each multiprocessing thread with a copy of the copc reader
-def init_mp(copc_path):
+def init_mp(copc_path, mp_init_function, mp_init_function_args):
     _transform_node.copc_reader = copc.FileReader(copc_path)
+    if mp_init_function:
+        mp_init_function(**mp_init_function_args)
 
 
 def transform_multithreaded(
@@ -26,6 +28,8 @@ def transform_multithreaded(
     chunk_size=1024,
     max_workers=None,
     update_minmax=False,
+    mp_init_function=None,
+    mp_init_function_args={},
 ):
     """Scaffolding for reading COPC files and writing them back out in a multithreaded way.
     It queues all nodes from either the provided list of nodes or nodes within the given resolution to be processed.
@@ -57,6 +61,9 @@ def transform_multithreaded(
         max_workers (int, optional): Manually set the number of processors to use when multiprocessing. Defaults to all processors.
         update_minmax (bool, optional): If true, updates the header of the new COPC file with the correct XYZ min/max.
             Defaults to False.
+        mp_init_function: (function, optional): A function that gets called in the ProcessPoolExeuctor initializer
+        mp_init_function_args: (dict, optional): A key/value pair of keyword arguments that get passed to `mp_init_function`.
+            Defaults to {}.
 
     Raises:
         RuntimeError
@@ -86,7 +93,7 @@ def transform_multithreaded(
     with concurrent.futures.ProcessPoolExecutor(
         max_workers=max_workers,
         initializer=init_mp,
-        initargs=(reader.path,),
+        initargs=(reader.path, mp_init_function, mp_init_function_args),
     ) as executor:
         # Chunk the nodes so we're not flooding executor.submit
         for chunk in chunks(nodes, chunk_size):