Threads

The threaded algorithm supports the use of multiple threads.

	mpl2005	mpl2014	serial	threaded
supports_threads				Yes

threaded shares most of its code with serial except for the high-level processing of chunks which it performs in parallel using a thread pool, and the creation of NumPy arrays is limited to a single thread at a time.

Note

The domain must be divided into chunks for multithreaded contouring.

Create a ThreadedContourGenerator by calling contour_generator() in the usual way, ensuring that the domain is chunked:

>>> from contourpy import contour_generator
>>> import numpy as np
>>> z = np.ones((100, 50))  # Sample z data.
>>> cont_gen = contour_generator(z=z, name="threaded", chunk_count=5, thread_count=4)
>>> cont_gen.thread_count
4
>>> cont_gen.chunk_count
25

Here the 25 chunks will be divided up between the 4 threads.

The thread_count argument is optional, if not specified the default is thread_count=0 which means it will use the maximum number of threads available. This number can be checked using:

>>> import contourpy
>>> contourpy.max_threads()

Note

max_threads() is implemented using the C++ function std::thread::hardware_concurrency.

If you request more threads than the number of chunks, the thread count will be reduced accordingly.

Warning

The order of processing chunks is not deterministic. If you use a LineType or FillType that do not arrange the results by chunk, the order of returned lines/polygons is also not deterministic. This includes LineType.Separate, LineType.SeparateCode, FillType.OuterCode and FillType.OuterOffset.