Metadata-Version: 2.1
Name: arrayqueues
Version: 1.3.1
Summary: Multiprocessing queues for numpy arrays using shared memory
Home-page: https://github.com/portugueslab/arrayqueues
Author: Vilim Stih @portugueslab
Author-email: vilim@neuro.mpg.de
License: MIT
Description: # ArrayQueues
        
        [![Build Status](https://travis-ci.org/portugueslab/arrayqueues.svg?branch=master)](https://travis-ci.org/portugueslab/arrayqueues)
        [![Coverage Status](https://coveralls.io/repos/github/portugueslab/arrayqueues/badge.svg?branch=master)](https://coveralls.io/github/portugueslab/arrayqueues?branch=master)
        [![PyPI version](https://badge.fury.io/py/arrayqueues.svg)](https://badge.fury.io/py/arrayqueues)
        
        This package provides a drop-in replacement for the Python multiprocessing Queue class which handles transport of large numpy arrays.
        It avoids pickling and uses the multiprocessing Array class in the background.
        The major difference between this implementation and the normal queue is that the maximal amount of memory that the queue can have must be specified beforehand.
        
        Attempting to send an array of a different shape or datatype of the previously inserted one resets the queue.
        Only passing of numpy arrays is supported, optionally annotated with timestamps if using the TimestampedArrayQueue class,
        but other object types can be supported by extending the class.
        
        The package has been tested on Python 3.6/3/7 on Windows and MacOS and Linux with Travis. Python 2.7 is not supported.
        
        # Usage example
        ```python
        from arrayqueues.shared_arrays import ArrayQueue
        from multiprocessing import Process
        import numpy as np
        
        class ReadProcess(Process):
            def __init__(self, source_queue):
                super().__init__()
                self.source_queue = source_queue
              
            def run(self):
                print(self.source_queue.get())
        
        if __name__ == "__main__":
            q = ArrayQueue(1) # intitialises an ArrayQueue which can hold 1MB of data
            n = np.full((5,5), 5)
            q.put(n)
            r = ReadProcess(q)
            r.start()
            r.join()
            
        ```
        
        Further examples can be found in tests.
        
Keywords: multiprocessing queues arrays
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Multimedia :: Video
Classifier: Topic :: Software Development :: Libraries
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Provides-Extra: dev
