.. currentmodule:: brian2

.. standalone_multiple_processes:

Example: standalone_multiple_processes
======================================


        .. only:: html

            .. |launchbinder| image:: file:///usr/share/doc/python-brian-doc/docs/badge.svg
            .. _launchbinder: https://mybinder.org/v2/gh/brian-team/brian2-binder/master?filepath=examples/standalone/standalone_multiple_processes.ipynb

            .. note::
               You can launch an interactive, editable version of this
               example without installing any local files
               using the Binder service (although note that at some times this
               may be slow or fail to open): |launchbinder|_

        

This example shows how to run several, independent simulations in standalone mode using multiple processes to run the
simulations in parallel.
Given that this example only involves a single neuron, an alternative – and arguably more elegant – solution
would be to run the simulations in a single `NeuronGroup`, where each neuron receives input with a different rate.

The example is a standalone equivalent of the one presented in :doc:`/tutorials/3-intro-to-brian-simulations`.

Note that Python's `multiprocessing` module cannot deal with user-defined functions (including `TimedArray`) and other
complex code structures. If you run into `PicklingError` or `AttributeError` exceptions, you might
have to use the `pathos` (https://pypi.org/project/pathos) package instead, which can handle more complex
code structures.

::

    import numpy as np
    import matplotlib.pyplot as plt
    import brian2 as b2
    from time import time
    
    b2.set_device('cpp_standalone', build_on_run=False)
    
    class SimWrapper:
        def __init__(self):
            self.net = b2.Network()
            P = b2.PoissonGroup(num_inputs, rates=input_rate)
            eqs = """
                dv/dt = -v/tau : 1
                tau : second (constant)
                """
            G = b2.NeuronGroup(1, eqs, threshold='v>1', reset='v=0', method='euler', name='neuron')
            S = b2.Synapses(P, G, on_pre='v += weight')
            S.connect()
            M = b2.SpikeMonitor(G, name='spike_monitor')
            self.net.add([P, G, S, M])
    
            self.net.run(1000 * b2.ms)
    
            self.device = b2.get_device()
            self.device.build(run=False, directory=None)  # compile the code, but don't run it yet
    
        def do_run(self, tau_i):
            # Workaround to set the device globally in this context
            from brian2.devices import device_module
            device_module.active_device = self.device
    
            result_dir = f'result_{tau_i}'
            self.device.run(run_args={self.net['neuron'].tau: tau_i},
                            results_directory=result_dir)
            return self.net["spike_monitor"].num_spikes/ b2.second
    
    
    if __name__ == "__main__":
        start_time = time()
        num_inputs = 100
        input_rate = 10 * b2.Hz
        weight = 0.1
    
        npoints = 15
        tau_range = np.linspace(1, 15, npoints) * b2.ms
    
        sim = SimWrapper()
    
        from multiprocessing import Pool
        with Pool(npoints) as pool:
            output_rates = pool.map(sim.do_run, tau_range)
    
        print(f"Done in {time() - start_time}")
    
        plt.plot(tau_range/b2.ms, output_rates)
        plt.xlabel(r"$\tau$ (ms)")
        plt.ylabel("Firing rate (sp/s)")
        plt.show()
    

