.. currentmodule:: playdoh

.. _example-monte_carlo:

Example: monte_carlo
====================

Monte Carlo simulation example of pi estimation.
This example shows how to use the Playdoh interface
to execute loosely coupled parallel tasks.

::

    from playdoh import *
    import numpy as np
    
    
    # Any task class must derive from the ParallelTask
    class PiMonteCarlo(ParallelTask):
        def initialize(self, n):
            # Specify the number of samples on this node
            self.n = n
    
        def start(self):
            # Draw n points uniformly in [0,1]^2
            samples = np.random.rand(2, self.n)
            # Count the number of points inside the quarter unit circle
            self.count = np.sum(samples[0, :] ** 2 + samples[1, :] ** 2 < 1)
    
        def get_result(self):
            # Return the result
            return self.count
    
    
    def pi_montecarlo(samples, nodes):
        # Calculate the number of samples for each node
        split_samples = [samples / nodes] * nodes
        # Launch the task on the local CPUs
        task = start_task(PiMonteCarlo,  # name of the task class
                          cpu=nodes,  # use <nodes> CPUs on the local machine
                          args=(split_samples,))  # arguments of
                                                  # MonteCarlo.initialize
                                                  # as a list,
                                                  # node #i receives
                                                  # split_samples[i]
                                                  # as argument
        # Retrieve the result, as a list with one element returned
        # by MonteCarlo.get_result per node
        result = task.get_result()
    
        # Return the estimation of Pi
        return sum(result) * 4.0 / samples
    
    
    if __name__ == '__main__':
        # Evaluate Pi with 10,000 samples and 2 CPUs
        print pi_montecarlo(1000000, 2)