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"""An example of how to use IPython for plotting remote parallel data
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The two files plotting_frontend.py and plotting_backend.py go together.
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This file (plotting_backend.py) performs the actual computation. For this
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example, the computation just generates a set of random numbers that
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look like a distribution of particles with 2D position (x,y) and
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momentum (px,py). In a real situation, this file would do some time
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consuming and complicated calculation, and could possibly make calls
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to MPI.
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One important feature is that this script can also be run standalone without
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IPython. This is nice as it allows it to be run in more traditional
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settings where IPython isn't being used.
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When used with IPython.parallel, this code is run on the engines. Because this
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code doesn't make any plots, the engines don't have to have any plotting
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packages installed.
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"""
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# Imports
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import numpy as N
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import time
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import random
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# Functions
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def compute_particles(number):
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x = N.random.standard_normal(number)
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y = N.random.standard_normal(number)
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px = N.random.standard_normal(number)
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py = N.random.standard_normal(number)
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return x, y, px, py
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def downsample(array, k):
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"""Choose k random elements of array."""
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length = array.shape[0]
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indices = random.sample(xrange(length), k)
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return array[indices]
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# Parameters of the run
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number = 100000
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d_number = 1000
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# The actual run
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time.sleep(0) # Pretend it took a while
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x, y, px, py = compute_particles(number)
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# Now downsample the data
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downx = downsample(x, d_number)
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downy = downsample(x, d_number)
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downpx = downsample(px, d_number)
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downpy = downsample(py, d_number)
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print "downx: ", downx[:10]
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print "downy: ", downy[:10]
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print "downpx: ", downpx[:10]
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print "downpy: ", downpy[:10]
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