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"""An example of how to use IPython1 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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To run this example, first start the IPython controller and 4
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engines::
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ipcluster start -n 4
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Then start ipython in pylab mode::
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ipython -pylab
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Then a simple "run plotting_frontend.py" in IPython will run the
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example. When this is done, all the variables (such as number, downx, etc.)
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are available in IPython, so for example you can make additional plots.
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"""
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from __future__ import print_function
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import numpy as N
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from pylab import *
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from IPython.parallel import Client
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# Connect to the cluster
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rc = Client()
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view = rc[:]
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# Run the simulation on all the engines
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view.run('plotting_backend.py')
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# Bring back the data. These are all AsyncResult objects
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number = view.pull('number')
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d_number = view.pull('d_number')
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downx = view.gather('downx')
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downy = view.gather('downy')
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downpx = view.gather('downpx')
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downpy = view.gather('downpy')
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# but we can still iterate through AsyncResults before they are done
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print("number: ", sum(number))
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print("downsampled number: ", sum(d_number))
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# Make a scatter plot of the gathered data
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# These calls to matplotlib could be replaced by calls to pygist or
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# another plotting package.
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figure(1)
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# wait for downx/y
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downx = downx.get()
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downy = downy.get()
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scatter(downx, downy)
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xlabel('x')
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ylabel('y')
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figure(2)
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# wait for downpx/y
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downpx = downpx.get()
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downpy = downpy.get()
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scatter(downpx, downpy)
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xlabel('px')
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ylabel('py')
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show()
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