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Updating nb tutorials
Updating nb tutorials

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plotting_frontend.py
60 lines | 1.5 KiB | text/x-python | PythonLexer
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r3670 """An example of how to use IPython1 for plotting remote parallel data
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r3675 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
engines::
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r6457 ipcluster start -n 4
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r15185 Then start ipython with matplotlib integration::
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r15185 ipython --matplotlib
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r3675 Then a simple "run plotting_frontend.py" in IPython will run the
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r3670 example. When this is done, all the variables (such as number, downx, etc.)
are available in IPython, so for example you can make additional plots.
"""
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r6455 from __future__ import print_function
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r15185 import matplotlib.pyplot as plt
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r3675 from IPython.parallel import Client
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r3675 # Connect to the cluster
rc = Client()
view = rc[:]
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# Run the simulation on all the engines
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r3675 view.run('plotting_backend.py')
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r3675 # Bring back the data. These are all AsyncResult objects
number = view.pull('number')
d_number = view.pull('d_number')
downx = view.gather('downx')
downy = view.gather('downy')
downpx = view.gather('downpx')
downpy = view.gather('downpy')
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r3675 # but we can still iterate through AsyncResults before they are done
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r6455 print("number: ", sum(number))
print("downsampled number: ", sum(d_number))
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r3670 # Make a scatter plot of the gathered data
# These calls to matplotlib could be replaced by calls to pygist or
# another plotting package.
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r15185 plt.figure(1)
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r3675 # wait for downx/y
downx = downx.get()
downy = downy.get()
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r15185 plt.scatter(downx, downy)
plt.xlabel('x')
plt.ylabel('y')
plt.figure(2)
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r3675 # wait for downpx/y
downpx = downpx.get()
downpy = downpy.get()
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r15185 plt.scatter(downpx, downpy)
plt.xlabel('px')
plt.ylabel('py')
plt.show()