upstream/ipython Files · docs/examples/kernel/phistogram.py

Ported the IPython Sphinx directive to 0.11....

Ported the IPython Sphinx directive to 0.11. This was originally written by John Hunter for the 0.10 API, now it works with 0.11. We still need to automate its test suite, but at least now it runs and the script itself can be executed as a test that produces screen output and figures in a subdir.

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             phistogram.py
        
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      """Parallel histogram function"""

      import numpy

      def phistogram(rc, a, bins=10, rng=None, normed=False):

          """Compute the histogram of a remote array a.

          :Parameters:

              rc

                  IPython RemoteController instance

              a : str

                  String name of the remote array

              bins : int

                  Number of histogram bins

              rng : (float, float)

                  Tuple of min, max of the range to histogram

              normed : boolean

                  Should the histogram counts be normalized to 1

          """

          nengines = len(rc)

          rc.push(dict(bins=bins, rng=rng))

          rc.execute('import numpy')

          rc.execute('hist, lower_edges = numpy.histogram(%s, bins, rng)' % a)

          lower_edges = rc.pull('lower_edges', targets=0)

          hist_array = rc.gather('hist')

          hist_array.shape = (nengines,-1)

          total_hist = numpy.sum(hist_array, 0)

          if normed:

              total_hist = total_hist/numpy.sum(total_hist,dtype=float)

          return total_hist, lower_edges

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				"""Parallel histogram function"""
				import numpy

				def phistogram(rc, a, bins=10, rng=None, normed=False):
				"""Compute the histogram of a remote array a.

				:Parameters:
				rc
				IPython RemoteController instance
				a : str
				String name of the remote array
				bins : int
				Number of histogram bins
				rng : (float, float)
				Tuple of min, max of the range to histogram
				normed : boolean
				Should the histogram counts be normalized to 1
				"""
				nengines = len(rc)
				rc.push(dict(bins=bins, rng=rng))
				rc.execute('import numpy')
				rc.execute('hist, lower_edges = numpy.histogram(%s, bins, rng)' % a)
				lower_edges = rc.pull('lower_edges', targets=0)
				hist_array = rc.gather('hist')
				hist_array.shape = (nengines,-1)
				total_hist = numpy.sum(hist_array, 0)
				if normed:
				total_hist = total_hist/numpy.sum(total_hist,dtype=float)
				return total_hist, lower_edges