1 import matplotlib
   2 import numarray as na
   3 
   4 def hist(binsIn, dataIn, normed=False):
   5     """
   6     Make a histogram that can be plotted with plot() so that
   7     the histogram just has the outline rather than bars as it
   8     usually does.
   9 
  10     Example Usage:
  11     binsIn = numarray.arange(0, 1, 0.1)
  12     angle = pylab.rand(50)
  13 
  14     (bins, data) = histOutline(binsIn, angle)
  15     plot(bins, data, 'k-', linewidth=2)
  16 
  17     """
  18     (en, eb) = matplotlib.mlab.hist(dataIn, bins=binsIn, normed=normed)
  19 
  20     stepSize = binsIn[1] - binsIn[0]
  21 
  22     bins = na.zeros(len(eb)*2 + 2, type=na.Float)
  23     data = na.zeros(len(eb)*2 + 2, type=na.Float)    
  24     for bb in range(len(binsIn)):
  25         bins[2*bb + 1] = binsIn[bb]
  26         bins[2*bb + 2] = binsIn[bb] + stepSize
  27         data[2*bb + 1] = en[bb]
  28         data[2*bb + 2] = en[bb]
  29 
  30     bins[0] = bins[1]
  31     bins[-1] = bins[-2]
  32     data[0] = 0
  33     data[-1] = 0
  34     
  35     return (bins, data)
  36 
  37 def convertForPlot(binsIn, histIn):
  38     """
  39     Take the output from a normal histogram and turn it into
  40     a histogram that can be plotted (square tops, etc.).
  41 
  42     binsIn - The bins output by matplotlib.mlab.hist()
  43     histIn - The histogram output by matplotlib.mlab.hist()
  44     """
  45     stepSize = binsIn[1] - binsIn[0]
  46 
  47     bins = na.zeros(len(binsIn)*2 + 2, type=na.Float)
  48     data = na.zeros(len(binsIn)*2 + 2, type=na.Float)    
  49     for bb in range(len(binsIn)):
  50         bins[2*bb + 1] = binsIn[bb]
  51         bins[2*bb + 2] = binsIn[bb] + stepSize
  52         data[2*bb + 1] = histIn[bb]
  53         data[2*bb + 2] = histIn[bb]
  54 
  55     bins[0] = bins[1]
  56     bins[-1] = bins[-2]
  57     data[0] = 0
  58     data[-1] = 0
  59     
  60     return (bins, data)