Class Histogram

Known Subclasses:

• WeightedHistogram

Histogram in one variable

The bin index and the number of points in a bin can be obtained by indexing the histogram with the bin number. Application of len() yields the number of bins. A histogram thus behaves like a sequence of bin index - bin count pairs.

Here is an example on usage:

>>> nsamples = 1000
>>> from numpy import random
>>> data = random.normal(1.0, 0.5, nsamples)
>>> import Scientific.Statistics as S
>>> S.mean(data)
0.9607056871982641
>>> S.standardDeviation(data)
0.50251811830486681
>>> S.median(data)
0.94853870756924152
>>> S.skewness(data)   # should be 0
0.038940041870334556
>>> S.kurtosis(data)   # should be 3
2.865582791273765
>>> 
>>> from Scientific.Statistics.Histogram import Histogram
>>> h = Histogram(data, 50)  # use 50 bins between min & max samples
>>> h.normalizeArea()        # make probabilities in histogram
>>> h[3]                     # bin index and frequency in the 4th bin
array([-0.45791018,  0.01553658])
>>> x = h.getBinIndices()
>>> y = h.getBinCounts()
>>> # can plot the y vector against the x vector (see below)
>>> 
>>> # add many more samples:
>>> nsamples2 = nsamples*100
>>> data = random.normal(1.0, 0.5, nsamples2)
>>> h.addData(data)
>>> h.normalizeArea()
>>> x2 = h.getBinIndices()
>>> y2 = h.getBinCounts()

>>> plot (x,y) and (x2,y2):
>>> import Gnuplot
>>> g = Gnuplot.Gnuplot(persist=1)
>>> g.xlabel('sample value');  g.ylabel('probability')
>>> d1 = Gnuplot.Data(x, y, with='lines',
...                   title='%d samples' % nsamples)
>>> d2 = Gnuplot.Data(x2, y2, with='lines',
...                   title='%d samples' % nsamples2)
>>> g.plot(d1,d2)