Class WeightedHistogram
Histogram --+
|
WeightedHistogram
Weighted histogram in one variable
Constructor: WeightedHistogram(|data|, |weights|, |bins|,
|range|=None)
Arguments:
In a weighted histogram, each point has a specific weight. If all
weights are one, the result is equivalent to a standard histogram. 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.
|
|
| __init__(self,
data,
weights,
nbins,
range=None) |
|
|
|
|
addData(self,
data,
weights)
Add values to the originally supplied data sequence. |
|
|
|
Inherited from Histogram:
__getitem__,
__getslice__,
__len__,
getBinCounts,
getBinIndices,
normalize,
normalizeArea
|
__init__(self,
data,
weights,
nbins,
range=None)
(Constructor)
|
|
- Parameters:
data (Numeric.array) - a sequence of data pointsweights (Numeric.array) - a sequence of weights, same length as datanbins (int) - the number of bins into which the data is to be sortedrange (tuple or NoneType) - a tuple of two values, specifying the lower and the upper end of
the interval spanned by the bins. Any data point outside this
interval will be ignored. If no range is given, the smallest and
largest data values are used to define the interval. - Overrides:
Histogram.__init__
|
addData(self,
data,
weights)
|
|
Add values to the originally supplied data sequence. Use this method
to feed long data sequences in multiple parts to avoid memory
shortages.
- Parameters:
data (Numeric.array) - a sequence of data points - Overrides:
Histogram.addData
Note:
this does not affect the default range of the histogram, which is
fixed when the histogram is created.
|