Primary User Interface: ImageStats

Compute desired statistics values for input array objects.

Author:Warren Hack, Christopher Hanley (for help, contact HST Help Desk)
class stsci.imagestats.ImageStats(image, fields='npix, min, max, mean, stddev', lower=None, upper=None, nclip=0, lsig=3.0, usig=3.0, binwidth=0.1)[source]

Class to compute desired statistics from array objects.

image : str

input image data array.

fields : str

comma-separated list of values to be computed. The following are the available fields:

image image data array
npix the number of pixels used to do the statistics
mean the mean of the pixel distribution
midpt estimate of the median of the pixel distribution
mode the mode of the pixel distribution
stddev the standard deviation of the pixel distribution
min the minimum pixel value
max the maximum pixel value

Only those fields specified upon instantiation will be computed and available as an output value.

lower : float

Lowest valid value in the input array to be used for computing the statistical values

upper : float

Largest valid value in the input array to be used in computing the statistical values

nclip : int

Number of clipping iterations to apply in computing the results

lsig : float

Lower sigma clipping limit (in sigma)

usig : float

Upper sigma clipping limit (in sigma)

binwidth : float

Width of bins (in sigma) to use in generating histograms for computing median-related values


The mean, standard deviation, min and max are computed in a single pass through the image using the expressions listed below. Only the quantities selected by the fields parameter are actually computed.

    mean = sum (x1,...,xN) / N
       y = x - mean
variance = sum (y1 ** 2,...,yN ** 2) / (N-1)
  stddev = sqrt (variance)

The midpoint and mode are computed in two passes through the image. In the first pass the standard deviation of the pixels is calculated and used with the binwidth parameter to compute the resolution of the data histogram. The midpoint is estimated by integrating the histogram and computing by interpolation the data value at which exactly half the pixels are below that data value and half are above it. The mode is computed by locating the maximum of the data histogram and fitting the peak by parabolic interpolation.

This data will be promoted down to float32 if provided as 64-bit datatype.


This class can can be instantiated using the following syntax:

>>> import stsci.imagestats as imagestats
>>> i = imagestats.ImageStats(image,
>>> i.printStats()
>>> i.mean

The statistical quantities specified by the parameter fields are computed and printed for the input image array. The results are available as attributes of the class object as well.


Compute the array of bin center positions.


Print the requested statistics values for those fields specified on input.