Synopsis
Calculate several basic statistic values for a data array
Syntax
simple_stats(array)
Description
The simple_stats() routine calculates several simple statistics for the input data (the array argument), such as the mean, median, and standard deviation. For more control over the calculation use the routines in the numpy module.
Loading the routine
The routine can be loaded into a Python session by saying:
from ciao_contrib.utils import *
Arguments
Name | Description |
---|---|
array | The array of data used to calculate the statistic values. |
The return value
The return value is an object with the following fields:
Field | Value |
---|---|
npts | The number of elements in the array. |
min | The minimum value in the array. |
max | The maximum value in the array. |
total | The sum of all the array elements. |
mean | The mean value of the array. |
median | The median value of the array. |
stddev | The standard deviation of the array, calculated using the biased estimator (so the mean level is calculated using the number of elements and not "number of elements - 1"). |
Examples
Example 1
>>> x = [0, 1, 2, 3, 2, 3, 4, 2, 1, 5] >>> s = simple_stats(x)
The return value of s is an object which contains the calculated statistics of the array:
>>> print(s) npts = 10 min = 0 max = 5 total = 23 mean = 2.3 median = 2.0 stddev = 1.41774468788 >>> print("The mean is {0} +- {1}".format(s.mean, s.stddev)) The mean is 2.3 +- 1.4177446878757827
Example 2
>>> img = read_image("emap.fits") >>> ivals = get_imagevals(img) >>> ix = numpy.isfinite(ivals) >>> simg = simple_stats(ivals[ix])
Here we use the Crates read_image() and get_imagevals() to read in the contents of the image "emap.fits". Since it may contain "invalid" numbers, such as NaN and Infinity, we filter out these values before calculating the statistics (simg).
See Also
- contrib
- simple_hist
- tools::gratings
- detilt, dewiggle, symmetrize