scipy tutorial - Baustatik-Info-Server

1.9.4 Comparing two samples
SciPy Reference Guide, Release 0.8.dev
In the following, we are given two samples, which can come either from the same or from different distribution, and
we want to test whether these samples have the same statistical properties.
Comparing means
Test with sample with identical means:
>>> rvs1 = stats.norm.rvs(loc=5, scale=10, size=500)
>>> rvs2 = stats.norm.rvs(loc=5, scale=10, size=500)
>>> stats.ttest_ind(rvs1, rvs2)
(-0.54890361750888583, 0.5831943748663857)
Test with sample with different means:
>>> rvs3 = stats.norm.rvs(loc=8, scale=10, size=500)
>>> stats.ttest_ind(rvs1, rvs3)
(-4.5334142901750321, 6.507128186505895e-006)
Kolmogorov-Smirnov test for two samples ks_2samp
For the example where both samples are drawn from the same distribution, we cannot reject the null hypothesis since
the pvalue is high
>>> stats.ks_2samp(rvs1, rvs2)
(0.025999999999999995, 0.99541195173064878)
In the second example, with different location, i.e. means, we can reject the null hypothesis since the pvalue is below
1%
>>> stats.ks_2samp(rvs1, rvs3)
(0.11399999999999999, 0.0027132103661283141)
1.10 Multi-dimensional image processing (ndimage)
1.10.1 Introduction
Image processing and analysis are generally seen as operations on two-dimensional arrays of values. There are however
a number of fields where images of higher dimensionality must be analyzed. Good examples of these are medical
imaging and biological imaging. numpy is suited very well for this type of applications due its inherent multidimensional
nature. The scipy.ndimage packages provides a number of general image processing and analysis
functions that are designed to operate with arrays of arbitrary dimensionality. The packages currently includes functions
for linear and non-linear filtering, binary morphology, B-spline interpolation, and object measurements.
1.10. Multi-dimensional image processing (ndimage) 57