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SciPy Reference Guide, Release 0.8.dev The (limits[0])th lowest values are set to the (limits[0])th percentile, and the (limits[1])th highest values are set to the (limits[1])th percentile. Masked values are skipped. Parameters a : sequence z(a, score) z is deprecated! Input array. limits : {None, tuple of float} optional Tuple of the percentages to cut on each side of the array, with respect to the number of unmasked data, as floats between 0. and 1. Noting n the number of unmasked data before trimming, the (n*limits[0])th smallest data and the (n*limits[1])th largest data are masked, and the total number of unmasked data after trimming is n*(1.sum(limits)) The value of one limit can be set to None to indicate an open interval. inclusive : {(True, True) tuple} optional Tuple indicating whether the number of data being masked on each side should be rounded (True) or truncated (False). inplace : {False, True} optional Whether to winsorize in place (True) or to use a copy (False) axis : {None, int} optional Axis along which to trim. If None, the whole array is trimmed, but its shape is maintained. scipy.stats.z is deprecated; please update your code to use scipy.stats.zscore_compare. Returns the z-score of a given input score, given thearray from which that score came. Not appropriate for population calculations, nor for arrays > 1D. zmap(scores, compare, axis=0) Returns an array of z-scores the shape of scores (e.g., [x,y]), compared to array passed to compare (e.g., [time,x,y]). Assumes collapsing over dim 0 of the compare array. zs(a) zs is deprecated! scipy.stats.zs is deprecated; please update your code to use scipy.stats.zscore. Returns a 1D array of z-scores, one for each score in the passed array, computed relative to the passed array. 3.18.6 Univariate and multivariate kernel density estimation (scipy.stats.kde) gaussian_kde(dataset) Representation of a kernel-density estimate using Gaussian kernels. class gaussian_kde(dataset) Representation of a kernel-density estimate using Gaussian kernels. Parameters dataset : (# of dims, # of data)-array datapoints to estimate from 696 Chapter 3. Reference
Attributes d int number of dimensions n int number of datapoints Methods SciPy Reference Guide, Release 0.8.dev kde.evaluate(points) array evaluate the estimated pdf on a provided set of points kde(points) array same as kde.evaluate(points) kde.integrate_gaussian(mean, cov) float multiply pdf with a specified Gaussian and integrate over the whole domain kde.integrate_box_1d(low, high) float integrate pdf (1D only) between two bounds kde.integrate_box(low_bounds, float integrate pdf over a rectangular space between low_bounds and high_bounds) high_bounds kde.integrate_kde(other_kde) float integrate two kernel density estimates multiplied together For many more stat related functions install the software R and the interface package rpy. 3.19 Image Array Manipulation and Convolution (scipy.stsci) 3.19.1 Image Array manipulation Functions (scipy.stsci.image) average combine median minimum threshhold translate 3.19.2 Image Array Convolution Functions (scipy.stsci.convolve) boxcar convolution_modes convolve convolve2d correlate correlate2d cross_correlate dft iraf_frame pix_modes 3.20 C/C++ integration (scipy.weave) Warning: This documentation is work-in-progress and unorganized. 3.19. Image Array Manipulation and Convolution (scipy.stsci) 697
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