scipy tutorial - Baustatik-Info-Server

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