scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
Parameters
a : array
moment : int
axis : int or None
Returns
The appropriate moment along the given axis or over all values if axis is :
None. :
mquantiles(a, prob=, [0.25, 0.5, 0.75], alphap=0.40000000000000002, betap=0.40000000000000002,
axis=None, limit=())
Computes empirical quantiles for a data array.
Samples quantile are defined by Q(p) = (1 − g).x[i] + g.x[i + 1], where x[j] is the *j*th order statistic, and i
= (floor(n*p+m)), m=alpha+p*(1-alpha-beta) and g = n*p + m - i.
Typical values of (alpha,beta) are:
• (0,1) : p(k) = k/n : linear interpolation of cdf (R, type 4)
• (.5,.5) : p(k) = (k+1/2.)/n : piecewise linear function (R, type 5)
• (0,0) : p(k) = k/(n+1) : (R type 6)
• (1,1) : p(k) = (k-1)/(n-1). In this case, p(k) = mode[F(x[k])]. That’s R default (R type 7)
• (1/3,1/3): p(k) = (k-1/3)/(n+1/3). Then p(k) ~ median[F(x[k])]. The resulting quantile estimates are
approximately median-unbiased regardless of the distribution of x. (R type 8)
• (3/8,3/8): p(k) = (k-3/8)/(n+1/4). Blom. The resulting quantile estimates are approximately unbiased
if x is normally distributed (R type 9)
• (.4,.4) : approximately quantile unbiased (Cunnane)
• (.35,.35): APL, used with PWM
Parameters
a : array-like
Input data, as a sequence or array of dimension at most 2.
prob : array-like, optional
List of quantiles to compute.
alpha : float, optional
Plotting positions parameter, default is 0.4.
beta : float, optional
Plotting positions parameter, default is 0.4.
axis : int, optional
Axis along which to perform the trimming. If None (default), the input array is first
flattened.
limit : tuple
Tuple of (lower, upper) values. Values of a outside this closed interval are ignored.
Returns
quants : MaskedArray
680 Chapter 3. Reference