scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
limits : None or (lower limit, upper limit), optional
Values in the input array less than the lower limit or greater than the upper limit will
be ignored. When limits is None, then all values are used. Either of the limit values
in the tuple can also be None representing a half-open interval. The default value is
None.
inclusive : (bool, bool), optional
Returns
tmean : float
A tuple consisting of the (lower flag, upper flag). These flags determine whether
values exactly equal to the lower or upper limits are included. The default value is
(True, True).
tmin(a, lowerlimit=None, axis=0, inclusive=True)
Compute the trimmed minimum
This function finds the miminum value of an array a along the specified axis, but only considering values greater
than a specified lower limit.
Parameters
a : array_like
array of values
lowerlimit : None or float, optional
Values in the input array less than the given limit will be ignored. When lowerlimit
is None, then all values are used. The default value is None.
axis : None or int, optional
Operate along this axis. None means to use the flattened array and the default is zero
inclusive : {True, False}, optional
Returns
tmin: float :
This flag determines whether values exactly equal to the lower limit are included.
The default value is True.
trim(a, limits=None, inclusive=(True, True), relative=False, axis=None)
Trims an array by masking the data outside some given limits. Returns a masked version of the input array.
Parameters
a : sequence
Input array
limits : {None, tuple} optional
If relative == False, tuple (lower limit, upper limit) in absolute values. Values of
the input array lower (greater) than the lower (upper) limit are masked. If relative
== True, tuple (lower percentage, upper percentage) to cut on each side of the array,
with respect to the number of unmasked data. 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))
In each case, the value of one limit can be set to None to indicate an
open interval. If limits is None, no trimming is performed
inclusive : {(True, True) tuple} optional
688 Chapter 3. Reference