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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
Examples SciPy Reference Guide, Release 0.8.dev If relative==False, tuple indicating whether values exactly equal to the absolute limits are allowed. If relative==True, tuple indicating whether the number of data being masked on each side should be rounded (True) or truncated (False). relative : {False, True} optional Whether to consider the limits as absolute values (False) or proportions to cut (True). axis : {None, integer}, optional Axis along which to trim. >>>z = [ 1, 2, 3, 4, 5, 6, 7, 8, 9,10] >>>trim(z,(3,8)) [–,–, 3, 4, 5, 6, 7, 8,–,–] >>>trim(z,(0.1,0.2),relative=True) [–, 2, 3, 4, 5, 6, 7, 8,–,–] trima(a, limits=None, inclusive=(True, True)) 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 Tuple of (lower limit, upper limit) in absolute values. Values of the input array lower (greater) than the lower (upper) limit will be masked. A limit is None indicates an open interval. inclusive : {(True,True) tuple} optional Tuple of (lower flag, upper flag), indicating whether values exactly equal to the lower (upper) limit are allowed. trimboth(data, proportiontocut=0.20000000000000001, inclusive=(True, True), axis=None) Trims the data by masking the int(proportiontocut*n) smallest and int(proportiontocut*n) largest values of data along the given axis, where n is the number of unmasked values before trimming. Parameters data : ndarray Data to trim. proportiontocut [{0.2, float} optional] Percentage of trimming (as a float between 0 and 1). If n is the number of unmasked values before trimming, the number of values after trimming is: (1-2*proportiontocut)*n. inclusive [{(True, True) tuple} optional] Tuple indicating whether the number of data being masked on each side should be rounded (True) or truncated (False). axis [{None, integer}, optional] Axis along which to perform the trimming. If None, the input array is first flattened. 3.18. Statistical functions (<strong>scipy</strong>.stats) 689
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