ndarray.__ifloordiv__ x.__ifloordiv__(y) <==> x//y

recarray.put(indices, values, mode='raise') Set a.flat[n] = values[n] for all n in indices. Refer to numpy.put for full documentation. See also numpy.put equivalent function

Introduction The Simple Wrapper and Interface Generator (or SWIG) is a powerful tool for generating wrapper code for interfacing to a wide variety of scripting languages. SWIG can parse header files, and using only the code prototypes, create an interface to the target language. But SWIG is not omnipotent. For example, it cannot know from the prototype: double rms(double* seq, int n); what exactly seq is. Is it a single value to be altered in-place? Is it an array, and if so what is its leng

numpy.random.bytes(length) Return random bytes. Parameters: length : int Number of random bytes. Returns: out : str String of length length. Examples >>> np.random.bytes(10) ' eh\x85\x022SZ\xbf\xa4' #random

MaskedArray.__le__ x.__le__(y) <==> x<=y

Fanaticism consists of redoubling your efforts when you have forgotten your aim. ? George Santayana An authority is a person who can tell you more about something than you really care to know. ? Unknown This Chapter attempts to explain the logic behind some of the new pieces of code. The purpose behind these explanations is to enable somebody to be able to understand the ideas behind the implementation somewhat more easily than just staring at the code. Perhaps in this way, the algorithms ca

recarray.max(axis=None, out=None) Return the maximum along a given axis. Refer to numpy.amax for full documentation. See also numpy.amax equivalent function

numpy.triu(m, k=0) [source] Upper triangle of an array. Return a copy of a matrix with the elements below the k-th diagonal zeroed. Please refer to the documentation for tril for further details. See also tril lower triangle of an array Examples >>> np.triu([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1) array([[ 1, 2, 3], [ 4, 5, 6], [ 0, 8, 9], [ 0, 0, 12]])

ndarray.__len__() <==> len(x)

generic.min() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The