generic.strides tuple of bytes steps in each dimension

numpy.asscalar(a) [source] Convert an array of size 1 to its scalar equivalent. Parameters: a : ndarray Input array of size 1. Returns: out : scalar Scalar representation of a. The output data type is the same type returned by the input?s item method. Examples >>> np.asscalar(np.array([24])) 24

numpy.sinc(x) [source] Return the sinc function. The sinc function is . Parameters: x : ndarray Array (possibly multi-dimensional) of values for which to to calculate sinc(x). Returns: out : ndarray sinc(x), which has the same shape as the input. Notes sinc(0) is the limit value 1. The name sinc is short for ?sine cardinal? or ?sinus cardinalis?. The sinc function is used in various signal processing applications, including in anti-aliasing, in the construction of a Lanczos resampli

generic.data pointer to start of data

generic.flat a 1-d view of scalar

chararray.setfield(val, dtype, offset=0) Put a value into a specified place in a field defined by a data-type. Place val into a?s field defined by dtype and beginning offset bytes into the field. Parameters: val : object Value to be placed in field. dtype : dtype object Data-type of the field in which to place val. offset : int, optional The number of bytes into the field at which to place val. Returns: None See also getfield Examples >>> x = np.eye(3) >>> x.ge

generic.size number of elements in the gentype

record.copy() 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

recarray.copy(order='C') Return a copy of the array. Parameters: order : {?C?, ?F?, ?A?, ?K?}, optional Controls the memory layout of the copy. ?C? means C-order, ?F? means F-order, ?A? means ?F? if a is Fortran contiguous, ?C? otherwise. ?K? means match the layout of a as closely as possible. (Note that this function and :func:numpy.copy are very similar, but have different default values for their order= arguments.) See also numpy.copy, numpy.copyto Examples >>> x = np.arra

ndarray.sum(axis=None, dtype=None, out=None, keepdims=False) Return the sum of the array elements over the given axis. Refer to numpy.sum for full documentation. See also numpy.sum equivalent function