pad(array, pad_width, mode, **kwargs) Pads an array.

chararray.dumps() Returns the pickle of the array as a string. pickle.loads or numpy.loads will convert the string back to an array. Parameters: None

dtype.__setstate__()

Setting and getting error handling seterr([all, divide, over, under, invalid]) Set how floating-point errors are handled. geterr() Get the current way of handling floating-point errors. seterrcall(func) Set the floating-point error callback function or log object. geterrcall() Return the current callback function used on floating-point errors. errstate(**kwargs) Context manager for floating-point error handling. Internal functions seterrobj(errobj) Set the object that defines floating

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

recarray.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None) Return the sum along diagonals of the array. Refer to numpy.trace for full documentation. See also numpy.trace equivalent function

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