Legendre.convert(domain=None, kind=None, window=None) [source] Convert series to a different kind and/or domain and/or window. Parameters: domain : array_like, optional The domain of the converted series. If the value is None, the default domain of kind is used. kind : class, optional The polynomial series type class to which the current instance should be converted. If kind is None, then the class of the current instance is used. window : array_like, optional The window of the conver

MaskedArray.__oct__() <==> oct(x)

MaskedArray.imag Imaginary part.

recarray.round(decimals=0, out=None) Return a with each element rounded to the given number of decimals. Refer to numpy.around for full documentation. See also numpy.around equivalent function

chararray.ravel([order]) Return a flattened array. Refer to numpy.ravel for full documentation. See also numpy.ravel equivalent function ndarray.flat a flat iterator on the array.

recarray.itemsize Length of one array element in bytes. Examples >>> x = np.array([1,2,3], dtype=np.float64) >>> x.itemsize 8 >>> x = np.array([1,2,3], dtype=np.complex128) >>> x.itemsize 16

numpy.polyint(p, m=1, k=None) [source] Return an antiderivative (indefinite integral) of a polynomial. The returned order m antiderivative P of polynomial p satisfies and is defined up to m - 1 integration constants k. The constants determine the low-order polynomial part of P so that . Parameters: p : array_like or poly1d Polynomial to differentiate. A sequence is interpreted as polynomial coefficients, see poly1d. m : int, optional Order of the antiderivative. (Default: 1) k : li

generic.setfield() 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

numpy.polynomial.laguerre.lagval2d(x, y, c) [source] Evaluate a 2-D Laguerre series at points (x, y). This function returns the values: The parameters x and y are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either x and y or their elements must support multiplication and addition both with themselves and with the elements of c. If c is a 1-D array a one is implicitly ap

generic.itemset() 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