matrix.view(dtype=None, type=None) New view of array with the same data. Parameters: dtype : data-type or ndarray sub-class, optional Data-type descriptor of the returned view, e.g., float32 or int16. The default, None, results in the view having the same data-type as a. This argument can also be specified as an ndarray sub-class, which then specifies the type of the returned object (this is equivalent to setting the type parameter). type : Python type, optional Type of the returned vie

MaskedArray.__setmask__(mask, copy=False) [source] Set the mask.

MaskedArray.__rfloordiv__(other) [source] Divide self into other, and return a new masked array.

generic.clip() 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

dtype.__reduce__()

numpy.polynomial.hermite_e.hermevander2d(x, y, deg) [source] Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees deg and sample points (x, y). The pseudo-Vandermonde matrix is defined by where 0 <= i <= deg[0] and 0 <= j <= deg[1]. The leading indices of V index the points (x, y) and the last index encodes the degrees of the HermiteE polynomials. If V = hermevander2d(x, y, [xdeg, ydeg]), then the columns of V correspond to the elements

numpy.polynomial.hermite.hermvander3d(x, y, z, deg) [source] Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees deg and sample points (x, y, z). If l, m, n are the given degrees in x, y, z, then The pseudo-Vandermonde matrix is defined by where 0 <= i <= l, 0 <= j <= m, and 0 <= j <= n. The leading indices of V index the points (x, y, z) and the last index encodes the degrees of the Hermite polynomials. If V = hermvander3d(x, y,

RandomState.chisquare(df, size=None) Draw samples from a chi-square distribution. When df independent random variables, each with standard normal distributions (mean 0, variance 1), are squared and summed, the resulting distribution is chi-square (see Notes). This distribution is often used in hypothesis testing. Parameters: df : int Number of degrees of freedom. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn.

Constants ma.MaskType alias of bool_ Creation From existing data ma.masked_array alias of MaskedArray ma.array(data[, dtype, copy, order, mask, ...]) An array class with possibly masked values. ma.copy(self, *args, **params) a.copy(order=) Return a copy of the array. ma.frombuffer(buffer[, dtype, count, offset]) Interpret a buffer as a 1-dimensional array. ma.fromfunction(function, shape, **kwargs) Construct an array by executing a function over each coordinate. ma.MaskedArray.copy(

MaskedArray.__iadd__(other) [source] Add other to self in-place.