generic.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

record.T transpose

matrix.dump(file) Dump a pickle of the array to the specified file. The array can be read back with pickle.load or numpy.load. Parameters: file : str A string naming the dump file.

RandomState.rayleigh(scale=1.0, size=None) Draw samples from a Rayleigh distribution. The and Weibull distributions are generalizations of the Rayleigh. Parameters: scale : scalar Scale, also equals the mode. Should be >= 0. 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. Default is None, in which case a single value is returned. Notes The probability density function for the Rayleigh distribution is

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.

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.

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,

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

class numpy.vectorize(pyfunc, otypes='', doc=None, excluded=None, cache=False) [source] Generalized function class. Define a vectorized function which takes a nested sequence of objects or numpy arrays as inputs and returns a numpy array as output. The vectorized function evaluates pyfunc over successive tuples of the input arrays like the python map function, except it uses the broadcasting rules of numpy. The data type of the output of vectorized is determined by calling the function with