matrix.prod(axis=None, dtype=None, out=None) [source] Return the product of the array elements over the given axis. Refer to prod for full documentation. See also prod, ndarray.prod Notes Same as ndarray.prod, except, where that returns an ndarray, this returns a matrix object instead. Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x matrix([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> x.prod() 0 >>> x.prod(0) matrix([[

numpy.polynomial.laguerre.lagvander2d(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 Laguerre polynomials. If V = lagvander2d(x, y, [xdeg, ydeg]), then the columns of V correspond to the elements of a

numpy.core.defchararray.splitlines(a, keepends=None) [source] For each element in a, return a list of the lines in the element, breaking at line boundaries. Calls str.splitlines element-wise. Parameters: a : array_like of str or unicode keepends : bool, optional Line breaks are not included in the resulting list unless keepends is given and true. Returns: out : ndarray Array of list objects See also str.splitlines

numpy.polynomial.laguerre.lagadd(c1, c2) [source] Add one Laguerre series to another. Returns the sum of two Laguerre series c1 + c2. The arguments are sequences of coefficients ordered from lowest order term to highest, i.e., [1,2,3] represents the series P_0 + 2*P_1 + 3*P_2. Parameters: c1, c2 : array_like 1-D arrays of Laguerre series coefficients ordered from low to high. Returns: out : ndarray Array representing the Laguerre series of their sum. See also lagsub, lagmul, lagdiv

numpy.core.defchararray.translate(a, table, deletechars=None) [source] For each element in a, return a copy of the string where all characters occurring in the optional argument deletechars are removed, and the remaining characters have been mapped through the given translation table. Calls str.translate element-wise. Parameters: a : array-like of str or unicode table : str of length 256 deletechars : str Returns: out : ndarray Output array of str or unicode, depending on input type

numpy.apply_over_axes(func, a, axes) [source] Apply a function repeatedly over multiple axes. func is called as res = func(a, axis), where axis is the first element of axes. The result res of the function call must have either the same dimensions as a or one less dimension. If res has one less dimension than a, a dimension is inserted before axis. The call to func is then repeated for each axis in axes, with res as the first argument. Parameters: func : function This function must take tw

MaskedArray.data Return the current data, as a view of the original underlying data.

numpy.logical_not(x[, out]) = Compute the truth value of NOT x element-wise. Parameters: x : array_like Logical NOT is applied to the elements of x. Returns: y : bool or ndarray of bool Boolean result with the same shape as x of the NOT operation on elements of x. See also logical_and, logical_or, logical_xor Examples >>> np.logical_not(3) False >>> np.logical_not([True, False, 0, 1]) array([False, True, True, False], dtype=bool) >>> x = np.arange(5)

numpy.polynomial.hermite.hermint(c, m=1, k=[], lbnd=0, scl=1, axis=0) [source] Integrate a Hermite series. Returns the Hermite series coefficients c integrated m times from lbnd along axis. At each iteration the resulting series is multiplied by scl and an integration constant, k, is added. The scaling factor is for use in a linear change of variable. (?Buyer beware?: note that, depending on what one is doing, one may want scl to be the reciprocal of what one might expect; for more informat

ndarray.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 vi