numpy.polynomial.hermite.hermgrid2d(x, y, c) [source] Evaluate a 2-D Hermite series on the Cartesian product of x and y. This function returns the values: where the points (a, b) consist of all pairs formed by taking a from x and b from y. The resulting points form a grid with x in the first dimension and y in the second. The parameters x and y are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars. In either case, either x and y or their elem

ndarray.__nonzero__ x.__nonzero__() <==> x != 0

numpy.ma.vander(x, n=None) [source] Generate a Vandermonde matrix. The columns of the output matrix are powers of the input vector. The order of the powers is determined by the increasing boolean argument. Specifically, when increasing is False, the i-th output column is the input vector raised element-wise to the power of N - i - 1. Such a matrix with a geometric progression in each row is named for Alexandre- Theophile Vandermonde. Parameters: x : array_like 1-D input array. N : int, o

numpy.polynomial.legendre.legvander(x, deg) [source] Pseudo-Vandermonde matrix of given degree. Returns the pseudo-Vandermonde matrix of degree deg and sample points x. The pseudo-Vandermonde matrix is defined by where 0 <= i <= deg. The leading indices of V index the elements of x and the last index is the degree of the Legendre polynomial. If c is a 1-D array of coefficients of length n + 1 and V is the array V = legvander(x, n), then np.dot(V, c) and legval(x, c) are the same up

numpy.fabs(x[, out]) = Compute the absolute values element-wise. This function returns the absolute values (positive magnitude) of the data in x. Complex values are not handled, use absolute to find the absolute values of complex data. Parameters: x : array_like The array of numbers for which the absolute values are required. If x is a scalar, the result y will also be a scalar. out : ndarray, optional Array into which the output is placed. Its type is preserved and it must be of the r

MaskedArray.set_fill_value(value=None) [source] Set the filling value of the masked array. Parameters: value : scalar, optional The new filling value. Default is None, in which case a default based on the data type is used. See also ma.set_fill_value Equivalent function. Examples >>> x = np.ma.array([0, 1.], fill_value=-np.inf) >>> x.fill_value -inf >>> x.set_fill_value(np.pi) >>> x.fill_value 3.1415926535897931 Reset to default: >>> x.s

MaskedArray.__deepcopy__(memo=None) [source]

numpy.ma.compress_rows(a) [source] Suppress whole rows of a 2-D array that contain masked values. This is equivalent to np.ma.compress_rowcols(a, 0), see extras.compress_rowcols for details. See also extras.compress_rowcols

numpy.polynomial.legendre.legdiv(c1, c2) [source] Divide one Legendre series by another. Returns the quotient-with-remainder of two Legendre series c1 / c2. The arguments are sequences of coefficients from lowest order ?term? to highest, e.g., [1,2,3] represents the series P_0 + 2*P_1 + 3*P_2. Parameters: c1, c2 : array_like 1-D arrays of Legendre series coefficients ordered from low to high. Returns: quo, rem : ndarrays Of Legendre series coefficients representing the quotient and re

numpy.testing.assert_array_less(x, y, err_msg='', verbose=True) [source] Raises an AssertionError if two array_like objects are not ordered by less than. Given two array_like objects, check that the shape is equal and all elements of the first object are strictly smaller than those of the second object. An exception is raised at shape mismatch or incorrectly ordered values. Shape mismatch does not raise if an object has zero dimension. In contrast to the standard usage in numpy, NaNs are co