numpy.polynomial.legendre.leggauss(deg) [source] Gauss-Legendre quadrature. Computes the sample points and weights for Gauss-Legendre quadrature. These sample points and weights will correctly integrate polynomials of degree or less over the interval with the weight function . Parameters: deg : int Number of sample points and weights. It must be >= 1. Returns: x : ndarray 1-D ndarray containing the sample points. y : ndarray 1-D ndarray containing the weights. Notes The resul

numpy.polynomial.polynomial.polyder(c, m=1, scl=1, axis=0) [source] Differentiate a polynomial. Returns the polynomial coefficients c differentiated m times along axis. At each iteration the result is multiplied by scl (the scaling factor is for use in a linear change of variable). The argument c is an array of coefficients from low to high degree along each axis, e.g., [1,2,3] represents the polynomial 1 + 2*x + 3*x**2 while [[1,2],[1,2]] represents 1 + 1*x + 2*y + 2*x*y if axis=0 is x and

numpy.floor_divide(x1, x2[, out]) = Return the largest integer smaller or equal to the division of the inputs. It is equivalent to the Python // operator and pairs with the Python % (remainder), function so that b = a % b + b * (a // b) up to roundoff. Parameters: x1 : array_like Numerator. x2 : array_like Denominator. Returns: y : ndarray y = floor(x1/x2) See also remainder Remainder complementary to floor_divide. divide Standard division. floor Round a number to the n

MaskedArray.__isub__(other) [source] Subtract other from self in-place.

numpy.ma.loads(strg) [source] Load a pickle from the current string. The result of cPickle.loads(strg) is returned. Parameters: strg : str The string to load. See also dumps Return a string corresponding to the pickling of a masked array.

numpy.vdot(a, b) Return the dot product of two vectors. The vdot(a, b) function handles complex numbers differently than dot(a, b). If the first argument is complex the complex conjugate of the first argument is used for the calculation of the dot product. Note that vdot handles multidimensional arrays differently than dot: it does not perform a matrix product, but flattens input arguments to 1-D vectors first. Consequently, it should only be used for vectors. Parameters: a : array_like I

generic.__array__() sc.__array__(|type) return 0-dim array

numpy.random.set_state(state) Set the internal state of the generator from a tuple. For use if one has reason to manually (re-)set the internal state of the ?Mersenne Twister?[R261] pseudo-random number generating algorithm. Parameters: state : tuple(str, ndarray of 624 uints, int, int, float) The state tuple has the following items: the string ?MT19937?, specifying the Mersenne Twister algorithm. a 1-D array of 624 unsigned integers keys. an integer pos. an integer has_gauss. a float cac

ndarray.transpose(*axes) Returns a view of the array with axes transposed. For a 1-D array, this has no effect. (To change between column and row vectors, first cast the 1-D array into a matrix object.) For a 2-D array, this is the usual matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided and a.shape = (i[0], i[1], ... i[n-2], i[n-1]), then a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]). Para

classmethod Polynomial.fit(x, y, deg, domain=None, rcond=None, full=False, w=None, window=None) [source] Least squares fit to data. Return a series instance that is the least squares fit to the data y sampled at x. The domain of the returned instance can be specified and this will often result in a superior fit with less chance of ill conditioning. Parameters: x : array_like, shape (M,) x-coordinates of the M sample points (x[i], y[i]). y : array_like, shape (M,) or (M, K) y-coordinates