RandomState.logistic(loc=0.0, scale=1.0, size=None) Draw samples from a logistic distribution. Samples are drawn from a logistic distribution with specified parameters, loc (location or mean, also median), and scale (>0). Parameters: loc : float scale : float > 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. Returns: samples : ndarray or scal

record.dump() 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

nditer.remove_axis(i) Removes axis i from the iterator. Requires that the flag ?multi_index? be enabled.

numpy.ma.append(a, b, axis=None) [source] Append values to the end of an array. New in version 1.9.0. Parameters: a : array_like Values are appended to a copy of this array. b : array_like These values are appended to a copy of a. It must be of the correct shape (the same shape as a, excluding axis). If axis is not specified, b can be any shape and will be flattened before use. axis : int, optional The axis along which v are appended. If axis is not given, both a and b are flattened

matrix.all(axis=None, out=None) [source] Test whether all matrix elements along a given axis evaluate to True. Parameters: See `numpy.all` for complete descriptions See also numpy.all Notes This is the same as ndarray.all, but it returns a matrix object. Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x matrix([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> y = x[0]; y matrix([[0, 1, 2, 3]]) >>> (x == y) matrix([[ True, Tru

numpy.polynomial.legendre.legadd(c1, c2) [source] Add one Legendre series to another. Returns the sum of two Legendre 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 Legendre series coefficients ordered from low to high. Returns: out : ndarray Array representing the Legendre series of their sum. See also legsub, legmul, legdiv

chararray.flatten(order='C') Return a copy of the array collapsed into one dimension. Parameters: order : {?C?, ?F?, ?A?, ?K?}, optional ?C? means to flatten in row-major (C-style) order. ?F? means to flatten in column-major (Fortran- style) order. ?A? means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ?K? means to flatten a in the order the elements occur in memory. The default is ?C?. Returns: y : ndarray A copy of the input array

numpy.bitwise_xor(x1, x2[, out]) = Compute the bit-wise XOR of two arrays element-wise. Computes the bit-wise XOR of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ^. Parameters: x1, x2 : array_like Only integer and boolean types are handled. Returns: out : array_like Result. See also logical_xor, bitwise_and, bitwise_or binary_repr Return the binary representation of the input number as a string. Examples

numpy.polynomial.hermite_e.hermeder(c, m=1, scl=1, axis=0) [source] Differentiate a Hermite_e series. Returns the series 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 series 1*He_0 + 2*He_1 + 3*He_2 while [[1,2],[1,2]] represents 1*He_0(x)*He_0(y) + 1*He_1(x)*He_0

numpy.ma.choose(indices, choices, out=None, mode='raise') [source] Use an index array to construct a new array from a set of choices. Given an array of integers and a set of n choice arrays, this method will create a new array that merges each of the choice arrays. Where a value in a is i, the new array will have the value that choices[i] contains in the same place. Parameters: a : ndarray of ints This array must contain integers in [0, n-1], where n is the number of choices. choices : s