ndarray.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False) Returns the variance of the array elements, along given axis. Refer to numpy.var for full documentation. See also numpy.var equivalent function

matrix.reshape(shape, order='C') Returns an array containing the same data with a new shape. Refer to numpy.reshape for full documentation. See also numpy.reshape equivalent function

ndindex.ndincr() [source] Increment the multi-dimensional index by one. This method is for backward compatibility only: do not use.

numpy.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None) [source] Return numbers spaced evenly on a log scale. In linear space, the sequence starts at base ** start (base to the power of start) and ends with base ** stop (see endpoint below). Parameters: start : float base ** start is the starting value of the sequence. stop : float base ** stop is the final value of the sequence, unless endpoint is False. In that case, num + 1 values are spaced over the interval in log

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

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

HermiteE.truncate(size) [source] Truncate series to length size. Reduce the series to length size by discarding the high degree terms. The value of size must be a positive integer. This can be useful in least squares where the coefficients of the high degree terms may be very small. Parameters: size : positive int The series is reduced to length size by discarding the high degree terms. The value of size must be a positive integer. Returns: new_series : series New instance of series w