numpy.real_if_close(a, tol=100) [source] If complex input returns a real array if complex parts are close to zero. ?Close to zero? is defined as tol * (machine epsilon of the type for a). Parameters: a : array_like Input array. tol : float Tolerance in machine epsilons for the complex part of the elements in the array. Returns: out : ndarray If a is real, the type of a is used for the output. If a has complex elements, the returned type is float. See also real, imag, angle Notes

numpy.polynomial.hermite_e.hermesub(c1, c2) [source] Subtract one Hermite series from another. Returns the difference of two Hermite series c1 - c2. The sequences of coefficients are 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 Hermite series coefficients ordered from low to high. Returns: out : ndarray Of Hermite series coefficients representing their difference. See also hermeadd, hermemu

class numpy.broadcast [source] Produce an object that mimics broadcasting. Parameters: in1, in2, ... : array_like Input parameters. Returns: b : broadcast object Broadcast the input parameters against one another, and return an object that encapsulates the result. Amongst others, it has shape and nd properties, and may be used as an iterator. Examples Manually adding two vectors, using broadcasting: >>> x = np.array([[1], [2], [3]]) >>> y = np.array([4, 5, 6]) >

record.cumprod() 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

numpy.random.seed(seed=None) Seed the generator. This method is called when RandomState is initialized. It can be called again to re-seed the generator. For details, see RandomState. Parameters: seed : int or array_like, optional Seed for RandomState. Must be convertible to 32 bit unsigned integers. See also RandomState

Chebyshev.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

HermiteE.deriv(m=1) [source] Differentiate. Return a series instance of that is the derivative of the current series. Parameters: m : non-negative int Find the derivative of order m. Returns: new_series : series A new series representing the derivative. The domain is the same as the domain of the differentiated series.

Hermite.mapparms() [source] Return the mapping parameters. The returned values define a linear map off + scl*x that is applied to the input arguments before the series is evaluated. The map depends on the domain and window; if the current domain is equal to the window the resulting map is the identity. If the coefficients of the series instance are to be used by themselves outside this class, then the linear function must be substituted for the x in the standard representation of the base p

numpy.meshgrid(*xi, **kwargs) [source] Return coordinate matrices from coordinate vectors. Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given one-dimensional coordinate arrays x1, x2,..., xn. Changed in version 1.9: 1-D and 0-D cases are allowed. Parameters: x1, x2,..., xn : array_like 1-D arrays representing the coordinates of a grid. indexing : {?xy?, ?ij?}, optional Cartesian (?xy?, default) or matrix (?ij?) indexing of output. S

numpy.ma.mask_cols(a, axis=None) [source] Mask columns of a 2D array that contain masked values. This function is a shortcut to mask_rowcols with axis equal to 1. See also mask_rowcols Mask rows and/or columns of a 2D array. masked_where Mask where a condition is met. Examples >>> import numpy.ma as ma >>> a = np.zeros((3, 3), dtype=np.int) >>> a[1, 1] = 1 >>> a array([[0, 0, 0], [0, 1, 0], [0, 0, 0]]) >>> a = ma.masked_equa