numpy.histogram2d(x, y, bins=10, range=None, normed=False, weights=None) [source] Compute the bi-dimensional histogram of two data samples. Parameters: x : array_like, shape (N,) An array containing the x coordinates of the points to be histogrammed. y : array_like, shape (N,) An array containing the y coordinates of the points to be histogrammed. bins : int or array_like or [int, int] or [array, array], optional The bin specification: If int, the number of bins for the two dimension

numpy.random.uniform(low=0.0, high=1.0, size=None) Draw samples from a uniform distribution. Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn by uniform. Parameters: low : float, optional Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0. high : float Upper boundary of the outp

Hermite.integ(m=1, k=[], lbnd=None) [source] Integrate. Return a series instance that is the definite integral of the current series. Parameters: m : non-negative int The number of integrations to perform. k : array_like Integration constants. The first constant is applied to the first integration, the second to the second, and so on. The list of values must less than or equal to m in length and any missing values are set to zero. lbnd : Scalar The lower bound of the definite integral

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.copy(a, order='K') [source] Return an array copy of the given object. Parameters: a : array_like Input data. order : {?C?, ?F?, ?A?, ?K?}, optional Controls the memory layout of the copy. ?C? means C-order, ?F? means F-order, ?A? means ?F? if a is Fortran contiguous, ?C? otherwise. ?K? means match the layout of a as closely as possible. (Note that this function and :meth:ndarray.copy are very similar, but have different default values for their order= arguments.) Returns: arr :

RandomState.standard_gamma(shape, size=None) Draw samples from a standard Gamma distribution. Samples are drawn from a Gamma distribution with specified parameters, shape (sometimes designated ?k?) and scale=1. Parameters: shape : float Parameter, should be > 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 scalar

MaskedArray.soften_mask() [source] Force the mask to soft. Whether the mask of a masked array is hard or soft is determined by its hardmask property. soften_mask sets hardmask to False. See also hardmask

generic.prod() 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.isrealobj(x) [source] Return True if x is a not complex type or an array of complex numbers. The type of the input is checked, not the value. So even if the input has an imaginary part equal to zero, isrealobj evaluates to False if the data type is complex. Parameters: x : any The input can be of any type and shape. Returns: y : bool The return value, False if x is of a complex type. See also iscomplexobj, isreal Examples >>> np.isrealobj(1) True >>> np.isr

Laguerre.convert(domain=None, kind=None, window=None) [source] Convert series to a different kind and/or domain and/or window. Parameters: domain : array_like, optional The domain of the converted series. If the value is None, the default domain of kind is used. kind : class, optional The polynomial series type class to which the current instance should be converted. If kind is None, then the class of the current instance is used. window : array_like, optional The window of the conver