numpy.ma.ediff1d(arr, to_end=None, to_begin=None) [source] Compute the differences between consecutive elements of an array. This function is the equivalent of numpy.ediff1d that takes masked values into account, see numpy.ediff1d for details. See also numpy.ediff1d Equivalent function for ndarrays.

numpy.polynomial.hermite.hermint(c, m=1, k=[], lbnd=0, scl=1, axis=0) [source] Integrate a Hermite series. Returns the Hermite series coefficients c integrated m times from lbnd along axis. At each iteration the resulting series is multiplied by scl and an integration constant, k, is added. The scaling factor is for use in a linear change of variable. (?Buyer beware?: note that, depending on what one is doing, one may want scl to be the reciprocal of what one might expect; for more informat

ndarray.view(dtype=None, type=None) New view of array with the same data. Parameters: dtype : data-type or ndarray sub-class, optional Data-type descriptor of the returned view, e.g., float32 or int16. The default, None, results in the view having the same data-type as a. This argument can also be specified as an ndarray sub-class, which then specifies the type of the returned object (this is equivalent to setting the type parameter). type : Python type, optional Type of the returned vi

HermiteE.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

numpy.logical_not(x[, out]) = Compute the truth value of NOT x element-wise. Parameters: x : array_like Logical NOT is applied to the elements of x. Returns: y : bool or ndarray of bool Boolean result with the same shape as x of the NOT operation on elements of x. See also logical_and, logical_or, logical_xor Examples >>> np.logical_not(3) False >>> np.logical_not([True, False, 0, 1]) array([False, True, True, False], dtype=bool) >>> x = np.arange(5)

numpy.swapaxes(a, axis1, axis2) [source] Interchange two axes of an array. Parameters: a : array_like Input array. axis1 : int First axis. axis2 : int Second axis. Returns: a_swapped : ndarray For Numpy >= 1.10, if a is an ndarray, then a view of a is returned; otherwise a new array is created. For earlier Numpy versions a view of a is returned only if the order of the axes is changed, otherwise the input array is returned. Examples >>> x = np.array([[1,2,3]]) >&g

numpy.rint(x[, out]) = Round elements of the array to the nearest integer. Parameters: x : array_like Input array. Returns: out : ndarray or scalar Output array is same shape and type as x. See also ceil, floor, trunc Examples >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) >>> np.rint(a) array([-2., -2., -0., 0., 2., 2., 2.])

numpy.mgrid = nd_grid instance which returns a dense multi-dimensional ?meshgrid?. An instance of numpy.lib.index_tricks.nd_grid which returns an dense (or fleshed out) mesh-grid when indexed, so that each returned argument has the same shape. The dimensions and number of the output arrays are equal to the number of indexing dimensions. If the step length is not a complex number, then the stop is not inclusive. However, if the step length is a complex number (e.g. 5j), then the integer par

Chebyshev.has_samedomain(other) [source] Check if domains match. New in version 1.6.0. Parameters: other : class instance The other class must have the domain attribute. Returns: bool : boolean True if the domains are the same, False otherwise.

numpy.fft.rfftfreq(n, d=1.0) [source] Return the Discrete Fourier Transform sample frequencies (for usage with rfft, irfft). The returned float array f contains the frequency bin centers in cycles per unit of the sample spacing (with zero at the start). For instance, if the sample spacing is in seconds, then the frequency unit is cycles/second. Given a window length n and a sample spacing d: f = [0, 1, ..., n/2-1, n/2] / (d*n) if n is even f = [0, 1, ..., (n-1)/2-1, (n-1)/2] / (d*