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

matrix.tobytes(order='C') Construct Python bytes containing the raw data bytes in the array. Constructs Python bytes showing a copy of the raw contents of data memory. The bytes object can be produced in either ?C? or ?Fortran?, or ?Any? order (the default is ?C?-order). ?Any? order means C-order unless the F_CONTIGUOUS flag in the array is set, in which case it means ?Fortran? order. New in version 1.9.0. Parameters: order : {?C?, ?F?, None}, optional Order of the data for multidimensi

numpy.fft.irfft2(a, s=None, axes=(-2, -1), norm=None) [source] Compute the 2-dimensional inverse FFT of a real array. Parameters: a : array_like The input array s : sequence of ints, optional Shape of the inverse FFT. axes : sequence of ints, optional The axes over which to compute the inverse fft. Default is the last two axes. norm : {None, ?ortho?}, optional New in version 1.10.0. Normalization mode (see numpy.fft). Default is None. Returns: out : ndarray The result of the i

numpy.issubclass_(arg1, arg2) [source] Determine if a class is a subclass of a second class. issubclass_ is equivalent to the Python built-in issubclass, except that it returns False instead of raising a TypeError if one of the arguments is not a class. Parameters: arg1 : class Input class. True is returned if arg1 is a subclass of arg2. arg2 : class or tuple of classes. Input class. If a tuple of classes, True is returned if arg1 is a subclass of any of the tuple elements. Returns:

numpy.gradient(f, *varargs, **kwargs) [source] Return the gradient of an N-dimensional array. The gradient is computed using second order accurate central differences in the interior and either first differences or second order accurate one-sides (forward or backwards) differences at the boundaries. The returned gradient hence has the same shape as the input array. Parameters: f : array_like An N-dimensional array containing samples of a scalar function. varargs : scalar or list of scala

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

matrix.tofile(fid, sep="", format="%s") Write array to a file as text or binary (default). Data is always written in ?C? order, independent of the order of a. The data produced by this method can be recovered using the function fromfile(). Parameters: fid : file or str An open file object, or a string containing a filename. sep : str Separator between array items for text output. If ?? (empty), a binary file is written, equivalent to file.write(a.tobytes()). format : str Format string

numpy.ma.where(condition, x=, y=) [source] Return a masked array with elements from x or y, depending on condition. Returns a masked array, shaped like condition, where the elements are from x when condition is True, and from y otherwise. If neither x nor y are given, the function returns a tuple of indices where condition is True (the result of condition.nonzero()). Parameters: condition : array_like, bool The condition to meet. For each True element, yield the corresponding element from

recarray.flat A 1-D iterator over the array. This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python?s built-in iterator object. See also flatten Return a copy of the array collapsed into one dimension. flatiter Examples >>> x = np.arange(1, 7).reshape(2, 3) >>> x array([[1, 2, 3], [4, 5, 6]]) >>> x.flat[3] 4 >>> x.T array([[1, 4], [2, 5], [3, 6]]) >>> x.T.flat[3] 5 >>> type(

numpy.polynomial.laguerre.laggauss(deg) [source] Gauss-Laguerre quadrature. Computes the sample points and weights for Gauss-Laguerre quadrature. These sample points and weights will correctly integrate polynomials of degree or less over the interval with the weight function . Parameters: deg : int Number of sample points and weights. It must be >= 1. Returns: x : ndarray 1-D ndarray containing the sample points. y : ndarray 1-D ndarray containing the weights. Notes The resul