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

recarray.dot(b, out=None) Dot product of two arrays. Refer to numpy.dot for full documentation. See also numpy.dot equivalent function Examples >>> a = np.eye(2) >>> b = np.ones((2, 2)) * 2 >>> a.dot(b) array([[ 2., 2.], [ 2., 2.]]) This array method can be conveniently chained: >>> a.dot(b).dot(b) array([[ 8., 8.], [ 8., 8.]])

exception numpy.RankWarning [source] Issued by polyfit when the Vandermonde matrix is rank deficient. For more information, a way to suppress the warning, and an example of RankWarning being issued, see polyfit.

numpy.isfortran(a) [source] Returns True if the array is Fortran contiguous but not C contiguous. This function is obsolete and, because of changes due to relaxed stride checking, its return value for the same array may differ for versions of Numpy >= 1.10 and previous versions. If you only want to check if an array is Fortran contiguous use a.flags.f_contiguous instead. Parameters: a : ndarray Input array. Examples np.array allows to specify whether the array is written in C-contigu

numpy.bitwise_xor(x1, x2[, out]) = Compute the bit-wise XOR of two arrays element-wise. Computes the bit-wise XOR of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ^. Parameters: x1, x2 : array_like Only integer and boolean types are handled. Returns: out : array_like Result. See also logical_xor, bitwise_and, bitwise_or binary_repr Return the binary representation of the input number as a string. Examples

generic.ndim number of array dimensions

recarray.swapaxes(axis1, axis2) Return a view of the array with axis1 and axis2 interchanged. Refer to numpy.swapaxes for full documentation. See also numpy.swapaxes equivalent function

numpy.asanyarray(a, dtype=None, order=None) [source] Convert the input to an ndarray, but pass ndarray subclasses through. Parameters: a : array_like Input data, in any form that can be converted to an array. This includes scalars, lists, lists of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays. dtype : data-type, optional By default, the data-type is inferred from the input data. order : {?C?, ?F?}, optional Whether to use row-major (C-style) or column-major (Fortran-

numpy.matlib.randn(*args) [source] Return a random matrix with data from the ?standard normal? distribution. randn generates a matrix filled with random floats sampled from a univariate ?normal? (Gaussian) distribution of mean 0 and variance 1. Parameters: *args : Arguments Shape of the output. If given as N integers, each integer specifies the size of one dimension. If given as a tuple, this tuple gives the complete shape. Returns: Z : matrix of floats A matrix of floating-point samp

dtype.newbyteorder(new_order='S') Return a new dtype with a different byte order. Changes are also made in all fields and sub-arrays of the data type. Parameters: new_order : string, optional Byte order to force; a value from the byte order specifications below. The default value (?S?) results in swapping the current byte order. new_order codes can be any of: ?S? - swap dtype from current to opposite endian {?<?, ?L?} - little endian {?>?, ?B?} - big endian {?=?, ?N?} - native order