generic.flags integer value of flags

numpy.linalg.slogdet(a) [source] Compute the sign and (natural) logarithm of the determinant of an array. If an array has a very small or very large determinant, then a call to det may overflow or underflow. This routine is more robust against such issues, because it computes the logarithm of the determinant rather than the determinant itself. Parameters: a : (..., M, M) array_like Input array, has to be a square 2-D array. Returns: sign : (...) array_like A number representing the si

recarray.imag The imaginary part of the array. Examples >>> x = np.sqrt([1+0j, 0+1j]) >>> x.imag array([ 0. , 0.70710678]) >>> x.imag.dtype dtype('float64')

numpy.r_ = Translates slice objects to concatenation along the first axis. This is a simple way to build up arrays quickly. There are two use cases. If the index expression contains comma separated arrays, then stack them along their first axis. If the index expression contains slice notation or scalars then create a 1-D array with a range indicated by the slice notation. If slice notation is used, the syntax start:stop:step is equivalent to np.arange(start, stop, step) inside of the brack

ndarray.__getitem__ x.__getitem__(y) <==> x[y]

MaskedArray.reshape(*s, **kwargs) [source] Give a new shape to the array without changing its data. Returns a masked array containing the same data, but with a new shape. The result is a view on the original array; if this is not possible, a ValueError is raised. Parameters: shape : int or tuple of ints The new shape should be compatible with the original shape. If an integer is supplied, then the result will be a 1-D array of that length. order : {?C?, ?F?}, optional Determines whether

numpy.imag(val) [source] Return the imaginary part of the elements of the array. Parameters: val : array_like Input array. Returns: out : ndarray Output array. If val is real, the type of val is used for the output. If val has complex elements, the returned type is float. See also real, angle, real_if_close Examples >>> a = np.array([1+2j, 3+4j, 5+6j]) >>> a.imag array([ 2., 4., 6.]) >>> a.imag = np.array([8, 10, 12]) >>> a array([ 1. +8.j, 3

generic.ravel() 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

MaskedArray.__ilshift__ x.__ilshift__(y) <==> x<<=y

numpy.triu_indices(n, k=0, m=None) [source] Return the indices for the upper-triangle of an (n, m) array. Parameters: n : int The size of the arrays for which the returned indices will be valid. k : int, optional Diagonal offset (see triu for details). m : int, optional New in version 1.9.0. The column dimension of the arrays for which the returned arrays will be valid. By default m is taken equal to n. Returns: inds : tuple, shape(2) of ndarrays, shape(n) The indices for the tr