class numpy.lib.user_array.container(data, dtype=None, copy=True) [source] Standard container-class for easy multiple-inheritance. Methods copy tostring byteswap astype

class numpy.lib.NumpyVersion(vstring) [source] Parse and compare numpy version strings. Numpy has the following versioning scheme (numbers given are examples; they can be > 9) in principle): Released version: ?1.8.0?, ?1.8.1?, etc. Alpha: ?1.8.0a1?, ?1.8.0a2?, etc. Beta: ?1.8.0b1?, ?1.8.0b2?, etc. Release candidates: ?1.8.0rc1?, ?1.8.0rc2?, etc. Development versions: ?1.8.0.dev-f1234afa? (git commit hash appended) Development versions after a1: ?1.8.0a1.dev-f1234afa?, ?1.8.0b2.dev

class numpy.lib.Arrayterator(var, buf_size=None) [source] Buffered iterator for big arrays. Arrayterator creates a buffered iterator for reading big arrays in small contiguous blocks. The class is useful for objects stored in the file system. It allows iteration over the object without reading everything in memory; instead, small blocks are read and iterated over. Arrayterator can be used with any object that supports multidimensional slices. This includes NumPy arrays, but also variables f

numpy.lexsort(keys, axis=-1) Perform an indirect sort using a sequence of keys. Given multiple sorting keys, which can be interpreted as columns in a spreadsheet, lexsort returns an array of integer indices that describes the sort order by multiple columns. The last key in the sequence is used for the primary sort order, the second-to-last key for the secondary sort order, and so on. The keys argument must be a sequence of objects that can be converted to arrays of the same shape. If a 2D a

numpy.less_equal(x1, x2[, out]) = Return the truth value of (x1 =< x2) element-wise. Parameters: x1, x2 : array_like Input arrays. If x1.shape != x2.shape, they must be broadcastable to a common shape (which may be the shape of one or the other). Returns: out : bool or ndarray of bool Array of bools, or a single bool if x1 and x2 are scalars. See also greater, less, greater_equal, equal, not_equal Examples >>> np.less_equal([4, 2, 1], [2, 2, 2]) array([False, True,

numpy.less(x1, x2[, out]) = Return the truth value of (x1 < x2) element-wise. Parameters: x1, x2 : array_like Input arrays. If x1.shape != x2.shape, they must be broadcastable to a common shape (which may be the shape of one or the other). Returns: out : bool or ndarray of bool Array of bools, or a single bool if x1 and x2 are scalars. See also greater, less_equal, greater_equal, equal, not_equal Examples >>> np.less([1, 2], [2, 2]) array([ True, False], dtype=bool)

numpy.left_shift(x1, x2[, out]) = Shift the bits of an integer to the left. Bits are shifted to the left by appending x2 0s at the right of x1. Since the internal representation of numbers is in binary format, this operation is equivalent to multiplying x1 by 2**x2. Parameters: x1 : array_like of integer type Input values. x2 : array_like of integer type Number of zeros to append to x1. Has to be non-negative. Returns: out : array of integer type Return x1 with bits shifted x2 time

numpy.ldexp(x1, x2[, out]) = Returns x1 * 2**x2, element-wise. The mantissas x1 and twos exponents x2 are used to construct floating point numbers x1 * 2**x2. Parameters: x1 : array_like Array of multipliers. x2 : array_like, int Array of twos exponents. out : ndarray, optional Output array for the result. Returns: y : ndarray or scalar The result of x1 * 2**x2. See also frexp Return (y1, y2) from x = y1 * 2**y2, inverse to ldexp. Notes Complex dtypes are not supported, th

numpy.kron(a, b) [source] Kronecker product of two arrays. Computes the Kronecker product, a composite array made of blocks of the second array scaled by the first. Parameters: a, b : array_like Returns: out : ndarray See also outer The outer product Notes The function assumes that the number of dimensions of a and b are the same, if necessary prepending the smallest with ones. If a.shape = (r0,r1,..,rN) and b.shape = (s0,s1,...,sN), the Kronecker product has shape (r0*s0, r1*s1, ...

numpy.kaiser(M, beta) [source] Return the Kaiser window. The Kaiser window is a taper formed by using a Bessel function. Parameters: M : int Number of points in the output window. If zero or less, an empty array is returned. beta : float Shape parameter for window. Returns: out : array The window, with the maximum value normalized to one (the value one appears only if the number of samples is odd). See also bartlett, blackman, hamming, hanning Notes The Kaiser window is defined