# Don't add these to the __all__ variable though

Package Contents
		__init__ alter_code1 -- This module converts code written for Numeric to run with numpy alter_code2 array_printer arrayfns -- Backward compatible with arrayfns from Numeric compat fft fix_default_axis functions linear_algebra -- Backward compatible with LinearAlgebra from Numeric ma matrix misc mlab precision random_array rng rng_stats setup setupscons typeconv ufuncs user_array

Functions
		_move_axis_to_0(a, axis) dump(...) dump(obj, file, protocol=0) -- Write an object in pickle format to the given file.   See the Pickler docstring for the meaning of optional argument proto. dumps(...) dumps(obj, protocol=0) -- Return a string containing an object in pickle format.   See the Pickler docstring for the meaning of optional argument proto. innerproduct = inner(...) inner(a, b)   Inner product of two arrays.   Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes.   Parameters ---------- a, b : array_like     If `a` and `b` are nonscalar, their last dimensions of must match.   Returns ------- out : ndarray     `out.shape = a.shape[:-1] + b.shape[:-1]`   Raises ------ ValueError     If the last dimension of `a` and `b` has different size.   See Also -------- tensordot : Sum products over arbitrary axes. dot : Generalised matrix product, using second last dimension of `b`.   Notes ----- For vectors (1-D arrays) it computes the ordinary inner-product::       np.inner(a, b) = sum(a[:]*b[:])   More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`::       np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))   or explicitly::       np.inner(a, b)[i0,...,ir-1,j0,...,js-1]          = sum(a[i0,...,ir-1,:]*b[j0,...,js-1,:])   In addition `a` or `b` may be scalars, in which case::      np.inner(a,b) = a*b   Examples -------- Ordinary inner product for vectors:   >>> a = np.array([1,2,3]) >>> b = np.array([0,1,0]) >>> np.inner(a, b) 2   A multidimensional example:   >>> a = np.arange(24).reshape((2,3,4)) >>> b = np.arange(4) >>> np.inner(a, b) array([[ 14,  38,  62],        [ 86, 110, 134]])   An example where `b` is a scalar:   >>> np.inner(np.eye(2), 7) array([[ 7.,  0.],        [ 0.,  7.]])

Data
		Character = 'c' Complex = 'D' Complex0 = 'F' Complex16 = 'F' Complex32 = 'F' Complex64 = 'D' Complex8 = 'F' Float = 'd' Float0 = 'f' Float16 = 'f' Float32 = 'f' Float64 = 'd' Float8 = 'f' Int = 'l' Int0 = 'b' Int16 = 'h' Int32 = 'i' Int64 = 'l' Int8 = 'b' LittleEndian = True NewAxis = None PyObject = 'O' UInt = 'u' UInt16 = 'H' UInt32 = 'I' UInt64 = 'L' UInt8 = 'B' UnsignedInt = 'u' UnsignedInt16 = 'H' UnsignedInt32 = 'I' UnsignedInt64 = 'L' UnsignedInt8 = 'B' UnsignedInteger = 'u' __all__ = ['__version__', 'NewAxis', 'UFuncType', 'UfuncType', 'ArrayType', 'arraytype', 'LittleEndian', 'arrayrange', 'matrixmultiply', 'array_constructor', 'pickle_array', 'DumpArray', 'LoadArray', 'multiarray', 'dump', 'dumps', 'load', 'loads', 'Unpickler', 'Pickler', ...] __file__ = '/usr/lib/python2.6/dist-packages/numpy/oldnumeric/__init__.pyc' __name__ = 'numpy.oldnumeric' __package__ = 'numpy.oldnumeric' __path__ = ['/usr/lib/python2.6/dist-packages/numpy/oldnumeric'] divide_safe = <ufunc 'divide'> typecodes = {'Character': 'c', 'Complex': 'FD', 'Float': 'fd', 'Integer': 'bhil', 'UnsignedInteger': 'BHIL'}