stats.py 文件源码

def amean (inarray,dimension=None,keepdims=0):
    """
    Calculates the arithmatic mean of the values in the passed array.
    That is:  1/n * (x1 + x2 + ... + xn).  Defaults to ALL values in the
    passed array.  Use dimension=None to flatten array first.  REMEMBER: if
    dimension=0, it collapses over dimension 0 ('rows' in a 2D array) only, and
    if dimension is a sequence, it collapses over all specified dimensions.  If
    keepdims is set to 1, the resulting array will have as many dimensions as
    inarray, with only 1 'level' per dim that was collapsed over.

    Usage:   amean(inarray,dimension=None,keepdims=0)
    Returns: arithematic mean calculated over dim(s) in dimension
    """
    if inarray.dtype in [N.int_, N.short,N.ubyte]:
        inarray = inarray.astype(N.float_)
    if dimension == None:
        inarray = N.ravel(inarray)
        sum = N.add.reduce(inarray)
        denom = float(len(inarray))
    elif type(dimension) in [IntType,FloatType]:
        sum = asum(inarray,dimension)
        denom = float(inarray.shape[dimension])
        if keepdims == 1:
            shp = list(inarray.shape)
            shp[dimension] = 1
            sum = N.reshape(sum,shp)
    else: # must be a TUPLE of dims to average over
        dims = list(dimension)
        dims.sort()
        dims.reverse()
        sum = inarray *1.0
        for dim in dims:
            sum = N.add.reduce(sum,dim)
        denom = N.array(N.multiply.reduce(N.take(inarray.shape,dims)),N.float_)
        if keepdims == 1:
            shp = list(inarray.shape)
            for dim in dims:
                shp[dim] = 1
            sum = N.reshape(sum,shp)
    return sum/denom