def avar (inarray, dimension=None,keepdims=0):
    if dimension == None:
        inarray = N.ravel(inarray)
        dimension = 0
    mn = amean(inarray,dimension,1)
    deviations = inarray - mn
    if type(dimension) == ListType:
        n = 1
        for d in dimension:
            n = n*inarray.shape[d]
    else:
        n = inarray.shape[dimension]
    var = ass(deviations,dimension,keepdims)/float(n-1)
    return var
Return J diag(d) as LinearOperator.
dataset/ETHPy150Open radlab/sparrow/src/main/python/third_party/stats.py/avar--crossover with--dataset/ETHPy150Open scipy/scipy/scipy/optimize/_lsq/common.py/right_multiplied_operator