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src/n/i/nipy-0.3.0/nipy/algorithms/statistics/tests/test_onesample.py   nipy(Download)
 
    W = 1. / (sd**2 + results['random'])
    mu = onesample.estimate_mean(Y, np.sqrt(sd**2 + results['random']))['effect']
    yield assert_almost_equal, mu, (W*Y).sum(0) / W.sum(0)
 

src/n/i/nipy-0.3.0/examples/fiac/fiac_example.py   nipy(Download)
    adjusted_sd = np.sqrt(adjusted_var)
 
    results = onesample.estimate_mean(Y, adjusted_sd) 
    for n in ['effect', 'sd', 't']:
        im = api.Image(results[n], copy(coordmap))
        adjusted_sd = np.sqrt(adjusted_var)
 
        results = onesample.estimate_mean(Y, adjusted_sd) 
        T = results['t']
        minT[i], maxT[i] = np.nanmin(T), np.nanmax(T)