replay.py 文件源码

def linregress_bst(bst, tuningcurve):
    """perform linear regression on all the events in bst, and return the slopes, intercepts, and R^2 values"""

    posterior, bdries, mode_pth, mean_pth = decode(bst=bst, ratemap=tuningcurve)

    slopes = np.zeros(bst.n_epochs)
    intercepts = np.zeros(bst.n_epochs)
    r2values = np.zeros(bst.n_epochs)
    for idx in range(bst.n_epochs):
        y = mode_pth[bdries[idx]:bdries[idx+1]]
        x = np.arange(bdries[idx],bdries[idx+1], step=1)
        x = x[~np.isnan(y)]
        y = y[~np.isnan(y)]

        if len(y) > 0:
            slope, intercept, rvalue, pvalue, stderr = stats.linregress(x, y)
            slopes[idx] = slope
            intercepts[idx] = intercept
            r2values[idx] = rvalue**2
        else:
            slopes[idx] = np.nan
            intercepts[idx] = np.nan
            r2values[idx] = np.nan #
#     if bst.n_epochs == 1:
#         return np.asscalar(slopes), np.asscalar(intercepts), np.asscalar(r2values)
    return slopes, intercepts, r2values