figrc.py 文件源码

def hist_with_err(x, xerr, bins=None, normed=False, step=False, *kwargs):
    from scipy import integrate

    # check inputs
    assert( len(x) == len(xerr) ), 'data size mismatch'
    _x = np.asarray(x).astype(float)
    _xerr = np.asarray(xerr).astype(float)

    # def the evaluation points
    if (bins is None) | (not hasattr(bins, '__iter__')):
        m = (_x - _xerr).min()
        M = (_x + _xerr).max()
        dx = M - m
        m -= 0.2 * dx
        M += 0.2 * dx
        if bins is not None:
            N = int(bins)
        else:
            N = 10
        _xp = np.linspace(m, M, N)
    else:
        _xp = 0.5 * (bins[1:] + bins[:-1])

    def normal(v, mu, sig):
        norm_pdf = 1. / (np.sqrt(2. * np.pi) * sig ) * np.exp( - ( (v - mu ) / (2. * sig) ) ** 2 )
        return norm_pdf / integrate.simps(norm_pdf, v)

    _yp = np.array([normal(_xp, xk, xerrk) for xk, xerrk in zip(_x, _xerr) ]).sum(axis=0)

    if normed:
        _yp /= integrate.simps(_yp, _xp)

    if step:
        return steppify(_xp, _yp)
    else:
        return _xp, _yp