poissonianlikemethods.py 文件源码

def estimate_poissonian(self, x_axis, data, params):
    """ Provide an estimator for initial values of a poissonian function.

    @param numpy.array x_axis: 1D axis values
    @param numpy.array data: 1D data, should have the same dimension as x_axis.
    @param lmfit.Parameters params: object includes parameter dictionary which
                                    can be set

    @return tuple (error, params):

    Explanation of the return parameter:
        int error: error code (0:OK, -1:error)
        Parameters object params: set parameters of initial values
    """

    error = self._check_1D_input(x_axis=x_axis, data=data, params=params)

    # a gaussian filter is appropriate due to the well approximation of poisson
    # distribution
    # gaus = gaussian(10,10)
    # data_smooth = filters.convolve1d(data, gaus/gaus.sum(), mode='mirror')
    data_smooth = self.gaussian_smoothing(data=data, filter_len=10,
                                          filter_sigma=10)

    # set parameters
    mu = x_axis[np.argmax(data_smooth)]
    params['mu'].value = mu
    params['amplitude'].value = data_smooth.max() / self.poisson(mu, mu)

    return error, params