poissonianlikemethods.py 文件源码

def make_poissonian_fit(self, x_axis, data, estimator, units=None, add_params=None):
    """ Performe a poissonian fit on the provided data.

    @param numpy.array x_axis: 1D axis values
    @param numpy.array data: 1D data, should have the same dimension as x_axis.
    @param method estimator: Pointer to the estimator method
    @param list units: List containing the ['horizontal', 'vertical'] units as strings
    @param Parameters or dict add_params: optional, additional parameters of
                type lmfit.parameter.Parameters, OrderedDict or dict for the fit
                which will be used instead of the values from the estimator.

    @return object result: lmfit.model.ModelFit object, all parameters
                           provided about the fitting, like: success,
                           initial fitting values, best fitting values, data
                           with best fit with given axis,...
    """

    poissonian_model, params = self.make_poissonian_model()

    error, params = estimator(x_axis, data, params)

    params = self._substitute_params(initial_params=params,
                                     update_params=add_params)

    try:
        result = poissonian_model.fit(data, x=x_axis, params=params)
    except:
        self.log.warning('The poissonian fit did not work. Check if a poisson '
                         'distribution is needed or a normal approximation can be'
                         'used. For values above 10 a normal/ gaussian distribution '
                         'is a good approximation.')
        result = poissonian_model.fit(data, x=x_axis, params=params)
        print(result.message)

    if units is None:
        units = ['arb. unit', 'arb. unit']

    result_str_dict = dict()  # create result string for gui   oder OrderedDict()

    result_str_dict['Amplitude'] = {'value': result.params['amplitude'].value,
                                    'error': result.params['amplitude'].stderr,
                                    'unit': units[1]}     # Amplitude

    result_str_dict['Event rate'] = {'value': result.params['mu'].value,
                                    'error': result.params['mu'].stderr,
                                    'unit': units[0]}      # event rate

    result.result_str_dict = result_str_dict

    return result