gridlib.fit_multi_exp

gridlib.fit_multi_exp(parameters, data, disp: bool = True)

Function fits one or more n-exponentials to the provided data and returns the fit results.

Parameters

• parameters (Dict) – Dictionary containing all the parameters needed to perform the multi-exponential fitting.

• data (Dict[str, Dict[str, np.ndarray]]) –

  A dictionary mapping keys (time-lapse conditions) to the corresponding time and value arrays of the survival functions. For example:

  {
      "0.05s": {
          "time": array([0.05, 0.1, 0.15, ...]),
          "value": array([1.000e+04, 8.464e+03, 7.396e+03, ...]),
      },
      "1s": {
          "time": array([1., 2., 3., 4., ...]),
          "value": array([1.000e+04, 6.925e+03, 5.541e+03, 4.756e+03, ...]),
      },
  }
  

• disp (bool, optional) – If True, then messages and final minimization results are printed out, otherwise the there are no messages printed, by default True.

Returns

fit_results – A dictionary mapping keys (fitting procedure) to the corresponding fit results. For example, if parameters[“n_exp”] = [1, 2, 3]:

{
    "1-exp": {
        "k": array([0.02563639]),
        "s": array([1.]),
        "a": 0.08514936433699753,
        "loss": 1.2825570522448484
    },
    "2-exp": {
        "k": array([0.03715506, 1.7248619]),
        "s": array([0.17296989, 0.82703011]),
        "a": 0.011938572088673213,
        "loss": 0.2868809590425386
    },
    "3-exp": {
        "k": array([0.0137423 , 0.27889073, 3.6560956]),
        "s": array([0.06850312, 0.23560175, 0.69589513]),
        "a": 0.011125323730424764,
        "loss": 0.0379697542735324
    },
}

Return type

Dict[str, Dict[str, Union[np.ndarray, float]]]

Raises

ValueError – If an incorrect parameter value is provided or a value is missing.