Resampling

gridlib.resample_and_fit(parameters: Dict, data: Dict[str, Dict[str, ndarray]], n: int = 200, perc: float = 0.8, fit_mode: str = 'grid', multiprocess_flag: bool = True, max_workers: Optional[int] = None) → Tuple[Dict[str, Dict[str, Union[ndarray, float]]], List[Dict[str, Dict[str, Union[ndarray, float]]]]][source]

This function performs a resampling procedure to fit survival time and value data with a given set of parameters. It first fits the full dataset with the parameters, then resamples the data and fits the resampled datasets with the parameters. The function returns the fit results for the full dataset and the resampled datasets.

Parameters

  • parameters (Dict) – Dictionary containing all the parameters needed to perform the GRID and/or 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., ...]),
        "value": array([1.000e+04, 6.925e+03, 5.541e+03, ...]),
    },
}

  • n (int, optional) – The number of times you want to resample from the full dataset. Defaults to 200.

  • perc (float, optional) – The percentage of data points to include in each resampled dataset. Defaults to 0.8.

  • fit_mode ({"grid", "multi-exp", "all"}, optional) –

    The fitting procedure to perform:

    • ”grid”: perform GRID fitting procedure on the data.

    • ”multi-exp”: perform multi-exponential fitting procedure on the data.

    • ”all”: perform both GRID fitting and multi-exponential fitting procedure on the data.

  • multiprocess_flag (bool, optional) – A flag indicating whether to use parallel processing when fitting the resampled datasets. Defaults to True.

  • max_workers (int, optional) – The maximum number of logical cores to use for the multiprocessing. This number is only used if multiprocess_flag is set to True and the value has to be between 1 and the maximum # logical cores - 1. If value is set to None, than it will be set to # logical cores - 1. Default is None.

Returns

  • fit_results_full (Dict[str, Dict[str, Union[np.ndarray, float]]]) – A dictionary mapping keys (fitting procedure) to the corresponding fit results. For example:

    {
    "grid": {
        "k": array([1.00000000e-03, 1.04737090e-03, ...]),
        "s": array([3.85818587e-17, 6.42847878e-18, ...]),
        "a": 0.010564217803906671,
        "loss": 0.004705659331508584,
    },
}

  • fit_results_resampled (List[Dict[str, Dict[str, Union[np.ndarray, float]]]]) – A list of dictionaries, each a dictionary mapping keys (fitting procedure) to the corresponding fit results. For example:

    [
    {
        "grid": {
            "k": array([1.00000000e-03, 1.04737090e-03, ...]),
            "s": array([3.85818587e-17, 6.42847878e-18, ...]),
            "a": 0.010564217803906671,
            "loss": 0.004705659331508584,
        },
    },
    {
        "grid": {
            "k": array([1.00000000e-03, 1.04737090e-03, ...]),
            "s": array([3.85818587e-17, 6.42847878e-18, ...]),
            "a": 0.010564217803906671,
            "loss": 0.004705659331508584,
        },
    },
    ...
]