pyActigraphy.analysis.Cosinor.fit

Cosinor.fit(ts, params=None, method='leastsq', nan_policy='raise', reduce_fcn=None, verbose=False)[source]

Fit the actigraphy data using a cosinor function.

Parameters

  • ts (pandas.Series) – Input time series.

  • params (instance of Parameters [1], optional.) – Initial fit parameters. If None, use the default parameters. Default is None.

  • method (str, optional) – Name of the fitting method to use [1]. Default is ‘leastsq’.

  • nan_policy (str, optional) –

    Specifies action if the objective function returns NaN values. One of:

    • ’raise’: a ValueError is raised

    • ’propagate’: the values returned from userfcn are un-altered

    • ’omit’: non-finite values are filtered

    Default is ‘raise’.

  • reduce_fcn (str, optional) –

    Function to convert a residual array to a scalar value for the scalar minimizers. Optional values are:

    • ’None’ : sum of squares of residual

    • ’negentropy’ : neg entropy, using normal distribution

    • ’neglogcauchy’: neg log likelihood, using Cauchy distribution

    Default is None.

  • verbose (bool, optional) – If set to True, display fit informations. Default is False.

Returns

fit_results – Fit results.

Return type

MinimizerResult

References

1(1,2)

Non-Linear Least-Squares Minimization and Curve-Fitting for Python. https://lmfit.github.io/lmfit-py/index.html