def _rm_outlier_by_amp(self, params, model, signal, ii):
        """
        Helper function to reject outliers based on mean amplitude
        """
        maxamps = np.nanmax(np.abs(model),0)
        z_score = (maxamps - np.nanmean(maxamps,0))/np.nanstd(maxamps,0)
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            outlier_idx = np.where(np.abs(z_score)>2.0)[0]
            nan_idx = np.where(np.isnan(params))[0]
            outlier_idx = np.unique(np.hstack([nan_idx, outlier_idx]))
            ii[outlier_idx] = 0
            model[outlier_idx] = np.nan
            signal[outlier_idx] = np.nan
            params[outlier_idx] = np.nan

        return model, signal, params, ii
def _rm_outlier_by_amp ( self , params , model , signal , ii ) : maxamps = np . nanmax ( np . abs ( model ) , 0 ) z_score = ( maxamps - np . nanmean ( maxamps , 0 ) ) / np . nanstd ( maxamps , 0 ) with warnings . catch_warnings ( ) : warnings . simplefilter ( "ignore" ) outlier_idx = np . where ( np . abs ( z_score ) > 2.0 ) [ 0 ] nan_idx = np . where ( np . isnan ( params ) ) [ 0 ] outlier_idx = np . unique ( np . hstack ( [ nan_idx , outlier_idx ] ) ) ii [ outlier_idx ] = 0 model [ outlier_idx ] = np . nan signal [ outlier_idx ] = np . nan params [ outlier_idx ] = np . nan return model , signal , params , ii
Helper function to reject outliers based on mean amplitude