Method selected_range_indices

pyxrf/model/lineplot.py:1383–1421

The function computes the range of indices based on the selected energy range and parameters for the energy axis. Parameters ---------- e_low, e_high: float or None Energy values (in keV) that set the selected range n_indexes: int

(self, *, e_low=None, e_high=None, n_indexes=None, margin=2.0)

    #         Functions for plotting spectrum preview

    def selected_range_indices(self, *, e_low=None, e_high=None, n_indexes=None, margin=2.0):
        """
        The function computes the range of indices based on the selected energy range
        and parameters for the energy axis.

        Parameters
        ----------
        e_low, e_high: float or None
            Energy values (in keV) that set the selected range
        n_indexes: int
            Total number of indexes in the energy array (typically 4096)
        margin: float
            The displayed energy range is extended by the value of `margin` in both directions.

        Returns
        -------
        n_low, n_high: int
            The range of indices of the energy array (n_low..n_high-1) that cover the selected energy range
        """
        # The range of energy selected for analysis
        if e_low is None:
            e_low = self.param_model.param_new["non_fitting_values"]["energy_bound_low"]["value"]
        if e_high is None:
            e_high = self.param_model.param_new["non_fitting_values"]["energy_bound_high"]["value"]
        # Protection for the case if e_high < e_low
        e_high = e_high if e_high > e_low else e_low
        # Extend the range (by the value of 'margin')
        e_low, e_high = e_low - margin, e_high + margin

        # The following calculations ignore quadratic term, which is expected to be small
        c0 = self.param_model.param_new["e_offset"]["value"]
        c1 = self.param_model.param_new["e_linear"]["value"]
        # If more precision if needed, then implement more complicated algorithm using
        #   the quadratic term: c2 = self.param_model.param_new['e_quadratic']['value']

        n_low = int(np.clip(int((e_low - c0) / c1), a_min=0, a_max=n_indexes - 1))
        n_high = int(np.clip(int((e_high - c0) / c1) + 1, a_min=1, a_max=n_indexes))

        return n_low, n_high

    def _datasets_max_size(self, *, only_displayed=True):
        """