def _evaluate(self, x, y):
    '\n        Returns the level of the interpolated function at each value in x,y.\n        Only called internally by HARKinterpolator2D.__call__ (etc).\n        '
    if _isscalar(x):
        y_pos = max(min(np.searchsorted(self.y_list, y), (self.y_n - 1)), 1)
        alpha = ((y - self.y_list[(y_pos - 1)]) / (self.y_list[y_pos] - self.y_list[(y_pos - 1)]))
        f = (((1 - alpha) * self.xInterpolators[(y_pos - 1)](x)) + (alpha * self.xInterpolators[y_pos](x)))
    else:
        m = len(x)
        y_pos = np.searchsorted(self.y_list, y)
        y_pos[(y_pos > (self.y_n - 1))] = (self.y_n - 1)
        y_pos[(y_pos < 1)] = 1
        f = (np.zeros(m) + np.nan)
        if (y.size > 0):
            for i in range(1, self.y_n):
                c = (y_pos == i)
                if np.any(c):
                    alpha = ((y[c] - self.y_list[(i - 1)]) / (self.y_list[i] - self.y_list[(i - 1)]))
                    f[c] = (((1 - alpha) * self.xInterpolators[(i - 1)](x[c])) + (alpha * self.xInterpolators[i](x[c])))
    return f
Returns the level of the interpolated function at each value in x,y.
Only called internally by HARKinterpolator2D.__call__ (etc).
def _evaluate(self, x, y):
    '\n        Returns the level of the interpolated function at each value in x,y.\n        Only called internally by HARKinterpolator2D.__call__ (etc).\n        '
    if _isscalar(x):
        y_pos = max(min(np.searchsorted(self.y_list, y), (self.y_n - 1)), 1)
        alpha = ((y - self.y_list[(y_pos - 1)]) / (self.y_list[y_pos] - self.y_list[(y_pos - 1)]))
        f = (((1 - alpha) * self.xInterpolators[(y_pos - 1)](x)) + (alpha * self.xInterpolators[y_pos](x)))
    else:
        m = len(x)
        y_pos = np.searchsorted(self.y_list, y)
        y_pos[(y_pos > (self.y_n - 1))] = (self.y_n - 1)
        y_pos[(y_pos < 1)] = 1
        f = (np.zeros(m) + np.nan)
        if (y.size > 0):
            for i in range(1, self.y_n):
                c = (y_pos == i)
                if np.any(c):
                    alpha = ((y[c] - self.y_list[(i - 1)]) / (self.y_list[i] - self.y_list[(i - 1)]))
                    f[c] = (((1 - alpha) * self.xInterpolators[(i - 1)](x[c])) + (alpha * self.xInterpolators[i](x[c])))
    return f