def _derY(self, x, y):
    '\n        Returns the derivative with respect to y of the interpolated function\n        at each value in x,y. Only called internally by HARKinterpolator2D.derivativeY.\n        '
    if _isscalar(x):
        x_pos = max(min(self.xSearchFunc(self.x_list, x), (self.x_n - 1)), 1)
        y_pos = max(min(self.ySearchFunc(self.y_list, y), (self.y_n - 1)), 1)
    else:
        x_pos = self.xSearchFunc(self.x_list, x)
        x_pos[(x_pos < 1)] = 1
        x_pos[(x_pos > (self.x_n - 1))] = (self.x_n - 1)
        y_pos = self.ySearchFunc(self.y_list, y)
        y_pos[(y_pos < 1)] = 1
        y_pos[(y_pos > (self.y_n - 1))] = (self.y_n - 1)
    alpha = ((x - self.x_list[(x_pos - 1)]) / (self.x_list[x_pos] - self.x_list[(x_pos - 1)]))
    dfdy = (((((1 - alpha) * self.f_values[((x_pos - 1), y_pos)]) + (alpha * self.f_values[(x_pos, y_pos)])) - (((1 - alpha) * self.f_values[((x_pos - 1), (y_pos - 1))]) + (alpha * self.f_values[(x_pos, (y_pos - 1))]))) / (self.y_list[y_pos] - self.y_list[(y_pos - 1)]))
    return dfdy
Returns the derivative with respect to y of the interpolated function
at each value in x,y. Only called internally by HARKinterpolator2D.derivativeY.
def _derY(self, x, y):
    '\n        Returns the derivative with respect to y of the interpolated function\n        at each value in x,y. Only called internally by HARKinterpolator2D.derivativeY.\n        '
    if _isscalar(x):
        x_pos = max(min(self.xSearchFunc(self.x_list, x), (self.x_n - 1)), 1)
        y_pos = max(min(self.ySearchFunc(self.y_list, y), (self.y_n - 1)), 1)
    else:
        x_pos = self.xSearchFunc(self.x_list, x)
        x_pos[(x_pos < 1)] = 1
        x_pos[(x_pos > (self.x_n - 1))] = (self.x_n - 1)
        y_pos = self.ySearchFunc(self.y_list, y)
        y_pos[(y_pos < 1)] = 1
        y_pos[(y_pos > (self.y_n - 1))] = (self.y_n - 1)
    alpha = ((x - self.x_list[(x_pos - 1)]) / (self.x_list[x_pos] - self.x_list[(x_pos - 1)]))
    dfdy = (((((1 - alpha) * self.f_values[((x_pos - 1), y_pos)]) + (alpha * self.f_values[(x_pos, y_pos)])) - (((1 - alpha) * self.f_values[((x_pos - 1), (y_pos - 1))]) + (alpha * self.f_values[(x_pos, (y_pos - 1))]))) / (self.y_list[y_pos] - self.y_list[(y_pos - 1)]))
    return dfdy