bug fix in generate_probe_positions()

            grid_steps_x, grid_steps_y = np.floor_divide(grid_steps, 2)  

            grid_range_x = np.array([grid_range_start[0], grid_range_stop[0]])

            grid_range_y = np.array([grid_range_start[1], grid_range_stop[1]])

            probe_pos = np.array([[-x, y] for y, x in zip(y_pos.flatten()[::-1], x_pos.flatten())])

        else:                            

            grid_steps_x, grid_steps_y = np.floor(np.diff(probe_range).flatten() * self.dims[:2] / probe_step).astype(np.int)

            grid_range_x, grid_range_y = [(probe_range[i] - np.ones((2,)) * 0.5) * self.dims[i]

        atexit.register(_clean_up)

        return ctx # return context in case of manual clean-up

#     @staticmethod

#     def clean_up(ctx):

#         ctx.pop()

#         ctx.detach()

#         ctx = None

#         from pycuda.tools import clear_context_caches

#         clear_context_caches()

    def plan_simulation(self, num_probes=None):

        if num_probes is None:

            num_probes = self.num_probes