An improvement about Obstacle calculation

[0oshowero0]

Hi, thanks for your great work about this social force simulator. I've tried this simulator and find it very useful.
I have a suggestion to improve the computation efficiency about the force calculation, that is the ObstacleForce class.
the _get_force() func will calculate the distance first for all the points in obstacle lines, then it add masks. When there are lots of obstacles this function will greatly slow-down the process.

To speed up this process, now I add a simple judgement to pre-select nearby obstacles. This improvement will speed up the process from 14sec/step to 0.05sec/step in our case. Note that the solution here is not very elegant. You can try to improve by this thought.

class ObstacleForce(Force):
    def _get_force(self):
        sigma = self.config("sigma", 0.2)
        threshold = self.config("threshold", 0.2) + self.peds.agent_radius
        force = np.zeros((self.peds.size(), 2))
        if len(self.scene.get_obstacles()) == 0:
            return force
        obstacles = np.vstack(self.scene.get_obstacles())
        pos = self.peds.pos()

        for i, p in enumerate(pos):
            diff = p - obstacles
            diff_select = diff[np.logical_and(np.logical_and(diff[:,0]<10,diff[:,0]>-10),np.logical_and(diff[:,1]<10,diff[:,1]>-10))]
            if diff_select.shape[0] == 0:
                continue
            else:
                directions, dist = stateutils.normalize(diff_select)
                dist = dist - self.peds.agent_radius
                if np.all(dist >= threshold):
                    continue
                dist_mask = dist < threshold
                directions[dist_mask] *= np.exp(-dist[dist_mask].reshape(-1, 1) / sigma)
                force[i] = np.sum(directions[dist_mask], axis=0)

        return force * self.factor