Fixing Pooltool: Memory Leaks And Simulation Stalls

Unraveling the Pooltool Puzzle: Inefficiency and Memory Woes

Have you ever found yourself in a situation where your pooltool simulations, designed to meticulously replicate the physics of billiards, suddenly grind to a halt, consuming an alarming amount of system memory? It's a common frustration, and one that often points to underlying inefficiency and memory issue within the simulation loop itself. The core of this problem frequently manifests as the simulation getting stuck at the final shot, eventually leading to a complete exhaustion of available RAM. This isn't just a minor hiccup; it can severely impede scientific research, game development, or any project relying on accurate and performant pooltool models. The provided code snippet showcases a classic example of this, where a series of seemingly innocuous actions cause the pooltool engine to spiral into an endless loop of event resolution, specifically involving ball_circular_cushion events. These events, occurring with extremely small time steps (Δt), are a tell-tale sign of numerical instability, where the simulation struggles to resolve a collision or boundary condition cleanly. Instead of progressing the game, it gets caught in a microscopic dance between the cue ball and a pocket or cushion, recalculating the same interaction over and over again. Understanding why this happens and how to fix it is crucial for anyone working with pooltool, ensuring your simulations run smoothly and efficiently without unexpected memory bloat. This article aims to demystify these complex behaviors, offering clear explanations and actionable strategies to overcome these simulation roadblocks, transforming your pooltool experience from frustrating to flawlessly fluid.

Diving Deep into the Code: Understanding the Problematic Scenario

Let's meticulously examine the provided Python code, which beautifully illustrates the pooltool inefficiency and memory issue at hand. The script initiates a series of ten distinct billiard actions, each defining specific parameters for a shot, such as initial velocity (V0), strike angle (phi, theta), and spin components (a, b). For each of these actions, the code performs a full pooltool simulation. Initially, a default table is set up, and balls are racked for an eight-ball game using pt.get_rack, with a fixed seed to ensure reproducibility. A cue object is also initialized. The for action in actions: loop is where the trouble begins. Inside this loop, for every action, a new pt.System object, named shot, is created. This shot object encapsulates the current state of the table, balls, and cue. Subsequently, the cue.set_state method applies the parameters of the current action to the cue ball. The critical line is pt.simulate(shot, inplace=True). This function is designed to run the physics simulation until all motion ceases or a specified time limit is reached. The inplace=True argument is vital here, as it instructs pooltool to modify the shot object directly rather than returning a new one, which might seem like a memory-efficient approach. After the simulation, the state of the table, balls, and cue is updated by re-assigning them from the shot object's attributes: table, balls, cue = shot.table, shot.balls, shot.cue. This ensures that the state from the previous shot carries over to the next. The problem, as evidenced by the debug output, arises predominantly during the final shots. The simulation gets caught in a loop of ball_circular_cushion events. These events indicate a collision between a ball (specifically the cue ball, identified as 'cue', and ball '16t', likely the 8-ball or another specific ball in pooltool's internal numbering) and a circular cushion, which usually means a pocket opening. The alarming pattern is the extremely small and rapidly increasing sequence of time values for these events (e.g., 0.47855498413402703, 0.47856050117842885, etc.). This suggests that the balls are not resolving their collision or interaction with the pocket cleanly. Instead, they are repeatedly