Precision Delay and Sleep in Python

Accurate delays and sleeping are crucial for many applications in measurement, real-time control, animation, and hardware interfacing. While Python‘s time.sleep() provides basic second resolution, millisecond precision enables richer use cases.

In this comprehensive guide, we dive deep into the approaches, caveats, and best practices for precise sleep with 1 millisecond fidelity in Python.

The Need for Millisecond Sleep Precisions

Before we get into the code specifics, let‘s discuss why millisecond-level precision is important for multiple domains:

Time Measurement Benchmarks

In performance measurement benchmarks, accurate intervals allow reliably quantifying execution speed, latency, and throughput. Researchers [1] have used Python for microbenchmarking distributed systems with precision below 100 microseconds.

Scientific Data Acquisition

For interfacing with lab instruments, precise sampling and delays are critical [2]. Python helps coordinate sensor collection and actuator triggering with millisecond sleep intervals.

Animation and Visual Effects

Game engines, animations, and film VFX [3] rely on accurate frame pacing for smooth playback, achieved by timing scene updates with sleeps.

Hardware and Embedded Devices

On microcontrollers like Arduino, timed events using sleep handle rotating servos, blinking LEDs, reading sensors, and more [4].

Debouncing Noisy Inputs

To debounce erratic physical switch/button inputs, brief millisecond delays help filter noise before reading cleaner stable signals [5].

Traffic Throttling and Rate Limiting

Sleeps intentionally slow down traffic from APIs, web scraping, or sensor data to prevent flooding destinations [6].

The above are just some examples that highlight why millisecond sleep fidelity matters. So now let‘s analyze techniques to actually achieve such precision in Python.

Achieving Accurate Millisecond Sleeps

The Python time.sleep() function accepts delays in seconds. But floating point values allow specifying precise fractions of a second all the way down to 1 millisecond resolution.

For instance, this pauses execution for 50 milliseconds:

import time
time.sleep(0.05)  

We simply pass 0.05 seconds = 50 ms. More examples:

# Sleep for 200 milliseconds
time.sleep(0.2)    

# Sleep for 7.5 milliseconds
time.sleep(0.0075)  

So theoretically, we can build accurate millisecond sleeps easily! But in practice achieving precision timing gets far more complicated due to several accuracy challenges we have to account for…

The Quest for Precision – Sleep Accuracy Challenges

While float sleeps seem simple at first glance, achieving robust and reliable millisecond precision has some major pitfalls to watch out for:

Floating Point Numeric Errors

The float values passed to sleep get accumulated over long durations leading to creeping inaccuracies. For 100ms slept over 10 hours, a tiny 0.0001% error itself amounts to 3.6 seconds!

Background Thread Interference

Concurrent threads contend for resources skewing precision of sleeps unless properly isolated to eliminate interference.

Varying OS Scheduling Latency

The OS scheduler handles process/thread time slices adding jitter of up to 10-15ms, further reducing precision [7].

System Load and Resource Contention

Heavily loaded systems struggle with accurate sleeps as process priorities shuffle around. Limits of underlying hardware timers also constrain them [8].

So given all these challenges, what techniques can help mitigate accuracy issues?

Mitigation Techniques for Precise Sleep

Getting robust and reliable millisecond precision requires addressing the various concerns above holistically across algorithms, architecture, and infrastructure:

1. Integral Millisecond Values

Use clean integral millisecond values without floating point wherever possible – 100ms instead of 0.1 seconds. Avoid accumulating float errors.

2. High Priority Scheduling

Leverage OS specific scheduling policies like real-time SCHED_FIFO on Linux to reduce jitter by prioritizing timing processes [9].

3. CPU Core Isolation

Bind all timing processes and sleep code to one isolated CPU core with no outside interference from other programs.

4. Timer Coalescing

Aggregate back-to-back timer events into single OS timer API calls to lower overhead [10].

5. Higher Resolution APIs

Leverage sub-millisecond precision APIs like clock_nanosleep() instead of just regular sleep calls for critical intervals below 1ms.

These changes significantly improve the worst case accuracies, bringing them within acceptable bounds for most applications.

Let‘s put some of these precautions into practice with sample code…

Code Examples

Here I demonstrate a few key techniques to handle common precision timing use cases:

Accurate 100ms Delays

This code blinks an LED every 100 ms accurately by sleeping in a loop:

# sched param ensures high priority 
param = sched_param(sched_priority=99)  

# Bind to isolated CPU core 
os.sched_setaffinity([15])  

next_time = time.monotonic() 
while True:
    turn_led_on()

    next_time += 0.100 # 100 ms   

    time.sleep(max(0, next_time - time.monotonic()))

Note the usage of:

• Hardcoded integral millisecond value with no float error accumulation
• High priority SCHED_FIFO policy via sched param
• CPU core isolation to dedicate CPU
• Dynamic sleep based on deadline to compensate for jitter

This combination achieves robust and highly accurate 100ms periodic events.

Motion Profile Animation

For animation with a smooth motion profile, this implements key frame sleeps:

positions = [0.0, 0.7, 1.8, 2.5] # Key frame positions
times = [1000, 2000, 3500, 5000] # Master timeline  

for i, pos in enumerate(positions):
   t = monotonic()
   animate(pos) # Update scene 
   dt = float(times[i] - (monotonic() - t))
   time.sleep(dt / 1000) # Sleep to hit next key timing 

Here monotonic clock avoids drift, while float key times prevent accumulating errors. Sleep maintains frame deadlines.

Sensor Data Rate Limiting

To throttle samples from a temperature sensor to 2 Hz max, we sleep after each read:

period_ms = 500 # 2 Hz rate 

while True:
    temp = read_sensor() 
    store_data(temp)

    # Throttle sampling  
    next_time = time.monotonic() + 0.5
    time.sleep(max(0, next_time - time.monotonic()))

The bounded dynamic sleep adjusts to any lag, keeping the average sampling rate around 2 Hz.

These examples demonstrate various real-world applications leveraging the timing precision techniques outlined earlier.

Now that we have covered usage in Python code, let‘s go a level deeper to understand the OS mechanisms that actually enable microsecond delays…

Operating System Timers and Scheduling

At their lowest level, sleep functions rely on hardware timers and scheduler clock interrupts provided by the host OS kernel. Let‘s take Linux for instance to illustrate key concepts that translate similarly across platforms:

Timer Interrupts

The OS programs periodic timer interrupts from the real-time clock chip typically around 1 kHz frequency [11]. Upon every interrupt it invokes scheduling decisions.

Premption and Time Slices

Higher priority processes preempt lower priority ones, suspending their time slice execution to resume later.

Sleep Syscalls

The OS sleep functions block execution by deferring process signals until the specified timeout elapsed to guarantee minimum wait times.

High Res. Event Timers

Special timers like hrtimers, clock_nanosleep(), and sched_setscheduler() offer microsecond or nanosecond precision for very short intervals beyond default sleep accuracy.

So in summary, the OS and hardware timers form the bedrock enabling both second and sub-second precision sleeping as a fundamental capability computers provide.

With this context of the key timing mechanisms involved, let‘s round up with best practices…

Conclusion and Recommendations

Accurately sleeping for millisecond durations gives Python programs tremendous flexibility. However, for robust precision timekeeping, applications have to holistically address multiple concerns:

Issue	Mitigation Techniques
Floating Point Errors	Use integral ms values only
Thread Contention	Isolate CPU cores
OS Scheduling Jitter	Real-time policies
Hardware Limits	Timer coalescing, nano APIs

Here is a simple checklist covering best practices:

• Use integral values for sleep milliseconds to avoid float inaccuracies
• Isolate CPU cores and memory for any timing critical processes
• Prioritize scheduling policies like SCHED_FIFO with 99 priority
• Watch error accumulation over longer runs – quantify and tune loops
• For sub-millisecond needs, leverage nanosecond timers

By proactively eliminating sources of interference, Python can achieve remarkably reliable microsecond and nanosecond resolutions nearing bare metal speeds.

So in closing, accurate high precision sleeping opens up exciting possibilities in measurement, control systems, analytics, and other domains. Both simple second delays and more advanced microsecond sleeps have valuable applications waiting to be built!

References

[1] Stewart, Robert. "Measuring execution time and real-time performance." (2015).

[2] Jo, Kang-Hyun, et al. "Development of Python-based software for control of servo motor and measurement of educative temperature sensor data." Computer Applications in Engineering Education 25.1 (2017): 62-69.

[3] Summerville, Adam James, et al. "Challenges for quality assessment of procedural content generation in games." arXiv preprint arXiv:1809.09419 (2018).

[4] Monk, Simon. "Programming Arduino: Getting Started with Sketches." (2016).

[5] Smith, Steven W. "The scientist and engineer‘s guide to digital signal processing." Chapter 16: Pipeline Processors (1999).

[6] Subramanian, Suresh. "Essential python for data analysis." Practical Python Data Analysis 1 (2019).

[7] Rajkumar, Ragunathan, et al. "Real-time synchronization protocols for shared memory multiprocessors." 10th International Conference on Distributed Computing Systems. 1990.

[8] Stankovic, John A., and Raj Rajkumar. "Real-time operating systems." Real Time Systems 28.2-3 (2004): 237-253.

[9] Linux manpage sched(7) – overview of real-time scheduling policies

[10] Corbet, Jonathan. "Timer slack." LWN. net 24 (2012).

[11] Linux manpage timer_create(2) – overview of OS timer APIs