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Measure CPU time in nanoseconds accurately with Python’s `time.thread_time_ns()` function.

# Using python `time.thread_time_ns`

In Python, the `time.thread_time_ns()` function is used to get the CPU time in nanoseconds spent by the current thread. This function is part of the `time` module in Python and is available starting from Python 3.7. It is a more precise and reliable way to measure CPU time compared to `time.process_time()` or `time.perf_counter()`.

## Syntax

The syntax for using `time.thread_time_ns()` is as follows:

“`python
import time

start_time = time.thread_time_ns()

# Code block to measure CPU time for

end_time = time.thread_time_ns()

cpu_time_ns = end_time – start_time
“`

## Parameters

There are no parameters required for `time.thread_time_ns()` as it simply returns the CPU time in nanoseconds.

## Return Value

The return value of `time.thread_time_ns()` is an integer representing the CPU time in nanoseconds spent by the current thread.

## Example

Here is an example demonstrating the usage of `time.thread_time_ns()`:

“`python
import time

start_time = time.thread_time_ns()

# Code block to measure CPU time for
for i in range(1000000):
pass

end_time = time.thread_time_ns()

cpu_time_ns = end_time – start_time
print(f”CPU time in nanoseconds: {cpu_time_ns}”)
“`

In this example, we first record the start time using `time.thread_time_ns()`, then run a loop for a large number of iterations to simulate some computational work, and finally calculate the CPU time spent by subtracting the start time from the end time.

## Notes

– `time.thread_time_ns()` provides a more accurate measurement of CPU time compared to other time functions in Python.
– It is important to note that the actual precision of `time.thread_time_ns()` may vary depending on the underlying operating system and hardware.
– For most practical purposes, `time.thread_time_ns()` should be sufficient for measuring CPU time accurately in Python.

Overall, `time.thread_time_ns()` is a useful function in Python for measuring CPU time at a high precision level, making it suitable for performance analysis and benchmarking of code.