Diagnostics Profiling

Performance profiling tools.

Profiling Module

Performance profiling utilities.

This submodule provides utilities for measuring execution time, memory usage, and function call counts.

Examples

from rite.diagnostics.profiling import ( ... profiling_timer, ... profiling_stopwatch, ... profiling_memory ... ) @profiling_timer() ... def slow_function(): ... pass with profiling_stopwatch("task") as sw: ... pass

Modules

profiling_count_calls

Function Call Counter

Count how many times a function is called.

Examples

from rite.diagnostics.profiling import profiling_count_calls @profiling_count_calls() ... def api_call(): ... pass

Functions

profiling_count_calls(print_every: int | None = None) -> Callable[[Callable[..., T]], Callable[..., T]]

Decorator to count function calls.

Parameters:

Name	Type	Description	Default
print_every	int | None	Print count every N calls (None = don't print).	None

Returns:

Type	Description
Callable[[Callable[..., T]], Callable[..., T]]	Decorated function with call counter.

Examples:

>>> @profiling_count_calls()
... def process():
...     pass
>>> process()
>>> process.call_count
1
>>> @profiling_count_calls(print_every=10)
... def api_request():
...     pass

profiling_memory

Memory Profiler

Measure memory usage of function.

Examples

from rite.diagnostics.profiling import profiling_memory @profiling_memory() ... def memory_intensive(): ... data = [0] * 1000000

Functions

profiling_memory(print_result: bool = True) -> Callable[[Callable[..., T]], Callable[..., T]]

Decorator to measure memory usage of function.

Parameters:

Name	Type	Description	Default
print_result	bool	If True, print memory usage.	True

Returns:

Type	Description
Callable[[Callable[..., T]], Callable[..., T]]	Decorated function that measures memory.

Examples:

>>> @profiling_memory()
... def create_large_list():
...     return [0] * 1000000
>>> @profiling_memory(print_result=False)
... def process_data():
...     pass

Notes

Uses sys.getsizeof for basic measurement. For detailed profiling, use memory_profiler package.

profiling_stopwatch

Timer Context Manager

Context manager to measure elapsed time.

Examples

from rite.diagnostics.profiling import profiling_stopwatch with profiling_stopwatch("operation") as sw: ... time.sleep(0.1) print(sw.elapsed)

Classes

profiling_stopwatch(name: str = 'operation')

Context manager to measure elapsed time.

Attributes:

Name	Type	Description
name		Operation name.
elapsed	float	Elapsed time in seconds.
start_time	float	Start timestamp.
end_time	float	End timestamp.

Examples:

>>> with profiling_stopwatch("task") as sw:
...     time.sleep(0.1)
>>> sw.elapsed > 0.1
True

Initialize stopwatch.

Parameters:

Name	Type	Description	Default
name	str	Operation name for display.	'operation'

profiling_timer

Timer Decorator

Decorator to measure function execution time.

Examples

from rite.diagnostics.profiling import profiling_timer @profiling_timer() ... def slow_function(): ... time.sleep(1)

Functions

profiling_timer(name: str | None = None, print_result: bool = True) -> Callable[[Callable[..., T]], Callable[..., T]]

Decorator to measure function execution time.

Parameters:

Name	Type	Description	Default
name	str | None	Optional name for the operation being timed.	None
print_result	bool	If True, print timing result.	True

Returns:

Type	Description
Callable[[Callable[..., T]], Callable[..., T]]	Decorated function that measures execution time.

Examples:

>>> @profiling_timer()
... def compute():
...     sum(range(1000000))
>>> @profiling_timer(name="API Call", print_result=False)
... def api_request():
...     pass

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