flexprofiler 0.1.3

A flexible profiling utility for Python.

🚀 About

FlexProfiler is a lightweight, flexible profiling utility for Python that makes it easy to measure function and method performance with minimal code changes. It supports both function-level and line-level profiling, recursive class method tracking, and customizable statistics reporting. Designed for developers who want actionable insights into their code’s runtime behavior, FlexProfiler helps you identify bottlenecks and optimize performance with simple decorators and clear output.

📦 Installation

pip install flexprofiler

:computer: Usage

The most common usage involves adding simple decorators to your functions or classes, enabling you to profile execution time and other metrics with minimal changes to your codebase. This approach makes it easy to identify bottlenecks and optimize your code efficiently.

• @track() - decorate a function to collect call counts and timing statistics. Args:
  • lines: boolean, collect per-line timing inside the function.
• @track_all() - Decorate a class to profile all its methods. Args:
  • max_depth: integer, how deep to recursively profile composed objects (default: 10).
  • include: list of method names to track (default: None to track all).
  • exclude: list of method names to skip (default: None to skip none).
  • arg_sensitive: list of method names to track argument values (default: None).
• stats() - print aggregated profiling statistics (function-level and, when enabled, line-level). Args:
  • unit: the time unit to use for displaying statistics (default: "ms").

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⬇️ Examples

1. Tracking a Simple Function

import time
from flexprofiler import stats, track

@track()  # Use @track() decorator to profile the function
def simple_func():
    time.sleep(0.1)

simple_func()
simple_func()

stats() # display the profiling statistics

output:

2. Tracking All Methods in a Class

import time
from flexprofiler import track_all, stats

@track_all()  # Use @track_all() decorator to profile all methods in the class
class Foo:
    def example_method(self):
        self.another_method()
        time.sleep(0.1)
    def another_method(self):
        time.sleep(0.2)

Foo().example_method()
Foo().another_method()

stats()

output:

3. Tracking All Methods in a Class Recursively

import time
from flexprofiler import track_all, stats

@track_all(max_depth=3)
class Foo:
    def __init__(self):
        self.sub_class = Bar()
    def example_method(self):
        self.another_method()
        time.sleep(0.1)
    def another_method(self):
        time.sleep(0.2)
    def calling_subclass_method(self):
        for i in range(3):
            self.sub_class.subclass_method_1()
            self.sub_class.subclass_method_2()
            self.sub_class.subclass_method_3()

class Bar:
    def subclass_method_1(self):
        time.sleep(0.05)
    def subclass_method_2(self):
        self.a()
        self.b()
    def subclass_method_3(self):
        self.a()
        self.b()
    def a(self):
        time.sleep(0.02)
    def b(self):
        time.sleep(0.01)

obj = Foo()
obj.example_method()
obj.calling_subclass_method()

stats()

output:

4. Track All lines in a function

import time
from flexprofiler import track, stats


@track(lines=True)  # with line tracking
def bar(n):
    total = 0
    for i in range(n):
        total += i
        if i % 2 == 0:
            time.sleep(0.001)
        else:
            time.sleep(0.0005)
    return total

@track()  # no line tracking
def baz():
    time.sleep(0.1)

@track()  # no line tracking
def foo():
    for _ in range(3):
        bar(50)
    baz()

foo()
stats()  # display the profiling statistics

outputs:

🤝 Feedback and Contributions

Contributions, suggestions, and feedback are welcome! Feel free to open issues or submit pull requests on GitHub.

Author

Arthur Fender C Bucker