GitHub - Infinitode/FuncProfiler: An open-source Python library for identifying bottlenecks in code. It includes function profiling, data exports, logging, and line-by-line profiling for more granular control.

An open-source Python library for identifying bottlenecks in code. It includes function profiling, data exports, logging, and line-by-line profiling for more granular control.

Changelog (v.1.1.0):

• Added support for 2 new export formats: yaml and toml.
• Exports now include more information: peak memory usage, timestamp, arguments, return value, filepath, line number, and docstring.
• Added enabled and log_level options to the decorators.
• Improved export formats for better readability.

Installation

You can install FuncProfiler using pip:

Supported Python Versions

FuncProfiler supports Python 3.6 and later.

Features

• Function Profiling: Monitor a function's memory usage and execution time.
• Line-by-Line Profiling: Get execution time and memory usage for each line of a function.
• Shared Logging: Log profiler outputs to a .txt file.
• File Exports: Export profiling data in various formats.
• New Options:
  • enabled: A boolean to enable or disable profiling.
  • log_level: Set the logging level to "info" or "debug".

Export Formats

Usage

Function Profiling

from funcprofiler import function_profile

@function_profile(export_format="html", shared_log=True, log_level="debug")
def some_function():
    return "Hello World."

message = some_function()

Line-by-Line Profiling

from funcprofiler import line_by_line_profile

@line_by_line_profile(shared_log=True, enabled=True)
def some_complicated_function(n):
    total = 0
    for i in range(n):
        for j in range(i):
            total += (i * j) ** 0.5
    return total

total = some_complicated_function(1000)

Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

License

FuncProfiler is released under the MIT License (Modified). See the LICENSE file for details.