FuncProfiler Package Documentation

Package documentation for FuncProfiler, a Python library for identifying bottlenecks in code. It includes function profiling, data exports, logging, and line-by-line profiling for more control.

Changelog

• 1.1.0 (Latest):

  • 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.

• 1.0.2: Created 2 new export formats: xml, md for both function profiling and line-by-line profiling.

• 1.0.1: Updated PYPI project description.

• 1.0.0: Initial release.

Installation

You can install FuncProfiler using PyPi, please make sure that you are using Python 3.6 or later before installing FuncProfiler:

pip install funcprofiler

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Example Usage

Function Profiling

from funcprofiler import function_profile

# Exporting as `html` with logging enabled
@function_profile(export_format="html", shared_log=True)
def some_function():
    return "Hello World."

# Call the function
message = some_function()

Line-by-Line Profiling

from funcprofiler import line_by_line_profile

# Logging enabled without exports
@line_by_line_profile(shared_log=True)
def some_complicated_function(n):
    total = 0
    for i in range(n):
        for j in range(i):
            total += (i * j) ** 0.5  # Square root calculation
    return total

# Call the function
total = some_complicated_function(1000)

FuncProfiler can be added to any function using the callable format: @funcprofiler_function_name(expected_arguments).