runmetricsvisualizer 3.1.2

A package for storing function runtime data and graphing it via GUI!

RunMetrics Visualizer

A handy developer tool for storing function runtime data and graphing it via GUI!

Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgments

About The Project

This personal project is meant to be a developer tool, aimed at mass running functions, storing their runtimes as CSV data, aggregating it, and using a GUI to create a graph of this data. This tool is pip installable, making it extremely easy for Python developers to use.

Built With

• Python
• Matplotlib
• Pandas
• Tkinter
• Poetry
• PyPi

Getting Started

Prerequisites

pip

pip --version

Installation

Simply pip install the module

pip install runmetricsvisualizer

Usage

import runmetricsvisualizer as rmv

RunMetrics.run()

Runs a specified function desired amount of times, outputs runtime data to CSV file as provided by user. Uses *args and **kwargs for function parameters.\

RunMetrics.run(function, output_csv_file, *function_args, function_run_count, **function_kwargs)

Example

# our function
def do_something(num, iterations):
    for x in range(num):
        sum([x**4 for x in range(iterations)])

# (num = 100, iterations = 1000) Will generate 50 datapoints
RunMetrics.run(do_something, 'data/test.csv', 100, 1000, count=50)

RunMetrics.plot()

Opens a Tkinter-generated GUI window for customizing graph settings: datapoint colors, chart style, background theme, and chart title.

RunMetrics.plot(CSVfile_to_plot_from)

Example

RunMetrics.plot('data/test.csv')
# opens the following GUI menu

Roadmap

See the open issues for a full list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE.txt for more information.

Contact

Rahul Vikram - LinkedIn

Acknowledgments

• GitHub Pages
• README.md Template