PyGamLab 0.1.1

A scientific Python library with constants, unit convertors, formulas, and data analysis tools.

PyGamLab

PyGamLab is a scientific Python library developed for researchers, engineers, and students who need access to fundamental constants, conversion tools, engineering formulas, and data analysis utilities. The package is designed with simplicity, clarity, and usability in mind.

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📌 Overview

PyGAMLab stands for Python GAMLAb tools, a collection of scientific tools and functions developed at the GAMLab by Ali Pilehvar Meibody under the supervision of Prof. Malek Naderi at Gamlab (Graphene and Advanced Material Laboratory) under supervision of Amirkabir University of Technology (AUT).

• Author: Ali Pilehvar Meibody
• Supervisor: Prof. Naderi
• Affiliation: GAMLab, Amirkabir University of Technology (AUT)

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📦 Modules

PyGAMLab is composed of four core modules, each focused on a specific area of scientific computation:

🔹 Constants.py

This module includes a comprehensive set of scientific constants used in physics, chemistry, and engineering.

Examples:

• Planck's constant
• Boltzmann constant
• Speed of light
• Universal gas constant
• Density of Metals
• Tm of Metals
• And many more...

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🔹 Convertors.py

Contains unit conversion functions that follow the format:
FirstUnit_To_SecondUnit()

Examples:

• Kelvin_To_Celsius(k)
• Celsius_To_Kelvin(c)
• Meter_To_Foot(m)
• ...and many more standard conversions used in science and engineering.

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🔹 Functions.py

This module provides a wide collection of scientific formulas and functional tools commonly used in engineering applications.

Examples:

• Thermodynamics equations
• Mechanical stress and strain calculations
• Fluid dynamics formulas
• General utility functions

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🔹 Data_Analysis.py

Provides tools for working with data, either from a file path or directly from a DataFrame.

Features include:

• Reading and preprocessing datasets
• Performing scientific calculations
• Creating visualizations (e.g., line plots, scatter plots, histograms)

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📦 Requirements

To use PyGamLab, make sure you have the following Python packages installed:

• numpy
• pandas
• scipy
• matplotlib
• seaborn
• scikit-learn

You can install all dependencies using:

pip install numpy pandas scipy matplotlib seaborn scikit-learn

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🚀 Installation

To install PyGAMLab via pip (after uploading it to PyPI):

pip install pygamlab

or

git clone https://github.com/APMaii/pygamlab.git

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📖 Usage Example

import PyGamLab

#--------------Constants-----------------------

print(PyGamLab.Constants.melting_point_of_Cu)
print(PyGamLab.Constants.melting_point_of_Al)
print(PyGamLab.Constants.Fe_Tm_Alpha)
print(PyGamLab.Constants.Fe_Tm_Gama)

print(PyGamLab.Constants.Boltzmann_Constant)
print(PyGamLab.Constants.Faraday_Constant)

#----------------Convertors---------------------

print(PyGamLab.Convertos.Kelvin_to_Celcius(300))
print(PyGamLab.Convertos.Coulomb_To_Electron_volt())
print(PyGamLab.Convertos.Angstrom_To_Milimeter())
print(PyGamLab.Convertos.Bar_To_Pascal())

#------------Functions-----------------------

PyGamLab.Functions.Gibs_free_energy(H0,T,S0)


PyGamLab.Functions.Bragg_Law(h, k, l, a, y)


PyGamLab.Functions.Electrical_Resistance(v,i)


PyGamLab.Functions.Hall_Petch(d_grain,sigma0,k)

#-----------Data_Analysis--------------------
import pandas as pd

df= pd.read_csv('/users/apm/....../data.csv')
PyGamLab.Data_Analysis.Stress_Strain1(df, 'PLOT')
my_uts=PyGamLab.Data_Analysis.Stress_Strain1(df, 'UTS')


data=pd.read_csv('/users/apm/....../data.csv')
my_max=PyGamLab.Data_Analysis.Xrd_Analysis(data,'max intensity')
PyGamLab.Data_Analysis.Xrd_Analysis(data,'scatter plot')
PyGamLab.Data_Analysis.Xrd_Analysis(data,'line graph')

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Structure

pygamlab/
├── __init__.py
├── Constants.py
├── Convertors.py
├── Functions.py
├── Data_Analysis.py
└── contributers.md

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🤝 Contributing

Contributions are welcome! Here's how to get started:

Fork the repository. Create your feature branch

git checkout -b feature/my-feature

Commit your changes

git commit -am 'Add some feature'

Push to the branch

git push origin feature/my-feature

Create a new Pull Request. Please make sure to update tests as appropriate and follow PEP8 guidelines.

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📄 License

This project is licensed under the MIT License — see the LICENSE.txt file for details

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🙏 Acknowledgements

This project is part of the scientific research activities at GAMLab (Generalized Applied Mechanics Laboratory) at Amirkabir University of Technology (AUT).

Special thanks to:

• Prof. Nader – For his guidance, mentorship, and continuous support.
• Ali Pilehvar Meibody – Main developer and author of PyGamLab.
• GAMLab Research Group – For providing a collaborative and innovative environment.

We would also like to thank all the students who participated in the GAMLab AI course and contributed to the growth and feedback of this project. Their names are proudly listed in the contributors.md file.

This project was made possible thanks to the powerful Python open-source ecosystem:
NumPy, SciPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and many more.

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