PyGamLab 1.2.4

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 powerful tools for nanostructure generation, alloy design, material data exploration, and AI-driven analysis. 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 (Graphene and Advanced Material Laboratory) by Ali Pilehvar Meibody under the supervision of Prof. Malek Naderi at Amirkabir University of Technology (AUT).

• Main Author: Ali Pilehvar Meibody
• Supervisor: Prof. Malek Naderi
• Co Author: Danial Nekoonam
• Contributor Shokoofeh Karimi
• Affiliation: GAMLab, Amirkabir University of Technology (AUT)

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📦 Modules

PyGAMLab is composed of several core scientific modules, each designed to address a specific domain of materials modeling, nanoscience, and data-driven discovery.

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🧱 Structure

The Structure module is the foundation of PyGAMLab, providing advanced tools to generate, manipulate, and analyze nanoscale and bulk materials.

Key Features:

• Generate 0D (clusters, nanoparticles), 1D (nanowires, nanotubes), and 2D (nanosheets, thin films) structures, as well as bulk crystals.
• Automated builders for nanoclusters, nanotubes, and supercells.
• Perform geometric operations such as rotation, translation, scaling, merging, slicing, and symmetry analysis.
• Comprehensive I/O support: read, write, and convert structures in formats like .cif, .xyz, .pdb, .vasp, .json, and more.
• Direct integration with the ASE (Atomic Simulation Environment) via built-in converters.

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🎨 GAMVis (GAM Visualizer)

GAMVis is the internal visualization engine of PyGAMLab — an interactive and high-performance visualizer for atomic and molecular structures.

Capabilities:

• Real-time 2D and 3D visualization of molecules, nanostructures, and crystal lattices.
• Graphical representation of bonds, surfaces, and charge distributions.
• Export of rendered structures and animations.
• Ideal for publication-quality figures and teaching demonstrations.

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🤖 Nano_AI

Nano_AI integrates machine learning into materials research, providing tools for automatic model training, fine-tuning, and prediction.

Highlights:

• Automated ML workflows for regression, classification, and clustering.
• Access to over 120 pre-trained machine learning models trained on nanomaterials datasets.
• Easy fine-tuning and inference on user-provided data.
• Includes property predictors for band gap, formation energy, hardness, and more.
• Built-in tools for data splitting, validation, and model evaluation.

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🧬 DB_Explorer

The DB_Explorer module provides seamless access to multiple materials databases, allowing users to retrieve both structural and property data from leading open repositories.

Supported Databases:

• Materials Project (MP)
• AFLOW
• JARVIS
• Crystallography Open Database (COD)

Available Data:

• Mechanical, electronic, and thermodynamic properties
• Structural and crystallographic information
• Chemical composition and symmetry details

A universal parent class, GAM_Explorer, synchronizes and unifies data from all sources, enabling consistent data retrieval and cross-database comparison.

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📊 Data_Analysis

The Data_Analysis module offers advanced tools for data preprocessing, analysis, and scientific visualization.

Main Features:

• Read and preprocess data from files or DataFrames.
• Perform filtering, normalization, and feature extraction.
• Generate publication-ready graphs (line, scatter, histogram, heatmap, etc.).
• Support for 68+ experimental analysis tools, including NMR, XPS, XRD, UV-Vis, and Raman spectroscopy.
• Includes scientific constants, unit converters, and mathematical utilities for laboratory and computational work.

🔹 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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📦 Requirements

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

• numpy
• pandas
• scipy
• matplotlib
• seaborn
• scikit-learn
• ase
• plotly
• PyQt5

You can install all dependencies using:

pip install numpy pandas scipy matplotlib seaborn scikit-learn json scipy ase plotly PyQt5

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🚀 Installation

To install PyGAMLab via pip:

pip install pygamlab

or

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

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📖 Usage Example

import PyGamLab


import PyGamLab.Constants as gamcn
import PyGamLab.Convertos as gamcv
import PyGamLab.Functions as gamfunc
import PyGamLab.Data_Analysis as gamdat



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

print(gamcn.melting_point_of_Cu)
print(gamcn.melting_point_of_Al)
print(gamcn.Fe_Tm_Alpha)
print(gamcn.Fe_Tm_Gama)

print(gamcn.Boltzmann_Constant)
print(gamcn.Faraday_Constant)


#----------Converters------------------------

print(gamcv.Kelvin_to_Celcius(300))           # Convert 300 K to °C
print(gamcv.Coulomb_To_Electron_volt(1))      # Convert 1 Coulomb to eV
print(gamcv.Angstrom_To_Milimeter(1))         # Convert 1 Å to mm
print(gamcv.Bar_To_Pascal(1))                 # Convert 1 bar to Pascal

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

# Gibb's Free Energy: G = H0 - T*S0
H0 = 100  # Enthalpy in kJ/mol
T = 298   # Temperature in Kelvin
S0 = 0.2  # Entropy in kJ/mol·K
print(gamfunc.Gibs_free_energy(H0, T, S0))


# Electrical Resistance: R = V / I
voltage = 10         # in Volts
current = 2          # in Amperes
print(gamfunc.Electrical_Resistance(voltage, current))

# Hall-Petch Relationship: σ = σ0 + k / √d
d_grain = 0.01       # Grain diameter in mm
sigma0 = 150         # Friction stress in MPa
k = 0.5              # Strengthening coefficient in MPa·mm^0.5
print(gamfunc.Hall_Petch(d_grain, sigma0, k))

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

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


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

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📚 Documentation

For detailed documentation, please visit the official PyGamLab Documentation.

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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. Malek Naderi – For his guidance, mentorship, and continuous support.
• Ali Pilehvar Meibody – Main developer and author of PyGamLab.
• Danial Nekoonam – Co-Author of PyGamLab.
• Shokoofeh Karimi - For her Contribution of verify and testing most functions
• GAMLab Research Group – For providing a collaborative and innovative environment.
• Hossein Behjoo – For his guidance in taking the AI courses and his creative work in the development of the logo.

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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