A collection of simulation and modeling lab assignments covering numerical computation, random number generation, matrix operations, and data visualization using Python, NumPy, Matplotlib, and Seaborn.
| # | Topic | Key Concepts |
|---|---|---|
| 1 | Random Vector Visualization | Generate two vectors of 15 random floats, plot on shared graph with labeled axes |
| 2 | Matrix Heatmap | Create a 4×4 random matrix, visualize as heatmap with row/column labels |
| 3 | Matrix Arithmetic & Bar Plots | Generate two 4×4 matrices, perform addition/subtraction/multiplication, visualize results |
| 4 | Advanced Matrix Analysis | Extended matrix operations with comparative bar plot analysis |
- Random Number Generation —
numpy.randomfor float/matrix generation - Data Visualization —
matplotlib.pyplotfor line plots, bar charts - Heatmaps —
seaborn.heatmapfor matrix representation - Matrix Operations — Addition, subtraction, multiplication with
numpy - Statistical Analysis — Mean, variance, distribution patterns
- Plot Customization — Labels, titles, legends, color maps
python -m pip install numpy matplotlib seabornjupyter notebook "Lab_Assignment(1).ipynb"
jupyter notebook "Lab_Assignment(2).ipynb"
jupyter notebook "Lab_Assignment(3).ipynb"
jupyter notebook "Lab_Assignment(4).ipynb"- Generating and manipulating numerical data in Python
- Understanding the role of visualization in data pattern analysis
- Effects of mathematical operations on matrices
- Best practices for scientific plotting and figure design
| University | University of Information Technology and Sciences |
| Course | Simulation and Modelling Lab |
| Student | Sheikh Muhammad Asaj Ibtisam Nipun |
| ID | 2215151017 |
MIT
A repository for simulation and modeling projects, covering mathematical, computational, and statistical techniques to analyze complex systems. Includes implementations of discrete-event simulation, agent-based modeling, Monte Carlo methods, and more.