Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

https://github.com/satvikpraveen/pcc-vizforge

๐ŸŽจ Personal data visualization toolkit generating synthetic datasets across multiple domains (random walks, dice simulations, weather patterns, earthquakes, GitHub analytics) with beautiful Matplotlib & Plotly visualizations. Includes Jupyter notebooks, interactive dashboards & statistical analysis. Perfect for learning data science! ๐Ÿš€๐Ÿ“Š
https://github.com/satvikpraveen/pcc-vizforge

analytics dashboard data-analysis data-generation data-science data-visualization github-analytics interactive-visualization jupyter-notebook matplotlib plotly probability python random-walk scientific-computing seismology statistical-analysis synthetic-data time-series weather-data

Last synced: 2 months ago
JSON representation

๐ŸŽจ Personal data visualization toolkit generating synthetic datasets across multiple domains (random walks, dice simulations, weather patterns, earthquakes, GitHub analytics) with beautiful Matplotlib & Plotly visualizations. Includes Jupyter notebooks, interactive dashboards & statistical analysis. Perfect for learning data science! ๐Ÿš€๐Ÿ“Š

Awesome Lists containing this project

README 

          
# PCC-VizForge



**A Personal Data Visualization Playground**



[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[![Personal Project](https://img.shields.io/badge/type-personal%20project-green.svg)](https://github.com/SatvikPraveen/PCC-VizForge)



> _"Creating beautiful visualizations from synthetic data across multiple domains - because data should tell compelling stories."_



## ๐ŸŽฏ About This Project



**PCC-VizForge** is my personal exploration into the world of data visualization and synthetic data generation. This toolkit demonstrates my ability to:



- Generate realistic synthetic datasets across diverse domains

- Create publication-quality static visualizations with Matplotlib

- Build interactive web-ready charts with Plotly

- Implement modular, scalable code architecture

- Apply statistical analysis and data science concepts



This project serves as both a learning exercise and a showcase of data visualization capabilities, perfect for educational purposes, prototyping, or when you need realistic data without privacy concerns.



## ๐Ÿš€ Key Features



### ๐ŸŽฒ Multi-Domain Data Generation



- **Random Walks**: Stochastic processes with 1D/2D simulations and statistical analysis

- **Dice Simulations**: Probability theory demonstrations with Gutenberg-Richter law applications

- **Weather Patterns**: Realistic meteorological data with seasonal variations and extreme events

- **Earthquake Data**: Seismic activity simulations following geological principles

- **GitHub Analytics**: Repository metrics and developer activity patterns



### ๐Ÿ“Š Dual Visualization Approach



- **Matplotlib**: Professional static plots for research papers and presentations

- **Plotly**: Interactive dashboards with hover effects, zooming, and web deployment



### ๐Ÿ’พ Flexible Export Options



- **Images**: High-resolution PNG/JPG for publications

- **Interactive HTML**: Web-ready visualizations for portfolios

- **Data Formats**: CSV/JSON for further analysis and sharing



## ๐Ÿ› ๏ธ Project Architecture



```

PCC-VizForge/

โ”œโ”€โ”€ ๐Ÿ“‹ README.md                    # You are here!

โ”œโ”€โ”€ โš™๏ธ pyproject.toml              # Python package configuration

โ”œโ”€โ”€ ๐Ÿ”ง Makefile                    # Development automation

โ”œโ”€โ”€ ๏ฟฝ doc/                        # Documentation (changelog, guides, etc.)

โ”œโ”€โ”€ ๏ฟฝ๐Ÿ“ config/                     # YAML configuration files

โ”‚   โ”œโ”€โ”€ random_walk.yaml          # Random walk parameters

โ”‚   โ”œโ”€โ”€ dice.yaml                 # Dice simulation settings

โ”‚   โ”œโ”€โ”€ weather.yaml              # Weather generation config

โ”‚   โ”œโ”€โ”€ quakes.yaml               # Earthquake data parameters

โ”‚   โ””โ”€โ”€ github.yaml               # GitHub statistics config

โ”œโ”€โ”€ ๐Ÿงฌ src/                        # Core source code

โ”‚   โ”œโ”€โ”€ generators/               # Synthetic data generators

โ”‚   โ”‚   โ”œโ”€โ”€ random_walk.py       # Stochastic process simulation

โ”‚   โ”‚   โ”œโ”€โ”€ dice.py              # Probability distributions

โ”‚   โ”‚   โ”œโ”€โ”€ weather.py           # Meteorological data

โ”‚   โ”‚   โ”œโ”€โ”€ quakes.py            # Seismic activity

โ”‚   โ”‚   โ””โ”€โ”€ github.py            # Repository analytics

โ”‚   โ”œโ”€โ”€ plots/                   # Visualization modules

โ”‚   โ”‚   โ”œโ”€โ”€ *_mpl.py            # Matplotlib implementations

โ”‚   โ”‚   โ””โ”€โ”€ *_plotly.py         # Plotly implementations

โ”‚   โ”œโ”€โ”€ utils/                   # Shared utilities

โ”‚   โ”‚   โ”œโ”€โ”€ io.py               # Data I/O operations

โ”‚   โ”‚   โ””โ”€โ”€ theming.py          # Consistent styling

โ”‚   โ””โ”€โ”€ cli.py                  # Command-line interface

โ”œโ”€โ”€ ๐Ÿ“Š data/synthetic/            # Generated datasets

โ”‚   โ”œโ”€โ”€ random_walk/             # Random walk data

โ”‚   โ”œโ”€โ”€ dice/                    # Dice roll results

โ”‚   โ”œโ”€โ”€ weather/                 # Weather time series

โ”‚   โ”œโ”€โ”€ quakes/                  # Earthquake catalogs

โ”‚   โ””โ”€โ”€ github/                  # Repository metrics

โ”œโ”€โ”€ ๐ŸŽจ exports/                   # Output visualizations

โ”‚   โ”œโ”€โ”€ images/                  # Static plots (PNG/JPG)

โ”‚   โ””โ”€โ”€ html/                    # Interactive charts

โ”œโ”€โ”€ ๐Ÿ““ notebooks/                 # Jupyter analysis notebooks

โ”‚   โ”œโ”€โ”€ 01_random_walk.ipynb     # Random walk analysis

โ”‚   โ”œโ”€โ”€ 02_dice.ipynb            # Probability simulations

โ”‚   โ”œโ”€โ”€ 03_weather.ipynb         # Weather data exploration

โ”‚   โ”œโ”€โ”€ 04_quakes.ipynb          # Seismic analysis

โ”‚   โ””โ”€โ”€ 05_github.ipynb          # Repository analytics

โ””โ”€โ”€ ๐Ÿงช tests/                     # Unit tests

    โ”œโ”€โ”€ test_generators.py       # Data generation tests

    โ””โ”€โ”€ test_plots.py            # Visualization tests

```



## ๐Ÿš€ Quick Start



### Installation



```bash

# Clone my repository

git clone https://github.com/SatvikPraveen/PCC-VizForge.git

cd PCC-VizForge



# Install the package

pip install -e .



# Or use the setup script

chmod +x setup_pcc_vizforge.sh

./setup_pcc_vizforge.sh

```



### Command Line Usage



```bash

# Generate a random walk visualization

pcc-vizforge random_walk --library matplotlib --export-type image



# Create interactive dice probability analysis

pcc-vizforge dice --library plotly --export-type html



# Build a weather dashboard

pcc-vizforge weather --library plotly --export-type html



# Generate earthquake analysis

pcc-vizforge quakes --library matplotlib --export-type image



# Create GitHub repository analytics

pcc-vizforge github --library plotly --export-type html



# Run a complete demonstration

pcc-vizforge demo

```



### Python API



```python

from src.generators.random_walk import RandomWalkGenerator

from src.plots.random_walk_mpl import RandomWalkMatplotlib



# Generate synthetic random walk data

config = {'n_steps': 1000, 'n_walks': 5, 'step_size': 1.0}

generator = RandomWalkGenerator(config)

walk_data = generator.generate_1d_walk(n_steps=1000)



# Create visualization

plotter = RandomWalkMatplotlib(config)

fig, ax = plt.subplots(figsize=(12, 8))

plotter.plot_1d_timeseries(walk_data, ax=ax)

plt.show()

```



### Jupyter Notebooks



```bash

# Launch Jupyter and explore the analysis notebooks

jupyter notebook notebooks/



# Or explore specific domains

jupyter notebook notebooks/01_random_walk.ipynb

jupyter notebook notebooks/03_weather.ipynb

```



## ๐Ÿ“Š Visualization Showcase



### 1. Random Walk Analysis ๐Ÿšถโ€โ™‚๏ธ



- **1D Time Series**: Multiple trajectory comparisons with statistical convergence

- **2D Spatial Paths**: Beautiful walk patterns with start/end markers

- **Statistical Analysis**: Mean square displacement and diffusion coefficients

- **Interactive Features**: Hover data, zoom controls, animation sequences



### 2. Dice Probability Simulations ๐ŸŽฒ



- **Frequency Distributions**: Single die and multiple dice sum analysis

- **Convergence Demonstrations**: Law of large numbers visualizations

- **Theoretical vs Empirical**: Chi-square goodness of fit testing

- **Interactive Probability**: Dynamic probability calculators



### 3. Weather Pattern Analysis ๐ŸŒฆ๏ธ



- **Time Series Decomposition**: Trend, seasonal, and residual components

- **Climate Comparisons**: Temperate, tropical, arid, and polar zones

- **Extreme Events**: Heatwaves, cold snaps, and storm systems

- **3D Weather Space**: Multi-parameter relationship exploration



### 4. Earthquake Seismology ๐ŸŒ



- **Magnitude-Frequency**: Gutenberg-Richter law demonstrations

- **Spatial Clustering**: Geographic hotspot identification using DBSCAN

- **Aftershock Analysis**: Omori's law decay patterns

- **Interactive Maps**: Global seismic activity with magnitude scaling



### 5. GitHub Repository Analytics ๐Ÿ“ˆ



- **Development Patterns**: Commit frequency and contributor analysis

- **Collaboration Networks**: Developer interaction graphs

- **Repository Health**: Comprehensive scoring system

- **Project Evolution**: Timeline visualization of repository growth



## โš™๏ธ Configuration System



Each domain has dedicated YAML configuration files for easy customization:



```yaml

# config/random_walk.yaml

data_generation:

  n_steps: 1000

  n_walks: 5

  step_size: 1.0

  random_seed: 42



visualization:

  matplotlib:

    figsize: [12, 8]

    style: "seaborn-v0_8"

    colors: ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd"]



  plotly:

    template: "plotly_white"

    width: 900

    height: 600

    animation_duration: 1000

```



## ๐ŸŽจ Theming and Aesthetics



Built-in themes for consistent, professional visualizations:



```python

from src.utils.theming import get_plot_theme, apply_style



# Available themes

themes = ['clean', 'dark', 'minimal', 'scientific', 'vibrant']



# Apply theme

theme = get_plot_theme('clean')

apply_style(theme)



# Custom color palettes

colors = theme.get_color_palette('qualitative', n_colors=8)

```



## ๐Ÿงช Development Workflow



### Available Make Commands



```bash

make install-dev    # Install with development dependencies

make test          # Run comprehensive unit tests

make lint          # Code quality checks (flake8, mypy)

make format        # Auto-format code with black

make clean         # Remove generated files and __pycache__

make demo          # Quick demonstration of all features

make docs          # Generate documentation

```



### Testing and Quality Assurance



```bash

# Run the full test suite

pytest tests/ -v --cov=src --cov-report=html



# Test specific modules

pytest tests/test_generators.py -k "test_random_walk"

pytest tests/test_plots.py -k "test_matplotlib_plots"



# Code formatting and linting

black src/ tests/

flake8 src/ tests/ --max-line-length=100

mypy src/ --ignore-missing-imports

```



## ๐Ÿ“ˆ Advanced Examples



### Multi-Scenario Analysis



```python

from src.generators.weather import WeatherGenerator



# Generate multiple climate scenarios

generator = WeatherGenerator()

scenarios = {

    'tropical': {'temperature_base': 28, 'humidity_base': 85},

    'temperate': {'temperature_base': 15, 'humidity_base': 65},

    'arid': {'temperature_base': 25, 'humidity_base': 30}

}



results = {}

for climate, params in scenarios.items():

    data = generator.generate_climate_specific_data(

        climate_type=climate, **params

    )

    results[climate] = data

```



### Custom Visualization Pipeline



```python

from src.plots.quakes_plotly import QuakePlotly

from src.utils.io import save_data, load_data



# Load earthquake data

quake_data = load_data('data/synthetic/quakes/earthquake_catalog.csv')



# Create interactive 3D visualization

plotter = QuakePlotly()

fig = plotter.plot_3d_seismicity(quake_data)



# Customize and save

fig.update_layout(

    title="Global Seismic Activity - Interactive 3D View",

    scene=dict(bgcolor='black'),

    template='plotly_dark'

)

fig.write_html('exports/html/custom_earthquake_3d.html')

```



### Statistical Analysis Integration



```python

from scipy import stats

import numpy as np



# Analyze dice rolling fairness

dice_data = load_data('data/synthetic/dice/all_dice_rolls.csv')

single_die = dice_data['single_die'].dropna()



# Chi-square goodness of fit test

observed = [np.sum(single_die == i) for i in range(1, 7)]

expected = [len(single_die) / 6] * 6

chi2_stat, p_value = stats.chisquare(observed, expected)



print(f"Chi-square statistic: {chi2_stat:.4f}")

print(f"P-value: {p_value:.4f}")

print(f"Fair die? {'Yes' if p_value > 0.05 else 'No'}")

```



## ๐ŸŽฏ Personal Learning Outcomes



Through this project, I've demonstrated proficiency in:



### Technical Skills



- **Data Generation**: Synthetic data creation following statistical distributions

- **Visualization**: Both static (Matplotlib) and interactive (Plotly) plotting

- **Statistical Analysis**: Hypothesis testing, regression analysis, clustering

- **Software Architecture**: Modular design with clear separation of concerns

- **Testing**: Unit tests with pytest and code coverage analysis



### Domain Knowledge



- **Probability Theory**: Random processes, central limit theorem, statistical distributions

- **Meteorology**: Weather patterns, seasonal analysis, extreme event modeling

- **Seismology**: Earthquake statistics, magnitude-frequency relationships, spatial analysis

- **Software Engineering**: Development metrics, collaboration patterns, repository health



### Best Practices



- **Code Quality**: Type hints, documentation, consistent styling

- **Configuration Management**: YAML-based settings for reproducibility

- **Data Pipeline**: ETL processes with validation and error handling

- **Version Control**: Structured git workflow with meaningful commits



## ๐Ÿค Contributing



While this is a personal project, I welcome feedback, suggestions, and contributions:



1. **Fork** the repository

2. **Create** a feature branch (`git checkout -b feature/awesome-addition`)

3. **Commit** your changes (`git commit -m 'Add awesome feature'`)

4. **Push** to the branch (`git push origin feature/awesome-addition`)

5. **Open** a Pull Request with detailed description



### Development Setup



```bash

git clone https://github.com/SatvikPraveen/PCC-VizForge.git

cd PCC-VizForge

make install-dev

make test

```



## ๐Ÿ“š Educational Resources



This project can serve as a learning resource for:



- **Data Science Students**: Real-world data generation and analysis examples

- **Visualization Enthusiasts**: Best practices for both static and interactive plots

- **Python Developers**: Clean, modular code architecture patterns

- **Statistics Learners**: Applied statistical concepts with visual demonstrations



## ๐Ÿ”ฎ Future Enhancements



Potential areas for expansion:



- [ ] **Time Series Forecasting**: ARIMA, LSTM models for weather/earthquake prediction

- [ ] **Machine Learning Integration**: Clustering, classification, anomaly detection

- [ ] **Real Data Integration**: APIs for live data feeds (weather, seismic, GitHub)

- [ ] **Web Dashboard**: Flask/Streamlit app for interactive exploration

- [ ] **3D Visualizations**: Advanced 3D plotting with better interaction

- [ ] **Performance Optimization**: Cython/Numba for large dataset handling



## ๐Ÿ“„ License



This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.



**What this means:**



- โœ… Commercial use allowed

- โœ… Modification allowed

- โœ… Distribution allowed

- โœ… Private use allowed

- โ— License and copyright notice required



## ๐Ÿ™ Acknowledgments



- **Libraries**: Built with love using [Matplotlib](https://matplotlib.org/), [Plotly](https://plotly.com/), [NumPy](https://numpy.org/), [Pandas](https://pandas.pydata.org/), and [SciPy](https://scipy.org/)

- **Inspiration**: Scientific visualization best practices and educational data science resources

- **Community**: Stack Overflow, GitHub, and the broader Python data science community



## ๐Ÿ“ž Connect With Me



- ๐Ÿ™ **GitHub**: [@SatvikPraveen](https://github.com/SatvikPraveen)

- ๐Ÿ’ผ **LinkedIn**: Connect for professional discussions about data science and visualization

- ๐Ÿ› **Issues**: Report bugs or request features via [GitHub Issues](https://github.com/SatvikPraveen/PCC-VizForge/issues)



---



**"Data visualization is not just about making pretty charts - it's about revealing the hidden stories within data and making complex concepts accessible to everyone."**



_Built with โค๏ธ and lots of โ˜• by Satvik Praveen_



---



### ๐Ÿ”ง Quick Reference



| Command                       | Description                |

| ----------------------------- | -------------------------- |

| `make demo`                   | Run complete demonstration |

| `pcc-vizforge --help`         | Show all CLI options       |

| `jupyter notebook notebooks/` | Explore analysis notebooks |

| `make test`                   | Run comprehensive tests    |

| `make clean && make demo`     | Fresh start demonstration  |



**Star โญ this repository if you find it useful for your data visualization journey!**