bezierv 1.1.5
This package serves as a computational framework for Bézier distributions.
License: MIT
Requires Python: <3.14,>=3.10
Keywords: bezier, distribution, optimization

<p align="center">
  <img src="docs/assets/logo.png" alt="bezierv logo" width="260"/>
</p>

<h1 align="center">bezierv</h1>
<p align="center">
  <em>Fit smooth Bézier random variables to empirical data</em>
</p>

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---

## Why Bézier Random Variables?

Traditional parametric distributions (normal, exponential, etc.) can be too rigid for real-world data. **bezierv** bridges the gap between non-parametric and parametric approaches by using Bézier curves to create smooth, flexible distributions that can fit virtually any shape.

**Key advantages:**
- 📈 **Flexible**: Fit any continuous distribution shape
- 🎛️ **Controllable**: Intuitive control points for fine-tuning
- 🔄 **Composable**: Built-in convolution for sums of random variables
- ⚡ **Fast**: Multiple efficient fitting algorithms
- 🎨 **Visual**: Interactive tools for exploration

---

## Quick Start

### Installation

```bash
pip install bezierv
```

### Basic Usage

```python
import numpy as np
from bezierv.classes.distfit import DistFit

# Generate or load your data
data = np.random.beta(2, 5, 1000)  # Example: skewed data

# Fit a Bézier distribution
fitter = DistFit(data, n=5)  # 5 control segments
bezier_rv, mse = fitter.fit(method="projgrad")

# Use the fitted distribution
samples = bezier_rv.random(10000)      # Generate new samples
q90 = bezier_rv.quantile(0.90)         # 90th percentile  
mean = bezier_rv.get_mean()            # Distribution mean
prob = bezier_rv.cdf_x(0.5)            # P(X ≤ 0.5)

# Visualize the fit
bezier_rv.plot_cdf(data)  # Compare with empirical CDF
bezier_rv.plot_pdf()      # Show probability density
```

### Advanced: Convolution of Random Variables

```python
from bezierv.classes.convolver import Convolver

# Fit two distributions
rv1 = DistFit(data1, n=4).fit()[0]
rv2 = DistFit(data2, n=4).fit()[0]

# Compute their sum: Z = X + Y
convolver = Convolver([rv1, rv2])

# Monte Carlo
sum_mc = convolver.convolve(n_sims=10000)

```

---

## Documentation

| Resource | Description |
|----------|-------------|
| [**📖 User Guide**](https://estebanleiva.github.io/bezierv/) | Complete tutorials and examples |
| [**🔧 API Reference**](https://estebanleiva.github.io/bezierv/reference/) | Detailed function documentation |
| [**🎮 Interactive Demo**](#interactive-tool) | Browser-based curve editor |
| [**📚 Tutorials**](https://estebanleiva.github.io/bezierv/tutorials/) | Step-by-step examples and guides |

---

## Interactive Tool

Launch an interactive Bézier curve editor in your browser:

```python
from bezierv.classes.bezierv import InteractiveBezierv
from bokeh.plotting import curdoc

# Create interactive editor
editor = InteractiveBezierv(
    controls_x=[0.0, 0.25, 0.75, 1.0],
    controls_z=[0.0, 0.1, 0.9, 1.0]
)

curdoc().add_root(editor.layout)
```

Then run: `bokeh serve --show your_app.py`

---

## Algorithms

bezierv includes multiple fitting algorithms optimized for different scenarios:

| Algorithm | 
|-----------|
| `projgrad` |
| `projsubgrad` |
| `nonlinear` | 
| `neldermead` |



## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.