semvec/pypi-packages huggingface dataset:
pytikhonov 0.0.1
Pure-Python toolkit for Tikhonov regularization of linear inverse problems, with GSVD-based diagnostics and parameter selection.
Requires Python: >=3.7
Keywords: GCV, discrepancy principle, inverse problems, lcurve, regularization parameter, tikhonov regularization

# PyTikhonov

PyTikhonov is a pure-Python toolkit for **Tikhonov regularization** of linear inverse problems, with a focus on **regularization parameter selection** and **GSVD-based diagnostics**.

It is primarily intended for small to moderate-scale problems (e.g., $N$ up to a few thousand) where matrix-like access to $A$ and $L$ is available. For larger problems, it provides a `ProjectedTikhonovFamily` interface designed to be combined with iterative methods (e.g., Krylov / model reduction bases).

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## Installation

    pip install pytikhonov

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## Features

- Core object: `TikhonovFamily(A, L, b, d, ...)` representing the entire family $$x_\lambda = \arg\min_x \|Ax - b\|_2^2 + \lambda \|Lx - d\|_2^2$$ for $\lambda > 0$.

- GSVD-based implementation (via [`easygsvd`](https://github.com/jlindbloom/easygsvd)):
  - Fast evaluation of $x_\lambda$ for many $\lambda$.
  - Efficient computation of $\|Ax - b\|_2^2$, $\|Lx - d\|_2^2$, and their derivatives.
  - Support for both $\lambda$ and reciprocal parameterization $\beta = 1/\lambda$.

- Diagnostic tools:
  - Picard plot (discrete Picard condition).
  - L-curve (and curvature-based “L-corner”).
  - Monitoring function and degrees of freedom.

- Regularization parameter selection:
  - L-corner heuristic.
  - Discrepancy principle (DP).
  - Generalized cross validation (GCV).
  - Convenience function to compare all methods on a given problem.
  - Randomization experiments to study robustness of parameter choices.

- Performing projections:
  - `ProjectedTikhonovFamily` for reduced problems on subspaces $\underline{x} + \mathrm{col}(V)$.
  - Designed to plug into Krylov / model-reduction pipelines (e.g., GKB, Arnoldi) for large-scale problems.

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## Minimal example

    import numpy as np
    from pytikhonov import TikhonovFamily, lcorner

    # Example problem (A, L, b, d as dense arrays)
    A = ...
    L = ...
    b = ...
    d = np.zeros(L.shape[0])

    # Build the Tikhonov family
    tf = TikhonovFamily(A, L, b, d)

    # Select lambda via the L-corner heuristic
    lcorner_data = lcorner(tf)
    lambdah_star = lcorner_data["lambdah"]
    x_star = lcorner_data["x_lambdah"]

For full details, mathematical background, and additional examples (Picard plots, monitoring function, IRLS, projected problems, etc.), see the documentation PDF included in the repository.
wayneGL 2.0.10
Wayne Graphics: a custom graphics library based off cmu-graphics but simpler
Requires Python: >=3.9

# Wayne Graphics

**Wayne Graphics** is a custom graphics engine inspired by CMU‑style graphics, rewritten with its own pixel renderer.  
It also includes two CLI tools: an **AI explainer** and an **API server** for remote shape creation and control.

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## ✨ Features

- 🎨 **Custom Renderer** — pixel‑based graphics engine with shapes, groups, events, and widgets.
- 🟢 **Shapes** — Arc, Circle, Polygon, Rectangle, Star, Line, Image, Point.
- 📦 **Groups** — combine shapes into reusable containers with transformations.
- 🖱️ **Events** — mouse, keyboard, and step callbacks via a clean event bus.
- 🧩 **Widgets** — buttons, sliders, labels, and more built on top of shapes.
- 🌐 **API Router** — HTTP server to create and manipulate shapes remotely.
- 🤖 **AI Explainer** — CLI tool that provides code explanations and tutoring.

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## 📦 Installation

```bash
pip install wayneGL