semvec/pypi-packages huggingface dataset:
promptmin 1.0.2
Prompt minimizer for LLM evals — shrink prompts to minimal failing inputs using delta debugging
Requires Python: >=3.9
Keywords: prompt, llm, eval, ddmin, delta-debugging, debugging, testing

# promptmin

[![PyPI version](https://img.shields.io/pypi/v/promptmin)](https://pypi.org/project/promptmin/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

**Prompt minimizer for LLM evals** — shrink a prompt to the smallest input that still reproduces a failure using delta debugging (ddmin).

## Why?

- **Fast debugging**: minimal repros beat 300-line prompts
- **Cheaper CI**: fewer tokens, fewer moving parts
- **Safer iteration**: smaller diffs, clearer "what changed"
- **Handles flakiness**: stability modes (`strict` / `k-of-n`) and `--confirm-final`

## Installation

This package is a Python wrapper that shells out to the `promptmin` Node CLI.

```bash
# 1. Install the CLI
npm install -g promptmin

# 2. Install the Python wrapper
pip install promptmin
```

## Usage

### Python API

```python
from promptmin import minimize

result = minimize(
    prompt_path="prompts/support.md",
    config_path="promptmin.config.json",
    target="test:refund_policy_01",
    budget_runs=60
)

print(f"Minimized: {result.minimized_path}")
print(f"Report: {result.report_path}")
```

### CLI

```bash
promptmin-py minimize \
  --prompt prompts/support.md \
  --config promptmin.config.json \
  --target test:refund_policy_01 \
  --budget-runs 60
```

## Config example

```json
{
  "runner": {
    "type": "openai_responses",
    "model": "gpt-4.1-mini"
  },
  "tests": [{
    "id": "refund_policy_01",
    "input": { "user": "Can I get a refund?" },
    "assert": { "type": "regex_not_match", "pattern": "competitor" }
  }]
}
```

Requires `OPENAI_API_KEY` for the `openai_responses` runner, or use `local_command` to run your own eval script.

## Artifacts

After minimization, you get:
- `baseline.prompt` / `minimized.prompt` — before and after
- `diff.patch` — what was removed
- `report.md` / `meta.json` — summary and metadata

## Options

| Flag | Description |
|------|-------------|
| `--strategy` | `ddmin` (default) or `greedy` |
| `--granularity` | `sections`, `blocks`, `sentences`, or `lines` |
| `--stability-mode` | `strict` or `kofn` for flaky tests |
| `--confirm-final` | Re-verify the final minimized prompt |
| `--no-trace-output` | Disable trace logging (for sensitive prompts) |

## Links

- [GitHub](https://github.com/10fra/promptmin)
- [Full documentation](https://github.com/10fra/promptmin#readme)

## License

MIT
roxieinterfaces 0.5.0
Interfaces implementation to roxie
Requires Python: <3.13,>=3.11

[//]: # "Comment" SPDX-FileCopyrightText: 2024 CERN
[//]: # "Comment" SPDX-License-Identifier: BSD-4-Clause

# Roxie Interfaces

This project provides python tools and classes to interface with Roxie.

See [The Roxie documentation](https://roxie.docs.cern.ch/) on how to install Roxie binaries.

## Installation

### As pip package

Simply install the package via pip:

    pip install roxie-interfaces

### Via Poetry

We use the poetry packaging manager. You can get poetry with pip:

    pip install poetry

After You have pulled this repository with git. Navigate to the main directory (where this readme file is located),
and run

    poetry install

## Features

### Step file generation (conductors and end spacers)

The most important example is plot_step_file_generation.
It shows You how to generate the step files for endspacers, wedges, posts and conductors.

### HMO file generator

Load mesh formats (hmascii, gmsh) and generate hmo file to use with roxie.
roxie-api 0.4.5
A Python API for ROXIE to build a model data input, modify cable database file, and control simulation with a tool adapter
Requires Python: >=3.9

# ROXIE API

This project provides a python interface to interact with Roxie, which is compatible with Roxie release >= 23.6.

See [The Roxie documentation](https://roxie.docs.cern.ch/) on how to install Roxie binaries.

## Features

### Input Parsing and generation

- Generate data files and access them in a structured way
- Parse existing data files (and modify them)
- Generate data files to execute with roxie

### Output parsing and plotting

- Parse roxie xml output
- Plot Graphs, 2D crosssections and 3D renders
- extract figures of merit and key information of a run (Design variables, objectives, harmonics)

### Execution

- Execute roxie on your local computer
- Execute roxie within a Docker container
- Execute roxie using the Rest API interface of a roxie-rest-api computing node
OSL-ANALYSIS 0.1.6
program for the analysis of physical processes of OSL curves
Requires Python: >=3.6

# OSL_ANALYSIS

A comprehensive simulation program for analyzing photostimulation, irradiation,
thermal washing, and optical washing processes in optically and thermally
stimulated materials.

## Author
**EDWIN JOEL PILCO QUISPE**  
Email: edwinpilco10@gmail.com

## Description
This package provides a sophisticated computational framework for modeling
charge carrier dynamics in materials subjected to optical and thermal
stimulation. It implements advanced physical models for optically stimulated
luminescence (OSL) and radiation dosimetry applications.

## Key Features

### Multi-Process Simulation
- **Photostimulation Analysis**: Wavelength-dependent photoionization
  cross-section calculations with Gaussian light source simulation
- **Irradiation Modeling**: Trap filling dynamics during radiation exposure
  with competition effects
- **Thermal Washing**: Thermal release of trapped charges using the
  TTOR (Two-Trap One-Recombination center) model
- **Optical Washing**: Combined thermal–optical stimulation effects analysis

### Advanced Physical Models
- Extended TTOR model with three eV electron traps (user selected)
- Temperature-dependent detrapping via Arrhenius equation
- Wavelength-dependent photoionization probabilities
- Charge neutrality verification and validation

### Flexible Input System
Supports:
- Single values: `470`
- Ranges: `300,400,25`
- Multiple values: `300-350-400`

## Output
- Three-panel diagnostic plots per simulation
- Publication-quality PNG images (300 DPI)
- Tab-delimited text files (Origin / Excel compatible)
- Pandas DataFrames
- Automatic Google Colab download support

## Scientific Applications
- Radiation dosimetry and OSL dosimeter characterization
- Material science defect studies

## Installation

### From PyPI
```bash
python -m pip install OSL_ANALYSIS
c1s-slingshot-sdk-py 2.1.0
Slingshot SDK
Requires Python: <3.14,>=3.9

# Capital One Slingshot SDK Python Library

![Capital One Slingshot Logo](https://raw.githubusercontent.com/capitalone/c1s-slingshot-sdk-py/main/docs/_static/slingshot-small-logo.png)

The official Python SDK for Capital One's Slingshot platform. This library provides a convenient way to interact with the Slingshot API from your Python applications.

## 📚 Documentation

**➤ [Complete Documentation & API Reference](https://capitalone.github.io/c1s-slingshot-sdk-py/)**

For comprehensive guides, examples, and API documentation, visit our GitHub Pages documentation site.

## Table of Contents

- [Capital One Slingshot SDK Python Library](#capital-one-slingshot-sdk-python-library)
  - [📚 Documentation](#-documentation)
  - [Table of Contents](#table-of-contents)
  - [Installation](#installation)
  - [Quick Start](#quick-start)
  - [Contributing](#contributing)
  - [License](#license)

## Installation

Install the SDK using pip:

```bash
pip install c1s-slingshot-sdk-py
```

## Quick Start

```python
from slingshot import SlingshotClient

# Initialize the client (uses SLINGSHOT_API_KEY environment variable)
client = SlingshotClient()
# If you want to pass your API key to the client without using the environment variable:
# client = SlingshotClient(api_key="your API key")

# List projects
all_projects = []
for project in client.projects.iterate_projects():
    all_projects.append(project)

print(f"Found {len(all_projects)} projects.")

# Get a specific project
project = client.projects.get_project(all_projects[0]["id"])
print(project)
```

## Contributing

> [!IMPORTANT]
> At this time, we are only accepting pull requests from Capital One employees. External pull requests will be closed.

🔧 **[Contributing Guide](CONTRIBUTING.md)** - Development setup, testing, and release process

## License

This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.