semvec/pypi-packages huggingface dataset:
mini-camera-intrinsic 0.0.2
按相机型号（DESAYSV/PONY）解析内参，返回统一 dict，支持保存为与 D02 一致的 YAML 格式。
Requires Python: >=3.10

# mini-camera-intrinsic

按相机型号解析内参，返回统一字典；支持保存为与 D02（pb_calibration）一致的本地 YAML 格式。

## 安装

```bash
pip install mini-camera-intrinsic
```

## 功能

- **parse_intrinsics(camera_model, **kwargs)**：按相机型号解析，返回 `List[dict]`，每项与 D02 单组内参 YAML 同构。
- **save_intrinsics_to_dir(intrinsics_list, output_dir)**：将上述列表写入 `output_dir/intrinsics/*.yaml`。

支持的相机型号（硬编码）：`DESAYSV`、`PONY`。

## 快速开始

### DESAYSV

需提供 log 来源（目录或文件列表）与 camera_id 映射（**camera_id 文件路径**或 dict，不能传文件夹路径）：

```python
from mini_camera_intrinsic import parse_intrinsics, save_intrinsics_to_dir, CAMERA_MODEL_DESAYSV

# 方式一：log 目录 + camera_id 文件
items = parse_intrinsics(
    CAMERA_MODEL_DESAYSV,
    log_dir="/path/to/log_dir",
    camera_id_path="/path/to/camera_id",
)

# 方式二：log 文件列表 + camera_id 映射
items = parse_intrinsics(
    CAMERA_MODEL_DESAYSV,
    log_paths=["/path/to/camera_30.log", "/path/to/camera_100.log"],
    camera_id_mapping={1: "front_30", 2: "front_120"},
)

# 返回 List[dict]，可保存到本地
save_intrinsics_to_dir(items, "/path/to/output_dir")
```

### PONY

需提供 log 文件路径：

```python
from mini_camera_intrinsic import parse_intrinsics, CAMERA_MODEL_PONY

items = parse_intrinsics(
    CAMERA_MODEL_PONY,
    log_path="/path/to/drivers_camera_pony.log.INFO.xxx",
)
# 或 file_path=...
```

### 返回格式（dict）

每条内参与 D02 单组 intrinsic YAML 一致，例如：

- `frame_id`: str  
- `model_type`: `"PINHOLE"` | `"FISHEYE"`  
- `pinhole`（或 `fisheye`）: `width`, `height`, `intrinsic`（9 个数）, `distortion`（4 或 8 个数）

便于 API 调用与 `save_intrinsics_to_dir` 入参。

## 依赖

- Python >= 3.10  
- PyYAML >= 6.0  

## License

MIT
reframodel 2026.2.2
Seismic refraction modeling and inversion tool
Requires Python: >=3.8
Keywords: seismic, refraction, geophysics, inversion, modeling

# RefraModel

Seismic refraction modeling and inversion tool using PyGIMLi.

## Features

- Interactive geological model builder
- Visual model editing and manipulation
- Forward modeling of seismic refraction data
- Inversion with flexible regularization
- Body-specific velocity constraints
- Anisotropic smoothing support

## Installation

### From source

```bash
git clone https://github.com/yourusername/reframodel.git
cd reframodel
pip install -e .
```

### From PyPI (when published)

```bash
pip install reframodel
```

## Usage

After installation, run the application:

```bash
reframodel
```

Or as a Python module:

```bash
python -m RefraModel
```

## Requirements

- Python >= 3.8
- numpy >= 1.20.0
- matplotlib >= 3.3.0
- PyQt5 >= 5.15.0
- pygimli >= 1.3.0

## License

MIT License

## Author

Hermann Zeyen, University Paris-Saclay <hermann.zeyen@universite-paris-saclay.fr>
ag2tracer 0.1.1
Failure attribution on multi-agent traces
Requires Python: >=3.9

# ag2tracer

Failure attribution on multi-agent traces. Given a conversation trace from a multi-agent system, `ag2tracer` identifies **which agent** made the decisive error and **at which step**.

## Installation

```bash
pip install ag2tracer
```

## Setup

Set at least one API key:

```bash
export OPENAI_API_KEY="your-openai-key"
export ANTHROPIC_API_KEY="your-anthropic-key"   # optional
export GOOGLE_API_KEY="your-google-key"          # optional
```

## Web Interface

```bash
# Launch the web UI
ag2tracer launch

# Or run in background
ag2tracer launch --daemon

# Other commands
ag2tracer status    # check if server is running
ag2tracer logs      # view server logs
ag2tracer logs -f   # follow logs
ag2tracer stop      # stop the background server
```

Then open http://127.0.0.1:8500 to:

1. Upload a trace JSON file (must contain a `history` field)
2. Browse the conversation with a sliding-window viewer
3. Select an attribution method and click **Start**
4. View the failure-responsible agent and decisive error step

## Python API

```python
import json
from ag2tracer import all_at_once, step_by_step, binary_search

with open("trace.json") as f:
    trace = json.load(f)

# Choose a method
result = all_at_once(trace, model="gpt-4o")
# result = step_by_step(trace, model="gpt-4o")
# result = binary_search(trace, model="gpt-4o")

print(f"Agent: {result.agent_name}")
print(f"Step:  {result.step_number}")
print(f"Reason: {result.reason}")
```

## Trace Format

A trace is a JSON object with a required `history` array:

```json
{
  "history": [
    {"role": "assistant", "name": "Excel_Expert", "content": "..."},
    {"role": "user", "name": "Computer_terminal", "content": "..."},
    ...
  ],
  "question": "optional problem description",
  "ground_truth": "optional expected answer"
}
```

## Attribution Methods

| Method | Description | LLM Calls |
|--------|-------------|-----------|
| **All-at-Once** | Feeds entire conversation to the LLM | 1 |
| **Step-by-Step** | Evaluates each step sequentially, stops at first error | Up to N |
| **Binary Search** | Recursively narrows down the error location | O(log N) |

## License

MIT
voraus-pipeline-utils 0.3.0
A library containing pipeline utils for both, local usage and usage in Jenkins.
Requires Python: >=3.11

# voraus Pipeline Utils

A library containing pipeline utils for both, local usage and usage in Jenkins.

## Installation

Install **voraus-pipeline-utils** in your venv.

```bash
pip install voraus-pipeline-utils

# Or via uv
uv add --dev voraus-pipeline-utils
```

## Basic usage

Get help for any CLI command with
```bash
vpu --help
```

## Documentation

Please read the [documentation](https://vorausrobotik.github.io/voraus-pipeline-utils/)_ for more a more
detailed introduction.
mirava-tui 0.1.2
TUI wizard for selecting the best Iran mirrors
Requires Python: >=3.9

# Mirava

> A TUI implementation of the [Mirava](https://github.com/GeeDook/mirava) project. It helps developers in I.R. Iran keep working during internet blackouts by finding accessible mirror endpoints.

Mirava is an interactive terminal app that helps you find working mirror endpoints for:

- Linux package repositories (Debian/Ubuntu/Arch/Alpine and more)
- Language/package registries (PyPI, npm, Docker Registry, etc.)

It checks both:

- Reachability (`OK`/`FAIL`)
- Package availability (`FOUND`/`NOT FOUND`)

## Quick Start

### Option 1: Install from PyPI (recommended)

```bash
pip install mirava-tui
mirava
```

### Option 2: Run a prebuilt binary

1. Open the repository's **Releases** page.
2. Download the binary for your platform.
3. Run it from your terminal.

Examples:

```bash
# Linux / macOS
chmod +x mirava-<target>
./mirava-<target>
```

```powershell
# Windows PowerShell
.\mirava-windows-x64.exe
```

### Option 3: Run from source

Requirements:

- Python 3.9+

Install and run:

```bash
pip install -e .
mirava
```

Or without installation:

```bash
python -m mirava
```

## How to Use

When Mirava starts, choose one of these modes:

- `OS mirrors`: Check OS repository mirrors. Package input is optional.
- `Registry mirrors`: Check registries like PyPI/npm/Docker. Package/image input is required.

Keyboard controls:

- `Up` / `Down` (or `k` / `j`) to move
- `Enter` to select
- `b` to go back
- `q` to quit

## Understanding Results

Mirava prints a results table with these columns:

- `Reach`: Endpoint health (`OK` or `FAIL`)
- `Package`: `FOUND`, `NOT FOUND`, or `SKIPPED`
- `Latency`: Lower is typically better
- `Mirror`, `Endpoint`, `Reason`: Context and failure details

Tips:

- Prefer rows with `Reach=OK` and lower latency.
- `NOT FOUND` usually means the mirror is reachable, but that specific package/image is missing.
- `SKIPPED` appears when package checking was optional and no package name was given.

## Notes

- Mirror checks use live network requests, so results can change over time.
- Some mirrors may be reachable but slow; run multiple checks if needed.