semvec/pypi-packages huggingface dataset:
balderhub-data 0.0.1b2
balderhub-data: BalderHub Project that can be used to manage data
License: MIT
Requires Python: >=3.9
Keywords: test, systemtest, reusable, scenario, junit, balder

# BalderHub Package ``balderhub-data``

This is a BalderHub package for the [Balder](https://docs.balder.dev) test framework. If you are new to Balder check out the
[official documentation](https://docs.balder.dev) first.

## Installation

You can install the latest release with pip:

```
python -m pip install balderhub-data
```

# Check out the documentation

If you need more information, 
[checkout the ``balderhub-data`` documentation](https://hub.balder.dev/projects/data).


# License

This BalderHub package is free and Open-Source

Copyright (c)  2025  balderhub-data

Distributed under the terms of the MIT license
nbgen-cli-amirbnsl 0.3.4
A CLI tool to generate Jupyter Notebooks with data loading scripts.
License: MIT
Requires Python: >=3.6

# nbgen

A CLI tool to generate Jupyter Notebooks with data loading scripts.

## Installation

```bash
pip install nbgen-cli-amirbnsl
```

## Usage

```bash
nbgen my_notebook.ipynb
```

With Kaggle support:
```bash
nbgen my_kaggle_notebook.ipynb -k
```

### Credentials
The tool looks for Kaggle credentials in the following order:
1. Command line arguments: `--kaggle-user` and `--kaggle-key`
2. Environment variables: `KAGGLE_USERNAME` and `KAGGLE_KEY`
3. Kaggle configuration file: `~/.kaggle/kaggle.json`

If the notebook already exists, the new cells will be prepended to the top of the notebook.
a42-proto 0.4.2
Protobuf Python bindings for A42 sensor data
License: MIT
Requires Python: >=3.6

# 📦 A42 Protobuf Bindings (`a42-proto`)

Dieses Python-Paket enthält die **Protobuf-Bindings für Sensordaten im A42-Format**. Es erlaubt das einfache Einlesen und Verarbeiten von LiDAR-Scans und zugehörigen Objektinformationen.

---

## 🔽 Datendownload

👉 [KIT Sync & Share Download](https://bwsyncandshare.kit.edu/s/6pLYbjB9Etxe3gY)

---

## 📥 Installation

```bash
pip install a42-proto
```

---

## 🧪 Beispielskripte

- [`analyze_proto.py`](https://github.com/HSE-VSV/DataReaderA42/blob/581dea222b6871f6ef4e66ad9e998c3d5a60af08/scripts/analyze_proto.py) – aggregierte Auswertung über Frames, Scans, Punkte, Objekte  
- [`visualize_pointcloud.py`](https://github.com/HSE-VSV/DataReaderA42/blob/581dea222b6871f6ef4e66ad9e998c3d5a60af08/scripts/visualize_pointcloud.py) – zeigt eine Punktwolke in Open3D

---

## 📄 Data Structure

### Frame
- `frame_timestamp_ns` – global timestamp in nanoseconds  
- `lidars[]` – list of `LidarScan` messages  
- `frame_id` - unique identifier for the frame

**Example:**
```python
first_scan = frame.lidars[0]
```

---

### LidarScan
- `laser_name` – sensor identifier (`LaserName` enum)  
- `scan_timestamp_ns` – timestamp of this scan (ns)  
- `pointcloud` – LiDAR point cloud (`PointCloud`)  
- `calibration` – sensor calibration (`SensorCalibration`)  
- `object_list[]` – detected objects (`ObjectBBox`)  

**Example:**
```python
objects = scan.object_list[0]
```

---

### PointCloud

All fields are stored as **byte arrays** and must be decoded into NumPy arrays.

- `cartesian` – XYZ coordinates (`float32`, shape N×3)  
- `intensity` – intensity values (`uint16`)  
- `ambient` – ambient light / signal quality (`uint16`)  
- `velocity` – point-wise velocity (`float32`)  
- `reflectivity` – reflectivity values (`uint16`)  
- `timestamp_offset` – per‑point timestamp offset (`uint64` ns)  
- `channel_id` – channel index (`uint16`)  

**Example decoding:**
```python
import numpy as np
xyz = np.frombuffer(pc.cartesian, dtype=np.float32).reshape(-1, 3)
intensity = np.frombuffer(pc.intensity, dtype=np.uint16)
```

---

### ObjectBBox
- `id` – object ID  
- `timestamp_ns` – timestamp of the object (ns)  
- `position {x,y,z}` – center position  
- `dimension {x,y,z}` – object size  
- `orientation {x,y,z,w}` – quaternion orientation  
- `velocity {x,y,z}` – velocity vector  
- `pointcloud` – sub‑pointcloud inside the bounding box  
- `obj_class` – semantic class  
- `obj_class_score` – classification confidence  

**Example:**
```python
pos = obj.position
```

---

### SensorCalibration
- `sensor_name` – sensor identifier  
- `extrinsic[]` – 4×4 row‑major extrinsic matrix  
- `vertical_fov`, `horizontal_fov` – field of view  
- `vertical_scanlines`, `horizontal_scanlines` – resolution  
- `horizontal_angle_spacing` – angle spacing (deg)  
- `beam_altitude_angles[]` – altitude angles  
- `beam_azimuth_angles[]` – azimuth angles  
- `frame_mode` – scan mode  
- `scan_pattern` – scan pattern identifier  

**Example:**
```python
vfov = scan.calibration.vertical_fov
```

---