semvec/pypi-packages huggingface dataset:
dbf2csv-dbase7 0.6.5
File converter from DBF to CSV (only for dBASE 7)
Requires Python: <4,>=3.10

# Conversión de formato DBF a CSV

Script simple para convertir ficheros de formato DBF a CSV. De momento sólo funciona para ficheros DBF "dBASE level 7".

Uso:

```sh
./dbfconv.py <fichero.dbf>
```

También se puede instalar desde <https://pypi.org> como:

```sh
pip install dbf2csv-dbase7
```

Una vez descargado se puede ejecutar del siguiente modo:

```sh
python -m dbf2csv <fichero.dbf>
```

O también se puede instalar como pipx:

```sh
pipx install dbf2csv-dbase7
```

Lo que añade la utilidad `dbf2csv`:

```sh
dbf2csv <fichero.dbf>
```

## Referencias: formatos de ficheros

- [Data File Header Structure for the dBASE Version 7 Table File][1]
- [dBASE File Format (with coding details): DBF and DBT/FPT file structure][2]
- [dBASE Table File Format (DBF)][3]
- [Xbase Data file (*.dbf)][4]

[1]: http://www.dbase.com/KnowledgeBase/int/db7_file_fmt.htm
[2]: http://www.independent-software.com/dbase-dbf-dbt-file-format.html
[3]: https://www.loc.gov/preservation/digital/formats/fdd/fdd000325.shtml
[4]: https://www.clicketyclick.dk/databases/xbase/format/dbf.html
hough-jax 0.2
JAX implementation of the probabilistic Hough transform for lines
Requires Python: >=3.12

# JAX implementation of `probabilistic_hough_line`

This is a JAX implementation of the `skimage.transform.probabilistic_hough_line` function.

## Installation

```bash
uv add hough-jax
```

or if you don't have uv:

```bash
pip install hough-jax
```

## Usage

```python
import jax
import jax.numpy as jnp
import numpy as np
from hough_jax import probabilistic_hough_line

# Create a simple image with a horizontal line
image = np.zeros((100, 100), dtype=np.float32)
image[50, 20:80] = 1.0

# Detect lines
lines, nlines = probabilistic_hough_line(
    jnp.array(image),
    threshold=10,
    line_length=30,
    line_gap=3,
    rng=jax.random.PRNGKey(0),
)

# lines: Array of shape (lines_max, 2, 2) with endpoints [[x0, y0], [x1, y1]]
# nlines: Number of valid lines detected
print(f"Detected {int(nlines)} line(s)")
print(f"First line endpoints: {lines[0]}")
```

### Converting to skimage format

To get the same output format as `skimage.transform.probabilistic_hough_line`:

```python
def to_skimage_format(lines, nlines):
    """Convert JAX output to list of ((x0, y0), (x1, y1)) tuples."""
    n = int(nlines)
    return [
        ((int(lines[i, 0, 0]), int(lines[i, 0, 1])), (int(lines[i, 1, 0]), int(lines[i, 1, 1])))
        for i in range(n)
    ]

skimage_lines = to_skimage_format(lines, nlines)
# [((79, 50), (20, 50))]
```
bn-connector 0.0.2
API for microservices on byneuron backend
Requires Python: >=3.7

# Service Connector



## About

Provides a method for microservices to connect to the byneuron platform


## Concept

Search in the master dataset for matching Gateways. This allows access to the Devices that feature those gateways.
Provides tools for iterating linked entities and store data like numberEvents.
continuum-objects 0.0.6
Python package to load, validate and export objects of the digital continuum
Requires Python: >=3.12

# Continuum Objects

This python library is used to describe objects of the digital continuum :

- computing clusters,

- data centers,

- user workspaces,

- ...

All objects are described through [pydantic BaseModel](https://docs.pydantic.dev), ensuring easy **importation**, **exportation** and **validation**.

## Examples

See file [example.py](example.py) to get an example of usage.

Example of object description :

```json
{
  "id": "c11a0535-b154-4deb-ba71-ed1613290271",
  "type": "Partition",
  "name": "cpu_p1",
  "totalNodeCount": 720,
  "nodes": [
    {
      "type": "NodeGroup",
      "id": "415a18d3-ae83-43e6-8013-a19d771ed3d5",
      "nodeComponents": [
        {
          "type": "CPU",
          "name": "Intel Xeon Gold 6248",
          "count": 2
        },
        {
          "type": "RAM",
          "capacity": {
            "value": 192,
            "unit": "GB"
          },
          "count": 1
        },
        {
          "type": "MotherBoard",
          "count": 1
        }
      ]
    }
  ]
}
```