semvec/pypi-packages huggingface dataset:
rskfd 0.7.1
Python Package for Instrument Control and Data Handling
License: MIT
Requires Python: >=3.0
Keywords: INSTRUMENT CONTROL, RAW SOCKET, I/Q DATA, WV, IQ.TAR

# rskfd

## Description

Python package for instrument control and I/Q data handling (R&S formats). Instrument control uses raw socket connections.

- Software is provided as is.
- Package APIs may change; pin the version you use.
- This is not an official Rohde & Schwarz package.

## Overview

Package layout:

```
rskfd/
- iq_data_handling/   # I/Q data and file handling
- remote_control/     # Remote connections to measurement instruments
- signal_generation/  # Signal generation
- helper/             # Additional utilities
- snp_handling/       # S-parameter file handling
```

## Installing

```bash
pip install rskfd
```

## Usage

### Instrument Control

Test connection to an instrument (default port 5025):

```python
import rskfd

myinstrument = rskfd.instrument("192.168.1.100")
myinstrument.Connect()
myinstrument.Write("*IDN?")
print(myinstrument.Read())
myinstrument.Close()
```

### iq.tar File Handling

Generate white Gaussian noise and save it as an `iq.tar` file (100 MHz sample rate):

```python
import rskfd

iq = rskfd.CreateWGNSignal(NoisePower=-40, RecordLength=10000, BandLimit=0.8)
rskfd.WriteIqTar(iq, 100e6, "demo.iq.tar")
```
nnUNet-package 0.2.9
Package to use nnUNet on 3D Slicer
License: MIT
Requires Python: >=3.9

# LungSegmentation nnUNetv2 Prediction Script

[![Python](https://img.shields.io/badge/Python-3.9%2B-blue)](https://www.python.org/)
[![License](https://img.shields.io/badge/License-MIT-green)](LICENSE)

A Python script to easily run **nnUNetv2** lung segmentation predictions
on medical images. Automatic model download, file preparation for
prediction, and result renaming included.

------------------------------------------------------------------------

## Features

-   Automatic download and extraction of models from a URL.
-   Preparation of `dataset.json` for nnUNet prediction.
-   Conversion of input images to `.nrrd` if necessary.
-   Prediction execution with detailed logs.
-   Automatic cleanup of temporary files.
-   Automatic renaming of the final prediction file.

------------------------------------------------------------------------

## Requirements

Before running the script, make sure you have installed and configured
the following:

``` bash
git clone https://github.com/FlorianDAVAUX/nnUNet_package.git
cd nnUNet_package
pip install -e .
```

------------------------------------------------------------------------

## Usage

| Option         | Description | Example |
|----------------|-------------|---------|
| `--mode`       | Prediction mode (`Invivo` or `Exvivo`) | `--mode Invivo` |  
| `--structure`  | Structure to segment (`Parenchyma`, `Airways`, `Vascular`, `ParenchymaAirways`, `All`, `Lobes`) | `--structure Parenchyma` |
| `--input`      | Path to the input image (.nii, .nii.gz, .mha, .nrrd) | `--input ~/data/scan_patient.nrrd` |
| `--output`     | Output directory for the prediction (default: `prediction`) | `--output ~/predictions` |
| `--models_dir` | Path to store or search for models | `--models_dir ~/models` |
| `--name`       | Final name of the prediction file (without extension) | `--name segmentation_parenchyma` |


### Full Example

``` bash
nnunet_predict --mode Invivo --structure Parenchyma --input ~/data/scan_patient.nrrd --output ~/predictions --models_dir ~/models --name segmentation_parenchyma
```