semvec/pypi-packages huggingface dataset:
ultrasound-processing-package-minimal 0.2.2
Ultrahangos képfeldolgozó Python csomag (minimal változat)
Requires Python: >=3.8

Ultrasound processing package is a Python toolbox designed for the purpose of preprocessing and transformation of ultrasound images. It provides three core capabilities: Convert curved ultrasound scans into flat images, Filtering the contour of a selected object by intensity thresholding, Convert back to curved ultrasound scans. The transformations are essential for the thresholding so we can search for peaks using Cartesian coordinates instead of polar coordinates. These modules will be the fundamentals of point cloud generation based on ultrasound images.

# Modules


## Transformation

In this module we are preprocessing the image and we transform it to a “flat” format. We convert the input image to grayscale and detect centimeter calibration marks along both axes. The image is then cleaned by removing irrelevant top and side regions and outlier pixel values. Based on peak detection, the module estimates the geometric parameters of the scan area and transducer settings, such as the offset and scanning depth. Using trigonometric relations, it constructs a polar-to-Cartesian grid. We use bilinear interpolation to project the image onto a uniform Cartesian space.

## Masking

In this module, we first generate a binary mask by thresholding the input image: pixels with intensity at or above the specified threshold are set to white (255), and those below are set to black (0). After removing noise components, we scan each column to select the very first pixel whose intensity exceeds the threshold, building an initial contour mask. Next, we apply OpenCV’s functions to thicken that contour for clear visualization. Finally, we apply the dilated contour as a mask to preserve and display the original pixel intensities.

## Backtransformation

In this module we reverse the transformation process, converting polar image data back into Cartesian coordinates. We first initialize key transformation parameters and build a 3D volume grid using spherical coordinates. The grid is filtered to exclude regions outside the region of interest. Using bilinear interpolation, we project back the masked image to the grid.

# Installation:

pip install ultrasound-processing-package-minimal

# Requirements

NumPy

OpenCV

Matplotlib

Scikit-image

Scipy

# Example
-------

Hyperlinked notebook: https://shorturl.at/BZ1bQ

GitHub repository link: https://github.com/Mart-SciecPyt/ultrasound-processing-minimal

Pypi: https://pypi.org/project/ultrasound-processing-package-minimal/

Readthedocs: https://ultrasound-processing-minimal.readthedocs.io/en/latest/
pyams-cad 0.1.4.1
Design and simulation of circuits and modeling of analog and mixed-signal electronic components using CAD systems.
Requires Python: >=3.6
Keywords: simulation, circuit, electronics, mixed-signal, analog, EDA, electronic modeling, electrical engineering


# PyAMS
PyAMS: Python for Analog and Mixed Signals

<h1 align="center">
    <a href="https://pyams.sourceforge.io/"><img src="https://pyams.sourceforge.io/logo.png" width="175px" alt="PyAMS"></a>
</h1>

---

<p align="center">
 
 <a href="#News">
    <img src="https://img.shields.io/badge/Version-0.1.4-blue" alt="V 0.1.4">
 </a>
  <a href="#Installation">
      <img src="https://img.shields.io/badge/Python->=3-blue" alt="Python 3+">
  </a>
    
  <a href="https://github.com/d-fathi/PyAMS/blob/main/LICENSE">
      <img src="https://img.shields.io/badge/GPLv3-blue" alt="GPLv3">
  </a>
</p>


**************
What is PyAMS?
**************

PyAMS is a software used to simplify simulate electronic circuit and  modeling analog and digital elements  using pyams_lib
The objectives of PyAMS is:

*	Drawing circuit by schematic;
*	Creating new symbols for models;
*	Creating new PyAMS models of electrical elements by using pyams_lib;
*	Simulating the circuit in the selected mode of operation;
*	Presenting simulation results in a dedicated waveform editor or probe;
*   PyAMS used Python3+ or PyPy and works on  Windows , Linux (future), and OSX(future).
*   **Licensed under:** PyAMS is free to use. GPLv3.


## Web Site of Software

* **Hom page** <a href="https://pyams.sf.net">https://pyams.sf.net</a>.
* **PyAMS’s documentation**  <a href="https://pyams.sf.net/doc">https://pyams.sf.net/doc</a>.

## Installation of Software

* **Download:** <a href="https://sourceforge.net/projects/pyams/">https://sourceforge.net/projects/pyams/</a>.

## Screenshots of schematic and data display areas

<h1 align="center">
    <img src="https://pyams.sourceforge.io/images/C.png" width="900px" alt="PyAMS">
</h1>


## Installation by pip

To install `pyams_cad`, use the following command:

```sh
pip install pyams_cad
```

To upgrade to the latest version:

```sh
pip install --upgrade pyams_cad
```

### Code:

```python

from pyams import win
win()

````

## Support the Project 💖

If you need more support or assistance with this project, and would like to contribute to the development of the PyAMS library, 
consider donating through my Ko-fi page: <a href='https://ko-fi.com/pyams/'>https://ko-fi.com/pyams/</a>
pyTSPA-toolbox 0.1.1
A toolbox for analyzing and visualizing team sports performance
Requires Python: >=3.10

# pyTSPA - Toolbox for Analyzing and Visualizing Team Sports Performance  

A toolbox for analyzing and visualizing team sports performance, with a particular focus on **football match data**.  
The toolbox includes modules for data processing, statistical analysis, match outcome prediction, and result visualization.

---

### Current Version: `0.1.1`  
### Planned Release: May 2025  

---

## Introduction  

`pyTSPA` was developed to support coaches, analysts, and sports scientists in extracting actionable insights from football match data.  
Designed with a **football-centric approach**, the toolbox facilitates data handling, statistical analysis, match outcome prediction using machine learning, and comprehensive data visualization.

---

## Description  

The toolbox is divided into the following modules:

- **Data Handling:**  
  - Data input/output, cleaning, and profiling (CSV, Excel).

- **Metrics:**  
  - Calculation of match and team statistics (`Win Percentage`, `Pythagorean Expectation`)  
  - Predictive models using **logistic regression** for match outcome prediction.

- **Visualization:**  
  - Graphical representation of match data, statistical summaries, and prediction results using bar charts, scatter plots, and pie charts.

---

**For more information, visit the [official documentation](https://pytspa-toolbox.readthedocs.io/en/latest/)**.


## Installation
### From PyPi
Install `pyTSPA` using pip

```
pip install pyTSPA-toolbox
```

### From source
Clone the git repository
```
git clone https://github.com/vargaheni05/pyTSPA-toolbox.git
```
Change directory to the cloned repository
```
cd pyTSPA-toolbox
```
Install with pip
```
pip install .
```

## Building the documentation
Install required packages
```
pip install -r docs/requirements.txt
```
Call Sphinx build command
```
sphinx-build -M html docs/source docs/build
```
On Windows you can also run the `make.bat` file
```
.\docs\make.bat html
```

The documentation should be available in the `docs/build` directory as html files<br>
This includes the example codes as tutorials

## Correspondence
Henrietta Varga (varga.henrietta.julianna@hallgato.ppke.hu)<br>
Marcell Szögi (szogi.marcell@hallgato.ppke.hu)<br>
Benedek Kardos (kardos.benedek.zoltan@hallgato.ppke.hu)