semvec/pypi-packages huggingface dataset:
EMGT 0.0.1
A general EMG processing and feature extraction package.
Requires Python: >=3.8

# EMGT Package Documentation

## Overview

The EMGT (EMG Toolkit) package is a general package for the analysis of EMG signals in Python.

## SignalFilterer Module

The `EMGT.SignalFilterer` module is designed to handle the pre-processing and analysis of EMG files. It contains key functions such as `NotchFilterSignals`, `BandpassFilterSignals`, `SmoothFilterSignals` and `AnalyzeSignals`, which make up the main part of the package's workflow.

## OutlierFinder Module

The `EMGT.OutlierFinder` module is designed to assist with finding outliers among your EMG files that may require special case filters.

## PlotSubjects Module

The `EMGT.PlotSubjects` module is designed to assist with visualizing your EMG signals, such as to compare files and visually identify outliers.
dLuxToliman 0.3.0
A repo to hold the canonical dLux Toliman models.
Requires Python: >=3.7,<4.0

# dLuxToliman

[![PyPI version](https://badge.fury.io/py/dLuxToliman.svg)](https://badge.fury.io/py/dLuxToliman)
[![License](https://img.shields.io/badge/license-BSD%203--Clause-blue.svg)](LICENSE)

![Banner Image](./assets/dLuxToliman_banner.jpg)

## Description
This repository/package contains pre-built ∂Lux models of the Toliman optical system, and pre-built parametrised ∂Lux source objects for Alpha Centauri.

[∂Lux](https://louisdesdoigts.github.io/dLux/) is an open-source differentiable optical modelling framework harnessing the structural isomorphism between optical systems and neural networks, giving forwards models of optical system as a _parametric neural network_.
∂Lux is built in [Zodiax](https://github.com/LouisDesdoigts/zodiax) which is an open-source object-oriented [Jax](https://github.com/google/jax) framework built as an extension of [Equinox](https://github.com/patrick-kidger/equinox) for scientific programming.

The primary goal of the Toliman mission is to discover Earth-sized exoplanets orbiting in Alpha Centauri, the closest star system to our own.
To achieve this, the mission will employ a novel telescope design that will be able to detect subtle changes in the positions of the Alpha Centauri binary pair.
These changes are caused by the gravitational reflex motion induced by an Earth-sized companion, and this cutting-edge technology will enable scientists to identify exoplanets too small to be detected by conventional telescopes.
Toliman utilises a binary phase diffraction pupil to grasp the expected microarcsecond-scale astrometric signal.

## Installation

`dLuxToliman` is pip-installable. To install the latest release, simply run:

```bash
pip install dLuxToliman
```

## Usage

A very basic example of how to use this package is as follows:

```python
# imports
import dLuxToliman as dlT
from matplotlib import pyplot as plt

osys = dlT.TolimanOpticalSystem(psf_npixels=128)  # creating Toliman optical system
source = dlT.AlphaCen(n_wavels=3, separation=8, position_angle=30)  # creating Alpha Centauri source
psf = osys.model(source)  # running optical simulation

# plotting
plt.imshow(psf ** .5, cmap='inferno')
plt.title('Toliman $\sqrt{PSF}$')
plt.show()
```

![Example Image](./assets/basic_toliman_psf.jpg)

## Contributors
[Max Charles](https://github.com/maxecharles), [Louis Desdoigts](https://github.com/LouisDesdoigts), [Benjamin Pope](https://github.com/benjaminpope), and [Peter Tuthill](https://github.com/ptuthill).

## License

This package is released under the BSD 3-Clause License. See the [LICENSE](LICENSE) file for more information.

## Support

If you encounter any problems or have any questions related to Toliman models, please raise an issue or contact me at `max.charles@sydney.edu.au`.
trio-serial-windows 0.1.1
Serial Port support for python-trio on Windows
Requires Python: >=3.8

# trio-serial-windows

Implements a serial port with a [Trio](https://trio.readthedocs.io/) `Stream` interface on Windows.
See [trio-serial](https://pypi.org/project/trio-serial/) for the equivalent functionality on Linux and MacOS.

Example usage (assuming a looped back serial connection):
```python
import trio
import trio_serial_windows


async def main():
    async with trio_serial_windows.SerialStream("COM5", baudrate=115200) as port:
        await port.send_all(b"Hello, World!")
        await trio.sleep(0.2)
        print(await port.receive_some())

trio.run(main)
```
hammer-vlsi 1.2.0
Hammer is a physical design framework that wraps around vendor specific technologies and tools to provide a single API to create ASICs.
Requires Python: >=3.9,<4.0

![Logo design by @kenhoberkeley](https://github.com/ucb-bar/hammer/raw/master/doc/logo_transparent.png)

![Hammer PR CI](https://github.com/ucb-bar/hammer/actions/workflows/pr.yml/badge.svg?event=push) ![Hammer publish CI](https://github.com/ucb-bar/hammer/actions/workflows/publish.yml/badge.svg)

Highly Agile Masks Made Effortlessly from RTL (Hammer)
=============================================

This is the master (unstable) development branch.

Documentation
=============
The documentation is found at https://hammer-vlsi.readthedocs.io
Doc sources are available at `doc/` for offline reading.

Hammer currently requires Python 3.9+.

For a deeper dive into available options and environment configuration:

* [Core Hammer settings](hammer/config/defaults.yml)
* [Setup documentation](https://hammer-vlsi.readthedocs.io/en/latest/Hammer-Basics/Hammer-Setup.html)
* [Hammer technology library schema](https://hammer-vlsi.readthedocs.io/en/latest/Technology/Tech-json.html#full-schema)
* For CAD tool settings, please see the relevant `defaults.yml` for those plugins.

Hammer is an integral component of UC Berkeley Architecture Research's [Chipyard framework](https://github.com/ucb-bar/chipyard).
Useful documentation for how an example block is pushed through the VLSI flow with Hammer in the free ASAP7 and Sky130 PDKs is at https://chipyard.readthedocs.io/en/latest/VLSI.

History
=======
The list of contributors can be found in the Git history of the project, or online at https://github.com/ucb-bar/hammer/graphs/contributors

The Hammer project builds upon the legacy of the PLSI project by Palmer Dabbelt, a previous project which aimed to build a portable VLSI flow. The Hammer project is grateful for the feedback and lessons learned which provided valuable insight that ultimately led to the design and software architecture of Hammer as it is now.

Below is the list of Hammer publications in reverse chronological order:
- [Hammer: a modular and reusable physical design flow tool (DAC 2022)](https://dl.acm.org/doi/abs/10.1145/3489517.3530672)
- [A Methodology for Reusable Physical Design (ISQED 2020)](https://ieeexplore.ieee.org/document/9136999)
- [PLSI: A Portable VLSI Flow (Master's Thesis, 2017)](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-77.html)
python-semantic-retrieval 0.1.3
Semantic Retrieval Python Library
Requires Python: >=3.7

# Semantic Retrieval Python

Python library for Semantic Retreival project

# Development

```
conda env create -f environment.yml
conda activate semantic_retrieval
```

Setup VSCode to use conda virtual environment

# Testing

In virtual env, link package to src directory, then run pytest

```
pip install -e .
```

```
pytest
```