semvec/pypi-packages huggingface dataset:
autctapi 0.0.4
Autct websocket API client in Python
Requires Python: >=3.8

### autct-api

This allows a Python application to make RPC calls to an [autct](https://github.com/AdamISZ/aut-ct) server, without any dependencies or cryptographic library calls. Read there for me details on what autct is.

The RPC calls provided are:

* prove

* verify

* createkeys

## Install

```
pip install autctapi
```

Projected published [on PyPI](https://pypi.org/project/autctapi/). A very small library that only relies on [websockets](https://github.com/python-websockets/websockets).

## Examples

To try it out, you need to run the [autct rust binary](https://github.com/AdamISZ/aut-ct/releases) **as a server**. The syntax for that is shown in the Worked Example section of that repo's readme. Then, run here:

```
python src/example.py
```

... although obviously the purpose here is for a Python developer to actually *read* that file, and look at the way that RPC calls are done. Individual scenarios will require different config settings, that can easily be modified as shown in that file.
gtodo-sprokkel78 1.0.4
A graphical user interface in PyGTK4 for performing TODO list items on Ubuntu and other Linux distro's.
Requires Python: >=3.10

gtodo-sprokkel78

A graphical user interface in PyGTK4 for performing TODO list items on Ubuntu and other Linux distro's. 
It requires Python3.10 or higher and the PyGTK apps. Developed and tested on Ubuntu 23.10.

Installation on Ubuntu 23.10

1. $sudo apt install python3 python3-dev
2. $sudo apt install python3-gi python3-gi-cairo gir1.2-gtk-4.0 gir1.2-adw-1
3. $python3 ./gtodo.py

Added 'install.sh' script for system-wide installation.
- The startup shell script will be /usr/bin/gtodo
- The application is installed in /usr/share/gtodo-sprokkel78
- The .desktop file is placed in /usr/share/applications/com.sprokkel78.gtodo.desktop

Added 'uninstall.sh' script for system-wide uninstallation.
- This will delete /usr/bin/gtodo and /usr/share/gtodo-sprokkel78,
  This will also remove /usr/share/applications/com.sprokkel78.gtodo.desktop
  
Check https://www.github.com/sprokkel78/gtodo for contributing, development features and pre-releases.

Funding: Paypal email: sprokkel78.bart@gmail.com
SungrowModbusTcpClient-jesseklm 0.1.9
A ModbusTcpClient wrapper for talking to Sungrow solar inverters
Requires Python: >=3.5.0

# Sungrow-Modbus

just a new release with pymodbus>=3.0 until the main project gets updated

https://pypi.org/project/SungrowModbusTcpClient/

### TCP Client for Sungrow Devices with AES128 ECB encryption

### Class based on pymodbus.ModbusTcpClient, completely interchangeable, just replace ModbusTcpClient() with SungrowModbusTcpClient()
django-improved-user 2.1.0
Compose new Django User models that follow best-practices for international names and authenticate via email instead of username.
Requires Python: >=3.6

Latest Release: |Version| |Tag|

Documentation: |StableDocs|

Compatibility: |Python| |Django| |License| |Black|

Tests: |Pre-commit| |Coverage|

.. |Version| image:: https://img.shields.io/pypi/v/django-improved-user
		:target: https://pypi.org/project/django-improved-user/
		:alt: PyPI Version

.. |Tag| image:: https://img.shields.io/github/v/tag/jambonrose/django-improved-user
		:target: https://github.com/jambonrose/django-improved-user/releases
		:alt: Github Tag

.. |StableDocs| image:: https://readthedocs.org/projects/django-improved-user/badge/?version=stable
		:target: https://django-improved-user.readthedocs.io/en/stable/?badge=stable
		:alt: Stable Documentation Status

.. |Python| image:: https://img.shields.io/pypi/pyversions/django-improved-user
		:target: https://pypi.org/project/django-improved-user/
		:alt: Python Support

.. |Django| image:: https://img.shields.io/pypi/frameworkversions/django/django-improved-user
		:target: https://pypi.org/project/django-improved-user/
		:alt: Django Support

.. |License| image:: https://img.shields.io/pypi/l/django-improved-user
		:target: http://opensource.org/licenses/BSD-2-Clause
		:alt: License

.. |Pre-commit| image:: https://results.pre-commit.ci/badge/github/jambonrose/django-improved-user/development.svg
		:target: https://results.pre-commit.ci/latest/github/jambonrose/django-improved-user/development
		:alt: pre-commit.ci status

.. |Coverage| image:: https://codecov.io/gh/jambonrose/django-improved-user/branch/development/graph/badge.svg
		:target: https://codecov.io/gh/jambonrose/django-improved-user
		:alt: Coverage Status

.. |Black| image:: https://img.shields.io/badge/code%20style-black-000000
		:target: https://github.com/psf/black

.. end-badges

Read Me
=======

This project provides a custom user model that improves on Django's
default by making a few modern and international changes.

* Uses email as the username to simplify login for users
* Replace :code:`first_name` and :code:`last_name` with international
  friendly :code:`short_name` and :code:`full_name` fields

The project also provides mix-in classes to make building custom User
models easier.

For an explanation of why and how the project was built, please see the
`Project Rationale`_.

For information about getting started, please refer to the `quickstart
documentation`_.

For information about how to help with the project, please see the
`contributing documentation`_.

.. _contributing documentation: https://django-improved-user.readthedocs.io/en/latest/contributing.html
.. _Project Rationale: https://django-improved-user.readthedocs.io/en/latest/rationale.html
.. _quickstart documentation: https://django-improved-user.readthedocs.io/en/latest/quickstart.html
soft-dtw-rust 0.1.2
A simple implementation of the soft-dtw algorithm in rust with python bindings
Requires Python: >=3.8

# Soft-DTW Rust
**EXPERIMENTAL** implementation of the [Soft-DTW](https://arxiv.org/abs/1703.01541) algorithm in Rust. The project is built with [maturin](https://www.maturin.rs/) so it can be used also with Python.

Note that this is primarily an exercise to learn how to code in Rust and integrate Rust scripts in Python. I'm not pretending that this is 100% functional or correct. 
This implementation is not created to be used as a loss function. It is designed only to compute the soft-dtw distance between two signals. Also for now works only if the signals to compare have the same length. In future I could modify the algorithm to work with signals of different lengths. 
The distance between samples of the two signals is computed through the L-2 norm.

## Installation
### Method 1
Use the command ```pip install soft-dtw-rust```

### Method 2

1) Download the repository and install maturin in your python environment (it is available both in conda and pip).
2) Install the package in your python environment running the command ```maturin develop --release```.
3) Import the module ```soft_dtw_rust``` and call one of the two methods `compute_sdtw_1d` or `compute_sdtw_2d`. For both methods, you can pass the hyperparameter `gamma`. If you not pass it by default will be set to 1.

## Use the library
Import the package `soft_dtw_rust` and call the function `compute_sdtw_1d` or `compute_sdtw_2d`. The differences between 1d and 2d version is described below.

### Example with code
```python
import soft_dtw_rust
x = np.random.rand(size)
y = np.random.rand(size)
output = soft_dtw_rust.compute_sdtw_1d(x, y, gamma)
```

A small example with synthetic data can be found inside the example folder.

### Extra notes on 1d and 2d version
Note that `compute_sdtw_1d` is used to compute the difference between two 1d signals and return a single float 64 value.

Instead `compute_sdtw_2d` is used to compute the difference between two matrices of signals. Also in this case the output is a single float 64 value. 
The matrices must have the same shape and each row must contain a signal (an example could be a multi-channel EEG signal). In this case, the algorithm use the soft-dtw to compare the signal in the first row of the first matrix with the signal in the first row of the second matrix and so on. 
The output of the various comparisons is summed and used as the final output of the function. 
Optionally you could pass an extra parameter called `average_along_channels`. As the name suggests, in this case, the final output will be divided by the number of signals inside the matrices.