semvec/pypi-packages huggingface dataset:
boinc-client 1.12.1
Python API for interacting with a BOINC client via RPC
Requires Python: <4.0,>=3.9

# BOINC Client

![Test and Release](https://github.com/SplinterHead/boinc-client/actions/workflows/test-and-release.yml/badge.svg)
[![boinc-client](https://snyk.io/advisor/python/boinc-client/badge.svg)](https://snyk.io/advisor/python/boinc-client)
![PyPI - Downloads](https://img.shields.io/pypi/dm/boinc-client)


Python native library for interacting with a BOINC client via RPC. This library has been designed to have consistent response types.

## Usage

### Setup

```python
from boinc_client import Boinc
from boinc_client.clients.rpc_client import RpcClient

# Hostname or IP of the running BOINC client
BOINC_HOSTNAME = "192.168.0.2"

# Create an RPC client to connect to the BOINC socket
rpc_client = RpcClient(hostname=BOINC_HOSTNAME)
rpc_client.authenticate()

# Create a BOINC client to interact with the RPC socket
boinc_client = Boinc(rpc_client=rpc_client)
```

#### RPC Client options
The following options can be passed when creating an `RpcClient` instance

| Argument   | Description                                                                | Required | Default |
|------------|----------------------------------------------------------------------------|----------|---------|
| `hostname` | Hostname or IP address of the BOINC client                                 | Yes      | None    |
| `port`     | Exposed port of the BOINC client                                           | No       | 31416   |
| `timeout`  | Seconds to wait for a successful connection to the RPC socket              | No       | 30      |
| `password` | Password to authenticate to the BOINC client, required for most operations | No       | None    |

#### Boinc options
The following options can be passed when creating a `Boinc` instance

| Argument     | Description                        | Required | Default |
|--------------|------------------------------------|----------|---------|
| `rpc_client` | Instance of a configured RpcClient | Yes      | None    |

### Interacting with Boinc

* [Unauthorised Operations](docs/unauthorised.md)
* [Project Operations](docs/project.md)

## Contributors

<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
<!-- prettier-ignore-start -->
<!-- markdownlint-disable -->

<!-- markdownlint-restore -->
<!-- prettier-ignore-end -->

<!-- ALL-CONTRIBUTORS-LIST:END -->
ift-dbms 0.0.2
IFT-DBMS PyPi Version
Requires Python: >=3.10

IFT NXOPEN API
===============

**Beschreibung**

This repository contains an API for the IFT in Siemens NX. The classes in this API use the NXOpen API to get important data from a Siemens NX Session. 

This package was generated for the SurfAlce project, but the functionalities of the different classes can be used 
in any other project. 

All classes and their methods can be found in the folder `./src/IFT` in the Folder `./scripts`however you can find scripts that can be executed from Siemens NX. 

Before you can use these functionalities, stick to the setup instructions below.

**Setup** 

- [Install Python 3.8](https://www.python.org/) if not already installed and remind the installation directory.
- Add following system variables to the  `%UGII_BASE_DIR%/UGII/ugii_env.dat` file.
- `UGII_PYTHON_LIBRARY_DIR = <installation directory>/Python38`
- `UGII_PYTHONPATH = %UGII_PYTHON_LIBRARY_DIR%;%UGII_PYTHON_LIBRARY_DIR%\DLLs;%UGII_PYTHON_LIBRARY_DIR%\Lib;%UGII_PYTHON_LIBRARY_DIR%\Lib\site-packages;%UGII_BASE_DIR%\nxbin\python`
- `UGII_CAM_WRITE_MRR = <abitrary directory with writing permisions>`
- Install the API into the python distribution via pip with the following command `pip install git+https://git.ift.tuwien.ac.at/lab/ift/sis/data-science/nxopen-export.git`
- Open a Siemens NX CAM-Setup
- If not already done, enable the Developer tab, by right-click on the blue ribbon at the top of NX and set a tick to "Developer"
- Press "Play" and search for the `./scripts/nx_data_export.py` file available in the gitlab repository.
- The script can be executed via the callback-function of a personalized button in the NX Ribbon. 

> NOTE: None of the system variables specified above, expecially `UGII_PYTHONPATH`,  may exceed 256 characters.
MUSIC-RECOMMENDATION 0.1.0
A music recommendation system
Requires Python: >=3.6

# MUSIC_RECOMMENDATION

A music recommendation system using Spotify's API and cosine similarity to suggest songs based on a user's chosen track.

## Features

- Fetches top 50 tracks from Spotify Japan Weekly Ranking
- Analyzes track features such as danceability, energy, loudness, etc.
- Recommends similar tracks based on cosine similarity

## Installation

1. Clone the repository:
    ```bash
    git clone https://github.com/sayamiyaji/MUSIC_RECOMMENDATION.git
    cd MUSIC_RECOMMENDATION
    ```

2. Create a virtual environment and activate it:
    ```bash
    python -m venv music
    source music/bin/activate
    ```

3. Install the required packages:
    ```bash
    pip install -r requirements.txt
    ```

## Usage

1. Set up your Spotify API credentials:
   - Create an application on the [Spotify Developer Dashboard](https://developer.spotify.com/dashboard/applications)
   - Obtain your `client_id` and `client_secret`

2. Export your Spotify API credentials:
    ```bash
    export SPOTIPY_CLIENT_ID='your_client_id'
    export SPOTIPY_CLIENT_SECRET='your_client_secret'
    export SPOTIPY_REDIRECT_URI='http://localhost:8080'
    ```

3. Run the web application:
    ```bash
    python -m your_project.app
    ```

4. Open your web browser and go to `http://localhost:5000`

## Project Structure

music-recommendation/
├── data/               # データ保存用
├── notebooks/          # Jupyter Notebooks用
├── src/                # ソースコード
│   ├── __init__.py
│   └── recommender.py
├── requirements.txt    # 必要なパッケージリスト
└── README.md           # プロジェクト説明

## Contributing

1. Fork the repository
2. Create a new branch (`git checkout -b feature-foo`)
3. Commit your changes (`git commit -am 'Add some foo'`)
4. Push to the branch (`git push origin feature-foo`)
5. Create a new Pull Request

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Acknowledgements

- [Spotipy](https://spotipy.readthedocs.io/) - A lightweight Python library for the Spotify Web API
- [Flask](https://flask.palletsprojects.com/) - A micro web framework written in Python
stereoid 0.4
The stereoid tools for the Harmony mission.
Requires Python: >=3.9

#############################################################
stereoid
#############################################################

Stereoid packages together a set of software tools that are used in the design
and analysis of the Harmony satellite mission.

=======================================================
Setting up your development environment to run stereoid
=======================================================
To be able to run the Jupyter Notebooks and use the modules defined in the
library you will need to install a few dependencies. Setting up a virtual
environment and running "``pip install .``" should automatically fetch and install
the necessary packages. Detailed instructions on how to do so follow:

You will need a recent version of Python installed on your system. We suggest
3.9 or later but 3.8 is also know to work. Once you have Python, you can create
a virtual environment so that you can install packages in isolation. You can do
this by running the following command on Unix/macOS:

``python3 -m venv drama-env``

or on Windows:

``py -m venv drama-env``

This will create a folder called "``drama-env``" in the folder where the previous
command was ran. All packages will be installed in this folder. Now to activate
this environment run this on Unix/macOS:

``source drama-env/bin/activate``

or on Windows:

``.\drama-env\Scripts\activate``

Now change to the directory where you have downloaded the stereoid
package to and execute:
git switch develop
pip install -e .

The first command switches to the develop branch and the second installs the
package. Make sure that "``pip install -e .``" is ran from the root level of the git
repository, ie from the directory where the ``setup.py`` and ``README.rst`` files
are.

After this you can install Jupyter Lab (if you don't already have it)
and start running the Jupyter Notebooks.

========================================
Running the Harmony scientific workbench
========================================
The package provides a cli program that sets up a simulation scenario, reads the
wave spectra from the SWAN model and runs the ocean scientific workbench for the
Harmony mission. To run, execute:
``harmulator_fwd [--debug] /path/to/params_scientific_workbench.py``
The --debug flag provides additional information in the console log. A sample
``params_scientific_workbench.py`` is provided in the ``PAR`` directory of this
repository.
JupyterExport 0.1.7
Convert .ipynb files to .pdf/.html using a simple graphical interface.
Requires Python: >=3.6

<span style="color:red">**Warning:** I highly recommend creating a virtual environment to install this package instead of installing it in the global environment. </span>


# JupyterExport

JupyterExport is a simple GUI application built in Python using Tkinter that allows you to effortlessly convert Jupyter Notebook files to HTML and PDF formats. This tool aims to streamline the process of sharing and distributing Jupyter notebooks by providing a user-friendly interface for exporting them into more accessible formats.

## Features

- **Easy Conversion**: Convert Jupyter Notebook files (.ipynb) to HTML or PDF format with just a few clicks.
- **Graphical User Interface**: Intuitive GUI interface makes it easy for users to select input files and output formats.

## Installation

To install JupyterExport, simply use pip:


Alternatively, you can download the source code from the GitHub repository and install it manually.
https://github.com/Alan-Analyst/JupyterExport

## Usage

After installing the package, you can run the GUI application by executing one of the following commands in your terminal or command prompt:
> JupyterExport
> 
> jupyterexport
> 
> JR

Once the application is running, follow these steps:

1. Click on the "Select File" button to choose the Jupyter Notebook file you want to convert.
2. Choose the desired output format (HTML or PDF) from the dropdown menu.
3. Optionally, specify the filename.
4. Click the "Convert" button to start the conversion process.

Note: If your notebook contains image files place the image in the app directory.
aitelegrambot 0.8.4
aitelegrambot is a Telegram bot that uses the Ollama backend to run the LLM rationalAI (by default).

**HELP NEEDED, if you think you can help me maintain this project, please create a issue.**

# Introduction

aitelegrambot is a Telegram bot that uses the
[Ollama](http://ollama.com/) backend to run the LLM
[rationalAI](https://ollama.com/tusharhero/rationalai)(by
default). This bot is designed to provide access to the full
capabilities of Ollama through Telegram, allowing users to interact
with LLMs in a convenient and interactive manner.

Find the [CHANGELOG](CHANGELOG.md), and [Documentation.](docs/).
tacalendar-ttime 0.2
Handles date time manipulation with simplicity
Keywords: ttime, tcalendar, tcalendar_time, tc, tt

# Tcalendar and Ttime

This project provides two classes, `Tcalendar` and `Ttime`, which allow for easy manipulation and handling of calendar dates and times in Python. 

## Introduction

- `Tcalendar`: This class represents a date on the calendar. It allows for the representation and manipulation of dates, including addition and subtraction of days, comparison of dates, and conversion to and from string representation.
  
- `Ttime`: This class represents a specific time of day. It allows for the representation and manipulation of time, including addition and subtraction of seconds, comparison of times, and conversion to and from string representation.

## Features

- **Tcalendar**
  - Creation of dates based on year, month, and day.
  - Leap year detection.
  - Getting the name of the month.
  - Finding the maximum number of days in a month.
  - Determining the day of the week.
  - Generating calendar pages.
  - Calculating the next and previous days.
  - Getting today's date.
  - Sorting dates.
  
- **Ttime**
  - Creation of times based on hour, minute, and second.
  - Switching between 12-hour and 24-hour formats.
  - Getting the current time.
  - Sorting times.

- **Tcalendar_time**
  - Combines features of `Tcalendar` and `Ttime` classes.
  - Creation of datetime objects with date and time components.
  - Addition and subtraction of datetime objects.
  - Comparison of datetime objects.
  - Conversion to and from string representation.

## Usage

Here's how you can use these classes in your Python code:

```python
from tcalendar_ttime import Tcalendar, Ttime

# Create a Tcalendar instance
date = Tcalendar(2024, 5, 24)

# Print the date
print(date)  # Output: 2024-05-24

# Create a Ttime instance
mytime = Ttime(13, 30, 0)
mytime.format12() # 12 hour formatting time

# Print the time
print(mytime)  # Output: 01:30:00 PM
```

## Installation
You can install the package using pip:

```
pip install tcalendar-ttime
```

## Examples
```
# Example: Adding days to a date
date = Tcalendar(2024, "May", 24)
new_date = date + 7

# Example: Sorting a list of dates
dates = [date, new_date, Tcalendar.today()]
sorted_dates = Tcalendar.sort(dates)

# Create a Tcalendar_time instance
datetime = Tcalendar_time(2024, 5, 24, 1, 30, 0)

# Print the datetime
print(datetime)  # Output: 2024-05-24 01:30:00 PM
```
orf-tools 0.0.4
Tools to work with open reading frames (ORFs)
Requires Python: >=3.6

# orf_tools

Package to manipulate open reading frames (ORFs).

Dependent on pandas.

As of today, one function available: extend_orfs()
See its docstring in the code

## Authors
Joan Pallares started the extend_orfs.
Marco Mariotti later optimized it.

## Installation
conda install -c conda-forge -c bioconda -c mmariotti orf_tools

Or:

pip install orf_tools
UniCor 0.0.1
Hierarchical Feature Selection through Propagation of Uniquely Correlated Entities
Requires Python: >=3.9

UniCor - Hierarchical Feature Selection through Propagation of Uniquely Correlated Entities

The idea is to utilize the natural hierarchy in high dimensional, hierarchical datasets (like taxonomic hierarchy in microbiome datasets) in order to make them appropriate for a bigger variety of methods through a reduction of their feature space without the loss of relevant information.
The UniCor Metric 
= |fcc| - ffc
identifies UNIquely CORrelated eNtities (UNICORNs) with
- high absolute correlation (feature [cont. target var.] correlation, |fcc|)
- negative or low uniqueness (average feature feature correlation, ffc)
sleep-detection-app 0.1.0
パッケージの簡単な説明
Requires Python: >=3.6

# Sleep Detection App

This Python application detects if the user is sleeping using the computer's webcam. It uses face detection and eye closure detection to determine if the user is asleep. If the user is away from the computer, they can press a button to temporarily pause the alarm and detection. They can resume detection by pressing another button when they return.

## Installation

You can install the Sleep Detection App via pip:

```bash
pip install sleep-detection-app