semvec/pypi-packages huggingface dataset:
queue-confirmer 0.0.6
Handling server and client information
Keywords: python, sockets, solar car, threading

# queue_confirmer

**Version**: 0.0.6  
**Author**: MichaeCarlsonDev (Michael Carlson)  
**Description**: A package that allows you to handle server and client interaction.

---

## Overview

`queue_confirmer` is a Python package designed to facilitate server and client communication. It supports handling of connections, threading, and communication protocols, which can be useful for applications that require robust client-server architecture. Thanks chat.

## Features

- **Socket Communication**: Establishes and manages socket connections between server and client.
- **Threading**: Manages concurrent threads to handle multiple clients.
- **Applications**: Suitable for projects like solar car telemetry or other IoT-based server/client models.

## Installation

To install:

```bash
pip install queue-confirmer
adsf-practical-astronomy 1.0.2
Astronomically useful functions
Requires Python: >=3.12.4

This project is based on the algorithms found in [Practical Astronomy with your Calculator or Spreadsheet](https://www.cambridge.org/de/universitypress/subjects/physics/amateur-and-popular-astronomy/practical-astronomy-your-calculator-or-spreadsheet-4th-edition?format=PB&isbn=9781108436076) by Peter Duffett-Smith and Jonathan Zwart.

This is a library of functions, with unit tests, written in Python 3.12, the standard library, unittest framework, and the math module.

All credit for the accuracy and usefulness of these algorithms goes to the original authors.

# Test

To run all tests run this command:

```bash
python -m unittest
```

# Local Development Setup

## Start/Activate a Virtual Environment

Start:

```bash
python3 -m venv venv
```

Activate:

```bash
source venv/bin/activate
```

## Install dependencies

```bash
pip install --upgrade -r requirements.txt
```

# Installation

```bash
pip install af_practical_astronomy
```

# Running

Currently, you will have to manually import these functions into your project if you wish.

# Future plans

- Finish implementing all of the algorithms from the book.
  - Note: skipped section 31, matrix method for coordinate conversions. Will implement later.
- Add a test runner.

# Updating and Repackaging the Project with `setuptools`

To update and repackage this Python project using `setuptools` on macOS, follow these steps:

## 1. Install or Activate the Virtual Environment

It's recommended to use a virtual environment for isolation. If you don't already have a virtual environment, create and activate one:

### Create and start virtual environment:

```bash
python3 -m venv venv
```

```bash
source venv/bin/activate
```

## 2. Install Required Dependencies

Ensure that setuptools and wheel are installed in your environment:

```bash
pip install setuptools wheel twine
```

or

```bash
pip install -r requirements.txt
```

## 3. Update version number

```bash
setup(
    name="astronomy_types",
    version="0.2.0",  # Update this to the new version number
    ...
)
```

## 4. Build the dist

```bash
python3 setup.py sdist bdist_wheel
```

## 5. Upload with `twine`

```bash
twine upload dist/*
```

And enter in the API token when prompted
pyplaato 0.0.19
Asynchronous Python client for getting Plaato Airlock and Keg data
Requires Python: >=3

# Python API client for fetching Plaato data

Fetches data for the Plaato Keg and Plaato Airlock using the official API handed by [blynk.cc](blynk.cc)

To be able to query the API an `auth_token` is required and which can be obtained by following [these](https://plaato.zendesk.com/hc/en-us/articles/360003234717-Auth-token) instructions

For more information about the available pins that can be retrieved please see the official [docs](https://plaato.zendesk.com/hc/en-us/articles/360003234877-Pins) from Plaato

## Usage
```
usage: cli.py [-h] -t AUTH_TOKEN -d {keg,airlock,both} [-u URL] [-k API_KEY]

optional arguments:
  -h, --help            show this help message and exit

required arguments:
  -t AUTH_TOKEN         Auth token received from Plaato
  -d {keg,airlock}

optional arguments:
  -u URL                Mock url
  -k API_KEY            Header key for mock url
```

## Available pins

### Keg
```python
    BEER_NAME = "v64"
    PERCENT_BEER_LEFT = "v48"
    POURING = "v49"
    BEER_LEFT = "v51"
    BEER_LEFT_UNIT = "v74"
    TEMPERATURE = "v56"
    UNIT_TYPE = "v71"
    MEASURE_UNIT = "v75"
    MASS_UNIT = "v73"
    VOLUME_UNIT = "v82"
    LAST_POUR = "v59"
    DATE = "v67"
    OG = "v65"
    FG = "v66"
    ABV = "v68"
    FIRMWARE_VERSION = "v93"
    LEAK_DETECTION = "v83"
    MODE = "v88"
```

### AirLock
```python
    BPM = "v102"
    TEMPERATURE = "v103"
    BATCH_VOLUME = "v104"
    OG = "v105"
    SG = "v106"
    ABV = "v107"
    TEMPERATURE_UNIT = "v108"
    VOLUME_UNIT = "v109"
    BUBBLES = "v110"
    CO2_VOLUME = "v119"
```

### Disclaimer
This python library was not made by Plaato. It is not official, not developed, and not supported by Plaato.
ml-energy 0.0.2
PhD work/exploration @ FEUP, PORTO
Requires Python: >=3.10

# ml_energy

Package for security in machine learning.


[![PyPi Version](https://img.shields.io/pypi/v/ml_energy)](https://pypi.org/project/ml_energy/)
[![GitHub](https://img.shields.io/github/stars/AndreCatarino/ml_energy?style=social)](https://github.com/AndreCatarino/ml_energy)
[![Documentation Status](https://readthedocs.org/projects/ml-security/badge/?version=latest)](https://ml-security.readthedocs.io/en/latest/?badge=latest)
[![Downloads](https://static.pepy.tech/badge/ml_energy)](https://pepy.tech/project/ml_energy)
[![Codacy Badge](https://app.codacy.com/project/badge/Grade/25cf2a7639dd468fa40868831caadeb7)](https://app.codacy.com/gh/AndreCatarino/ml_energy/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)

## Installation

```bash
conda create -n phd python=3.11
pip install ml_energy/
```
bioomics 0.2.9
Download, retrieve and process omics data for further bioinformatics
Keywords: pypi, cicd, python

\n# bio_omics
Download, retrieve and process omics data, or biological informatics data from public database

Comprehensive Databases
- NCBI: genome database, 
- UniProt: protein database, https://www.expasy.org/resources/uniprotkb-swiss-prot

Sepecific Databases
- miRBase: mircoRNA database, https://www.mirbase.org/
- RNACentral: non-coding RNA squence database, https://rnacentral.org/
- IEDB: immune epitope database, https://www.iedb.org/


See the help documents of example coding at https://www.fbridges.com/pipeline/bio_omics.

https://www.iedb.org/downloader.php?file_name=doc/epitope_full_v3.zip


## data model
ETL data processing is composed of some steps including downloads, retrieval, organization, combination, integration, enrichment, formation. This packages focus on downloads, retrieval, and combination of omics data.
It is suggested that the data model would be consistent. Data are organized by entity namely protein, or antigen. An example of data is showed as the below. Here the pair 'key' defines unique identifier of this entity. "ID" is automatically created. Retrieved data are pushed as one key-value. 
Note:
- Abundant data are possible and to be allowed.
- The key-value is defined by this corresponding database source.
- Used for Integration rather than enrichment. Therefore, data combination or aggregation is not recommended.
- Data from various source could be different or invalid. Those would be validated in the afterwards step rather than this step.
```
{
    "ID": "79541",
    "key": "H0YED9",
    "UniProt_SwissProt": {
        ....
    },
    "NCBI": {
        ....
    },
    "PDB": {
        ....
    },
    ....    
}
```