semvec/pypi-packages huggingface dataset:
nebis-cli 1.1.0
Terminal client for Nebis Cloud, in-memory database with disk persistence.
Requires Python: >=3.6

# Nebis CLI

Nebis CLI is a command line tool for managing databases on the Nebis platform. It allows users to register, login, and perform operations such as creating, deleting, and getting database connection URLs.

## Features

- User registration.
- Secure login with hidden password.
- Database management (create, list, and delete).
- Getting the connection URL in a secure format.

## Requirements

- Python 3.6 or higher.
- Required modules:
- `requests`
- `getpass`

You can install the required modules with:

```bash
pip install requests
```

## Installation

1. Clone this repository:

```bash
git clone https://github.com/nebis-db/nebis-cli.git
```

2. Go to the project directory:

```bash
cd nebis-cli
```

3. Run the main script:

```bash
python cli.py
```

## Usage

Running the script will display an interactive menu:

```bash
--- Nebis CLI ---
1. Register
2. Login
3. Exit
Select an option:
```

### User Registration

Select option `1` and enter the required information:

```
Enter your username: johndoe
Enter your password: ********
Enter your email: johndoe@example.com
```

### Login

Select option `2` and enter your credentials (It is important to confirm your account, otherwise you will not be able to log in no matter how many times you try):

```
Enter your username: johndoe
Enter your password: ********
```

### User Menu

After logging in, you will be able to perform the following actions:

1. View associated databases.
2. Create a new database.
3. Delete a database.
4. Get the Nebis URI.
5. Log out.

### Get the connection URL

To get the connection URL for a database, select the corresponding option and enter the password. The URL provided will be in the format:

```bash
nebis://username:password@nebisdb.pythonanywhere.com/database_name
```

## Contribution

If you would like to contribute to Nebis CLI, please follow these steps:

1. Fork the repository.
2. Create a new branch with your changes.
3. Submit a pull request for review.
sfapi-client 0.4.1
Python client for NERSC SF API
Requires Python: >=3.7

# Welcome to sfapi_client

sfapi_client is a Python 3 client for NERSC's [Superfacility API](https://docs.nersc.gov/services/sfapi/).

---

Install sfapi_client using pip:

```shell
$ pip install sfapi_client
```

Let's get started by checking the status of perlmutter:

```pycon
>>> from sfapi_client import Client
>>> from sfapi_client.compute import Machine
>>> with Client() as client:
...     status = client.compute(Machine.perlmutter)
...
>>> status
Compute(name='perlmutter', full_name='Perlmutter', description='System Degraded', system_type='compute', notes=['2023-04-26 18:16 -- 2023-04-28 09:30 PDT, System Degraded, Rolling reboots are complete, a final reboot is scheduled for 0930 PDT'], status=<StatusValue.degraded: 'degraded'>, updated_at=datetime.datetime(2023, 4, 26, 18, 16, tzinfo=datetime.timezone(datetime.timedelta(days=-1, seconds=61200))), client=<sfapi_client._sync.client.Client object at 0x102c871c0>)
```

## Features

* `async` interface and standard synchronous interface.
* Fully type annotated.

## Documentation

For the basics, head over to the [QuickStart](https://nersc.github.io/sfapi_client/quickstart/). We also have Jupyter Notebook [examples](https://nersc.github.io/sfapi_client/examples/).

More in depth developer documentation can be found in the [API reference](https://nersc.github.io/sfapi_client).

## Dependencies

The sfapi_client project relies on these libraries:

* `httpx` - HTTP support.
* `authlib` - OAuth 2.0 authentication.
* `pydantic` - Data models.
* `tenacity` - Retry.
* `datamodel-code-generator` - Generating data models from the Open API specification.
* `unasync` - Generating synchronous interface from the async implementation.


## Installation

Install with pip:

```shell
$ pip install sfapi_client
```
fasttea 1.1.0
Fast XiaoTea encryption and decryption

# FastTea
FastTea is a Python extension module that provides fast XiaoTea encryption and decryption.
It is >100x faster than the [original XiaoTea implementation](https://github.com/BotoX/xiaomi-m365-firmware-patcher/blob/master/xiaotea/xiaotea.py).

## Usage
`import fasttea`
### Encrypt
`fasttea.encrypt(<bytes>)`
### Decrypt
`fasttea.decrypt(<bytes>)`
### Using custom key
```python
fasttea.encrypt(<bytes>, b"FastTeaFastTeaFT")
# or using keywords
fastea.decrypt(data=<bytes>, key=b"FastTeaFastTeaFT")
```
playersftbl 0.0.1
Takes player information from Transfermarkt
Keywords: Football Players

# Transfermarkt Team Scraper

This is a Python library that allows you to scrape team data from Transfermarkt pages. By providing a team's URL, you can extract various information such as player data, squad details, and more.


Change Log
==========

0.0.1 (24/01/2024)
-------------------
- First Release
nebis 1.0.0
Nebis Python client
Requires Python: >=3.7

# pynebis

Pynebis is a Python client for interacting with Nebis, an in-memory database with disk persistence, designed to be ultra-fast, scalable, and easy to use.

## Features

- Secure data storage.
- Local and server synchronization.
- Full CRUD operations (Create, Read, Update, Delete).
- In-memory data handling with configurable TTL (Time-to-Live).

## Installation

Install the package using pip:

```bash
pip install nebis
```

## Usage

### Basic Example:

```python
from nebis_sdk import NebisSDK

n = NebisSDK("nebis://user:pass@host/db", m='method')
n.c({"key": "1", "value": {"name": "John"}})
print(n.r())
```

### Detailed Usage:

```python
from nebis_sdk import NebisSDK

try:
    # Connect to the database
    n = NebisSDK("nebis://user:pass@nebisdb.pythonanywhere.com/db", m='both')

    # Create (Save) data
    s = {
        "key": "1",
        "value": {
            "name": "John",
            "lastname": "Smith",
        }
    }
    n.c(s)

    # Read data
    l = n.r()
    print(l)

    # Update data
    u = {
        "key": "1",
        "value": {
            "name": "Luke",
            "lastname": "Smith",
            "age": 25
        }
    }
    n.u(u)

    # Delete data
    k = {"key": "1"}
    n.d(k)

except Exception as e:
    print(str(e))
```

## Available Methods

- `c(data)`: Stores data in memory, locally, and on the server based on the configuration.
- `r()`: Retrieves stored data.
- `u(data)`: Updates existing data.
- `d(data)`: Deletes data from storage.

## Configuration Parameters

- `m`: Storage mode (`local`, `server`, `both`).
- `ttl`: Time-to-live for in-memory data (default: 300 seconds).

## License

This project is licensed under the [LICENSE](LICENSE).

## Contributing

Feel free to submit issues or pull requests to improve Pynebis. Contributions are welcome!
latest-user-agents 0.0.5
Get the latest user agent strings for major browsers and OSs

# latest-user-agents

_Get the latest user agent strings for major browsers and OSs_

---

## Installation

```shell
pip install latest-user-agents
```

## Basic usage

```python
In [1]: from latest_user_agents import get_latest_user_agents, get_random_user_agent

In [2]: get_latest_user_agents()
Out[2]:
['Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:128.0) Gecko/20100101 Firefox/128.0',
 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:134.0) Gecko/20100101 Firefox/134.0',
 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36',
 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/132.0.0.0 Safari/537.36',
 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/18.2 Safari/605.1.15',
 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36',
 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/132.0.0.0 Safari/537.36',
 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/132.0.0.0 Safari/537.36 Edg/132.0.0.0',
 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:128.0) Gecko/20100101 Firefox/128.0',
 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:134.0) Gecko/20100101 Firefox/134.0',
 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36',
 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/132.0.0.0 Safari/537.36',
 'Mozilla/5.0 (X11; Linux x86_64; rv:128.0) Gecko/20100101 Firefox/128.0',
 'Mozilla/5.0 (X11; Linux x86_64; rv:134.0) Gecko/20100101 Firefox/134.0',
 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:128.0) Gecko/20100101 Firefox/128.0',
 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:134.0) Gecko/20100101 Firefox/134.0']

In [3]: get_random_user_agent()
Out[3]: 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/132.0.0.0 Safari/537.36'

In [4]: get_random_user_agent()
Out[4]: 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/132.0.0.0 Safari/537.36 Edg/132.0.0.0'

In [5]: get_random_user_agent()
Out[5]: 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:134.0) Gecko/20100101 Firefox/134.0'
```

## Bugs, requests, questions, etc.

Please create an [issue on GitHub](https://github.com/jnrbsn/python-latest-user-agents/issues).
nvidia-binarchive 0.5.0
Parser for NVIDIA redistributable JSON manifests
Requires Python: >=3.8

# build-system-archive-import-examples

[![License](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT-license)
[![Contributing](https://img.shields.io/badge/Contributing-Developer%20Certificate%20of%20Origin-violet)](https://developercertificate.org)


## Overview

Examples for importing precompiled binary `tarball` and `zip` archives into various CI/CD build and packaging systems


## Redistrib JSON

Sample scripts for parsing `redistrib_${label}.json` manifests ([JSON schema](https://developer.download.nvidia.com/compute/redist/redistrib-v2.schema.json)).

  - Downloads each archive
  - Validates SHA256 checksums
  - Extracts archives
  - Flattens into a collapsed directory structure

### Python Usage

```shell
usage: parse_redist.py (-u URL | [-l LABEL] [-p PRODUCT]) [-o OUTPUT]
       option filters: [--component COMPONENT] ([--os OS] [--arch ARCH])
       option toggles: [--no-download] [--no-checksum] [--no-extract] [--no-flatten]
```
> note: for this reference script, Python 3.8 or later is required


#### Example (python)

```shell
python3 ./parse_redist.py --product cuda --label 11.4.2
```

or equivalent

```shell
python3 ./parse_redist.py --url https://developer.download.nvidia.com/compute/cuda/redist/redistrib_11.4.2.json
```

### BASH Usage

```shell
USAGE: ./shell-parse.sh (--url= | [--product=] [--label=]) [--input=] [--output=]
          [--component=] [--os=] [--arch=] [--variant=]
          [-W] [-S] [-X] [-F] [--list] [--latest] [--help]
```
> note: for this reference script, `jq`, `curl`, `wget`, etc. are required


#### Example (bash)

```shell
./shell-parse.sh --product=cuda --label=12.8.0
```

or equivalent

```shell
./shell-parse.sh --product=cuda --latest
```


## CMake

### FindCUDAToolkit

See example [cmake/1_FindCUDAToolkit/](cmake/1_FindCUDAToolkit/)

### ExternalProject

See example [cmake/2_ExternalProject/](cmake/2_ExternalProject/)

## Bazel

### pkg_tar

See example [bazel/1_pkg_tar/](bazel/1_pkg_tar/)

## Contributing

See [CONTRIBUTING.md](CONTRIBUTING.md)
vizblend 2.0.0
Generate multi-page HTML file for Interactive analytical reporting with Python and Plotly
Requires Python: >=3.9

# VizBlend

[![PyPI Version](https://img.shields.io/pypi/v/vizblend)](https://pypi.org/project/vizblend/) [![License](https://img.shields.io/pypi/l/vizblend)](https://github.com/MahmoudHousam/VizBlend/blob/master/LICENSE) [![Build Status](https://github.com/MahmoudHousam/VizBlend/actions/workflows/release.yml/badge.svg)](https://github.com/MahmoudHousam/VizBlend/actions) [![CI/CD](https://github.com/MahmoudHousam/VizBlend/actions/workflows/main.yml/badge.svg)](https://github.com/MahmoudHousam/VizBlend/actions) [![Code Style: Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://black.readthedocs.io/en/stable/) [![PyPI Downloads](https://static.pepy.tech/badge/vizblend)](https://pepy.tech/projects/vizblend)

VizBlend is a Python package that simplifies creating interactive analytical reports by consolidating Plotly figures into a single HTML file. It’s designed to function like a modern PowerPoint presentation but with the power of stunning and interactive data visualizations.

Whether you’re a data analyst, scientist, or developer, VizBlend streamlines the visualization workflow and enhances collaboration.

# Demo
Below is a preview of a report generated with VizBlend:

⭐ If You Like It, Start it, please! ⭐
![VizBlend Thumbnail](https://cdn.jsdelivr.net/gh/MahmoudHousam/VizBlend@master/demo/preview5.gif)
[VizBlend Thumbnail](https://cdn.jsdelivr.net/gh/MahmoudHousam/VizBlend@master/demo/preview5.gif)

# Installation and use case

Install vizBlend using pip:
```
pip install vizBlend
```
Import VizBlend and add figures
```
from vizblend import CreateReport  
import plotly.graph_objects as go  

report = CreateReport(report_title="Sales Report")  
bar_fig = go.Figure(go.Bar(x=["A", "B", "C"], y=[10, 20, 30]))  
report.add_figure(bar_fig, {"title": "Sales Data"})
```
Generate the report
```
report_file = report.blend_graphs_to_html()  
print(f"Report saved to {report_file}")
```

# Learning Purpose

Beside its main job, this repo intends to teach aspiring data analysts or even data scientists who cannot have full control over their coding cycle. With a simple mission: create visualizations and append them to an HTML file, you can take your learning curve up to include useful skills:
* Write unit and integration tests with edge cases to ensure your code is not error-prone.

* Write CI/CD workflows to run on every push or pull request to ensure the newly committed code is compatible enough.

* Creating, managing and deploying your package to PyPi so that you can simply run `pip install <package_name>` and start using it.


    ##### Useful Resources

    * [Building and testing Python | Official GitHub Actions Docs](https://docs.github.com/en/actions/use-cases-and-examples/building-and-testing/building-and-testing-python)

    * [pypi-publish | GitHub Action](https://github.com/marketplace/actions/pypi-publish)
overseerr-py 1.0.1
Overseerr API
Keywords: OpenAPI, OpenAPI-Generator, Overseerr API

    This is the documentation for the Overseerr API backend.  Two primary authentication methods are supported:  - **Cookie Authentication**: A valid sign-in to the &#x60;/auth/plex&#x60; or &#x60;/auth/local&#x60; will generate a valid authentication cookie. - **API Key Authentication**: Sign-in is also possible by passing an &#x60;X-Api-Key&#x60; header along with a valid API Key generated by Overseerr.