semvec/pypi-packages huggingface dataset:
websole 1.0.1
A solution to expose command-line tools through web-based console, through docker.
Requires Python: >=3.8
Keywords: cli, web, web console

# Websole

Websole is a web-based console for you to expose command-line tools to a web server.

## Features

1. Protect your terminal with a login password.
2. Customizable logos, links, and icons.
3. Simple to use as a base docker image in other projects.

## Screenshot

![](https://github.com/jackzzs/websole/raw/main/images/example_console.png)

![](https://github.com/jackzzs/websole/raw/main/images/example_login.png)

## Install

You can expose any program (for example, `bash`) by running:

```bash
docker run -p 80:1818 --rm -it jackzzs/websole:main bash
```

This will expose a terminal running `bash` on port 80.

# Use in other projects

You can use websole as a base docker image, to expose your program.

See the `example` directory for examples.

# Commandline options

```
Usage: websole [OPTIONS] COMMAND

    --host              Host for web console to listen on (default: 127.0.0.1).
    --port              Port for web console to listen on (default: 1818).
    --webpass           Password for logging into the web console (default: <no password>).
    --brand             Brand to be shown in web console header (default: Web Console).
    --icon              Add an icon to be shown in web console footer (format: <icon>:<url>).
    --link              Add a link to be shown in web console footer (format: <label>:<url>).
    --config            Location of the config file (default: websole.yml).
    --start/--no-start  Whether to start the program immediately, rather than on first connection. (default: False)
    --version           Print version and exit.
    --help              Show help message and exit.

NOTE: If COMMAND contains spaces, you need to enclose it in quotes.
```

# Configuration file

Default config:
```yaml
# Program to be exposed to web console.
command: bash

# Host for web console to listen on.
host: 0.0.0.0

# Port for web console to listen on.
port: 1818

# Brand to be shown in web console header.
brand: Your Program

# Link to be shown in web console footer.
links:
  - label: Github
    url: https://github.com/jackzzs/websole
  - label: Example
    url: https://websole.onrender.com

# Icons to be shown in web console footer.
# icon names can be found from https://icons.getbootstrap.com/
icons:
  - icon: bi-github
    url: https://github.com/jackzzs/websole
  - icon: bi-fire
    url: https://websole.onrender.com

# Password for logging into the web console.
webpass: 123456

# Whether to start the program immediately, rather than on first connection.
start: yes

# URL for "What is web console password?" link on the login page.
what_is_webpass_url: https://github.com/jackzzs/websole

# Allow users to restart the program through the refresh button in the upper right corner of the console.
allow_restart: yes

# By default, allow users to use Ctrl+C/V to copy/paste (but prevent the shortcut key from reaching the program).
use_shortcut: no

# Hide the switch for "use Ctrl+C/V to copy/paste".
hide_use_shortcut_switch: no
```
LLM-PCT 0.0.1
A package for taking and scoring the PCT
Requires Python: >=3.8

# Political Compass Test (PCT)

[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)

* GitHub: https://github.com/isabellahoch/LLM_PCT
* Free software: MIT license

## Overview

The Political Compass Test (PCT) is a Python package that allows users to proctor the political compass test algorithmically by allowing LLMs to react to PCT questions with open-ended responses. It classifies responses in agreement levels according to a specified threshold, then directly interacts with the [PCT website](https://www.politicalcompass.org/test) to determine the generator's political position on the compass.

## Features

- Elicits responses to questions for the political compass test
- Classifies generated responses to determine their political position
- Programmatically intearacts with PCT website to take the test and determine results
- Displays PCT results on interactive plot

## Setup

Expects a file path to a `pct-assets`s directory with `crx/adblock.crx` extension and subdirectories for generated `response`, `score`, `results` files.
fremu 0.0.9
Emulator for f(R) gravity

<h1 align="center">
<img src="https://github.com/astrobai/fremu/blob/master/fremu.png" width="700">
</h1><br>

$\texttt{FREmu}$ offers fast and accurate predictions of non-linear power spectra for the specific Hu-Sawicki $f(R)$ gravity model. $\texttt{FREmu}$ employs principal component analysis and artificial neural networks to establish a parameter-to-power-spectrum map, with training data sourced from the Quijote-MG simulation suite. With a parameter space encompassing 7 dimensions, including $\Omega_m$, $\Omega_b$, $h$, $n_s$, $\sigma_8$, $M_{\nu}$, and $f_{R_0}$, the emulator achieves an accuracy better than 5% for the majority of cases and it's proved highly efficient for parameter constraints.

## Installation

You can install $\texttt{FREmu}$ via pip:

```
pip install fremu
```

## Usage

```python
from fremu import fremu

# Initialize the emulator
emu = fremu.emulator()

# Set cosmological parameters
emu.set_cosmo(Om=0.3, Ob=0.05, h=0.7, ns=1.0, sigma8=0.8, mnu=0.05, fR0=-1e-5)

# Get power spectrum
k_values = emu.get_k_values()
power_spectrum = emu.get_power_spectrum(k=k_values, z=0.5)

# Get boost factor
boost_factor = emu.get_boost(k=k_values, z=0.5)

```

## Documentation

You can find the documentation of the emulator here: [FREmu documentation](https://astrobai.github.io/codes/fremu).

## Contributing

If you find any issues or have any suggestions for improvement, feel free to submit an issue or pull request on GitHub.
to-literal 1.1.2
Librery to use it in a simple way to do a cast of an array to Literal[], to use it for validation with Pydantic

# To_literal

Is a micro librery to use it with pydantic, we can do now convert Literal[] to simple array in python, or reverse in a simple way.

Repository:
![Static Badge](https://img.shields.io/badge/build-passing-brightgreen?style=for-the-badge&logo=GitHub&logoColor=%23ffffff&label=LINK&color=white&link=https%3A%2F%2Fgithub.com%2FHarryEddward%2Fto_literal%3Ftab%3Dreadme-ov-file)


## Why i do it?

I'm still doing a RestAPI with FastAPI with React Native, and when you need to obtain from your database with nosql an array, you can't convert in the simplest way array to Literal[] and you can't validate data in a flexible way. To save before data an after apply to your new values to use it, from your database to validate with Pydantic.


## How to use it

```python
from pydantic import BaseModel
from to_literal.v1 import toLiteral

hoursL = toLiteral([
    '9:00',
    '9:30',
])

class testLiteral(BaseModel):
    hours: hoursL


#   Good Example:

test = testLiteral(hours='9:30')
print(test.model_dump())

#   Result:
#   { 
#       'hours': '9:30'
#   }



#   Bad Example -> ERROR:

test = testLiteral(hours='10:30')
print(test.model_dump())

```
mltoolkit-laht 0.2.2
A data science and machine learning toolkit
Requires Python: >=3.9

# mltoolkit-laht 0.2.2

This is a Python library for data science and machine learning tasks. It provides utilities for data processing, modeling, and visualization.

## Installation

To install this package, clone the repository and run the setup script:

```
git clone https://github.com/lehidalgo/mltoolkit-laht
cd mltoolkit-laht
pip install .
```

## Usage

Full examples in Tutorials directory.

## Testing

To run the tests, use the following command:

```
python -m unittest discover tests
```

## Dependencies

This package requires the following Python libraries, which are listed in the `requirements.txt` file:

- numpy
- pandas
- scikit-learn
- matplotlib

Please make sure to install these dependencies before using this toolkit.

## Contributing

Contributions are welcome! Please submit a pull request or create an issue to propose changes or additions.
squice 0.6
Example library for scientific software.
Requires Python: >=3.6

# SQUICE

*Slicing through squares: visualise and interpolate numerical matrix data.*

This is a python library with accompanying demonstration streamlit web application, to visualise and interpolate numerical matrix data.

This github site, streamlit application and pypi library has been created from the scientific application template [app-lib-py](https://github.com/RachelAlcraft/app-lib-py).

### Use as an application
To use the application simply go to the open source publicly hosted for free application [squice@streamlit](https://squice.streamlit.app/)

### Use as a python library
```
pip install squice
```
The documentation for the library is here: [squice docs](https://rachelalcraft.github.io/squice/index.html).
Note the application [squice@streamlit](https://squice.streamlit.app/) also shows demonstrations of how to use the library.


### Use as a self-hosted web app
This can also be [downloaded from docker](https://hub.docker.com/repository/docker/raea/squice/general) and self-hosted.
```
# docker pull raea/squice
docker run --rm --name temp-name-p 8001:8501 raea/squice
```
easy-db-lib 0.1.0
A very simple db lib.

# Easy DB Lib

Welcome to **Easy DB Lib**, a Python library designed to simplify database interactions for small projects. This library allows you to define and manage your database structure using Python dictionaries, making it easy to modify your database schema as your project evolves.

## Features

- **Programmatic Table Definition**: Define your database tables and their relationships using Python dictionaries.
- **Automatic Table Creation**: Automatically create tables in the database if they do not exist.
- **Flexible Queries**: Easily perform CRUD operations and custom queries.
- **PostgreSQL Support**: Specifically designed to work with PostgreSQL databases.


## Contributing

We welcome contributions from the community! To contribute:

1. Fork the repository.
2. Create a new branch with your feature or bugfix.
3. Make your changes and commit them with clear commit messages.
4. Push your changes to your fork.
5. Create a pull request to the main repository.

Please ensure your code adheres to the project's coding standards and includes appropriate tests.
License

This project is licensed under the MIT License. See the LICENSE file for more details.