semvec/pypi-packages huggingface dataset:
MiniGPTWrapper 0.999
A minimalistic wrapper for the GPT.
Requires Python: >=3.6

# MiniGPTWrapper

A Python wrapper for the GPT.

## Installation

You can install the package using pip:

```pip install MiniGPTWrapper```


## Usage

```python
from MiniGPTWrapper import GPTWrapper

gpt = GPTWrapper()
response = gpt.send_message("Hello")
print(response)
```
pygamelibrary 1.0
my python game library
Requires Python: >=3.1

# Pygamelib
- It is python game library.
- It is not pygame.
## Console
### Print2D
- Print 2D List.
- bool(x) is True: print t else: print f
## GUI
### Messagebox
- str message box! (normal, password)
- it's korean.
### Sprite
- sprite
- it can move.
- it can set x.
- it can set y.
- it can get x, y. (tuple)
## Sound
### Sound
- sound
- it's pygame sound.
- but it have loopplay function (name : loopplay)
otbenchmark 0.2.1
A benchmark library for OpenTURNS
Requires Python: >=3.8
Keywords: reliability, sensitivity, benchmark

[![GHA](https://github.com/openturns/otbenchmark/actions/workflows/build.yml/badge.svg?branch=master)](https://github.com/openturns/otbenchmark/actions/workflows/build.yml)

# otbenchmark

## What is it?

The goal of this project is to provide benchmark classes for OpenTURNS. 
It provides a framework to create use-cases which are associated with
reference values.
Such a benchmark problem may be used in order to check that a given
algorithm works as expected and to measure its performance in terms 
of accuracy and speed.

Two categories of benchmark classes are currently provided:
* reliability problems, i.e. estimating the probability that 
the output of a function is less than a threshold,
* sensitivity problems, i.e. estimating sensitivity indices, 
for example Sobol' indices.

Most of the reliability problems were adapted from the RPRepo

https://rprepo.readthedocs.io/en/latest/

This module allows you to create a problem, run an algorithm and 
compare the computed probability with a reference probability: 
Moreover, we can loop over all problems and run several methods on these 
problems.

## Authors

* Michaël Baudin
* Youssef Jebroun
* Elias Fekhari
* Vincent Chabridon

## Installation

To install the module, we can use either pip or conda: 

```
pip install otbenchmark
```

## Documentation

The documentation is available here: https://openturns.github.io/otbenchmark/master/