semvec/pypi-packages huggingface dataset:
tep 3.0.6
tep is a testing framework to help you write pytest more easily. Try Easy Pytest!
License: Apache-2.0
Requires Python: <4.0,>=3.8
Keywords: testing, http, framework, pytest

# 简介

tep是Try Easy Pytest的首字母缩写，帮你轻松上手pytest。

框架特点：

- 基于pytest封装，成熟、稳定且扩展性强。
- 框架完全由Python构建，没有混杂其他语言。
- 原生Python语法，学习Python，零成本使用框架。
- HAR包转换pytest用例。
- 函数v()支持${}占位符语法，便捷管理接口数据。

# 安装

支持Python3.8以上版本

创建虚拟环境：`python -m venv .venv`

激活虚拟环境，Windows用户：`activate.bat` Mac用户：`source .venv/bin/activate`

安装tep：`pip install tep`

验证安装成功：`tep -V`

# 更多介绍

[原创接口测试框架tep](https://dongfanger.github.io/chapters/%E5%8E%9F%E5%88%9B%E6%8E%A5%E5%8F%A3%E6%B5%8B%E8%AF%95%E6%A1%86%E6%9E%B6tep.html)

# 重要版本说明

- V3.0.0 纯粹Python接口测试框架
- V2.0.0 关键字驱动框架
- V1.0.0 tep小工具完整教程
- V0.2.3 tep小工具首次开源
egobox 0.31.0
A python binding for egobox crates
License: Apache-2.0
Requires Python: >=3.9

# EGObox - Efficient Global Optimization toolbox

[![pytests](https://github.com/relf/egobox/workflows/pytest/badge.svg)](https://github.com/relf/egobox/actions?query=workflow%3Apytest)
[![DOI](https://joss.theoj.org/papers/10.21105/joss.04737/status.svg)](https://doi.org/10.21105/joss.04737)

`egobox` package is the Python binding of the optimizer named `Egor` and the surrogate model `Gpx`, mixture of Gaussian processes, from the [EGObox libraries](https://github.com/relf/egobox?tab=readme-ov-file#egobox---efficient-global-optimization-toolbox) written in Rust.

## Installation

```bash
pip install egobox
```

### Egor optimizer

```python
import numpy as np
import egobox as egx

# Objective function
def f_obj(x: np.ndarray) -> np.ndarray:
    return (x - 3.5) * np.sin((x - 3.5) / (np.pi))

# Minimize f_opt in [0, 25]
res = egx.Egor([[0.0, 25.0]], seed=42).minimize(f_obj, max_iters=20)
print(f"Optimization f={res.y_opt} at {res.x_opt}")  # Optimization f=[-15.12510323] at [18.93525454]
```

### Gpx surrogate model

```python
import numpy as np
import matplotlib.pyplot as plt
import egobox as egx

# Training
xtrain = np.array([0.0, 1.0, 2.0, 3.0, 4.0])
ytrain = np.array([0.0, 1.0, 1.5, 0.9, 1.0])
gpx = egx.Gpx.builder().fit(xtrain, ytrain)

# Prediction
xtest = np.linspace(0, 4, 100).reshape((-1, 1))
ytest = gpx.predict(xtest)

# Plot
plt.plot(xtest, ytest)
plt.plot(xtrain, ytrain, "o")
plt.show()
```

See the [tutorial notebooks](https://github.com/relf/egobox/tree/master/doc/README.md) and [examples folder](https://github.com/relf/egobox/tree/d9db0248199558f23d966796737d7ffa8f5de589/python/egobox/examples) for more information on the usage of the optimizer and mixture of Gaussian processes surrogate model.