semvec/pypi-packages huggingface dataset:
bayesfm 0.2.2
Bayesian Fama-MacBeth Regressions
Keywords: asset pricing, factor models, risk premia, bayesian methods


# bayesfm - Bayesian Fama-MacBeth

Implementation of ["Bayesian Fama-MacBeth Regressions"](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4989615) from Bryzgalova, Huang and Julliard (2024).
As presented by the authors, this methodology provides reliable risk premia estimates for both tradable and nontradable factors, 
detects those weakly identified, delivers valid credible intervals for all objects of interest, and is intuitive, fast and simple to implement.

## Installation
```bash
pip install bayesfm
```

## Usage
There is a self-contained [example file](https://github.com/gusamarante/bayesfm/blob/main/example.py) using the Fama-French 25 sorted portfolios and their 5 factors.

There are 3 classes available:
- `BFM`: Bayesian Fama-MacBeth
- `BFMGLS`: Bayesian Fama-MacBeth with the GLS precision matrix for the cross-sectional step
- `BFMOMIT`: Bayesian Fama-MacBeth with omitted factors
  - As noted by the authors, the use of this model requires us to include a sufficient number of latent factors in the cross-sectional step, which is chosen with the `p` argument of this class

All three class save the draws of all elements of interest as attributes, and have a method called `plot_lambda`, which plots the posteriors of the risk premia parameters.
This method outputs the chart below, where the blue density are the posterior draws and the orange lines are the canonical Fama-MacBeth two-pass OLS regression estiamtes.

<p align="center">
  <img src="https://github.com/gusamarante/bayesfm/blob/main/images/bfm_lambda_posterior.png?raw=true" alt="Risk Premia Posterior"/>
</p>
xy-console 1.0.6
xy_console 模块
Requires Python: >=3.8

# xy_console

| [简体中文](./README.md)         | [繁體中文](readme/README.zh-hant.md)        |                      [English](readme/README.en.md)          |
| ----------- | -------------|---------------------------------------|

# 说明
简单Python控制台输入输出工具封装.

## 源码仓库

| [Github](https://github.com/xy-base/xy_console.git)         | [Gitee](https://gitee.com/xy-opensource/xy_console.git)        |                      [GitCode](https://gitcode.com/xy-opensource/xy_console.git)          |
| ----------- | -------------|---------------------------------------|

## 安装

```bash
# bash
pip install xy_console
```

## 开始

```python
from xy_console.utils import *
# 普通打印
printt("hello world")
# 打印成功
print_s("hello world")
# 打印执行
print_exe("hello world")
# 打印警告(warning)
print_w("hello world")
# 打印运行(running)
print_r("hello world")
# 打印运行(error)
print_e("hello world")

```

![运行结果](./readme/image.png)

## 许可证
xy_console 根据 <木兰宽松许可证, 第2版> 获得许可。有关详细信息，请参阅 [LICENSE](LICENSE) 文件。

## 捐赠

如果小伙伴们觉得这些工具还不错的话，能否请咱喝一杯咖啡呢?  

![Pay-Total](./readme/Pay-Total.png)


## 联系方式
```
微信: yuyangiit
邮箱: yuyangit.0515@qq.com
```
unwatermark-ai 0.2.0
A library for asynchronous and synchronous watermark removal from images
Requires Python: >=3.6

Unwatermark-AI
==============

**Unwatermark-AI** is a Python library for removing watermarks from images, supporting both asynchronous and synchronous operations via the Unwatermark API.

Installation
------------

Install the package using pip:

    pip install unwatermark-ai

Usage
-----

This library includes two main classes: `AsyncUnwater` for asynchronous operations and `Unwater` for synchronous operations.

### Example Usage

#### Asynchronous

Using the `AsyncUnwater` class to remove a watermark asynchronously:

    import asyncio
    from unwatermark_ai import AsyncUnwater
    
    async def main():
        async_unwater = AsyncUnwater()
        result_url = await async_unwater.remove_watermark("path/to/image.jpg")
        print(f"Watermark removed image URL: {result_url}")
    
    asyncio.run(main())
    

#### Synchronous

Using the `Unwater` class to remove a watermark synchronously:

    from unwatermark_ai import Unwater
    
    unwater = Unwater()
    result_url = unwater.remove_watermark("path/to/image.jpg")
    print(f"Watermark removed image URL: {result_url}")
    

Project Details
---------------

*   **Version:** 0.1.0
*   **Author:** FSystem88
*   **Author Email:** ivan@fsystem88.ru
*   **Repository URL:** [GitHub](https://github.com/FSystem88/unwatermark-ai)

Dependencies
------------

This package depends on:

*   `httpx`
*   `aiofiles`

Requirements
------------

Python 3.6 or higher

License
-------

Unwatermark-AI is licensed under the MIT License. See the LICENSE file for more details.

Classifiers
-----------

*   Programming Language :: Python :: 3
*   License :: OSI Approved :: MIT License
*   Operating System :: OS Independent
jetshift-core 1.0.0a31
JetShift is a powerful and lightweight ETL framework that simplifies the process of building data pipelines.
Requires Python: <4.0,>=3.9

<p align="center">
  <a href="https://github.com/mdobydullah/jetshift-core">
    <img src="https://cdn.shouts.dev/media/435/jetshift-github.png" alt="JefShift" width="120">
  </a>
</p>

<p align="center">
<strong>JetShift Core</strong>
</p>

> JetShift is a powerful and lightweight ETL framework that simplifies the process of building data pipelines.

## Intro

JetShift allows seamless extraction, transformation, and loading of data from multiple sources. Its intuitive API and flexible architecture make it easy to handle complex data transformations at scale, whether the data is structured or unstructured. This makes it ideal for developing robust workflows with minimal effort.

Get your data moving swiftly and reliably with JetShift. 🚀

## Usage

For detailed usage instructions, please refer to the [JetShift Documentation](https://jetshift.vercel.app).

## Linked Repositories

- [JetShift](https://github.com/jetshift/jetshift)
- [JetShift Docs](https://github.com/jetshift/docs)

## License

The JetShift is open-sourced software licensed under the [MIT license](https://github.com/mdobydullah/jetshift/blob/master/LICENSE).