semvec/pypi-packages huggingface dataset:
rabbitmq-request-response 0.0.2
Common python package
Requires Python: >=3.6

# rabbitmq-request-response

#### 介绍
把lycium包对rabbitmq的封装独立出来，同时加上兼容金蝶的mq中的rabbitmq 功能。要使用金蝶mq，要配置环境变量 _jindie_mq=1

#### 软件架构
软件架构说明


#### 安装教程

1.  xxxx
2.  xxxx
3.  xxxx

#### 使用说明

1.  xxxx
2.  xxxx
3.  xxxx

#### 参与贡献

1.  Fork 本仓库
2.  新建 Feat_xxx 分支
3.  提交代码
4.  新建 Pull Request


#### 特技

1.  使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md
2.  Gitee 官方博客 [blog.gitee.com](https://blog.gitee.com)
3.  你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解 Gitee 上的优秀开源项目
4.  [GVP](https://gitee.com/gvp) 全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目
5.  Gitee 官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help)
6.  Gitee 封面人物是一档用来展示 Gitee 会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/)
rust-enum 1.1.5
Rust-style enums
Requires Python: >=3.7

# Rust-style enums for Python

Easily-defined enumerations that can contain data and be matched.

Here they are:

```python
@enum
class DivisionResult:
    Undefined = Case()
    Some = Case(number=float)

def divide(a: float, b: float) -> DivisionResult:
    if b == 0: return DivisionResult.Undefined()
    return DivisionResult.Some(a / b)

match divide(3, 3):
    case DivisionResult.Some(n): assert n == 1
    case _: assert False
```

Also Option is implemented, so you can do it even faster in most cases:

```python
def divide(a: float, b: float) -> Option[float]:
    if b == 0: return Option.Nothing()
    return Option.Some(a / b)

assert divide(6, 2).unwrap() == 3
assert divide(6, 2).unwrap_or(None) == 3
assert divide(6, 0).unwrap_or(None) is None
assert divide(6, 2).map(lambda v: v * 3) == Option.Some(9)
assert divide(6, 2).and_then(lambda v: divide(v, 3)) == Option.Some(1)
```

## Installation

```bash
pip install rust_enum
```
stratus-api-document 0.0.5
An API stratus_api for simplified development
Requires Python: >=3.11

The Stratus API document is a python package that is built on top of Google Cloud Firestore API. Functionality includes  create, update, get, and delete objects in the firestore collections.

### Installation
Install this library using pip.

### Supported Python Versions
Python >= 3.5

### Example Usage
```
from stratus_api.document.base import create_db_client

client = create_db_client()
doc_ref = client.collection('example_collection').document('document_id')
document = doc_ref.get()
```
### Example Usage
```
from stratus_api.document.get import get_objects

objects = get_objects(collection_name='example_collection', active=True, limit=10, full_collection_name=True)

for object in objects:
    print(object)
```
alfi 1.0.2
An approximate latent force model library with variational inference for non-linear ODEs and PDEs.
Requires Python: >=3.6

# Alfi: Approximate Latent Force Inference

> _Don't miss out!_

[![Latest PyPI Version][pb]][pypi] [![PyPI Downloads][db]][pypi] [![Documentation Status](https://readthedocs.org/projects/lafomo/badge/?version=latest)](https://lafomo.readthedocs.io/en/latest/?badge=latest)


[pb]: https://img.shields.io/pypi/v/lafomo.svg
[pypi]: https://pypi.org/project/lafomo/

[db]: https://img.shields.io/pypi/dm/lafomo?label=pypi%20downloads


### Implement Latent Force Models in under 10 lines of code!

This library implements several Latent Force Models. These are all implemented in building blocks simplifying the creation of novel models or combinations.

We support analytical (exact) inference in addition to inducing point approximations for non-linear LFMs written in PyTorch.


## Installation

`pip install alfi`

## Documentation


See Jupyter notebooks in the documentation [here](https://alfi.readthedocs.io/en/latest/notebooks_list.html). Alternatively, directly browse the notebooks in the `docs/notebooks/` directory. The docs contain linear, non-linear (both variational and MCMC methods), and partial Latent Force Models. The notebooks also contain complete examples from the literature such as a replication of the analytical linear Latent Force Model from [Lawrence et al., 2006](https://papers.nips.cc/paper/3119-modelling-transcriptional-regulation-using-gaussian-processes.pdf)