semvec/pypi-packages huggingface dataset:
nanorm 1.9.14
A simple ORM framework for Python ( Nano ORM )
License: MIT
Keywords: orm namo mini sample database sqlite nanorm nanoorm

Nanorm : A simple ORM of Python
=========================================


这是一个精简的Python ORM框架。旨在于用一个文件写出ORM，提供开发小型或微型项目时的一些基本功能。


为什么要使用nanorm？
-------------------
当你的项目足够小，以至于不想与其他的多余库产生依赖关系，但是你又需要使用一些简单的ORM功能（SQLAlchemy对你来说过于庞大），这时你就可以选择Nanorm。它只有一个py文件，直接把它放在你的项目目录下就行，你甚至可以把它嵌入在你的源代码中。


为什么只支持sqlite3？
--------------------
这个ORM的目的只在于解决一些小微项目的需求，sqlite3对于小微项目来说是很好的选择。因为在Python标准库中就有对sqlite3的支持，不需要像使用mysql时那样另外安装mysqldb库。另一方面，你也不用在你的电脑或服务器上安装数据库服务，大多数windows和unix的操作系统都自带了sqlite3的支持。我们的目的就是精简，极简。


如何使用？
---------
安装：
```
pip install nanorm
```

下面是一个最简单的例子

```python
from nanorm import *

set_db_name("example.db")

class User(Model):
    name = CharField()
    age = IntegerField(default=20)
    sex = BooleanField()

s1 = User()
s1.name = "Joe"
s1.age = 45
s1.sex = True
s1.save()

joe = User.get(age=45)
print(joe)

```

具体的高级功能和用法可参照 `nanorm_example.py` 文件中各场景的用例
topsis-102103357 0.0.4
Comparison of models using Topsis
License: MIT
Requires Python: >=3.6

## Introduction

TOPSIS (technique for order performance by similarity to ideal solution) is a useful technique in dealing with multi-attribute or multi-criteria decision making (MADM/MCDM) problems in the real world.

## Installation
Use the package manager pip to install.

```pip install topsis-102103357```

## Usage
Enter csv filename followed by .csv extentsion, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma separated signs (+,-) and enter the output file name followed by .csv extension.

```topsis-102103357 [InputDataFile as .csv] [Weights as a string] [Impacts as a string] [ResultFileName as .csv]```

## Sample Input

| Fund Name| P1	  | P2	 | P3  | P4	  | P5    |
|----------|------|------|-----|------|-------|
| M1	   | 0.84 |	0.71 | 6.7 | 42.1 |	12.59 |
| M2	   | 0.91 |	0.83 | 7   | 31.7 |	10.11 |
| M3	   | 0.79 |	0.62 | 4.8 | 46.7 |	13.23 |
| M4	   | 0.78 |	0.61 | 6.4 | 42.4 |	12.55 |
| M5	   | 0.94 |	0.88 | 3.6 | 62.2 | 16.91 |
| M6	   | 0.88 | 0.77 | 6.5 | 51.5 | 14.91 |
| M7	   | 0.66 | 0.44 | 5.3 | 48.9 | 13.83 |
| M8	   | 0.93 | 0.86 | 3.4 | 37   | 10.55 |

```topsis-102103357 data.csv "1,1,1,1,1" "+,-,+,-,+" output.csv```

## Sample Output

Results saved to output.csv

| Fund Name| P1	  | P2	 | P3  | P4	  | P5    |     Performance   | Rank |
|----------|------|------|-----|------|-------|-------------------|------|
| M1	   | 0.84 |	0.71 | 6.7 | 42.1 |	12.59 | 0.404268469809145 | 5.0  |
| M2	   | 0.91 |	0.83 | 7   | 31.7 |	10.11 | 0.699297825503612 | 1.0  |
| M3	   | 0.79 |	0.62 | 4.8 | 46.7 |	13.23 | 0.333581741928051 | 8.0  |
| M4	   | 0.78 |	0.61 | 6.4 | 42.4 |	12.55 | 0.364968017290041 | 6.0  |
| M5	   | 0.94 |	0.88 | 3.6 | 62.2 | 16.91 | 0.534831688649816 | 2.0  |
| M6	   | 0.88 | 0.77 | 6.5 | 51.5 | 14.91 | 0.439693012540145 | 4.0  |
| M7	   | 0.66 | 0.44 | 5.3 | 48.9 | 13.83 | 0.526356720373482 | 3.0  |
| M8	   | 0.93 | 0.86 | 3.4 | 37   | 10.55 | 0.341972356097968 | 7.0  |

The best model is M2

## License

This repository is licensed under the MIT license. See LICENSE for details.
fastapi-healthchecks 1.1.0
FastAPI Healthchecks
License: MIT
Requires Python: >=3.10,<4.0
Keywords: fastapi,healthcheck

# FastAPI health checks

Configurable health checks endpoints for FastAPI applications.

## Quickstart

```python
app = FastAPI()
app.include_router(
    HealthcheckRouter(
        Probe(
            name="readiness",
            checks=[
                PostgreSqlCheck(host="db.example.com", username=..., password=...),
                RedisCheck(host="redis.example.com", username=..., password=...),
            ],
        ),
        Probe(
            name="liveness",
            checks=[
                ...,
            ],
        ),
    ),
    prefix="/health",
)
```

The probes from this example will be available as `GET /health/readiness` and `GET /health/liveness`.

## Bundled checks

* `PostgreSqlCheck` – checks PostgreSQL server availability
* `RedisCheck` – checks Redis server availability
* `RabbitMqCheck` – checks RabbitMQ server availability
* `SettingsCheck` – validates settings models based on pydantic BaseModel
* `HttpCheck` – checks availability of specified URL
* `CephCheck` – checks Ceph server availability

## Custom checks

You can create your own checks by providing custom `fastapi_healthchecks.checks.Check` implementations. Like this:

```python
class MaintenanceCheck(Check):
    async def __call__(self) -> CheckResult:
        if is_maintenance():
            return CheckResult(name="Maintenance", passed=False, details="Closed for maintenance")
        else:
            return CheckResult(name="Maintenance", passed=True)
```