semvec/pypi-packages huggingface dataset:
topsis-dhruv-102303645 1.0.3
TOPSIS command line tool
Requires Python: >=3.7

# Project Description

## topsis-dhruv-102303645

**For:** Project-1 (UCS654)  
**Submitted by:** Dhruv  
**Roll No:** 102303645  

`topsis-dhruv-102303645` is a Python library for solving **Multiple Criteria Decision Making (MCDM)** problems using the  
**Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)**.

The package provides a **command-line interface (CLI)** to rank alternatives based on multiple criteria by supplying
weights and impacts.

---

## Installation

Use the Python package manager **pip** to install the library:

```bash
pip install topsis-dhruv-102303645
py-mini-sh 0.1.6
Python Mini Shell


py_mini_sh
==========

.. note:: Version: 0.1.6

This package provides helper functions to simplify the writing of
platform-independent sh-like scripts in Python.

Full documentation can be found at `Read-the-Docs
<http://py-mini-sh.readthedocs.io>`_, the source for which is in the
``docs`` folder.


Using this package
------------------

Either fetch this package directly from Git, or pull it from the Python
Packaging index.

#. Pip-install from Git::

       pip install -U git+https://bitbucket.org/abelana/py_mini_sh.git

#. Pip-install from PyPI::

       pip install -U py_mini_sh

       

Dog Food
--------

The script used to develop and test this component uses the py_mini_sh
itself. To start development of this component, follow these steps:

#. Clone this repo onto a machine from ``https://bitbucket.org/abelana/py_mini_sh.git``::

       git clone https://bitbucket.org/abelana/py_mini_sh.git

#. Cd to the ``py_mini_sh`` folder and run the ``buildall`` script in
   this folder::

       buildall 36

   The argument specifies which version of Python you wish to use. 27,
   34, 35, and 36 are supported.


This ``buildall`` script will perform the following steps:

#. Builds a virtualenv derived from the specified version of Python.

#. Installs the py_mini_sh package into this virtualenv in "develop"
   mode.

#. Builds the documentation into the SHIP folder.

#. Runs pylint over the source and fails if the results are worse than
   the previous run.

#. Runs pytest tests which generate a coverage report into the SHIP
   folder.

#. Builds the bdist_wheel for this package into the SHIP folder.


License
-------

This software is made available under the MIT License (see LICENSE.txt).
easemysql 1.1.3
简单易用的 mysql 封装
Requires Python: >=3.6

# EaseMySQL

一个简单易用的MySQL封装库，帮助开发者更高效地处理MySQL数据库操作。

## 主要功能

- 数据库连接池管理
- 简化的CRUD操作接口
- SQL语句构建辅助函数
- 支持参数化查询，防止SQL注入
- 单例模式确保每个连接配置只创建一个连接池

## 安装方法

pip install --upgrade easemysql -i https://pypi.org/simple/

> 依赖的库
```
pip install pymysql --upgrade
pip install DBUtils --upgrade
```

## 快速开始

```python
from easemysql import EaseMySQL

# 配置数据库连接
config = dict(host='localhost', user='username', password='password', database='dbname')
db = EaseMySQL(**config)

# 查询单条记录
result = db.one('table_name', dict(id=1))

# 查询多条记录
results = db.all('table_name', 'status = ?', [1])

# 使用条件列表进行复杂查询
results = db.all('table_name', [
    'or',
    ['in', 'id', [1, 3, 4]],
    ['and', ['>', 'id', 5], 'id < 8', dict(id=[6, 7])],
    dict(id=8),
    dict(id=[9]),
])

# 插入记录
db.insert('table_name', dict(name='test', value=123))

# 更新记录
db.update('table_name', dict(value=456), 'id = 1')

# 删除记录
db.delete('table_name', 'id = 1')
```
Topsis-Tushargarg-102303674 0.1.1
A Python package for TOPSIS multi-criteria decision making
Requires Python: >=3.7

# TOPSIS Package

## Installation
pip install Topsis-Tushargarg-102303674

## Usage
topsis <InputFile> <Weights> <Impacts> <OutputFile>

Example:
topsis data.csv "1,1,1,1,1" "+,+,-,+,+" result.csv

## Input Format
- First column: alternative name
- Remaining columns: numeric criteria

## Output
- Adds TOPSIS Score and Rank
# TOPSIS-TUSHARGARG-102303674
A Python package for TOPSIS multi-criteria decision making
dcm2niiw 0.1.5
Thin dcm2niix wrapper with Python and CLI interfaces.
Requires Python: >=3.9

# `dcm2niiw`

Thin wrapper around [dcm2niix](https://github.com/rordenlab/dcm2niix) with some defaults modified.

## CLI

```shell
uv tool install dcm2niiw
dcm2niiw --help
dcm2hdr --help
```

## Python

```shell
uv init
uv add dcm2niiw
uv run python
```

```python
>>> from dcm2niiw import dcm2nii
>>> help(dcm2nii)
```
argus-notification-msteams 1.0.0
Allow argus-server to send notifications to MS Teams
Requires Python: >=3.8

argus_notification_msteams
==========================

This is a plugin to send notifications to MSTeams from
`Argus <https://github.com/Uninett/argus-server>`_

Different levels of incidents have hard-coded icon colors, based on those used in the Argus frontend.

Version 0.5.1 and older can be used by argus-server version 1.9.x to 1.13.x.

Django settings
---------------

Add ``argus_notification_msteams.MSTeamsNotification`` to ``MEDIA_PLUGINS``::

    MEDIA_PLUGINS = [
        ..
        "argus_notification_msteams.MSTeamsNotification",
    ]

The plugin uses the setting ``NOTIFICATION_SUBJECT_PREFIX``.

Configuration
-------------

Create a workflow inside MS Teams to send webhook alerts to a chat or channel.
This lets you use "Copy webhook link" to get a long url that needs to be
stored in the ``settings``-field.

You can test without invoking the frontend by adding the webhook manually in
Django admin.

POST-ing to the API:

/api/v2/notificationprofiles/destinations/, POSTed body::

    {
      "media": "msteams",
      "label": "whatever",
      "settings": {
        "webhook": "https://msteams.domain/some-very-long-webhook-specific-path"
      }
    }

GET-ing from the API:

/api/v2/notificationprofiles/destinations/{id}/, received result::

  {
    "pk": 0,
    "media": {
      "slug": "msteams",
      "name": "MS Teams"
    },
    "label": "whatever",
    "suggested_label": "whatever",
    "settings": {
      "webhook": "https://msteams.domain/some-very-long-webhook-specific-path"
    }
  }
passagefy 1.0.0
“通途”是一个基于Django的应用，帮助用户创建一个技术反馈表，并且与飞书的多维表格进行同步
Requires Python: >=3.7

# passagefy 「通途」 使用说明

我们结合飞书的多维表格，实现了一个基于Django的应用，帮助用户创建一个技术反馈表，并且与飞书的多维表格进行同步。

集成了HAdmin的配置文件生成功能，帮助前端进行渲染。

# 快速开始

## 安装

```bash
pip install passagefy
```

## 使用方式

- 在 settings.py 中添加 passagefy 到 INSTALLED_APPS 中

```python
INSTALLED_APPS = [
    ...,
    'passagefy'
]
```

- 并且设定两个变量，也放在settings.py中

```python
PASSAGEFY_WEBHOOK_URL = "用于发送给飞书多维表格的webhook地址"
PASSAGEFY_WEBHOOK_TOKEN = "用于验证的token，如果没设定，就不要这个"
```

**重点说明：token如果设定，那么接收和发送都是用相同的token，请注意配置飞书多维表格时设定好**

- 最后，在urls.py中添加如下代码

```python
from django.urls import path, include
from passagefy.urls import passagefyAdminRouter
from passagefy.views import passagefy_webhook

urlpatterns = [
    ...,
    path('v1/passagefy/', include(passagefyAdminRouter.urls)),  # HAdmin配置文件相关接口
    path('v1/passagefy/webhook', passagefy_webhook),  # 飞书多维表格同步接口
]
```

# 版本历史

1.0.0 初始版本
Topsis-Anjani-102303480 1.0.0
TOPSIS implementation in Python

# Topsis-Anjani-102303480

**TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)** implementation in Python for multi-criteria decision analysis.

## Description

TOPSIS is a multi-criteria decision analysis method that ranks alternatives based on their similarity to the ideal solution. This package provides a simple command-line tool to perform TOPSIS analysis on CSV data files.

## Installation

Install the package using pip:

```bash
pip install Topsis-Anjani-102303480
```

## Usage

After installation, you can use the `topsis` command from anywhere in your terminal:

```bash
topsis <InputDataFile> <Weights> <Impacts> <OutputResultFileName>
```

### Parameters

- **InputDataFile**: Path to the input CSV file
- **Weights**: Comma-separated weights for each criterion (e.g., "1,1,1,2")
- **Impacts**: Comma-separated impacts for each criterion ('+' for maximize, '-' for minimize)
- **OutputResultFileName**: Path for the output CSV file

### Example

```bash
topsis data.csv "1,1,1,2" "+,+,-,+" result.csv
```

## Input File Format

The input CSV file must follow this structure:

- **First column**: Names of alternatives/options
- **Remaining columns**: Numeric values for each criterion
- **Minimum**: 3 columns (1 name column + at least 2 criteria)

### Example Input (`data.csv`)

```csv
Model,Price,Storage,Camera,Battery
P1,250,64,12,4000
P2,200,32,8,3500
P3,300,128,16,4500
P4,275,64,12,4200
P5,225,32,16,3800
```

## Output Format

The output CSV includes all original columns plus:

- **Topsis Score**: Score between 0 and 1 (higher is better)
- **Rank**: Ranking based on TOPSIS score (1 is best)

### Example Output (`result.csv`)

```csv
Model,Price,Storage,Camera,Battery,Topsis Score,Rank
P3,300,128,16,4500,0.691,1
P4,275,64,12,4200,0.535,2
P1,250,64,12,4000,0.534,3
P5,225,32,16,3800,0.401,4
P2,200,32,8,3500,0.308,5
```
lica 3.0.7
Assorte collection of utilities (functions, classes, etc.) used in other projects at LICA, UCM
Requires Python: >=3.9

# lica
 
 Assorte collection of source code with utilities (functions, classes, etc.) 
 used in other projects at LICA (UCM) or STARS4ALL

## Installation

### Stable version

```bash
pip install lica
```

### Development version using UV 
This must be handed a source code in your client package with one of the two commands below.

```bash
uv add git+https://github.com/guaix-ucm/lica --branch main
uv add git+https://github.com/guaix-ucm/lica --tag x.y.z
```

***Note:***
lica library uses different modules in its subpackages, so you must use one or more the following extras:
* lica[jinja]
* lica[tabular]
* lica[raw]
* lica[sqlalchemy]
* lica[aiosqlalchemy]
* lica[photometer]
* lica[lab]