semvec/pypi-packages huggingface dataset:
yandex-market-api 0.1.1.1
Async Python SDK for Yandex Market Partner API
Requires Python: >=3.10

# yandex-market-api

Async Python client for the **Yandex Market Partner API** (unofficial).

This library provides an asynchronous, resource-oriented interface for interacting with
the Yandex Market Partner API. It is designed as a thin, explicit SDK that mirrors the API
structure while following Python and async best practices.

---

## Features

- Fully asynchronous HTTP client based on `httpx`
- Resource-oriented design (`client.offers`, `client.campaigns`, etc.)
- Explicit and predictable method naming
- Python 3.10+ support
- Designed to be extended with typed models (Pydantic) over time

---

## Installation

The library can be installed from PyPI:

```bash
pip install yandex-market-api
```

Python 3.10 or newer is required.

---

## Quick start

```python
from yandex_market_api import YandexMarketClient
import asyncio

async def main() -> None:
    client = YandexMarketClient(api_key="YOUR_API_TOKEN")

    campaigns = await client.campaigns.list_campaigns()
    print(campaigns)

    await client.close()

asyncio.run(main())
```

The client is asynchronous, so it should be used inside an async context
(e.g. with `asyncio.run`).

---

## Project status

**Work in progress**

- Some endpoints are not implemented yet
- Request and response schemas are mostly returned as plain dictionaries
- Error handling and retries are still evolving
Topsis-Naman-102317229 1.2
A Python implementation of the TOPSIS decision-making method.


# TOPSIS Decision Support System

A Python implementation of the **Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)** used for multi-criteria decision making.  
This project provides both a **PyPI package** and a **Flask-based web application** that allows users to upload datasets, assign weights and impacts, and receive ranked results via email.

---

## 🚀 Features

- Implements the TOPSIS algorithm from scratch
- Accepts CSV/XLSX datasets
- Supports customizable weights and impacts
- Generates TOPSIS score and rank automatically
- Published as an installable PyPI package
- Integrated with a simple and user-friendly Flask web interface
- Automated result delivery through email
- Input validation for weights, impacts, and email format

---

## 📦 PyPI Package

Install directly using:

```bash
pip install Topsis-Naman-102317229
```

PyPI Link:  
https://pypi.org/project/Topsis-Naman-102317229/

---

## 💻 Local Usage

After installing the package:

```python
from topsis.topsis import main

main()
```

Or via command line:

```bash
topsis <inputfile> <weights> <impacts> <outputfile>
```

Example:

```bash
topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
```

---

## 🌐 Live Web Application

Access the deployed web app here:

👉 https://naman25.pythonanywhere.com

### Web App Workflow:
1. Upload the dataset  
2. Enter weights (comma separated)  
3. Enter impacts (+ or -)  
4. Provide your email address  
5. Receive the ranked result file via email  

---

## 📊 Input File Requirements

- First column: Alternative names (non-numeric)
- Remaining columns: Numeric criteria values
- Minimum 3 columns required
- Number of weights must equal number of impacts
- Impacts must be either **+** (benefit) or **-** (cost)

---

## 🛠 Tech Stack

- Python  
- Pandas & NumPy  
- Flask  
- SMTP (Email Automation)  
- PyPI Packaging
test-poetry-app3 2.0.0
A short description of the project
Requires Python: >=3.10

# test-poetry-app3

A short description of the project

## Overview

This documentation covers the test-poetry-app3 application.

## Installation

```bash
pip install test-poetry-app3
```

Or, if you use Poetry:

```bash
poetry add test-poetry-app3
```

## Usage

Run the application:

```bash
test_poetry_app3
```

Or run as a module:

```bash
python -m test_poetry_app3
```

## Quick Start

```bash
# Install it
pip install test-poetry-app3
# Run it
test_poetry_app3
```
myo-tools 0.3.0
A Python package for the myo api.
Requires Python: >=3.8

# myo_tools

`myo_tools` is a collection of functions and utilities designed to support common workflows involving human embodiment and embodied motion. While originally developed to support MyoLab.ai’s work on human embodied intelligence, the APIs are general-purpose and can be extended to other embodied systems and applications.

An overview of the available functionality is provided below. Refer to the individual module folders for detailed feature descriptions and functionality details.

## Modules

### → MuJoCo ([mj](myo_tools/mj))
[MuJoCo](https://mujoco.readthedocs.io/en/stable/APIreference/index.html) is the core physics engine used to simulate motion at MyoLab. This module provides core routines for working directly with MuJoCo’s **compiled** data structures: `MjModel` and `MjData`.

These APIs are intended for robust instantiation, inspection, and manipulation of compiled MuJoCo models and simulation state.


### → MuJoCo Spec ([mjs](myo_tools/mjs))
As of MuJoCo 3.2.0, models can be created and modified using the
[MjSpec](https://mujoco.readthedocs.io/en/stable/programming/modeledit.html)
structure and its associated API. This data structure has a one-to-one correspondence with MJCF; MuJoCo’s own MJCF and URDF XML parsers use this API internally.

This module provides foundational APIs that operate on **uncompiled** models and produce MuJoCo `mjSpec` structures. These tools are useful for editing and programmatically generating models while preserving editability prior to compilation.


### → Utilities ([utils](myo_tools/utils))
The utilities module contains functionality that is independent of MuJoCo, including:
- File and path handling
- Logging helpers
- Mathematical utilities (e.g., tensor and quaternion operations)

This module provide helpful utilities to accelerate your workflows.


## Installation

Install the latest release from [PyPI](https://pypi.org/project/myo_tools/):

```bash
pip install myo_tools
```
Or, install in editable mode using
```bash
git clone https://github.com/myolab/myo_tools.git
cd myo_tools
pip install -e '.[test]'
```