semvec/pypi-packages huggingface dataset:
tryonai 0.1.0
TryOn AI Python SDK - Use this SDK to interact with TryOn AI API. Integrate virtual try-on, model swap, outfit generation and captioning in your application.
Requires Python: >=3.10

# TryOn AI Python SDK
Use this SDK to interact with TryOn AI API. Integrate virtual try-on, model swap, outfit generation and captioning in your application.

## Installation
```bash
pip install tryonai
```

## Environment Variables
```bash
export TRYONAI_SERVER_URL=https://prod.server.tryonlabs.ai
export TRYONAI_EMAIL=your_email@example.com
export TRYONAI_PASSWORD=your_password
```

# Usage
## Get access token
```python
from tryonai import get_access_token

# Get access token
access_token = get_access_token(email=os.getenv('TRYONAI_EMAIL'), password=os.getenv('TRYONAI_PASSWORD'))
print("access_token:", access_token)
```

## Upload Image
```python
from tryonai import upload_image

# Upload image
uploaded_image = upload_image(image_path="data/garment.jpg", data={"type": "garment", "gender": "male", "preprocess": "true"}, access_token=access_token)
print("uploaded_image:", uploaded_image)
```

## Virtual Try On
```python
from tryonai import perform_virtual_try_on

# Perform virtual try on
experiment = perform_virtual_try_on(garment_id=garment_id, person_id=person_id)
print("experiment:", experiment)
```

## Model Swap
```python
from tryonai import perform_model_swap

# Perform model swap
experiment = perform_model_swap(garment_id=garment_id, params={"prompt": "Indian male model with straight hair, blue eyes, round face, 4k resolution, high-resolution", "guidance_scale": 4.5, "num_inference_steps": 30, "seed":-1, "num_images": 3, "width":512, "height": 512})
print("experiment:", experiment)
```

## Outfit Generation
```python
from tryonai import generate_outfit

# Generate outfit
experiment = generate_outfit(params={"prompt": "A flat lay image of a T-shirt, round neck, blue color, high-resolution, 4k resolution", "height":256, "width": 256, "seed": -1, "guidance_scale": 4.5, "num_inference_steps": 20, "num_images_per_prompt": 2, "strength": 0.8})
print("experiment:", experiment)
```

## Outfit Captioning
```python
from tryonai import caption_outfit

# Caption outfit
experiment = caption_outfit(garment_id=garment_id, params={"output_format": "text"})
print("experiment:", experiment)
```

## Get Experiment
```python
from tryonai import get_experiment

# Get experiment
experiment = get_experiment(experiment_id=experiment_id)
print("experiment:", experiment)
```

## Download Image
```python
from tryonai import download_image

# Download image
download_image(image_url=experiment_result_image_url, image_path="data/result.png")
```
slack-notif 0.1.0
A simple slack notification tool to alert you when scripts finish running.
Keywords: slack, notification, alert

# slack-notif

A simple Python package to send well-structured Slack notifications for your scripts, including success/error status, execution time, and custom content.

## Installation

```bash
pip install slack-notif
```

## Usage

First, you'll need a Slack bot token. Create one at https://api.slack.com/apps.

### Basic Usage

```python
from slack_notif import NotificationManager

# Initialize the notification manager
notif = NotificationManager(
    token="xoxb-your-token",
    channel="#your-channel"
)

# Use as a decorator
@notif.notify
def my_function():
    # Your code here
    pass

# Add extra content to notifications
@notif.notify(extra_content={"Environment": "Production", "Version": "1.0.0"})
def another_function():
    # Your code here
    pass

# Or send notifications manually
notif.send_notification(
    status="success",
    message="Data processing complete",
    duration=42.5,  # seconds
    extra_content={"Rows Processed": 1000}
)
```

### Features

- ✨ Clean, modern Slack message formatting
- ⏱️ Automatic execution time tracking
- 🎯 Success/error status with appropriate colors
- 📝 Support for custom additional content
- 🔄 Easy-to-use decorator syntax
- ⚡ Simple manual notification sending

### Example Output

The notifications will include:
- A header with the script status (Success/Error)
- The main message
- Execution duration (if provided)
- Any extra content in a clean key-value format

## Development

To set up for development:

```bash
pip install -e ".[dev]"
```

## License

Not affiliated with Slack.

MIT
esma 0.0.502
Quantum Espresso automation tool
Requires Python: >=3.10

# Espresso Machine
Automation library for Quantum Espresso via python

Start discovering by checking out the Tutorials section for different calculations
It is suggested to use a virtual environment to avaoid any compatibility issues.
## Automated Calculation
Any kind of choosen calculation type will be initialized after automatically generating the input files
## Parameter Adjustment
DFT parameters can be adjusted using the functional approach to keep the bugs away
## Utility Tools
Various kind of utility tools added to make the workflow smooth

### How to use
#### 1. Prepare the python environment for avoiding compatibility issues
  > python -m venv .venv

  > source .venv/bin/activate 

  > pip install esma
#### 2. Create work script
- Initialize model and define paths
  > model = esma.project(project_id="Si") #Define project
  > model.set_cores(4) #Define number of processing cores
  > model.get_structure(format='poscar',path='./Structures/Si.poscar') 
- Pseudopotential names should be as same as the the. For example for Si atom it should be named as Si.UPF.
  > model.set_pseudo(path="./Pseudopotentials") 
- Adjust system specific parameters
  > model.ecutwfc(80) #Set wavefunction cutoff
  > model.k_points([4,4,4]) #Set number of k points
- Start calculations 
  > model.calculate('vc-relax')
  > model.calculate('scf')
- Define band path and calculate band structure 
  > path = ['L','GAMMA','X','K','GAMMA']
  > num_points = 100 
  > model.band_points(path,num_points) 
  > model.calculate('bands') 
- Plot band structure
  > model.plot('electron',ylim=[-13,12]) #plot electron bands
llm-researcher 0.1.0
LLM plugin for comprehensive research using Jina AI's Search Foundation APIs
Requires Python: >=3.8

# llm-jina-research

[![PyPI](https://img.shields.io/pypi/v/llm-jina-research.svg)](https://pypi.org/project/llm-jina-research/)
[![Changelog](https://img.shields.io/github/v/release/simonw/llm-jina-research?include_prereleases&label=changelog)](https://github.com/simonw/llm-jina-research/releases)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/llm-jina-research/blob/main/LICENSE)

LLM plugin for comprehensive research using Jina AI's Search Foundation APIs

## Installation

Install this plugin in the same environment as LLM:

```bash
llm install llm-jina-research
```

Set your Jina AI API key:

```bash
export JINA_API_KEY="your_api_key_here"
```

Get your Jina AI API key for free: https://jina.ai/?sui=apikey

## Usage

Perform comprehensive research using Jina AI's APIs:

```bash
llm research "What are the latest advancements in AI safety?"
```

Options:
- `query`: The research question to investigate
- `--output`: Path to save results as JSON file

## Features

- 🔍 Web search with relevance filtering
- 📥 Advanced content extraction with reader API
- 🧬 Query embedding generation
- 🔬 Document reranking based on relevance
- 📊 Interactive terminal report generation
- 📄 JSON output for programmatic use
- 📝 Automatic research session logging

## Example

```bash
llm research "What are the latest advancements in AI safety?" --output results.json
```

This will:
1. Search the web for relevant information
2. Extract and process content from top results
3. Analyze and rank the content
4. Generate an interactive report
5. Save the results to a JSON file

## Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:

```bash
cd llm-jina-research
python -m venv venv
source venv/bin/activate
```

Now install the dependencies and test dependencies:

```bash
pip install -e '.[test]'
```

To run the tests:

```bash
pytest
```

## Contributing

Contributions to llm-jina-research are welcome! Please refer to the [GitHub repository](https://github.com/simonw/llm-jina-research) for more information on how to contribute.

## License

This project is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for details.
sbcli-allow 0.0.1
CLI for managing SimplyBlock cluster


# Simply Block
[![Docker Image Build](https://github.com/simplyblock-io/sbcli/actions/workflows/docker-image.yml/badge.svg)](https://github.com/simplyblock-io/sbcli/actions/workflows/docker-image.yml)

[![Python Unit Testing](https://github.com/simplyblock-io/sbcli/actions/workflows/python-testing.yml/badge.svg)](https://github.com/simplyblock-io/sbcli/actions/workflows/python-testing.yml)


## Install
Add the package repo from AWS CodeArtifact using [awscli](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html)

```bash
aws codeartifact login --tool pip --repository sbcli --domain simplyblock --domain-owner 565979732541 --region eu-west-1
```
Install package
```bash
pip install --extra-index-url https://pypi.org/simple sbcli-dev
```

# Components

## Simply Block Core
Contains core logic and controllers for the simplyblock cluster

## Simply Block CLI
Please see this document
[README.md](../main/simplyblock_cli/README.md)


## Simply Block Web API
Please see this document
[README.md](../main/simplyblock_web/README.md)



### local development

FoundationDB requires a client library (libfdb_c.dylib) for the Python bindings to interact with the database.
Depending on the OS architecture, please install the appropriate version from the official github repo

```
wget https://github.com/apple/foundationdb/releases/download/7.3.3/FoundationDB-7.3.3_arm64.pkg
```

setup the code on a management node and the webApp code can be developed by building the `docker-compose-dev.yml` file.


```
sudo docker compose -f docker-compose-dev.yml up --build -d
```
pycppstreams 0.0.2
A C++ style stream library for Python

Use C++ style stream to read and write data in Python
It supports:
- C++ style iostream
- C++ style stringstream
- C++ style fstream
- C++ style but only in this library:
    bytesstream
    voicestream (use voicestream_init(pyttsxn.init()) to initialize (n ∈ {blank, 2, 3, 4}))
    only on Windows: keystream (use keystream_init() to initialize, use keybd >> var to read key and keybd << bytes to write chars)

Special thanks to:
- pyttsx4
Zmap-sc 1.0.1
An algorithm framework employing multi-layered regional constraints to accurately assign cell locations, enhancing spatial accuracy and resilience to noise.
Requires Python: >=3.8.0

<img src='logo.png' align="right" height="200" /></a>
Mapping cell locations via multi-layer regionalization constraints
=========================================================================

Introduction
------------
Resolving spatial cell arrangement is crucial for understanding physiological and pathological processes. While scRNA-seq captures gene expression at single-cell resolution, it loses spatial context, and current spatial transcriptomics methods often compromise on throughput or resolution. Existing integration methods face challenges with accuracy and scalability due to noise from molecular diffusion, cell segmentation errors, and disproportionate cell-type representation. We present Zmap, an algorithm framework employing multi-layered regional constraints to accurately assign cell locations, enhancing spatial accuracy and resilience to noise. Comparative analysis on benchmark datasets demonstrates Zmap’s superior accuracy and scalability over existing methods. Applied to liver cancer tissue, Zmap revealed spatial heterogeneity of cDC cells, a detail missed by deconvolution-based techniques, and achieved cell-cell interaction resolution beyond traditional mapping approaches. Additionally, Zmap outperforms current techniques in computational efficiency and resource usage, particularly with large-scale stereo-seq data, underscoring its potential for broad application.

![](overview.jpg)

Installation
------------
Zmap can be installed either through GitHub or PyPI.

To install from GitHub:

    git clone https://github.com/caiquanyou/Zmap
    cd Zmap
    python setup.py install # or pip install .

Alternatively, install via PyPI using:

    pip install Zmap-sc

Usage
-----
After installation, Zmap can be used in Python as follows:
 ```python
    import Zmap as zm
    import scanpy as sc
    scdata = sc.read_h5ad('/Path/to/scdata.h5ad')
    stdata = sc.read_h5ad('/Path/to/stdata.h5ad')
    stdata_grid = zm.generate_grid(stdata,width=none)
    zm_object = zm.Zmap(scdata,stdata_grid,cluster_time=1,device='cpu')
    zm_object.allocate()
    cell_alocated_data = zm.sc2sc(scdata, stdata, zm_object.spot_matrix,thres=0.1,method='max')
```
Also can running in terminal:
 ```bash
zmap \
    -sc SC_path \
    -st ST_path \
    -w Width_of_grid \
    [-o OUTPUT] \
    [--cluster_time CLUSTER_TIME] \
    [--custom_region CUSTOM_REGION] \
    [--cluster_thres CLUSTER_THRES] \
    [--thres THRES] \
    [--method {max,lap}] \
    [--device {cpu,cuda}]
```

Contributing
------------
Contributions to Zmap are welcome. Please refer to the project's issues and pull requests for areas where you can help.

License
-------
**Free Software**

Support and Contact
-------------------
For support or to contact the developers, please use the project's GitHub Issues page.
cppstreams 1.0.4
A C++ style stream library for Python

Use C++ style stream to read and write data in Python
It supports:
- C++ style iostream
- C++ style stringstream
- C++ style fstream
- C++ style but only in this library:
    bytesstream
    voicestream (use voicestream_init(pyttsxn.init()) to initialize (n ∈ {blank, 2, 3, 4}))
    only on Windows: keystream (use keystream_init() to initialize, use keybd >> var to read key and keybd << bytes to write chars)

Special thanks to:
- pyttsx4
pinterest-downloader 2.0.6
A Pinterest media downloader
Requires Python: >=3.7

# pinterest-downloader

This Python library allows you to search for images on Pinterest and download them asynchronously. It uses asynchronous programming to efficiently search

## Features

- Search Pinterest for images based on a query
- Download images and videos from Pinterest
- Asynchronous operations for improved performance
- Supports both image and video content from Pinterest


## Requirements

- Python 3.7+
- aiohttp


## Installation

You can install this package using pip:

```shellscript
pip3 install -U pinterest-downloader
```

## Usage

You can use this library in your Python projects by importing the necessary functions:

```python
import asyncio
from pinterest_downloader import *

async def main():
    query = "cute cats"
    results = await pinterest_search(query)

    for result in results:
        print(result['url'])

    result = await download_pinterest_media("https://pin.it/6nEi5NMe9", return_url=True)
    if result['success']:
        print(f"Type: {result['type']}, URL: {result['url']}")

if __name__ == "__main__":
    asyncio.run(main())
```

## Available Functions

1. `pinterest_search(query: str) -> List[Dict[str, str]]`

    1. Searches Pinterest for images based on the given query.
    2. Returns a list of dictionaries containing image URLs and thumbnails.

2. `download_pinterest_media(url: str, output_dir: str = '.', return_url: bool = False) -> Dict[str, Any]`

    1. Downloads a single image or video from Pinterest.
    2. Returns a dictionary with information about the downloaded media.
html-scrubber 0.0.3
A simple HTML cleaner utility built with BeautifulSoup. This library allows you to easily clean HTML content by removing unwanted tags like styles, scripts, iframes, and more. It provides an easy-to-use interface for cleaning HTML from various sources, making it ideal for web scraping, data extraction, or sanitizing HTML content before processing.
Requires Python: >=3.12

# html-scrubber

`html-scrubber` is a simple Python library built with BeautifulSoup that allows you to clean HTML content by removing unwanted tags such as styles, scripts, iframes, and more. It's ideal for web scraping, data extraction, or sanitizing HTML content before further processing.

## Features

- Clean HTML content by removing unnecessary elements
- Supports removal of `<style>`, `<script>`, `<iframe>`, `<svg>`, `<meta>`, and `<noscript>` tags
- Easily extendable with custom tags to remove
- Option to return the cleaned HTML as a BeautifulSoup object or a plain string

## Installation

To install `html-scrubber`, you can use pip:

```bash
pip install html-scrubber
```

## Usage

Here is an example of how to use the `html-scrubber` library to clean HTML content:

```python
from html_scrubber import clean_html

raw_html = """
<html>
    <head><style>body {color: red;}</style></head>
    <body>
        <script>alert("test");</script>
        <div>Test content</div>
        <iframe src="https://example.com"></iframe>
    </body>
</html>
"""

# Clean the HTML content by removing style, script, and iframe tags
cleaned_html = clean_html(
    html_content=raw_html,
    remove_styles=True,
    remove_scripts=True,
    remove_iframes=True,
    return_as_string=True,
)
numplot 1.0.0
Package mathstats contains mathematical and statistical functions, distributions, tests
Requires Python: >=3.6
Keywords: files speedfiles

# Numplot File Library #

## What is this? ##
The module allows you to work with files in just one line of code, without the need to manually open and close the file each time

## Quick Guide ##
The module is based on the following structure:

    
    f = open('data.txt')
    data = f.readlines()
    f.close()
    
Which Python provides by standard.


----------
soda-conan-server 2025.1.27.0
soda's conan server
Requires Python: >=3.10

# Soda's conan server

<a href="https://pypi.org/project/soda-conan-server/">
    <img alt="PyPI - Version" src="https://img.shields.io/pypi/v/soda-conan-server">
</a>

Currently(2025-01-27) conan-server was not supported in python 3.13 due to cgi module removed and (unupgraded) bottle dependency. So I created this package.

Conflict with official `conan` and `conan_server` package. Use virtual environment.

Will not keep sync with official repo; use at your own risk.
nnumplot 1.0.0
Package mathstats contains mathematical and statistical functions, distributions, tests
Requires Python: >=3.6
Keywords: files speedfiles

# Numplot File Library #

## What is this? ##
The module allows you to work with files in just one line of code, without the need to manually open and close the file each time

## Quick Guide ##
The module is based on the following structure:

    
    f = open('data.txt')
    data = f.readlines()
    f.close()
    
Which Python provides by standard.


----------
mathstat 1.0.2
Package mathstats contains mathematical and statistical functions, distributions, tests
Requires Python: >=3.6
Keywords: files speedfiles

# Numplot File Library #

## What is this? ##
The module allows you to work with files in just one line of code, without the need to manually open and close the file each time

## Quick Guide ##
The module is based on the following structure:

    
    f = open('data.txt')
    data = f.readlines()
    f.close()
    
Which Python provides by standard.


----------
simple-photo-organizer 0.1.3.1
A CLI tool for sorting photos into folders according to their modification date and numbering them. Needs exiftool to work and asks for permission to install it, if it is not found.
Requires Python: >=3.6

# Simple Photo Organizer

Beginner project: A simple CLI tool for sorting photos into folders according to their modification date and numbering them. Needs exiftool to work and asks for permission to install it, if it is not found.

Takes a source folder with images (or technically anything) inside and an ideally empty destination folder, in which the images are moved, sorted into folders and numbered (YYYY-number, YYYY-MM-number or YYYY-MM-DD-number) based on the modification date in their Metadata.