semvec/pypi-packages huggingface dataset:
image-processing-studies-mvbs 0.0.1
image processing package using skimage to study
Requires Python: >=3.5

# image_processing_studies_mvbs

Description.
The package image_processing_studies_mvbs is used to:
Processing - Histogram matching - Structural similarity - Resize image
Utils - Read image - Save image - Plot image - Plot result - Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install package_name
```

## Author

Autora real: Karina
(Aluno que sestÃ¡ utilizando esse repositorio a fim de estudos: Marcelo Victor)

## License

[MIT](https://choosealicense.com/licenses/mit/)
awsmate 0.4.2
A companion for your AWS Python developments.
Requires Python: >=3.8
Keywords: aws, aws-apigateway, aws-eventbridge, aws-lambda, aws-s3, python

[![Version](https://img.shields.io/pypi/v/awsmate)](https://pypi.org/project/awsmate/ "Project page - PyPI")
[![Python](https://img.shields.io/pypi/pyversions/awsmate)](https://github.com/shlublu/awsmate/tree/master/src/awsmate "Source code - Github")
[![License](https://img.shields.io/github/license/shlublu/awsmate)](https://github.com/shlublu/awsmate/blob/master/LICENSE.txt "License - Github")
[![Tests](https://github.com/shlublu/awsmate/actions/workflows/tests.yaml/badge.svg?branch=master)](https://github.com/shlublu/awsmate/actions "Workflows - Github")
[![Documentation](https://readthedocs.org/projects/awsmate/badge/?version=latest)](https://awsmate.readthedocs.io/ "Documentation - ReadTheDocs.io")


`awsmate` is a companion for your AWS developments in Python.
It provides you with helper code that facilitates the use of various AWS services.

## What it is, what it does

`awsmate` pools AWS specific code that is rewriten and retested for each new project:
* it simplifies the use of some AWS services
* it simplifies the error handling related to the use of these services

`awsmate` does not do what others have already done and sticks to simplifying the use of AWS:
* it does not replace existing major libraries such as [s3fs](https://s3fs.readthedocs.io/en/latest/), [pynamodb](https://pynamodb.readthedocs.io/en/latest/), [aws-lambda-powertools](https://pypi.org/project/aws-lambda-powertools/) or [boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html)
* it does not implement functional use cases or algorithms

## Installation

* Command line: `pip3 install awsmate`
* [PyPI page](https://pypi.org/project/awsmate/ "PyPI page")

## Documentation

The complete documentation is available at [ReadTheDocs](https://awsmate.readthedocs.io/ "Documentation - ReadTheDocs.io").

## Example application

An example application is available to demonstrate the main features of this library. It is documented [in this chapter](https://awsmate.readthedocs.io/en/latest/example_application.html).

## Issues

Should you encounter any bug or problem of any kind, please feel free to open an issue according to our [contribution guidelines](https://awsmate.readthedocs.io/en/latest/contributing.html).

## License

This project is licensed under the EUROPEAN UNION PUBLIC LICENCE (EUPL) v. 1.2, which is compatible with the majority of open source licences.

* [View all translations](https://joinup.ec.europa.eu/collection/eupl/eupl-text-eupl-12 "Translations of the EUPL-1.2")
* [OSI Compatibility matrix](https://joinup.ec.europa.eu/collection/eupl/matrix-eupl-compatible-open-source-licences "Compatibility matrix of the EUPL-1.2")
sharepoint-to-df 0.5.0
A library to fetch SharePoint list data and return it as a Pandas DataFrame.

# SharePoint to DataFrame

A Python library to fetch data from a SharePoint list and return it as a Pandas DataFrame.

## Installation

You can install the package using pip:

```bash
pip install sharepoint_to_df
```

## Usage

Here is a simple example of how to use the library:

```python
import pandas as pd
from sharepoint_to_df import sharepoint_utils

# Define your SharePoint credentials and site
username = 'your_username'
password = 'your_password'
sharepoint_site = 'https://yourtenant.sharepoint.com/sites/yoursite'
list_name = 'Your List Name'

# Fetch the data from the SharePoint list
df = sharepoint_utils.get_list_view_test(username, password, sharepoint_site, list_name)

# Display the DataFrame
print(df.head())
```

### Parameters

- `username` (str): Your SharePoint username.
- `password` (str): Your SharePoint password.
- `sharepoint_site` (str): URL of your SharePoint site.
- `list_name` (str): Name of the SharePoint list.
- `view_name` (str, optional): Name of the SharePoint view. Default is "All Items".

### Requirements

- `pandas`
- `office365-rest-python-client`

You can install the required dependencies using:

```bash
pip install pandas office365-rest-python-client
```
didall 0.1.5
An SDK for cross-platform identity authentication and end-to-end encrypted communication based on DID
Requires Python: >=3.7

# Cross-Platform Identity Authentication and End-to-End Encrypted Communication Technology Based on DID

**Author**: Chang Gaowei  
**Email**: chgaowei@gmail.com  
**Website**: [pi-unlimited.com](http://pi-unlimited.com)  
**中文版本**: [中文版本readme](https://github.com/chgaowei/didall/blob/main/README.cn.md)  

## Abstract

This project is an open-source SDK implementation based on Decentralized Identifier (DID) and end-to-end encrypted communication technology. For technical details, refer to the [Technical White Paper: A Cross-Platform Identity Authentication and End-to-End Encrypted Communication Technology Based on DID](https://egp0uc2jnx.feishu.cn/wiki/JyaIwTwngiWi9qkJjjycI4XcnXe?from=from_copylink). With the didall open-source project, any intelligent agent or server can connect to the DID server, register its DID, accept connections from other users, connect to other users, and engage in end-to-end encrypted communication.

## Features

- **Cross-Platform Identity Authentication**: Achieves identity interoperability across different platforms using DID.
- **End-to-End Encrypted Communication**: Uses ECDHE for short-term key agreement to ensure communication security.
- **Efficient and Secure**: Simplifies the identity verification process, ensuring data confidentiality and integrity.

### Installation

The latest version has been removed from PyPI, so install directly:

```bash
pip install didall
```

### Usage

After installing the didall library, you can run the sample code in the examples directory to generate DID files for Alice and Bob, save Alice's DID file to the DID server, and then have Bob connect to Alice's DID for end-to-end encrypted communication.

1. Generate two DID documents, alice.json and bob.json, save them to the specified files, and register them with the DID server:
```bash
python sample_did.py alice.json
python sample_did.py bob.json
```

2. Start Alice's demo:
```bash
python sample_alice.py alice.json
```

3. Start Bob's demo:
```bash
python sample_bob.py bob.json
```

By checking the logs, you can see that Alice and Bob have successfully connected and engaged in end-to-end encrypted communication.

## Contributing

Contributions to this project are welcome. Please read the contribution guidelines before submitting a pull request.

## License
    
This project is open-sourced under the MIT License. For more details, please refer to the LICENSE file.

## Packaging and Uploading (update the version number in setup.py first)

```bash
python setup.py sdist bdist_wheel 
twine upload dist/*        
```
ctweather 0.0.7
get weather information of cities
Requires Python: >=3.6

# ctweather
get information of weather with 3days
## 목적
OpenWeatherMap API를 활용하여 3일간의 날씨 정보를 조회하는 파이썬 클라이언트 소스코드입니다. 

날씨를 알고 싶은 도시와 시간(오늘, 내일, 모레)를 물어보면 해당 도시의 기온, 습도등의 정보를 얻을 수 있습니다.

### 사용 하기

1. 아래의 웹사이트에서 OpenWeather의 API KEY를 얻습니다.('owm_apikey.txt'에 api key를 입력합니다) 

    - <https://home.openweathermap.org/>
      
2. 필요 파이썬 패키지를 설치합니다.

    - 'pip install requests'
llm-price-scraper 1.0.3
A simple Python SDK to scrape and retrieve pricing information for Large Language Models (LLMs) from an external webpage, with structured models for easy integration and usage.
Requires Python: >=3.6

# LLM Price Scraper
LLM Price Scraper is a Python package designed to scrape and organize pricing information for large language models (LLMs)
from the following sources:
- https://docsbot.ai/tools/gpt-openai-api-pricing-calculator (best source for now)
- https://huggingface.co/spaces/philschmid/llm-pricing
- https://www.botgenuity.com/tools/llm-pricing
- https://llm-price.com

## Installation
You can install the package using pip:
```bash
pip install llm-price-scraper==1.0.3
```

## Usage
Once you have installed the scrapper, you can use it to quickly retrieve the current pricing information from the website.
```python
from llm_price_scraper.scrapers import LlmPricingScraper

# Get the pricing information
pricing_data = LlmPricingScraper.scrape()

# Loop through each pricing entry and print data
for entry in pricing_data:
    print(f"Model: {entry.model}")
    print(f"Provider: {entry.provider}")
    print(f"1M input tokens: {entry.input_tokens_price}$")
    print(f"1M output tokens: {entry.output_tokens_price}$")
    print(f"Context: {entry.context}")
    print(f"Source: {entry.source}")
    print(f"Updated: {entry.updated}")
    print("-" * 40)
    
# get all gpt-4o models
gpt_4o_models = [entry for entry in pricing_data if "gpt-4o" in entry.model.lower()]
print("GPT-4o models:")
for entry in gpt_4o_models:
    print(f"Model: {entry.model}")
    print(f"Provider: {entry.provider}")
    print(f"1M input tokens: {entry.input_tokens_price}$")
    print(f"1M output tokens: {entry.output_tokens_price}$")
    print(f"Context: {entry.context}")
    print(f"Source: {entry.source}")
    print(f"Updated: {entry.updated}")
    print("-" * 40)
```
You can also chose the source of the data you want to scrape by passing the source as an argument to the `scrape` method. The available sources are defined in the `DataSources` enum.
```python
from llm_price_scraper.scrapers import LlmPricingScraper, DataSources

pricing_data = LlmPricingScraper.scrape(DataSources.HUGGINGFACE)
```

### Example Output
After running the above code, you should see an output like this:

```
Model: gpt-4-32k
Provider: OpenAI
1M input tokens: 60
1M output tokens: 120
Context: 32K
Source: https://www.botgenuity.com/tools/llm-pricing
Updated: March 16, 2024
----------------------------------------
...
```

### Error Handling
In case of a failure to connect to the webpage, an exception will be thrown with an appropriate message. For example:

```
Exception: Failed to retrieve the webpage. Status code: 404
```

## Contributing
Contributions, bug reports, and feature requests are welcome! Feel free to submit a pull request or open an issue on GitHub.

## License
This project is licensed under the MIT License - see the LICENSE file for details.