semvec/pypi-packages huggingface dataset:
names-data-David.Spencer 0.0.1
A small example package
Requires Python: >=3.8

NamesData
=============

## Overview

This repository contains a collection of name datasets gathered from multiple sources, including government records, historical documents, and public databases. The data has been cleaned, standardized, and organized to facilitate easy use in data science projects, demographic studies, and name-related research.

## Features

- **Extensive Name Collection**: A wide range of first and last names from multiple countries and cultures.
- **Python Tools**: Utilities and scripts to efficiently work with the name datasets.
- **Easy Integration**: Simple and intuitive integration with your Python projects.
- **Open Source**: Released under the MIT License, allowing for free use and distribution.

## Installation

To use NameDatabases in your project, you can clone the repository and install the necessary dependencies.

```bash
git clone https://github.com/DecisionNerd/NameDatabases.git
cd NameDatabases
pip install -r requirements.txt
```

## Usage

### Loading the Datasets

You can easily load the name datasets using the provided Python tools. Below is an example of how to load and use the data.

```python
import pandas as pd

# Load last names from a specific country
last_names_df = pd.read_csv('data/last_names/usa.csv')

# Display the first few entries
print(last_names_df.head())
```

### Searching for a Name

The tools provided also include functionality to search for specific names within the datasets.

```python
from tools import name_search

# Search for a specific last name
results = name_search.search_last_name('Smith', 'usa')
print(results)
```

### Generating Random Names

You can also generate random names using the datasets for purposes such as testing or anonymizing data.

```python
from tools import name_generator

# Generate a random full name
random_name = name_generator.generate_random_name('usa')
print(random_name)
```

## Contributing

We welcome contributions from the community! If you would like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Make your changes.
4. Submit a pull request with a detailed description of your changes.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.

## Acknowledgements

A big thank you to Matthew Hager [smashew](https://github.com/smashew) for his original work on the text databases of last names. This project builds upon the foundation he created.

A thank you to Philippe Rémy [philipperemy](https://github.com/philipperemy) for posting the username dataset.  While Philippe's work is not integrated into this project, his data preparation has greatly accelerated our work on NamesData.

## Contact

For any questions or suggestions, feel free to open an issue or start a discussion thread or contact us at hello@frontieranalytica.com.
ema-emb 0.0.5
A library for comparing embedding spaces
Requires Python: >=3.9

# ema-tool

ema-tool is a Python library designed to facilitate the initial **comparison** of diverse **embedding spaces** in biomedical data. By incorporating **user-defined metadata** on the natural grouping of data points, ema-tool enables users to compare global statistics and understand the differences in clustering of natural groupings across different embedding spaces.


## Overview

- **[Features](#features)**
- **[Installation](#installation)**
- **[Colab Notebook Examples](#colab-notebook-examples)**
- **[Links to Embedding Scripts](#links-to-embedding-scripts)**
- **[License](#license)**

## Features

Given a set of samples and metadata, and at least two embedding spaces, the **ema-tool** provides visualisations to compare the following aspects of the embedding spaces:

- **Unsupervised Clusters**: ema-tool provides a simple interface to cluster samples in the embedding space using the KMeans algorithm and compare against user-defined metadata.
- **Dimensionality Reduction**: ema-tool allows users to reduce the dimensionality of the embedding space using PCA, t-SNE, or UMAP.
- **Pairwise Distances**: ema-tool computes pairwise distances between samples in the embedding space. Different distance metrics are available, including Euclidean, Cosine, and Mahalanobis.

The following figure provides an overview of the ema-tool workflow:

![ema-tool](images/ema_overview.jpg)


## Installation

You can install the ema library through pip, or access examples locally by cloning the github repo.

### Installing the ema library
```
pip install ema-emb
```

### Cloning the ema repo
```
git clone https://github/pia-francesca/ema

cd ema                         # enter project directory
pip3 install .                 # install dependencies
jupyter lab colab_notebooks    # open notebook examples in jupyter for local exploration
```

## Colab Notebook Example

An example of how to use the ema-tool library is provided in the following colab notebook: 

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pia-francesca/ema/blob/main/colab_notebooks/ema_tool_application_example.ipynb)


## Links to Embedding Scripts

To allow a flexible use, ema-tool does not include the scripts for generating the embeddings. However, here are some links to external scripts for generating protein embeddings from fasta files using the following models:

- [ESM model](https://github.com/facebookresearch/esm?tab=readme-ov-file#compute-embeddings-in-bulk-from-fasta-)
- [ProtT5 model](https://github.com/agemagician/ProtTrans#-quick-start)

## Contact 

If you have any questions or suggestions, please feel free to reach out to the authors: francesca.risom@hpi.de.

## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
SimplesAPI 0.0.0a14
SimplesAPI is intended to be the easiest API to implementation in Python
Requires Python: <4.0,>=3.12

# SimplesAPI

**SimplesAPI** is a **Python-based application framework** designed to simplify the implementation of various functionalities such as database connections, structured logging, event dispatching, bucket access, and queue processing. By **reducing code repetition** and **enhancing code readability**, SimpleAPI allows developers to focus on solving business problems and rapidly create Proof of Concepts (POCs).

<p align="center">
  <img width="119" alt="image" src="https://github.com/oandersonmagalhaes/simplesapi/assets/83456692/7374e4f8-f9a8-4530-9e71-785e524e2b58">
</p>

## Features (WIP)
- Database Connections: Seamlessly connect to different types of databases with minimal configuration.
- Structured Logging: Implement structured and meaningful logs effortlessly.
- Event Dispatching: Send and handle events with ease.
- Bucket Access: Access and manage data stored in buckets.
- Queue Processing: Send messages to and retrieve messages from processing queues.
- Code Simplicity: Reduce boilerplate code to keep your codebase clean and maintainable.
- Rapid POC Development: Quickly develop and iterate on Proof of Concepts.

## Installation
To install SimplesAPI, simply run:

```bash
pip install simplesapi
```

# Getting Started

## Basic Setup
The main entry point of your application (main.py) should be set up as follows:

```python
from simplesapi.app import SimplesAPI
app = SimplesAPI(routes_path="routes")
```

## Folder Structure
SimpleAPI uses a folder structure to define routes. For example, to create a GET route for /users/{userId}, the folder structure should be:
```
routes/health-check__GET.py
routes/users/POST.py
routes/users/[userId]__GET.py
routes/users/DELETE.py
routes/users/PATCH.py
```

## Route Handler
Inside the route file ([userId]__GET.py), you should define an asynchronous handler function:
```python
async def handler(userId: str):
    return {"id": userId}
```

## Running
```
uvicorn main:app
```

## Contributing
We welcome contributions to SimpleAPI! Please read our contributing guidelines before submitting a pull request.

## License
SimpleAPI is licensed under the MIT License. See the LICENSE file for more details.

## Contact
For any questions or feedback, please contact in project Github.

By leveraging SimpleAPI, developers can streamline their application development process, focus on core business logic, and quickly bring their ideas to life.