semvec/pypi-packages huggingface dataset:
lilymodBETA 1.0.4
So simple, its criminal

![Lily Mod Banner](https://private-user-images.githubusercontent.com/118138305/370608403-fdc5a337-8fd8-4dbb-a4f6-b53edd92c81c.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MjkwMjMyNTQsIm5iZiI6MTcyOTAyMjk1NCwicGF0aCI6Ii8xMTgxMzgzMDUvMzcwNjA4NDAzLWZkYzVhMzM3LThmZDgtNGRiYi1hNGY2LWI1M2VkZDkyYzgxYy5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQxMDE1JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MTAxNVQyMDA5MTRaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT1lZjExOTM0NDcyOGJlNWRiNDQ5MDAxMDIzZTY2OWY0NTM1Yjg1OTU2MGU1YTBlMjMyYmQ5YTEyOTkyMjk4OGY4JlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCJ9.inpj0QaS4r8NJS63BDF6C5aKQMnH7hSQCTith3LJxBE)
<h3>Lily Module</h3>
[ The official Python wrapper for the Anntwinetta Engine ]
<hr>
<br>

### Features
- Includes the Lily-Mod scripting layer for the Anntwinetta Engine
- Includes the Pytwin sub-module wrapping the Anntwinetta Engine
- Includes many 'pythonic' utility functions and classes for a quickend development pace
hive-config 0.0.9
Configuration management for Hive

[![version badge]](https://pypi.org/project/hive-config/)

[version badge]: https://img.shields.io/pypi/v/hive-config?color=limegreen

# hive-config

Configuration management for Hive.

## Installation

### With PIP

```sh
pip install hive-config
```

### From source

```sh
git clone https://github.com/gbenson/hive.git
cd hive/libs/common
python3 -m venv .venv
. .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
pip install -e .
flake8 && pytest
```
glutamate 0.0.1a7
Python library for querying and downloading posts from e621
Requires Python: >=3.10
Keywords: e621, glutamate

# glutamate

Easy to use Python library for querying and downloading posts from e621.net.

## Installation

```bash
pip install glutamate
```

## Example

```python
from pathlib import Path

from glutamate.database import E621, E621Data, E621PostsCSV, E621TagsCSV, Query, autoinit_from_directory
from glutamate.dataset import get_captions, write_captions, write_stats
from glutamate.download import download_posts


# Init with qualified file paths
e621_data_directory = Path('./e621-data/')
posts_csv = e621_data_directory / 'posts.csv'
posts = E621PostsCSV(posts_csv)
tags_csv = e621_data_directory / 'tags.csv'
tags = E621TagsCSV(tags_csv)
e621: E621 = E621Data(posts, tags)

# or simple automatic init with directory contains CSVs
e621_data_directory = Path('./e621-data/')
e621 = autoinit_from_directory(e621_data_directory)

query = Query(("kisha", "solo"))
kisha_dataset = e621.select(query)

target_directory = Path().cwd() / 'tmp' / 'kisha_solo'
target_directory.mkdir(parents=True, exist_ok=True)

results = download_posts(posts, target_directory, naming='id')
failed = [result for result in results if not result.ok]
if failed:
    print(f"Failed to download {len(failed)} posts")

captions = kisha_dataset.get_captions(
    naming='id',
    remove_underscores=True,
    tags_to_head=('kisha', 'kisha (character)')
)
write_captions(captions, target_directory)

counts_csv = target_directory / 'tags.csv'
counts = kisha_dataset.get_tags_stats()
write_stats(counts, counts_csv)

```
infantnetwork 0.1.2
Python package to construct and describe neonatal transfer networks.
Requires Python: >=3.6

# infantnetwork

Python package for constructing networks from infant transfer records. Functions and example code is provided to construct networks and describe their shape and structure.

infantnetwork uses the computational backends [NetworkX](https://networkx.org/) and [igraph](https://igraph.org/python/).


Code is released under the open-source [MIT License](LICENSE).


## Installation

Install the package using pip:

```bash
pip install infantnetwork
```

### Usage

``` 
from infantnetwork import computeNetwork, sample_transfers

#Compute network using default settings
output = computeNetwork(sample_transfers)

#Extract graphs
graph_networkx = output['graph_networkx']
graph_igraph = output['graph_igraph']

#Metrics
network_metrics = output['metrics']

#Extract a single network metric
network_metrics['centrality_mean_receiving']

```

The network_metrics atribute of computeNetwork returns the following quantatitve network metrics:

| Metric                            | Description                                                                                      |
|------------------------------------|--------------------------------------------------------------------------------------------------|
| `n_nodes`                         | Number of nodes in the network.                                                                  |
| `n_edges`                     | Total number of edges in the network.                                                |
| `n_transfers`                     | Total number of transfers in the network.                                                |
| `n_self_loops`                    | Number of self-loops.                                   |
| `centrality_median`               | Median node Katz centrality.                     |
| `centrality_mean_receiving`        | Mean node Katz centrality of nodes with incoming transfers.        |
| `density_unweighted`              | Network density (total proportion of possible edges observed)                                         |
| `density_weighted`                | Weighted network density: $$\frac{\sum \text{(edge weights)}}{\text{(n edges)} \cdot \max(\text{(edge weights)})}$$ |
| `efficiency_global`               | Global network efficiency.                 |
| `efficiency_median_local`         | Median of local node efficiencies.          |
| `modularity_greedy`               | Network modularity using the greedy community modularity algorithm.      |
| `modularity_randomwalk`           | Network modularity using random walktrap algorithm             |
| `max_node_percentage_by_component`| Percentage of nodes in the largest connected component.                                   |
| `max_weight_percentage_by_component`| Percentage of weight (transfers) within the largest connected component.                  |
capacho 0.0.5
A tool for registering implementations and provisioning them with containers, facilitating seamless integration and management
Requires Python: >=3.9
Keywords: Container, Dependency Injection, Register

# capacho 🧺

[![version](https://img.shields.io/github/release/alice-biometrics/capacho/all.svg)](https://github.com/alice-biometrics/capacho/releases)
[![ci](https://github.com/alice-biometrics/capacho/workflows/ci/badge.svg)](https://github.com/alice-biometrics/capacho/actions)
[![pypi](https://img.shields.io/pypi/dm/capacho)](https://pypi.org/project/capacho/)
[![codecov](https://codecov.io/gh/alice-biometrics/capacho/branch/main/graph/badge.svg?token=BX1IZJZLJQ)](https://codecov.io/gh/alice-biometrics/capacho)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) 
[![Pydantic v2](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/pydantic/pydantic/main/docs/badge/v2.json)](https://docs.pydantic.dev/latest/contributing/#badges)
[![license](https://img.shields.io/github/license/alice-biometrics/capacho.svg)](https://github.com/alice-biometrics/capacho/blob/main/LICENSE)
[![versions](https://img.shields.io/pypi/pyversions/capacho.svg)](https://github.com/alice-biometrics/capacho)

<img src="https://github.com/alice-biometrics/custom-emojis/blob/master/images/alice_header.png?raw=true" width=auto>

---



**Documentation**: <a href="https://alice-biometrics.github.io/capacho/" target="_blank">https://alice-biometrics.github.io/capacho/</a>

**Source Code**: <a href="https://github.com/alice-biometrics/capacho" target="_blank">https://github.com/alice-biometrics/capacho</a>

---

## What is capacho 🧺?

A tool for registering implementations and provisioning them with containers, facilitating seamless integration and management

## How could capacho 🧺 help me?

If you want to create abstractions and implement their corresponding solutions, this approach is highly effective.
It allows you to register implementations and define aliases for use within a container, making it particularly useful
for configuring implementations via configuration files.

## Installation 💻

```console
pip install capacho
```

## Contribute

We'd love you to contribute to *capacho* 🥳🥳🥳🥳🥳🥳️️!

For more information, check our [documentation](https://alice-biometrics.github.io/capacho/contributing/)

## Contact 📬

support@alicebiometrics.com