semvec/pypi-packages huggingface dataset:
louvain-enhanced 0.2
Community detection package using louvain's algorithm
Requires Python: >=3.6

# Louvain-Enhanced

This package has some functions taken from [python-louvain](https://github.com/taynaud/python-louvain) package

Louvain-Enhanced is a Python package for community detection in large networks using the Louvain method. This package provides enhanced functionalities and optimizations for efficient and accurate community detection.

## Features

- Efficient implementation of the Louvain method for community detection.
- Support for weighted and unweighted graphs.
- Easy integration with NetworkX.
- Randomized node evaluation for different partitions at each call.
- Modular and extensible design.

## Installation

You can install Louvain-Enhanced using pip:

```bash
pip install louvain-enhanced
```
## Usage

### Importing the Package
```python
import networkx as nx
```
```python
from louvain_enhanced import (
    get_partition_at_level,
    calculate_modularity,
    find_best_partition,
    create_dendrogram,
    create_induced_graph,
    load_binary_graph,
)
```

### Creating a Graph
```python
G = nx.karate_club_graph()
```

### Finding the Best Partition
```python
partition = find_best_partition(G)
print(partition)
```

### Calculating Modularity
```python
modularity = calculate_modularity(partition, G)
print(f"Modularity: {modularity}")
```

### Creating a Dendrogram
```python
dendrogram = create_dendrogram(G)
print(dendrogram)
```

### Getting Partition at a Specific Level
```python
level = 1
partition_at_level = get_partition_at_level(dendrogram, level)
print(partition_at_level)
```

### Creating an Induced Graph
```python
induced_graph = create_induced_graph(partition, G)
print(induced_graph)
```

### Loading a Binary Graph
```python
binary_graph = load_binary_graph("path_to_binary_file")
print(binary_graph)
```

## Contributing
Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

## License
This project is licensed under the MIT License. See the LICENSE file for details.

## Author
[Himangshu Singh](https://github.com/himangshusingh)
myuplink 0.7.0
API package for myUplink
Requires Python: >=3.9

# myUplink

Package for getting data from the myUplink API.

Primary usage is for [Home Assistant](https://www.home-assistant.io/integrations/myuplink/)

Package is published to [PyPI](https://pypi.org/project/myuplink/#history)

## Supported features
- Invoke ping and protected ping
- Get systems
- Get notifications
- Get devices on a system
- Get data points on a device

## Requirements
- aiohttp

## Test before commit
```
pip install .
python examples/example1.py
```
flodger 1.0.4
A package for function-driven logging.
Requires Python: >=3.11

# flodger 
A package for function-driven logging.

The ethos of `flodger` is to have all logging within a project driven by
function calls and function outputs.

No explicit log statements need inserting - just the addition of the `log_function_call`
decorator onto any and all functions that you want to log.
zanon 0.3.4
A module for z-anonymity
Requires Python: >=3.6

# zanon

A module to anonymize data streams with zero-delay called z-anonymity.

When instantiating the *zanon* object, the constructor receives a value in seconds for **Delta_t**, the desired probability of reaching k-anonymity expressed with a value between 0 and 1 (**pk**) and a value for **k**.

The *anonymize()* method accepts a tuple with 3 arguments **(t,u,a)** , meaning that at time **t** a user **u** exposes an attribute **a**.
Also a tuple of kind **(t,u, latitude, longitude)** is handled (in that case you need the `pyproj` module). 

If the tuple exposes an attribute that has not been exposed by at least other **z - 1** users in the past **Delta_t**, the tuple is simply ignored. Otherwise, the tuple is printed in the file 'output.txt'.

The algorithm can handle generalization when providing the attribute with a hierarchy using \* as separator (*max_generalization\*...\*min_generalization\*attribute*).
Whenever releasing the attribute is not possible, the algorithm will look for the most specific generalization exposed by at least other **z - 1** users in the past **Delta_t**. If none is found, nothing is print out.
For the geolocation case, the algorithm divides in cells the territory (only Italy) with cells of differents sizes and outputs the cell with at most 5 level of details (3km, 5km, 10km, 30km, 500km).


### Other methods

Run the *study_output.py* after the simulation to plot the distribution of z, pk and traffic during time. (You will find the plot 'z_tuning.pdf').

*endFiles()*: the file output.txt and counters.txt are intended for testing. They need to be closed at the end of the simulation (see example below).

*duration()* print the range of time covered.

## Install
```
pip install zanon
```
[Link to PyPI](https://pypi.org/project/zanon/)


## Example of usage
```python
from zanonymity import zanon

file_in = "trace.txt"
deltat = 3600 #in seconds
pk = 0.8
k = 2

z = zanon.zanon(deltat, pk, k)

for line in open(file_in, 'r'):
    t,u,a = line.split(",")
    z.anonymize((t,u,a))
z.endFiles()
z.duration()

```
hyperlpr2 2.0.0
hyperlpr添加opencv>=4.11与numpy>1.22.0支持
Requires Python: >=3.6

基于北京智云视图科技有限公司/HyperLPR修改新版本cv及numpy报错问题
原始链接:https://gitee.com/zeusees/HyperLPR/blob/master/README.md
侵权请联系删除
shakhbanov 0.0.12
ML tools for time series
Requires Python: >=3.7

<p align="center">
  <img src="https://s3.shakhbanov.org/logo.jpeg" alt="Лого" width="200" />
</p>

# shakhbanov

**ML инструменты для временного ряда**

Добро пожаловать в `shakhbanov_ml` - набор инструментов для обработки и анализа временных рядов. Этот пакет включает в себя методы для восстановления пропусков, очистки данных и прогнозирования, специально разработанные для упрощения работы с временными рядами.

---

## 🚀 Установка

```bash
pip install shakhbanov
```