semvec/pypi-packages huggingface dataset:
nitter 0.0.5
Nitter is a simple library to scrap Twitter data using the Nitter website
License: MIT
Keywords: Nitter,Twitter,Twitter Scraper

# Nitter

Simple **Twitter** scraper using **[Nitter](https://nitter.moomoo.me/)**

For now, It's used only to scrap media (images)

## How To Install

You can use the following command to install it.

```
pip install nitter
```

## Getting Started

```python
from nitter import Nitter

n = Nitter()
from nitter import Nitter

nitter = Nitter()
username = "X"

for media in nitter.get_media(username):
    print("Caption:", media.caption)
    print("Pub Date:", media.pub_date)
    print("Images:", "\n".join(img.src for img in media.images), "\n")
```

## Licence

[MIT](https://github.com/iAliF/Nitter/blob/master/LICENSE)
kgenealogic 0.2.3
Genetic genealogy tool for tree structure inference
License: MIT
Requires Python: >=3.8,<3.12

# kgenealogic

`kgenealogic` is a tool for genetic genealogy. It clusters relatives into a hypothesized family
tree based on shared and unshared DNA.

## Features

- Supports GEDmatch pairwise segment and triangulation files
- Uses both presence and inferred absence of triangulation
- Allows, but doesn't require, specifying known relatives at each level of the family tree

## Installation

It is highly recommended to use a virtual environment for installing kgenealogic and its
dependencies (e.g., conda, venv, etc.). Within the virtual environment, just run:

```
pip install kgenealogic
```


## Usage

```
python3 -m kgenealogic init [-p <project-file>]
python3 -m kgenealogic add [-p <project-file>] ... # files to import
python3 -m kgenealogic cluster [-p <project-file>] [-o <out-file>] <config-file>

python3 -m kgenealogic --help # general help
python3 -m kgenealogic <command> --help # help for <command>, e.g. init/add/build/cluster
```

## Algorithms

### Clustering

Clustering is performed by recursive greedy approximation of a min-cut of a genetic closeness
graph.

Specifically, the sums of the (cM) lengths of pairwise matches form the edge-weights of a base
graph. We add to these weights the positive and negative lengths of triangulations for which the
"source" kit of the triangle is listed as a "seed" in the clustering configuration file. When
processing a particular node of the tree, we use triangulations for seeds at that node or on its
path to the root (descendants when viewed as a family tree, ancestors when viewed as a tree data
structure).

Having formed this graph at a particular node of the tree, we consider seeds listed in the
clustering configuration in the maternal or paternal branches of the node. For each connected
component of the graph, if there is at least one seed present in the component, we find an
approximate minimum cut separating the maternal and paternal seeds using a greedy algorithm. 
If there are only, e.g., maternal seeds present and no negative weights (from imputed negative
triangulations), then the entire component will be classified as maternal.

Kits that are classified as either maternal or paternal at a particular node are considered
recursively in the maternal or paternal branches of that mode.

### Inferred negative triangulations

Suppose triangulations and pairwise matches are available for a kit S. And suppose there are kits
T1 and T2 that each match pairwise with kit S on a particular segment, but fail to triangulate with
S on that segment. Then we have a negative triangulation for source S between T1 and T2, indicating
that T1 and T2 likely belong to different branches of the tree. This is represented with a negative
weight between T1 and T2 in the graph used for clustering.

We find all negative triangulations for kits S whose triangulations and pairwise matches are
available. This is somewhat computationally expensive, because the segments where negative
triangulations occur are generally sub-segments of those appearing in the data files.