semvec/pypi-packages huggingface dataset:
sequmap 0.1.0
An AA/NT sequence encoder using umap & pca
Requires Python: >=3.6

# sequmap

Encode and embed sequences as kmer vectors (both NT and AA) and generate projections using UMAP

### Setup

```
pip install sequmap
```

### Usage

Embed and project sequences using

```
from sequmap import sequmap, NT_DICT
proj = sequmap(seqs, ndim, k = 5, lookup_dic = NT_DICT, n_neighbors = 30, min_dist = 0.001) 

```

where `seqs` is an array of strings, `ndim` is the number of projected dimensions and `k` is the kmer size. By default, sequences are embedded by counting all unique kmers and the distance between sequences is estimated using the SquaredEuclidean distance between dimensionality-reduced kmer vector.

Additionally, a simpler `seqpca` method is included which just performs PCA in the kmer domain, used as follows:

```
from sequmap import seqpca, NT_DICT
proj = seqpca(seqs, ndim, k = 5, lookup_dic = NT_DICT) 

```

The following lookup dictionaries are included for sequence encoding:

```
AA_DICT = Dict(
    'A' => 1, 'C' => 2, 'D' => 3, 'E' => 4, 'F' => 5, 
    'G' => 6, 'H' => 7, 'I' => 8, 'K' => 9, 'L' => 10, 
    'M' => 11, 'N' => 12, 'P' => 13, 'Q' => 14, 'R' => 15, 
    'S' => 16, 'T' => 17, 'V' => 18, 'W' => 19, 'Y' => 20, 
    '*' => 21, 'X' => 22
    )

NT_DICT = Dict('A' => 1, 'C' => 2, 'G' => 3, 'T' => 4)
```
example-pkg-bergsigfinn 0.0.1
An AA/NT sequence encoder using umap & pca
Requires Python: >=3.6

# sequmap

Encode and embed sequences as kmer vectors (both NT and AA) and generate projections using UMAP

### Setup

```
pip install sequmap
```

### Usage

Embed and project sequences using

```
from sequmap import sequmap
proj = sequmap(seqs, ndim, k = 5, lookup_dic = NT_DICT, n_neighbors = 30, min_dist = 0.001) 
``` 

where `seqs` is an array of strings, `ndim` is the number of projected dimensions and `k` is the kmer size. By default, sequences are embedded by counting all unique kmers and the distance between sequences is estimated using the SquaredEuclidean distance between dimensionality-reduced kmer vector.

Alternatively, if PCA is not used, you can use corrected_kmer_distance to estimate distance between kmer vectors:

```
from sequmap import sequmap, corrected_kmer_distance
proj = sequmap(seqs, ndim, k = 5, lookup_dic = NT_DICT, n_neighbors = 30, min_dist = 0.001, pca = False, metric = corrected_kmer_distance, metric_kwds = {"k": 5}) 

```

The following lookup dictionaries are included for sequence encoding:

```
AA_DICT = Dict(
    'A' => 1, 'C' => 2, 'D' => 3, 'E' => 4, 'F' => 5, 
    'G' => 6, 'H' => 7, 'I' => 8, 'K' => 9, 'L' => 10, 
    'M' => 11, 'N' => 12, 'P' => 13, 'Q' => 14, 'R' => 15, 
    'S' => 16, 'T' => 17, 'V' => 18, 'W' => 19, 'Y' => 20, 
    '*' => 21, 'X' => 22
    )

NT_DICT = Dict('A' => 1, 'C' => 2, 'G' => 3, 'T' => 4)
```
GameTools-spidertyler2005 0.1.2
A bunch of tools for making games in Pygame and other tools.
Requires Python: >=3.6

![Version](https://img.shields.io/badge/Version-0.1.2-green) ![Status](https://img.shields.io/badge/Status-Operational-mint) ![issues](https://img.shields.io/github/issues/spidertyler2005/GameTools)

# GameTools

GameTools is a collection of packages that helps you create your game.

## Features
|Feature|Description|State|
|:--:|:--:|:--:|
|Templates|Templates make game creation easier by allowing you to write less code.|:white_check_mark:|
|DeltaTime Calculator|Calculates the deltaTime from the previous call of the function|:white_check_mark:|
|Messaging System|Have the ability to send messages and have functions listen to those Messages via a decorator -> ``@recv_Message("Message Name")``|:white_check_mark:|
|Entity System|The ability to create entitys|:memo:|
|UI System|A proper game Ui System is essentially for any menu in a game.|:black_large_square:|

:memo: -> started
:white_check_mark: -> finished or satisfactory
:red_circle: -> broken
:black_large_square: -> not started

---

## Getting Started

### Installation
 in your terminal run `python -m pip install GameTools`.Alternatively, you can download the source directly and throw it into your project.

### examples
If you look into Testing.py you can see me playing around with everything. There are also examples in the docs

### Documentation
Documentation can be found under the wiki tab in github.

---

## Other Information

### licensing information:
  We operate with an MIT liscense which can be found in
  [LICENSE.txt](LICENSE.txt)