semvec/pypi-packages huggingface dataset:
syscarg 1.1
Sys control argument
Requires Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*

****
# syscarg 1.1
#### Sys Control Argument
The latest version: 1.1
<br>

## Installation
#### Use pip:<i>
> ``` pip install -U syscarg```
</i>

## Owner&Maintainer
##### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;👇👇👇👇
* [Raskodi](https://pypi.org/user/Raskodi/ "Go and see him") (Owner)
* [Raskody](https://pypi.org/user/Raskody/ "Go and see him") (Owner)
<br>

## Join us
#### If you would like to join us, please contact us privately:<i>
> [Raskody](mailto:Raskody@outlook.com "Write him an e-mail")

> [Raskodi](mailto:Raskodi@outlook.com "Write him an e-mail")
</i>
<br>

### Next version: 1.2.1
<br>


****
eigenpip 0.1.0
Eigen Library for Python Linking

# eigenpip: Recent Version of Eigen Library for Python

*Mohan Wu, Martin Lysy*

---

## Description

**eigenpip** is a recent distribution of the [C++ Eigen library](http://eigen.tuxfamily.org/) for Python linking. Some applications of the library can be found in our [Kalman package](https://github.com/mlysy/kalmantv). There are two main functions to **eigenpip**. The first is installing the Eigen library to give access to other Python packages. The second is give the user access to the path of the Eigen library.

## Installation

To instal from pypi:

```bash
pip install eigenpip
```

For a normal installation: 

```bash
git clone https://github.com/mohanwu/eigenpip
cd eigenpip
pip install .
```

If you would like to your own version of the Eigen library then set the environment variable `EIGEN_PATH` to the path of your library. 

On Linux:

```bash
EIGEN_PATH="/ABSOLUTE/PATH/TO/EIGEN/LIBRARY" pip install .
```

On Windows Powershell:

```bash
$env:EIGEN_PATH = "/ABSOLUTE/PATH/TO/EIGEN/LIBRARY"; pip install .
```

## Usage

To get the path of the Eigen library:

```Python
import eigenpip

EIGEN_PATH = eigenpip.get_include()
```
ms-collect 0.0.4
A series of modules that provide easy storage/accesss to Mass Spec related Raw Data/Groupings of Data.
Requires Python: >=3.7

# ms-collect
A series of modules that provide easy storage/accesss to Mass Spec related Raw Data / Groupings of Data.
- Documentation: https://jakuzo.github.io/ms-collect/
- Bug Reports/Feature Requests: https://github.com/jakuzo/ms-collect/issues
- Source Code: https://github.com/jakuzo/ms-collect
- The Why: https://jakuzo.github.io/ms-collect/html/background.html
- Contributing: https://jakuzo.github.io/ms-collect/html/contribute.html

> This package is currently in its BETA phase. Your understanding is greatly appreciated.

## Installation
```sh
pip install ms-collect
```
## Background
In regions of m/z and Retention Time, there exists various signals, features, and collections of data.
This package provides:
- Interfaces to entities that can represent these signals and collections.
- Seamless visual utilities for these entities.
- Input/Output utilities for parsing and organizing MS data.
- Various conversion utilities for MS data.

MS-1 3D Plot             |  MS-1 3D Spectrum
:-------------------------:|:-------------------------:
![3D collection](threeD_collection.png "3D plot of MS-1 Data")  |  ![3D Spectrum](threeD_spectrum.png "3D spectrum plot of MS-1 Data")

![3D collection as spectrum](spectrum.png "Standard Spectrum representation of an MS-1 Collection")
## Usage
For usage examples and Api documentation refer to: https://jakuzo.github.io/ms-collect/

### Upcoming features
- Visual utilities for the various types of collections.
- Optimizations on resource intensive class methods.
- Better import/export and IO related functionality.
- Converting a collection to a dataframe.
- Better Api doc experience.
- If you would like to see something here we are accepting feature requests! Please submit an issue or ping us!
armadillin 0.53
Lineage prediction from SARS-CoV-2 sequences
Requires Python: >=3.6

# Armadillin

### This is an experimental tool under development. The recommended method for calling lineages remains normal Pangolin: https://github.com/cov-lineages/pangolin

_A Re-engineered Method Allowing DetermInation of viraL LINeages_

Armadillin is an experimental alternative approach to training models on [lineages designated by the PANGO team](https://github.com/cov-lineages/pango-designation).

Armadillin uses dense neural networks for assignment, which means it doesn't have to assume that positions with an N are the reference sequence. Armadillin is still very fast, in part because it sparsifies the feature input to this neural net during training.

## Installation

### With pipx (self-contained):
```
pip install --local pipx
pipx install  armadillin
```

### or in your environment:
```
pip3 install armadillin
```

## Usage

We'll use a NextStrain open sampled file for a demo:

```
wget https://data.nextstrain.org/files/ncov/open/global/sequences.fasta.xz
```

```
armadillin sequences.fasta.xz
```

or

```
armadillin sequences.fasta.xz > output.tsv
```

If you have sequences already aligned to the reference you can make inference much faster using the `--seqs_are_aligned` parameter: 

```
wget https://data.nextstrain.org/files/ncov/open/global/aligned.fasta.xz
armadillin aligned.fasta.xz --seqs_are_aligned > output.tsv
```

## Related tools

[Pangolin](https://github.com/cov-lineages/pangolin) is the OG for assigning lineages