semvec/pypi-packages huggingface dataset:
rescaler 0.0.3
Transform features by scaling each feature to a given range.
Requires Python: >=3.6

# rescaler
A package that can transform features by scaling each feature to a given range. The input should be (input list, new minimum value, new maximum value). This is more light weight and easy to use than sklearn.preprocessing.MinMaxScaler. This package can simply and quickly rescale each element of the input list into a new value within the range, and generate a new list.

### Parameters
**`rescaler.rescaler.rescale`**

* `input_list` A list.
* `newmin` The minimum value of the new scale.
* `newmax` The maximum value of the new scale.
engram 0.0.2
Encoding Graphical Representations         of Activated Memories (ENGRAM)
Requires Python: ==3.7

# ENGRAM

**Encoding Graphical Representations of Activated Memories**
is an open-source Python package for developing cognitive neural prostheses.

[![PyPI badge](https://img.shields.io/pypi/v/engram.svg?logo=python&logoColor=white)](https://pypi.org/project/engram/)
[![Github badge](https://img.shields.io/badge/github-source_code-blue.svg?logo=github&logoColor=white)](https://github.com/garrettmflynn/engram)
[![Docs badge](https://img.shields.io/readthedocs/engram/latest.svg?logo=read-the-docs&logoColor=white)](https://readthedocs.org/projects/engram)
[![License badge](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)

## Installation

Install ENGRAM by running:

    install engram

## Contribute

* Issue Tracker: github.com/garrettmflynn/engram/issues
* Source Code: github.com/garrettmflynn/engram

## Support

If you are having issues, please email me at garrett@garrettflynn.com
BioPro 0.1.7
BioPro is a bioinformatics pipeline that predicts the functional targets of RNA-binding protein and the biological processes to which these functional targets contribute.

# Predicting Biochemical Pathways Involving Target RBP

BioPro is a bioinformatics pipeline that processes and analyzes eCLIP data of RNA-binding proteins (RBP) to predict the functional targets of RBP and biological processes to which the functional targets of RBP contributes.

BioPro pipeline cleans raw eCLIP data of a target RBP with Cutadapt and aligns the RNA sequence with STAR. Peak calling is then performed with PureCLIP. Then, based on the peaks, PRAS predicts the functional target genes of RBP and produces a ranked gene list. The pipeline then performs gene ontology (GO) analysis on the top percent ranked genes to predict the biological processes. 


## Install

### Install via source code

To install via source code, clone our github repo and run:

```
git clone https://github.com/Crazy-Jack/SuperdopeBioinformatics
cd SuperdopeBioinformatics
bash install.sh
```


### Install via pip

Or you can directly install our software from pip:

`pip install BioPro`

Our package can be found at: https://pypi.org/project/BioPro/


## Attribution

This project is created as a course project for 03-713 Bioinformatics Data Integration Practicum (Spring 2020). All memebers of our team contributed equally to the final product. Great thanks to Tianqin Li, Zeyuan Zuo, Jui-Chia Chung, Snigdha Agarwal and Serena Abraham. 

## Maintainance

Tianqin Li (tianqinl@andrew.cmu.edu)

Carnegie Mellon University

Pittsburgh, PA, 15213, US.
cmdtime 0.0.4
Similar to the Linux time utility for timing commands. Useful under Windows.
Requires Python: >=3.5

[![Build Status](https://travis-ci.org/ravi-chandran/cmdtime.svg?branch=master)](https://travis-ci.org/ravi-chandran/cmdtime)

# cmdtime
- Similar to the Linux `time` utility for measuring how long a command takes to run
- Useful for the Windows Command Prompt
- Uses pure Python
- No point using this under Linux
- No need to install Cygwin

# Installation
- Requires Python 3.5+.
- Install from PyPi:

```bat
python -m pip install cmdtime
```

# Usage Examples
- Time a Python script

```bat
cmdtime python whateverscript.py
```

- Time anything else

```bat
cmdtime dir
```

# Notes
For some machine learning projects, I use the Windows command prompt to execute scripts. I use Windows so that Tensorflow can access my PC's NVIDIA GPU. (Unfortunately, using Linux under VirtualBox can't access GPUs.)

In addition, there is no built-in equivalent of the Linux `time` utility in the Windows 10 command prompt. Yes, there are other options such as those discussed [here](https://stackoverflow.com/questions/673523/how-do-i-measure-execution-time-of-a-command-on-the-windows-command-line) and [here](https://www.raymond.cc/blog/measure-time-taken-to-complete-a-batch-file-or-command-line-execution/). But they either require installing old binaries that I'd rather avoid, or involve more cumbersome approaches. I wanted something where I can just prefix the command I want to run similar to the Linux `time` utility.

Hence the creation of this really trivial but useful Linux `time`-like utility which I'm calling `cmdtime`. And it's pure Python.

Probably someone else has already created such a utility under PyPi, but I couldn't find it.


## Developer Notes
```bat
python setup.py sdist

```
- [Work in development mode](https://packaging.python.org/guides/distributing-packages-using-setuptools/#working-in-development-mode):
```bat
python -m pip install --editable .
```

- Push to TestPyPI
```bat
python -m twine upload --repository testpypi dist/*
```

- Push to PyPI
```bat
python -m twine upload dist/*
```

## Testing Notes
Tests are written to support both Windows and Linux, although this utility is not really needed in Linux.

- Install `pytest`:
```bat
python -m pip install --upgrade pytest
```

- Local testing:
```bat
pytest -v
```

- Travis CI:
  - Right now, Travis CI doesn't support Python on Windows, so this can only be tested on Linux.
  - The tests only work on Python 3.7 or later as the `subprocess.run()` had significant changes in parameters (`capture_output`, `text`) not available in earlier versions. While it's possible to create version-specific test code to be compatible with earlier versions, it's not worth the effort...