semvec/pypi-packages huggingface dataset:
gemstool 0.0.2.1
some small tools for my modeling work
Requires Python: >=3.6

# GEMstool

some small tools to build GEMs and other metabolic model.

![FRAMED](logo.png)
## install :
    pip install gemstool

## upload update in Terminal:
    python setup.py sdist
    twine upload dist/*

* username: bioluohao
* pw: l*4********

## requriment:
    pip install cobra
loon-filter 0.1.1
a Redis-backed scalable bloom filter
Requires Python: >=3.6

# Loon Filter

A simple scalable bloom filter implementation, inspired by previous related work.
The goal of this project is not to be the best possible Redis-backed bloom filter,
but to be a good-enough Redis-backed bloom filter.

If you want the best possible Redis-backed bloom filter, you should consider
using the Redisbloom module of RedisLabs' Redis Enterprise Software.

## Inspiration

- https://github.com/jaybaird/python-bloomfilter
- https://github.com/benhuds/yarb
- http://repositorium.sdum.uminho.pt/bitstream/1822/6627/1/dbloom_cmb.pdf
- http://www.isthe.com/chongo/tech/comp/fnv/index.html
- https://www.eecs.harvard.edu/~michaelm/postscripts/tr-02-05.pdf
Outlier-Removal-Naman-Goyal 0.0.1
Outlier Removal in the dataset using Z-score method
Requires Python: >=3

# Outlier Removal

A python package for implementation of outlier removal in the dataset using Z-score method.

The Z-score is the signed number of standard deviations by which the value of an observation or data point is above the mean value of what is being observed or measured.
The intuition behind Z-score is to describe any data point by finding their relationship with the Standard Deviation and Mean of the group of data points. Z-score is finding the distribution of data where mean is 0 and standard deviation is 1 i.e. normal distribution.
While calculating the Z-score we re-scale and center the data and look for data points which are too far from zero. These data points which are way too far from zero will be treated as the outliers. In most of the cases a threshold of 3 or -3 is used i.e if the Z-score value is greater than or less than 3 or -3 respectively, that data point will be identified as outliers.
apac 0.1.0
Automatically parcellate putative human core using myelin content and curvature

# APAC

Automatic parcellation of auditory cortex (APAC)

APAC is a lightweight module for parcellate human brain auditory cortex.
It is primarily intended for dividing the aduitory cortex into subregions

## License

-

## Support

-

## Paper

-

## Conference

K. Byeon, B.-y. Park, H. S. Lee, S.-G. Kim and H. Park, "Fully automated parcellation of the primary auditory cortex"
Society for Neuroscience - Neuroscience 2019


## Core development team

- Sean H. Lee - Max Planck Institute for Empirical Aesthetic
- Bo-yong Park, MICA Lab - Montreal Neurological Institute
- Kyoungseob Byeon, MIPL - Sungkyunkwan University