semvec/pypi-packages huggingface dataset:
permgroups 1.0.2
Python implementation of permutation groups
Requires Python: >=3.7

# permgroups package

Python implementation of permutation groups is presented. 
Two classes are introduced: *Perm* for permutations, 
*Group* for permutation groups (everal versions). 

The class *Perm* from the *perms* module
is based on Python dictionaries and utilize cycle notation. 
The methods of calculation for the perm order, parity, ranking and unranking 
are given. A random permutation generation is also shown. 

The class *Group* from the *groups* module
is very simple and it is also based on dictionaries. 
It is mainly the presentation of the permutation groups interface with 
methods for the group order, subgroups (normalizer, centralizer, center, 
stabilizer), orbits, and several tests.

The class *Group* from the *setgroups* module
is also simple and it is based on sets.

The class *Group* from the *simsgroups* module
is advanced and it is bases on the Sims theory.

The project is moved from *Google Code*.

## Download

To install an official release do

    python3 -m pip install permgroups

To get the git version do

    git clone https://github.com/ufkapano/permgroups.git

## Usage

~~~python
>>> from permgroups.perms import Perm
>>> from permgroups.groups import Group
>>> p = Perm()(0, 1, 2, 4)(3, 5)
>>> ~p
Perm()(0, 4, 2, 1)(3, 5)
>>> p.is_identity(), p.parity(), p.is_even(), p.sign()
(False, 0, True, 1)
>>> p.order(), pow(p, 4)
(4, Perm())
>>> Perm.random(10)
Perm()(0, 1, 4, 2, 7)(3, 9, 8)(5, 6)
>>> G = Group()
>>> G.insert(p)
>>> G.order()
4
>>> list(G.iterperms())
[Perm(), Perm()(0, 1, 2, 4)(3, 5), Perm()(0, 2)(1, 4), Perm()(0, 4, 2, 1)(3, 5)]
>>> Perm()(1, 4)(0, 2) in G, Perm()(0, 1, 2, 3) in G
(True, False)
>>> G.is_abelian()
True
~~~

## References

[1] A. Kapanowski, *Python for education: permutations*. 
http://arxiv.org/abs/1307.7042 [draft]

[2] A. Kapanowski, The Python Papers 9, 3 (2014). 
*Python for education: permutations*. [final version]
http://ojs.pythonpapers.org/index.php/tpp/article/view/258 

## Contributors

Andrzej Kapanowski (project leader)

Tomasz Gądek

EOF
jin-test-packet 0.0.1
这是我的第一个资源包，仅仅是测试如何制作资源包并上传PYPI

+ 这是第一次编写Python资源包，这里仅仅是做一个测试，没有任何的实际使用价值
+ 这里是对项目的详细描述，也就是`long_description`对应的内容
+ 在这里尽量描述资源包的作用，资源包的用法，每个方法的使用，最好能给出使用范例，方便其他人使用
ribonorma 1.2
Implements additional normalisation methods for RNA Sequencing.
Requires Python: >=3.6

# Ribonorma
As part of the paper


## Installation
Installation can be done with Pip. Python 3.6+.

`pip install ribonorma`

`pip3 install ribonorma`

## How to use

### Command line usage
![Pipeline](/images/Ribonorma%20analysis%20pipeline.png)

1. Suppose you have two files, one file with the raw RNASeq count data ([example](/test/maqc_count_with_lengths.tsv)) and one file with the phenotype file data ([example](/test/maqc_phenotypes.tsv)).

2. Run `ribonorma-normalise`

### Python code usage
```py
import csv
from ribonorma import ribonorma

reads = list(csv.read( ... )) # Reads
gene_length = [] # List of gene lengths

conditions = ["Standard media", "Standard media", "Standard media", "Super media", "Super media", "Super media"] # Example conditions

normalised_counts = ribonorma.tpmr(reads, gene_length, conditions, percent_housekeep=10)
```


`ribonorma.tpm(reads, gene_length)`
* `reads`: 1D list of read counts
* `gene_length`: 1D list of individual gene lengths


`ribonorma.tpmm(samples, gene_length)`
* `samples`: 2D list of sample read counts - [sample x count]
* `gene_length`: 1D list of individual gene lengths


`ribonorma.tpmr(samples, gene_length, experimental_conditions, percent_housekeep=10)`
* `samples`: 2D list of sample read counts - [sample x count]
* `gene_length`: 1D list of individual gene lengths
* `experimental_conditions`: 1D list of individual experimental conditions, same size as `samples`
* `alpha`: floating point value of percent housekeep as stated in the paper


`ribonorma.tpmr_2(samples, gene_length, experimental_conditions, percent_housekeep=10)`
* `samples`: 2D list of sample read counts - [sample x count]
* `gene_length`: 1D list of individual gene lengths
* `experimental_conditions`: 1D list of individual experimental conditions, same size as `samples`
* `alpha`: floating point value of percent housekeep as stated in the paper
timeConverter 3.7
A GUI tool to convert time across all time zones. To use this tool, just type 'timeConverter' on your terminal.

# TimeConverter

## Description

A GUI tool to convert time across all time zones.
To use this tool just type 'timeConverter' on your terminal.


## Installation

```pip install timeConverter```

## Usage

![img1](https://github.com/k-aishwarya/TimeConverter/raw/master/images/img1.png)
![img2](https://github.com/k-aishwarya/TimeConverter/raw/master/images/img2.png)
![img3](https://github.com/k-aishwarya/TimeConverter/raw/master/images/img3.png)
![img4](https://github.com/k-aishwarya/TimeConverter/raw/master/images/img4.png)