semvec/pypi-packages huggingface dataset:
pennywise 0.0.11
pennywise - A docker based bioinformatics shape shifting tool.
Requires Python: >=3.4

# Pennywise

Pennywise has the ability to shapeshift into the bioinformatic tool you are most afraid to install.

## Requirements

You must have docker and python3 available at your system.

## Install

    pip install pennywise 

## Usage

### List available tools

    pennywise list 

### Basic usage

    pennywise <tool> <regular-tool-options> 

### Example 1: Running trimmomatic

    pennywise trimmomatic PE r1.fq r2.fq r1_p.fq r1_u.fq r2_p.fq r2_u.fq SLIDINGWINDOW:4:20 MINLEN:100

### Example 2: Running spades

```shell
pennywise spades -1 r1_p.fq -2 r2_p.fq -o assembly -meta 
```
    
### First time using a tool

If it's the first time you are running a particular command, pennywise will need to pull the image before it actually 
runs the tool. Most images will range from 200MB to 2GB, and will get installed in less than 10 minutes providing you
have a good internet connection.

```shell
pascal@dione:~/Sandbox/$ pennywise quast genome.fa
- It looks like it's your first time running this command!
- pennywise will download the image (staphb/quast:latest) so it can shapeshift
- This can take some time...
```

## Adding your own tools

 1. Fork this repository
 2. Update pennywise.toml
 3. Run `pennywise source <your-github-user>`

Pennywise will download pennywise.toml from your fork and use it for command reference.

### pennywise.toml format

    [quast]
    image = "staphb/quast:latest"
    prefix_cmd = "quast.py"
    
    [prokka]
    image = "staphb/prokka:latest"
    prefix_cmd = "prokka"

### I don't want to use github as source

It's fine, just update ~/.pennywise/pennywise.toml.

## Tools Available

 - FastANI
 - Prokka
 - Quast
 - Spades
 - Trimmomatic
dittotk 0.0.0.1
pennywise - A docker based bioinformatics shape shifting tool.
Requires Python: >=3.4

# Pennywise

Pennywise has the ability to shapeshift into the bioinformatic tool you are most afraid to install.

## Requirements

You must have docker and python3 available at your system.

## Install

    pip install pennywise 

## Usage

### List available tools

    pennywise list 

### Basic usage

    pennywise <tool> <regular-tool-options> 

### Example 1: Running trimmomatic

    pennywise trimmomatic PE r1.fq r2.fq r1_p.fq r1_u.fq r2_p.fq r2_u.fq SLIDINGWINDOW:4:20 MINLEN:100

### Example 2: Running spades

```shell
pennywise spades -1 r1_p.fq -2 r2_p.fq -o assembly -meta 
```
    
### First time using a tool

If it's the first time you are running a particular command, pennywise will need to pull the image before it actually 
runs the tool. Most images will range from 200MB to 2GB, and will get installed in less than 10 minutes providing you
have a good internet connection.

```shell
pascal@dione:~/Sandbox/$ pennywise quast genome.fa
- It looks like it's your first time running this command!
- pennywise will download the image (staphb/quast:latest) so it can shapeshift
- This can take some time...
```

## Adding your own tools

 1. Fork this repository
 2. Update pennywise.toml
 3. Run `pennywise source <your-github-user>`

Pennywise will download pennywise.toml from your fork and use it for command reference.

### pennywise.toml format

    [quast]
    image = "staphb/quast:latest"
    prefix_cmd = "quast.py"
    
    [prokka]
    image = "staphb/prokka:latest"
    prefix_cmd = "prokka"

### I don't want to use github as source

It's fine, just update ~/.pennywise/pennywise.toml.

## Tools Available

 - FastANI
 - Prokka
 - Quast
 - Spades
 - Trimmomatic
cn-sentiment-measures 1.0.0.dev5
A toolkit for estimating Chinese sentiment scores with multiple measures.
Requires Python: >=3.6, <4
Keywords: sentiment analysis,Chinese,measures,text analysis,natural language processing

# Chinese Sentiment Measures 

A toolkit for estimating Chinese sentiment scores with multiple measures. 

```pip
pip install cn-sentiment-measures
```

## Measures
1. Absolute Proportional Difference - APD. Bounds: [-1, 1]
2. Relative Proportional Difference - RPD. Bounds: [-1, 1]
3. Logit scale - LC.  Bounds: [-infinity, +infinity]

Improved versions of the above measures by integrating degree words are marked with `_with_degree` in the toolkit. 

## Examples
```python

from cn_sentiment_measures.sentiment_measures import SentimentMeasures

sm = SentimentMeasures()

word_list=["我","今天","很","高兴"]

print("Here are measure values with normal measures!")
print('APD = ',sm.APD(word_list))
print('RPD = ',sm.RPD(word_list))
print('LS = ',sm.LS(word_list))

print()
print("Here are measure values optimized by using adverb words!")
print('APD* = ',sm.APD_with_degree(word_list))
print('RPD* = ',sm.RPD_with_degree(word_list))
print('LS* = ',sm.LS_with_degree(word_list))

```

## License
The project is provided by [Donghua Chen](https://github.com/dhchenx).
correlation-kit 1.0.0.dev2
A toolkit for estimating the correlation between variables
Requires Python: >=3.6, <4
Keywords: correlation analysis,relation mining

# Correlation Kit

A toolkit for estimating the correlation values between variables

## Installation
```pip
pip install correlation-kit
```

## Correlation between two continual variables
```python
import pandas as pd
from correlation_kit.ck_wrapper import CorrelationKit

# set a dataframe or read from a csv file
d = {'x': [1, 2, 3.5, 4], 'y': [3, 4, 4.5, 6]}
df = pd.DataFrame(data=d)

# set x label and y label for correlation
x = "x"
y = "y"

# calc
def get_correlation(x, y, corr_type):
    stat = 0
    p = 0
    if corr_type == "pearson":
        stat, p = CorrelationKit(df).get_pearson(x, y)
    elif corr_type == "spearman":
        stat, p = CorrelationKit(df).get_spearman(x, y)
    elif corr_type == "kendalltau":
        stat, p = CorrelationKit(df).get_kendalltau(x, y)
    return stat, p

# print results
print("pearson = ", get_correlation(x, y, "pearson"))
print("spearman = ", get_correlation(x, y, "spearman"))
print("kendalltau = ", get_correlation(x, y, "kendalltau"))
```

## Estimate correlation between binary and continual variables
```python
import pandas as pd
from correlation_kit.ck_wrapper import CorrelationKit

# set a dataframe or read from a csv file
d = {'x': ['large', 'large', 'small', 'small'], 'y': ['hot', 'hot', 'cold', 'cold'],'z':[0,1,2.5,3]}
df = pd.DataFrame(data=d)

# set x label and y label for correlation, which is suitable for binary variables
r_p,r_s,r_k=CorrelationKit(df).get_corr_between_category_and_continual('x','large','z') # large=1; otherewise 0

# results
print('pearson: ',r_p)
print('speraman: ',r_s)
print('kendalltau: ',r_k)

```

## Estimate F value between multiple-category variable and continual variables

```python
import pandas as pd
from ck_wrapper import CorrelationKit

# set a dataframe or read from a csv file
d = {'x': ['large', 'large', 'middle','small', 'small'], 'y': ['hot', 'hot','warm', 'cold', 'cold'],'z':[0,1,2,2.5,3]}
df = pd.DataFrame(data=d)

# set x label and y label for correlation, which is suitable for multiple-category variables
F,p=CorrelationKit(df).get_f_oneway('x',['large','middle','small'],'z')

# results
print('F: ',F)
print('p: ',p)


```

## License
The `Correlation-Kit` project is provided by [Donghua Chen](https://github.com/dhchenx).
roblox 2.0.0
ro.py is a modern object-oriented asynchronous Python wrapper for the Roblox web API.
Requires Python: >=3.7

<p align="center" width="100%">
    <img src="https://raw.githubusercontent.com/ro-py/ro.py/main/resources/textlogo.svg" alt="ro.py" height="128em" />
    <br />
</p>
<p align="center">
    <a href="https://github.com/ro-py/ro.py">GitHub</a> |
    <a href="https://discord.gg/UKPaPu4teg">Discord</a> |
    <a href="https://pypi.org/project/roblox/">PyPI</a> |
    <a href="https://ro.py.jmk.gg/">Documentation</a> |
    <a href="https://github.com/ro-py/ro.py/tree/main/examples">Examples</a> |
    <a href="https://github.com/ro-py/ro.py/blob/main/LICENSE">License</a>
</p>
<p align="center">
    <a href="https://discord.gg/UKPaPu4teg"><img src="https://img.shields.io/discord/761603917490159676?style=flat-square&logo=discord" alt="RoAPI Discord"/></a>
    <a href="https://pypi.org/project/ro-py/"><img src="https://img.shields.io/pypi/v/roblox?style=flat-square" alt="ro.py PyPI"/></a>
    <a href="https://pypi.org/project/roblox/"><img src="https://img.shields.io/pypi/dm/roblox?style=flat-square" alt="ro.py PyPI Downloads"/></a>
    <a href="https://pypi.org/project/roblox/"><img src="https://img.shields.io/pypi/dm/ro-py?style=flat-square" alt="ro.py PyPI Downloads (Legacy)"/></a>
    <a href="https://pypi.org/project/ro-py/"><img src="https://img.shields.io/pypi/l/roblox?style=flat-square" alt="ro.py PyPI License"/></a>
    <a href="https://github.com/ro-py/ro.py"><img src="https://img.shields.io/github/commit-activity/w/rbx-libdev/ro.py?style=flat-square" alt="ro.py GitHub Commit Activity"/></a>
    <a href="https://github.com/ro-py/ro.py"><img src="https://img.shields.io/github/last-commit/rbx-libdev/ro.py?style=flat-square" alt="ro.py GitHub Last Commit"/></a>
</p>

# Overview
Welcome to ro.py! ro.py is an asynchronous, object-oriented wrapper for the Roblox web API.

# Features
The key features are:  

- **Asynchronous**: ro.py works well with asynchronous frameworks like [FastAPI](https://fastapi.tiangolo.com/) and 
[discord.py](https://github.com/Rapptz/discord.py).  
- **Easy**: ro.py's client-based model is intuitive and easy to learn for both the beginner and expert developer. It
  abstracts away API requests and leaves you with simple objects that represent data types on the Roblox platform.
- **Flexible**: ro.py's builtin Requests object allows the user to do things that we haven't already implemented
ourselves without dealing with advanced Roblox-specific concepts.

# Installation
To install ro.py from PyPI, you can install with pip:
```
pip install roblox
```

To install the latest unstable version of ro.py, install [git-scm](https://git-scm.com/downloads) and run the following:
```
pip install git+git://github.com/ro-py/ro.py.git
```

# Tutorial
Learn how to use ro.py in our docs:
https://ro.py.jmk.gg/tutorial/
prothomaloscraper 0.0.1
'A web scraper for grabbing Bengali content from ProthomAlo.'
Requires Python: >=3.6

# prothomaloscraper Package

This is a python package to scrape different types of content from the leading Bengali newspaper 'Prothom Alo' 
(https://www.prothomalo.com). The objective of this package is to grab Bengali text 
mainly for the data scientists who need bengai content for research. 
This package was created for absolutely for non-commercial use only, 
and the author does not encourage to use this package for any business 
other than any research tasks. You can check the content from the following link as well: 
(https://github.com/neolithian/prothomalo-scraper/)