semvec/pypi-packages huggingface dataset:
PyShawpack 0.7
SHAWPACK NLP processing package
Keywords: NLP,text-data,AI,DS,Shahrour,arabic-py,arabic-nlp

# PyShawpack
 ![image](https://github.com/AbdelrahmanShahrour/PyShawpack/blob/main/logo/SHAWPACK-LOGO.png?raw=true) 

###  Now with PyShawpack, dealing with Arabic texts has become easy, and in the future it will become easier with PyShawpack 

## Installation

```python
pip3 install PyShawpack==0.6
```
---
## Structure 

|> PyShawpack

| ------> | `Normalization`

|---------| ------> | `Normalization_ar`

|---------|---------| ------> | alef

|---------|---------| ------> | lamalef

|---------|---------| ------> | hamza

|---------|---------| ------> | tah

|---------|---------| ------> | madah

|---------|---------| ------> | normalization_all

| ------> `Processor`

|---------| ------> | `Arabic_Processor`

|---------|---------| ------> | remove_stopword

|---------|---------| ------> | remove_other_lang

|---------|---------| ------> | remove_diacritics

|---------|---------| ------> | remove_arabic_punctuations

|---------|---------| ------> | arabic_only

|---------|---------| ------> | with_out_num

|---------| ------> | `English_Processor`

|---------|---------| ------> | remove_stopword

|---------|---------| ------> | english_only

|---------|---------| ------> | remove_english_punctuations

|---------|---------| ------> | with_out_num

|---------| ------> | `General_Processor`

|---------|---------| ------> | remove_mentions

|---------|---------| ------> | remove_hasgtag

|---------|---------| ------> | remove_links

|---------|---------| ------> | remove_punctation

|---------|---------| ------> | keep_text

|---------|---------| ------> | remove_emojis

|---------|---------| ------> | remove_whitespace

---

## Example:

```python
pip3 install PyShawpack==0.6
```

```python
from PyShawpack.TweetsCleaner import Clean_tweet
```



```python
text = 'akhhh !تَوقَعْت إذا جات داريا بشوفهم كاملين بس لي للحين احس فيه احد ناقْصهم 💔 #Avlu https://www.messenger.com/ @pyshawpack '

Clean_tweet.ar(text)
```

output:
`' توقعت اذا جات داريا بشوفهم كاملين بس لي للحين احس فيه احد ناقصهم '
`
ChorusFruit2 0.0.1
an Easy way to Make TUIs
Requires Python: >=3.7

# ChorusFruit2

### A Cool and New TUI Framework
+ Easy to Use

+ Object Orianted

+ Color Support

## Easy to Use

For Example This Code Prints a Blue and Bold Text:

```py
from ChorusFruit2 import Terminal
terminal = Terminal()
print("This Text is [BLUE][BOLD]Blue and Bold")
```

### Usage:
To Make an TUI!

## More Info

For Documentation See Folder docs

For Source Code See Folder src

For Example See Folder examples

---

### Hope You Liked The Project, More Updates Comming in Future
## if you Have an Issues Plase Open one
ramanfitter 0.0.3
A Raman peak finder and model fitter
Requires Python: >=3.6
Keywords: python,raman,fitter,raman fitter,lorentzian,gaussian,voigt


# ramanfitter

This package seeks to provide and easy and efficient matter for fitting Raman data with Lorentzian, Gaussian, or Voigt models.

Developed by John Ferrier, NEU Physics (c) 2022

## Quickstart Example

For a simple run,
```python
import os
import numpy as np
from ramanfitter import RamanFitter

filename    = os.path.join( 'path', 'to', 'data.csv' )      # Get File
data        = np.genfromtxt( filename, delimiter = ',' )    # Open File

x           = data[ :, 0 ]                                  # Parse x-values
y           = data[ :, 1 ]                                  # Parse y-values

RF          = RamanFitter( x = x, y = y, autorun = True )   # Run Fitter automatically

components  = RF.comps                                      # Returns a dictionary of each curve plot
curveParams = RF.params                                     # Returns a dictionary of the parameters of each Lorentzian, Gaussian, or Voigt curve
bestFitLine = RF.fit_line                                   # Returns the plot data of the model
```

For more control over parameters

```python
import os
import numpy as np
from ramanfitter import RamanFitter

filename    = os.path.join( 'path', 'to', 'data.csv' )      # Get File
data        = np.genfromtxt( filename, delimiter = ',' )    # Open File

x           = data[ :, 0 ]                                  # Parse x-values
y           = data[ :, 1 ]                                  # Parse y-values

RF          = RamanFitter( x = x, y = y, autorun = False )  # Run Fitter automatically


''' Each step ran when autorun = False '''
RF.NormalizeData()
RF.Denoise( ShowPlot = True )
RF.FindPeaks( showPlot = True )
RF.FitData( type = 'Voigt', showPlot = True )


components  = RF.comps                                      # Returns a dictionary of each curve plot
curveParams = RF.params                                     # Returns a dictionary of the parameters of each Lorentzian, Gaussian, or Voigt curve
bestFitLine = RF.fit_line                                   # Returns the plot data of the model
```


On 0.0.3, a new Mapper feature is added for handling mapped data. This example below creates a 4K video across a sweep of frequencies.

```
from ramanfitter.mapper import Mapper

here    = os.path.abspath( os.path.dirname( __file__ ) )
fname   = os.path.join( here, 'sample_map', 'sample.txt' )
MP      = Mapper( file = fname, display_name = "Sample 1", output_path = os.path.join( here, 'sample_map' ), delimiter = ',', provided_img=img, makePlot=False, makeVideo=True, plotType='imshow', resolution = '4k')
```