semvec/pypi-packages huggingface dataset:
Bimbmaan 0.1
Bimbmaan: A Shaktiman to process high quality images for research publication
Requires Python: >=3.8

# **BIMBMAAN**: A Shaktiman to process high quality images for research publication

BIMBMAAN is a powerful tool designed to assist researchers in dealing with image resizing and conversion challenges encountered during manuscript preparation for research publication. Its primary goal is to simplify the process of handling high-quality images, ensuring they meet the standards required for publication.

# Details and Descriptions

## Features
* Efficient image resizing to desired dimensions (e.g., 600 DPI, 1200 DPI).
* Seamless conversion between different image formats (e.g., PNG, JPEG, TIFF).
* Batch processing capabilities for handling multiple images simultaneously.
* Preservation of image quality and resolution to meet publication requirements.
* Easy to install and operate and works with single commands without requiring any customisation

## How to Use
* Install BIMBMAAN using `pip install bimbmaan`.
* Navigate to your images directory where your images are ready to process.
* Use command `bimbkar` or `bimbman` for batch processing.
* Customised examples:
    - `bimbkar -d 72 600 1200 -e jpg tiff`

## Benefits
* Saves time and effort by automating image processing tasks.
* Ensures compliance with publication guidelines regarding image quality and resolution.
* Reduces the risk of errors and inconsistencies in image conversion and dimensions of the images.

# Feedback and Support
We welcome any feedback or suggestions for improving BIMBMAAN. Please report issues through 
[GitHub issues](https://github.com/TheBiomics/bimbmaan/issues).
PathMePy-Gustoon 0.5
A tool to add scripts to Path
Requires Python: >=3.2


# PathMePy
A tool to add scripts to Path

## Installation
You have to install the package using pip : `pip install PathMePy-Gustoon`

## Content
You need to import this package with `import PathMePy_Gustoon`
This packages add three functions : 
`PathMePyDir(path)`,
`PathMePyUserScriptFolder()`,
`IsAlreadyOnPath(path)`,
`UserScriptFolderIsAlreadyOnPath()`,
two variables `Current_Path` and `Current_Path_Formated`

## Explanation
`PathMePyDir(path)` function is for temporaly add a directory in your user PATH variable,
`PathMePyUserScriptFolder()` function is for add the Python User Script Folder to the user PATH variable,
`IsAlreadyOnPath(path)` function return if an element is on the PATH or not,
`UserScriptFolderIsAlreadyOnPath()` function return if the User Script folder is on the PATH or not,
`Current_Path` is a variable with the current path,
`Current_Path_Formated` is a formatted version of `Current_Path` that changes each PATH separator with a newline.
solargazer 0.2
A simple wrapper for linearmodels to generate clean panel regression tables in LaTeX.
Requires Python: >=3.6

# SolarGazer: A Simple Panel Regressions Wrapper for Latex Tables in Python

## Description

solargazer is an extremely simple library designed to offer additional functionalities relative to stargazer for Latex table generation in Python. The library is compatible with the PanelOLS class from statsmodels for clean tables in Latex. 

IMPORTANT: Follow this Colab tutorial notebook for the easiest setup guide: https://colab.research.google.com/drive/1s9IpYR2xxvgrQBbqTk9GKFTPutxAgMMU?usp=sharing

## Installation

Installation is extremely simple using pip. If you are using this to make regression tabels, you will need to have linearmodels installed. Open your terminal or command prompt and run:

```bash
pip install linearmodels
```

Then you can install solargazer using: 
```bash
pip install solargazer
``` 

To run the tables in Latex, you will also need to include this in your preamble:
```latex
\usepackage{amssymb}
\usepackage{threeparttable}
\usepackage{booktabs}
```

## Use

There is only one simple function. 

```python
from solargazer.solargazer import SolarGazer
SolarGazer().make_table(model_list,model_names, groupings = group_dict)
``` 

The function takes three parameters.
1. *model_list*: a list of your fitted PanelOLS models. This is post-processing, etc.
2. *model_names*: a list of strings which are the names you want for the columns in your regressions.
3. *grouping*: an optional dictionary to add groupings at the top of the regression table, if you want to make clear that several of the regressions are of the same type. If you don't provide a dictionary here, there will be no additional title at the top, just the model names. 

So a full example might be:
```python
SolarGazer().make_table([model1, model2, model3],['model 1','model 2','model 3'], groupings = {'Republican':[model1, model2],'Democratic':[model3]})
```

The library will output a LateX string that you can directly transfer to your file.
mecmitool 0.1.2
some repetitive wheels
Requires Python: >=3.7

###### this project contain some small tool which are use in my other project.  
##### 0.1.02:  
add rgb; refix timer
##### 0.1.01:  
add functimer 
##### 0.1.00:  
refix pathchecker with @classmethod 
##### 0.0.12:  
add checkpoint  
##### 0.0.11:  
fix timer   
##### 0.0.10:  
improve timer and pathchecker   
##### 0.0.9:  
repair auto_upload.py   
##### 0.0.8:  
rewrite twine.__main__      
##### 0.0.7:  
add auto_upload.py      
##### 0.0.6:  
add verified twine   
##### 0.0.5:  
error add OutRangeError  
fix path_checker.check_infile  
##### 0.0.4:  
add error.py  
##### 0.0.3:  
add playsound.py  
##### 0.0.2:  
add timer.py  
##### 0.0.1:  
add path_checker.py