semvec/pypi-packages huggingface dataset:
uaua 0.1
A command line program to test the writing speed of your terminal
Requires Python: >=3.6

# uaua
uaua is a command-line program to check the writing speed of your terminal.

## Example
```sh
$ python -m uaua --line-count 100000
uaua
uaua
uaua
uaua
uaua
...
uaua
uaua
   0.43807244 seconds taken
       100000 uaua
    228272.75 uaua per second
  0.000004381 seconds per uaua
$ python -m uaua --duration 1 > /dev/null
   1.00000167 seconds taken
      1626917 uaua
   1626914.28 uaua per second
  0.000000615 seconds per uaua
$ python -m uaua -c 100000 1 > kočka.txt
   0.02503824 seconds taken
       100000 uaua
   3993890.57 uaua per second
  0.000000250 seconds per uaua
$ python -m uaua -h
```
filexl 1.0.2
CLI tool to list files and export to Excel
Requires Python: >=3.7

# 📂 filexl

A simple Python CLI tool to list all files in a given directory and export them to an Excel file. Supports filtering by file extension, optional path naming, and detection of duplicate file names.

---

## 🚀 Features

- 🔍 Recursively lists all files in a directory (including hidden files)
- 📝 Outputs to Excel with columns:
  - File Name
  - Extension
  - Full Path
  - Path Name (optional)
- 🔁 Appends to existing Excel files
- 🎯 Filter by file extension (`--ext`)
- 🔎 Export only duplicate file names with `--only-duplicates`
- 🔠 Optional case-insensitive extension filtering
- ✅ Simple CLI interface with `--help` support

---

## 🧰 Installation

```bash
pip install filexl
```

## ⚙️ Options

| Option | Description |
|:-------|:------------|
| `--extensions` | Filter files by specific extensions (e.g., `.jpg .png`) |
| `--case-sensitive` | Match extensions case-sensitively |
| `--only-duplicates` | Save only files with duplicate file names |
| `--extract-duplicates-from` | Create a new Excel file containing only duplicates from an existing Excel file |
| `--out` | Output Excel file name for extracted duplicates |
| `--help` | Show help message and usage instructions |


## 📦 Usage

Basic usage
```
filexl --path D:\data

```

With optional path name and Excel file name
```
filexl --path D:\data --name "MyData" --filename myfiles.xlsx

```

Filter by extensions (space or comma-separated)
```
filexl --path D:\images --ext .jpg .png --ignore-case

```

Export only duplicate file names
```
# With default output name (duplicates.xlsx)
filexl --path D:\data --only-duplicates

# Or with a custom output file
filexl --path D:\data --only-duplicates mydups.xlsx

```

Extract Duplicates from an Existing Excel
```
filexl --extract-duplicates-from myfiles.xlsx --out only_duplicates.xlsx
```
This command reads myfiles.xlsx, finds duplicate file names, and writes them to only_duplicates.xlsx.

## 🧪 Example Output

| File Name | Extension | Path                              | Path Name |
|-----------|-----------|-----------------------------------|-----------|
| report    | pdf       | D:\docs\2023\report.pdf           | Documents |
| report    | pdf       | D:\docs\2022\report.pdf           | Documents |
| image     | jpg       | D:\media\photos\image.jpg         | Media     |
| notes     | txt       | D:\projects\docs\notes.txt        | Projects  |

## 🧱 Development
To build a standalone executable:
```
pip install pyinstaller
pyinstaller --onefile filexl/cli.py

```

## 📜 License
MIT License. Use it freely in your own projects!

## 👨‍💻 Author
Developed by Kiarash Gharahgozloo
GitHub: @kiarash-gh
colab-easy-ui2 0.1.12
Add your description here
Requires Python: >=3.10

## ビルド＆デプロイ

UI を内包するパッケージとなるなるため、UI を更新したら、ビルドしてこのパッケージにコピー（デプロイ）する必要がある。
詳細は、UI の README を参照すること。

新 UI を作成する場合は、pyproject.toml で、その UI のファイルもパッケージに取り込むように設定する必要がある。

```
uv build
uv publish --token pypi-.....
```
image-processing-dudac 0.0.1
Image processing Package using Skimage
Requires Python: >=3.5

# package_name

Description. 
The package package_name is used to:
	 Processing:  
		- Histogram matching
		- Structural similarity
		- Resize image

	Utils: 
		- Read image
		- Save image
		- Plot image 
		- Plot result
		- Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install image_processing
```

## Usage

```python
from package_name.module1_name import file1_name
file1_name.my_function()
```

## Author
Duda

## License
[MIT](https://choosealicense.com/licenses/mit/)
EShAY 1.0.4
A package to look at similarity between two genetic datasets. Useful for finding duplicate tissue donors betwen studies.
Requires Python: >=3

# Examine Shared Alleles? Yes! (EShAY)

A small python package with a very tortured acronym. Contains functions to compare two genetic datasets to each other (in VCF format) to assess similarity.
It was developed to find duplicate tissue donors between experiments across scRNA datasets. 

## Description

An in-depth paragraph about your project and overview of use.

## Getting Started

The main function of the package is:
```
Similarity(file1, file2, method)
```
This function take in two VCF files ('file1' and 'file2').
'method' tells the package which calculation to use for, either using a comparison based on Expected Heterozygosity (1-sum(p*2)) or Shannons Information (-sum(p*ln(p))).
For Expected Heterozygosity set 'method' to either the string "ExpHet" or the integer 2.
For Shannons Information set 'method' to either the string "Shan" or the integer 1.

results will be printed and returned as a list, in the format:
```
["Sample1","Sample2","Similarity", "Pooled diversity", "N_Loc"]
```
Sample1/2 are just the names of the samples taken from the VCF headers. If a VCF file has more than one sample in it, only the 1st sample will be used. 

Similarity is the main result. Any value over 0.8 suggests the same donor (see 'N_Loc' below), with a value of 1 indicating a completly identical dataset.

Pooled diversity is the total ammount of genotype diversity when all loci between both samples is pooled. This is used as a baseline of diveristy for the method, but can be used as a QC metric. If your samples have 0 pooled diversity it can indicate some data quality issues (even identical samples will have some pooled diversity).

N_Loc is thr number of shared loci between your two samples. For this package to work, it is recommended that you have atleast 250,000 shared alleles between your samples for ths package to be accurate - and the more the merrier. 

## Author

Dr Gabe O'Reilly
g.oreilly@garvan.org.au
elt-pipeline 0.1.0
A simple ELT pipeline package to extract, transform, and load data.
Requires Python: >=3.7

# ELT pipeline 

A simple ELT (Extract, Load, Transform) pipeline package for CSV, API, and Database operations.

- db_connection.py - Creates the engine to your database

- extractor.py - Uses the engine to extract data into a DataFrame

- transformer.py - Cleans and prepares the DataFrame

- loader.py - Saves the cleaned DataFrame to CSV or JSON