semvec/pypi-packages huggingface dataset:
rolo-tools 0.0.7
A collection of python tools for use in Windows Environments
Requires Python: >=3.8

# Rolo's Tools

## A collection of reusable python tools for use in Windows Environments

### Logger
Write to a log file in C:\windows\temp and also display in the console. 
Easily log plain strings, dated strings, and json objects

### Manip
Clean and manipulate different variable and data types

### Secure Env
securely store and retrieve environment variables

More details to come...
aws-flask-lambda 0.1.4
Library to run applications on AWS Lambda Function using Flask

# aws_flask_lambda

Version 0.1.3 updates Flask and werkzeug version requirements to 3.0.3 and updates werkzeug.wrappers import, from base_request.py to request.py

Version 0.1.4 fixes an error when extracting the event['body'] on the AWS environment request, on the environ variable 'wsgi.input'

## Installation

Install the package using pip:

```sh
pip install aws-flask-lambda
```

## Usage

```python
from aws_flask_lambda import FlaskLambda
from flask import request, jsonify

app = FlaskLambda(__name__)


@app.route('/greet', methods=['GET', 'POST'])
def greet():
    name = request.form.get('name', 'World')
    message = f'Hello, {name}!'
    return (
        jsonify({'message': message}),
        200,
        {'Content-Type': 'application/json'}
    )


if __name__ == '__main__':
    app.run(debug=True)

```
liver-annotation 0.1.2
A machine learning model for classification of cells and annotation of clusters in scRNA-seq data from liver samples.
Requires Python: >=3.10

# Liver Annotation

Liver Annotation is a Python package designed to annotate clusters in single-cell RNA sequencing (scRNA-seq) data from liver samples. This package provides a machine learning model that is specifically trained on liver cells, enabling out-of-the-box functionality without the need for pre-existing expert-annotated data.

## Features

- Machine learning model trained specifically on liver cells.
- Supports both neural network and random forest classifier models.
- Annotates clusters using either the most common annotation or probability-based methods.

## Installation

To install the package, use pip:

```bash
pip install liver_annotation
```

## Usage

### Classification of Cells

You can classify cells by cell type using the classify_cells function. The function requires an input in_data which is a standard scanpy/anndata object with gene expression data.

```python
from liver_annotation import classify_cells

# Example usage
classify_cells(ann_data_obj, species="human", model_type="nn")
```

- `species`: Choose between `"human"` or `"mouse"`.
- `model_type`: Choose between `"rfc"` (random forest classifier) or `"nn"` (neural network).

### Cluster Annotation

Annotate clusters using the cluster_annotations function. This function requires an input in_data and allows you to specify the clustering algorithm and model type.

```python
from liver_annotation import cluster_annotations

# Example usage
cluster_annotations(in_data, species="human", clusters="louvain", algorithm="mode", model_type="nn")
```

- `clusters`: The column in `in_data.obs` to use for cluster data.
- `algorithm`: Choose between `"mode"` or `"prob"` for cluster annotation.
- `model_type`: Choose between `"rfc"` or `"nn"`.

## Dependencies

- `torch`
- `joblib`
- `scipy`
- `numpy`
- `scanpy`

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

## Contact

For any questions or issues, please contact Madhavendra Thakur at madhavendra.thakur@gmail.com.
Cryptix 0.1.1
A versatile cryptography library for various encryption and decryption techniques.
Requires Python: >=3.6

# Cryptix Package Usage

## Overview

The Cryptix a powerful python library which provides decryption functions for various ciphers. Below are examples demonstrating how to use the decryption functions for different ciphers included in the package.

## Supported Ciphers

- **Vigenère Cipher**
- **Affine Cipher**
- **Hill Cipher**
- **Beaufort Cipher**
- **Rail Fence Cipher**
- **Playfair Cipher**
- **One-Time Pad Cipher**
- **XOR Cipher**
- **RSA Cipher**
- **Caeser Cipher**
- **Bacon Cipher**
- **AtBash Cipher**
- **RO13**
- **ROT5**
- **Columnar Transposition Cipher**

## Supported Encodings
- **Binary**
- **Hexadecimal**
- **Octal**
- **ASCII**
- **URL Encoding**
- **Unicode Point**
- **Base64**
- **Base32**
- **Base58**
- **Morse Code**

## Sample Usage

### Caesar Cipher

```python
from Cryptix import Cryptix

ciphertext = "Khoor Zruog"  # Encrypted with a shift of 3
shift = 3
plaintext = Cryptix.decrypt_caesar(ciphertext, shift)
print("Caesar Cipher Decryption:", plaintext)
```

## Conclusion
Simply there is the logic to decrypt all these algorthims, like if you want any cipher to decrypt, follow the trick below:
```python
from Cryptix import Cryptix
ciphertext = "Xhpc yb fggw"  # Example encrypted text
key_matrix = [[6, 24, 1], [13, 16, 10], [20, 17, 15]]
plaintext = Cryptix.decrypt_hill(ciphertext, key_matrix)
print("Hill Cipher Decryption:", plaintext)

```
Just you want to type `decrypt_name`, like `decrypt_hill_cipher`, then either you want to print it or use a variable to store it and then print it.
The above example showed us that if you want to print it, first by saving its returing value to variable and then printing it.

### Other way to do
```python
from Cryptix import Cryptix
print(decrypt_caeser_cipher("HERE CIPHER TEXT COMES", "HERE COMES THE SWIFT FOR IT"))
```

Similary you can do it for decoding the mentioned encodings you have to use.
```python
encoded_text = "+++++++......++++++,-------"  # Example encoded text
plaintext = Cryptix.decode_morse_code(encoded_text)
print("Morse Code decoded text:", encoded_text)
```

### Other way to do it
```python
from Cryptix import Cryptix
print("Morse Code decoded text:", encoded_text)
```