semvec/pypi-packages huggingface dataset:
hadro 0.5.0a6
Storage Engine

<div align="center">

![Hadro](https://raw.githubusercontent.com/mabel-dev/hadro/main/hadro.png)

Hadro is a database storage engine for [Opteryx](https://opteryx.dev).

[![PyPI Latest Release](https://img.shields.io/pypi/v/hadrodb.svg)](https://pypi.org/project/hadro/)
[![codecov](https://codecov.io/gh/mabel-dev/hadro/branch/main/graph/badge.svg?token=nl9JwOVdPs)](https://codecov.io/gh/mabel-dev/hadro)
[![Downloads](https://static.pepy.tech/badge/hadro)](https://pepy.tech/project/hadro)

</div>

## License

[![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/mabel-dev/hadro/blob/main/LICENSE)
[![Notices](https://img.shields.io/badge/-Notices-darkgreen.svg)](https://github.com/mabel-dev/hadro/blob/main/NOTICES)

## Status

[![Status](https://img.shields.io/badge/Status-alpha-orange)](https://github.com/mabel-dev/hadro)

Hadro is in alpha. Alpha means different things to different people, to us, being alpha means:

- Interfaces may be significantly changed
- The data file format is unstable
- Expected functionality is missing
- Things that worked yesterday, don't work today
- The results of the system may be unreliable

As such, we really don't recommend using HadroDB anywhere where your data matters.

## File Format

### Magic Bytes

`HADRO`

### Version

`001`

### Header

Section table - Type, Offset, Size, Compression Algo

Record Count

Values Hash

Flags (bytes)
    Value Store Compression Algo (0 = none, 1 = LZ4, 2 = zSTD)

Column Names (not every record has every column)

PK column name

### Statistics

Column, Count, Min, Max, unique values 

### Indexes

Key, Version, Offset, Length

- used for the PK index (mandatory) and other values (optional)

Key is binary encoded and limited to 64 bytes, if the value is longer, it needs to be filtered from the value

OR

number of entries 
value, bitmap

### Values

Entries of MsgPack bytes
average-methylation 0.1.0
A package to calculate average methylation scores.
Requires Python: >=3.6

Average Methylation Calculation

This Python package, average_methylation, is designed to process large datasets of methylation scores, calculate average methylation scores per gene per sample in defined windows upstream of gene start sites, and compile the results for further genomic analysis. It is intended for bioinformaticians and researchers studying epigenetic modifications.
Features

    1. Split large datasets: Break down comprehensive methylation datasets into manageable parts by chromosome.
    2. Calculate averages: Compute average methylation scores for genes in specific genomic windows.
    3. Combine results: Aggregate results into a concise format for downstream analysis.

Installation

You can install average_methylation directly via pip from PyPI:

    pip install average_methylation

Alternatively, if you have access to the GitHub repository and prefer to install the latest development version, you can install it directly using:

    pip install git+https://github.com/mrimis/average-methylation-calculation.git

Usage

After installation, the package can be used to run methylation analysis workflows. Here's a basic example of how to use the package in your Python scripts:

    from average_methylation.methylation_analysis import run_full_pipeline
    
    # Define the paths to your input files and output directory
    methylation_filepath = 'path/to/your/final_merged_dataset.csv'
    bed_filepath = 'path/to/your/hglft_genome_Methlylation_EPIC_GRCh38.bed'
    gene_coords_filepath = 'path/to/your/gene_coords.txt'
    output_directory = 'path/to/your/output/directory'
    
    # Run the full methylation analysis pipeline
    run_full_pipeline(methylation_filepath, bed_filepath, gene_coords_filepath, output_directory)

Function Parameters

    1. methylation_filepath: Path to the CSV file containing methylation scores.
    2. bed_filepath: Path to the BED file with genomic coordinates for methylation sites.
    3. gene_coords_filepath: Path to the file containing gene start and end coordinates.
    4. output_directory: Directory where the output files will be saved.

Contributing

Contributions to the average_methylation package are welcome! To contribute, please fork the repository, make your changes, and submit a pull request. We appreciate contributions in the form of code improvements, additional features, or bug fixes.

License

This project is licensed under the MIT License - see the LICENSE file for details.
Support

If you have any questions or encounter issues using the package, please open an issue on the GitHub repository issue tracker.
minsprojectr 0.0.3
A small package for basic mathematical operations
Requires Python: >=3.8

#  Package

This is a simple  package. You can use functions defined in this package
to perform basic additions, subtractions, and multiplications of two numbers
with add(), sub(), and multiply() respectively. 

usage: ->>
            from minsprojectr import operations
        
            given two integers 'a' and 'b', provided methods are
            operations.add(a,b), operations.sub(a,b) and operations.multiply(a,b)
autofiller 1.0.2
A small popup to input various random data for you
Requires Python: >=3.10

# autofiller
[![PyPI - Version](https://img.shields.io/pypi/v/autofiller.svg)](https://pypi.org/project/autofiller)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/autofiller.svg)](https://pypi.org/project/autofiller)

A small popup to input various random data for you.

There's 2 options to use this. By default, the popup opens as soon as you run the program, so adding "python -m autofiller" as a global short works great.

You can also run "python -m autofiller -d &" to have it run in the background, detect when you press the keyboard shortcut, and open the window.

You can also customize the keyboard shortcut like this:
```bash
python -m autofiller -d -m meta -k r
# or
python -m autofiller --deamon --modifier ctrl --key 5
# or
python -m autofiller -d -m none -k f3
```

-----

**Table of Contents**

- [Installation](#installation)
- [License](#license)

## Installation
Note that, due to one of the dependancies, clang needs to be installed on your system:
```bash
sudo apt-get install clang
sudo dnf install clang
# ...etc
```
Then you can install the library itself:
```console
pip install autofiller
```

## License

`autofiller` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
soleil 0.1.1
Object configuration system
Requires Python: >=3.8
Keywords: configuration, Machine Learning, object oriented


Soleil is a Python object configuration mechanism inspired by [Meta's Hydra](https://hydra.cc/). It's main goal is to provide a Python mechanism for machine learning researchers to assemble flexible experiments in a way that is **declarative**, **inheritable**, **automatically CLI-invokable** and **intuitively overridable**.

* Install soleil using

```
pip install soleil
```


* See [Soleil's full documentation](https://soleil.readthedocs.io/en/latest/) for more information


* Contribute at [Soleil's github page](https://github.com/zepedaj/soleil)
carbonation 0.1.1
You can't have fizz without carbonation
Requires Python: >=3.8

# Carbonation

[![PyPI - Version](https://img.shields.io/pypi/v/carbonation.svg)](https://pypi.org/project/carbonation)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/carbonation.svg)](https://pypi.org/project/carbonation)

-----

**Table of Contents**

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install carbonation
```

## License

`carbonation` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
drtensor 0.0.2
Pytorch modeling easier
Keywords: pytorch, debug, tensor

# Doctor Tensor 🩺

```
pip install drtensor
```

> Debug tool to help pytorch modeling

## 📦 Installation
We assume you have the pytorch already installed. Or this library doesn't serve any purpose.

```bash
pip install drtensor
```

## 🚀 Usage
### With clause
```python
from drtensor.doctor import DrTensor


model = reset_model()

dr_tensor = DrTensor(resnet=model)

with dr_tensor:
    y_ = model(torch.randn(1, 3, 224, 224))

# check the logs
print(dr_tensor.logs[-5:])
```
wolf-smart 0.0.7
A package to communicate with Wolf SmartSet Cloud

## Library to handle Wolf SmartSet communication.

### Features:
- Built-in authentication
- Session creation
- Fetch all devices you have
- Fetch all parameters description
- Fetch value for specific parameter

### Parameter descriptions
Parameters verified only for FGB-28 system.
Other should work.
Keep in mind that core implementation of fetching parameters is removing duplications by value_id and name.
dynamic-reader-aws 0.1
Directly use AWS Secrets Manager with Django settings.py



# dynamic_reader_aws

dynamic_reader_aws is a Python library that simplifies the integration of AWS Secrets Manager with Django applications. It enables the seamless retrieval of secret values from AWS Secrets Manager and their direct integration into Django settings.py files.

## Features

- **Seamless Integration:** Utilizes boto3 to seamlessly interact with AWS Secrets Manager, enabling smooth retrieval of secret values.
- **Direct Integration with Django Settings:** Streamlines the integration of secret key values into Django settings.py files, eliminating the need for manual configuration.
- **Enhanced Security:** Ensures the secure handling of sensitive information by leveraging AWS Secrets Manager's encryption and access control features.
- **Simplified Configuration:** Requires minimal configuration, including the Secrets Manager name, AWS region, and AWS credentials (AWS_ACCESS_KEY and AWS_SECRET_ACCESS_KEY), making it easy to get started.

## Installation

```bash
pip install dynamic_reader_aws



from dynamic_reader_aws import dynamic_secret_manager

# Provide AWS credentials and secret details
secrets_manager_name = "your_secrets_manager_name"
secret_key_name = "your_secret_key_name_in_secrets_manager"
region = "your_aws_region"
aws_access_key = "your_aws_access_key"
aws_secret_access_key = "your_aws_secret_access_key"
secret_key_name = "your_secret_key_name_in_secrets_manager"
# Integrate secrets with Django settings
dynamic_secret_manager(secrets_manager_name, secret_key_name, region, aws_access_key, aws_secret_access_key)