semvec/pypi-packages huggingface dataset:
smartfarm-iot 0.0.14
A package for Smart Farm IoT data collection and publishing using Modbus TCP and MQTT
Requires Python: >=3.6


<img src="https://data.doctor-ag.com/static/assets/images/brand/drag-logo.png" alt="logo" width="150" height="150" style="margin-bottom: -60px;">

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# [Dr.AG Smart Farm IoT Project](http://www.doctor-ag.com/)

This project collects data from IoT devices in a smart farm using Modbus TCP protocol and publishes the data to an MQTT broker.

## Project Structure

- `config/`: Configuration files including farm information and device details.
- `src/`: Source code for reading Modbus data and publishing to MQTT.
- `logs/`: Directory for log files.
- `requirements.txt`: Python dependencies.
- `README.md`: Project documentation.

## Setup

1. Install the required Python packages:
    ```bash
    pip install -r requirements.txt
    ```

2. Configure your MQTT broker and device information in the `config` directory.

3. Run the main program:
    ```bash
    ./run.sh
    ```
   
## Building and Distributing the Package

1. Install necessary packages:
    ```bash
    pip3 install setuptools wheel twine
    ```
   
2. Increase version, build package, and upload PyPI:
    ```bash
    ./build.sh
    ```
   
3. View at:
   - https://pypi.org/project/smartfarm-iot
   
## Copyright

ⓒ 2024. NDS all rights reserved.
HeliosMedPrep 0.0.2
HeliosMedPrep
Requires Python: >=3.7

# HeliosMedPrep

HeliosMedPrep is a specialized Python library for medical data preprocessing, helping to normalize, encode, and transform data from EHR systems for use in machine learning models. It is part of the Helios ecosystem, developed to improve efficiency and usability in medical research using MIMIC data. HeliosMedPrep includes various functions to prepare hospitalization data for machine learning models. This library is handy in handling different data types, such as drug coding, diagnoses, medical procedures, prescriptions, laboratory events, and hospital admission information. This library is introduced in the article "DiagLumina: A Trustworthy and Explainable Multi-Task Framework for Clinical and Para-clinical Diagnosis Using EHR Data."

HeliosMedPrep is a library designed to facilitate data preprocessing from the MIMIC database in the newest version.