succulent 0.3.4

Collect POST requests easily

succulent - Collect POST requests easily

🔍 Detailed Insights • 📦 Installation • 🐳 Container • 🚀 Usage • 🔧 Configuration • 🔑 License • 🫂 Contributors

Do you ever find it challenging and tricky to send sensor measurements 📏, data 📊, or GPS positions from embedded devices 📱, microcontrollers, and smartwatches to a central server? 📡 Setting up the primary data collection scripts can be a time-consuming ⏳ process, involving selecting a protocol, framework, API, and testing them out. Moreover, these scripts are often tailored for specific tasks, making them difficult to adapt to different scenarios.

But fear not! Introducing succulent 🌵, a pure Python framework that simplifies the configuration, management, collection, and preprocessing of data collected via POST requests. This framework draws inspiration from real-world data collection challenges in smart agriculture 🧠🌿, specifically plant monitoring using ESP32 devices. The main goal behind succulent is to streamline the process of configuring various data parameters and provide a range of useful functions for data transformations. By leveraging succulent, you can set up your entire data collection endpoint within minutes, freeing you from the hassle of dealing with server-side scripts. 🚀🔧

• Free software: MIT license
• Documentation: https://succulent.readthedocs.io/en/latest
• Python versions: 3.8.x, 3.9.x, 3.10.x, 3.11.x, 3.12.x
• Tested OS: Windows, Ubuntu, Fedora, Alpine, Arch, macOS. However, that does not mean it does not work on others

🔍 Detailed Insights

The current version of succulent comes packed with exciting features, including, but not limited to:

• Hassle-free generation of request URLs for seamless data collection 🌐
• Effortless data retrieval from POST requests 📥
• Versatile data storage options, such as CSV, JSON, SQLite, XML, and even images 🗂️📊🖼️
• Customisable boundaries for collected data, allowing you to set minimum and maximum thresholds ⚙️

With succulent, the process of collecting, managing, and preprocessing data becomes a breeze, empowering you to focus on what truly matters—gaining valuable insights from your embedded devices, microcontrollers, and smartwatches. ⌚ So why waste precious time? ⏳ Dive into the world of succulent and unlock the true potential of your data! 💪📈

📦 Installation

pip

To install succulent with pip, use:

pip install succulent

Alpine Linux

To install succulent on Alpine Linux, use:

$ apk add py3-succulent

Arch Linux

To install succulent on Arch Linux, use an AUR helper:

$ yay -Syyu python-succulent

Fedora Linux

To install succulent on Fedora, use:

$ dnf install python3-succulent

🐳 Container

Create a docker-compose.yml file with the following content in the root directory:

version: '3.8'

services:
  app:
    image: codeberg.org/firefly-cpp/succulent:v3
    ports:
      - "8080:8080"
    volumes:
      - ./run.py:/succulent-app/run.py
      - ./configuration.yml:/succulent-app/configuration.yml
    environment:
      - GUNICORN_WORKERS=2

Next create a configuration.yml file in the root directory. Here's an example of a configuration file:

data:
  - name: 'temperature'
  - name: 'humidity'
  - name: 'light'
  - name: 'time'
  - name: 'date'

More information regarding the configuration file and its settings can be found in the configuration section.

Then create a Python file named run.py with the following content in the root directory:

from succulent.api import SucculentAPI

api = SucculentAPI(config='configuration.yml', format='csv')

# Flask app instance, called by gunicorn
app = api.app

Once you have set up the configuration file and the Python file, build the Docker image with the following command:

docker compose build

Finally, run the Docker container with the following command:

docker compose up

🚀 Usage

Example

from succulent.api import SucculentAPI
api = SucculentAPI(host='0.0.0.0', port=8080, config='configuration.yml', format='csv')
api.start()

🔧 Configuration

Data collection

In the root directory, create a configuration.yml file and define the following:

data:
  - name: # Measure name
    min:  # Minimum value (optional)
    max:  # Maximum value (optional)

To collect images, create a configuration.yml file in the root directory and define the following:

data:
  - key: # Key in POST request

To store data collection timestamps, create a configuration.yml file in the root directory and define the following:

timestamp: true # false by default

To access the URL for data collection, send a GET request (or navigate) to http://localhost:8080/measure.

Data access

To access data via the Succulent API, enable the results option in the configuration file:

results:
  - enable: true # false by default

To access the collected data, send a GET request (or navigate) to http://localhost:8080/data.

Data export

To export the data, enable the export option in the configuration file:

results:
  - export: true # false by default

To export the data, send a GET request (or navigate) to http://localhost:8080/export. The data will be downloaded in the format specified in the configuration file.

🔑 License

This package is distributed under the MIT License. This license can be found online at http://www.opensource.org/licenses/MIT.

Disclaimer

This framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!

🫂 Contributors

Thanks goes to these wonderful people (emoji key):

Tadej Lahovnik 💻 🐛 🤔 📖 ✅

Ayan Das 💻 ⚠️

Iztok Fister Jr. 💻 🤔 🧑‍🏫

Oromion 🐛 📦

rhododendrom 🎨

Zala Lahovnik 📖

This project follows the all-contributors specification. Contributions of any kind welcome!