A new data model, SolarTracker in the subject dataModel.GreenEnergy
Thanks to the contributors of the organization Altanet for their contribution
- SolarTracker. Description of the data model SolarTracker
Category: Smart Energy domain
Updated the test fpr NGSIv2 payloads
Calling all SDM contributors!
To ensure a smooth integration process, we want to remind everyone that all contributions undergo automated testing prior to submission. To help you streamline your workflow and catch potential issues early, we’ve made our testing suite available for local use.
🛠️ Test Locally, Submit with Confidence
You can access the full source code for our testing framework under an open-source license. Running these tests on your own machine not only boosts the quality of your code but also saves valuable time for both you and the maintainers.
Getting Started
-
Download the Source: Head over to our repository to grab the testing suite.
-
Configure: Follow the simple instructions in the README. (Quick tip: You only need to update
config.jsonwith your specific local directories). -
Run Anywhere: The suite is flexible—it supports testing against both remote repositories and local file systems.
By validating your work locally before you push, you’re helping us maintain a high standard for the SDM project.
Happy coding!
List of tests
test_array_object_structure.py- test_duplicated_attributes.py
- test_file_exists.py
- test_name_attributes.py
- test_schema_descriptions.py
- test_schema_metadata.py
- test_string_incorrect.py
- test_valid_json.py
- test_valid_keyvalues_examples.py
- test_valid_ngsild.py
- test_valid_ngsiv2.py
- test_yaml_files.py
updated the test file test_duplicated_attributes
The contributors of new data models can test their data models in their local repositories with the source code of the testing tool than also can use online
Home -> tools -> test your data model
it has been updated to deal with those attributes coming from languageMap properties in NGSI
New pysmartdatamodels Version 0.8.0.2: Enhanced with More Models and Automated Contributor Tools
We are thrilled to announce a significant new release of our Python package, pysmartdatamodels, designed to empower developers and streamline the contribution process for our community.
This update is packed with new data models and, most notably, powerful new validation and testing services.
What’s New in This Version?
An Expanded Library of Data Models
The heart of the Smart Data Models initiative is our comprehensive library of open-licensed data models. With this new release, we have expanded it further, adding new models across our thirteen domains, from Smart Cities to Smart Energy. This package continues to provide powerful functions to integrate more than 1,000 standardized data models into your projects, digital twins, and data spaces.
New! Automated Testing for Contributors
To improve the quality and speed of contributions, we are excited to launch a brand new service for our contributors. You can now automatically test your data models before submitting them. This automated validation ensures that new models comply with our standards, making the review and integration process smoother for everyone.
For those who want to integrate this validation into their own workflows, the source code for the testing tool is also available.
Online Validation for Examples
In addition to the new contributor tool, you can also use the online service to validate payloads against existing data model examples.
Get the Latest Version
You can find all the details, explore the functions, and get the latest package from the Python Package Index (PyPI). The updated README file includes comprehensive documentation on all the new features.
Our Commitment to Open and Interoperable Data
We are committed to making data interoperability easier for everyone. These updates are a direct result of community feedback and the hard work of our contributors. A huge thank you to everyone who has helped make this possible!
Use smart data models as a service. First draft
In the data-models repository you can access to the first version to use smart data models as a service. Thanks to the works for the Cyclops project.
The files available create a wrap up around pysmartdatamodels package and also add one service for the online validation of NGSI-LD payloads.
Here is the readme contents to have an explanation of this first version
Smart Data Models API and Demo
This project consists of two main components:
- A FastAPI-based web service (
pysdm_api3.py) that provides access to Smart Data Models functionality - A demo script (
demo_script2.py) that demonstrates the API endpoints - A requirements file for the components use
- A bash script to run it
API Service (pysdm_api3.py)
A RESTful API that interfaces with the pysmartdatamodels library to provide access to Smart Data Models functionality.
Key Features
- Payload Validation: Validate JSON payloads against Smart Data Models schemas
- Data Model Exploration: Browse subjects, data models, and their attributes
- Search Functionality: Find data models by exact or approximate name matching
- Context Retrieval: Get @context information for data models
- if you need other please email us to info @ smartdatamodels. org
Endpoints
| Endpoint | Method | Description |
|---|---|---|
/validate-url |
GET | Validate a JSON payload from a URL against Smart Data Models |
/subjects |
GET | List all available subjects |
/datamodels/{subject_name} |
GET | List data models for a subject |
/datamodels/{subject_name}/{datamodel_name}/attributes |
GET | Get attributes of a data model |
/datamodels/{subject_name}/{datamodel_name}/example |
GET | Get an example payload of a data model |
/search/datamodels/{name_pattern}/{likelihood} |
GET | Search for data models by approximate name |
/datamodels/exact-match/{datamodel_name} |
GET | Find a data model by exact name |
/subjects/exact-match/{subject_name} |
GET | Check if a subject exists by exact name |
/datamodels/{datamodel_name}/contexts |
GET | Get @context(s) for a data model name |
Validation Process
The /validate-url endpoint performs comprehensive validation:
- Fetches JSON from the provided URL
- Normalizes NGSI-LD payloads to key-values format
- Extracts the payload type
- Finds all subjects containing this type
- Retrieves schemas for each subject
- Validates against all schemas
- Returns consolidated results
Demo Script (demo_script2.py)
A simple interactive script that demonstrates the API endpoints by opening a series of pre-configured URLs in your default web browser.
Features
- Opens each URL in a new browser tab
- Pauses between URLs for user input
- Allows early termination with ‘exit’ command
- Provides clear progress indicators
Usage
- Configure the URLs in the
my_web_urlslist - Run the script:
python demo_script2.py - Follow the on-screen instructions
The demo includes examples of:
- Payload validation
- Subject listing
- Data model exploration
- Attribute retrieval
- Example payloads
- Search functionality
- Exact matching
- Context retrieval
Requirements
- Python 3.7+
- FastAPI
- Uvicorn
- httpx
- pydantic
- jsonschema
- webbrowser (standard library)
Installation
pip install fastapi uvicorn httpx pydantic jsonschema
Running the API
python pysdm_api3.py
Running the demo
python demo_script2.py
Configuration
Edit the my_web_urls list in demo_script2.py to change which endpoints are demonstrated.
License
Apache 2.0
New script for testing several data models at the same time.
Most of the files of the testing process have been updated and make it available the source code:
https://github.com/smart-data-models/data-models/tree/master/test_data_model
But also there is a new file
This file enables you to test all the data models located in a internal subject (subdirectories of the root one). Currently this option is not available as a form but if you send us an email to our infno@smartdatamodels.org
we could create a specific form for that
See here an example of the outcome.
Another tiny improvement on the new testing process (ngsild payloads)
In the new testing process, 4th option in the tools menu, now it is available a new test that checks if the example-normalized.jsonld is a valid NGSI LD file.
This process helps contributors to debug their data models before submit them officially (where there will be new tests before final approval)
The source code for the test is available at the repo.
Remember that if you want to improve / create a new test, just create a PR on the repo.
Tiny improvement on the new testing process
In the new testing process, 4th option in the tools menu, now it is available a new test that checks if the example-normalized.json is a valid NGSIv2 file.
This process helps contributors to debug their data models before submit them officially (where there will be new tests before final approval)
The source code for the test is available at the repo.
Remember that if you want to improve / create a new test, just create a PR on the repo.
Improved test method for data models
When you want to contribute a new data model (or an improvement in an existing one) you need to pass a test.
The current process (3rd option in tools menu) keeps on working as it was.
But we have drafted a new method because
– We need to be more explicit about the tests passed and the errors
– We need to improve the performance
So you can check the new method in the 4th option of the Tools menu
Besides this, the tests are very modular so if you are a python programmer you can use them in your own system because the code is being released or indeed you can write new tests that would be included in the official site. Make a PR on the data-models repo and we will add it eventually. Check this post.
New testing process in progress were you can contribute your code
Current test process for new and extended data models
In order to approve a new data model a test needs to be passed. It cold be accessed in the 3rd option in the tools menu at the front page:
Pro: it is currently working
Con: It is mostly created in a single file for testing and error messages are not very explicit about the errors detected
The new process
1) Every test is an independent file:
2) To test the new data model it copies to local the files and then run the tests, which is quicker.
What can you do with basic knowledge of python (or with a good AI service)
Here you can see the current files available in the github repository data-models subdirectory test_data_model
Instructions
