pyHQL is HighQSoft's Python library for accessing ASAM ODS test data through the HQL Web Service. Instead of exporting files and converting formats, you write HQL queries directly in your Python scripts and receive results as Python-native objects ready for pandas, numpy, or matplotlib.
HQL (HighQSoft Query Language) is the same query language used in production at BMW, Ford, Bosch, and other major automotive OEMs to manage millions of test measurements. pyHQL brings that full capability to your Python environment - not a subset, not a simplified wrapper, but the complete query language including mass data access, transactions, and file operations.
Whether you're building automated analysis pipelines with Merlin, creating custom reports, or exploring data interactively in Jupyter notebooks, pyHQL keeps your data where it belongs - in your governed ASAM ODS repository - while giving you the programmatic access modern data science workflows require.
Write any query you need using SQL-like syntax. Select, filter, join, aggregate - access your entire ASAM ODS data model without limitations. No predefined endpoints, no artificial restrictions.
Create and update records with full transaction control. Commit when ready, rollback on errors. Your data integrity is protected, and every change is traceable through your ASAM ODS governance model.
Results come as Python-native objects. Dates are datetime objects. Arrays are numpy-compatible. Build DataFrames with one line. pyHQL fits into your existing analysis workflow, not the other way around.
Most Python libraries for test data provide thin wrappers around REST APIs. They let you fetch predefined data structures, but the moment you need something custom - a complex join, a filter the API doesn't expose, access to mass data - you're stuck exporting files again.
pyHQL takes a fundamentally different approach. Instead of wrapping a limited API, it gives you direct access to HQL - a complete query language designed specifically for ASAM ODS. Your queries execute server-side for performance, then return as Python objects ready for analysis.
This means you can query metadata, retrieve time series with millions of points, write results back to the server, and manage file attachments - all from Python, all with the same query language your MATLAB colleagues use with the ASAM ODS Toolbox.
Python ASAM ODS Tutorial: Using HQL is key to simplicity.
This tutorial series takes you from installation through advanced operations. Each part builds on the previous while answering a specific question test engineers commonly ask.
By the end of this series, you'll be able to build Python scripts that query test metadata, retrieve measurement channels into pandas DataFrames, create and update records, and manage file attachments.
To use pyHQL, you need:
Don't have access to an HQL Web Service yet? Contact us for an evaluation license or request a demo with your own data.
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