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Notebook Tools

ODSBox Python Library

The ODSBox Python Library is a lightweight Python wrapper on the standardized ASAM ODS REST API.

Using intuitive JAQuel Queries and pandas.DataFrames the ODSBox makes dealing with ASAM ODS data in Python more fun.

Simply pip-install the library or download from our GihHub repository.

JAQuel Queries

JAQuel allows you to query your data in a simple and intuitive way following the concepts of the MongoDB Query Language (MQL). The definition of query expression as JSON easyly integrates with the Python language - a win-win situation.

Example Notebooks

To show you how to deal with structured data in your Python code, we have created some simple Jupyter Notebooks which guides you through the major concepts:

• 1_query-asam-server.ipynb - Query Peak ODS Server
• 2_load_mearesult.ipynb - Loading time series data
• 3_explore_the_data_model.ipynb - Explore the Data Model

All Notebooks executing queries against the Peak ODS Server - the data kernel of the Peak Test Data Manager.

Query Peak ODS Server

In the notebook example "1_query-asam-server.ipynb" you learn how to connect to a Peak ODS Server and query for data (objects) in the server. Starting from simple queries, over defining the returned data sets up to using referenced objects, all major aspects of programmatically data exploration are covered.

Load measurement data

The notebook example "2_load_mearesult.ipynb" explains how to receive time series (measurement) data from the Peak ODS Server and converts it to pandas.DataFrames for further processing.

Explore the Data Model

The strength and flexibility of the Peak Test Data Manager is the storage of data according to a data model which suites your data needs. Like an Entity-relationship model it defines your business objects. Working with JAQuel Queries requires some knowledge of your application model and the ASAM ODS Base Model. The Notebook example "3_explore_the_data_model.ipynb" gives some ideas for looking into the data model.

You can find the notebooks and other tools like the ODSBox in our open-source repository.

Are you interested in a customized solution for ODS Test Data Analytics with Jupyter Notebooks?

Contact us today to find out how you can explore test data in an ASAM ODS datastore with Jupyter Notebooks. Our team of experts will be happy to provide you with personalized advice and show you how you can benefit from our ODSBox Python library and example notebooks.