Embedded Python refers to the integration of the Python programming language into the InterSystems IRIS kernel, allowing developers to operate with data and develop business logic for server-side applications using Python.
In this article we are going to demonstrate the great potential that IRIS/HealthConnect makes available to all its users with the use of Embedded Python and we are going to do it by developing a small production that will allow us to recognize and identify the faces present in a JPG file from some images that we will use as a reference.
With the introduction of vector data types and the Vector Search functionality in IRIS, a whole world of possibilities opens up for the development of applications and an example of these applications is the one that I recently saw published in a public contest by the Ministry of Health from Valencia in which they requested a tool to assist in ICD-10 coding using AI models.
How could we implement an application similar to the one requested? Let's see what we would need:
Google Forms is the most popular solution on the market for collecting data, answering questionnaires and quizzes. So, it is the ideal solution for collecting patient data and responses in a practical way, without the need to expand or develop systems. In this article, I will detail how to create an account on Google Cloud, register the application that will consume the Google Forms API, generate the service user necessary to consume the API and finally perform actions to create new forms and collect data filled in them in an automated way in embedded Python and IRIS.
As you have seen in the latest community publications, InterSystems IRIS has included since version 2024.1 the possibility of including vector data types in its database and based on this type of data vector searches have been implemented. Well, these new features reminded me of the article I published a while ago that was based on facial recognition using Embedded Python.
Template engines are essential for producing customizable dynamic content, without the need for compilation and opening up great possibilities of extensibility for the template user. Especially if the template engine supports Python. A popular solution for template engine is Mko. It is is a template library written in Python. It provides a familiar, non-XML syntax which compiles into Python modules for maximum performance. Mako's syntax and API borrows from the best ideas of many others, including Django and Jinja2 templates, Cheetah, Myghty, and Genshi.
I have created a package to export a Global into JSON object file and to re-create it by reloading from this file embeddedPython refers to the new available technologies. It should be understood as a learning exercise of how to handle the language interfaces. Only Global nodes containing data are presented in the generated JSON file.
I have created a package to export a Global into JSON object file and to re-create it by reloading from this file embeddedPython refers to the new available technologies. It should be understood as a learning exercise of how to handle the language interfaces. Only Global nodes containing data are presented in the generated JSON file. Differently from the previous example, this one is using embedded Python only, no ObjectScript. Therefore PURE
In the ever-evolving landscape of data science and machine learning, having the right tools at your disposal can make all the difference. In this article, we want to shine a spotlight on two essential Python libraries that have become indispensable for data scientists and machine learning practitioners alike: Matplotlib and scikit-learn.
as it took me some time to figure out what's wrong, I would like to share this experience, so that you do not fall into the same trap.
I've just noticed that if you name your package "code" (all lowercase), in a class using some embedded python using [Language = python], you'll face the <THROW> *%Exception.PythonException <PYTHON EXCEPTION> 246 <class 'ModuleNotFoundError'>: No module named 'code.basics'; 'code' is not a package
In this article I will demonstrate the functionality of my app iris-energy-isodata . Application is accessing energy data (production, demand and supply) from the major Independent System Operators (ISOs) in the United States to ensure sustainable consumption and production patterns (SDG's 12)
This post is a introduction of my open exchange iris-fhir-client application.
iris-fhir-client can connect to any open FHIR Server by using embedded python with the help of fhirpy Library. Get Resource information by terminal and by using CSP web application.
This post is a introduction of my openexchange iris-python-apps application. Build by using Embedded Python and Python Flask Web Framework. Application also demonstrates some of the Python functionalities like Data Science, Data Plotting, Data Visualization and QR Code generation.
Features
Responsive bootstrap IRIS Dashboard
View dashboard details along with interoperability events log and messages.
In this article I will explain how to display management portal dashboard by using Python Flask web and Boostrap framework with the help of embedded python
Date and Time is an important factors in our life. Because all applications are engaging based on Date/Time. But our world is split into multiple time zones. if our product is launched in the world, to maintain the history of events we are definitely required to convert all times to our local time or UTC ( Coordinated Universal Time ). As I know, many known programming languages of C#, JavaScript, Java, etc., provided the library to convert the date and time. i.e with a time zone name we can be able to convert without knowing the UTC offset.
We are happy to share interesting information with you, as well as tell you why Python is good, where it is used.
Among the most used libraries are NumPy and Pandas. NumPy (Numerical Python) is used to sort large datasets. It simplifies mathematical operations and their vectorization on arrays. Pandas offers two data structures: Series (a list of elements) and Data Frames (a table with multiple columns). This library converts data into a Data Frame, allowing you to remove and add new columns, as well as perform various operations.
You may have seen my series of articles on GlobalToJSON closing with Embedded Python. And to me, Embedded means inside a Class and not PEX or other external variants. The first variants showed that ePy is not an ultimate requirement. ISOS/COS may do it as well. Here I try to position ePy inside IRIS.
In the previous articles, we learned the basics of using IMAP protocol to handle messages from mailboxes in an e-mail server. That was cool and interesting, but you could take advantage of implementations created by other ones, available in libraries ready to use.
This example demonstrates the difference you may experience when you write to Gllobals directly from Embedded Python compared to native ObjectScript.
To make this demo useful I start 2 background jobs that simply write sequentially to a dedicated global. A common control method signals for a synchronous start. Similar a common stop & view interrupts data feeding.
Applications that work with bill payments and receipts, as well as the delivery and inventory of items, generally require the use of barcodes or QR Codes. The latter is used in even broader scenarios since the QR Code can store more information than a simple bar code. Thus, it is important to have the ability to generate barcodes and QR Codes or read the data stored in them from an image or a PDF. This article will show you how to do this using Python and some of its free libraries.
While starting the development with IRIS we have a distribution kit or in case of Docker we are pulling the docker image and then often we need to initialize it and setup the development environment. We might need to create databases, namespaces, turn on/off some services, create resources. We often need to import code and data into IRIS instance and run some custom code to init the solution.
And there plenty of templates on Open Exchange where we suggest how to init REST, Interoperability, Analytics, Fullstack and many other templates with ObjectScript. What if we want to use only Python to setup the development environment for Embedded Python project with IRIS?
So, the recent release of Embedded Python template is the pure python boilerplate that could be a starting point for developers that build python projects with no need to use and learn ObjectScript. This article expresses how this template could be used to initialize IRIS. Here we go!
This project was thought of when I was thinking of how to let Python code deal naturally with the scalable storage and efficient retrieving mechanism given by IRIS globals, through Embedded Python.
My initial idea was to create a kind of Python dictionary implementation using globals, but soon I realized that I should deal with object abstraction first.
So, I started creating some Python classes that could wrap Python objects, storing and retrieving their data in globals, i.e., serializing and deserializing Python objects in IRIS globals.
Using embedded Python while building your InterSystems-based solution can add very powerful and deep capabilities to your toolbox.
I'd like to share one sample use-case I encountered - enabling a CDC (Change Data Capture) for a mongoDB Collection - capturing those changes, digesting them through an Interoperability flow, and eventually updating an EMR via a REST API.
In a previous question, I have illustrated a few problems using Embedded Python interactively as you would do from Docker console or IRIS terminal. Investigation of the causes brought a rather clear picture. It's a classic impedance mismatch
In the previous article we presented the d[IA]gnosis application developed to support the coding of diagnoses in ICD-10. In this article we will see how InterSystems IRIS for Health provides us with the necessary tools for the generation of vectors from the ICD-10 code list using a pre-trained language model, its storage and the subsequent search for similarities on all these generated vectors.
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