Overview

We started to use Azure Service Bus (ASB) as an enterprise messaging solution 3 years ago. It is being used to publish and consume data between many applications in the organization. Since the data flow is complex, and one application’s data is usually needed in multi applications the “publisher” ---> ”multiple subscribers” model was a great fit. The ASB usage in the organization is dozens of millions of messages per day, while IRIS platform is having around 2-3 million messages/day.

In this GitHub based on this InterSystems community rest api template Guillaume and I have created this example of all the import CRUD operations usable using ONLY Python on IRIS and using Flask.

Using the IRIS ORM or by simply doing SQL requests as both methods are seen in the GitHub.

With the latest improvements in support for Python in IRIS, and continued work on Python DB-API support by InterSystems. I've implemented IRIS support to the Django project where is Python DB-API is used to work with some other databases.

Let's try a simple application on Django, which stores its data in IRIS.

ipython-iris-magic

%%iris magic for IPython, can run ObjectScript command in Notebooks

It can be run in any Notebook and does not require any special setup on IRIS or the Notebook.

Demo

Native Api

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.

Hi Community,

This article will demonstrate how to create Patient and Patient Observation Resources by using iris-fhir-client application.

I recommend to read my first article about this app and watch Youtube Video before continue

so let's start

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

The invention and popularization of Large Language Models (such as OpenAI's GPT-4) has launched a wave of innovative solutions that can leverage large volumes of unstructured data that was impractical or even impossible to process manually until recently.

Overview

Cross-Skilling from IRIS objectScript to Python it becomes clear there are some fascinating differences in syntax.

One of these areas was how Python returns Tuples from a method with automatic unpacking.

Effectively this presents as a method that returns multiple values. What an awesome invention :)

out1, out2 = some_function(in1, in2)

ObjectScript has an alternative approach with ByRef and Output parameters.

Do ##class(some_class).SomeMethod(.inAndOut1, in2, .out2)

Where:

I am documenting a demo of InterSystems IRIS featuring Embedded Python and Jupyter Notebook deployed on the same container, and an Embedded Python application developed on that Jupyter Notebook IDE.

Named parameters can be achieved with SQLAlchemy :

from sqlalchemy import create_engine, text,types,engine

_engine = create_engine('iris+emb:///')

with _engine.connect() as conn:
    rs = conn.execute(text("select :some_private_name"), {"some_private_name": 1})
    print(rs.all())

or with native api

from sqlalchemy import create_engine, text,types,engine

# set URL for SQLAlchemy
url = engine.url.URL.create('iris', username='SuperUser', password='SYS', host='localhost', port=33782, database='FHIRSERVER')

_engine = create_engine(url)

with _engine.connect() as conn:
    rs = conn.execute(text("select :some_private_name"), {"some_private_name": 1})
    print(rs.all())

Welcome, dear members of the community!

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.

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.

Written in reply to community post for can Python create HL7 Message dynamically.

Pre-requisites and setup

Use an integration enabled namespace.
Note: USER namespace is not enabled for interoperability by default.
If following suggest create a new interoperatibility namespace to explore functionality.

# Switch to
ZN "[Interoperability Namespace Name]"

# Launch interactive Python shell:
Do $SYSTEM.Python.Shell()