Article
· Dec 7, 2020 6m read
IRIS Python Native API in AWS Lambda

If you are looking for a slick way to integrate your IRIS solution in the Amazon Web Services ecosystem, server less application, or boto3 powered python script, using the IRIS Python Native API could be the way to go. You don't have to build out to far with a production implementation until you'll need to reach out and get something or set something in IRIS to make your application do its awesome sauce, so hopefully you will find value in this article and build something that matters or doesn't matter at all to anybody else but you as that is equally important.

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Article
· Mar 29, 2023 1m read
Named Parameter In SQL with Python

Quick Tips: Total Productive Maintenance

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())

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Article
· Jul 27, 2018 4m read
Load a ML model into InterSystems IRIS

Hi all. Today we are going to upload a ML model into IRIS Manager and test it.

Note: I have done the following on Ubuntu 18.04, Apache Zeppelin 0.8.0, Python 3.6.5.

Introduction

These days many available different tools for Data Mining enable you to develop predictive models and analyze the data you have with unprecedented ease. InterSystems IRIS Data Platform provide a stable foundation for your big data and fast data applications, providing interoperability with modern DataMining tools.

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Article
· Apr 19, 2023 2m read
Apache Superset now with IRIS

Apache Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams. Superset integrates well with a variety of data sources.

And now it is possible to use with InterSystems IRIS as well.

An online demo is available and it uses IRIS Cloud SQL as a data source.

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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.

Lajos Simicska declares war on Viktor Orban: "It's either him or me!" - The  Budapest Beacon

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!

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On this GitHub you can find all the information on how to use a HuggingFace machine learning / AI model on the IRIS Framework using python.

1. iris-huggingface

Usage of Machine Learning models in IRIS using Python; For text-to-text, text-to-image or image-to-image models.

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I am demonstrating a use case of how we can create an IRIS Interoperability Production for special use in an external language. InterSystems IRIS, within Interoperability has a framework called Production Extension (PEX), using which we can create productions and program them as per their purpose using external languages like Java, Python etc, and also develop custom inbound and outbound adapters to communicate with other applications.

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Index

Part 1

  • Introducing Flask: a quick review of the Flask Docs, where you will find all the information you need for this tutorial;
  • Connecting to InterSystems IRIS: a detailed step-by-step of how to use SQLAlchemy to connect to an IRIS instance;

Part 2

  • A discussion about this kind of implementation: why we should use it and situations where it is applicable.
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Hello Community,

SQL language remains the most practical way to retrieve information stored in a database.

The JSON format is very often used in data exchange.

It is therefore common to seek to obtain data in JSON format from SQL queries.

Below you will find simple examples that can help you meet this need using ObjectScript and Python code.

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   _________ ___ ____  
  |__  /  _ \_ _|  _ \ 
    / /| |_) | || |_) |
   / /_|  __/| ||  __/ 
  /____|_|  |___|_|    

Starting in version 2021.1, InterSystems IRIS began shipping with a python runtime in the engine's kernel. However, there was no way to install packages from within the instance. The main draw of python is its enormous package ecosystem. With that in mind, I introduce my side project zpip, a pip wrapper that is callable from the iris terminal.

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Article
· Apr 5, 2022 4m read
Serializing Python objects in globals

Motivation

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.

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Article
· Jul 7, 2023 8m read
Iris FHIR Python Strategy

Description

With InterSystems IRIS FHIR Server you can build a Strategy to customize the behavior of the server (see documentation for more details).

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This repository contains a Python Strategy that can be used as a starting point to build your own Strategy in python.

This demo strategy provides the following features:

  • Update the capability statement to remove the Account resource
  • Simulate a consent management system to allow or not access to the Observation resource
    • If the User has sufficient rights, the Observation resource is returned
    • Otherwise, the Observation resource is not returned
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I think it's a known fact that Populate Utility has very limited functionality. It supports only one language and one country. The list of possible values does not have so many options.

There is a kind of tool that now can help with it, named Faker. It has implementations in different languages, including Python. Since IRIS has now had the Embedded Python feature, Python faker can be implemented in IRIS.

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For the upcoming Python contest, I would like to make a small demo, on how to create a simple REST application using Python, which will use IRIS as a database. Using this tools

  • FastAPI framework, high performance, easy to learn, fast to code, ready for production
  • SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL
  • Alembic is a lightweight database migration tool for usage with the SQLAlchemy Database Toolkit for Python.
  • Uvicorn is an ASGI web server implementation for Python.

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