Hi Community,

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.

•  Use of Python plotting from IRIS

•  Use of Jupyter Notebook

•  Introduction to Data Science, Data Plotting and Data Visualization.

•  QR Code generator from python.

If your embedded python code calls tkinter library (which is used by a lot of graphic producing libraries, including matplotlib), you might get this error:

<THROW> *%Exception.PythonException <CLASS DOES NOT EXIST> 230 ^^0^DO ##CLASS(User.Test).Test() 
<class '_tkinter.TclError'>: Can't find a usable init.tcl in the following directories:

c:/intersystems/irispy/lib/python/lib/tcl8.6
c:/intersystems/irispy/lib/tcl8.6
c:/intersystems/lib/tcl8.6
c:/intersystems/irispy/library
c:/intersystems/library
c:/intersystems/tcl8.6.9/library
c:/tcl8.6.

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.

How does it work?

Hi Community
In this article, I will introduce my application irisChatGPT which is built on LangChain Framework.
First of all, let us have a brief overview of the framework.

The entire world is talking about ChatGPT and how Large Language Models(LLMs) have become so powerful and has been performing beyond expectations, giving human-like conversations. This is just the beginning of how this can be applied to every enterprise and every domain! 

1. interoperability-embedded-python

This proof of concept aims to show how the iris interoperability framework can be used with embedded python.

1.1. Table of Contents

• 1. interoperability-embedded-python
  • 1.1. Table of Contents
  • 1.2. Example
  • 1.3. Register a component
• 2. Demo
• 3. Prerequisites
• 4. Installation
  • 4.1. With Docker
  • 4.2. Without Docker
  • 4.3. With ZPM
  • 4.4. With PyPI
    • 4.4.1. Known issues
• 5. How to Run the Sample
  • 5.1. Docker containers
  • 5.2. Management Portal and VSCode
  • 5.3. Open the production
• 6. What's inside the repository
  • 6.1. Dockerfile
  • 6.2. .vscode/settings.json
  • 6.3. .

Hi Community,

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
 

Hi, Community,

This post will demonstrate how to display data on the web by using Embedded Python , Python Flask Web Framework and Jquery datatable

We will display processes from %SYS.ProcessQuery table.

Step 1: Add table to HTML page and write below javascript code to display passed data from app.py :

HTML

  <table id="myTable" class="table table-bordered table-striped">                 
   </table>

Javascript

  <script>
    
$(document).ready(function() {
      // parse the data to local variable passed from app.py file
      let my_data = JSON.

Introduction

This article aims to give an introduction to what gRPC is and an example of how to play with the official Hello World using IRIS Embedded Python.

You can find all the code exposed here, in this project repo.

gRPC

The gRPC (gRPC remote procedure call) is an API architectural style based on the RPC protocol. The project was created by Google in 2015 and is licensed under Apache 2.0. Currently, the project is supported by the Cloud Native Computing Foundation (CNCF).

If you are using Python, you can use the built-in venv module to create a virtual environment. This module is the recommended way to create and manage virtual environments.

A virtual environment is a tool that helps to keep dependencies required by different projects separate by creating isolated python virtual environments for them. It solves the “Project X depends on version 1.x but, Project Y needs 4.x” dilemma, and keeps your global site-packages directory clean and manageable.

So if like me you work a lot with Python, you can use the venv module to create a virtual environment for your project. This will allow you to install packages without affecting the global Python installation.

You will find here two neat alias to create and activate a virtual environment.

Python aliases

alias venv="python3 -m venv .venv; source .venv/bin/activate"
alias irisvenv="python3 -m venv .venv; source .venv/bin/activate; pip install https://github.com/grongierisc/iris-embedded-python-wrapper/releases/download/v0.0.3/iris-0.0.3-py3-none-any.whl"

All of us know that money is important. We constantly need to monitor all expenses to avoid looking back to the bank statement and thinking: “So, where’s my money?”

To evade financial stress, we must keep an eye on the inflow and outflow of money into our accounts.It is also important to tack when and how we spend and earn. Manually recording all transactions in order to understand where our money goes requires an effort. It demands consistency, and it is boring. Today there is a bunch of mobile or SaaS options that help you manage your finances.

If you develop in IRIS you are confronted with two major phenomena:

• an incredibly fast and excellent designed data storage engine
• a scripting language to work on this storage engine, actually named ObjectScript 

I have been using embedded python for more than 2 years now on a daily basis. May be it's time to share some feedback about this journey.

Why write this feedback? Because, I guess, I'm like most of the people here, an ObjectScript developer, and I think that the community would benefit from this feedback and could better understand the pros & cons of chosing embedded python for developing stuff in IRIS. And also avoid some pitfalls.

• Feedback : Using embedded python daily for 2 years
• Introduction
• Starting with Python
  • Python is not ObjectScript
    • Pep8
    • Modules
    • Dunders
  • Conclusion
• Embedded Python
  • What is Embedded Python ?
  • How to use Embedded Python ?
  • How I use Embedded Python
    • Use Python libraries and code as they were ObjectScript classes
      • With the language tag
      • Without the language tag
      • Conclusion
    • Use a python first approach
      • Example : iris-fhir-python-strategy
      • Remarks
      • Where to find the code
        • How to implement a Strategy
        • Implementation of InteractionsStrategy
        • Implementation of Interactions
        • Interactions in Python
        • Implementation of the abstract python class
      • Too long, do a summary

What is Web Scraping:

In simple terms, Web scraping, web harvesting, or web data extraction is an automated process of collecting large data(unstructured) from websites. The user can extract all the data on particular sites or the specific data as per the requirement. The data collected can be stored in a structured format for further analysis.

Steps involved in web scraping:

1. Find the URL of the webpage that you want to scrape
2. Select the particular elements by inspecting
3. Write the code to get the content of the selected elements
4. Store the data in the required format

It’s that simple !

Previous post - Using AI to Simplify Clinical Documents Storage, Retrieval and Search

This post explores the potential of OpenAI's advanced language models to revolutionize healthcare through AI-powered transcription and summarization. We will delve into the process of leveraging OpenAI's cutting-edge APIs to convert audio recordings into written transcripts and employ natural language processing algorithms to extract crucial insights for generating concise summaries.

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.

Introduction

Data analytics is a crucial aspect of business decision-making in today's fast-paced world. Organizations rely heavily on data analysis to make informed decisions and stay ahead of the competition. In this article, we will explore how data analytics can be performed using Pandas and Intersystems Embedded Python. We will discuss the basics of Pandas, the benefits of using Intersystems Embedded Python, and how they can be used together to perform efficient data analytics.

Easy, easy, I'm not promoting a war against the machines in the best sci-fi way to avoid world domination of Ultron or Skynet. Not yet, not yet 🤔

I invite you to challenge the machines through the creation of a very simple game using ObjectScript with embedded Python.

I have to say that I got super excited with the feature of Embedded Python on InterSystems IRIS, it's incredible the bunch of possibilities that opens to create fantastic apps.

Let's build a tic tac toe, the rules are quite simple and I believe that everyone knows how to play.

In this article, I will show you how one can easily create and read Microsoft Word documents using InterSystems IRIS with the leverage power of embedded Python.

Setup

First things first, let’s install the Python module called python-docx. There are a lot of modules to write MS Word files in Python. However, this one is the easiest one to use.

Just execute the following command on the terminal:

!

My major interest is  Working with Globals in Embedded Python.
So I checked the available official documentation.

#1 Introduction to Globals
an attempt of a generic description of what a global is. Pointing to

#2 A Closer Look at ObjectScript
But where is Embedded Python ?
Way down you see

#3 Embedded Python

3.1 Embedded Python Overview
3.1.1 Work with Globals
Great if you have never seen a Global before
Otherwise a shocking primitive example
3.2 Using Embedded Python
Last hope: >>> but there is just NOTHING visible.
This is more than just disappointing! Even IRIS Native API for Python is more detailed.

Python has become the most used programming language in the world (source: https://www.tiobe.com/tiobe-index/) and SQL continues to lead the way as a database language. Wouldn't it be great for Python and SQL to work together to deliver new functionality that SQL alone cannot? After all, Python has more than 380,000 published libraries (source: https://pypi.org/) with very interesting capabilities to extend your SQL queries within Python. This article details how to create new SQL Stored Procedures in InterSystems IRIS Database using Embedded Python.

Nowadays so much noise around LLM, AI, and so on. Vector databases are kind of a part of it, and already many different realizations for the support in the world outside of IRIS. 

Why Vector?

• Similarity Search: Vectors allow for efficient similarity search, such as finding the most similar items or documents in a dataset. Traditional relational databases are designed for exact match searches, which are not suitable for tasks like image or text similarity search.
• Flexibility: Vector representations are versatile and can be derived from various data types, such as text (via embeddings like Word2Vec, BERT), images (via deep learning models), and more.
• Cross-Modal Searches: Vectors enable searching across different data modalities. For instance, given a vector representation of an image, one can search for similar images or related texts in a multimodal database.

And many other reasons.

So, for this pyhon contest, I decided to try to implement this support. And unfortunately I did not manage to finish it in time, below I'll explain why.

Hi Community,
In this article I will demonstrate the usage of InterSystems Embedded Python, We will cover below topics:

• 1-Embedded Python Overview
• 2-Usage of Embedded Python
  • 2.1- Using a Python Library from ObjectScript
  • 2.2- Calling the InterSystems APIs from Python
  • 2.3- Using ObjectScript and Python together
• 3-Using python Built-in Functions
• 4-Python Modules/Libraries 
• 5-Embedded Python Use Cases
  • 5.1- Printing PDF by using python Reportlab Library
  • 5.2- Generating QR code by using Python Qrcode Library
  • 5.3- Get GEO location by using Python Folium Library
  • 5.

This is a first attempt to use Embedded Python in IRIS
The Python code is adapted from solutions for Advent of Code 2020 contest.
Test data are all input to my personal challenge.

Prerequisites

Make sure you have git and Docker desktop installed.

Installation

Clone/git pull this repo into any local directory

$ git clone https://github.

In this article you will have access to the curated base of articles from the InterSystems Developer Community of the most relevant topics to learning InterSystems IRIS. Find top published articles ranked by Machine Learning, Embedded Python, JSON, API and REST Applications, Manage and Configure InterSystems Environments, Docker and Cloud, VSCode, SQL, Analytics/BI, Globals, Security, DevOps, Interoperability, Native API. Learn and Enjoy!

Machine Learning

Machine Learning is a mandatory technology to build advanced data analysis and automate manual activities with excellent efficiency.

Hi developers!

Let me share with you a minimal embedded python template, that I can recommend as a starting point for any general project with InterSystems IRIS that will use embedded python.

Features:

• Embedded Python ready;
• Examples of 3 ways of Embedded python development;
• VSCode development ready;
• Docker enabled;
• Online demo enabled;
• ZPM First development ready.

Let's discuss the features below!

Following this GitHub we will see how the FIX protocol can be implemented easily using IRIS and Python.

If you don't have much time focus on the Send a Quote before the Order part near the end, as it will, in a matter of minute, tell you how to send a Quote Request followed by an Order Request and show you the result from the server, and that in no more than five clicks.

IRIS-fix-protocol

Implementation of the fix protocol using an IRIS python container for the initiator and a regular python container for the acceptor.

Programmatic Production Access

To Programmatically Edit Productions (interfaces) you can use a combination of the interoperability apis and SQL queries.

Current Namespace

At a high level, it is important to know the namespace and production you are working in at the moment.

// Object script 
// The active namespace is stored in this variable
$$$NAMESPACE 
// Print namespace
Write $$$NAMESPACE

# Python
import iris
# The active namespace is returned from this method
iris.utils._OriginalNamespace()
# Print namespace
print(iris.utils.

Hi developers!

Recently we announced the preview of Embedded Python technology in InterSystems IRIS.

Check the Sneak Peak video by @Robert.Kuszewski.

Embedded python gives the option to load and run python code in the InterSystems IRIS server. You can either use library modules from Python pip, like numpy, pandas, etc, or you can write your own python modules in the form of standalone py files.

So once you are happy with the development phase of the IRIS Embedded Python solution there is another very important question of how the solution could be deployed.

One of the options you can consider is using the ZPM Package manager which is described in this article.

Hi Community,

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.

I'd like to share an example on how the new Embedded Python feature in IRIS helped me in my daily routines.

While I’m participating in the iris-kaggle-socrata-generator project with Henrique Dias, I got to unzip datasets from Kaggle in order to import them.

Such a task was easily achieved by using the zipfile lib in Python (this code was copied from this stackoverflow):

Method UnZip(pZipFileName As %String, pExtractToDir As %String) As %DynamicArray [ Language = python ]
{
    import zipfile
    import iris
    with zipfile.ZipFile(pZipFileName, 'r') as zip_ref:
        zip_ref.