Python is an interpreted high-level programming language for general-purpose programming. Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, notably using significant whitespace
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.
The reason is that ActiveState Python version 2.7.X is built with Visual Studio 2008 and Microsoft provides Visual Studio 2008, which one must install, so that Python C extensions can be built.
In last week's discussion we created a simple graph based on the data input from one file. Now, as we all know, sometimes we have multiple different datafiles to parse and correlate. So this week we are going to load additional perfmon data and learn how to plot that into the same graph.
Since we might want to use our generated graphs in reports or on a webpage, we'll also look into ways to export the generated graphs.
In this article I'm going to show how to integrate the InterSystems IRIS Database with Python to serve a Machine
Learning Model of Natural Language Processing (NLP).
In this article, we will cover the basics of topics listed below:
1. Routing in Flask Framework 2. Folder structure for a Flask app (Static and Template) 3. Getting and displaying data in the Flask application from IRIS.
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.
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.
Hello everyone, I’m a French student in academical exchange for my fifth year of engineering school and here is my participation in the FHIR for Women's Health contest.
This project is supposed to be seen as the backend of a bigger application. It can be plugged into a Front End app and help you gather information from your patients. It will read your data in local and use a Data Transformation to make it into a FHIR object before sending it to the included local FHIR server.
FHIR has revolutionized the healthcare industry by providing a standardized data model for building healthcare applications and promoting data exchange between different healthcare systems. As the FHIR standard is based on modern API-driven approaches, making it more accessible to mobile and web developers. However, interacting with FHIR APIs can still be challenging especially when it comes to querying data using natural language.
Schematron is a rule-based validation language for making assertions about the presence or absence of certain patterns in XML documents. A schematron refers to a collection of one or more rules containing tests. Schematrons are written in a form of XML, making them relatively easy for everyone, even non-programmers, to inspect, understand, and write
Headache-free stored objects: a simple example of working with InterSystems Caché objects in ObjectScript and Python
Neuschwanstein Castle
Tabular data storages based on what is formally known as the relational data model will be celebrating their 50th anniversary in June 2020. Here is an official document – that very famous article. Many thanks for it to Doctor Edgar Frank Codd. By the way, the relational data model is on the list of the most important global innovations of the past 100 years published by Forbes.
On the other hand, oddly enough, Codd viewed relational databases and SQL as a distorted implementation of his theory. For general guidance, he created 12 rules that any relational database management system must comply with (there are actually 13 rules). Honestly speaking, there is zero DBMS's on the market that observes at least Rule 0. Therefore, no one can call their DBMS 100% relational :) If you know any exceptions, please let me know.
Jupyter Notebook is an interactive environment consisting of cells that allow executing code in a great number of different markup and programming languages.
To do this Jupyter has to connect to an appropriate kernel. There was no ObjectScript Kernel, that is why I decided to create one.
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.
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.
Note: Please note that the exact functional equivalent of Python slices has never been implemented in Caché, since this functionality has never been required.
There are several great articles in the community showing how to use Jupyter and InterSystems IRIS together, and I encourage you to check them out in the link at the end of this article for more in depth understanding.
This is just another one, the difference is on the simplicity. Do you want to just start a container where Jupyter is already connected to an IRIS instance? Then this is for you!
This formation, accessible on my GitHub, will cover, in half a hour, how to read and write in csv and txt files, insert and get inside the IRIS database and a distant database using Postgres or how to use a FLASK API, all of that using the Interoperability framework using ONLY Python following the PEP8 convention.
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.
This demo showcases the powerful synergy between IRIS Vector Search and RAG (Retrieval Augmented Generation), providing a cutting-edge approach to interacting with documents through a conversational interface. Utilizing InterSystems IRIS's newly introduced Vector Search capabilities, this application sets a new standard for retrieving and generating information based on a knowledge base.
The backend, crafted in Python and leveraging the prowess of IRIS and IoP, the LLM model is orca-mini and served by the ollama server.
The frontend is an chatbot written with Streamlit.
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