(Originally posted by Timur Safin on Intersystems CODE, 3/2/15) This code snippet is a routine that parses options saved in an arguments array. The subroutine "test" runs the code:
This series of articles would cover Python Gateway for InterSystems Data Platforms. Execute Python code and more from InterSystems IRIS. This project brings you the power of Python right into your InterSystems IRIS environment:
In the first article in this series, we looked at the entity–attribute–value (EAV) model in relational databases, and took a look at the pros and cons of storing those entities, attributes and values in tables. We learned that, despite the benefits of this approach in terms of flexibility, there are some real disadvantages, in particular a basic mismatch between the logical structure of the data and its physical storage, which causes various difficulties.
I created the iris-fhir-portal as part of the current contest InterSystems IRIS for Health FHIR, and I'm writing this quick overview to introduce the features that my application offers.
The goal of iris-fhir-portal is to show how easy we can create a Patient Chart using FHIR capabilities in IRIS for Health and empower the user with their own data.
Spring Boot is the most used Java framework to create REST API and microservices. It can be used to deploy web or executable web or desktop self-contained apps, where the application and another dependencies are packaged toghether. Springboot allows you do to a lot of functions, see:
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.
If one of your packages on OEX receives a review you get notified by OEX only of YOUR own package. The rating reflects the experience of the reviewer with the status found at the time of review. It is kind of a snapshot and might have changed meanwhile. Reviews by other members of the community are marked by * in the last column.
I also placed a bunch of Pull Requests on GitHub when I found a problem I could fix. Some were accepted and merged, and some were just ignored. So if you did a major change and expect a changed review just let me know.
My newest app includes a java routine to read data from Excel 95, 97, 2000, XP, and 2003 workbooks and write the data into IRIS globals using Java Native API library.
If you have Git and Docker installed, clone/git pull the repo into any local directory
According Wikipedia a mind map is a diagram used to visually organize information into a hierarchy, showing relationships among pieces of the whole. It is often created around a single concept, drawn as an image in the center of a blank page, to which associated representations of ideas such as images, words and parts of words are added. Major ideas are connected directly to the central concept, and other ideas branch out from those major ideas.
I recently participated in a fantastically organized hands-on by @Patrick Jamieson in which an Angular application was configured together with an IRIS FHIR server following the protocols defined by SMART On FHIR and I found it really interesting, so I decided to develop my own Angular application and thus take advantage of what I learned to publish it in the Community.
SMART On FHIR
Let's see what Google tells us about SMART On FHIR:
Today my customer ask me a question about how to write the MDX with a summary row however this row with different aggregate functions for each column.
We know in DeepSee analyzer has pivot table option "Summary", user can select sum, avg ... aggregate functions to get the summary row/column. However we can not specify different aggregate function for each measure column.
Here show the example to use All level and IIF function achieve that. see the example (Holefood cube in Sample namespace) below
In previous articles on iKnow, we described a number of demo applications (iKnow demo apps parts 1, 2, 3, 4 & 5) that are either part of the regular kit or can be easily installed from GitHub. All of those applications assumed you already had your iKnow domain ready, with your data of interest loaded and ready for exploration. In this article, we'll shed more light on how exactly you can get to that stage: how you define and then build a domain.
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.
Quite a few enhancements have appeared over recent months in QEWD for easing and simplifying the creation of REST-based services. It's now even more slick and powerful, allowing you to very quickly create very high-performance, highly-scalable REST (and Web) services that make use of Cache.
I've therefore updated the training presentation deck (Part 31 on developing REST Services with QEWD). It describes all the new features with worked examples. See:
HealthShare HealthConnect and Information Exchange version 15.03 support import transformations from C-CDA 2.1 to SDA. You can find these transforms in your installation's csp/xslt/SDA3 directory. For general information about import transforms, see "CDA Documents and XSL Transforms in HealthShare" in Overview of Health Connect.
Among the enhancements to import functionality added in connection with C-CDA 2.1 support is the ability to preprocess your C-CDA input files prior to the transformation done for import.
This code snippet changes all passwords in a system to a specified string. The two literal strings at the beginning of the snippet can be adjusted to edit the system or password string. The class method "test" runs the code:
I'm not saying that this is in anyway "best practices," but I'm in a peculiar situation where I need to restrict users from starting a "retired" Ensemble Production in a namespace that's been renamed. It's still an "Ensemble-activated" namespace; we need to keep it available for Ensemble Message Viewer access ... fortunately, only for a little while.
It's a bit of a hack ...
Open the Production class in Studio and add the following classmethod:
Intersystem Cache is a commercial operational database management system from intersystem, used to develop healthcare management, banking and financial, government sectors software applications.
Dasha.AI is a platform that allows you to create and manage voice interfaces for your applications. One of Dasha’s distinctive features is that most users believe they are talking to a human, not a robot.
Voice is the most natural way for people to interact. Dasha allows to use voice interface to interact with your application as naturally as communication between people.
The Mockable.io (https://www.mockable.io/) is an online service to deploy REST API or SOAP services in seconds. This is useful to test the consumption of an API or SOAP service in your production or objectscript class without having to implement a real service, including https option.
We use the Caché JDBC Gateway to Oracle and SQL servers to directly invoke their stored procedures from Ensemble. Getting quick, inline data results back are typically handled within the Functions.Library class as a function to wrap the query and format the return appropriately.
What is Distributed Artificial Intelligence (DAI)?
Attempts to find a “bullet-proof” definition have not produced result: it seems like the term is slightly “ahead of time”. Still, we can analyze semantically the term itself – deriving that distributed artificial intelligence is the same AI (see our effort to suggest an “applied” definition) though partitioned across several computers that are not clustered together (neither data-wise, nor via applications, not by providing access to particular computers in principle). I.e., ideally, distributed artificial intelligence should be arranged in such a way that none of the computers participating in that “distribution” have direct access to data nor applications of another computer: the only alternative becomes transmission of data samples and executable scripts via “transparent” messaging. Any deviations from that ideal should lead to an advent of “partially distributed artificial intelligence” – an example being distributed data with a central application server. Or its inverse. One way or the other, we obtain as a result a set of “federated” models (i.e., either models trained each on their own data sources, or each trained by their own algorithms, or “both at once”).
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