Visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users.
In a previous question, I have illustrated a few problems using Embedded Python interactively as you would do from Docker console or IRIS terminal. Investigation of the causes brought a rather clear picture. It's a classic impedance mismatch
I am trying to use a %Status property on some of my Response classes. If the execution of a service goes wrong, I set the %Status property on Response with the value $$$ERROR($$$GeneralError, "pre-defined error message").
When I check the visual trace for this message and look for the %Status (property "status_code" on the image), it is displayed like this:
If I select to display the Body of the Response, I get the 'readable' form of %Status:
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.
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By design, DSW provides an implementation for every widget in DeepSee library. But there are some extra features in DSW which make solutions built with DSW dashboards more functional. This article describes it.
Users of analytical applications often need to generate and send out PDF reports comprised of elements of the analytical panel. In the InterSystems stack, this task is solved using the DSW Reports project that is an extension of DeepSeeWeb. In this article, we will explain how to use DSW Reports for generating PDF reports and emailing them.
Today we will talk about InterSystems Reports. This is a BI system that provides you with tools to create static reports and export them to different file formats. We will see how it works using the DC Analytics public analytical sample as an example. In this article, we will examine how to familiarize yourself with the reports available in the repository, how to make a new report based on a ready-made data structure, and how to prepare a data structure from scratch.
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 the previous part of this series, we saw how to define a basic portlet. Now we will look into making this portlet reference a web page that will enhance our dashboard experience.
In this example, we will be embedding a Developer Community article along side a couple of widgets displaying information related to the number of views on the Developer Community articles. This example is not hosted on the Community Analytics server, but if it was we could see the view counts going up as we interacted with the page.
Why use this?
In a real case, perhaps you have an embedded page from an external web site showing the current Emergency Room wait times for Hospitals in your area. This portlet can be used along side widgets from your Emergency Room showing how many people are waiting, how many doctors are active, and how many people are being treated. As other Emergency Room wait times grow, you can possibly expect your volume to increase as well. This can help you make decisions on how to allocate resources.
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.
Code coverage and performance optimization of code has come up a bunch of times already, so most of you should already be aware of the SYS.MONLBL utility.
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