Working with multiple scales in Spotfire visualizations can be a bit of a pain. I frequently find I have more scales than I need, and getting them synched up to the same min and max values can be frustrating. Last week, I built a project that allowed users to set multiple scale ranges with document properties. Not only does it give users more control, but it keeps multiple scales synched. It required a little bit of IronPython but was easy to setup. Read on to learn how.
In last week’s post, I showed you how to populate type curve inputs with IronPython and toggle between different sets of inputs. This week, I’ll go one step further using an R function to select type curve inputs from a table and then load them into document properties. Read on to find out how.
Type Curve generators are standard in reservoir engineering workflows. They are also a bit problematic. They require lots of inputs, which are time-consuming to build and fill out. You can’t easily recreate them in other projects. Therefore, my next two posts will show you how to make type curve inputs more efficient. The first post will demonstrate how to populate type curve inputs with IronPython and toggle between different sets of inputs. The second post will go further by allowing users to load type curve settings in a table. Then, the user tells an R function which row to pull from the table and place in the type curve inputs. Read on for these great solutions!
- Would you like to know how to reset marking when changing pages in an analysis?
- Are you worried about users getting confused with markings on different pages and would like them to reset?
- Are you using the same markings in multiple visualizations in an analysis?
If you answered yes to any of these questions, read on to find a simple solution that will reset all markings when a user moves to a different page.
This is my third week working thru IronPython scripts from the Spotfire IronPython quick reference. Last week, we looked at for loops. The week before, we used IP to modify titles. This week, we’ll insert visualizations with IronPython and explore the API as we go. We’ll get started learning how to add a bar chart with IronPython to an analysis.
I promised a return to Spotfire API for IronPython posts, and that is finally happening. I had to get a few drafts cleared out, like 5 Ways to Approach Troubleshooting Data Problems, but now I’m back on track discussing how to change visualization titles with IronPython
In October last year, I committed to spending 100 hours learning IronPython. I wrote several “starter” IronPython posts, which can be found in the Data Shop Talk Archive. I have lost count of how much time I’ve spent on IronPython, but the clock starts again today.
My goals haven’t changed. They are still to ….
- Get an understanding of IronPython structure
- Develop an understanding of the Spotfire API
- Apply that understanding in Spotfire code examples
For the next several weeks, I’ll be using the code snippets posted on The Spotfire IronPython Quick Reference to explore the API. I’ll punctate that with an article here and there are the IronPython/Python structure as I go. Read on for this week’s code snippet.