Hi all,
Design Explore has always been a popular tool on the forum. However, in previous versions, it only supported the method of uploading cloud files to preview the design data and image groups you created.
I drew on the main functions of the previous version and used React + Vit to create a deployable local program, allowing people to preview these data and images by uploading files.You can obtain the latest version in this project repository. It is a locally deployed application, packaged with Electron.
LoftyTao/design-explorer-react: Design Explorer - Visualize and explore design data with ease.
Or you can try out the online service provided by our micro servers.
This is the function introduction.
The entire page is divided into three sections: parallel coordinate graph, data table, and gallery.
What is different from before is that the interactions of these elements are all mutually responsive. You can select any data row in the data table or click on any thumbnail image in the gallery, and the highlighted display will focus on other constituent elements.

In this version, we recommend that you use the PO Fly and PO Fly ID components to prepare the data files.These two components are of the same type as those of Colibri, but they are directly provided by Pollination’s Grasshopper installation program. Users do not need to download any additional tools to use them.
You can easily package the file set generated by Pollination and drag it to upload into the program for preview.Even multiple sets of files that need to be previewed can be uploaded, and the actual set to be previewed can be selected through a drop-down list.

If you haven’t yet familiarized yourself with the components of Pollination Fly, you can watch this video tutorial which was recorded by @mostapha .
In addition, this version also provides support for multiple sets of image groups. When you have multiple different illustrations for each set of data, you can switch to the part you need at any time, and the current selection status will not be overwritten.

For the presentation of the data set, I adopted the excellent design of HotPlot, which enables us to focus more on the selected results.Whenever we select any data, the data that is displayed is always the most prominent.

In addition, I have retained the sorting function of the data, and on this basis, I have also added the common color schemes that people are accustomed to.
This project has received limited usage and feedback from community users. Currently, all the main functions are operating quite well, so I have chosen to share it with everyone.
It should be noted that I would like to express my gratitude to @MingboPeng and @mikkel for their contributions to the two projects(projects1 and projects2) I referred to, as well as @AbrahamYezioro for his testing and feedback.
Please enjoy it!
Zhengrong



