In a nutshell, the PivotViewer Collection Tool for Excel enables you to represent a PivotViewer collection in a spreadsheet, and then export that spreadsheet to a format understood by PivotViewer.
Because it is an Excel add-in, PivotViewer Collection Tool files are stored as Excel files. The tool consists of the following key components:
All of the standard features of Excel are also at your disposal, including data import and transformation functionality. Using these features, data can be imported from text files, web pages, databases, etc., and then organized for display in PivotViewer.
When you are finished editing your collection, use the "Publish" button in the Ribbon. Publishing will create the collection ".cxml" and Deep Zoom image files required for display in PivotViewer.
It is important to note that the PivotViewer Collection Tool cannot be used to load or edit existing ".cxml" collection files. As such, any edits made to the files generated during publishing cannot be imported back into the tool. It is recommended that any such edits be made on the Excel file instead, and the publishing step be repeated.
This tutorial will walk you through making a small PivotViewer collection using sample Windows photos. For reference, use the following figure to identify the various parts of the PivotViewer client.

To create a new collection: start Excel, click the “PivotViewer Collections” Ribbon tab, then press the “New Collection” button.

A new workbook will be created containing several columns:
To add items to the collection:
The table will expand to include all imported images, and the images will appear in the "Preview" column:

To edit item Names, Hrefs, or Descriptions, simply type in the appropriate columns. In the picture below, the spreadsheet has been edited to include names, links to Wikipedia articles, and Wikipedia descriptions:

There is now enough information in the spreadsheet to preview the collection in PivotViewer. To preview the collection, make sure your cursor is inside the table, then click the “Quick Preview” button in the Ribbon.
After pressing "Quick Preview," PivotViewer will open to display the information in the collection. Note that, during preview, the items have placeholder images rather than the original images. Click on any of the items in the collection to see if the info panel contents match your expectations:

At this point, the collection could be published for use in PivotViewer. For instructional purposes, suppose you want to add some detailed information to the collection first.
Return to Excel to continue working on your collection. To make this a more interesting collection of photos, suppose you want to add “Rating,” “Date Taken” and “Subject” columns to the spreadsheet. To add columns, type their names in the first row of the columns to the right of the table.
Once you have added the columns, fill them with information for each item. See the following picture:

Click “Quick Preview” again to see the new information in PivotViewer. By default, the newly-added facet categories will appear in both the filter panel and the info panel. Use the “Category Properties” panel in the Ribbon to modify visibility settings for each category.
The fields that can be modified are:
For more information on adjusting visibility, see Collection Design.
In the above example, no items were assigned multiple values in the same category. Suppose you want an item to have two values for the “Subject” category.
The tool supports two ways of assigning multiple values in the same category:

In either case, the display in the PivotViewer info panel is the same:

Note: the approach of separating values with '||' works only on facet categories of string type. Duplicating the column name works on facet categories of all types.
PivotViewer supports five facet category types: String, LongString, Number, DateTime and Link. For detailed information about facet categories types, see Collection XML Schema. The PivotViewer Collection Tool infers the type of data when you preview or publish a collection based on the values currently in the cells. Some of the behaviors available to help you format facet category presentation in PivotViewer are:
In our example collection, suppose you want to display the "Date Taken" field in the form "weekday, month & date, year":
One of the final steps in collection authoring is configuring collection-wide properties. These are available on the “Collection Properties” sheet of the workbook.

The following properties can be edited:
For more information on these properties, see: Collection Design.
Once you are finished editing the collection-wide properties, switch back to the “Collection Items” sheet. Make sure your cursor is inside the table, then click the “Publish” button in the Ribbon. Publish will create the collection ".cxml" and Deep Zoom image files for your collection and then load the collection in PivotViewer:

To share your collection with others, you'll need to place it on a website or file share. This section contains basic information about sharing a collection. For more detailed information, see: Collection Hosting.
To share your collection, you need to move the files created during the publish step to a location that your audience can access. When you navigate to the directory where you published the collection, you will see the following files and directories:
To share the collection with others, copy the ".cxml" file, the images directory, the most recently modified ".xml" file, and the most recently modified directory. The ".xml" file and the directory should have matching names. All other ".xml" files and directories can be deleted.
The easiest way to ensure that only the latest files are copied is to delete all files and directories with randomly-generated names, then re-publish from the PivotViewer Collection Tool. The tool will create a new ".xml" file and directory with the latest information, ready to be copied.
Once you have copied the files, share a link to the ".cxml" file to let your audience open it in PivotViewer!
Join our technical discussion to interact directly with the PivotViewer team. We hope you’ll join this community and share your work.
Comments (0)