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In a month s time, you are likely to have completely forgotten what this script was intended to do, and you will be thankful for these little reminders Text strings: Text strings are delimited by quotation marks Delimited means that the text is enclosed between a matching pair of the specified characters Python uses a variety of forms of quotation marks, principally 'single' and "double" quotation marks There is a subtle difference between the two forms, which I ll cover in detail in the section on text strings in 3 The main reason for the different forms is to allow nesting of quotes Nesting means putting one inside the other like this: What on earth does nested delimiters mean she asked Like comments, quotation marks only work if the text is all on one line Fortunately, there are a variety of ways to get the interpreter to ignore line breaks.

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Figure 14-14. The info panel is actived when a data item is selected from the imagery canvas. At this point, you should be comfortable with the PivotViewer user interface. This topic was introduced before you looked at PivotViewer collections because, as you will see in the upcoming sections, collection design is a trial-and-error and learn-by-doing process. It is difficult to get the collection designed perfectly the first time. You will most likely have to tweak the collection when you see it rendered. Therefore, understanding all the components of the PivotViewer is essential in order to create great PivotViewer collections.

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Configure your chosen dialect by supplying the fully qualified dialect class name as the value for the hibernate.dialect configuration property. Through Hibernate Query Language (HQL), Hibernate provides object-querying functionality that is database-independent. Hibernate translates the HQL queries into database-specific SQL using hints provided by the SQL dialect classes. We discuss HQL in more detail in 9. Hibernate also provides a native SQL facility, which is especially useful for porting existing JDBC applications to Hibernate or for improving the performance of complicated queries.

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PivotViewer collections are the second main component of a complete PivotViewer application. In the previous section, you saw all the functionality that the PivotViewer user interface exposes. Now it s time to look at the data aspect of the PivotViewer and how to create your own collections. There are three types of PivotViewer collection architectures: simple, linked, and Just in Time collections. Figure 14-15 shows a data visualization that depicts each collection attributes. Essentially, the choice between the different collection architecture depends on the amount of data items you want to surface and expose to the PivotViewer. Collections that have less than 3,000 items can be easily created in a single static XML file that is a simple collection. If you need to surface additional items, it is recommended to use a linked collection, which is several XML files hosted on a server. A linked collection can surface up to several thousand data items. The Just in Time collection for enormous amounts of content (up to millions of records) is a just in time collection. This collection requires a server component that can dynamically build the XML responses for the PivotViewer; this is essential for collections that are the size of Wikipedia articles. Obviously these are the most complex collections to create.

In this example, I shall use the.

Summary

Warning Linked and Just in Time collections are well beyond the scope of this book. We will focus on the simple collections, so you can become familiar with the collection architecture and how it works. Additional resources for the advanced collections can be found at www.silverlight.net/learn/pivotviewer/collection-design/.

The PivotViewer collection is an XML document that applies the collection schema. This is why it is usually referred to as CXML (Collection XML). To create a simple CXML document, all you have to do is create a simple text document that adheres to the CXML schema. The CXML namespaces and XSD files are all publicly available on the PivotViewer web site: Overview of the CXML Schema: http://www.silverlight.net/learn/pivotviewer/collection-xml-schema/ PivotCollection XSD file: http://www.getpivot.com/collectioncode/pivotcollection.xsd

technique of triple quoting. Triple quotes can be either '''single''' or """double""", and they allow us to mark out an entire block of text along with all the line breaks. I m going to use triple quotes in a specialized way to create a documentation string The block of text in Listing 2-1 is going to form the basis of your documentation, so let s enclose it in triple quotes. A text string that occurs as the first statement of any module, function or class becomes a Python docstring, which makes the text available as part of the built-in documentation for this application. It won t do anything yet, but Listing 2-2 is now a legal Python file; the interpreter won t complain if you try to run it. Listing 2-2. Creating a Framework for hello_world.py """ Problem: Get the computer to output a message. Target Users: Me Target System: GNU/Linux Interface: Command-line Functional Requirements: Print out a message. User must be able to input some text. Testing: Simple run test - expecting a message to appear. Maintainer: maintainer@website.com """ # This is just a comment

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