Parameter Catalogs for Simulation

A good part of computer science was and is driven by the motivation to make it easier to develop computer programs of all sorts. "Higher" programming languages were invented to make programs human readable and soon special constructs for functional programming (computation without side effects) and structured programming (computation without go to statements) were introduced to help programmers writing and understanding ever growing programs. Then, between 1962 and 1967, program language Simula was developed especially to deal with the challenges of simulating systems comprising of many different types of objects. This opened the door to more direct computer representations of real world objects, their attributes, relationships and behavior, ultimately leading to object-oriented software development that today is embodied in programming languages like Java, C++, Python, and graphical notations like the Unified Modeling Language (UML).

While these achievements had boosted the productivity of software developers, still the creation of correct, efficient and maintainable programs — including simulations — required a big deal of expert knowledge and experience. To overcome this bottleneck, starting in the 70s, so called 4th generation languages entered the stage. These languages were tailored to specific tasks like statistics ("S" 1976, "R" being its successor), database programming (SQL 1979), or simulation (MATLAB around 1979, Mathematica 1988, Modellica 1999) to name a few. By sacrificing generality, these special languages become more accessible to domain experts, not just trained software developers. To flatten the learning curve even more, formal graphical languages for special purposes were invented, e.g. Simulink for block diagram simulation models in 1984, Entity-Relationship-Diagrams for data modeling in 1976, UML for object-oriented systems design in the 1990s, or graphical languages to specify business and also scientific workflows around 2000.

This very short history of technologies for development of software in general, and simulations in particular, shall illuminate the tools at our disposal:

Now back to the task at hand.

Some domains deal with a few types of simple objects to be simulated. Take the building blocks of an electric circuit as an example. The algorithms to simulate these correctly and efficiently may be quite complex — the model elements usually can be described by very few parameters like resistance or capacity. More complex domains like (regenerative) energy systems or building physics deal with more complex objects to be simulated, e.g. PV modules or layered walls of buildings, often coming in different types and configurations, and dozens of possibly interdependent parameters.

First a note on terminology: Instead of parameter catalogs in SimStadt we used term library like in building physics library. Obviously this was not a good choice, since library is used a lot in IT and programming with all sorts of meaning. Instead we started to talk about data catalogs, but in data science this term has specific meaning, namely: catalogs of data and data sources. Since our catalogs, first of all, shall grant structured access to parameters for simulated entities parameter catalog sounds more appropriate to me.

The problem of navigating huge parameter spaces and assembling complex simulation models popped up as the author worked on a diagram editor for INSEL, a simulation language and runtime environment developed for renewable energy systems simulation. To make existing catalogs on weather data, solar panels and inverter modules accessible to the modeler, special dialogs were added to the INSEL user interface that allowed browsing through the catalogs. Using this browsers, the modeler would choose a weather station, panel or inverter to parameterize a corresponding INSEL function-block. However, there are some severe disadvantages with this approach:

From 2013 to 2016, the simulation platform SimStadt was developed to make specific modeling and simulation workflows accessible to experts in urban planning and energy systems. Using INSEL and other simulators under the hood, the usage of 3D city data, provided as CityGML files, was a core requirement of this project.

To enable simulation of, say, the heating demand of a district, geometric building data had to be enriched with data on building physics and usage. To do so, existing informations about building physics and usage — often only available as informal typologies or tables — had to be provided to the SimStadt user on an abstract level, e.g. to choose between refurbishment scenarios. At the same time, specific building configurations and parameter sets had to be injected into the simulation models to obtain the desired results.

Again, we implemented parameter catalogs to fulfill these requirements, but compared to the quite simple catalogs used in INSEL, the data for building materials, window, wall and roof types as well as the typologies of buildings, households, usage patterns, and so on were more intricate. They had to be created iteratively in collaboration with domain experts. In this situation, manual coding data formats and access with a general programming language would have led to relatively long iteration cycles and high communication effort between programmer and domain expert. Instead, we decided to use a DSL for data modeling and use code generation whenever possible. Since SimStadt was developed within the Java eco-system we followed this standard approach:^[1]

After the data model for building physics catalogs had matured, we developed a desktop application for convenient creation and maintenance of building physics data catalogs separate from SimStadt. It was developed in Java with a user interface written in JavaFX and was well received by domain experts.

However, as a different catalog — this time for building usages — had to be created, it was quite difficult to reuse the XML schema and application code from the building physics catalog: The usage catalog data model was "pressed" into a form similar to the building physics catalog data model, and the UI code was "over-engineered" to accommodate both catalog’s requirements.

From INSEL and SimStadt we learned, that manual and automatic construction and parameterization of complex simulation models with many types of interrelated objects should be supported be the means of domain specific parameter catalogs.

Close collaboration with domain experts in designing and implementing these catalogs in short development cycles is desirable.

Parameter catalogs and the software for their creation, maintenance and deployment should be independent of any specific simulation software, (a) to be reusable and (b) not to overload simulation applications.

In SimStadt, catalog development was partly facilitated by a textual DSL for data modeling (XML schema language) and automatic generation of Java code from it. On the other hand, user interfaces and generation and parameterization of simulations from templates within SimStadt workflows had still to be coded manually hindering the routinely creation of new catalogs.

Now, in 2020, several developments in different projects provide an opportunity to re-think the topic of parameter catalogs for simulations, namely:

Plans are to use the same approach also for implementation of (3). Since task (2) and maybe (1) will use Eclipse, too, close integration of parameter catalogs and simulation environments seems feasible. E.g., a user could drag an electric system component from a catalog onto an INSEL block for parametrization.

The Eclipse application framework offers:

As always, all that glitters is not gold. When we go through the details below, some bugs and inconsistencies, typical for open source projects of this age and size, have to be addressed.

Eclipse was originally developed by IBM and became Open Source in 2001. It is best known for its Integrated Development Environments (Eclipse IDEs), not only for Java, but also for C++, Python and many other programming languages. These IDEs are created on top of the Eclipse Rich Client Platform (Eclipse RCP), an application framework and plug-in system based on Java and OSGi. Eclipse RCP is foundation of a plethora of general-purpose applications, too.

First time users of Eclipse better understand the following concepts.

An Eclipse package is an Eclipse distribution dedicated to a specific type of task.^[3] A list of packages is available at eclipse.org. Beside others it contains Eclipse IDE for Java Developers, Eclipse IDE for Scientific Computing, and the package we will use: Eclipse Modeling Tools. Note that third parties offer many other packages, e.g. GAMA for multi-agent-simulation or Obeo Designer Community for creating Sirius diagram editors.

An installed Eclipse package consists of a runtime core and a bunch of additional plug-ins. Technically, a plug-in is just a special kind of Java archive (JAR file) that uses and can be used by other plug-ins with regard to OSGi specifications. Groups of plug-ins that belong together are called a feature.

Sometimes, a user will add plug-ins or features to an Eclipse installation to add new capabilities. E.g. writing this documentation within my Eclipse IDE is facilitated by the plug-in Asciidoctor Editor. Plug-ins can easily be installed via main menu command Help → Eclipse Marketplace…​ or Help → Install New Software…​. Some plug-ins may be self-made like our City Units plug-in that enables Ecore to deal with physical quantities.

Git is the industry standard for collaborative work on, and versioning of, source code and other textual data. Collaborative development of parameter catalogs benefits massively from using Git. Git support is built into Eclipse Modeling Tools, the Eclipse package we will use. However, if Eclipse needs to connect to a Git server that uses SSH protocol (not HTTPS with credentials), access configuration is more involved and may be dependent on your operating system.

Some users, anyway, prefer to use Git from the command line or with one of the client application listed here, e.g. TortoiseGit for Windows.

While it is required to get Git working at some point, we won’t refer to it in this document and, for now, do not cover the installation of Git on your machine or configuration of Git in Eclipse.

When you start a new Eclipse installation for the first time, you are asked to designate a new directory in your file system to store an Eclipse workspace. Eclipse is always running with exact one workspace open. As the name implies, a workspace stores everything needed in a given context of work, namely a set of related projects the user is working on as well as meta-data like preference settings, the current status of projects, to do lists, and more. In case a user wants to work in different contexts, e.g. on different tasks, command File → Switch Workspace allows to create additional workspaces and to switch between them.

An Eclipse project is a technical term for a directory that often contains:

File → New → Project…​ offers many different types of projects that the user can choose from, e.g. Java projects to create Java programs, Ecore modeling projects, or general projects, that simple hold some arbitrary files.^[4]

The projects belonging to a workspace can either be directly stored within the workspace as sub-directories (the default offered to the user when creating a new project), or linked from it, that is the workspace just holds a link to the project directory that lives somewhere in the file system outside of the workspace. Linking allows to work with the same projects in different workspaces.

While it sometimes makes sense to share or exchange workspaces between users,^[5], I do not recommend this for now. Projects, in contrast, are shared between users most of the time, usually via Git. In general, I would suggest to store Eclipse projects outside workspaces at dedicated locations in the user’s file system. That way, we can follow the convention that local Git repositories should all be located under <userhome>/git.

Eclipse runs on 64-bit versions of Windows, Linux, and macOS and requires an according Java Development Kit (JDK), version 11 or higher, to be installed on your machine. Even if such JDK already exists, please download OpenJDK, version 16 or newer for your operating system from AdoptOpenJDK. ^[6] Choose HotSpot as Java Virtual Machine. Installation process is straight forward, but you can also find links to exhaustive instructions for your operating system.

New Java versions appear every six months, so one could tend to stick with older version 11 that comes with long time support (LTE) until next LTE version 17 arrives in autumn 2021. However, actual version 16 conforms to the latest security measures built into macOS Catalina, so it is a must if software we build here shall be deployed to these systems, too.

Note that different versions of Java coexist peacefully.

Now its time to download and install the correct Eclipse package Eclipse Modeling Tools, version 2021-03 or newer. Please go to Eclipse download page for packages. On this page you may see "Try the Eclipse Installer" or similar. Do not follow this advice, since we want more control over what versions of Java and Eclipse shall be installed. Instead, look for package Eclipse Modeling Tools and follow the link for your operating system on the right:

Finally, you can click on Download and wait for the 400 something MB package to arrive.

The downloaded installation file contains the application simply named Eclipse ready to be copied into Applications on macOS or be installed in Programs on Windows. Since later you may add other Eclipse packages — or different versions of the same package — I suggest to rename the application more significantly to EclipseModeling2103 or similar.

After installation has finished launch Eclipse for the first time and you will see a dialog for choosing a new empty directory as its workspace.

Again, more workspaces might come into existence later, so replace the proposed generic directory path and name with a more specific one, e.g.EclipseModelingWS. The Eclipse main window appears with a Welcome Screen open. It contains links to exhaustive documentation on concept, features and usage of Eclipse that might be of interest later, especially:

For now, you can dismiss the welcome screen. It can be opened anytime by executing Help → Welcome. Now you should see the initial layout of Eclipse with Model Explorer and Outline on the left and a big empty editing area to the right with a Properties view below.

It takes time and effort to come along with good names for model entities, projects, files, and so on. Also, specific naming conventions are in place to enhance readability of models and program code. Since it is not always clear where names provided during modeling are used later, I compiled a list of names important in Ecore projects and added examples and comments to elucidate their meaning and naming conventions.

Classes are written in Camel case notation starting with an upper case letter. Associations and attributes are written the same way, but starting with a lower case letter.

All other names should be derived from the globally unique name space of the project, in our example: example.org/democatalog. It consists of a global unique domain name and a path to the project, unique within that domain.

Use the names of example Demo Catalog to create your first Ecore modeling project:

Eclipse should look like below with an new empty graphical Ecore diagram editor opened. The diagram is automatically named democatalog after the package name for the Java classes that will be generated from it (provided above). The Model Explorer shows the contents of the new Ecore modeling project.

To get your feet wet, do this:

Technically, everything is in place now to begin modeling the data that the projected catalog shall contain. Except …​ understanding the basics of object-oriented modeling would be helpful. This is why developers should support domain experts at this stage.

Ecore diagrams are simplified UML class diagrams. Here some resources on what this is all about:

We will touch central object-oriented concepts Class, Object, Attribute, Association, Composition, and Multiplicity in an example below, but work through above sources to get a deeper understanding and to enhance your modeling skills.

Note that above sources differentiate between conceptual and detailed models. We go for detailed models, since only these contain enough information to generate code. Having said this, it is usually a good idea to have two or three conceptual iterations at a white board to agree on the broad approach before going too much into detail. But even if one starts with Ecore models right away, these also can be adapted any time to follow a new train of thought.

See here the essential and typical structure of a parameter catalog in a class diagram. Instead of artificial example classes like Foo and Bar it shows classes from an existing catalog, albeit in very condensed form.

The diagram models four types of technical components whose data shall be stored in the catalog, e.g. for parameterization of simulation models later: Boiler, CombinedHeatPower, SolarPanel, and Inverter.

The catalog itself is represented by class EnergyComponentsCatalog. Unlike dozens, hundreds, or even thousands of objects to be cataloged — Boilers, Inverters etc. — there will be just exactly one catalog object in the data representing the catalog itself. Its "singularity" is not visible in the class diagram, but an Ecore convention requires that all objects must form a composition hierarchy with only one root object.

If, in the domain, one object is composed of others, this is expressed by a special kind of association called composition. Compositions are depicted as a link with a diamond shape attached to the containing object. In the Boiler case said link translates to: The EnergyComponentsCatalog contains — or is composed of — zero or more (0..*) boiler objects stored in a list named boilers.

Besides composition of objects, the model above shows another, completely different, kind of hierarchy: the inheritance hierarchy between classes. Whenever classes of objects share the same attributes or associations, we don’t like to repeat ourselves by adding that attribute or relation to all classes again and again. Instead, we add a super class to define common attributes and associations and connect it to sub classes that will automatically inherit all the features of their super class.

In our example above, common to all four energy components are attributes modelName and revisionYear, thus these are modeled by class EnergyComponent that is directly or indirectly a super class of Boiler, CombinedHeatPower, SolarPanel, and Inverter. Similar, Boiler and CombinedHeatPower share attribute installedThermalPower factored out by class ChemicalDevice. SolarPanel and Inverter share attribute nominalPower modeled in abstract class ElectricalDevice.

You probably noticed a fifth type of objects contained in the catalog, namely Manufacturer objects stored in list manufactureres. How come? Ok, here is the story:

Observe in our data model, reference producedBy points from EnergyComponent to Manufacturer making it uni-directional reference. One can simply query the manufacturer of a product, but not the other way around. With a bi-directional reference both queries would be available.

Observe also the annotations 0..* and 1..1 near class Manufacturer. These are multiplicities of associations: An EnergyComponentsCatalog contains zero, one, or many objects of class Manufacturer and an EnergyComponent must reference exactly one manufacturer — not less, not more.

Obviously, attributes are central in data modeling. Create one by dragging it from the palette onto our one and only class so far: EnergyComponentsCatalog. The class symbol will turn red to indicate an error. Hover with the mouse pointer over the new attribute and a tooltip with a more or less helpful error message will appear. Current error is caused by that no data type was set for the new attribute. Data types for attributes can be integer or floating point numbers, strings, dates, booleans, and more. To get rid of the error:

There exists one other means to define the values an attribute can take, namely enumerations of distinct literals. Take Monday, Tuesday, Wednesday, …​ as a typical example for representing weekdays. In our example data model you’ll find one Enumeration named BoilerType with values LowTemperature and Condensing.

The next section deals with generation of Java code from data models. To have more to play with, please implement our example model in Ecore now.

Two more tips and you are ready to rock and roll! — At least with your homework.

By now, your Ecore model should look like this:

Let us bring the model to life, that is, generate code from it that creates, reads, updates, and deletes concrete data objects of modeled classes in computers. I would like to tell you that this is done with just one click but, actually, you need two or three:

In this section you will learn how to generate and tweak a CRUD user interface based on Ecore data model and Java classes created for our demo parameters catalog above. Topics described here are discussed in more detail in tutorial Getting started with EMF Forms. To find out what user interface controls and layouts are provided by this framework have a look at EMF Forms – View Model Elements. EMF Forms is already part of package Eclipse Modeling Tools, so we can create a third Eclipse project/plugin that implements a user interface for editing catalog data without further ado:

According to these inputs a new project is created with file EnergyComponentsCatalog.view under directory viewmodels. This file opens automatically in a special View Editor.

Like the data of our catalog are modeled as Ecore file using a dedicated graphical editor, so will our catalog’s user interface be modeled in .view files, again using a special editor.

Since we opted for Fill view model with default layout the catalog’s UI is filled initially with default controls for all data items assigned to Ecore type EnergyComponentsCatalog like a string control for author or list controls for boilers, chps, and so on.

See red arrow in the above screen-shot? It points to a button that opens a functional preview of the modeled user interface.

Red input field and exclamation mark in the preview signal missing or inconsistent data. Ecore data model specifies attribute author with a lower bound of one, meaning it is a mandatory attribute. As soon as an author’s name is provided, the error indication disappears. This is what functional preview means. You can even create new boilers or other objects in lists provided, with all forms created "automagically" with respect to our underlying Ecore data model.

Of course, such automatic approach has its limits. In our case, a long list of lists is not very user-friendly, because one has to scroll up and down to find a specific list. Also, no specific object data are shown in the list and data can only be edited in a pop-up form (no inline editing).

How should a better UI look and feel like?

If there are many lists (types) of entities — the normal case for parameter catalogs — users should select what list they want to work with by selecting it from a list or tree view that is always visible, the master view. Once a type is selected in the master view, a table with all objects of this list/type shall appear sidelong in a detail view, ready for editing.

In EMF Forms master-detail-views can be modeled either with Categorization or Tree Master Detail UI components. The latter not only allows to edit information displayed in the detail view, but also the tree of elements in the master view. Opposed to that, a Categorization presents a fixed hierarchy of elements to choose from. This is exactly what we need as there are only a fixed number of types of objects to be edited in a parameters catalog.

As mentioned earlier, parameter catalogs should be able to represent quantities, not just bare numbers. See Unit of measurement libraries, their popularity and suitability for a systematic account of open source solutions in the this area.

Java provides an extensive framework to deal with quantities and their units defined in Java Specification Request (JSR) 385. The reference implementation for this framework is Indriya. Demos of its usage can be found at https://unitsofmeasurement.github.io/uom-demos/.

To make Indriya available Ecore data models and EMF Forms, the author has created two plug-ins that can easily be added to Eclipse. To do so, open dialog Help → Install New Software…​ and enter site https://transfer.hft-stuttgart.de/pages/neqmodplus/indriya-p2/release_target_211/ like depicted below.

Select Indriya plug-in, press Next > and acknowledge all following dialogs, including security warnings.

Do the same for the City Units plug-in available at site https://transfer.hft-stuttgart.de/pages/neqmodplus/de.hft-stuttgart.cityunits/release_target_101/ Finally, restart Eclipse to complete plug-in installation.

While the first plug-in installs Indriya, the second plug-in adds some specific units for urban simulation, EMF Forms editor fields for quantities, and Ecore types used for modeling quantities as attributes of classes.

Make the new Ecore types QuantityLong and QuantityDouble available like so:

From now on, the new attribute types are available to model quantities with integer or floating point values as can be seen on the screenshot to the right.

Note here, that I also changed the default value from 0.0 to 0.0 V to indicate that maxDCVoltage of inverters is given in Volt.

The symbols for defining units follow SI and other standards, including decimal prefixes like m for Milli or G for Giga as well as derived units, that is: mV, GV or kW·h/m³ are all valid unit definitions. This is all documented well in the resources mentioned at the top of this section, but for convenience, a table with valid units, including some specific units for urban simulation, is compiled in UnitsExamples.md.

If a unit symbol cannot be interpreted, this error is not already detected while generating and compiling code from model, but not before run time when the application tries to create the default value. In that case, you will see an error message like this:

One last technicality. Before code from an Ecore model with attributes of type QuantityLong or QuantityDouble can be generated correctly, we must tell Eclipse to reuse the corresponding generator model from the City Units plug-in:

If no generator model is available for selection, press button Add…​ to add it first (this only works if QuantityLong or QuantityDouble were used at least once in the Ecore model).

Congratulations on making it this far. What have we achieved?

We get to know the Eclipse Modeling Tools IDE and created a graphical Ecore data model with one catalog class and five classes/types of domain objects therein. Classes have been defined by name, attributes, and relationships between them, often with cardinalities. Whenever classes shared some attributes or relationships we factored these out into super classes. An enumeration introduced a new attribute type as a set of named values.

From this data model, we issued commands to create Java code for representing the data in memory as well as to store and retrieve them on and from disk. Methods to create, read, update and delete data objects (CRUD) were generated, too.

We reflected on a good user interface for this data and used EMF Forms to model such an interface resulting in a full functional prototype.

Lastly, we enhanced Eclipse, Ecore and EMF Forms with two plug-ins for modeling, editing and persisting physical quantities as numerical values with defined units.

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catalog.xml muss durch den richtigen Pfad zum XML-Katalog ersetzt werden.

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Handlebar templates to access data catalogs and create/parameterize textual simulation models.

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We are going to create a complete Eclipse desktop application from generated code. We also want to deploy this application for Linux, macOS and Windows operating systems. Eclipse offers several approaches for compiling and deploying such an application, traditionally with Ant scripts.

Creation and maintenance of these scripts turned out to be tedious and error prone. For quite some years now, the proposed — and mostly supported — method for building Eclipse applications is to use Maven build system, more specifically, a couple of Maven plug-ins, subsumed under the name Tycho.

Many Eclipse platforms have Maven support M2Eclipse already built in, not so our Eclipse Modeling Tools. But don’t worry: Installation of required Eclipse feature is easy and straight forward. And, by the way, you will acquire the indispensable skill of how to install new plug-ins/features to Eclipse.

First, tell your Eclipse installation where to look for the new software. Execute Help → Install new Software…​ to invoke dialog Available Software and press Add…​. Sub-dialog Add Repository pops up.

In there provide m2e as name and

as location. After confirmation with Add, choose the new site to Work with: Eclipse now looks up the site for available software.

Provided Group items by category is checked, above features are displayed. Check all features and confirm all following questions about licenses and security concerns. After download is complete — it can take a few minutes — restart Eclipse. Verify that Maven version 3.6.3 or above is now installed in Window → Preferences…​ (or Eclipse → Preferences…​ on macOS) under Maven → Installations.

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"Correct" way to add third party Java libraries as plugins

Example Indriya

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