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Project Goals

The main goal is to build a simple document object model (DOM) for STEP model data. The DOM can be used as an import/export layer for CAD like application or applications which need access to STEP model data. STEP defines various applications, one example application is IFC4 data (Industry Foundation Classes 1), this also will be the first application with explict support, because it is related to my profession and because there is a free documentation for IFC4 provided by buildingSMART International. Other applications of the ISO 10303 standard may not have such free resources, and I will not purchase ISO standards for a hobby project and because of the complexity it is not possible for me to support all application in an extensive way beyond a generic support.

The DOM should provide methods to travers the model and create or delete object nodes like xml.etree.ElementTree for XML data.

Loading and Storing Data

Many file formats of the STEP standard should be supported, as plain text file and also as compressed zip file:

  1. STEP - Standard for the Exchange of Product model data - p21.readfile() already works and is fast enough.
  2. XML - Extensible Markup Language, fast to read from Python with xml.etree.ElementTree
  3. JSON - JavaScript Object Notation, no examples available yet

EXPRESS Schema Parser

A Python data model generator has to be written for EXPRESS definition files, the solution I found online called STEPcode, is a starting point, but the generated Python code is ugly and does not correspond to PEP8, generated code is included in folder doc/stepcode.

  • EXPRESS data specification language, EXPRESS is a standard data modeling language for product data. EXPRESS is formalized in the ISO Standard for the Exchange of Product model STEP (ISO 10303), and standardized as ISO 10303-11.
  • iso-10303-11--2004.bnf: Backus-Naur-Form for EXPRESS
  • EXPRESS parser implemented by antlr4 works but is very slow. Generating a Python data model from EXPRESS schema is a done once and therefore hasn't to be very fast, but it is a pain in the development and testing phase (Caching AST!).
  • The pyparsing implementation does not work but is promising for speed, so I will not abandon this implementation complete - but for now I go the antlr4 route.

Abstract Syntax Tree for EXPRESS

It is required to create an AST from the parse tree, (EXP|XSD) -> AST -> Python Data Model.

Caching the AST could speed up developing and testing phase for the slow antlr4 parser!

XML Requirements & Resources

Import/Export by standard Python module xml.etree.ElementTree and the ability to use an faster alternative implementation like lxml.

JSON Requirements & Resources

Import/Export by the standard Python module json and the ability to use an faster alternative implementation like orjson.

Internal Data Model

Classes

  1. Static: create class declarations from EXPRESS file as a Python .py file

    • (+) manually modifications are possible
    • (-) modifications are lost at recreation process
    • (-) big code base

  2. Dynamic: use Python meta programming to create classes on the fly

    • (-) no modifications are possible by default, maybe extensible by mixins
    • (+) small code base

Instances

Instantiation by factory! args is a list of positional arguments and kwargs are keyword arguments as a dict.

ifc4 = steputils.model('IFC4')
root = ifc4.get_root()
entity = ifc4.entity('IfcRoot', IfcGloballyUniqueId=guid())

DOM Interface

Create new model:

ifc4 = steputils.model('IFC4')

Get root node:

root = ifc4.get_root()

Create new node:

entity = ifc4.entity('IfcRoot', IfcGloballyUniqueId=guid())

Adding new node to parent:

# add one child node
root.append(entity)

# add multiple child nodes
root.extend([entity, entity2, ...])

# insert at a specified position
root.insert(index, entity)

Iterate child nodes:

for entity in root:
    pass

Delete child nodes:

root.remove(entity)

Query Language - First Draft

Query entities in a XPath like way:

tag Selects all child elements with the given tag. For example, spam selects all child elements named spam, and spam/egg selects all grandchildren named egg in all children named spam.

. Selects the current node. This is mostly useful at the beginning of the path, to indicate that it’s a relative path.

* Selects all child elements. For example, */egg selects all grandchildren named egg

// Selects all subelements, on all levels beneath the current element. For example, .//egg selects all egg elements in the entire tree.

.. Selects the parent element.

[@attrib] Selects all elements that have the given attribute.

[@attrib='value'] Selects all elements for which the given attribute has the given value. The value cannot contain quotes.

[tag] Selects all elements that have a child named tag. Only immediate children are supported.

[tag='text'] Selects all elements that have a child named tag whose complete text content, including descendants, equals the given text.

[position] Selects all elements that are located at the given position. The position is an integer: 1 is the first position, -1 for the last position like Python list indices.

root.findall('spam')  # finds all matching child elements named spam
root.find('spam')  # find first matching child element named spam