sgraph 0.5.0

sgraph hierarchic graph data structure, format and algorithms

sgraph

sgraph contains data format, structures and algorithms to work with hierachic graph structures. Typically it is suitable for representing software structures.

Install

pip install sgraph

Contributions

The project is welcoming all contributions.

Core Ontology

A model, SGraph consists of a root SElement, which may have children of the same type (as in XML). Attribute information can be stored via key-value pairs into them. The SElement objects can be connected together via SElementAssociation objects.

Example model

nginx model has an nginx root element that represents the main directory. Inside it, there is a src element. And inside src, there is core.

https://github.com/nginx/nginx/tree/master/src inside core, there are several elements, e.g. nginx.c and nginx.h

https://github.com/nginx/nginx/blob/master/src/core/nginx.c

Because nginx.c contains #include directive to nginx.h, in the model it is formulated so that there is a relationship (also called as association) from nginx.c element to nginx.h

To make model more explicit, that particular relationship should be annotated with type "inc" to describe the dependency type.

It is also possible to have other attributes assigned to relationships other than type but typically this is rare.

XML format

In XML dataformat, minimalism is the goal to make it simple and clean. Integers are used as unique identifiers for the elements. In the example case, the nginx.h element is assigned with ID 2 and the relationship that is inside nginx.c refers this way to nginx.h

This integer reference system has been designed to make the data format highly performing even with 10 million element models.

Deps data format - line based simple format for easy scripting

In Deps data format (usually a .txt file), the above model can be described minimally this way:

/nginx/src/core/nginx.c:/nginx/src/core/nginx.h:inc

Although this might seem very compelling data format to use, it is not recommended for very large models, e.g. 10 million elements.

Using the API

Creating a simple model:

>>> from sgraph import SGraph
>>> from sgraph import SElement
>>> from sgraph import SElementAssociation
>>> x = SGraph(SElement(None, ''))
>>> x
<sgraph.sgraph.SGraph object at 0x7f2efae9ad30>

>>> x.to_deps(fname=None)

>>> e1 = x.createOrGetElementFromPath('/path/to/file.x')
>>> e2 = x.createOrGetElementFromPath('/path/to/file.y')
>>> x.to_deps(fname=None)
/path
/path/to
/path/to/file.x
/path/to/file.y

>>> x.to_xml(fname=None)
<model version="2.1">
  <elements>
  <e n="path" >
    <e n="to" >
      <e n="file.x" >
      </e>
      <e n="file.y" >
      </e>
    </e>
  </e>
</elements>
</model>

>>> ea = SElementAssociation(e1, e2, 'use')
>>> ea.initElems()  # ea is not connected to the model before this call.
>>> x.to_deps(fname=None)
/path/to/file.x:/path/to/file.y:use
/path
/path/to
>>>

>>> x.to_xml(fname=None)
<model version="2.1">
  <elements>
  <e n="path" >
    <e n="to" >
      <e n="file.x" >
        <r r="2" t="use" />
      </e>
      <e i="2" n="file.y" >
      </e>
    </e>
  </e>
 </elements>
</model>

Current utilization

Softagram uses it for building up the information model about the analyzed software.