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Design Specification - API Sandbox

Developer Notes

• Configuring your fork with Read the Docs
• Formatting copyright, authors, "the top of the module"
• Formatting docstrings to look nice in code and in sphinx. (Generally, follow numpydoc recommendations.)
• Guidelines for corner cases

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• How we tend to handle None

  When checking a value against None use the if var is None: construction. We also use None (as does Python itself) as a null argument to functions where the default argument must be newly computed at runtime. For example, if you use def f(nlist=[]): the list will be created when the function is defined and it will be the same list for all function calls. If you want a new list for each function call use

   def f(nlist=None):
       if nlist is None:
           nlist = []
  

  In some functions we allow nodes to be specified with a default of processing all nodes. We use None to indicate whether an argument was provided or not. Thus, None is special in that it cannot be a node in our graphs. We state that a node can be any hashable other than None.

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• Explicitly not supporting directed or multigraph in a function:

  Use the decorator not_implemented_for in networkx/utils/decorators.py to designate that a function doesn't accept 'directed', 'undirected', 'multigraph' or 'graph'. The function should have its first argument be the graph object to be checked.

   @nx.not_implemented_for('directed', 'multigraph')
   def function_not_for_MultiDiGraph(G, others):
       # function not for graphs that are directed *and* multigraph
       pass
  
   @nx.not_implemented_for('directed')
   @nx.not_implemented_for('multigraph')
   def function_only_for_Graph(G, others):
       # function not for directed graphs *or* for multigraphs
       pass
  

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