Runtime validator checks

[IzaakWN]

The current pydantic Models are useful to create valid JSON files. One could add some more basic checks during generation, like checking

• bin edges are in increasing order;
• #(edges-1)==#(content) for 1D binning, and similar for multibinning;
• #(keys)==#(content) for categories;
• #(axis)==#(input parameters) for multibinning (see issue Associate Content with input variable #2);
• input parameters of content blocks are defined (see issue Associate Content with input variable #2);
• ...

pydantic provides validator methods, e.g.

class Binning(Model):
    nodetype: Literal["binning"]
    input: str
    edges: List[float]
    "Edges of the binning, where edges[i] <= x < edges[i+1] => f(x, ...) = content[i](...)"
    content: List[Content]
    
    @validator('edges')
    def validate_edges(cls,edges,values):
        for i, lowedge in enumerate(edges[:-1]):
          if lowedge>=edges[i+1]:
            raise ValueError("bin edges not in increasing order: %s"%(edges))
        return edges
    
    @validator('content')
    def match_content_bins(cls,content,values):
        if 'edges' in values:
          nbins = len(values['edges'])-1
          if len(content)!=nbins:
            raise ValueError("number of content elements (%s) must match number of bins (%s)"%(len(content),nbins))
        return content

or one could override __init__.
More examples are here. (pydantic errors are quite verbose, so I added some highlighting.)

[nsmith-]

This is a great addition. If you are willing, feel free to make a PR with the validators you wrote.