Import and Export

evidencelib keeps data exchange small and explicit. JSON and CSV are intended for round trips. LaTeX is intended for paper-ready tables.

All imports require the target Frame. The frame carries the model semantics, especially for DSmT and hybrid models, so imported mass data is interpreted against the frame you provide.

Dictionaries

from evidencelib import Frame, MassFunction

frame = Frame.dst(["A", "B"])
m = MassFunction.from_dict(frame, {"A": 0.2, "A|B": 0.8})

assert m.to_dict() == {"A": 0.2, "A|B": 0.8}

from_dict() also accepts schema-wrapped dictionaries produced by decoding to_json().

JSON

text = m.to_json()
restored = MassFunction.from_json(frame, text)

The JSON payload contains:

schema: the evidencelib.mass.v2 schema name,
frame: atom, model, region-count, and exact possible-region metadata,
masses: proposition strings mapped to mass values.

The stored frame metadata is validation metadata only. It does not replace the Frame object passed to from_json().

Version 2 records the model’s possible Venn regions, so hybrid models with the same atom names and region count but different constraints cannot be confused. Legacy v1 data remains readable for DST and free DSmT frames. Because v1 cannot identify hybrid constraints safely, importing v1 into a hybrid target raises an error and requires re-export from the original model.

CSV

csv_text = m.to_csv()
restored = MassFunction.from_csv(frame, csv_text)

The CSV format has two columns:

proposition,mass
A,0.2
A|B,0.8

Headerless CSV can be imported with has_header=False:

restored = MassFunction.from_csv(frame, "A,0.2\nA|B,0.8\n", has_header=False)

LaTeX

Use to_latex() when you want a table for a paper or thesis.

latex = m.to_latex(
    columns=("mass", "belief", "plausibility"),
    caption="Mass assignment",
    label="tab:mass",
)

By default, LaTeX exports only focal propositions. Pass rows="all" to include every proposition generated by the frame:

m.to_latex(rows="all")

Use rows="all" carefully on DSmT frames because the hyper-power set can grow quickly.

Supported column names are:

Column	Aliases
`mass`	`m`
`belief`	`bel`
`plausibility`	`pl`
`commonality`	`q`

The default LaTeX output uses booktabs rules. Include \usepackage{booktabs} in your document preamble, or pass booktabs=False.