Python API¶

How you run a model. The model itself is the YAML file — SPEC is what it may contain and what it means; this page is the sixteen names that load, check, build, solve and read one back. The surface is pinned by a test and the reasoning behind its size is ARCHITECTURE.

Five verbs — check, load_schema, build, solve, write — and the exception tree rooted at LinopyYamlError: LanguageError (with SchemaError, DimensionError, PiecewiseExpansionError) for the model, DataError for what was bound to it.

import lpspec as lps

lps.check('model.yaml')  # parse → validate → lower, no data bound
schema = lps.load_schema('model.yaml')  # MathSchema

result = lps.solve('model.yaml', sources, solver_options={'time_limit': 60})
result.status, result.termination_condition, result.objective
result.is_ok  # rolled-up verdict: not an error, abort or refusal
result.has_primal  # narrower: are there values to read
result.primal('p')  # tidy frame (dims…, value) — the native shape
result.dual('power_balance')  # shadow prices, the same shape and the same join
result.to_pandas('p')  # the same, as a DataFrame
result.to_dataarray('p')  # the same, labelled: .sel / resample / plot
result.to_dataset()  # every variable by default; names for a subset
result.to_parquet(directory)  # streamed to disk, never through this process

lps.write('model.yaml', sources, 'model.lp')  # sink chosen by the suffix

Nothing has to be released. The built model is frames this process owns, so primal and the to_* readers stay valid for as long as the Result does. close() and the context-manager protocol exist to hand a large model back early, not because forgetting them breaks anything. lps.build returns the executor when one build should feed more than one sink:

ex = lps.build('model.yaml', sources)
ex.write_lp('model.lp')
result = ex.solve()

What sources accepts is SPEC §8. Nothing on this path imports linopy, and primal returns a polars.DataFrame — Arrow-backed, so it exports the same protocol the loader recognises. to_pandas and to_dataarray are the bridges out and need pandas / xarray, which ship with the [linopy] extra. The only build knob is coords; solver_options is not a build knob and is forwarded verbatim to the solver.

Reading a result:

Rule
`is_ok` is not `has_primal`	`is_ok` rolls up the termination condition; `has_primal` adds the solver's verdict on whether an incumbent exists, and is what every reader gates on. A MIP that hits `time_limit` before finding a feasible point is `ok` with nothing to read
reading anyway	`NoSolutionError`; `objective` is `nan`
`dual` raises rather than zero-filling	no values at all is `NoSolutionError`; values but no duals — any integer or binary variable makes them undefined — is `LinopyYamlError`, because only this quantity is missing
duals exist only on `solver_direct`	a model written to LP and solved elsewhere never passes back through here. Reduced costs and slacks ride the same join and are not exposed yet
`to_dataset` costs what it says	each variable arrives dense over its own dims — name a subset, or use `to_parquet`
`write`	`.lp` only today; `.mps` raises `NotImplementedError`

The linopy shim (lpspec.linopy.build / .extend, [linopy] extra) puts the same YAML math on a linopy.Model that already exists in memory. It is documented with everything else about that relationship in docs/design/linopy.md.