Skip to content

math_spec.model

The YAML surface's types — every block a file may contain, rooted at :class:Spec.

Nothing here has seen data.

Curvature = Literal['convex', 'concave', 'either'] module-attribute #

Expression = Annotated[str, BeforeValidator(_number_is_an_expression, json_schema_input_type=str | float)] module-attribute #

FORMULATIONS = ('piecewise', 'sos') module-attribute #

Formulation = Literal['piecewise', 'sos'] module-attribute #

NUMERIC_DTYPES = frozenset({'float', 'int'}) module-attribute #

PIECEWISE_METHODS = {'adjacency': 'a binary per segment, and a row making the two nonzero weights neighbours', 'sos2': 'the same weights, restricted by a set the solver branches on (the sos rules)', 'convex': 'nothing — the weights range over the hull, which is a pure LP', 'lp': 'no weights at all — one row per segment line, plus the two rows holding the domain'} module-attribute #

SOS_TYPES = frozenset(get_args(SosType)) module-attribute #

SUPPORTED_VERSIONS = (0,) module-attribute #

AssumptionBlock #

Bases: _StrictBlock

What the model assumes of its data: a predicate every coordinate it is checked at has to satisfy.

Written in YAML as a bare where string, or as a mapping once it carries a where: or a description:, and serialised back to whichever form it was written in::

assumptions:
  efficiency_is_a_fraction: "efficiency > 0 AND efficiency <= 1"
  bounds_do_not_cross:
    holds: "p_min <= p_max"
    where: "p_min"
    description: a unit with no minimum is unconstrained below

The language decides nothing about the numbers, so the consumer binding the data checks it, and refuses the data where it does not hold.

description = None class-attribute instance-attribute #

holds instance-attribute #

where = None class-attribute instance-attribute #

BoundsBlock #

Bases: _StrictBlock

Variable bounds — each side is a finite number, a parameter name, or None where it is open.

An omitted bound leaves the variable unbounded on that side, not implicitly non-negative. An infinity is refused: an open side is null, and the other infinity leaves no value at all.

lower = None class-attribute instance-attribute #

upper = None class-attribute instance-attribute #

ConstraintBlock #

Bases: _StrictBlock

A declared constraint: one rule, over one frame.

description = None class-attribute instance-attribute #

dims instance-attribute #

expression instance-attribute #

where = None class-attribute instance-attribute #

DimensionBlock #

Bases: _StrictBlock

A declared dimension, and the dtype its coordinates must be.

A dimension is an axis and nothing else: it declares that the axis exists and what its coordinates are typed as, never which coordinates there are — those are data, and arrive at bind time. The maps its members carry — a generator's bus, a snapshot's period — are top-level relations: (:class:RelationBlock), keyed by their own name.

description = None class-attribute instance-attribute #

dtype = 'str' class-attribute instance-attribute #

ExpressionBlock #

Bases: _StrictBlock

A named quantity: one arithmetic expression, referenced by the math or read back after a solve.

Written in YAML as a bare string, or as a mapping once it carries a description: — and serialised back to whichever form it was written in, so a round trip through :meth:Spec.to_yaml reproduces the file::

expressions:
  total_generation: sum(p, over=generator)
  emissions:
    expression: sum(p * rate, over=generator)
    description: CO2 released, the quantity the cap bounds

A quantity whose value varies by region is written as cases: over a declared dims:, with an otherwise: for the rest — see the language reference.

cases = {} class-attribute instance-attribute #

description = None class-attribute instance-attribute #

dims = None class-attribute instance-attribute #

expression = None class-attribute instance-attribute #

otherwise = None class-attribute instance-attribute #

ExpressionCase #

Bases: _StrictBlock

One region of a named expression: the value, and when it is the value.

Every case says where it applies. The value wherever none of them does is the block's otherwise:, which is written outside cases: because it is not a region like these — it is what is left::

cases:
  opening: { when: "position(snapshot) == 0", expression: p_max }
otherwise: 0

expression instance-attribute #

when instance-attribute #

MacroBlock #

Bases: _StrictBlock

A parameterised expression template, defined in the YAML itself.

Language, not code: formals (args positional, kwargs keyword) shadow model names inside the template, and every call site expands in the syntax tree before resolution reads the expression.

args = [] class-attribute instance-attribute #

description = None class-attribute instance-attribute #

kwargs = [] class-attribute instance-attribute #

template instance-attribute #

ObjectiveBlock #

Bases: _StrictBlock

A declared objective function.

description = None class-attribute instance-attribute #

expression instance-attribute #

sense = 'minimize' class-attribute instance-attribute #

ParameterBlock #

Bases: _StrictBlock

A declared parameter with dims and dtype.

description = None class-attribute instance-attribute #

dims instance-attribute #

dtype = 'float' class-attribute instance-attribute #

PiecewiseBlock #

Bases: _StrictBlock

N expressions jointly pinned to a breakpoint-indexed piecewise curve.

Mirrors linopy.Spec.add_piecewise_formulation. Each link is [expression, values_parameter] or [expression, values_parameter, sign]: expression is any affine expression string, values_parameter names a parameter carrying the over dim, and sign bounds the link by the curve instead of pinning it (at most one non-"==", and only with exactly two links).

activity = None class-attribute instance-attribute #

curve property #

The two links as (x, y), the bounded one last.

Two-link blocks only.

description = None class-attribute instance-attribute #

method = 'adjacency' class-attribute instance-attribute #

nominated property #

The block's own values parameter points: names, so the mask is derived from it — or None.

over instance-attribute #

points = None class-attribute instance-attribute #

Bases: _StrictBlock

One link of a piecewise block: an expression pinned to a values curve.

Written in YAML as [expression, values] or [expression, values, sign] and serialised back to exactly that form, so a round trip through :meth:Spec.to_yaml reproduces the file.

expression instance-attribute #

sign = '==' class-attribute instance-attribute #

values instance-attribute #

RelationBlock #

Bases: _StrictBlock

A named relation between dimensions: the columns a row is keyed by, and the columns that key determines.

Each side is a dimension, a list of them, or a mapping of column name to dimension where two columns share one. key: is the claim the language checks at bind: one row per key tuple, so every values: column is a function of it. A relation with no values: is bare — every column is in its key, a row is its own identity, and nothing reads it::

relations:
  gen_bus: {key: generator, values: bus}
  gen_bt: {key: [generator], values: [bus, technology]}
  zone_of: {key: [generator, period], values: zone}
  ends: {key: line, values: {bus0: bus, bus1: bus}}
  connection: {key: [generator, bus]}

An operator reads the table in the direction the call names (over=, into=), joining on the other key columns; the declaration fixes no direction. The map itself is data, and arrives at bind time under the relation's name, one column per role.

description = None class-attribute instance-attribute #

dims property #

key instance-attribute #

key_roles property #

The key roles, however key: was written.

pairs property #

(role, dimension) per column, the key's columns first.

The program calls the same thing :attr:~math_spec.program.RelationDeclaration.columns; here the table has no field of its own, being what the two sides make.

roles property #

value_roles property #

The roles the key determines; empty for a bare relation.

values = None class-attribute instance-attribute #

SosBlock #

Bases: _StrictBlock

A special-ordered set over one dimension of one variable.

One set per coordinate of the variable's dims minus along; the members are the variable's existing coordinates along along, in that dimension's declared order.

type: 1 admits at most one nonzero member, type: 2 at most two, and those two consecutive. A consumer with the concept takes the set as one; :meth:Spec.expand states it as binaries instead, and the rows it writes multiply by the member's own bounds, which is why a member needs both.

along instance-attribute #

description = None class-attribute instance-attribute #

type instance-attribute #

variable instance-attribute #

Spec #

Bases: _StrictBlock

The declared math — one YAML file, or one dict, validated. Nothing here has seen data.

A Spec that exists has passed the whole language: constructing one by any route — to_spec, :meth:model_validate, the constructor — runs every load-time check, expression pass included, and raises :class:~math_spec.errors.LanguageError on a model the language refuses. Holding one is the proof, so nothing downstream checks it again.

The API is the eleven declaration sections plus version and description, three ways back out — :meth:to_dict for the model as data, :meth:to_yaml for the file a reviewer reads, :meth:expand for the same math with its formulations written out — and :attr:program, the model typed, which every reader after load walks. Everything else on this class is pydantic's, not a contract this package keeps.

assumptions = {} class-attribute instance-attribute #

constraints = {} class-attribute instance-attribute #

description = None class-attribute instance-attribute #

dimensions = {} class-attribute instance-attribute #

expressions = {} class-attribute instance-attribute #

macros = {} class-attribute instance-attribute #

objective = None class-attribute instance-attribute #

parameters = {} class-attribute instance-attribute #

piecewise = {} class-attribute instance-attribute #

program cached property #

This model typed, section for section — what every reader after load walks.

Computing it is the expression pass, so a model the language refuses raises here; loading forces it, so every ask on a model in hand is the one object. It mirrors the model: a piecewise: block still in it is a curve under program.piecewise and a sos: block a set under program.sos, and :meth:expand is what writes either out as rows, so a consumer building rows reads spec.expand(...).program and refuses a block it does not take.

relations = {} class-attribute instance-attribute #

sos = {} class-attribute instance-attribute #

variables = {} class-attribute instance-attribute #

version = 0 class-attribute instance-attribute #

expand(*kinds) #

This model with its formulations written out as plain variables and constraints.

A formulation states rows rather than being one — piecewise: states a curve, sos: states which members of a family may be nonzero — and expanding one writes those rows under names prefixed with the block's own, then drops the block. The math is the same afterwards, and so is the data that binds it: neither a set nor a curve emits a parameter, and a curve's rows sit on where predicates over the file's own.

PARAMETER DESCRIPTION
kinds

Which formulations to write out — 'piecewise', 'sos', or none of them for every one. They go in :data:FORMULATIONS order whatever order they are asked in, because a method: sos2 curve emits a set and no set emits a curve.

TYPE: Formulation DEFAULT: ()

RETURNS DESCRIPTION
Spec

The model those blocks wrote out, or this one where it declares

Spec

none of them. It is a model like any other: :meth:to_yaml writes

Spec

it, and the file binds the same data as the one it came from.

RAISES DESCRIPTION
ValueError

kinds names something that is not a formulation.

Source code in src/math_spec/model.py
def expand(self, *kinds: Formulation) -> Spec:
    """This model with its formulations written out as plain variables and constraints.

    A formulation states rows rather than being one — ``piecewise:`` states
    a curve, ``sos:`` states which members of a family may be nonzero — and
    expanding one writes those rows under names prefixed with the block's
    own, then drops the block. The math is the same afterwards, and so is
    the data that binds it: neither a set nor a curve emits a parameter,
    and a curve's rows sit on ``where`` predicates over the file's own.

    Args:
        kinds: Which formulations to write out — ``'piecewise'``,
            ``'sos'``, or none of them for every one. They go in
            :data:`FORMULATIONS` order whatever order they are asked in,
            because a ``method: sos2`` curve emits a set and no set emits a
            curve.

    Returns:
        The model those blocks wrote out, or this one where it declares
        none of them. It is a model like any other: :meth:`to_yaml` writes
        it, and the file binds the same data as the one it came from.

    Raises:
        ValueError: *kinds* names something that is not a formulation.
    """
    wanted = _formulations(kinds)
    from math_spec.piecewise import expand_piecewise
    from math_spec.sos import expand_sets

    expanded = expand_piecewise(self) if 'piecewise' in wanted else self
    if 'sos' in wanted and expanded.sos:
        expanded = expand_sets(expanded)
    return expanded

model_validate(obj, *, strict=None, extra=None, from_attributes=None, context=None, by_alias=None, by_name=None) classmethod #

Validate a mapping, raising this package's exception tree rather than pydantic's.

__init__ is not wrapped the same way, because defining one makes pydantic run every after-validator twice.

Source code in src/math_spec/model.py
@classmethod
@override
def model_validate(
    cls,
    obj: object,
    *,
    strict: bool | None = None,
    extra: ExtraValues | None = None,
    from_attributes: bool | None = None,
    context: object = None,
    by_alias: bool | None = None,
    by_name: bool | None = None,
) -> Self:
    """Validate a mapping, raising this package's exception tree rather than pydantic's.

    ``__init__`` is not wrapped the same way, because defining one makes
    pydantic run every after-validator twice.
    """
    try:
        return super().model_validate(
            obj,
            strict=strict,
            extra=extra,
            from_attributes=from_attributes,
            context=context,
            by_alias=by_alias,
            by_name=by_name,
        )
    except ValidationError as exc:
        raise schema_error(exc) from None

to_dict() #

The model as plain data. to_spec(m.to_dict()) reproduces it.

Source code in src/math_spec/model.py
def to_dict(self) -> dict[str, object]:
    """The model as plain data. ``to_spec(m.to_dict())`` reproduces it."""
    return self.model_dump()

to_yaml() #

The file a reviewer reads — including for a model that never had one.

Source code in src/math_spec/model.py
def to_yaml(self) -> str:
    """The file a reviewer reads — including for a model that never had one."""
    import yaml

    return yaml.safe_dump(self.to_dict(), sort_keys=False, allow_unicode=True)

VariableBlock #

Bases: _StrictBlock

A declared decision variable.

absence = 'undefined' class-attribute instance-attribute #

bounds = BoundsBlock() class-attribute instance-attribute #

description = None class-attribute instance-attribute #

dims instance-attribute #

domain = 'continuous' class-attribute instance-attribute #

where = None class-attribute instance-attribute #

side_columns(written) #

(role, dimension) per column of one side of a relation, in written order.

A bare name or a list names each column after the dimension it is over; a mapping names the roles, which is what two columns over one dimension need.

Source code in src/math_spec/model.py
def side_columns(written: str | list[str] | dict[str, str] | None) -> tuple[tuple[str, str], ...]:
    """``(role, dimension)`` per column of one side of a relation, in written order.

    A bare name or a list names each column after the dimension it is over; a
    mapping names the roles, which is what two columns over one dimension need.
    """
    if written is None:
        return ()
    if isinstance(written, dict):
        return tuple(written.items())
    return tuple((d, d) for d in ((written,) if isinstance(written, str) else written))