Two-stage stochastic models

from collections import namedtuple

import examodels as exa

nscen = 3
Row = namedtuple("Row", "i t")
rows = [Row(i, 0.5 * i) for i in range(nscen * 4)]

core = exa.TwoStageCore(nscen)

d = exa.add_var(core, 2)                            # design, shared
v = exa.add_var(core, exa.EachScenario(), 4)        # recourse, per scenario

exa.add_obj(core, lambda r: (v[r.i] - r.t)**2, over=rows)
exa.add_con(core, exa.EachScenario(), lambda i: v[i] - d[0],
            over=range(nscen * 4))

EachScenario() marks a declaration as recourse rather than design. A per-scenario variable declaration is replicated by the backend, so the block holds nscen times what was asked for and is indexed flat. A per-scenario constraint is tagged rather than replicated: supply the full index set, spanning all scenarios.

Once built:

model = exa.Model(core)

exa.get_nscen(model)       # how many scenarios
exa.get_var_scen(model)    # scenario of each variable, 0 for the shared first stage
exa.get_con_scen(model)    # scenario of each constraint row

Tags are available directly — FirstStageTag, SecondStageTag, FirstStageConstraintTag, SecondStageConstraintTag — and new_tag(name, kind) defines one of your own, which can be attached with tag= on a declaration.