from __future__ import annotations
import builtins
import itertools
import os
import threading
from enum import Enum
from typing import TYPE_CHECKING, Any
import gams.transfer as gt
from gams.core.gdx import GMS_DT_VAR
from gams.transfer._internals import (
TRANSFER_TO_GAMS_VARIABLE_SUBTYPES,
)
import gamspy as gp
import gamspy._algebra.condition as condition
import gamspy._algebra.expression as expression
import gamspy._algebra.operable as operable
import gamspy._symbols.implicits as implicits
import gamspy._validation as validation
import gamspy.utils as utils
from gamspy._symbols.symbol import Symbol
from gamspy.exceptions import ValidationError
if TYPE_CHECKING:
from collections.abc import Sequence
from types import EllipsisType
import pandas as pd
from gamspy import Alias, Container, Set
from gamspy._algebra.expression import Expression
from gamspy.math.matrix import Dim
[docs]
class VariableType(Enum):
"""
Enumeration of available variable types.
"""
BINARY = "binary"
"""Discrete variable that can only take values of 0 or 1."""
INTEGER = "integer"
"""Discrete variable that can only take integer values between the bounds."""
POSITIVE = "positive"
"""No negative values are allowed for variable. The user may change both bounds from the default value."""
NEGATIVE = "negative"
"""No positive values are allowed for variables. The user may change both bounds from the default value."""
FREE = "free"
"""No bounds on variable. Both bounds may be changed from the default values by the user."""
SOS1 = "sos1"
"""A set of variables, such that at most one variable within a group may have a non-zero value."""
SOS2 = "sos2"
"""A set of variables, such that at most two variables within a group may have non-zero values and the two non-zero values are adjacent."""
SEMICONT = "semicont"
"""Semi-continuous, must be zero or above a given minimum level."""
SEMIINT = "semiint"
"""Semi-integer, must be zero or above a given minimum level and integer."""
@classmethod
def values(cls):
"""Convenience function to return all values of enum"""
return list(cls._value2member_map_.keys())
def __str__(self) -> str:
return self.value
[docs]
class Variable(gt.Variable, operable.Operable, Symbol):
"""
Represents a Variable symbol in GAMS.
Variables are the decision entities in a mathematical model. They can be
free, positive, binary, integer, etc.
See https://gamspy.readthedocs.io/en/latest/user/basics/variable.html
Parameters
----------
container : Container
The Container object that this variable belongs to.
name : str, optional
Name of the variable. If not provided, a unique name is generated automatically.
type : str, optional
Type of the variable. Options: "free", "positive", "negative", "binary",
"integer", "sos1", "sos2", "semicont", "semiint". Default is "free".
domain : Sequence[Set | Alias | str] | Set | Alias | Dim | str, optional
The domain of the variable. Can be a list of Sets/Aliases, a single Set/Alias,
or strings representing set names. Use "*" for the universe set. Default is [] (scalar).
records : Any, optional
Initial records (level/marginal/bounds) to populate the variable.
domain_forwarding : bool | list[bool], optional
If True, adding records to this variable will implicitly add new elements to the
domain sets (if they are dynamic). Default is False.
description : str, optional
A human-readable description of the variable.
is_miro_output : bool, optional
If True, flags this variable as an output symbol for GAMS MIRO. Default is False.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, "i", records=['i1', 'i2'])
>>> v = gp.Variable(m, "v", domain=[i], type="positive", description="Production quantity")
"""
@classmethod
def _constructor_bypass(
cls,
container: Container,
name: str,
type: str = "free",
domain: Sequence[Set | Alias | str] | Set | Alias | Dim | str | None = None,
records: Any | None = None,
description: str = "",
):
if domain is None:
domain = []
if isinstance(domain, (gp.Set, gp.Alias, str)):
domain = [domain]
if isinstance(domain, gp.math.Dim):
domain = gp.math._generate_dims(container, domain.dims)
# create new symbol object
obj = Variable.__new__(
cls,
container,
name,
type,
domain,
records,
description=description,
)
# set private properties directly
obj._type = type
obj._assignment = None
obj._gams_type = GMS_DT_VAR
obj._gams_subtype = TRANSFER_TO_GAMS_VARIABLE_SUBTYPES[type]
obj._requires_state_check = False
obj._container = container
container._requires_state_check = True
obj._name = name
obj._domain = domain
obj._domain_forwarding = False
obj._description = description
obj._records = records
obj._modified = True
obj._domain_violations = None
# add to container
container.data.update({name: obj})
# gamspy attributes
obj.where = condition.Condition(obj)
obj._latex_name = name.replace("_", r"\_")
obj.container._add_statement(obj)
obj._synchronize = True
obj._metadata = {}
obj._winner = "python"
# create attributes
obj._l, obj._m, obj._lo, obj._up, obj._s = obj._init_attributes()
obj._fx = obj._create_attr("fx")
obj._prior = obj._create_attr("prior")
obj._stage = obj._create_attr("stage")
# miro support
obj._is_miro_output = False
return obj
def __new__(
cls,
container: Container | None = None,
name: str | None = None,
type: str = "free",
domain: Sequence[Set | Alias | str] | Set | Alias | Dim | str | None = None,
records: Any | None = None,
domain_forwarding: bool | list[bool] = False,
description: str = "",
uels_on_axes: bool = False,
is_miro_output: bool = False,
):
if container is not None and not isinstance(container, gp.Container):
invalid_type = builtins.type(container)
raise TypeError(
f"Container must of type `Container` but found {invalid_type}"
)
if name is None:
return object.__new__(cls)
else:
if not isinstance(name, str):
raise TypeError(
f"Name must of type `str` but found {builtins.type(name)}"
)
try:
if not container:
container = gp._ctx_managers[
(os.getpid(), threading.get_native_id())
]
symbol = container[name]
if isinstance(symbol, cls):
return symbol
raise TypeError(
f"Cannot overwrite symbol `{symbol.name}` in container"
" because it is not a Variable object)"
)
except KeyError:
return object.__new__(cls)
def __init__(
self,
container: Container | None = None,
name: str | None = None,
type: str = "free",
domain: Sequence[Set | Alias | str] | Set | Alias | Dim | str | None = None,
records: Any | None = None,
domain_forwarding: bool | list[bool] = False,
description: str = "",
uels_on_axes: bool = False,
is_miro_output: bool = False,
):
self._metadata: dict[str, Any] = {}
self._assignment: Expression | None = None
if is_miro_output and name is None:
raise ValidationError("Please specify a name for miro symbols.")
# miro support
self._is_miro_output = is_miro_output
self._domain_violations = None
self._synchronize = True
self._winner = "python"
# domain handling
if domain is None:
domain = []
if isinstance(domain, (gp.Set, gp.Alias, str)):
domain = [domain]
if isinstance(domain, gp.math.Dim):
domain = gp.math._generate_dims(container, domain.dims) # type: ignore
# does symbol exist
has_symbol = False
if isinstance(getattr(self, "container", None), gp.Container):
has_symbol = True
if has_symbol:
if self.type != type.casefold():
raise TypeError(
"Cannot overwrite symbol in container unless variable"
f" types are equal: `{self.type}` !="
f" `{type.casefold()}`"
)
if any(d1 != d2 for d1, d2 in itertools.zip_longest(self._domain, domain)):
raise ValueError(
"Cannot overwrite symbol in container unless symbol"
" domains are equal"
)
if self._domain_forwarding != domain_forwarding:
raise ValueError(
"Cannot overwrite symbol in container unless"
" 'domain_forwarding' is left unchanged"
)
# reset some properties
self._requires_state_check = True
self.container._requires_state_check = True
if description != "":
self.description = description
previous_state = self.container._options.miro_protect
self.container._options.miro_protect = False
self._records = None
self._modified = True
# only set records if records are provided
if records is not None:
self.setRecords(records, uels_on_axes=uels_on_axes)
self.container._options.miro_protect = previous_state
else:
if container is None:
try:
container = gp._ctx_managers[
(os.getpid(), threading.get_native_id())
]
except KeyError as e:
raise ValidationError("Variable requires a container.") from e
assert container is not None
type = cast_type(type)
if name is not None:
name = validation.validate_name(name)
if is_miro_output:
name = name.lower() # type: ignore
else:
name = container._get_symbol_name(prefix="v")
previous_state = container._options.miro_protect
container._options.miro_protect = False
super().__init__(
container,
name,
type,
domain,
domain_forwarding=domain_forwarding,
description=description,
uels_on_axes=uels_on_axes,
)
self._latex_name = self.name.replace("_", r"\_")
if is_miro_output:
container._miro_output_symbols.append(self.name)
validation.validate_container(self, self._domain)
self.where = condition.Condition(self)
self.container._add_statement(self)
# create attributes
(
self._l,
self._m,
self._lo,
self._up,
self._s,
) = self._init_attributes()
self._fx = self._create_attr("fx")
self._prior = self._create_attr("prior")
self._stage = self._create_attr("stage")
if records is not None:
self.setRecords(records, uels_on_axes=uels_on_axes)
else:
if not self._is_miro_output:
self._modified = False
self.container._synch_with_gams()
container._options.miro_protect = True
def _serialize(self) -> dict:
info = {
"_domain_forwarding": self._domain_forwarding,
"_is_miro_output": self._is_miro_output,
"_metadata": self._metadata,
"_synchronize": self._synchronize,
}
if self._assignment is not None:
info["_assignment"] = self._assignment.getDeclaration()
return info
def _deserialize(self, info: dict) -> None:
for key, value in info.items():
if key == "_assignment":
left, right = value.split(" = ")
value = expression.Expression(left, "=", right[:-1])
setattr(self, key, value)
# Relink domain symbols
new_domain = []
for elem in self._domain:
if elem == "*":
new_domain.append(elem)
continue
new_domain.append(self.container[elem])
self.domain = new_domain
def __getitem__(
self, indices: Sequence | str | EllipsisType | slice
) -> implicits.ImplicitVariable:
domain = validation.validate_domain(self, indices)
return implicits.ImplicitVariable(self, name=self.name, domain=domain)
def __eq__(self, other):
return expression.Expression(self, "=e=", other)
def __ne__(self, other):
return expression.Expression(self, "ne", other)
def __repr__(self) -> str:
return f"Variable(name='{self.name}', domain={self.domain}, type='{self.type}')"
@property
def T(self) -> implicits.ImplicitVariable:
"""
Alias for the `.t()` method.
Returns
-------
ImplicitVariable
"""
return self.t()
def t(self) -> implicits.ImplicitVariable:
"""
Returns an ImplicitVariable derived from this
variable by swapping its last two indices. This operation
does not generate a new variable in GAMS.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, "i", records=['i1','i2'])
>>> j = gp.Set(m, "j", records=['j1','j2'])
>>> v = gp.Variable(m, "v", domain=[i, j])
>>> v_t = v.t()
>>> v_t.domain
[Set(name='j', domain=['*']), Set(name='i', domain=['*'])]
>>> v_t[i, j] # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
gamspy.exceptions.ValidationError:
>>> v_t["j1", "i1"].gamsRepr()
'v("i1","j1")'
"""
from gamspy.math.matrix import permute
dims = list(range(len(self.domain)))
if len(dims) < 2:
raise ValidationError(
"Variable must contain at least 2 dimensions to transpose"
)
x = dims[-1]
dims[-1] = dims[-2]
dims[-2] = x
return permute(self, dims) # type: ignore
def _init_attributes(self):
level = self._create_attr("l")
marginal = self._create_attr("m")
lower = self._create_attr("lo")
upper = self._create_attr("up")
scale = self._create_attr("scale")
return level, marginal, lower, upper, scale
def _create_attr(self, attr_name):
domain = self.domain
return implicits.ImplicitParameter(
self,
name=f"{self.name}.{attr_name}",
records=self.records,
domain=domain,
)
def _update_attr_domains(self):
self._l.__init__(
self,
name=f"{self.name}.l",
records=self.records,
domain=self.domain,
)
self._m.__init__(
self,
name=f"{self.name}.m",
records=self.records,
domain=self.domain,
)
self._lo.__init__(
self,
name=f"{self.name}.lo",
records=self.records,
domain=self.domain,
)
self._up.__init__(
self,
name=f"{self.name}.up",
records=self.records,
domain=self.domain,
)
self._s.__init__(
self,
name=f"{self.name}.scale",
records=self.records,
domain=self.domain,
)
self._fx.__init__(
self,
name=f"{self.name}.fx",
records=self.records,
domain=self.domain,
)
self._prior.__init__(
self,
name=f"{self.name}.prior",
records=self.records,
domain=self.domain,
)
self._stage.__init__(
self,
name=f"{self.name}.stage",
records=self.records,
domain=self.domain,
)
@property
def l(self):
"""
The Level of the variable (its current value).
This corresponds to the `.l` suffix in GAMS. After a solve, this holds the solution value.
Returns
-------
ImplicitParameter
Examples
--------
>>> import gamspy as gp
>>> import numpy as np
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego"])
>>> d = gp.Parameter(m, name="d", domain=i, records=np.array([7, 18]))
>>> x = gp.Variable(m, name="x", domain=i)
>>> x.l[i] = d[i]
>>> x.records.values.tolist()
[['seattle', 7.0, 0.0, -inf, inf, 1.0], ['san-diego', 18.0, 0.0, -inf, inf, 1.0]]
"""
return self._l
@l.setter
def l(self, value: int | float | Expression):
self._l[...] = value
@property
def m(self):
"""
The Marginal (dual value) of the variable.
This corresponds to the `.m` suffix in GAMS. Represents the reduced cost.
Returns
-------
ImplicitParameter
Examples
--------
>>> import gamspy as gp
>>> import numpy as np
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego"])
>>> d = gp.Parameter(m, name="d", domain=i, records=np.array([7, 18]))
>>> x = gp.Variable(m, name="x", domain=i)
>>> x.m[i] = d[i]
>>> x.records.values.tolist()
[['seattle', 0.0, 7.0, -inf, inf, 1.0], ['san-diego', 0.0, 18.0, -inf, inf, 1.0]]
"""
return self._m
@m.setter
def m(self, value: int | float | Expression):
self._m[...] = value
@property
def lo(self):
"""
The Lower Bound of the variable.
This corresponds to the `.lo` suffix in GAMS.
Returns
-------
ImplicitParameter
Examples
--------
>>> import gamspy as gp
>>> import numpy as np
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego"])
>>> d = gp.Parameter(m, name="d", domain=i, records=np.array([7, 18]))
>>> x = gp.Variable(m, name="x", domain=i)
>>> x.lo[i] = d[i]
>>> x.records.values.tolist()
[['seattle', 0.0, 0.0, 7.0, inf, 1.0], ['san-diego', 0.0, 0.0, 18.0, inf, 1.0]]
"""
return self._lo
@lo.setter
def lo(self, value: int | float | Expression):
self._lo[...] = value
@property
def up(self):
"""
The Upper Bound of the variable.
This corresponds to the `.up` suffix in GAMS.
Returns
-------
ImplicitParameter
Examples
--------
>>> import gamspy as gp
>>> import numpy as np
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego"])
>>> d = gp.Parameter(m, name="d", domain=i, records=np.array([7, 18]))
>>> x = gp.Variable(m, name="x", domain=i)
>>> x.up[i] = d[i]
>>> x.records.values.tolist()
[['seattle', 0.0, 0.0, -inf, 7.0, 1.0], ['san-diego', 0.0, 0.0, -inf, 18.0, 1.0]]
"""
return self._up
@up.setter
def up(self, value: int | float | Expression):
self._up[...] = value
@property
def scale(self):
"""
The Scale factor of the variable.
This corresponds to the `.scale` suffix in GAMS, used for scaling the variable
to improve numerical stability.
Returns
-------
ImplicitParameter
Examples
--------
>>> import gamspy as gp
>>> import numpy as np
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego"])
>>> d = gp.Parameter(m, name="d", domain=i, records=np.array([7, 18]))
>>> x = gp.Variable(m, name="x", domain=i)
>>> x.scale[i] = d[i]
>>> x.records.values.tolist()
[['seattle', 0.0, 0.0, -inf, inf, 7.0], ['san-diego', 0.0, 0.0, -inf, inf, 18.0]]
"""
return self._s
@scale.setter
def scale(self, value: int | float | Expression):
if self.type in ("integer", "binary"):
raise ValidationError("Scales cannot be applied to discrete variables.")
self._s[...] = value
@property
def fx(self):
"""
Fixed value of the variable.
Setting `.fx` implies setting both `.lo` and `.up` to the same value.
Reading `.fx` returns the current fixed level (if fixed).
Returns
-------
ImplicitParameter
Examples
--------
>>> import gamspy as gp
>>> import numpy as np
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego"])
>>> d = gp.Parameter(m, name="d", domain=i, records=np.array([7, 18]))
>>> x = gp.Variable(m, name="x", domain=i)
>>> x.fx[i] = d[i]
>>> x.records.values.tolist()
[['seattle', 7.0, 0.0, 7.0, 7.0, 1.0], ['san-diego', 18.0, 0.0, 18.0, 18.0, 1.0]]
"""
return self._fx
@fx.setter
def fx(self, value: int | float | Expression):
self._fx[...] = value
@property
def prior(self):
"""
Branching Priority.
This corresponds to the `.prior` suffix in GAMS. Allows identifying a priority for
branching on discrete variables. Valid only for discrete variable types (integer, binary).
Returns
-------
ImplicitParameter
Examples
--------
>>> import gamspy as gp
>>> import numpy as np
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego"])
>>> d = gp.Parameter(m, name="d", domain=i, records=np.array([7, 18]))
>>> x = gp.Variable(m, name="x", domain=i, type="integer")
>>> x.prior[i] = d[i]
>>> x.records.values.tolist()
[['seattle', 0.0, 0.0, 0.0, inf, 7.0], ['san-diego', 0.0, 0.0, 0.0, inf, 18.0]]
"""
return self._prior
@prior.setter
def prior(self, value: int | float | Expression):
if self.type not in ("integer", "binary"):
raise ValidationError("Priorities can only be used on discrete variables.")
self._prior[...] = value
@property
def stage(self):
"""
Branching Stage.
This corresponds to the `.stage` suffix in GAMS. Used in stochastic programming or
advanced branching strategies.
Returns
-------
ImplicitParameter
Examples
--------
>>> import gamspy as gp
>>> import numpy as np
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego"])
>>> d = gp.Parameter(m, name="d", domain=i, records=np.array([7, 18]))
>>> x = gp.Variable(m, name="x", domain=i, type="integer")
>>> x.stage[i] = d[i]
>>> x.records.values.tolist()
[['seattle', 0.0, 0.0, 0.0, inf, 7.0], ['san-diego', 0.0, 0.0, 0.0, inf, 18.0]]
"""
return self._stage
@stage.setter
def stage(self, value: int | float | Expression):
self._stage[...] = value
[docs]
def computeInfeasibilities(self) -> pd.DataFrame | None:
"""
Computes infeasibilities of the variable.
Checks if the level value `.l` lies outside the bounds `.lo` and `.up`
and returns a DataFrame containing the violations.
Returns
-------
pd.DataFrame | None
DataFrame showing the infeasible records.
Examples
--------
>>> import gamspy as gp
>>> import numpy as np
>>> m = gp.Container()
>>> x = gp.Variable(m, name="x")
>>> x.l[...] = -10
>>> x.lo[...] = 5
>>> x.computeInfeasibilities().values.tolist()
[[-10.0, 0.0, 5.0, inf, 1.0, 15.0]]
"""
return utils._calculate_infeasibilities(self)
[docs]
def getVariableListing(
self,
n: int | None = None,
filters: list[list[str]] | None = None,
) -> str:
"""
Returns the variable listing (log output) from the last solve.
This requires the model to have been solved with the `variable_listing_limit` option enabled.
Parameters
----------
n : int, optional
Maximum number of variables to return.
filters : list[list[str]], optional
Filters to select specific elements for the listing.
The list size must match the variable's dimension.
Returns
-------
str
The text listing of the variable's status and values.
Raises
------
ValidationError
If the model was not solved with `variable_listing_limit`.
ValidationError
If the filter size does not match the variable dimension.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, records=["item1", "item2"])
>>> v = gp.Variable(m, domain=i)
>>> z = gp.Variable(m)
>>> e = gp.Equation(m, domain=i)
>>> e[i] = v[i] * z >= 5
>>> model = gp.Model(m, "test", equations=[e], problem="NLP", sense="MIN", objective=z)
>>> summary = model.solve(options=gp.Options(variable_listing_limit=10))
>>> print(v.getVariableListing())
v(item1)
(.LO, .L, .UP, .M = -INF, 0, +INF, 0)
(0) e(item1)
<BLANKLINE>
v(item2)
(.LO, .L, .UP, .M = -INF, 0, +INF, 0)
(0) e(item2)
<BLANKLINE>
"""
if not hasattr(self, "_column_listing"):
raise ValidationError(
"The model must be solved with `variable_listing_limit` option for this functionality to work."
)
listings = self._column_listing if filters is None else []
if filters is not None:
for listing in self._column_listing:
lhs, *_ = listing.split("\n")
# symbol(elem1, elem2)
_, domain = lhs[:-1].split("(")
sets = domain.split(",") # ["elem1", "elem2"]
if len(filters) != len(sets):
raise ValidationError(
f"Filter size {len(filters)} must be equal to the domain size {len(sets)}"
)
matches = 0
for user_filter, set in zip(filters, sets, strict=False):
if set in user_filter or user_filter == []:
matches += 1
# infeasibility = float(listing.split("INFES = ")[-1][:-6])
if matches == len(sets):
listings.append(listing)
return "\n".join(listings[:n])
@property
def records(self) -> pd.DataFrame | None:
"""
Returns the records (data) of the Variable as a DataFrame.
The DataFrame contains columns for the domain sets, and columns for
level, marginal, lower, upper, and scale.
Returns
-------
DataFrame | None
Examples
--------
>>> import gamspy as gp
>>> import numpy as np
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego"])
>>> d = gp.Parameter(m, name="d", domain=i, records=np.array([7, 18]))
>>> x = gp.Variable(m, name="x", domain=i)
>>> x.fx[i] = d[i]
>>> x.records.values.tolist()
[['seattle', 7.0, 0.0, 7.0, 7.0, 1.0], ['san-diego', 18.0, 0.0, 18.0, 18.0, 1.0]]
"""
return self._records
@records.setter
def records(self, records):
import pandas as pd
if records is not None and not isinstance(records, pd.DataFrame):
raise TypeError("Symbol 'records' must be type DataFrame")
# set records
self._records = records
self._requires_state_check = True
self._modified = True
self.container._requires_state_check = True
self.container.modified = True
if self._records is not None and self._domain_forwarding:
self._domainForwarding()
# reset state check flags for all symbols in the container
for _, symbol in self.container.data.items():
symbol._requires_state_check = True
def __hash__(self):
return id(self)
def _setRecords(self, records: Any, *, uels_on_axes: bool = False) -> None:
super().setRecords(records, uels_on_axes)
if gp.get_option("DROP_DOMAIN_VIOLATIONS"):
if self.hasDomainViolations():
self._domain_violations = self.getDomainViolations()
self.dropDomainViolations()
else:
self._domain_violations = None
[docs]
def setRecords(self, records: Any, uels_on_axes: bool = False) -> None:
"""
Sets the records (data) of the Variable.
This is a convenience method to load data. It accepts various input formats.
If `uels_on_axes=True`, it assumes domain information is in the pandas axes.
Parameters
----------
records : Any
The data to load (e.g., list, numpy array, DataFrame).
uels_on_axes : bool, optional
If True, assumes domain elements are in the axes of the DataFrame. Default is False.
Examples
--------
>>> import gamspy as gp
>>> import numpy as np
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego"])
>>> x = gp.Variable(m, name="x", domain=i)
>>> x.setRecords(records=np.array([7, 18]))
>>> x.records.values.tolist()
[['seattle', 7.0, 0.0, -inf, inf, 1.0], ['san-diego', 18.0, 0.0, -inf, inf, 1.0]]
"""
self._setRecords(records, uels_on_axes=uels_on_axes)
self.container._synch_with_gams()
self._winner = "python"
@property
def type(self):
return self._type
@type.setter
def type(self, var_type: str | VariableType):
"""
The type of variable; [binary, integer, positive, negative, free, sos1, sos2, semicont, semiint]
Parameters
----------
var_type : str
The type of variable
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego"])
>>> x = gp.Variable(m, name="x", domain=i, type="positive")
>>> x.type = "negative"
>>> x.type
'negative'
"""
given_type = cast_type(var_type)
gt.Variable.type.fset(self, given_type)
[docs]
def gamsRepr(self) -> str:
"""
Returns the string representation of this Variable in the GAMS language.
(e.g., 'x(i)').
Returns
-------
str
The GAMS string representation.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego"])
>>> x = gp.Variable(m, name="x", domain=i, type="positive")
>>> x.gamsRepr()
'x(i)'
"""
representation = self.name
if self.domain:
representation += self._get_domain_str(self._domain_forwarding)
return representation
[docs]
def getDeclaration(self) -> str:
"""
Returns the GAMS declaration statement for this Variable.
(e.g., 'Positive Variable x(i);').
Returns
-------
str
The GAMS declaration string.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, "i", records=['i1','i2'])
>>> v = gp.Variable(m, "v", domain=[i])
>>> v.getDeclaration()
'free Variable v(i) / /;'
"""
output = self.type + " "
output += f"Variable {self.gamsRepr()}"
if self.description:
output += ' "' + self.description + '"'
if self.records is None:
output += " / /"
output += ";"
return output
[docs]
def getAssignment(self) -> str:
"""
Returns the latest GAMS assignment statement for this Variable.
Returns
-------
str
The GAMS assignment string.
Raises
------
ValidationError
If the variable has not been assigned.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, "i", records=['i1','i2'])
>>> v = gp.Variable(m, "v", domain=[i])
>>> v.l[i] = 0;
>>> v.getAssignment()
'v.l(i) = 0;'
"""
if self._assignment is None:
raise ValidationError("Variable is not assigned!")
return self._assignment.getDeclaration()
def cast_type(type: str | VariableType) -> str:
if isinstance(type, str) and type.lower() not in VariableType.values():
raise ValueError(
f"Allowed variable types: {VariableType.values()} but found {type}."
)
if isinstance(type, VariableType):
type = type.value
return type.lower()
