from __future__ import annotations
import itertools
import os
import threading
import weakref
from typing import TYPE_CHECKING, Any, Literal, cast
import pandas as pd
from gams.core.gdx import GMS_DT_SET
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._records_ingestion import SetIngestor
from gamspy._symbols.base import DomainSymbol
from gamspy._symbols.equals import equals_set
from gamspy._symbols.generate_records import generate_records_set
from gamspy._symbols.pivot import pivot_set
from gamspy.exceptions import ValidationError
if TYPE_CHECKING:
from collections.abc import Sequence
from gamspy import Alias, Container, UniverseAlias
from gamspy._algebra.condition import Condition
from gamspy._algebra.expression import Expression
from gamspy._algebra.operation import Operation
from gamspy._symbols.implicits import ImplicitParameter, ImplicitSet
from gamspy._types import DomainType, IndexType, OperableType, SetRecordsType
from gamspy.math.misc import MathOp
class SetMixin:
@property
def pos(self: Set | Alias) -> ImplicitParameter:
"""
Returns the element position in the current set, starting with 1.
This attribute corresponds to the `.pos` attribute in GAMS.
Returns
-------
ImplicitParameter
The position of the element.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego", "new-york"], description="canning plants")
>>> i.pos.records.values.tolist()
[['seattle', 'position', 1.0], ['san-diego', 'position', 2.0], ['new-york', 'position', 3.0]]
"""
return implicits.ImplicitParameter(self, f"{self.name}.pos")
@property
def ord(self: Set | Alias) -> ImplicitParameter:
"""
Same as .pos but for ordered sets only.
Returns
-------
ImplicitParameter
The ordinal position of the element.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego", "new-york"], description="canning plants")
>>> i.ord.records.values.tolist()
[['seattle', 'order', 1.0], ['san-diego', 'order', 2.0], ['new-york', 'order', 3.0]]
"""
return implicits.ImplicitParameter(self, f"{self.name}.ord")
@property
def off(self: Set | Alias) -> ImplicitParameter:
"""
Returns the element position in the current set minus 1.
Mathematically: `.off` = `.pos` - 1.
This attribute corresponds to the `.off` attribute in GAMS.
Returns
-------
ImplicitParameter
The offset position of the element.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego", "new-york"], description="canning plants")
>>> i.off.records.values.tolist()
[['san-diego', 'off', 1.0], ['new-york', 'off', 2.0]]
"""
return implicits.ImplicitParameter(self, f"{self.name}.off")
@property
def rev(self: Set | Alias) -> ImplicitParameter:
"""
Returns the reverse element position in the current set.
The value for the last element is 0, the penultimate is 1, and so on.
This attribute corresponds to the `.rev` attribute in GAMS.
Returns
-------
ImplicitParameter
The reverse position value.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego", "new-york"], description="canning plants")
>>> i.rev.records.values.tolist()
[['seattle', 'reverse', 2.0], ['san-diego', 'reverse', 1.0]]
"""
return implicits.ImplicitParameter(self, f"{self.name}.rev")
@property
def uel(self: Set | Alias) -> ImplicitParameter:
"""
Returns the element position in the global Unique Element List (UEL).
This attribute corresponds to the `.uel` attribute in GAMS.
Returns
-------
ImplicitParameter
The position index in the UEL table.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego", "new-york"], description="canning plants")
>>> i.uel.records.values.tolist()
[['seattle', 'uel_position', 1.0], ['san-diego', 'uel_position', 2.0], ['new-york', 'uel_position', 3.0]]
"""
return implicits.ImplicitParameter(self, f"{self.name}.uel")
@property
def len(self: Set | Alias) -> ImplicitParameter:
"""
Returns the length of the set element name (count of characters).
This attribute corresponds to the `.len` attribute in GAMS.
Returns
-------
ImplicitParameter
The character count of the element name.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego", "new-york"], description="canning plants")
>>> i.len.records.values.tolist()
[['seattle', 'length', 7.0], ['san-diego', 'length', 9.0], ['new-york', 'length', 8.0]]
"""
return implicits.ImplicitParameter(self, f"{self.name}.len")
@property
def tlen(self: Set | Alias) -> ImplicitParameter:
"""
Returns the length of the set element explanatory text (count of characters).
This attribute corresponds to the `.tlen` attribute in GAMS.
Returns
-------
ImplicitParameter
The character count of the element text.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=[("seattle", "Wisconsin"), ("san-diego", ""), ("new-york", " ")], description="canning plants")
>>> i.tlen.records.values.tolist()
[['seattle', 'text_length', 9.0], ['new-york', 'text_length', 1.0]]
"""
return implicits.ImplicitParameter(self, f"{self.name}.tlen")
@property
def val(self: Set | Alias) -> ImplicitParameter:
"""
Returns the numerical value if the set element name is a number.
If the element is not a number, this attribute is undefined and may result in an error or ignored record.
This attribute corresponds to the `.val` attribute in GAMS.
Returns
-------
ImplicitParameter
The numerical value of the element name.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["12", "20", "-13.4"], description="canning plants")
>>> i.val.records.values.tolist()
[['12', 'value', 12.0], ['20', 'value', 20.0], ['-13.4', 'value', -13.4]]
"""
return implicits.ImplicitParameter(self, f"{self.name}.val")
@property
def tval(self: Set | Alias) -> ImplicitParameter:
"""
Returns the numerical value if the set element text is a number.
If the element text is not a number, this attribute is undefined.
This attribute corresponds to the `.tval` attribute in GAMS.
Returns
-------
ImplicitParameter
The numerical value of the element text.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=[("seattle", "12"), ("san-diego", ""), ("new-york", "-13.4")], description="canning plants")
>>> i.tval.records.values.tolist()
[['seattle', 'text_value', 12.0], ['new-york', 'text_value', -13.4]]
"""
return implicits.ImplicitParameter(self, f"{self.name}.tval")
@property
def first(self: Set | Alias) -> ImplicitParameter:
"""
Returns 1 if the element is the first in the set, otherwise 0.
This attribute corresponds to the `.first` attribute in GAMS.
Returns
-------
ImplicitParameter
An implicit parameter with value 1 for the first element.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego", "new-york"], description="canning plants")
>>> i.first.records.values.tolist()
[['seattle', 'is_first', 1.0]]
"""
return implicits.ImplicitParameter(self, f"{self.name}.first")
@property
def last(self: Set | Alias) -> ImplicitParameter:
"""
Returns 1 if the element is the last in the set, otherwise 0.
This attribute corresponds to the `.last` attribute in GAMS.
Returns
-------
ImplicitParameter
An implicit parameter with value 1 for the last element.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego", "new-york"], description="canning plants")
>>> i.last.records.values.tolist()
[['new-york', 'is_last', 1.0]]
"""
return implicits.ImplicitParameter(self, f"{self.name}.last")
def lag(
self: Set | Alias,
n: OperableType,
type: Literal["linear", "circular"] = "linear",
) -> ImplicitSet:
"""
Shifts the values of a Set or Alias by `n` positions to the left (lag).
Parameters
----------
n : OperableType
The number of positions to shift. Can be an integer or a GAMS symbol.
type : 'linear' or 'circular', optional
The type of lag to perform:
- 'linear' (default): Elements shifted out of bounds are dropped.
- 'circular': Elements shifted out of bounds wrap around to the end.
Returns
-------
ImplicitSet
The shifted set expression.
Raises
------
ValueError
If `type` is not 'linear' or 'circular'.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> t = gp.Set(m, name="t", description="time sequence", records=[f"y-{x}" for x in range(1987, 1992)])
>>> a = gp.Parameter(m, name="a", domain=[t])
>>> b = gp.Parameter(m, name="b", domain=[t])
>>> a[t] = 1986 + gp.Ord(t)
>>> b[t] = a[t.lag(1, "linear")]
>>> b.records.values.tolist()
[['y-1988', 1987.0], ['y-1989', 1988.0], ['y-1990', 1989.0], ['y-1991', 1990.0]]
"""
jump = n if isinstance(n, int) else n.gamsRepr() # type: ignore
if type == "circular":
return implicits.ImplicitSet(self, name=self.name, extension=f" -- {jump}")
if type == "linear":
return implicits.ImplicitSet(self, name=self.name, extension=f" - {jump}")
raise ValueError("Lag type must be linear or circular")
def lead(
self: Set | Alias,
n: OperableType,
type: Literal["linear", "circular"] = "linear",
) -> ImplicitSet:
"""
Shifts the values of a Set or Alias by `n` positions to the right (lead).
Parameters
----------
n : OperableType
The number of positions to shift. Can be an integer or a GAMS symbol.
type : 'linear' or 'circular', optional
The type of lead to perform:
- 'linear' (default): Elements shifted out of bounds are dropped.
- 'circular': Elements shifted out of bounds wrap around to the beginning.
Returns
-------
ImplicitSet
The shifted set expression.
Raises
------
ValueError
If `type` is not 'linear' or 'circular'.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> t = gp.Set(m, name="t", description="time sequence", records=[f"y-{x}" for x in range(1987, 1992)])
>>> a = gp.Parameter(m, name="a", domain=[t])
>>> c = gp.Parameter(m, name="c", domain=[t])
>>> a[t] = 1986 + gp.Ord(t)
>>> c[t.lead(2, "linear")] = a[t]
>>> c.records.values.tolist()
[['y-1989', 1987.0], ['y-1990', 1988.0], ['y-1991', 1989.0]]
"""
jump = n if isinstance(n, int) else f"({n.gamsRepr()})" # type: ignore
if type == "circular":
return implicits.ImplicitSet(self, name=self.name, extension=f" ++ {jump}")
if type == "linear":
return implicits.ImplicitSet(self, name=self.name, extension=f" + {jump}")
raise ValueError("Lead type must be linear or circular")
def sameAs(self: Set | Alias, other: Set | Alias | str) -> MathOp:
"""
Evaluates to True if the current set element is identical to the given symbol or string.
This corresponds to the `sameAs` operator in GAMS.
Parameters
----------
other : Set | Alias | str
The other set, alias, or string label to compare against.
Returns
-------
MathOp
A boolean expression that evaluates to True (1) if they match, False (0) otherwise.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego"])
>>> j = gp.Set(m, name="j", records=["new-york", "seattle"])
>>> attr = gp.Parameter(m, "attr", domain=[i, j])
>>> attr[i,j] = i.sameAs(j)
>>> attr.records.values.tolist()
[['seattle', 'seattle', 1.0]]
"""
assert isinstance(self, (gp.Set, gp.Alias))
assert isinstance(other, (gp.Set, gp.Alias, str))
return gp.math.same_as(self, other)
[docs]
class Set(operable.Operable, DomainSymbol, SetMixin):
"""
Represents a Set symbol in GAMS.
See https://gamspy.readthedocs.io/en/latest/user/basics/set.html for more details.
Parameters
----------
container : Container
The Container object that this set belongs to.
name : str, optional
Name of the set. If not provided, a unique name is generated automatically.
domain : Sequence[Set | Alias | str] | Set | Alias | str, optional
The domain of the set. Can be a list of other Sets/Aliases, a single Set/Alias,
or strings representing set names. Use "*" for the universe set. Default is ["*"].
is_singleton : bool, optional
If True, restricts the set to contain at most one element. Default is False.
records : pd.DataFrame | np.ndarray | list, optional
Initial elements to populate the set.
domain_forwarding : bool | list[bool], optional
If True, adding records to this set 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 set.
uels_on_axes : bool, optional
If True, implies that the Unique Element Labels (UELs) for the domain are
contained in the axes (index/columns) of the provided `records` object
(e.g., pandas DataFrame). Default is False.
is_miro_input : bool, optional
If True, flags this set as an input symbol for GAMS MIRO. Default is False.
is_miro_output : bool, optional
If True, flags this set as an output symbol for GAMS MIRO. Default is False.
Examples
--------
Simple set:
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = m.addSet("i", records=["a", "b"])
Indexed set:
>>> j = m.addSet("j", domain=i)
Singleton set:
>>> s = m.addSet("s", is_singleton=True, records=["s1"])
"""
@classmethod
def _constructor_bypass(
cls,
container: Container,
name: str,
domain: DomainType | None = None,
records: SetRecordsType | None = None,
description: str = "",
*,
is_singleton: bool = False,
) -> Set:
# create new symbol object
obj = object.__new__(cls)
# legacy gtp attributes
## set private properties directly
obj._container = cast(
"Container",
weakref.proxy(container)
if not isinstance(container, weakref.ProxyType)
else container,
)
obj.name = name
obj._domain = obj._normalize_domain(obj._container, domain, default="*")
obj._domain_forwarding = False
obj._description = description
obj._records = records
obj._is_singleton = is_singleton
obj._gams_type = GMS_DT_SET
obj._gams_subtype = 1 if obj.is_singleton else 0
obj._container._data.update({name: obj})
## gamspy attributes
obj._domain_violations = None
obj.where = condition.Condition(obj)
obj._latex_name = name.replace("_", r"\_")
obj._container._add_statement(obj)
obj._metadata = {}
obj._should_load_from_gams = False
obj._should_unload_to_gams = False
# miro support
obj._is_miro_input = False
obj._is_miro_output = False
return obj
def __new__(
cls,
container: Container | None = None,
name: str | None = None,
domain: DomainType | None = None,
is_singleton: bool = False,
records: SetRecordsType | None = None,
domain_forwarding: bool | list[bool] = False,
description: str = "",
uels_on_axes: bool = False,
is_miro_input: bool = False,
is_miro_output: bool = False,
):
if container is not None and not isinstance(container, gp.Container):
raise TypeError(
f"Container must of type `Container` but found {type(container)}"
)
if name is None:
return object.__new__(cls)
else:
if not isinstance(name, str):
raise TypeError(f"Name must of type `str` but found {type(name)}")
try:
if not container:
container = gp._ctx_managers[
(os.getpid(), threading.get_native_id())
]
symbol = container._data[name]
except KeyError:
return object.__new__(cls)
if isinstance(symbol, cls):
return symbol
raise TypeError(
f"Cannot overwrite symbol `{name}` in container"
" because it is not a Set object)"
)
def __init__(
self,
container: Container | None = None,
name: str | None = None,
domain: DomainType | None = None,
is_singleton: bool = False,
records: SetRecordsType | None = None,
domain_forwarding: bool | list[bool] = False,
description: str = "",
uels_on_axes: bool = False,
is_miro_input: bool = False,
is_miro_output: bool = False,
):
self._metadata: dict[str, Any] = {}
if (is_miro_input or is_miro_output) and name is None:
raise ValidationError("Please specify a name for miro symbols.")
self._is_miro_input = is_miro_input
self._is_miro_output = is_miro_output
self._is_miro_symbol = is_miro_input or is_miro_output
self._domain_violations = None
# does symbol exist
has_symbol = False
if isinstance(getattr(self, "container", None), gp.Container):
has_symbol = True
if has_symbol:
domain = self._normalize_domain(self.container, domain, default="*")
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.is_singleton != is_singleton:
raise ValueError(
"Cannot overwrite symbol in container unless"
" 'is_singleton' is left unchanged"
)
if self._domain_forwarding != domain_forwarding:
raise ValueError(
"Cannot overwrite symbol in container unless"
" 'domain_forwarding' is left unchanged"
)
# reset some properties
self._records: pd.DataFrame | None = None
# only set records if records are provided
previous_state = self._container._options.miro_protect
self._container._options.miro_protect = False
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("Set requires a container.") from e
self._container = cast("Container", weakref.proxy(container))
self._assignment: Expression | None = None
if name is not None:
name = validation.validate_name(name)
if is_miro_input or is_miro_output:
name = name.lower()
else:
name = container._get_symbol_name(prefix="s")
self.name = name
domain = self._normalize_domain(self.container, domain, default="*")
self._domain = self._validate_domain(domain)
self._singleton_check(is_singleton, records, domain)
self._is_singleton = is_singleton
self._domain_forwarding = domain_forwarding
self._description = description
self._records = None
self._gams_type = GMS_DT_SET
self._gams_subtype = 1 if self._is_singleton else 0
self.where = condition.Condition(self)
self._latex_name = self.name.replace("_", r"\_")
self._should_load_from_gams = False
self._should_unload_to_gams = False
self._container._data.update({name: self})
if is_miro_input:
self._already_loaded = False
self._container._miro_input_symbols.append(self.name)
if is_miro_output:
self._container._miro_output_symbols.append(self.name)
validation.validate_container(self, self._domain)
self._container._add_statement(self)
previous_state = self._container._options.miro_protect
self._container._options.miro_protect = False
if records is not None:
self.setRecords(records, uels_on_axes=uels_on_axes)
else:
if self._is_miro_symbol:
self._should_unload_to_gams = True
self._container._synch_with_gams()
self._container._options.miro_protect = previous_state
def _serialize(self) -> dict:
info: dict[str, Any] = {
"_domain_forwarding": self._domain_forwarding,
"_is_miro_input": self._is_miro_input,
"_is_miro_output": self._is_miro_output,
"_metadata": self._metadata,
}
if self._assignment is not None:
info["_assignment"] = self._assignment.getDeclaration()
return info
def _deserialize(self, info: dict) -> None:
# Set attributes
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.name])
self._domain = new_domain
def __getitem__(self, indices: IndexType) -> ImplicitSet:
domain = validation.validate_domain(self, indices)
return implicits.ImplicitSet(self, name=self.name, domain=domain)
def __setitem__(
self,
indices: IndexType,
rhs: Expression | Operation | Condition | ImplicitSet | bool | str,
):
# self[domain] = rhs
domain = validation.validate_domain(self, indices)
if isinstance(rhs, bool):
rhs = "yes" if rhs is True else "no"
statement = expression.Expression(
implicits.ImplicitSet(self, name=self.name, domain=domain),
"=",
rhs,
)
# Cannot validate definition if we are in a gp.Loop since the control indices can be provided by the gp.Loop
if not self._container._in_loop:
statement._validate_definition(utils._unpack(domain))
self._container._add_statement(statement)
self._assignment = statement
self._container._synch_with_gams()
self._should_load_from_gams = True
def __repr__(self) -> str:
return f"Set(name='{self.name}', domain={self.domain})"
def __hash__(self):
return id(self)
def _singleton_check(
self,
is_singleton: bool,
records: SetRecordsType | None,
domain: Sequence[Set | Alias | UniverseAlias | Literal["*"]],
):
if is_singleton:
if records is not None and len(records) != 1:
raise ValidationError("Singleton set records size must be one.")
if len(domain) != 1:
raise ValidationError(
f"Length of the domain of the singleton set must be 1 but found {len(domain)}"
)
@property
def _attributes(self):
return ["element_text"]
@property
def summary(self) -> dict:
return {
"name": self.name,
"description": self.description,
"is_singleton": self.is_singleton,
"domain": self.domain_names,
"domain_type": self.domain_type,
"dimension": self.dimension,
"number_records": self.number_records,
}
[docs]
def toList(self, *, include_element_text: bool = False) -> list:
"""
Converts the records of the Set to a Python list.
Parameters
----------
include_element_text : bool, optional
If True, includes the element explanatory text in the output. Defaults to False.
Returns
-------
list
A list of the set elements. If the set has dimension > 1, the elements are returned as tuples.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego"])
>>> i.toList()
['seattle', 'san-diego']
"""
from gamspy._symbols.utils import toListSet
return toListSet(self, include_element_text=include_element_text)
@property
def is_singleton(self) -> bool:
return self._is_singleton
[docs]
def pivot(
self,
index: str | list | None = None,
columns: str | list | None = None,
fill_value: int | float | str | None = None,
) -> pd.DataFrame:
"""
Convenience function to pivot records into a new shape (only symbols with >1D can be pivoted).
If index is None then it is set to dimensions [0..dimension-1]. If columns is None then it is
set to the last dimension. Missing values in the pivot will take the value provided by fill_value.
Parameters
----------
index : str | list | None, optional
Column(s) to use to make new frame's index.
columns : str | list | None, optional
Column(s) to use to make new frame's columns.
fill_value : int | float | str | None, optional
Value to use for missing values.
Returns
-------
pd.DataFrame
The pivoted DataFrame representing the set records.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego"])
>>> j = gp.Set(m, name="j", records=["new-york", "chicago"])
>>> # Pivot requires a symbol with dimension > 1
>>> ij = gp.Set(m, name="ij", domain=[i, j], records=[("seattle", "new-york")])
>>> df = ij.pivot()
"""
return pivot_set(self, index, columns, fill_value)
[docs]
def generateRecords(
self,
density: int | float | list | None = None,
seed: int | None = None,
) -> None:
"""
Automatically generates records for the Set based on a specified density.
Parameters
----------
density : int | float | list | None, optional
The target density for the generated records. Can be a single numeric value or a list. Provinig a list
allows users to specify a density per symbol dimension.
seed : int | None, optional
A random seed to ensure reproducibility of the generated records.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=range(10))
>>> a = gp.Parameter(m, name="a", domain=i)
>>> # Generate records with 50% density
>>> a.generateRecords(density=0.5, seed=42)
"""
generate_records_set(self, density, seed)
[docs]
def equals(
self,
other: Set | Alias,
*,
check_element_text: bool = True,
check_meta_data: bool = True,
) -> bool:
"""
Compares this Set with another Set or Alias for equality.
Parameters
----------
other : Set | Alias
The other Set or Alias object to compare against.
check_element_text : bool, optional
If True, includes the element explanatory text in the equality check. Defaults to True.
check_meta_data : bool, optional
If True, includes symbol metadata in the equality check. Defaults to True.
Returns
-------
bool
True if the sets are considered equal based on the given parameters, False otherwise.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["seattle", "san-diego"])
>>> j = gp.Set(m, name="j", records=["seattle", "san-diego"])
>>> i.equals(j, check_meta_data=False)
True
"""
return equals_set(self, other, check_element_text, check_meta_data)
@property
def records(self) -> pd.DataFrame | None:
"""
Records of the Set
Returns
-------
DataFrame | None
Examples
--------
>>> import gamspy as gp
>>> import numpy as np
>>> m = gp.Container()
>>> i = gp.Set(m, name="i")
>>> i.setRecords(["seattle", "san-diego"])
>>> i.records.values.tolist()
[['seattle', ''], ['san-diego', '']]
"""
if self._should_load_from_gams:
self._load_from_gams()
return self._records
@records.setter
def records(self, records: pd.DataFrame | None):
if (
hasattr(self, "_is_miro_input")
and self._is_miro_input
and self._container._options.miro_protect
):
raise ValidationError(
"Cannot assign to protected miro input symbols. `miro_protect`"
" attribute of the container can be set to False to allow"
" assigning to MIRO input symbols"
)
if records is not None and not isinstance(records, pd.DataFrame):
raise TypeError("Symbol 'records' must be type DataFrame")
self._records = records
self._should_unload_to_gams = True
self._handle_domain_forwarding()
def _setRecords(self, records: Any, *, uels_on_axes: bool = False) -> None:
SetIngestor(self).ingest(records, uels_on_axes=uels_on_axes)
self._handle_domain_violations()
[docs]
def setRecords(
self, records: SetRecordsType | None, uels_on_axes: bool = False
) -> None:
"""
Sets the records (elements) of the Set.
This is a convenience method to load data into the set. It handles various
input formats like lists and pandas DataFrames.
Parameters
----------
records : pd.DataFrame | pd.Series | Sequence
The data to load. Common formats:
- List of strings: `['i1', 'i2']`
- List of tuples (for multi-dimensional sets): `[('a', '1'), ('b', '2')]`
- pandas DataFrame.
uels_on_axes : bool, optional
If True, assumes that the domain information is located in the axes
(index/columns) of the `records` object rather than the data values.
Use this when passing a DataFrame where the indices represent the set elements.
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i")
>>> i.setRecords(["seattle", "san-diego"])
>>> i.records.values.tolist()
[['seattle', ''], ['san-diego', '']]
"""
if records is None:
self._container._add_statement(f"option clear={self.name};")
self._container._synch_with_gams()
self._records = None
return
self._setRecords(records, uels_on_axes=uels_on_axes)
self._container._synch_with_gams()
[docs]
def gamsRepr(self) -> str:
"""
Representation of this Set in GAMS language.
Returns
-------
str
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, "i", domain=["*"], records=['i1','i2'])
>>> i.gamsRepr()
'i'
"""
return self.name
[docs]
def getDeclaration(self) -> str:
"""
Declaration of the Set in GAMS
Returns
-------
str
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, "i", records=['i1','i2'])
>>> i.getDeclaration()
'Set i(*);'
"""
output = f"Set {self.name}"
if self.is_singleton:
output = f"Singleton {output}"
output += self._get_domain_str(self._domain_forwarding)
if self.description:
output += f' "{self.description}"'
if self._records is None:
output += " / /"
output += ";"
return output
[docs]
def getAssignment(self) -> str:
"""
Latest assignment to the Set in GAMS
Returns
-------
str
Raises
------
ValueError
When type is not circular or linear
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, "i", records=['i1','i2'])
>>> i['i1'] = False
>>> i.getAssignment()
'i("i1") = no;'
"""
if self._assignment is None:
raise ValidationError("Set was not assigned!")
return self._assignment.getDeclaration()
