Parameter#

Bases: Parameter, Operable, Symbol

Represents a parameter symbol in GAMS. https://www.gams.com/latest/docs/UG_DataEntry.html#UG_DataEntry_Parameters

Parameters:

containerContainer: Container of the parameter.
namestr, optional: Name of the parameter. Name is autogenerated by default.
domainlist[Set | Alias | str] | Set | Alias | str, optional: Domain of the parameter.
recordsint | float | pd.DataFrame | np.ndarray | list, optional: Records of the parameter.
domain_forwardingbool, optional: Whether the parameter forwards the domain. See: https://gams.com/latest/docs/UG_SetDefinition.html#UG_SetDefinition_ImplicitSetDefinition
descriptionstr, optional: Description of the parameter.
uels_on_axesbool: Assume that symbol domain information is contained in the axes of the given records.
is_miro_inputbool: Whether the symbol is a GAMS MIRO input symbol. See: https://gams.com/miro/tutorial.html
is_miro_outputbool: Whether the symbol is a GAMS MIRO output symbol. See: https://gams.com/miro/tutorial.html

Attributes:

container: Container of the symbol
description: Description of the symbol
dimension: The dimension of symbol
domain: List of domains given either as string (* for universe set) or as reference to the Set/Alias object
domain_forwarding: A boolean indicating whether domain forwarding is enabled
domain_labels: The column headings for the records DataFrame
domain_names: String version of domain names
domain_type: State of the domain links
is_scalar: Returns True if the len(self.domain) = 0
modified: Flag that identifies if the symbol has been modified
name: Name of symbol
number_records: Number of records
records: Records of the Parameter
shape: Returns a tuple describing the array dimensions if records were converted with .toDense()
summary: Returns a dict of only the metadata

Methods

`countEps`([columns])	Counts total number of SpecialValues.EPS across columns
`countNA`([columns])	Counts total number of SpecialValues.NA across columns
`countNegInf`([columns])	Counts total number of SpecialValues.NegInf across columns
`countPosInf`([columns])	Counts total number of SpecialValues.PosInf across columns
`countUndef`([columns])	Counts total number of SpecialValues.Undef across columns
`dropDefaults`()	Main convenience method to remove zero values from the symbol's records.
`dropEps`()	Main convenience method to remove epsilon values from the symbol's records.
`dropMissing`()	Main convenience method to remove missing values from the symbol's records.
`dropNA`()	Main convenience method to remove NA (Not Available) values from the symbol's records.
`dropUndef`()	Main convenience method to remove undefined values from the symbol's records.
`dropZeros`()	Main convenience method to remove zero values from the symbol's records.
`equals`(other[, check_uels, check_meta_data, ...])	Used to compare the symbol to another symbol
`findEps`([column])	Find positions of SpecialValues.EPS in value column
`findNA`([column])	Find positions of SpecialValues.NA in value column
`findNegInf`([column])	Find positions of SpecialValues.NegInf in value column
`findPosInf`([column])	Find positions of SpecialValues.PosInf in value column
`findSpecialValues`(values[, column])	Find positions of specified values in records columns
`findUndef`([column])	Find positions of SpecialValues.Undef in value column
`gamsRepr`()	Representation of this Parameter in GAMS language.
`generateRecords`([density, func, seed])	Convenience method to set standard pandas.DataFrame formatted records given domain set information.
`getDeclaration`()	Declaration of the Parameter in GAMS
`getMaxAbsValue`([columns])	Get the maximum absolute value across chosen columns
`getMaxValue`([columns])	Get the maximum value across chosen columns
`getMeanValue`([columns])	Get the mean value across chosen columns
`getMinValue`([columns])	Get the minimum value across chosen columns
`getSparsity`()	Get the sparsity of the symbol w.r.t the cardinality
`isValid`([verbose, force])	Checks if the symbol is in a valid format
`pivot`([index, columns, fill_value])	Convenience function to pivot records into a new shape (only symbols with >1D can be pivoted)
`setRecords`(records[, uels_on_axes])	Main convenience method to set standard pandas.DataFrame formatted records.
`toDense`()	Convert symbol to a dense numpy.array format
`toDict`([orient])	convenience method to return symbol records as a python dictionary, orient can take values natural or columns and will control the shape of the dict.
`toList`()	Convenience method to return symbol records as a python list
`toSparseCoo`()	Convert symbol to a sparse COOrdinate numpy.array format
`toValue`()	Convenience method to return symbol records as a python float.
`whereMax`([column])	Find the domain entry of records with a maximum value (return first instance only)
`whereMaxAbs`([column])	Find the domain entry of records with a maximum absolute value (return first instance only)
`whereMin`([column])	Find the domain entry of records with a minimum value (return first instance only)

Examples

>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, "i", records=['i1','i2'])
>>> a = gp.Parameter(m, "a", [i], records=[['i1',1],['i2',2]])

property description#: Description of the symbol

property modified#: Flag that identifies if the symbol has been modified

property records#

Records of the Parameter

Returns:

DataFrame

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])
>>> d.setRecords(np.array([10, 25]))
>>> d.records.values.tolist()
[['seattle', 10.0], ['san-diego', 25.0]]

setRecords(records: Any, uels_on_axes: bool = False) → None[source]#

Main convenience method to set standard pandas.DataFrame formatted records. If uels_on_axes=True setRecords will assume that all domain information is contained in the axes of the pandas object – data will be flattened (if necessary).

Parameters:

recordsAny
uels_on_axesbool, optional

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', '']]

gamsRepr() → str[source]#

Representation of this Parameter in GAMS language.

Returns:

str

Examples

>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, "i", records=['i1','i2'])
>>> d = gp.Parameter(m, "d", domain=i)
>>> d.gamsRepr()
'd(i)'

property container#: Container of the symbol

countEps(columns: str | List[str] | None = None) → int#

Counts total number of SpecialValues.EPS across columns

Parameters:

columnsstr | List[str], optional: Columns to count special values in, by default None

Returns:

Total number of SpecialValues.EPS across columns

countNA(columns: str | List[str] | None = None) → int#

Counts total number of SpecialValues.NA across columns

Parameters:

columnsstr | List[str], optional: Columns to count special values in, by default None

Returns:

Total number of SpecialValues.NA across columns

countNegInf(columns: str | List[str] | None = None) → int#

Counts total number of SpecialValues.NegInf across columns

Parameters:

columnsstr | List[str], optional: Columns to count special values in, by default None

Returns:

Total number of SpecialValues.NegInf across columns

countPosInf(columns: str | List[str] | None = None) → int#

Counts total number of SpecialValues.PosInf across columns

Parameters:

columnsstr | List[str], optional: Columns to count special values in, by default None

Returns:

Total number of SpecialValues.PosInf across columns

countUndef(columns: str | List[str] | None = None) → int#

Counts total number of SpecialValues.Undef across columns

Parameters:

columnsstr | List[str], optional: Columns to count special values in, by default None

Returns:

Total number of SpecialValues.Undef across columns

property dimension#: The dimension of symbol

property domain#: List of domains given either as string (* for universe set) or as reference to the Set/Alias object

property domain_forwarding#: A boolean indicating whether domain forwarding is enabled

property domain_labels#: The column headings for the records DataFrame

property domain_names#: String version of domain names

property domain_type#: State of the domain links

dropDefaults() → None[source]#: Main convenience method to remove zero values from the symbol’s records.

dropEps() → None[source]#: Main convenience method to remove epsilon values from the symbol’s records.

dropMissing() → None[source]#: Main convenience method to remove missing values from the symbol’s records.

dropNA() → None[source]#: Main convenience method to remove NA (Not Available) values from the symbol’s records.

dropUndef() → None[source]#: Main convenience method to remove undefined values from the symbol’s records.

dropZeros() → None[source]#: Main convenience method to remove zero values from the symbol’s records.

equals(other: Parameter, check_uels: bool = True, check_meta_data: bool = True, rtol: int | float | None = None, atol: int | float | None = None, verbose: bool = False) → bool#

Used to compare the symbol to another symbol

Parameters:

otherParameter: Other Symbol to compare with
check_uelsbool, optional: If True, check both used and unused UELs and confirm same order, otherwise only check used UELs in data and do not check UEL order. by default True
check_meta_databool, optional: If True, check that symbol name and description are the same, otherwise skip. by default True
rtolint | float, optional: relative tolerance, by default None
atolint | float, optional: absolute tolerance, by default None
verbosebool, optional: If True, will return an exception from the asserter describing the nature of the difference. by default False

Returns:

bool: True if symbols are equal, False otherwise

findEps(column: str | None = None) → DataFrame#

Find positions of SpecialValues.EPS in value column

Parameters:

columnstr, optional: Column to find the special values in, by default None

Returns:

pd.DataFrame: Dataframe containing special values

findNA(column: str | None = None) → DataFrame#

Find positions of SpecialValues.NA in value column

Parameters:

columnstr, optional: Column to find the special values in, by default None

Returns:

pd.DataFrame: Dataframe containing special values

findNegInf(column: str | None = None) → DataFrame#

Find positions of SpecialValues.NegInf in value column

Parameters:

columnstr, optional: Column to find the special values in, by default None

Returns:

pd.DataFrame: Dataframe containing special values

findPosInf(column: str | None = None) → DataFrame#

Find positions of SpecialValues.PosInf in value column

Parameters:

columnstr, optional: Column to find the special values in, by default None

Returns:

pd.DataFrame: Dataframe containing special values

findSpecialValues(values: float | List[float], column: str | None = None) → DataFrame#

Find positions of specified values in records columns

Parameters:

valuesfloat | List[float]: Values to look for
columnstr, optional: Column to find the special values in, by default None

Returns:

pd.DataFrame: Dataframe containing special values

findUndef(column: str | None = None) → DataFrame#

Find positions of SpecialValues.Undef in value column

Parameters:

columnstr, optional: Column to find the special values in, by default None

Returns:

pd.DataFrame: Dataframe containing special values

Convenience method to set standard pandas.DataFrame formatted records given domain set information. Will generate records with the Cartesian product of all domain sets.

Parameters:

densityint | float | list, optional: Takes any value on the interval [0,1]. If density is <1 then randomly selected records will be removed. density will accept a list of length dimension – allows users to specify a density per symbol dimension, by default None
funcCallable, optional: Functions to generate the records, by default None; numpy.random.uniform(0,1)
seedint, optional: Random number state can be set with seed argument, by default None

getDeclaration() → str[source]#

Declaration of the Parameter in GAMS

Returns:

str

Examples

>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, "i", records=['i1','i2'])
>>> a = gp.Parameter(m, "a", [i], records=[['i1',1],['i2',2]])
>>> a.getDeclaration()
'Parameter a(i);'

getMaxAbsValue(columns: str | List[str] | None = None) → float#

Get the maximum absolute value across chosen columns

Parameters:

columnsstr | List[str], optional: Columns to find maximum absolute values in, by default None

Returns:

float: Maximum absolute value

getMaxValue(columns: str | List[str] | None = None) → float#

Get the maximum value across chosen columns

Parameters:

columnsstr | List[str], optional: Columns to find maximum values in, by default None

Returns:

float: Maximum value

getMeanValue(columns: str | List[str] | None = None) → float#

Get the mean value across chosen columns

Parameters:

columnsstr | List[str], optional: Columns to find mean values in, by default None

Returns:

float: Mean value

getMinValue(columns: str | List[str] | None = None) → float#

Get the minimum value across chosen columns

Parameters:

columnsstr | List[str], optional: Columns to find minimum values in, by default None

Returns:

float: Minimum value

getSparsity() → float#

Get the sparsity of the symbol w.r.t the cardinality

Returns:

float: Sparsity of the symbol w.r.t the cardinality

isValid(verbose: bool = False, force: bool = False) → bool#

Checks if the symbol is in a valid format

Parameters:

verbosebool, optional: Throw exceptions if verbose=True, by default False
forcebool, optional: Recheck a symbol if force=True, by default False

Returns:

bool: True if a symbol is in valid format, False otherwise (throws exceptions if verbose=True)

property is_scalar: bool#

Returns True if the len(self.domain) = 0

Returns:

bool: True if the len(self.domain) = 0

property name#: Name of symbol

property number_records#: Number of records

Convenience function to pivot records into a new shape (only symbols with >1D can be pivoted)

Parameters:

indexstr | list, optional: If index is None then it is set to dimensions [0..dimension-1], by default None
columnsstr | list, optional: If columns is None then it is set to the last dimension, by default None
fill_valueint | float | str, optional: Missing values in the pivot will take the value provided by fill_value, by default None

Returns:

DataFrame: Pivoted records dataframe

property shape: tuple#

Returns a tuple describing the array dimensions if records were converted with .toDense()

Returns:

tuple: A tuple describing the records dimensions

property summary: dict#

Returns a dict of only the metadata

Returns:

dict: Outputs a dict of only the metadata

toDense() → ndarray | None[source]#

Convert symbol to a dense numpy.array format

Returns:

ndarray | None: A numpy array with symbol records, None if no records were assigned

Examples

>>> m = gt.Container()
>>> j = gt.Set(m, "j", records=["new-york", "chicago", "topeka"])
>>> s = gt.Parameter(m, "s", [j], records=np.array([3,4,5]))
>>> print(s.toDense())
[3. 4. 5.]

toDict(orient: str | None = None) → dict | None[source]#

convenience method to return symbol records as a python dictionary, orient can take values natural or columns and will control the shape of the dict. Must use orient=”columns” if attempting to set symbol records with setRecords

Parameters:

orientstr, optional: Takes ‘natural’ or ‘columns’, by default None which sets it to ‘natural’.

Returns:

dict | None: A dictionary with symbol records, None if no records were assigned

Examples

>>> m = gt.Container()
>>> j = gt.Set(m, "j", records=["new-york", "chicago", "topeka"])
>>> s = gt.Parameter(m, "s", [j], records=np.array([3,4,5]))
>>> print(s.toDict(orient="natural"))
{'new-york': 3.0, 'chicago': 4.0, 'topeka': 5.0}
>>> print(s.toDict(orient="columns"))
{'j': {0: 'new-york', 1: 'chicago', 2: 'topeka'}, 'value': {0: 3.0, 1: 4.0, 2: 5.0}}

toList() → list | None[source]#

Convenience method to return symbol records as a python list

Returns:

list | None: A list of symbol records, None if no records were assigned

toSparseCoo() → coo_matrix | None[source]#

Convert symbol to a sparse COOrdinate numpy.array format

Returns:

coo_matrix | None

toValue() → float | None[source]#

Convenience method to return symbol records as a python float. Only possible with scalar symbols.

Returns:

float | None: Scalar’s record, None if no record was assigned

whereMax(column: str | None = None) → List[str]#

Find the domain entry of records with a maximum value (return first instance only)

Parameters:

columnstr, optional: Columns to find maximum values in, by default None

Returns:

List[str]: List of symbol names where maximum values exist

whereMaxAbs(column: str | None = None) → List[str]#

Find the domain entry of records with a maximum absolute value (return first instance only)

Parameters:

columnstr, optional: Columns to find maximum absolute values in, by default None

Returns:

List[str]: List of symbol names where maximum absolute values exist

whereMin(column: str | None = None) → List[str]#

Find the domain entry of records with a minimum value (return first instance only)

Parameters:

columnstr, optional: Columns to find minimum values in, by default None

Returns:

List[str]: List of symbol names where minimum values exist