Parameter#

class gamspy.Parameter(container: Container, name: str, domain: list[str | Set] | None = None, records: Any | None = None, domain_forwarding: bool = False, description: str = '', uels_on_axes: bool = False, is_miro_input: bool = False, is_miro_output: bool = False, is_miro_table: bool = False)[source]#

Bases: Parameter, Operable, Symbol

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

Parameters:

containerContainer
namestr
domainlist, optional
recordsAny, optional
domain_forwardingbool, optional
descriptionstr, optional
uels_on_axesbool
is_miro_inputbool
is_miro_outputbool

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]])

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

`addUELs`(uels[, dimensions])	Adds UELs to the symbol dimensions.
`capitalizeUELs`([dimensions])	Will capitalize all UELs in the Container or a subset of specified symbols, can be chained with other *UELs string operations
`casefoldUELs`([dimensions])	Will casefold all UELs in the Container or a subset of specified symbols, can be chained with other *UELs string operations
`countDomainViolations`()	Returns the count of how many records contain at least one domain violation
`countDuplicateRecords`()	Returns the count of how many (case insensitive) duplicate records exist
`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.
`dropDomainViolations`()	drop records from the symbol that contain a domain violation
`dropDuplicateRecords`([keep])	Drop records with (case insensitive) duplicate domains from the symbol
`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
`findDomainViolations`()	Get a view of the records DataFrame that contain any domain violations
`findDuplicateRecords`([keep])	Get a view of the records DataFrame that contain any (case insensitive) duplicate domains – keep argument can take values of "first" (finds all duplicates while keeping the first instance as unique), "last" (finds all duplicates while keeping the last instance as unique), or False (finds all duplicates)
`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.
`getDomainViolations`()	Returns a list of DomainViolation objects if any (None otherwise)
`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
`getStatement`()	Statement of the Parameter definition
`getUELs`([dimensions, codes, ignore_unused, ...])	Gets UELs from symbol dimensions.
`hasDomainViolations`()	Returns True if there are domain violations in the records, returns False if not.
`hasDuplicateRecords`()	Returns True if there are (case insensitive) duplicate records in the symbol, returns False if not
`isValid`([verbose, force])	Checks if the symbol is in a valid format
`ljustUELs`(length[, fill_character, dimensions])	Will left justify all UELs in the symbol or a subset of specified dimensions, can be chained with other *UELs string operations
`lowerUELs`([dimensions])	Will lowercase all UELs in the parent symbol or a subset of specified dimensions in the parent symbol, can be chain with other *UELs string operations
`lstripUELs`([dimensions])	Will left strip whitespace from all UELs in the parent set or a subset of specified dimensions in the parent set, can be chain with other *UELs string operations
`pivot`([index, columns, fill_value])	Convenience function to pivot records into a new shape (only symbols with >1D can be pivoted)
`removeUELs`([uels, dimensions])	Removes UELs that appear in the symbol dimensions, If uels is None then remove all unused UELs (categories).
`renameUELs`(uels[, dimensions, allow_merge])	Renames UELs (case-sensitive) that appear in the symbol dimensions.
`reorderUELs`([uels, dimensions])	Reorders the UELs in the symbol dimensions.
`rjustUELs`(length[, fill_character, dimensions])	Will right justify all UELs in the symbol or a subset of specified dimensions, can be chained with other *UELs string operations
`rstripUELs`([dimensions])	Will right strip whitespace from all UELs in the parent set or a subset of specified dimensions in the parent set, can be chain with other *UELs string operations
`setRecords`(records[, uels_on_axes])	main convenience method to set standard pandas.DataFrame formatted records.
`setUELs`(uels[, dimensions, rename])	Set the UELs for symbol dimensions.
`stripUELs`([dimensions])	Will strip whitespace from all UELs in the parent set or a subset of specified dimensions in the parent set, can be chain with other *UELs string operations
`titleUELs`([dimensions])	Will title (capitalize all individual words) in all UELs in the Container or a subset of specified symbols, can be chained with other *UELs string operations
`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.
`upperUELs`([dimensions])	Will uppercase all UELs in the parent symbol or a subset of specified dimensions in the parent symbol, can be chain with other *UELs string operations
`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)

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

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

addUELs(uels: str | List[str], dimensions: int | list | None = None) → None#

Adds UELs to the symbol dimensions. If dimensions is None then add UELs to all dimensions. ** All trailing whitespace is trimmed **

Parameters:

uelsstr | List[str]: List of UELs
dimensionsint | list, optional: Symbol dimensions, by default None

capitalizeUELs(dimensions: int | List[int] | None = None)#

Will capitalize all UELs in the Container or a subset of specified symbols, can be chained with other *UELs string operations

Parameters:

dimensionsint | List[int], optional: Symbols’ dimensions, by default None

casefoldUELs(dimensions: int | List[int] | None = None)#

Will casefold all UELs in the Container or a subset of specified symbols, can be chained with other *UELs string operations

Parameters:

dimensionsint | List[int], optional: Symbols’ dimensions, by default None

property container#: Container of the symbol

countDomainViolations() → int#

Returns the count of how many records contain at least one domain violation

Returns:

int: Count of how many records contain at least one domain violation

countDuplicateRecords() → int#

Returns the count of how many (case insensitive) duplicate records exist

Returns:

int: Count of how many (case insensitive) duplicate records exist

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.

dropDomainViolations()#: drop records from the symbol that contain a domain violation

dropDuplicateRecords(keep: str | bool = 'first') → None#

Drop records with (case insensitive) duplicate domains from the symbol

Parameters:

keepstr | bool, optional: keep argument can take values of “first” (keeps the first instance of a duplicate record), “last” (keeps the last instance of a record), or False (drops all duplicates including the first and last), by default “first”

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

findDomainViolations() → DataFrame | None#

Get a view of the records DataFrame that contain any domain violations

Returns:

Optional[pd.DataFrame]: Records DataFrame that contain any domain violations

findDuplicateRecords(keep: str | bool = 'first') → DataFrame#

Get a view of the records DataFrame that contain any (case insensitive) duplicate domains – keep argument can take values of “first” (finds all duplicates while keeping the first instance as unique), “last” (finds all duplicates while keeping the last instance as unique), or False (finds all duplicates)

Parameters:

keepstr | bool, optional: Argument ‘keep’ must be either ‘first’ (returns duplicates except for the first occurrence), ‘last’ (returns duplicates except for the last occurrence), or False (returns all duplicates), by default “first”

Returns:

pd.DataFrame

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

gamsRepr() → str[source]#

Representation of this Parameter in GAMS language.

Returns:

str

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

getDomainViolations() → List[DomainViolation] | None#

Returns a list of DomainViolation objects if any (None otherwise)

Returns:

Optional[DomainViolation]: List of DomainViolation objects if any (None otherwise)

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

getUELs(dimensions: int | list | None = None, codes: int | list | None = None, ignore_unused: bool = False, unique_only: bool = False) → List[str]#

Gets UELs from symbol dimensions. If dimensions is None then get UELs from all dimensions (maintains order). The argument codes accepts a list of str UELs and will return the corresponding int; must specify a single dimension if passing codes.

Parameters:

dimensionsint | list, optional: Symbols’ dimensions, by default None
codesint | list, optional: Symbols’ codes, by default None
ignore_unusedbool, optional: Flag to ignore unused UELs, by default False
unique_onlybool, optional: Flag to check only unique UELs, by default False

Returns:

List[str]: Only UELs in the data if ignore_unused=True, otherwise return all UELs.

hasDomainViolations() → bool#

Returns True if there are domain violations in the records, returns False if not.

Returns:

bool: True if there are domain violations in the records, returns False if not.

hasDuplicateRecords() → bool#

Returns True if there are (case insensitive) duplicate records in the symbol, returns False if not

Returns:

bool: True if there are (case insensitive) duplicate records in the symbol, returns False if not

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

ljustUELs(length: int, fill_character: str | None = None, dimensions: int | List[int] | None = None)#

Will left justify all UELs in the symbol or a subset of specified dimensions, can be chained with other *UELs string operations

Parameters:

lengthint
fill_characterstr, optional: Characters to fill in the empty, by default None
dimensionsint | List[int], optional: Symbols’ dimensions, by default None

lowerUELs(dimensions: int | List[int] | None = None)#

Will lowercase all UELs in the parent symbol or a subset of specified dimensions in the parent symbol, can be chain with other *UELs string operations

Parameters:

dimensionsint | List[int], optional: Symbols’ dimensions, by default None

lstripUELs(dimensions: int | List[int] | None = None)#

Will left strip whitespace from all UELs in the parent set or a subset of specified dimensions in the parent set, can be chain with other *UELs string operations

Parameters:

dimensionsint | List[int], optional: Symbols’ dimensions, by default None

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

Removes UELs that appear in the symbol dimensions, If uels is None then remove all unused UELs (categories). If dimensions is None then operate on all dimensions.

Parameters:

uelsdict | list | str: List of UELs (case-sensitive) that appear in the symbol dimensions
dimensionsint | list, optional: Symbols’ dimensions, by default None

renameUELs(uels: dict | list | str, dimensions: int | list | None = None, allow_merge: bool = False) → None#

Renames UELs (case-sensitive) that appear in the symbol dimensions. If dimensions is None then operate on all dimensions of the symbol. ** All trailing whitespace is trimmed **

Parameters:

uelsdict | list | str: List of UELs (case-sensitive) that appear in the symbol dimensions
dimensionsint | list, optional: Symbols’ dimensions, by default None
allow_mergebool, optional: If True, the categorical object will be re-created to offer additional data flexibility. By default False

reorderUELs(uels: str | List[str] | None = None, dimensions: int | list | None = None) → None#

Reorders the UELs in the symbol dimensions. If uels is None, reorder UELs to data order and append any unused categories. If dimensions is None then reorder UELs in all dimensions of the symbol.

Parameters:

uelsstr | List[str], optional: List of UELs, by default None
dimensionsint | list, optional: Symbol dimensions, by default None

rjustUELs(length: int, fill_character: str | None = None, dimensions: int | List[int] | None = None)#

Will right justify all UELs in the symbol or a subset of specified dimensions, can be chained with other *UELs string operations

Parameters:

lengthint
fill_characterstr, optional: Characters to fill in the empty, by default None
dimensionsint | List[int], optional: Symbols’ dimensions, by default None

rstripUELs(dimensions: int | List[int] | None = None)#

Will right strip whitespace from all UELs in the parent set or a subset of specified dimensions in the parent set, can be chain with other *UELs string operations

Parameters:

dimensionsint | List[int], optional: Symbols’ dimensions, by default None

setUELs(uels: str | List[str], dimensions: int | list | None = None, rename: bool = False) → None#

Set the UELs for symbol dimensions. If dimensions is None then set UELs for all dimensions. ** All trailing whitespace is trimmed **

Parameters:

uelsstr | List[str]: List of UELs (case-sensitive) that appear in the symbol dimensions
dimensionsint | list, optional: Symbols’ dimensions, by default None
renamebool, optional: If True, the old UEL names will be renamed with the new UEL names. By default False

property shape: tuple#

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

Returns:

tuple: A tuple describing the records dimensions

stripUELs(dimensions: int | List[int] | None = None)#

Will strip whitespace from all UELs in the parent set or a subset of specified dimensions in the parent set, can be chain with other *UELs string operations

Parameters:

dimensionsint | List[int], optional: Symbols’ dimensions, by default None

property summary: dict#

Returns a dict of only the metadata

Returns:

dict: Outputs a dict of only the metadata

titleUELs(dimensions: int | List[int] | None = None)#

Will title (capitalize all individual words) in all UELs in the Container or a subset of specified symbols, can be chained with other *UELs string operations

Parameters:

dimensionsint | List[int], optional: Symbols’ dimensions, by default None

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

upperUELs(dimensions: int | List[int] | None = None)#

Will uppercase all UELs in the parent symbol or a subset of specified dimensions in the parent symbol, can be chain with other *UELs string operations

Parameters:

dimensionsint | List[int], optional: Symbols’ dimensions, by default None

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

getStatement() → str[source]#

Statement of the Parameter definition

Returns:

str