SpecialValues#

class gamspy.SpecialValues[source]#

Bases: object

Methods

`isEps`()	Check if the input records represent a value close to zero with specific considerations for different data types.
`isNA`()	Check if values in records represent GAMS NA (Not Available) values.
`isNegInf`()	Check if the input records represent negative infinity.
`isPosInf`()	Check if the input records represent positive infinity.
`isUndef`()	Determine if the given input(s) represent GAMS "undef" values.

NA = nan#

EPS = -0.0#

UNDEF = nan#

POSINF = inf#

NEGINF = -inf#

isEps() → bool[source]#

Check if the input records represent a value close to zero with specific considerations for different data types.

Parameters:

records: int | float | str | pd.Series | pd.DataFrame | array-like: The input records to be checked for proximity to zero.

Returns:

bool: True if the input records represent a value close to zero according to the specified conditions, False otherwise.

Raises:

Exception: If the input (string) records cannot be converted to a float.
Exception: If the data structure passed in ‘records’ could not be converted to a numpy array (dtype=float) for testing.

isNA() → bool[source]#

Check if values in records represent GAMS NA (Not Available) values.

Parameters:

records: int | float | str | pd.Series | pd.DataFrame | array-like: The input records to be checked for GAMS NA values.

Returns:

bool: True if the values in records represent GAMS NA values; otherwise, False.

Raises:

Exception: If the input (string) records cannot be converted to a float.
Exception: If the data structure passed in ‘records’ could not be converted to a numpy array (dtype=float) for testing.

isUndef() → bool[source]#

Determine if the given input(s) represent GAMS “undef” values.

Parameters:

records: int | float | str | pd.Series | pd.DataFrame | array-like: The input records to be checked for GAMS “undef” values.

Returns:

bool: True if the values in records represent GAMS “undef” values; otherwise, False.

Raises:

Exception: If the input (string) records cannot be converted to a float.
Exception: If the data structure passed in ‘records’ could not be converted to a numpy array (dtype=float) for testing.

isPosInf() → bool[source]#

Check if the input records represent positive infinity.

Parameters:

records: int | float | str | pd.Series | pd.DataFrame | array-like: The input records to be checked for positive infinity values.

Returns:

bool: True if the values in records represent positive infinity values; otherwise, False.

Raises:

Exception: If the input (string) records cannot be converted to a float.
Exception: If the data structure passed in ‘records’ could not be converted to a numpy array (dtype=float) for testing.

isNegInf() → bool[source]#

Check if the input records represent negative infinity.

Parameters:

records: int | float | str | pd.Series | pd.DataFrame | array-like: The input records to be checked for negative infinity values.

Returns:

bool: True if the values in records represent negative infinity values; otherwise, False.

Raises:

Exception: If the input (string) records cannot be converted to a float.
Exception: If the data structure passed in ‘records’ could not be converted to a numpy array (dtype=float) for testing.