SpecialValues#

class gamspy.SpecialValues[source]#

Bases: object

Utility class representing GAMS special values and methods to test for their presence.

In GAMS, special values are used to represent missing data, undefined operations, infinities, or structural zeros (EPS). This class defines GAMSPy equivalents for these values. It also provides vectorized static methods to efficiently check for these special values across integers, floats, strings, pandas Series, pandas DataFrames, or array-like objects.

Attributes:

NAfloat: GAMS NA (Not Available), unpacked from a specific hexadecimal representation (“fffffffffffffffe”).
EPSfloat: GAMS EPS (Epsilon), represented as a negative zero (-0.0).
UNDEFfloat: GAMS UNDEF (Undefined), represented as Not a Number (NaN).
POSINFfloat: GAMS POSINF (Positive Infinity), represented as float(“inf”).
NEGINFfloat: GAMS NEGINF (Negative Infinity), represented as float(“-inf”).

Methods

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

Examples

Basic usage of attributes

>>> from gamspy._special_values import SpecialValues
>>> SpecialValues.EPS
-0.0
>>> SpecialValues.POSINF
inf

Testing for special values in data structures

>>> import numpy as np
>>> import pandas as pd
>>> data = pd.Series([1.5, -0.0, float("inf"), SpecialValues.NA])
>>> SpecialValues.isEps(data)
array([False,  True, False, False])
>>> SpecialValues.isNA(data)
array([False, False, False,  True])

static isEps(records: int | float | str | pd.Series | pd.DataFrame) → np.ndarray[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:

np.ndarray: 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.

static isNA(records: int | float | str | pd.Series | pd.DataFrame) → np.ndarray[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:

np.ndarray: 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.

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:

np.ndarray | np.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.

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:

np.ndarray | np.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.

static isUndef(records: int | float | str | pd.Series | pd.DataFrame) → np.ndarray[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:

np.ndarray: 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.

EPS = -0.0#

NA = nan#

NEGINF = -inf#

POSINF = inf#

UNDEF = nan#