numpy.core.getlimits.finfo

Attributes

bits : int

The number of bits occupied by the type.

eps : float

The difference between 1.0 and the next smallest representable float larger than 1.0. For example, for 64-bit binary floats in the IEEE-754 standard, eps = 2**-52, approximately 2.22e-16.

epsneg : float

The difference between 1.0 and the next smallest representable float less than 1.0. For example, for 64-bit binary floats in the IEEE-754 standard, epsneg = 2**-53, approximately 1.11e-16.

iexp : int

The number of bits in the exponent portion of the floating point representation.

machar : MachAr

The object which calculated these parameters and holds more detailed information.

Deprecated since version 1.22.

machep : int

The exponent that yields eps.

max : floating point number of the appropriate type

The largest representable number.

maxexp : int

The smallest positive power of the base (2) that causes overflow.

min : floating point number of the appropriate type

The smallest representable number, typically -max.

minexp : int

The most negative power of the base (2) consistent with there being no leading 0's in the mantissa.

negep : int

The exponent that yields epsneg.

nexp : int

The number of bits in the exponent including its sign and bias.

nmant : int

The number of bits in the mantissa.

precision : int

The approximate number of decimal digits to which this kind of float is precise.

resolution : floating point number of the appropriate type

The approximate decimal resolution of this type, i.e., 10**-precision.

tiny : float

An alias for smallest_normal, kept for backwards compatibility.

smallest_normal : float

The smallest positive floating point number with 1 as leading bit in the mantissa following IEEE-754 (see Notes).

smallest_subnormal : float

The smallest positive floating point number with 0 as leading bit in the mantissa following IEEE-754.

Parameters

dtype : float, dtype, or instance

Kind of floating point data-type about which to get information.

See Also

MachAr : The implementation of the tests that produce this information. iinfo : The equivalent for integer data types. spacing : The distance between a value and the nearest adjacent number nextafter : The next floating point value after x1 towards x2

Notes

For developers of NumPy: do not instantiate this at the module level. The initial calculation of these parameters is expensive and negatively impacts import times. These objects are cached, so calling finfo() repeatedly inside your functions is not a problem.

Note that smallest_normal is not actually the smallest positive representable value in a NumPy floating point type. As in the IEEE-754 standard [1], NumPy floating point types make use of subnormal numbers to fill the gap between 0 and smallest_normal. However, subnormal numbers may have significantly reduced precision [2].

References