Document

finfo(dtype)

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
machep : int: The exponent that yields :None:None:`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 :None:None:`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 :None:None:`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.

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 , 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 .

Parameters

dtype : float, dtype, or instance: Kind of floating point data-type about which to get information.

Machine limits for floating point types.

Examples

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

dask.array.ufunc.spacing dask.array.ufunc.nextafter scipy.linalg._decomp_svd.subspace_angles numpy.iinfo numpy.MachAr scipy.optimize._optimize.approx_fprime

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them