`numpy:finfo`

source: /dev/numpy/build-install/usr/lib/python3.14/site-packages/numpy/__init__.py

Signature

class finfo ( dtype )

Summary

Machine limits for floating point types.

Parameters

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

Attributes

bits : int: The number of bits occupied by the type.
dtype : dtype: Returns the dtype for which finfo returns information. For complex input, the returned dtype is the associated float* dtype for its real and complex components.
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.
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. Corresponds to the C standard MAX_EXP.
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. Corresponds to the C standard MIN_EXP - 1.
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 explicit bits in the mantissa (excluding the implicit leading bit for normalized numbers).
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.

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].

For longdouble, the representation varies across platforms. On most platforms it is IEEE 754 binary128 (quad precision) or binary64-extended (80-bit extended precision). On PowerPC systems, it may use the IBM double-double format (a pair of float64 values), which has special characteristics for precision and range.

This function can also be used for complex data types as well. If used, the output will be the same as the corresponding real float type (e.g. numpy.finfo(numpy.csingle) is the same as numpy.finfo(numpy.single)). However, the output is true for the real and imaginary components.

Examples

import numpy as np
np.finfo(np.float64).dtype
np.finfo(np.complex64).dtype

`numpy:finfo`

Signature

Summary

Parameters

Attributes

Notes

Examples

See also

Aliases

Referenced by

This package