brainpy.math.finfo#
- class brainpy.math.finfo(dtype)[source]#
Machine limits for floating point types.
- eps#
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.- Type:
- epsneg#
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.- Type:
- machar#
The object which calculated these parameters and holds more detailed information.
- Type:
MachAr
- max#
The largest representable number.
- Type:
floating point number of the appropriate type
- min#
The smallest representable number, typically
-max
.- Type:
floating point number of the appropriate type
- minexp#
The most negative power of the base (2) consistent with there being no leading 0’s in the mantissa.
- Type:
- precision#
The approximate number of decimal digits to which this kind of float is precise.
- Type:
- resolution#
The approximate decimal resolution of this type, i.e.,
10**-precision
.- Type:
floating point number of the appropriate type
- Parameters:
dtype (float, dtype, or instance) – Kind of floating point data-type about which to get information.
See also
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
tiny
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 andtiny
. However, subnormal numbers may have significantly reduced precision [2].References
- __init__()#
Methods
__init__
()