Machine limits for floating point types.
The number of bits occupied by the type.
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.
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.
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.
The number of bits in the exponent portion of the floating point representation.
The exponent that yields eps.
The largest representable number.
floating point number of the appropriate type
The smallest positive power of the base (2) that causes overflow.
The smallest representable number, typically -max.
floating point number of the appropriate type
The most negative power of the base (2) consistent with there being no leading 0’s in the mantissa.
The exponent that yields epsneg.
The number of bits in the exponent including its sign and bias.
The number of bits in the mantissa.
The approximate number of decimal digits to which this kind of float is precise.
The approximate decimal resolution of this type, i.e., 10**-precision.
floating point number of the appropriate type
An alias for smallest_normal, kept for backwards compatibility.
The smallest positive floating point number with 1 as leading bit in the mantissa following IEEE-754 (see Notes).
The smallest positive floating point number with 0 as leading bit in the mantissa following IEEE-754.
dtype (float, dtype, or instance) – Kind of floating point or complex floating point data-type about which to get information.
See also
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].
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.
References
Examples
>>> import numpy as np
>>> np.finfo(np.float64).dtype
dtype('float64')
>>> np.finfo(np.complex64).dtype
dtype('float32')
Methods
Attributes
Return the value for the smallest normal.
Return the value for tiny, alias of smallest_normal.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4