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# numpy.finfo

class numpy.finfo(dtype)

  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.

  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].

  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.

  References

  [1]

  IEEE Standard for Floating-Point Arithmetic, IEEE Std 754-2008, pp.1-70, 2008, https://doi.org/10.1109/IEEESTD.2008.4610935

  [2]

  Wikipedia, “Denormal Numbers”, https://en.wikipedia.org/wiki/Denormal_number

  Examples

      >>> import numpy as np
      >>> np.finfo(np.float64).dtype
      dtype('float64')
      >>> np.finfo(np.complex64).dtype
      dtype('float32')