The following rounding is incorrect:
```
>>> float_round(6.6 * 0.175, precision_digits=2)
1.15
```
Indeed, 6.6 * 0.175 = 1.155 ≈ 1.16.
In this specific case, the `epsilon` computed is not sufficient. A
precision of 53 gives:
normalized_value = 115.49999999999997
epsilon = 1.2823075934420547e-14
=> new normalized_value = 115.49999999999999
Bad luck, this is just not enough to tip the value in the right
direction.
However, a precision of 52 is sufficient:
normalized_value = 115.49999999999997
epsilon = 2.5646151868841094e-14
=> new normalized_value = 115.5
The value of 53 was chosen from the `binary64` number format precision.
In case of Python, the corresponding machine epsilon is 2^-52 [1].
Therefore, using 52 instead of 53 does make sense.
It is worth noting that the value of the machine epsilon
2^-52 = 2.2204460492503131e-16, which is still 2 orders of magnitude
below our dynamic estimation.
[1] https://en.wikipedia.org/wiki/Machine_epsilon
[2] `numpy.finfo(float).eps = 2.2204460492503131e-16`
opw-2047368
closesodoo/odoo#35565
Signed-off-by: Nicolas Martinelli (nim) <nim@odoo.com>
Nicolas Martinelli
0f878e4839