[FIX] tools: epsilon magnitude

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

closes odoo/odoo#35521

Signed-off-by: Nicolas Martinelli (nim) <nim@odoo.com>

1 files changed, 1 insertions, 1 deletions

diff --git a/addons/web/static/src/js/core/utils.js b/addons/web/static/src/js/core/utils.js
index a21c4241864..231ad97d202 100644
--- a/addons/web/static/src/js/core/utils.js
+++ b/addons/web/static/src/js/core/utils.js
@@ -309,7 +309,7 @@ var utils = {
         }
         var normalized_value = value / precision;
         var epsilon_magnitude = Math.log(Math.abs(normalized_value))/Math.log(2);
-        var epsilon = Math.pow(2, epsilon_magnitude - 53);
+        var epsilon = Math.pow(2, epsilon_magnitude - 52);
         normalized_value += normalized_value >= 0 ? epsilon : -epsilon;
 
         /**