fixed the __correct_for_negatives function to work properly. It used …

[fabcamo]

The negative values were not properly replaced with zero for the friction, tip and friciton_nbr; in the __correct_for_negatives function in the pre_process steps... I checked the tests and 3 remain failing as it was before the changes....

In the original code, the condition is never True, and thus data never gets fixed.

if data.size != 0 and not data.ndim:
     data[data < 0] = 0

Why the condition is never True?

data.ndim returns the number of dimensions (or axes) of a NumPy array

If data is:
A scalar (single number) → data.ndim == 0
A 1D array (like a list) → data.ndim == 1
A 2D array (like a table) → data.ndim == 2, and so on

Then, the condition:
if data.size != 0 and not data.ndim:
--> is only true if data.ndim == 0 (meaning data is a single scalar value).

What Happens?
If data is a scalar
(e.g., np.array(5)):

not data.ndim → True, so the function executes data[data < 0] = 0.
✅ Works fine.
If data is a 1D or 2D array (which it usually is in real use cases):

not data.ndim → False, because data.ndim is 1 or greater.
The condition fails, so negative values are NOT changed. ❌

[sonarqubecloud]

Quality Gate passed

Issues
0 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

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