BUG: StochasticModel visualize attributes of a uniform distribution

It showed the nominal and the standard deviation values and it doesn't make sense in a uniform distribution. In a np.random.uniform the 'nominal value' is the lower bound of the distribution, and the 'standard deviation' value is the upper bound. Now, a new condition has been added for the uniform distributions where the mean and semi range are calculated and showed. This way the visualize_attribute function will show the whole range where the random values are uniformly taken in
RocketPy-Team · Gui-FernandesBR · Mar 23, 2025 · Mar 14, 2025 · Mar 15, 2025 · Mar 15, 2025
commit f6efa816442a740279ebaf880458049d28799b3e
@@ -486,11 +486,20 @@ def format_attribute(attr, value):
                 )
             elif isinstance(value, tuple):
                 nominal_value, std_dev, dist_func = value
-                return (
-                    f"\t{attr.ljust(max_str_length)} "
-                    f"{nominal_value:.5f} ± "
-                    f"{std_dev:.5f} ({dist_func.__name__})"
-                )
+                if dist_func == np.random.uniform:
+                    mean = (std_dev + nominal_value) / 2
+                    semi_range = (std_dev - nominal_value) / 2
+                    return (
+                        f"\t{attr.ljust(max_str_length)} "
+                        f"{mean:.5f} ± "
+                        f"{semi_range:.5f} ({dist_func.__name__})"
+                    )
+                else:
+                    return (
+                        f"\t{attr.ljust(max_str_length)} "
+                        f"{nominal_value:.5f} ± "
+                        f"{std_dev:.5f} ({dist_func.__name__})"
+                    )
             return None
 
         attributes = {k: v for k, v in self.__dict__.items() if not k.startswith("_")}