MNT: improve random number generator naming.

Author: phmbressan

rocketpy/stochastic/stochastic_model.py

@@ -78,7 +78,7 @@ def _set_stochastic(self, seed=None):
         seed : int, optional
             Seed for the random number generator.
         """
-        self.__rng_generator = np.random.default_rng(seed)
+        self.__random_number_generator = np.random.default_rng(seed)
         self.last_rnd_dict = {}
 
         # TODO: This code block is too complex. Refactor it.
@@ -181,7 +181,7 @@ def _validate_tuple_length_two(
             # is the standard deviation, and the second item is the distribution
             # function. In this case, the nominal value will be taken from the
             # object passed.
-            dist_func = get_distribution(input_value[1], self.__rng_generator)
+            dist_func = get_distribution(input_value[1], self.__random_number_generator)
             return (getattr(self.obj, input_name), input_value[0], dist_func)
         else:
             # if second item is an int or float, then it is assumed that the
@@ -191,7 +191,7 @@ def _validate_tuple_length_two(
             return (
                 input_value[0],
                 input_value[1],
-                get_distribution("normal", self.__rng_generator),
+                get_distribution("normal", self.__random_number_generator),
             )
 
     def _validate_tuple_length_three(
@@ -228,7 +228,7 @@ def _validate_tuple_length_three(
             f"'{input_name}': Third item of tuple must be a string containing the "
             "name of a valid numpy.random distribution function."
         )
-        dist_func = get_distribution(input_value[2], self.__rng_generator)
+        dist_func = get_distribution(input_value[2], self.__random_number_generator)
         return (input_value[0], input_value[1], dist_func)
 
     def _validate_list(
@@ -287,7 +287,7 @@ def _validate_scalar(
         return (
             getattr(self.obj, input_name),
             input_value,
-            get_distribution("normal", self.__rng_generator),
+            get_distribution("normal", self.__random_number_generator),
         )
 
     def _validate_factors(self, input_name, input_value):
@@ -355,14 +355,16 @@ def _validate_tuple_factor(self, input_name, factor_tuple):
             return (
                 factor_tuple[0],
                 factor_tuple[1],
-                get_distribution("normal", self.__rng_generator),
+                get_distribution("normal", self.__random_number_generator),
             )
         elif len(factor_tuple) == 3:
             assert isinstance(factor_tuple[2], str), (
                 f"'{input_name}`: Third item of tuple must be a string containing "
                 "the name of a valid numpy.random distribution function"
             )
-            dist_func = get_distribution(factor_tuple[2], self.__rng_generator)
+            dist_func = get_distribution(
+                factor_tuple[2], self.__random_number_generator
+            )
             return (factor_tuple[0], factor_tuple[1], dist_func)
 
     def _validate_list_factor(self, input_name, factor_list):

rocketpy/tools.py

@@ -232,39 +232,39 @@ def bilinear_interpolation(x, y, x1, x2, y1, y2, z11, z12, z21, z22):
     ) / ((x2 - x1) * (y2 - y1))
 
 
-def get_distribution(distribution_function_name, rng_generator=None):
+def get_distribution(distribution_function_name, random_number_generator=None):
     """Sets the distribution function to be used in the monte carlo analysis.
 
     Parameters
     ----------
     distribution_function_name : string
         The type of distribution to be used in the analysis. It can be
         'uniform', 'normal', 'lognormal', etc.
-    rng_generator : np.random.Generator, optional
+    random_number_generator : np.random.Generator, optional
         The random number generator to be used. If None, the default generator
-        is used.
+        ``numpy.random.default_rng`` is used.
 
     Returns
     -------
     np.random distribution function
         The distribution function to be used in the analysis.
     """
-    if rng_generator is None:
-        rng_generator = np.random.default_rng()
+    if random_number_generator is None:
+        random_number_generator = np.random.default_rng()
 
     # Dictionary mapping distribution names to RNG methods
     distributions = {
-        "normal": rng_generator.normal,
-        "binomial": rng_generator.binomial,
-        "chisquare": rng_generator.chisquare,
-        "exponential": rng_generator.exponential,
-        "gamma": rng_generator.gamma,
-        "gumbel": rng_generator.gumbel,
-        "laplace": rng_generator.laplace,
-        "logistic": rng_generator.logistic,
-        "poisson": rng_generator.poisson,
-        "uniform": rng_generator.uniform,
-        "wald": rng_generator.wald,
+        "normal": random_number_generator.normal,
+        "binomial": random_number_generator.binomial,
+        "chisquare": random_number_generator.chisquare,
+        "exponential": random_number_generator.exponential,
+        "gamma": random_number_generator.gamma,
+        "gumbel": random_number_generator.gumbel,
+        "laplace": random_number_generator.laplace,
+        "logistic": random_number_generator.logistic,
+        "poisson": random_number_generator.poisson,
+        "uniform": random_number_generator.uniform,
+        "wald": random_number_generator.wald,
     }
     try:
         return distributions[distribution_function_name]