Update to probmatching comments to keep in track with main

pysteps/postprocessing/probmatching.py

@@ -274,7 +274,9 @@ def _get_error(scale):
     return shift, scale, R.reshape(shape)
 
 
-def resample_distributions(first_array, second_array, probability_first_array, randgen):
+def resample_distributions(
+    first_array, second_array, probability_first_array, randgen=np.random
+):
     """
     Merges two distributions (e.g., from the extrapolation nowcast and NWP in the blending module)
     to effectively combine two distributions for probability matching without losing extremes.
@@ -287,10 +289,13 @@ def resample_distributions(first_array, second_array, probability_first_array, r
         cascade). It must be of the same shape as `second_array`. Input must not contain NaNs.
     second_array: array_like
         One of the two arrays from which the distribution should be sampled (e.g., the NWP (model)
-        cascade). It must be of the same shape as `first_array`.. Input must not contain NaNs.
+        cascade). It must be of the same shape as `first_array`. Input must not contain NaNs.
     probability_first_array: float
         The weight that `first_array` should get (a value between 0 and 1). This determines the
         likelihood of selecting elements from `first_array` over `second_array`.
+    randgen: numpy.random or numpy.RandomState
+        The random number generator to be used for the binomial distribution. You can pass a seeded
+        random state here for reproducibility. Default is numpy.random.
 
     Returns
     -------

pysteps/tests/test_postprocessing_probmatching.py

@@ -19,7 +19,7 @@ def test_valid_inputs(self):
         second_array = np.array([2, 4, 6, 8, 10])
         probability_first_array = 0.6
         result = resample_distributions(
-            first_array, second_array, probability_first_array, np.random
+            first_array, second_array, probability_first_array
         )
         expected_result = np.array([9, 8, 6, 3, 1])  # Expected result based on the seed
         assert result.shape == first_array.shape
@@ -30,7 +30,7 @@ def test_probability_zero(self):
         second_array = np.array([2, 4, 6, 8, 10])
         probability_first_array = 0.0
         result = resample_distributions(
-            first_array, second_array, probability_first_array, np.random
+            first_array, second_array, probability_first_array
         )
         assert np.array_equal(result, np.sort(second_array)[::-1])
 
@@ -39,7 +39,7 @@ def test_probability_one(self):
         second_array = np.array([2, 4, 6, 8, 10])
         probability_first_array = 1.0
         result = resample_distributions(
-            first_array, second_array, probability_first_array, np.random
+            first_array, second_array, probability_first_array
         )
         assert np.array_equal(result, np.sort(first_array)[::-1])
 
@@ -48,7 +48,7 @@ def test_nan_in_arr1_prob_1(self):
         array_without_nan = np.array([2.0, 4, 6, 8, 10])
         probability_first_array = 1.0
         result = resample_distributions(
-            array_with_nan, array_without_nan, probability_first_array, np.random
+            array_with_nan, array_without_nan, probability_first_array
         )
         expected_result = np.array([np.nan, 9, 7, 3, 1], dtype=float)
         assert np.allclose(result, expected_result, equal_nan=True)
@@ -58,7 +58,7 @@ def test_nan_in_arr1_prob_0(self):
         array_without_nan = np.array([2, 4, 6, 8, 10])
         probability_first_array = 0.0
         result = resample_distributions(
-            array_with_nan, array_without_nan, probability_first_array, np.random
+            array_with_nan, array_without_nan, probability_first_array
         )
         expected_result = np.array([np.nan, 10, 8, 4, 2], dtype=float)
         assert np.allclose(result, expected_result, equal_nan=True)
@@ -68,7 +68,7 @@ def test_nan_in_arr2_prob_1(self):
         array_with_nan = np.array([2.0, 4, 6, np.nan, 10])
         probability_first_array = 1.0
         result = resample_distributions(
-            array_without_nan, array_with_nan, probability_first_array, np.random
+            array_without_nan, array_with_nan, probability_first_array
         )
         expected_result = np.array([np.nan, 9, 5, 3, 1], dtype=float)
         assert np.allclose(result, expected_result, equal_nan=True)
@@ -78,7 +78,7 @@ def test_nan_in_arr2_prob_0(self):
         array_with_nan = np.array([2, 4, 6, np.nan, 10])
         probability_first_array = 0.0
         result = resample_distributions(
-            array_without_nan, array_with_nan, probability_first_array, np.random
+            array_without_nan, array_with_nan, probability_first_array
         )
         expected_result = np.array([np.nan, 10, 6, 4, 2], dtype=float)
         assert np.allclose(result, expected_result, equal_nan=True)
@@ -88,7 +88,7 @@ def test_nan_in_both_prob_1(self):
         array2_with_nan = np.array([2.0, 4, np.nan, np.nan, 10])
         probability_first_array = 1.0
         result = resample_distributions(
-            array1_with_nan, array2_with_nan, probability_first_array, np.random
+            array1_with_nan, array2_with_nan, probability_first_array
         )
         expected_result = np.array([np.nan, np.nan, np.nan, 9, 1], dtype=float)
         assert np.allclose(result, expected_result, equal_nan=True)
@@ -98,7 +98,7 @@ def test_nan_in_both_prob_0(self):
         array2_with_nan = np.array([2.0, 4, np.nan, np.nan, 10])
         probability_first_array = 0.0
         result = resample_distributions(
-            array1_with_nan, array2_with_nan, probability_first_array, np.random
+            array1_with_nan, array2_with_nan, probability_first_array
         )
         expected_result = np.array([np.nan, np.nan, np.nan, 10, 2], dtype=float)
         assert np.allclose(result, expected_result, equal_nan=True)