change argument name

Bears-R-Us · Jan 20, 2025 · d6503ed · d6503ed
1 parent 508bfa2
commit d6503ed
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Showing 2 changed files with 40 additions and 14 deletions.
diff --git a/arkouda/numpy/random/_generator.py b/arkouda/numpy/random/_generator.py
@@ -620,7 +620,7 @@ def permutation(self, x, method="Argsort"):
             make a copy and shuffle the elements randomly.
         method: str = "Argsort"
             The method for generating the permutation.
-            Allowed values: Serial, Argsort
+            Allowed values: FisherYates, Argsort
             If Argsort is selected, the permutation will be generated by
             an argsort performed on randomly generated floats.
         Returns
@@ -643,7 +643,7 @@ def permutation(self, x, method="Argsort"):
         else:
             raise TypeError("permutation only accepts a pdarray or int scalar.")
 
-        if method.lower() == "serial":
+        if method.lower() == "fisheryates":
             # we have to use the int version since we permute the domain
             name = self._name_dict[to_numpy_dtype(akint64)]
             rep_msg = generic_msg(

diff --git a/tests/numpy/random_test.py b/tests/numpy/random_test.py
@@ -87,7 +87,7 @@ def test_shuffle(self, data_type):
         assert check(pda, pda_prime, data_type)
 
     @pytest.mark.parametrize("data_type", INT_FLOAT)
-    @pytest.mark.parametrize("method", ["Serial", "Argsort"])
+    @pytest.mark.parametrize("method", ["FisherYates", "Argsort"])
     def test_permutation(self, data_type, method):
 
         # ints are checked for equality; floats are checked for closeness
@@ -206,7 +206,10 @@ def test_logistic(self):
             log_sample = rng.logistic(loc=loc, scale=scale, size=num_samples).to_list()
 
             rng = ak.random.default_rng(17)
-            assert rng.logistic(loc=loc, scale=scale, size=num_samples).to_list() == log_sample
+            assert (
+                rng.logistic(loc=loc, scale=scale, size=num_samples).to_list()
+                == log_sample
+            )
 
     def test_lognormal(self):
         scal = 2
@@ -215,25 +218,40 @@ def test_lognormal(self):
         for mean, sigma in product([scal, arr], [scal, arr]):
             rng = ak.random.default_rng(17)
             num_samples = 5
-            log_sample = rng.lognormal(mean=mean, sigma=sigma, size=num_samples).to_list()
+            log_sample = rng.lognormal(
+                mean=mean, sigma=sigma, size=num_samples
+            ).to_list()
 
             rng = ak.random.default_rng(17)
-            assert rng.lognormal(mean=mean, sigma=sigma, size=num_samples).to_list() == log_sample
+            assert (
+                rng.lognormal(mean=mean, sigma=sigma, size=num_samples).to_list()
+                == log_sample
+            )
 
     def test_normal(self):
         rng = ak.random.default_rng(17)
         both_scalar = rng.normal(loc=10, scale=2, size=10).to_list()
         scale_scalar = rng.normal(loc=ak.array([0, 10, 20]), scale=1, size=3).to_list()
         loc_scalar = rng.normal(loc=10, scale=ak.array([1, 2, 3]), size=3).to_list()
-        both_array = rng.normal(loc=ak.array([0, 10, 20]), scale=ak.array([1, 2, 3]), size=3).to_list()
+        both_array = rng.normal(
+            loc=ak.array([0, 10, 20]), scale=ak.array([1, 2, 3]), size=3
+        ).to_list()
 
         # redeclare rng with same seed to test reproducibility
         rng = ak.random.default_rng(17)
         assert rng.normal(loc=10, scale=2, size=10).to_list() == both_scalar
-        assert rng.normal(loc=ak.array([0, 10, 20]), scale=1, size=3).to_list() == scale_scalar
-        assert rng.normal(loc=10, scale=ak.array([1, 2, 3]), size=3).to_list() == loc_scalar
         assert (
-            rng.normal(loc=ak.array([0, 10, 20]), scale=ak.array([1, 2, 3]), size=3).to_list()
+            rng.normal(loc=ak.array([0, 10, 20]), scale=1, size=3).to_list()
+            == scale_scalar
+        )
+        assert (
+            rng.normal(loc=10, scale=ak.array([1, 2, 3]), size=3).to_list()
+            == loc_scalar
+        )
+        assert (
+            rng.normal(
+                loc=ak.array([0, 10, 20]), scale=ak.array([1, 2, 3]), size=3
+            ).to_list()
             == both_array
         )
 
@@ -280,8 +298,12 @@ def test_exponential(self):
 
         # reset rng with same seed and ensure we get same results
         rng = ak.random.default_rng(17)
-        assert rng.exponential(scale=scal_scale, size=num_samples).to_list() == scal_sample
-        assert rng.exponential(scale=arr_scale, size=num_samples).to_list() == arr_sample
+        assert (
+            rng.exponential(scale=scal_scale, size=num_samples).to_list() == scal_sample
+        )
+        assert (
+            rng.exponential(scale=arr_scale, size=num_samples).to_list() == arr_sample
+        )
 
     def test_choice_hypothesis_testing(self):
         # perform a weighted sample and use chisquare to test
@@ -360,7 +382,9 @@ def test_lognormal_hypothesis_testing(self, method):
 
         mean = rng.uniform(-10, 10)
         deviation = rng.uniform(0, 10)
-        sample = rng.lognormal(mean=mean, sigma=deviation, size=num_samples, method=method)
+        sample = rng.lognormal(
+            mean=mean, sigma=deviation, size=num_samples, method=method
+        )
 
         log_sample_list = np.log(sample.to_ndarray()).tolist()
 
@@ -571,7 +595,9 @@ def test_legacy_uniform(self):
             uArray.to_list(),
         )
 
-        uArray = ak.random.uniform(size=np.int64(3), low=np.int64(0), high=np.int64(5), seed=np.int64(0))
+        uArray = ak.random.uniform(
+            size=np.int64(3), low=np.int64(0), high=np.int64(5), seed=np.int64(0)
+        )
         assert np.allclose(
             [0.30013431967121934, 0.47383036230759112, 1.0441791878997098],
             uArray.to_list(),