From 137a5f7995fe50804b3172e399794ce0140387d4 Mon Sep 17 00:00:00 2001
From: Jim <james.schmitz@gmail.com>
Date: Sat, 11 Mar 2023 07:40:27 -0500
Subject: [PATCH 1/2] improve random_sample()

---
 py5_resources/py5_module/py5/mixins/math.py | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

diff --git a/py5_resources/py5_module/py5/mixins/math.py b/py5_resources/py5_module/py5/mixins/math.py
index 8ba772b6..59490f33 100644
--- a/py5_resources/py5_module/py5/mixins/math.py
+++ b/py5_resources/py5_module/py5/mixins/math.py
@@ -22,6 +22,7 @@
 import warnings
 import traceback
 from pathlib import Path
+import types
 from typing import overload, Union, Any
 
 import numpy as np
@@ -261,17 +262,25 @@ def random_int(self, *args: int) -> int:
 
     def random_choice(self, objects: list[Any]) -> Any:
         """$class_Sketch_random_choice"""
-        if objects:
+        if len(objects):
             return objects[self._rng.integers(0, len(objects))]
         else:
             return None
 
-    def random_sample(self, objects: list[Any], size: int=1, replace: bool=True) -> npt.NDArray:
+    def random_sample(self, objects: list[Any], size: int=1, replace: bool=True) -> list[Any]:
         """$class_Sketch_random_sample"""
-        if objects:
-            return self._rng.choice(objects, size=size, replace=replace)
+        if len(objects):
+            if isinstance(objects, types.GeneratorType):
+                objects = list(objects)
+            indices = self._rng.choice(range(len(objects)), size=size, replace=replace)
+            if not isinstance(objects, list):
+                try:
+                    return objects[indices]
+                except:
+                    pass
+            return [objects[idx] for idx in indices]
         else:
-            return np.array([], dtype='O')
+            return []
 
     @overload
     def random_gaussian(self) -> float:

From cbe912ce8cb308e91e051035e3dbf6d27bd2da5e Mon Sep 17 00:00:00 2001
From: Jim <james.schmitz@gmail.com>
Date: Sat, 11 Mar 2023 07:40:39 -0500
Subject: [PATCH 2/2] update docs for changes

---
 py5_docs/Reference/api_en/Sketch_random_sample.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/py5_docs/Reference/api_en/Sketch_random_sample.txt b/py5_docs/Reference/api_en/Sketch_random_sample.txt
index 7e8665ae..fa5775f1 100644
--- a/py5_docs/Reference/api_en/Sketch_random_sample.txt
+++ b/py5_docs/Reference/api_en/Sketch_random_sample.txt
@@ -5,7 +5,7 @@ category = math
 subcategory = random
 
 @@ signatures
-random_sample(objects: list[Any], size: int=1, replace: bool=True) -> npt.NDArray
+random_sample(objects: list[Any], size: int=1, replace: bool=True) -> list[Any]
 
 @@ variables
 objects: list[Any] - list of objects to choose from
@@ -15,7 +15,7 @@ size: int=1 - number of random items to select
 @@ description
 Select random items from a list. The list items can be of any type. If multiple items are selected, this function will by default allow the same item to be selected multiple times. Set the `replace` parameter to `False` to prevent the same item from being selected multiple times.
 
-The returned value will always be a numpy array, even if only one item is sampled. If the list of objects is empty, an empty numpy array will be returned.
+The returned value will always be a sequence such as a list or numpy array, even if only one item is sampled. If you only want to sample one item, consider using [](sketch_random_choice) instead. If the list of objects is empty, an empty list will be returned.
 
 This function's randomness can be influenced by [](sketch_random_seed), and makes calls to numpy to select the random items.