Updated tutorials to use new RNG

Author: seanlaw

README.rst

@@ -82,7 +82,7 @@ Typical usage (1-dimensional time series data) with `STUMP <https://stumpy.readt
     import numpy as np
     
     if __name__ == "__main__":
-        your_time_series = np.random.rand(10000)
+        your_time_series = np.random.default_rng().random(10000)
         window_size = 50  # Approximately, how many data points might be found in a pattern 
     
         matrix_profile = stumpy.stump(your_time_series, m=window_size)
@@ -97,7 +97,7 @@ Distributed usage for 1-dimensional time series data with Dask Distributed via `
 
     if __name__ == "__main__":
         with Client() as dask_client:
-            your_time_series = np.random.rand(10000)
+            your_time_series = np.random.default_rng().random(10000)
             window_size = 50  # Approximately, how many data points might be found in a pattern 
     
             matrix_profile = stumpy.stumped(dask_client, your_time_series, m=window_size)
@@ -111,7 +111,7 @@ GPU usage for 1-dimensional time series data with `GPU-STUMP <https://stumpy.rea
     from numba import cuda
 
     if __name__ == "__main__":
-        your_time_series = np.random.rand(10000)
+        your_time_series = np.random.default_rng().random(10000)
         window_size = 50  # Approximately, how many data points might be found in a pattern
         all_gpu_devices = [device.id for device in cuda.list_devices()]  # Get a list of all available GPU devices
 
@@ -125,7 +125,7 @@ Multi-dimensional time series data with `MSTUMP <https://stumpy.readthedocs.io/e
     import numpy as np
 
     if __name__ == "__main__":
-        your_time_series = np.random.rand(3, 1000)  # Each row represents data from a different dimension while each column represents data from the same dimension
+        your_time_series = np.random.default_rng().random((3, 1000))  # Each row represents data from a different dimension while each column represents data from the same dimension
         window_size = 50  # Approximately, how many data points might be found in a pattern
 
         matrix_profile, matrix_profile_indices = stumpy.mstump(your_time_series, m=window_size)
@@ -140,7 +140,7 @@ Distributed multi-dimensional time series data analysis with Dask Distributed `M
 
     if __name__ == "__main__":
         with Client() as dask_client:
-            your_time_series = np.random.rand(3, 1000)   # Each row represents data from a different dimension while each column represents data from the same dimension
+            your_time_series = np.random.default_rng().random((3, 1000))   # Each row represents data from a different dimension while each column represents data from the same dimension
             window_size = 50  # Approximately, how many data points might be found in a pattern
 
             matrix_profile, matrix_profile_indices = stumpy.mstumped(dask_client, your_time_series, m=window_size)
@@ -153,7 +153,7 @@ Time Series Chains with `Anchored Time Series Chains (ATSC) <https://stumpy.read
     import numpy as np
     
     if __name__ == "__main__":
-        your_time_series = np.random.rand(10000)
+        your_time_series = np.random.default_rng().random(10000)
         window_size = 50  # Approximately, how many data points might be found in a pattern 
         
         matrix_profile = stumpy.stump(your_time_series, m=window_size)
@@ -174,7 +174,7 @@ Semantic Segmentation with `Fast Low-cost Unipotent Semantic Segmentation (FLUSS
     import numpy as np
 
     if __name__ == "__main__":
-        your_time_series = np.random.rand(10000)
+        your_time_series = np.random.default_rng().random(10000)
         window_size = 50  # Approximately, how many data points might be found in a pattern
 
         matrix_profile = stumpy.stump(your_time_series, m=window_size)
@@ -236,7 +236,7 @@ In order to fully understand and appreciate the underlying algorithms and applic
 Performance
 -----------
 
-We tested the performance of computing the exact matrix profile using the Numba JIT compiled version of the code on randomly generated time series data with various lengths (i.e., ``np.random.rand(n)``) along with different `CPU and GPU hardware resources <hardware_>`_. 
+We tested the performance of computing the exact matrix profile using the Numba JIT compiled version of the code on randomly generated time series data with various lengths (i.e., ``np.random.default_rng().random(n)``) along with different `CPU and GPU hardware resources <hardware_>`_. 
 
 .. image:: https://raw.githubusercontent.com/stumpy-dev/stumpy/main/docs/images/performance.png
     :alt: STUMPY Performance Plot

docs/Tutorial_Fast_Approximate_Matrix_Profiles.ipynb

@@ -334,11 +334,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Please keep in mind that the approximate matrix profile is computed by randomly computing distances along a subset of diagonals. So, each time you initialize a new `scrump` object by calling `stumpy.scrump`, this will randomly shuffle the order that the distances are computed, which inevitably results in different approximate matrix profiles (except when `percentage=1.0`). Depending on your use case, to ensure reproducible results, you may consider setting random seed prior to calling `stumpy.scrump`:\n",
+    "Please keep in mind that the approximate matrix profile is computed by randomly computing distances along a subset of diagonals. So, each time you initialize a new `scrump` object by calling `stumpy.scrump`, this will randomly shuffle the order that the distances are computed, which inevitably results in different approximate matrix profiles (except when `percentage=1.0`). Depending on your use case, to ensure reproducible results, you may consider fixing the random state prior to calling `stumpy.scrump`:\n",
     "\n",
     "```\n",
-    "seed = np.random.randint(100000)\n",
-    "np.random.seed(seed)\n",
+    "# Get and set random state\n",
+    "from stumpy import rng\n",
+    "state = rng.get_state()\n",
+    "rng.set_state(state)\n",
+    "\n",
     "approx = stumpy.scrump(steam_df['steam flow'], m, percentage=0.01, pre_scrump=False)\n",
     "```\n",
     "\n",
@@ -503,7 +506,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.14.6"
   }
  },
  "nbformat": 4,

docs/Tutorial_Matrix_Profiles_For_Streaming_Data.ipynb

@@ -47,7 +47,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "T = np.random.rand(336)"
+    "rng = np.random.default_rng()\n",
+    "T = rng.random(336)"
    ]
   },
   {
@@ -122,7 +123,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "t = np.random.rand()"
+    "t = rng.random()"
    ]
   },
   {
@@ -155,7 +156,7 @@
    "outputs": [],
    "source": [
     "for i in range(1000):\n",
-    "    t = np.random.rand()\n",
+    "    t = rng.random()\n",
     "    stream.update(t)"
    ]
   },
@@ -183,7 +184,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "T_full = np.random.rand(64)\n",
+    "T_full = rng.random(64)\n",
     "m = 8\n",
     "\n",
     "mp = stumpy.stump(T_full, m)\n",
@@ -280,7 +281,7 @@
     }
    ],
    "source": [
-    "T_full = np.random.rand(1_000)\n",
+    "T_full = rng.random(1_000)\n",
     "T_stream = T_full[:20].copy()\n",
     "m = 10\n",
     "\n",
@@ -266241,7 +266242,7 @@
     }
    ],
    "source": [
-    "T_full = np.random.rand(64)\n",
+    "T_full = rng.random(64)\n",
     "m = 8\n",
     "\n",
     "T_stream = T_full[:10].copy()\n",
@@ -266302,7 +266303,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.14.6"
   }
  },
  "nbformat": 4,

docs/Tutorial_Multidimensional_Motif_Discovery.ipynb

@@ -571,7 +571,7 @@
    ],
    "source": [
     "for i in range(4, 11):\n",
-    "    df[f'T{i}'] = np.random.uniform(0.1, -0.1, size=df.shape[0]).cumsum()\n",
+    "    df[f'T{i}'] = np.random.default_rng().uniform(0.1, -0.1, size=df.shape[0]).cumsum()\n",
     "    \n",
     "df = df.sample(frac=1, axis=\"columns\")  # Randomly shuffle the columns\n",
     "\n",
@@ -804,7 +804,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.14.6"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {

docs/Tutorial_Pattern_Matching.ipynb

@@ -645,8 +645,9 @@
     }
    ],
    "source": [
-    "Q_random = np.random.rand(100)\n",
-    "T_random = np.random.rand(1_000_000)\n",
+    "rng = np.random.default_rng()\n",
+    "Q_random = rng.random(100)\n",
+    "T_random = rng.random(1_000_000)\n",
     "\n",
     "naive_distance_profile = compute_naive_distance_profile(Q_random, T_random)"
    ]
@@ -736,7 +737,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.14.6"
   }
  },
  "nbformat": 4,

docs/Tutorial_Time_Series_Chains.ipynb

@@ -70,11 +70,12 @@
     }
    ],
    "source": [
-    "x = np.random.rand(20)\n",
-    "y = np.random.rand(20)\n",
+    "rng = np.random.default_rng()\n",
+    "x = rng.random(20)\n",
+    "y = rng.random(20)\n",
     "n = 10\n",
-    "motifs_x = 0.5 * np.ones(n) + np.random.uniform(-0.05, 0.05, n)\n",
-    "motifs_y = 0.5 * np.ones(n) + np.random.uniform(-0.05, 0.05, n)\n",
+    "motifs_x = 0.5 * np.ones(n) + rng.uniform(-0.05, 0.05, n)\n",
+    "motifs_y = 0.5 * np.ones(n) + rng.uniform(-0.05, 0.05, n)\n",
     "sin_x = np.linspace(0, np.pi/2, n+1)\n",
     "sin_y = np.sin(sin_x)/4\n",
     "chains_x = 0.5 * np.ones(n+1) + 0.02 * np.arange(n+1)\n",
@@ -605,7 +606,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.14.6"
   }
  },
  "nbformat": 4,