DOC: Update link to scipy.datasets

docs/source/tutorials/np_linalg_tutorial_port.ipynb

@@ -29,7 +29,7 @@
     "\n",
     "## Content\n",
     "\n",
-    "In this tutorial, we will use a [matrix decomposition](https://en.wikipedia.org/wiki/Matrix_decomposition) from linear algebra, the Singular Value Decomposition, to generate a compressed approximation of an image. We'll use the `face` image from the [scipy.misc](https://docs.scipy.org/doc/scipy/reference/misc.html#module-scipy.misc) module:"
+    "In this tutorial, we will use a [matrix decomposition](https://en.wikipedia.org/wiki/Matrix_decomposition) from linear algebra, the Singular Value Decomposition, to generate a compressed approximation of an image. We'll use the `face` image from the [scipy.datasets](https://docs.scipy.org/doc/scipy/reference/datasets.html) module:"
    ]
   },
   {
@@ -53,7 +53,7 @@
    "metadata": {},
    "source": [
     ":::{note}\n",
-    "If you prefer, you can use your own image as you work through this tutorial. In order to transform your image into a NumPy array that can be manipulated, you can use the `imread` function from the [matplotlib.pyplot](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.html#module-matplotlib.pyplot) submodule. Alternatively, you can use the [imageio.imread](https://imageio.readthedocs.io/en/stable/userapi.html#imageio.imread) function from the `imageio` library. Be aware that if you use your own image, you might need to adapt some steps below.\n",
+    "If you prefer, you can use your own image as you work through this tutorial. In order to transform your image into a NumPy array that can be manipulated, you can use the `imread` function from the [matplotlib.pyplot](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.html#module-matplotlib.pyplot) submodule. Alternatively, you can use the [imageio.imread](https://imageio.readthedocs.io/en/stable/_autosummary/imageio.v3.imread.html) function from the `imageio` library. Be aware that if you use your own image, you might need to adapt some steps below.\n",
     ":::"
    ]
   },
@@ -1041,7 +1041,7 @@
    "metadata": {},
    "source": [
     "From the output above, we can see that every value in red color channel of `img` is an integer value between 0 and 255, representing the level of red in each corresponding image pixel (keep in mind that this might be different if you\n",
-    "use your own image instead of [scipy.misc.face](https://docs.scipy.org/doc/scipy/reference/generated/scipy.misc.face.html#scipy.misc.face)).\n",
+    "use your own image instead of [scipy.datasets.face](https://docs.scipy.org/doc/scipy/reference/generated/scipy.datasets.face.html#scipy.datasets.face)).\n",
     "\n",
     "Note the data subset now has two dimensions only: height and width of lengths 768 and 1024 respectively."
    ]
@@ -5848,7 +5848,7 @@
     "\n",
     "-  [Python tutorial](https://docs.python.org/dev/tutorial/index.html)\n",
     "-  [NumPy Reference](https://numpy.org/devdocs/reference/index.html#reference)\n",
-    "-  [SciPy Tutorial](https://docs.scipy.org/doc/scipy/reference/tutorial/index.html)\n",
+    "-  [SciPy Tutorial](https://docs.scipy.org/doc/scipy/tutorial/index.html)\n",
     "-  [SciPy Lecture Notes](https://scipy-lectures.org)\n",
     "-  [A matlab, R, IDL, NumPy/SciPy dictionary](http://mathesaurus.sf.net/)"
    ]
@@ -5896,9 +5896,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:base] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-base-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -5910,7 +5910,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,