MAINT: Handle warnings in tutorials

content/save-load-arrays.md

@@ -127,7 +127,7 @@ print(load_xy.files)
 ```
 
 ```{code-cell}
-whos
+%whos
 ```
 
 ## Reassign the NpzFile arrays to `x` and `y`

content/tutorial-plotting-fractals.md

@@ -301,14 +301,14 @@ For example, setting $c = \frac{\pi}{10}$ gives us a very elegant cloud shape, w
 
 ```{code-cell} ipython3
 output = julia(mesh, c=np.pi/10, num_iter=20)
-kwargs = {'title': 'f(z) = z^2 + \dfrac{\pi}{10}', 'cmap': 'plasma'}
+kwargs = {'title': r'f(z) = z^2 + \dfrac{\pi}{10}', 'cmap': 'plasma'}
 
 plot_fractal(output, **kwargs);
 ```
 
 ```{code-cell} ipython3
 output = julia(mesh, c=-0.75 + 0.4j, num_iter=20)
-kwargs = {'title': 'f(z) = z^2 - \dfrac{3}{4} + 0.4i', 'cmap': 'Greens_r'}
+kwargs = {'title': r'f(z) = z^2 - \dfrac{3}{4} + 0.4i', 'cmap': 'Greens_r'}
 
 plot_fractal(output, **kwargs);
 ```
@@ -334,7 +334,7 @@ def mandelbrot(mesh, num_iter=10, radius=2):
 
 ```{code-cell} ipython3
 output = mandelbrot(mesh, num_iter=50)
-kwargs = {'title': 'Mandelbrot \ set', 'cmap': 'hot'}
+kwargs = {'title': 'Mandelbrot \\ set', 'cmap': 'hot'}
 
 plot_fractal(output, **kwargs);
 ```
@@ -370,8 +370,6 @@ for deg, ax in enumerate(axes.ravel()):
     diverge_len = general_julia(mesh, f=power, num_iter=15)
     ax.imshow(diverge_len, extent=[-2, 2, -2, 2], cmap='binary')
     ax.set_title(f'$f(z) = z^{degree} -1$')
-
-fig.tight_layout();
 ```
 
 Needless to say, there is a large amount of exploring that can be done by fiddling with the inputted function, value of $c$, number of iterations, radius and even the density of the mesh and choice of colours.
@@ -419,7 +417,7 @@ p.deriv()
 
 ```{code-cell} ipython3
 output = newton_fractal(mesh, p, p.deriv(), num_iter=15, r=2)
-kwargs = {'title': 'f(z) = z - \dfrac{(z^8 + 15z^4 - 16)}{(8z^7 + 60z^3)}', 'cmap': 'copper'}
+kwargs = {'title': r'f(z) = z - \dfrac{(z^8 + 15z^4 - 16)}{(8z^7 + 60z^3)}', 'cmap': 'copper'}
 
 plot_fractal(output, **kwargs)
 ```
@@ -443,7 +441,7 @@ def d_tan(z):
 
 ```{code-cell} ipython3
 output = newton_fractal(mesh, f_tan, d_tan, num_iter=15, r=50)
-kwargs = {'title': 'f(z) = z - \dfrac{sin(z)cos(z)}{2}', 'cmap': 'binary'}
+kwargs = {'title': r'f(z) = z - \dfrac{sin(z)cos(z)}{2}', 'cmap': 'binary'}
 
 plot_fractal(output, **kwargs);
 ```
@@ -475,7 +473,7 @@ We will denote this one 'Wacky fractal', as its equation would not be fun to try
 
 ```{code-cell} ipython3
 output = newton_fractal(small_mesh, sin_sum, d_sin_sum, num_iter=10, r=1)
-kwargs = {'title': 'Wacky \ fractal', 'figsize': (6, 6), 'extent': [-1, 1, -1, 1], 'cmap': 'terrain'}
+kwargs = {'title': 'Wacky \\ fractal', 'figsize': (6, 6), 'extent': [-1, 1, -1, 1], 'cmap': 'terrain'}
 
 plot_fractal(output, **kwargs)
 ```
@@ -550,7 +548,7 @@ def accident(z):
 
 ```{code-cell} ipython3
 output = general_julia(mesh, f=accident, num_iter=15, c=0, radius=np.pi)
-kwargs = {'title': 'Accidental \ fractal', 'cmap': 'Blues'}
+kwargs = {'title': 'Accidental \\ fractal', 'cmap': 'Blues'}
 
 plot_fractal(output, **kwargs);
 ```

content/tutorial-svd.md

@@ -35,12 +35,16 @@ After this tutorial, you should be able to:
 
 ## Content
 
-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:
 
 ```{code-cell}
-from scipy import misc
+# TODO: Rm try-except with scipy 1.10 is the minimum supported version
+try:
+    from scipy.datasets import face
+except ImportError:  # Data was in scipy.misc prior to scipy v1.10
+    from scipy.misc import face
 
-img = misc.face()
+img = face()
 ```
 
 **Note**: 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'll likely need to adapt the steps below. For more information on how images are treated when converted to NumPy arrays, see [A crash course on NumPy for images](https://scikit-image.org/docs/stable/user_guide/numpy_images.html) from the `scikit-image` documentation.
@@ -91,7 +95,7 @@ img[:, :, 0]
 ```
 
 From the output above, we can see that every value in `img[:, :, 0]` 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
-use your own image instead of [scipy.misc.face](https://docs.scipy.org/doc/scipy/reference/generated/scipy.misc.face.html#scipy.misc.face)).
+use your own image instead of [scipy.datasets.face](https://docs.scipy.org/doc/scipy/reference/generated/scipy.datasets.face.html)).
 
 As expected, this is a 768x1024 matrix:
 

environment.yml

@@ -5,6 +5,7 @@ dependencies:
   # For running the tutorials
   - numpy
   - scipy
+  - pooch
   - matplotlib
   - pandas
   - imageio

requirements.txt

@@ -1,6 +1,7 @@
 # For the tutorials
 numpy
 scipy
+pooch  # for scipy.datasets
 matplotlib
 pandas
 imageio