Testing toc structure

website/content/adv1-lesion-quantification.md

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+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.10.3
+kernelspec:
+  display_name: Python 3
+  language: python
+  name: python3
+---
+
+```{code-cell} ipython3
+from __future__ import division, print_function
+%matplotlib inline
+```
+
+# Quantifying spinal cord regeneration in zebrafish
+
+We want to quantify the amount of fluorescent cells in a wounded zebrafish embryo spinal column:
+
+<img src="../images/zebrafish-spinal-cord-color.png" alt="Zebrafish spinal cord" style="width: 300px;"/>
+
+The key steps are:
+
+- estimating the position and width of the cord
+- estimating the average fluorescence along the length of the cord
+
+```{code-cell} ipython3
+from matplotlib import pyplot as plt, cm
+from skimage import io
+image = io.imread('../images/zebrafish-spinal-cord.png')
+```
+
+# SciPy to estimate coordinates
+
+First, we get just the top and bottom rows of pixels, and use a 1D gaussian filter to smooth the signal.
+
+```{code-cell} ipython3
+from scipy import ndimage as nd
+top, bottom = image[[0, -1], :]
+
+fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))
+
+top_smooth = nd.gaussian_filter1d(top, sigma=20)
+ax0.plot(top, color='blue', lw=2)
+ax0.plot(top_smooth, color='orange', lw=2)
+ax0.set_title('top')
+
+bottom_smooth = nd.gaussian_filter1d(bottom, sigma=20)
+ax1.plot(bottom, color='blue', lw=2)
+ax1.plot(bottom_smooth, color='orange', lw=2)
+ax1.set_title('bottom')
+```
+
+With smooth curves, we can get the mode (the position of the center) and width of the signal.
+
+```{code-cell} ipython3
+top_mode = top_smooth.argmax()
+top_max = top_smooth[top_mode]
+top_width = (top_smooth > float(top_max) / 2).sum()
+
+bottom_mode = bottom_smooth.argmax()
+bottom_max = bottom_smooth[bottom_mode]
+bottom_width = (bottom_smooth > float(bottom_max) / 2).sum()
+
+width = max(bottom_width, top_width)
+
+print(top_mode, top_width, bottom_mode, bottom_width)
+```
+
+# scikit-image to trace the profile
+
+Now, use `measure.profile_line` to trace from (0, `top_mode`) to (-1, `bottom_mode`).
+
+```{code-cell} ipython3
+from skimage import measure
+trace = measure.profile_line(None) # Replace `None` with correct args
+```
+
+Finally, plot the trace.
+
+```{code-cell} ipython3
+plt.plot(trace, color='black', lw=2)
+plt.xlabel('position along embryo')
+plt.ylabel('mean fluorescence intensity')
+```
+
+From this trace, we can compute various summary statistics (e.g. min/max, gap width, slope, etc), and plot these over time as the wound recovers.
+
++++
+
+---
+
+<div style="height: 400px;"></div>
+
+```{code-cell} ipython3
+%reload_ext load_style
+%load_style ../themes/tutorial.css
+```

website/content/applications.md

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+# scikit-image applications
+
+A collection of tutorials highlighting the use of scikit-image for applications in science.
+
+```{toctree}
+---
+maxdepth: 1
+---
+content/adv1-lesion-quantification.md
+```

website/index.md

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 ```{toctree}
 ---
-maxdepth: 1
+maxdepth: 2
 ---
 content/00_images_are_arrays.md
+applications
 ```