WIP

Author: stefanv

.gitignore

@@ -1,14 +1,15 @@
 \#*
 .#*
 _requirements.installed
-build
 *.DS_Store
 *.html
 *.ipynb_checkpoints
 *.pyc
 *~
-*pano-advanced-output.png
-book/lessons
 
-# ignoring website local builds
+# Ignore built website
 site/_build
+
+# Ignore auto-generated content
+site/lectures
+

Makefile

@@ -1,42 +1,13 @@
-.PHONY: html
+.PHONY: clean html
 
-OBSHELL=/bin/bash
-
-.DEFAULT_GOAL = html
-
-LESSONS_DIR = lessons
-GENERATED_LESSONS_DIR = site/lessons
+.DEFAULT_GOAL = book
 
 _requirements.installed:
 	pip install -q -r requirements.txt
 	touch _requirements.installed
 
-MARKDOWNS = $(wildcard $(LESSONS_DIR)/*.md)
-MD_OUTPUTS = $(patsubst $(LESSONS_DIR)/%.md, $(GENERATED_LESSONS_DIR)/%.md, $(MARKDOWNS))
-NOTEBOOKS = $(patsubst %.md, %.ipynb, $(MD_OUTPUTS))
-
-.SECONDARY: $(MD_OUTPUTS) $(NOTEBOOKS)
-
-$(GENERATED_LESSONS_DIR)/%.ipynb:$(LESSONS_DIR)/%.md site/lessons site/lessons/images
-	# This does not work, due to bug in notedown; see https://github.com/aaren/notedown/issues/53
-	#notedown --match=python --precode='%matplotlib inline' $< > $@
-	notedown --match=python $< > $@
-	jupyter nbconvert --execute --inplace $@ --ExecutePreprocessor.timeout=-1
-
-%.md:%.ipynb
-	jupyter nbconvert --to=mdoutput --output="$(notdir $@)" --output-dir=$(GENERATED_LESSONS_DIR) $<
-#	$(eval NBSTRING := [📂 Download lesson notebook](.\/$(basename $(notdir $@)).ipynb)\n\n)
-#	sed -i'.bak' '1s/^/$(NBSTRING)/' $@
-
-site/lessons:
-	mkdir -p book/lessons
-
-site/lessons/images:
-	ln -s ${PWD}/lessons/images ${PWD}/site/lessons/images
-
-html: site/lessons _requirements.installed $(NOTEBOOKS) $(MD_OUTPUTS)
-	@export SPHINXOPTS=-W; make -C site html
-	cp $(GENERATED_LESSONS_DIR)/*.ipynb book/build/html/lessons/
+book: _requirements.installed
+	@export SPHINXOPTS=-W; make -C book html
 
 clean:
-	rm -rf $(GENERATED_LESSONS_DIR)/*
+	rm -rf book/_build

_config.yml

@@ -4,15 +4,17 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.10.3
+    jupytext_version: 1.11.2
 kernelspec:
   display_name: Python 3
   language: python
   name: python3
 ---
 
-```{code-cell} python
-:tags: [remove-input, remove-output]
+```{code-cell}
+---
+tags: [remove-input, remove-output]
+---
 
 %matplotlib inline
 %config InlineBackend.figure_format = 'retina'
@@ -26,7 +28,7 @@ Images are represented in ``scikit-image`` using standard ``numpy`` arrays.  Thi
 
 Let's see how to build a grayscale image as a 2D array:
 
-```{code-cell} python
+```{code-cell}
 import numpy as np
 from matplotlib import pyplot as plt
 
@@ -38,7 +40,7 @@ plt.colorbar();
 
 The same holds for "real-world" images:
 
-```{code-cell} python
+```{code-cell}
 from skimage import data
 
 coins = data.coins()
@@ -52,7 +54,7 @@ plt.imshow(coins, cmap='gray');
 
 A color image is a 3D array, where the last dimension has size 3 and represents the red, green, and blue channels:
 
-```{code-cell} python
+```{code-cell}
 cat = data.chelsea()
 print("Shape:", cat.shape)
 print("Values min/max:", cat.min(), cat.max())
@@ -62,7 +64,7 @@ plt.imshow(cat);
 
 These are *just NumPy arrays*. E.g., we can make a red square by using standard array slicing and manipulation:
 
-```{code-cell} python
+```{code-cell}
 cat[10:110, 10:110, :] = [255, 0, 0]  # [red, green, blue]
 plt.imshow(cat);
 ```
@@ -84,14 +86,14 @@ Images can also include transparent regions by adding a 4th dimension, called an
 
 ## Displaying images using matplotlib
 
-```{code-cell} python
+```{code-cell}
 from skimage import data
 
 img0 = data.chelsea()
 img1 = data.rocket()
 ```
 
-```{code-cell} python
+```{code-cell}
 import matplotlib.pyplot as plt
 
 f, (ax0, ax1) = plt.subplots(1, 2, figsize=(20, 10))
@@ -128,7 +130,7 @@ data-type of the array.
 
 E.g., here, I generate two valid images:
 
-```{code-cell} python
+```{code-cell}
 linear0 = np.linspace(0, 1, 2500).reshape((50, 50))
 linear1 = np.linspace(0, 255, 2500).reshape((50, 50)).astype(np.uint8)
 
@@ -148,7 +150,7 @@ as long as the range is correct (0-1 for floating point images, 0-255 for unsign
 
 You can convert images between different representations by using ``img_as_float``, ``img_as_ubyte``, etc.:
 
-```{code-cell} python
+```{code-cell}
 from skimage import img_as_float, img_as_ubyte
 
 image = data.chelsea()
@@ -164,7 +166,7 @@ print("231/255 =", 231/255.)
 
 Your code would then typically look like this:
 
-```python
+```{code-cell}
 def my_function(any_image):
    float_image = img_as_float(any_image)
    # Proceed, knowing image is in [0, 1]
@@ -181,7 +183,7 @@ Mostly, we won't be using input images from the scikit-image example data sets.
 
 scikit-image conveniently wraps many of these in the `io` submodule, and will use whichever of the libraries mentioned above are installed:
 
-```{code-cell} python
+```{code-cell}
 from skimage import io
 
 image = io.imread('../images/balloon.jpg')
@@ -196,15 +198,15 @@ plt.imshow(image);
 
 We also have the ability to load multiple images, or multi-layer TIFF images:
 
-```{code-cell} python
+```{code-cell}
 ic = io.ImageCollection('../images/*.png:../images/*.jpg')
 
 print('Type:', type(ic))
 
 ic.files
 ```
 
-```{code-cell} python
+```{code-cell}
 import os
 
 f, axes = plt.subplots(nrows=3, ncols=len(ic) // 3 + 1, figsize=(20, 5))
@@ -227,11 +229,11 @@ plt.tight_layout()
 
 `enumerate` gives us each element in a container, along with its position.
 
-```{code-cell} python
+```{code-cell}
 animals = ['cat', 'dog', 'leopard']
 ```
 
-```{code-cell} python
+```{code-cell}
 for i, animal in enumerate(animals):
     print('The animal in position {} is {}'.format(i, animal))
 ```
@@ -244,8 +246,10 @@ The arms and strut of the H should have a width of 3 pixels, and the H itself sh
 
 Start with the following template:
 
-```{code-cell} python
-:tags: [hide-output]
+```{code-cell}
+---
+tags: [hide-output]
+---
 
 def draw_H(image, coords, color=(0, 255, 0)):
     out = image.copy()
@@ -257,8 +261,10 @@ def draw_H(image, coords, color=(0, 255, 0)):
 
 Test your function like so:
 
-```{code-cell} python
-:tags: [remove-output]
+```{code-cell}
+---
+tags: [remove-output]
+---
 
 cat = data.chelsea()
 cat_H = draw_H(cat, (50, -50))
@@ -269,8 +275,10 @@ plt.imshow(cat_H);
 
 Display the different color channels of the image along (each as a gray-scale image).  Start with the following template:
 
-```{code-cell} python
-:tags: [raises-exception, remove-output]
+```{code-cell}
+---
+tags: [raises-exception, remove-output]
+---
 
 # --- read in the image ---
 
@@ -308,20 +316,20 @@ ax_color.set_title('all channels');
 
 Now, take a look at the following R, G, and B channels.  How would their combination look? (Write some code to confirm your intuition.)
 
-```{code-cell} python
+```{code-cell}
 from skimage import draw
 
 red = np.zeros((300, 300))
 green = np.zeros((300, 300))
 blue = np.zeros((300, 300))
 
-r, c = draw.circle(100, 100, 100)
+r, c = draw.circle_perimeter(100, 100, 100, shape=red.shape)
 red[r, c] = 1
 
-r, c = draw.circle(100, 200, 100)
+r, c = draw.circle_perimeter(100, 200, 100, shape=green.shape)
 green[r, c] = 1
 
-r, c = draw.circle(200, 150, 100)
+r, c = draw.circle_perimeter(200, 150, 100, shape=blue.shape)
 blue[r, c] = 1
 
 f, axes = plt.subplots(1, 3)
@@ -344,8 +352,10 @@ Compare your results to that obtained with `skimage.color.rgb2gray`.
 
 Change the coefficients to 1/3 (i.e., take the mean of the red, green, and blue channels, to see how that approach compares with `rgb2gray`).
 
-```{code-cell} python
-:tags: [raises-exception, remove-output]
+```{code-cell}
+---
+tags: [raises-exception, remove-output]
+---
 
 from skimage import color, img_as_float
 

content/00_images_are_arrays.md

@@ -1,15 +1,11 @@
 scikit-image[data] >= 0.18
-numpy >= 1.12
-scipy >= 1.0
-matplotlib >= 2.1
+numpy >= 1.20
+scipy >= 1.7
+matplotlib >= 3.0
 notebook >= 4.0
 scikit-learn >= 0.18
-jupyter-book >= 0.10.2
 napari[all]
 jupytext >=1.10.3
-sphinx_autodoc_typehints>=1.11.0
-ghp-import
-pytest
-pytest-qt
-pooch
-furo
+furo
+myst-nb
+sphinx-copybutton

requirements.txt

@@ -1,5 +1,4 @@
-# Minimal makefile for Sphinx documentation
-#
+.PHONY: help Makefile clean
 
 # You can set these variables from the command line, and also
 # from the environment for the first two.
@@ -12,18 +11,10 @@ BUILDDIR      = _build
 help:
 	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
 
-notebooks:
-	mkdir -p notebooks
-	jupytext -k python3 ../content/*.md --from myst --to notebook
-	mv ../content/*.ipynb notebooks
-
 clean:
 	rm -rf _build
-	rm -rf notebooks
-
-.PHONY: help Makefile notebooks clean
 
 # Catch-all target: route all unknown targets to Sphinx using the new
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

site/Makefile

@@ -31,6 +31,7 @@
     'myst_nb',
     'sphinx_copybutton',
 ]
+jupyter_execute_notebooks = "off"
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
@@ -67,4 +68,4 @@
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
+html_static_path = ['_static']

site/conf.py

@@ -4,7 +4,7 @@
 
 [launch_binder]: http://mybinder.org/v2/gh/scikit-image/skimage-tutorials/main?urlpath=lab/tree/content
 
- ## Content
+## Content
 
 ```{toctree}
 ---
@@ -14,9 +14,8 @@ introduction
 applications
 ```
 
-
 ## Indices and tables
 
 * {ref}`genindex`
 * {ref}`modindex`
-* {ref}`search`
+* {ref}`search`

site/content

@@ -6,6 +6,6 @@ Basic tutorials to prepare you for your journey on scikit-image.
 ---
 maxdepth: 1
 ---
-content/tour_of_skimage.md
-content/00_images_are_arrays.md
-```
+content/tour_of_skimage
+content/00_images_are_arrays
+```