Typo fixes, clarifying binary image weirdness in exercise 16, clarifying installation docs

Author: mwhamgenomics

episodes/05-napari.md

@@ -86,7 +86,7 @@ at the image's shape:
 
 ```python
 from skimage.io import imread
-iage = imread('path/to/FluorescentCells_3channel.tif')
+image = imread('path/to/FluorescentCells_3channel.tif')
 image.shape
 # returns: (512, 512, 3)
 ```
@@ -127,7 +127,7 @@ channel axis is represented as a slider, meaning we can't view the two at the sa
 As an alternative, we can load the image in the Python console:
 
 ```python
-image = skimage.io.imread('path/to/confocal-series_zstack.tif')
+image = imread('path/to/confocal-series_zstack.tif')
 image.shape
 # returns: (25, 2, 400, 400)
 ```
@@ -161,7 +161,7 @@ Remember that instead of `matplotlib.pyplot.imshow()`, you'll need to use `viewe
 ```python
 import skimage
 
-image = skimage.io.cells3d()
+image = skimage.data.cells3d()
 image.shape
 # returns: (60, 2, 256, 256)
 
@@ -208,8 +208,9 @@ For example, we can go to 'Open Sample' -> 'napari builtins' and  -> 'Binary Blo
 - Split touching objects (nsbatwm). Try experimenting with different sigma values until you get some good cell separations.
 - Connected component labelling (scikit-image, nsbatwm)
 
-Note: The Otsu threshold shouldn't be required since the test image is already a binary, but for some reason the 'Split
-touching objects' Napari tool isn't able to recognise the original as a binary image.
+Note: The Otsu threshold shouldn't be required since the test image is already a binary, but the 'Split
+touching objects' Napari tool isn't able to recognise the original as a binary image. This is because Napari expects the values
+to be 0/1, whereas the test image uses the values True/False. Thresholding the image again converts it to the right type.
 
 ::::::::::::::::::::::::::::::::::::: challenge
 

learners/setup.md

@@ -84,9 +84,12 @@ browser tab.
 ### Jupyter installation
 
 If you don't have access to a JupyterHub instance but still want to use Jupyter, you can
-install it locally:
+install it locally either with Conda:
 
        $ conda install -c conda-forge jupyterlab  # with Conda
+
+Or with pip:
+
        $ pip install jupyterlab                   # with Pip
 
 Once installed, start Jupyter with:
@@ -95,10 +98,20 @@ Once installed, start Jupyter with:
 
 This will start the server process and open JupyterLab in a new browser window. Follow the same
 GUI or graphical folder setup as for JupyterHub above.
+
+You may need to install some extra packages, such as skimage. To do this, exit Jupyter and in the same
+terminal window, run:
+
+       $ pip install scikit-image
+
+Or in Conda:
+
+       $ conda install scikit-image
+
 :::::::::::::::::::::::::
 
 :::::::::::::::: solution
-### Local setup
+### Local setup with venv and pip
 
 If you wish to use Python on its own, then that is also possible.
 You will also need the packages skimage, imageio, numpy and matplotlib.
@@ -167,8 +180,8 @@ $ config config --set solver libmamba  # if the confgured solver is 'classic'
 :::::::::::::::: solution
 ### Installation on Windows with Conda
 
-This assumes that you already have Anaconda installed on your system. You will need to use the
-'Anaconda' prompt option to open a terminal session with Conda loaded:
+This assumes that you already have Anaconda installed on your system. There should be an 'Anaconda
+Prompt' option in the start menu. Select this to open a terminal session with Conda loaded, and run:
 
        > conda create --name devbio-napari napari=0.4.19 python=3.9 devbio-napari pyqt -c conda-forge -c pytorch