differences for PR #122

Author: actions-user

fig/layer-rename.png

@@ -325,11 +325,27 @@ Try increasing the 'sigma' value to three and clicking run again:
 of 3" width='40%'}
 
 You should see a new 'nuclei_gaussian_σ=3.0' layer that is much more heavily blurred. 
-If you used the `+`/`-` buttons to set the sigma value, you may see many more 
-decimal places in the layer name e.g. 'nuclei_gaussian_σ=3.00000000000000'. 
-If so, double click on the layer name to re-name it to `nuclei_gaussian_σ=3.0`.
 
-What's happening here? What exactly does a gaussian blur do? A gaussian blur is 
+::::::::::::::::::::::::::::::::::::: callout
+
+## Renaming Layer Names
+
+The Gaussian filter plugin uses the special character `σ` in the layer names.
+This naming convention makes it difficult to type layer names in the Napari
+console (unless your keyboard has the `σ` character). To make life easier
+change the name of the added layer now. Double click on the layer name, delete `σ`
+and change it to `sigma`
+
+![](fig/layer-rename.png){alt="Changing the name of an image layer." width='40%'}
+
+
+Similarly, if you used the `+`/`-` buttons to set the sigma value, you may see excessive
+decimal places in the layer name e.g. `nuclei_gaussian_σ=3.00000000000000`.
+If so, double click on the layer name to re-name it to `nuclei_gaussian_sigma=3.0`.
+
+:::::::::::::::::::::::::::::::::::::
+
+What's happening here? What exactly does a gaussian blur do? A gaussian blur is
 an example of a 'linear filter' which is used to manipulate pixel values in 
 images. When a filter is applied to an image, each pixel value is replaced by 
 some combination of the pixel values around it. For example, below is shown a 
@@ -521,12 +537,12 @@ Now let's try thresholding our image again. Make sure you set your gaussian blur
 sigma to three, then click 'Apply Gaussian Filter'.
 
 Then, we'll apply the same threshold as before, now to the 
-'nuclei_gaussian_σ=3.0' layer:
+'nuclei_gaussian_sigma=3.0' layer:
 
 ```python
 
 # Get the image data for the blurred nuclei
-blurred = viewer.layers["nuclei_gaussian_σ=3.0"].data
+blurred = viewer.layers["nuclei_gaussian_sigma=3.0"].data
 
 # Create mask with a threshold of 8266
 blurred_mask = blurred > 8266
@@ -578,7 +594,7 @@ Finally, open the `napari-matplotlib` histogram again with:
 the nuclei image after a gaussian blur. The left contrast limit is set 
 to 0.134."}
 
-Make sure you have 'nuclei_gaussian_σ=3.0' selected in the layer list (should 
+Make sure you have 'nuclei_gaussian_sigma=3.0' selected in the layer list (should 
 be highlighted in blue).
 
 You should see that this histogram now runs from 0 to 1, reflecting the new 
@@ -588,7 +604,7 @@ threshold between the two peaks - around 0.134:
 ```python
 
 # Get the image data for the blurred nuclei
-blurred = viewer.layers["nuclei_gaussian_σ=3.0"].data
+blurred = viewer.layers["nuclei_gaussian_sigma=3.0"].data
 
 # Create mask with a threshold of 0.134
 blurred_mask = blurred > 0.134
@@ -633,7 +649,7 @@ clear that some areas are still missed or incorrectly labelled.
 ## Automated thresholding
 
 First, let's clean up our layer list again. Make sure you only have the 
-'nuclei', 'mask', 'blurred_mask' and 'nuclei_gaussian_σ=3.0' layers in the 
+'nuclei', 'mask', 'blurred_mask' and 'nuclei_gaussian_sigma=3.0' layers in the 
 layer list - select any others and remove them by clicking the ![](
 https://raw.githubusercontent.com/napari/napari/main/src/napari/resources/icons/delete.svg
 ){alt="A screenshot of Napari's delete layer button" height='30px'} icon. Then, 
@@ -648,7 +664,7 @@ the most common methods is _Otsu thresholding_, which we will look at now.
 
 Let's go ahead and apply this to our blurred image:
 
-- Select 'nuclei_gaussian_σ=3.0' in the `Image` row
+- Select 'nuclei_gaussian_sigma=3.0' in the `Image` row
 - Select 'otsu' as the method
 - Click the 'Apply Thresholding' button
 

filters-and-thresholding.md

@@ -12,7 +12,7 @@
 "episodes/designing-a-light-microscopy-experiment.md" "9a236fbc9a48f560a786077ea9662c9c" "site/built/designing-a-light-microscopy-experiment.md" "2025-11-04"
 "episodes/choosing-acquisition-settings.md" "1bf362eeea03fe66d4e170d9803dc649" "site/built/choosing-acquisition-settings.md" "2024-08-16"
 "episodes/quality-control-and-manual-segmentation.md" "9b8a54c933bcd6ce1552ffd059c06045" "site/built/quality-control-and-manual-segmentation.md" "2025-06-25"
-"episodes/filters-and-thresholding.md" "e25c833b9cc4e93c7fe1ef7b0570ca07" "site/built/filters-and-thresholding.md" "2025-11-21"
+"episodes/filters-and-thresholding.md" "eff8ab1ac588ef608cc03ab169b68e5f" "site/built/filters-and-thresholding.md" "2026-06-04"
 "episodes/instance-segmentation-and-measurements.md" "e10dc9c644c27e3c40414057432575e8" "site/built/instance-segmentation-and-measurements.md" "2025-11-04"
 "episodes/additional-resources.md" "b9945f6270e488fe84524cb86e88485b" "site/built/additional-resources.md" "2026-05-05"
 "instructors/instructor-notes.md" "5cf113fd22defb29d17b64597f3c9bc0" "site/built/instructor-notes.md" "2024-08-16"