Merge branch 'swcarpentry:main' into main

.github/workflows/README.md

@@ -2,7 +2,7 @@
 
 This directory contains workflows to be used for Lessons using the {sandpaper}
 lesson infrastructure. Two of these workflows require R (`sandpaper-main.yaml`
-and `pr-recieve.yaml`) and the rest are bots to handle pull request management.
+and `pr-receive.yaml`) and the rest are bots to handle pull request management.
 
 These workflows will likely change as {sandpaper} evolves, so it is important to
 keep them up-to-date. To do this in your lesson you can do the following in your
@@ -94,7 +94,7 @@ branch called `update/workflows` and a pull request is created. Maintainers are
 encouraged to review the changes and accept the pull request if the outputs
 are okay.
 
-This update is run ~~weekly or~~ on demand.
+This update is run weekly or on demand.
 
 ### 03 Maintain: Update Package Cache (update-cache.yaml)
 
@@ -140,7 +140,7 @@ Once the checks are finished, a comment is issued to the pull request, which
 will allow maintainers to determine if it is safe to run the 
 "Receive Pull Request" workflow from new contributors.
 
-### Recieve Pull Request (pr-recieve.yaml)
+### Receive Pull Request (pr-receive.yaml)
 
 **Note of caution:** This workflow runs arbitrary code by anyone who creates a
 pull request. GitHub has safeguarded the token used in this workflow to have no
@@ -171,7 +171,7 @@ The artifacts produced are used by the next workflow.
 
 ### Comment on Pull Request (pr-comment.yaml)
 
-This workflow is triggered if the `pr-recieve.yaml` workflow is successful.
+This workflow is triggered if the `pr-receive.yaml` workflow is successful.
 The steps in this workflow are:
 
 1. Test if the workflow is valid and comment the validity of the workflow to the

.github/workflows/sandpaper-version.txt

@@ -1 +1 @@
-0.13.0
+0.16.5

episodes/16-writing-functions.md

@@ -573,21 +573,25 @@ density. In the model, time takes discrete values 0, 1, 2, ...
 
 ## Solution
 
-1. ```python
+1.
+  ```python
   def logistic_map(x, r):
       return r * x * (1 - x)
   ```
 
-2. ```python
+2.
+  ```python
   initial_population = 0.5
   t_final = 10
   r = 1.0
   population = [initial_population]
+
   for t in range(t_final):
       population.append( logistic_map(population[t], r) )
   ```
 
-3. ```python
+3.
+  ```python
   def iterate(initial_population, t_final, r):
       population = [initial_population]
       for t in range(t_final):
@@ -598,7 +602,7 @@ density. In the model, time takes discrete values 0, 1, 2, ...
       population = iterate(0.5, period, 1)
       print(population[-1])
   ```
-  
+
   ```output
   0.06945089389714401
   0.009395779870614648