The auto-style-correct keeps borking my spacing goddammit

Author: vdauwera

docs/side_quests/metadata.md

@@ -870,53 +870,53 @@ Applying this pattern in your own work will enable you to build robust, maintain
 
 ### Key patterns
 
-1. **Reading and Structuring Metadata:** Reading CSV files and creating organized metadata maps that stay associated with your data files.
+1.  **Reading and Structuring Metadata:** Reading CSV files and creating organized metadata maps that stay associated with your data files.
 
-   ```groovy
-   channel.fromPath('samplesheet.csv')
-     .splitCsv(header: true)
-     .map { row ->
-         [ [id:row.id, character:row.character], row.recording ]
-     }
-   ```
+        ```groovy
+        channel.fromPath('samplesheet.csv')
+          .splitCsv(header: true)
+          .map { row ->
+              [ [id:row.id, character:row.character], row.recording ]
+          }
+        ```
 
-2. **Expanding Metadata During Workflow** Adding new information to your metadata as your pipeline progresses by adding process outputs and deriving values through conditional logic
+2.  **Expanding Metadata During Workflow** Adding new information to your metadata as your pipeline progresses by adding process outputs and deriving values through conditional logic.
 
-   - Adding new keys based on process output
+        - Adding new keys based on process output
 
-   ```groovy
-   .map { meta, file, lang ->
-     [ meta + [lang:lang], file ]
-   }
-   ```
-
-   - Adding new keys using a conditional clause
-
-   ```groovy
-   .map{ meta, file ->
-       if ( meta.lang.equals("de") || meta.lang.equals('en') ){
-           lang_group = "germanic"
-       } else if ( meta.lang in ["fr", "es", "it"] ) {
-           lang_group = "romance"
-       } else {
-           lang_group = "unknown"
-       }
-   }
-   ```
+        ```groovy
+        .map { meta, file, lang ->
+          [ meta + [lang:lang], file ]
+        }
+        ```
+
+        - Adding new keys using a conditional clause
+
+        ```groovy
+        .map{ meta, file ->
+            if ( meta.lang.equals("de") || meta.lang.equals('en') ){
+                lang_group = "germanic"
+            } else if ( meta.lang in ["fr", "es", "it"] ) {
+                lang_group = "romance"
+            } else {
+                lang_group = "unknown"
+            }
+        }
+        ```
 
-3. **Customizing Process Behavior:** Using metadata to adapt how processes handle different files
+3.  **Customizing Process Behavior:** Using metadata to adapt how processes handle different files.
 
-   - Using meta values in Process Directives
+    - Using meta values in Process Directives
 
-   ```groovy
-   publishDir "results/${meta.lang_group}", mode: 'copy'
-   ```
+    ```groovy
+    publishDir "results/${meta.lang_group}", mode: 'copy'
+    ```
 
-   - Adapting tool parameters for individual files
+    - Adapting tool parameters for individual files
 
-   ```groovy
-   cat $input_file | cowpy -c ${meta.character} > cowpy-${input_file}
-   ```
+    ```groovy
+    cat $input_file | cowpy -c ${meta.character} > cowpy-${input_file}
+    ```
 
 ### Additional resources
 

docs/side_quests/splitting_and_grouping.md

@@ -1152,27 +1152,27 @@ Mastering these channel operations will enable you to build flexible, scalable p
        )
    ```
 
-4. **Distributing across intervals:** We used `combine` to create Cartesian products of samples with genomic intervals for parallel processing
+4. **Distributing across intervals:** We used `combine` to create Cartesian products of samples with genomic intervals for parallel processing.
 
    ```groovy
    samples_ch.combine(intervals_ch)
    ```
 
-5. **Aggregating by grouping keys:** We used `groupTuple` to group by the first element in each tuple, thereby collecting samples sharing `id` and `interval` fields and merging technical replicates
+5. **Aggregating by grouping keys:** We used `groupTuple` to group by the first element in each tuple, thereby collecting samples sharing `id` and `interval` fields and merging technical replicates.
 
    ```groovy
    channel.groupTuple()
    ```
 
-6. **Optimizing the data structure:** We used `subMap` to extract specific fields and created a named closure for making transformations reusable
+6. **Optimizing the data structure:** We used `subMap` to extract specific fields and created a named closure for making transformations reusable.
 
    - Extract specific fields from a map
 
    ```groovy
    meta.subMap(['id', 'repeat'])
    ```
 
-   - Named closure for reusable transformations
+   - Use named closure for reusable transformations
 
    ```groovy
    getSampleIdAndReplicate = { meta, file -> [meta.subMap(['id', 'repeat']), file] }