Docs fixes: tidy decoding docstrings, change hatch version get, add i…

…nits

docs/README.md

@@ -55,3 +55,6 @@ The following items can be commented out in `mkdocs.yml` to reduce build time:
 - `mkdocs-jupyter`: Generates tutorial pages from notebooks.
 
 To end the process in your console, use `ctrl+c`.
+
+If your new submodule is causing a build error (e.g., "Could not collect ..."),
+you may need to add `__init__.py` files to the submodule directories.

docs/build-docs.sh

@@ -10,13 +10,14 @@ cp ./LICENSE ./docs/src/LICENSE.md
 mkdir -p ./docs/src/notebooks
 cp ./notebooks/*ipynb ./docs/src/notebooks/
 cp ./notebooks/*md ./docs/src/notebooks/
-cp ./docs/src/notebooks/README.md ./docs/src/notebooks/index.md
+mv ./docs/src/notebooks/README.md ./docs/src/notebooks/index.md
 cp -r ./notebook-images ./docs/src/notebooks/
 cp -r ./notebook-images ./docs/src/
 
 # Get major version
-FULL_VERSION=$(hatch version) # Most recent tag, may include periods
-export MAJOR_VERSION="${FULL_VERSION:0:3}" # First 3 chars of tag
+version_line=$(grep "__version__ =" ./src/spyglass/_version.py)
+version_string=$(echo "$version_line" | awk -F"[\"']" '{print $2}')
+export MAJOR_VERSION="${version_string:0:3}"
 echo "$MAJOR_VERSION"
 
 # Get ahead of errors

docs/mkdocs.yml

@@ -16,9 +16,6 @@ theme:
   favicon: images/Spyglass.svg
   features:
     - toc.follow
-    # - navigation.expand # CBroz1: removed bc long tutorial list hides rest
-    # - toc.integrate
-    # - navigation.sections
     - navigation.top
     - navigation.instant # saves loading time - 1 browser page
     - navigation.tracking # even with above, changes URL by section
@@ -55,27 +52,30 @@ nav:
       - Database Management: misc/database_management.md
   - Tutorials:
       - Overview: notebooks/index.md
-      - General:
+      - Intro:
           - Setup: notebooks/00_Setup.ipynb
           - Insert Data: notebooks/01_Insert_Data.ipynb
           - Data Sync: notebooks/02_Data_Sync.ipynb
           - Merge Tables: notebooks/03_Merge_Tables.ipynb
-      - Ephys:
-          - Spike Sorting: notebooks/10_Spike_Sorting.ipynb
+          - Config Populate: notebooks/04_PopulateConfigFile.ipynb
+      - Spikes:
+          - Spike Sorting V0: notebooks/10_Spike_SortingV0.ipynb
+          - Spike Sorting V1: notebooks/10_Spike_SortingV1.ipynb
           - Curation: notebooks/11_Curation.ipynb
-          - LFP: notebooks/12_LFP.ipynb
-          - Theta: notebooks/14_Theta.ipynb
       - Position:
           - Position Trodes: notebooks/20_Position_Trodes.ipynb
           - DLC From Scratch: notebooks/21_Position_DLC_1.ipynb
           - DLC From Model: notebooks/22_Position_DLC_2.ipynb
           - DLC Prediction: notebooks/23_Position_DLC_3.ipynb
           - Linearization: notebooks/24_Linearization.ipynb
-      - Combined:
-          - Ripple Detection: notebooks/30_Ripple_Detection.ipynb
-          - Extract Mark Indicators: notebooks/31_Extract_Mark_Indicators.ipynb
-          - Decoding with GPUs: notebooks/32_Decoding_with_GPUs.ipynb
-          - Decoding Clusterless: notebooks/33_Decoding_Clusterless.ipynb
+      - LFP:
+          - LFP: notebooks/30_LFP.ipynb
+          - Theta: notebooks/31_Theta.ipynb
+          - Ripple Detection: notebooks/32_Ripple_Detection.ipynb
+      - Decoding:
+          - Extract Clusterless: notebooks/41_Extracting_Clusterless_Waveform_Features.ipynb
+          - Decoding Clusterless: notebooks/42_Decoding_Clusterless.ipynb
+          - Decoding Sorted Spikes: notebooks/43_Decoding_SortedSpikes.ipynb
   - API Reference: api/ # defer to gen-files + literate-nav
   - How to Contribute: contribute.md
   - Change Log: CHANGELOG.md

docs/src/api/make_pages.py

@@ -28,11 +28,6 @@
         else:
             break
 
-if add_limit is not None:
-    from IPython import embed
-
-    embed()
-
 
 with mkdocs_gen_files.open("api/navigation.md", "w") as nav_file:
     nav_file.write("* [Overview](../api/index.md)\n")

notebooks/README.md

@@ -8,32 +8,64 @@ described in the categories below.
 
 ## 0. Intro
 
-Everyone should complete the [Setup](./00_Setup.ipynb) and [Insert Data](./01_Insert_Data.ipynb) notebooks.
+Everyone should complete the [Setup](./00_Setup.ipynb) and
+[Insert Data](./01_Insert_Data.ipynb) notebooks.
 
-[Data Sync](./02_Data_Sync.ipynb) is an optional additional tool for collaborators that want to share analysis files.
+[Data Sync](./02_Data_Sync.ipynb) is an optional additional tool for
+collaborators that want to share analysis files.
 
-The [Merge Tables notebook](./03_Merge_Tables.ipynb) explains details on a new table tier unique to Spyglass that allows the user to use different versions of pipelines on the same data. This is important for understanding the later notebooks.
+The [Merge Tables notebook](./03_Merge_Tables.ipynb) explains details on a new
+table tier unique to Spyglass that allows the user to use different versions of
+pipelines on the same data. This is important for understanding the later
+notebooks.
 
 ## 1. Spike Sorting Pipeline
 
-This series of notebooks covers the process of spike sorting, from automated spike sorting to optional manual curation of the output of the automated sorting.
+This series of notebooks covers the process of spike sorting, from automated
+spike sorting to optional manual curation of the output of the automated
+sorting.
 
 ## 2. Position Pipeline
 
-This series of notebooks covers tracking the position(s) of the animal. The user can employ two different methods:
+This series of notebooks covers tracking the position(s) of the animal. The
+user can employ two different methods:
 
-1. the simple [Trodes](20_Position_Trodes.ipynb) methods of tracking LEDs on the animal's headstage
-2. [DLC (DeepLabCut)](./21_Position_DLC_1.ipynb) which uses a neural network to track the animal's body parts
+1. the simple [Trodes](20_Position_Trodes.ipynb) methods of tracking LEDs on
+   the animal's headstage
+2. [DLC (DeepLabCut)](./21_Position_DLC_1.ipynb) which uses a neural network to
+   track the animal's body parts
 
-Either case can be followed by the [Linearization notebook](./24_Linearization.ipynb) if the user wants to linearize the position data for later use.
+Either case can be followed by the
+[Linearization notebook](./24_Linearization.ipynb) if the user wants to
+linearize the position data for later use.
 
 ## 3. LFP Pipeline
 
-This series of notebooks covers the process of LFP analysis. The [LFP](./30_LFP.ipynb) covers the extraction of the LFP in specific bands from the raw data. The [Theta](./31_Theta.ipynb) notebook shows specifically how to extract the theta band power and phase from the LFP data. Finally the [Ripple Detection](./32_Ripple_Detection.ipynb) notebook shows how to detect ripples in the LFP data.
+This series of notebooks covers the process of LFP analysis. The
+[LFP](./30_LFP.ipynb) covers the extraction of the LFP in specific bands from
+the raw data. The [Theta](./31_Theta.ipynb) notebook shows specifically how to
+extract the theta band power and phase from the LFP data. Finally the
+[Ripple Detection](./32_Ripple_Detection.ipynb) notebook shows how to detect
+ripples in the LFP data.
 
 ## 4. Decoding Pipeline
 
-This series of notebooks covers the process of decoding the position of the animal from spiking data. It relies on the position data from the Position pipeline and the output of spike sorting from the Spike Sorting pipeline. Decoding can be from sorted or from unsorted data using spike waveform features (so-called clusterless decoding). The first notebook([Extracting Clusterless Waveform Features](./41_Extracting_Clusterless_Waveform_Features.ipynb)) in this series shows how to retrieve the spike waveform features used for clusterless decoding. The second notebook ([Clusterless Decoding](./42_Decoding_Clusterless.ipynb)) shows a detailed example of how to decode the position of the animal from the spike waveform features. The third notebook ([Decoding](./43_Decoding.ipynb)) shows how to decode the position of the animal from the sorted spikes.
+This series of notebooks covers the process of decoding the position of the
+animal from spiking data. It relies on the position data from the Position
+pipeline and the output of spike sorting from the Spike Sorting pipeline.
+Decoding can be from sorted or from unsorted data using spike waveform features
+(so-called clusterless decoding).
+
+The first notebook
+([Extracting Clusterless Waveform Features](./41_Extracting_Clusterless_Waveform_Features.ipynb))
+in this series shows how to retrieve the spike waveform features used for
+clusterless decoding.
+
+The second notebook
+([Clusterless Decoding](./42_Decoding_Clusterless.ipynb)) shows a detailed
+example of how to decode the position of the animal from the spike waveform
+features. The third notebook ([Decoding](./43_Decoding.ipynb)) shows how to
+decode the position of the animal from the sorted spikes.
 
 ## Developer note
 

src/spyglass/common/common_lab.py

@@ -108,7 +108,7 @@ def get_djuser_name(cls, dj_user) -> str:
 
         Parameters
         ----------
-        user: str
+        dj_user: str
             The datajoint user name.
 
         Returns