try update to python 3.11

.github/workflows/build.yml

@@ -4,7 +4,7 @@ on:
   push:
     branches: [ "main" ]
   pull_request:
-    branches: [ "main" ]
+    branches: [ "main" , "dev"]
 
   workflow_dispatch:
 
@@ -23,23 +23,26 @@ jobs:
           fetch-depth: 0
           ref: main
 
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        # with:
-        #   python-version: "3.9"
+      # - name: Set up Python
+      #   uses: actions/setup-python@v4
+      #   with:
+      #     python-version: "3.11"
 
       - name: Upgrading pip
         run: pip install --upgrade pip
 
       - name: Install deps
         run: pip install cython numpy
 
-      - name: Installing package
-        run: pip install -e .
+      - name: pip freeze
+        run: pip freeze
 
       - name: Installing packages again (this prevents a weird error)
         run: pip install -r requirements.txt
 
+      - name: Installing package
+        run: pip install -e .
+
       - name: Installing build dependencies
         run: |
           pip install markupsafe==2.0.1

docs/contribution.md

@@ -8,7 +8,7 @@ The Databook can be forked via the GitHub Web UI from the Databook's [GitHub rep
 ## Initialize Locally
 A local repo can be made by pressing the `code` button on the front page of the forked repo, and copying the HTTPS url. Then locally, run the command `git clone <copied_url_here>`. For more information on cloning GitHub repos, check out GitHub's [Cloning a Repository](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository) Page.
 
-Then the environment must be set up. You may set up a conda environment if you don't want to interfere with your local environment. After installing conda, this can be done with the commands `conda create --name databook python=3.9` followed by `activate databook` (Windows) or `source activate databook` (Mac/Linux). Within or without the conda environment, the dependencies for the databook can be installed by navigating to the openscope_databook directory and running `pip install -e . --user`.
+Then the environment must be set up. You may set up a conda environment if you don't want to interfere with your local environment. After installing conda, this can be done with the commands `conda create --name databook python=3.11` followed by `activate databook` (Windows) or `source activate databook` (Mac/Linux). Within or without the conda environment, the dependencies for the databook can be installed by navigating to the openscope_databook directory and running `pip install -e . --user`.
 
 Finally, notebooks can be run with Jupyter notebook by running `jupyter notebook ./docs`