O'Hare, et al., 2025, Neuron

Data and code to reproduce results in O'Hare et al., 2025 (Neuron). Everything in this folder is self-contained. Wherever you unzip it, you should run things exclusively from within it. Contents: 1. Jupyter notebook (made in Jupyter Lab) to reproduce all analytical plots 2. plotting_utils Python module to assist Jupyter notebook 3. lab Python module to illustrate operations for template-based event detection and plateau potential classification 4. Conda environment for all code. See instructions below. You'll need 1.33 GB to unpack this environment. To get going: 1. Unpack conda environments (locally, no need to permanently link to your conda install!) a. ohare2025 needs to be unpacked from .tar.gz file. to do so: i. cd /path/to/folder/OHare2025_code/envs ii. mkdir ohare2025 iii. tar -xzf ohare2025-env.tar.gz -C ohare2025 #unpack into empty ohare2025 folder. may take a while. 2. Register the ohare2025 env as a Python kernel in your jupyter lab/notebook distribution. a. open whatever conda environment you normally use to run jupyter b. install ipykernel if you don't have it: pip install ipykernel #do NOT use conda install here! c. conda deactivate #unless running jupyter from base, in which case skip this command d. source /path/to/folder/OHare2025_code/envs/ohare2025/bin/activate #activates the conda env in-place e. python -m ipykernel install --prefix /path/to/jupyterlab_env --name ohare2025 --display-name "Python 2.7 (OHare2025)" f. exit the ohare2025 env deactivate #don't use conda here source ~/.bashrc #if you still see ohare2025 hanging around in your command prompt g. return to your jupyter env and verify kernel installed: conda activate <env_you_use_for_jupyter> jupyter kernelspec list #you should see it here, located in its conda env, e.g. ~/miniconda3/envs/jupyter/share/jupyter/kernels/ohare2025 3. Launch jupyter lab/notebook. a. cd /path/to/folder/OHare2025_code #this is critical b. launch jupyter lab: jupyter lab #or jupyter notebook, if that's your jam c. open O'Hare_2025_Figs.ipynb from the file explorer tab and select the new OHare2025 kernel you created. Enjoy! Code is shown exactly as used to produce each data figure in our manuscript. Feel free to explore the datasets further - they are stored as Pandas DataFrames in .pkl files and are thoroughly annotated, e.g. with animal ID, dendrite type, dendrite order, dendrite path distance to soma, dendrite parent soma, ROI genotype, etc. Enjoy! *Note: The Jupyter notebook was created in JupyterLab to enable toggling by figure. It may also function in a classical Jupyter notebook with nbextensions enabled. Without toggling, these notebooks will be unwieldy.