iCAT-workflow

Post-processing workflow for volume CLEM image data.

Installation

Assumes you are logged into a remote Linux server with conda configured.

1. Vastly overcomplicated but highly recommended environment setup with conda.

   $ conda create -n icat jupyterlab altair vega_datasets
   $ conda activate icat
   $ (icat) conda install -c conda-forge nodejs=15
   $ (icat) pip install tqdm lxml ipympl ipywidgets imagecodecs ruamel.yaml
   $ (icat) pip install git+git://github.com/AllenInstitute/BigFeta/
   $ (icat) jupyter labextension install @jupyter-widgets/jupyterlab-manager
   $ (icat) jupyter labextension install jupyter-matplotlib
   $ (icat) jupyter nbextension enable --py widgetsnbextension

2. Install iCAT-workflow from github repo

   $ (icat) pip install git+https://github.com/hoogenboom-group/iCAT-workflow.git

3. Clone GitHub repo

   $ (icat) git clone https://github.com/hoogenboom-group/iCAT-workflow

Getting started

1. Connect to remote server with port forwarding e.g.

   ssh -L 8888:localhost:8888 {user}@{server}

2. (Optional) Download sample data (~3GB) to a convenient location (will take several minutes)

   $ (icat) cd /path/to/data/storage/
   $ (icat) svn export https://github.com/hoogenboom-group/iCAT-data.git/trunk/pancreas

3. Start jupyter lab session

   $ (icat) cd ./iCAT-workflow/
   $ (icat) jupyter lab --no-browser --port 8888

4. Open a browser and navigate to http://localhost:8888/lab to run jupyter lab session