Post-processing workflow for volume CLEM image data.
Assumes you are logged into a remote Linux server with conda configured.
- 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
- Install iCAT-workflow from github repo
$ (icat) pip install git+https://github.com/hoogenboom-group/iCAT-workflow.git
- Clone GitHub repo
$ (icat) git clone https://github.com/hoogenboom-group/iCAT-workflow
- Connect to remote server with port forwarding e.g.
ssh -L 8888:localhost:8888 {user}@{server}
- (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
- Start
jupyter labsession
$ (icat) cd ./iCAT-workflow/
$ (icat) jupyter lab --no-browser --port 8888
- Open a browser and navigate to http://localhost:8888/lab to run jupyter lab session