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escher-vmh

v1.7.4-beta2

Published

Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Metabolic Pathways

Downloads

8

Readme

PyPi NPM Gitter.im Documentation Status Travis Coverage Status MIT

Escher

Escher is a web-based tool to build, view, share, and embed metabolic maps. The easiest way to use Escher is to browse or build maps on the Escher website.

Visit the documentation to get started with Escher and explore the API.

Check out the developer docs, the Gitter chat room, and the Development Roadmap for information on Escher development. Feel free to submit bugs and feature requests as Issues, or, better yet, Pull Requests.

Follow @zakandrewking for Escher updates.

You can help support Escher by citing our publication when you use Escher or EscherConverter:

Zachary A. King, Andreas Dräger, Ali Ebrahim, Nikolaus Sonnenschein, Nathan E. Lewis, and Bernhard O. Palsson (2015) Escher: A web application for building, sharing, and embedding data-rich visualizations of biological pathways, PLOS Computational Biology 11(8): e1004321. doi:10.1371/journal.pcbi.1004321

Escher was developed at SBRG. Funding was provided by The National Science Foundation Graduate Research Fellowship under Grant no. DGE-1144086, The European Commission as part of a Marie Curie International Outgoing Fellowship within the EU 7th Framework Program for Research and Technological Development (EU project AMBiCon, 332020), and The Novo Nordisk Foundation through The Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517)

Building and testing Escher

JavaScript

First, install dependencies with npm (or you can use yarn):

brew install nvm pyenv
pyenv install 3.10.11
pyenv shell 3.10.11
python -m venv .venv
source .venv/bin/activate
nvm use
npm install

Escher uses webpack to manage the build process. To run typical build steps, just run:

npm run build

You can run a development server with:

npm run start
# or for live updates when the source code changes:
npm run watch

To test the JavaScript files, run:

npm run test

Python

Escher has a Python package for generating Escher visualizations from within a Python data anlaysis session. To learn more about using the features of the Python package, check out the documentation:

https://escher.readthedocs.io/en/latest/escher-python.html

You can install it with pip:

pip install escher

Jupyter extensions

When you pip install escher, the Jupyter notebook extension should be installed automatically. If that doesn't work, try:

# The notebook extenstion should install automatically. You can check by running:
jupyter nbextension list
# Make sure you have version >=5 of the `notebook` package
pip install "notebook>=5"
# To manually install the extension
jupyter nbextension install --py escher
jupyter nbextension enable --py escher
# depending on you environment, you might need the `--sysprefix` flag with those commands

To install the Jupyter lab extension, simply install Escher with pip install escher then install the extension:

jupyter labextension install @jupyter-widgets/jupyterlab-manager
jupyter labextension install escher

Python/Jupyter Development

For development of the Python package, first build the JavaScript package and copy it over to the py directory with these commands in the Escher root:

npm install
npm run build
npm run copy

Then in the py directory, install the Python package:

cd py
pip install -r requirements.txt

To use the widgets within notebooks from jupyterlab, you also need to run the following from the escher root directory:

jupyter labextension install @jupyter-widgets/jupyterlab-manager
jupyter labextension install escher

For Python testing, run this in the py directory:

cd py
pytest

To develop the Jupyter notebook and Jupyter Lab extensions, you will need install them with symlinks.

First, install the Python package for development as described above.

For the Jupyter notebooks, run:

cd py
jupyter nbextension install --py --symlink escher
jupyter nbextension enable --py escher

If you are using virtualenv or conda, you can add the --sys-prefix flag to those commands to keep your environment isolated and reproducible.

When you make changes, you will need to yarn build && yarn copy and refresh notebook browser tab.

For Jupyter Lab, run (in the root directory):

yarn watch # keep this running as a separate process
jupyter labextension install @jupyter-widgets/jupyterlab-manager
jupyter labextension link
jupyter lab --watch

If you don't see changes when you edit the code, try refreshing or restarting jupyter lab --watch.

Docs

Build and run the docs::

cd docs
./build_docs
cd _build/html
python -m SimpleHTTPServer # python 2
python -m http.server # python 3