jupyter-docker-pymol

Travis CI:

Container-based installation of PyMol, with interaction through the browser via ipymol and Jupyter notebook (based on jupyter/notebook). A convenient and portable way to render pretty pictures of molecules, and much more.

The installation also contains numpy and scipy among other things, so it can be used for a variety of scientific Python tasks.

NOTE TO POTENTIAL USERS

May 22, 2019. Hi, thanks for checking out this project. If you are interested in using this, you should look at github.com/BioContainers instead, which is a giant repository of dockerized bioinformatics software. I contributed jupyter-docker-pymol to that, and it now lives at this address: https://github.com/BioContainers/containers/tree/master/pymol-jupiter . I am not planning to touch this repository (github.com/ocramz/jupyter-docker-pymol) anymore. Thanks and good luck!

Instructions (setup)

First, note down the IP address of the currently running Docker machine (which is called dev in this example) with

docker-machine ip dev

Download the image from Docker hub :
```
 docker pull ocramz/jupyter-docker-pymol
```
You can see the list of locally available Docker images with the command docker images

Run the image :

 docker run --rm -it -p 8888:8888 ocramz/jupyter-docker-pymol

Point your browser to the IP address of the Docker machine found initially, and port 8888, i.e.
```
 <docker-machine-ip>:8888
```
where <docker-machine-ip> usually starts with 192.168.

A Jupyter session should appear in the browser at this point. Up and running !

Instructions (use)

Within Jupyter, start a Python 3 document (or just start by modifying the provided example notebook)
Declare inline figure rendering within Jupyter notebooks and setup the connection to PyMol:
```
 %pylab inline 

 from ipymol import viewer as pm
 pm.start()
```

Run your PyMol tasks, e.g. :

 pm.do('fetch 3odu; as cartoon; bg white;')
 f1 = pm.show()

Requirements

Docker (Windows and OSX users should install the Docker Toolbox : https://docs.docker.com/toolbox/overview/)
docker-machine running in the current shell (setup guide : https://docs.docker.com/machine/get-started/)

Notes

This project uses PyMol 1.8.2.0 and Python 3

Caveat

At present, this setup is intended for local use only (i.e. the Docker image, along with all the computational payload i.e. PyMol and the Python interpreter, is running on the same host that runs the browser).

There is NO authentication to the notebooks and the Jupyter user is root.

Credits

the PyMol project contributors , pymol.org

the Jupyter and iPython project contributors , jupyter.org

Carlos Hernandez for ipymol , https://github.com/cxhernandez/ipymol

Saulo Alves (https://github.com/sauloal) for helping merge with the BioDocker project (https://github.com/BioDocker , now https://github.com/BioContainers)

dagezh/jupyter-docker-pymol