#Tagged code templates for running PyMOL inside JupyterLab
Go to the GitHub Page for a list of the snippets and their descriptions.
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- New to scripting in PyMOL
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- jupyterlabpymolpysnips PyMOL Python snippets for use in JupyterLab with the jupyterlab-snippets extension.
- jupyternbclassicpymolpysnipsplus
- taggedpymolpysnips
- taggedpymolpysnipspymolpysnipsplus
- jupyter-vsc-pymolpysnips
- jupyter-st3-pymolpysnips
- jupyter-emacs-pymolpysnips
- jupyter-ultisnips-pymolpysnips
- jupyter-snipmate-pymolpysnips
- jupyter-neosnippets-pymolpysnips
- jupyter-atom-pymolpysnips
- easypymol
- pymolshortcuts
- orgpymolpysnips
- rstudiopymolpysnips
- jupyterlabpymolpysnips
- colabOpenSourcePyMOLpySnips
- colabPyMOLpySnips
- PyMOLwallhangings
The code cells are easy to execute and edit with help from the menu pull-downs. The keyboard shortcuts for Jupyter are modest in number and fast to master.
You can illustrate your JupyterLab documents with images created in PyMOL. You can also adjust molecular images from within JupyterLab without opening PyMOL, thereby saving time.
The Jupyter notebooks have a gentle learning curve because they are easy to edit. The code blocks can be run individually during the development of the code, or all of the code blocks can be run at once.
The output from structure analysis can be directly imported into a pandas DataFrame and then onto a Python or R statistical package for further analysis. The presence of output from PyMOL and statistical packages in the same document eases the tracking of the results and supports reproducible research.
The images generated by PyMOL can be stored in one Jupyter notebook file instead of having dozens of script and image files sequestered in numerous subfolders. Using one file greatly eases finding the code to make a particular figure because the code and image can be next to each other. This ability to easily find the required code later reduces the resistance to remaking a figure for manuscript resubmission, journal cover artwork, posters, platform presentations, lectures, book chapters, review articles, websites, and wall hangings.
Using one file also eases sharing images with collaborators because only one file needs to be shared. If the collaborators are not PyMOL or JupyterLab users, the notebook file can be reformatted as a PDF or HTML file.
However, remember that although the stored images and data in a Jupyter notebook will be displayed upon reloading the notebook, these objects are not in the computer's current state and cannot be accessed. The objects have to be recreated from scratch to be able to work on them.
Other software can be run from the same notebook during a molecular structure analysis project provided that this software has a Python API. This can improve the reproducibility of the computational aspects of your research.
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You can run the PyMOL GUI next to your JupyterLab session. You can adjust the molecule's orientation manually, run the get_view command, and copy the output in the command history window from PyMOL and paste it into a cell in the Jupyter notebook. The one line of settings returned by the rv shortcut is much easier to work with (see PyMOL shortcuts).
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You do not need the viewport. With 10-15 minutes of practice, you can master the rapid iterating of rotate and translate commands to adjust the molecule's orientation with greater precision than via manipulation of the mouse. You can use the up arrow to reuse prior commands and to edit and rerun prior commands.
If you are not ready to write PyMOL scripts, please consider using PyMOL shortcuts to enhance your productivity in PyMOL interactive sessions. For example, the above ambient occlusion effect can be invoked at any time by entering AO at the PyMOL prompt, if the pymolshortcuts.py file has been loaded. These shortcuts can also be invoked in JupyterLab by submitting them as arguments to the cmd.do() method, (e.g., cmd.do("AO") to generate the ambient occlusion effect.)
The taggedpymolpysnips library contains 260 code fragments (i.e., templates or snippets) written in Python to run PyMOL in JupyterLab via PyMOL's Python API. This API is only available for recent versions of PyMOL. This API is available for both the incentive and open-source versions of PyMOL.
| Technology | Version | Description |
|---|---|---|
| PyMOL | 2.5.1 | Molecular graphics program |
| Python from Anaconda | 3.8 | Programming language |
| conda from Anaconda | 4.10.1 | Program used to create Python environments. |
| git | 2.33 | Eases the downloading and updating of the libraries. |
| Node.js | >12.2 | Needed for Jupyter extensions. Often the Node.js from Anaconda is outdated. Install from Node.js website. |
| Jupyter | needed to create kernel mapped to PyMOL's Python interpreter | |
| JupyterLab | >3.0 | Required for Elyra snippets |
| elyra-code-snippet-extension | > 3.3 | supports tagged snippets |
Some of the snippets are limited to Python3 code. If you are using an ancient version of PyMOL that relies on Python2, you can buy a license to the current version of PyMOL, install a free open-source version of PyMOL that depends on Python3 (See the PyMOL Wiki), or you can rewrite the snippet's code to be Python2 compliant. This often merely involves replacing print functions in Python3 with print statements in Python2. Note that multiple versions of PyMOL can operate side-by-side on a computer, so you do not have to delete the old version of PyMOL.
For example, after installing PyMOL for Python 3.9, you can make a Python kernel for this Python with the following command:
/opt/local/bin/python3.9 -m ipykernel install --user --name mp39 --display-name "Macports Python 3.9"This kernel will appear as Macports Python 3.9 in the pulldown for kernels.
It is stored on the Mac in ~/Library/Jupyter/kernels/mp39.
The naming convention for the Jupyter package that is used by Macports is confusing.
Instead, I recommend creating a conda environment to install Jupyter Lab from Anaconda and then the Juptyer Lab extensions.
After launching Juptyer Lab, you can select the Macports Python 3.9 kernel to access the pymol API through Macports.
This works despite the claim that your conda env is isolated from the rest of your Python ecosystem.
# If the following directory is missing: ~/Library/Jupyter/metadata
mkdir ~/Library/Jupyter/metadata
cd ~/Library/Jupyter/metadata
git clone https://github.com/MooersLab/taggedpymolpysnips.git
cd taggedpymolpysnips
mv code-snippets ../.
cd ..
rm -rf taggedpymolsnipsWhen you open JupyterLab, you will find a </> icon in the left column.
Click on it to display a list of available snippets.
The snippets are clustered by language.
The PyMOL snippets are clustered as Python.
The snippet library is independent of PyMOL. No modification of PyMOL is required.
The planned animation at the top of the page will convey the essential knowledge for usage.
In the meantime, the static images will have to do.
First, select the appropriate kernel from the list of icons and open a new notebook.
Select a cell.
Second, select a snippet from the menu. For example, the selection of the ao snippet leads to the display of its content.
To insert the snippet into the selected cell of the notebook, select the second icon from the left in the upper right.
To add a new snippet, click on the + in the upper right of the Code Snippets icon.
This will open a new GUI (see below) for creating a snippet.
- The
Nameshould be a one-word compound word. - The
Descriptiondescribes what the snippet does in one or more sentences. - The Tags are used to narrow the listing of snippets in the menu.
- The Source is Python.
- The Code can be entered by selecting code in a notebook cell or copying and pasting from a script file.
- Save & Close.
Run the code cells in the ipynbs/ao.ipynb notebook to test that the connection to PyMOL's API is working. The output should be the same as in ipynbs/7JU5setview.png.
Please use the Issues tab above to request support for additional snippets or to ask questions. Alternatively, you can send e-mail to me.
Questions about PyMOL should be directed to the PyMOL Mailing List.
Snippets of new code are most welcome. Send to e-mail.
- Submit the Python code in a plain text file.
- Write the filenames and function names in camelCase.
- Describe what the code does in one to several sentences, an example of usage, and any citations or links to further information.
Use the Issues tab above to report bugs or send e-mail to me.
I plan to expand the library to include examples of the 500 commands and 600 settings in PyMOL.
We use the permissive MIT license. The license information for this project is found in the License.txt file above.
Reach me via the Issue tab above or via e-mail: blaine-mooers at ouhsc.edu.
If you use this library to make figures for publication, please see the Citation.md file above.
| Version | Changes | Date |
|---|---|---|
| Version 0.2 | Added update table. Made numerous edits of the README.m | 2024 May 14 |