Welcome to ZnDraw, a powerful tool for visualizing and interacting with your trajectories.
You can install ZnDraw directly from PyPi via:
pip install zndraw
Visualize your trajectories with a single command:
zndraw <file>
Note
ZnDraw's webapp-based approach allows you to use port forwarding to work with trajectories on remote systems.
ZnDraw supports multiple users and clients. Connect one or more Python clients to your ZnDraw instance:
- Click on
Python access
in the ZnDraw UI. - Connect using the following code:
from zndraw import ZnDraw
vis = ZnDraw(url="http://localhost:1234", token="<your-token>")
The vis
object provides direct access to your visualized scene. It inherits from abc.MutableSequence
, so any changes you make are reflected for all connected clients.
from ase.collections import s22
vis.extend(list(s22))
You can modify various aspects of the visualization:
vis.camera
vis.points
vis.selection
vis.step
vis.figures
vis.bookmarks
vis.geometries
For example, to add a geometry:
from zndraw import Box
vis.geometries = [Box(position=[0, 1, 2])]
ZnDraw enables you to analyze your data and generate plots using Plotly. It automatically detects available properties and offers a convenient drop-down menu for selection.
ZnDraw will look for the step
and atom
index in the customdata[0]
and [1]
respectively to highlight the steps and atoms.
Make your tools accessible via the ZnDraw UI by writing an extension:
from zndraw import Extension
class AddMolecule(Extension):
name: str
def run(self, vis, **kwargs) -> None:
structures = kwargs["structures"]
vis.append(structures[self.name])
vis.step = len(vis) - 1
vis.register(AddMolecule, run_kwargs={"structures": s22}, public=True)
vis.socket.wait() # This can be ignored when using Jupyter
The AddMolecule
extension will appear for all tokens
and can be used by any client.
A hosted version of ZnDraw is available at https://zndraw.icp.uni-stuttgart.de . To upload data, use:
zndraw <file> --url https://zndraw.icp.uni-stuttgart.de
To host your own version of ZnDraw, use the following docker-compose.yaml
setup:
version: "3.9"
services:
zndraw:
image: pythonf/zndraw:latest
command: --no-standalone /src/file.xyz
volumes:
- /path/to/files:/src
restart: unless-stopped
ports:
- 5003:5003
depends_on:
- redis
- worker
environment:
- FLASK_STORAGE=redis://redis:6379/0
- FLASK_AUTH_TOKEN=super-secret-token
worker:
image: pythonf/zndraw:latest
entrypoint: celery -A zndraw_app.make_celery worker --loglevel=info -P eventlet
volumes:
- /path/to/files:/src
restart: unless-stopped
depends_on:
- redis
environment:
- FLASK_STORAGE=redis://redis:6379/0
- FLASK_SERVER_URL="http://zndraw:5003"
- FLASK_AUTH_TOKEN=super-secret-token
redis:
image: redis:latest
restart: always
environment:
- REDIS_PORT=6379
If you want to host zndraw as subdirectory domain.com/zndraw
you need to adjust the environmental variables as well as update base: "/",
in the app/vite.config.ts
before building the ap..
If you use ZnDraw in your research and find it helpful please cite us.
@misc{elijosiusZeroShotMolecular2024,
title = {Zero {{Shot Molecular Generation}} via {{Similarity Kernels}}},
author = {Elijo{\v s}ius, Rokas and Zills, Fabian and Batatia, Ilyes and Norwood, Sam Walton and Kov{\'a}cs, D{\'a}vid P{\'e}ter and Holm, Christian and Cs{\'a}nyi, G{\'a}bor},
year = {2024},
eprint = {2402.08708},
archiveprefix = {arxiv},
}
The creation of ZnDraw was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in the framework of the priority program SPP 2363, “Utilization and Development of Machine Learning for Molecular Applications - Molecular Machine Learning” Project No. 497249646. Further funding though the DFG under Germany's Excellence Strategy - EXC 2075 - 390740016 and the Stuttgart Center for Simulation Science (SimTech) was provided.