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Structure-Property-Visualizer

Use this app to generate interactive visualizations like these for atomic structures and their properties.

Features

  • interactive scatter plots via bokeh server
  • interactive structure visualization via jsmol
  • simple input: provide CIF/XYZ files with structures and CSV file with their properties
  • simple deployment on materialscloud.org through Docker containers
  • driven by database backend:
    1. sqlite database (default)
    2. AiiDA database backend (requires customization)

Getting started

Prerequisites

Installation

git clone https://github.com/materialscloud-org/structure-property-visualizer.git
cd structure-property-visualizer
pip install -e .     # install python dependencies
./prepare.sh         # download test data (run only once)

Running the app

export PYTHONPATH="${PYTHONPATH}:/path/to/structure-property-visualizer"  # export pythonpath
bokeh serve --show figure detail select-figure   # run app

Customizing the app

Input data

  • a set of structures in data/structures
    • Allowed file extensions: cif, xyz
  • a CSV file data/properties.csv with their properties
    • has a column name whose value <name> links each row to a file in structures/<name>.<extension>.
  • adapt import_db.py accordingly and run it to create the database

Plots

The plots can be configured using a few YAML files in figure/static:

  • columns.yml: defines metadata for columns of CSV file
  • filters.yml: defines filters available in plot
  • presets.yml: defines presets for axis + filter settings

AiiDA support

Instead of querying an sqlite database, you can also query an AiiDA database. In order to keep the docker image size manageable, it is best to keep the AiiDA profile outside the image and to

  1. communicate the docker container how to connect to the AiiDA database
  2. mount the AiiDA file repository inside the docker container

For the first step you already find a couple of (commented) variables in the Dockerfile, as well a python function figure.aiida.load_profile to load an AiiDA profile environment from these variables.

For the second step, modify the docker-compose.yml to mount the AiiDA file repository instead of the data/ directory.

Docker deployment

pip install -e .
./prepare.sh
docker-compose build
docker-compose up
# open http://localhost:3245/select-figure