Database Exploration

Tip

We recommend using DBeaver to explore your database and all of its tables. DBeaver is free and production-ready for all database backends that we support (SQLite, Postgres, etc.).

Quick Guide

1. Ensure your database is initialized. This was done in earlier tutorials with the command simmate database reset. Do NOT rerun this command as it will clear your database and erase your results.

2. (optional) Open up your database (~/simmate/my_env-database.sqlite) using DBeaver. Open and view the workflows_staticenergy table.

3. The results table for Tutorial 2 is found in the StaticEnergy datatable class, which can be accessed via either of these options:

   # OPTION 1
   from simmate.database import connect # this connects to our database
   from simmate.database.workflow_results import StaticEnergy
   
   # OPTION 2
   from simmate.workflows.utilities import get_workflow
   workflow = get_workflow("static-energy.quantum-espresso.quality00")
   table = workflow.database_table  # yields the StaticEnergy class
   

4. Use show_columns to see all available table columns:

   table.show_columns()
   

5. Convert the full table to a pandas dataframe:

   df = table.objects.to_dataframe()
   

6. Use django-based queries to filter results. For example:

   filtered_results = table.objects.filter(
       formula_reduced="NaCl", 
       nsites__lte=2,
   ).all()
   

7. Convert the final structure from a database object (aka DatabaseStructure) to a structure object (aka ToolkitStructure).

   single_relaxation = table.objects.filter(
       formula_reduced="NaCl", 
       nsites__lte=2,
   ).first()
   nacl_structure = single_relaxation.to_toolkit()
   

8. Explore other tables and data available. For example, third-party data is automatically included in the prebuilt database (includes Material Project, AFLOW, COD, and more):

   from simmate.database import connect
   from simmate.apps.jarvis.models import JarvisStructure
   
   first_150_rows = JarvisStructure.objects.all()[:150]
   dataframe = first_150_rows.to_dataframe()