There are several ways to get data out of Sparrow's analytical schemas. Sample information can be extracted from Sparrow's web API, through Sparrow's SQLAlchemy ORM, or using custom SQL queries.

This is a SQL query to get data points matching the parameter '4He' and lab_id '22-00001' from the database:

SELECT * FROM datum
JOIN analysis ON analysis.id = datum.analysis
JOIN session ON session.id = analysis.session_id
JOIN sample ON sample.id = session.sample_id
JOIN datum_type ON datum_type.id = datum.type
WHERE sample.lab_id = '22-00001'
  AND datum_type.parameter = '4He'

The same query can be constructed in Python using the SQLAlchemy ORM bundled with Sparrow:

db = sparrow.get_database()
Session = db.model.session
Sample = db.model.sample
Analysis = db.model.analysis
Datum = db.model.datum
DatumType = db.model.datum_type
res = (self.db.session.query(Datum)
       .join(Analysis)
       .join(Session)
       .join(Sample)
       .join(DatumType)
       .filter(Sample.lab_id == '22-00001')
       .filter(DatumType.parameter == "4He")
       .all())

This does literally the same thing as the SQL query above. SQLAlchemy is nice because it generally works out which foreign key columns to join on. However, it is sometimes harder to get exactly the columns you want. Here's an example that selects all data for a single sample:

SELECT parameter, unit, value FROM datum
JOIN analysis ON analysis.id = datum.analysis
JOIN session ON session.id = analysis.session_id
JOIN sample ON sample.id = session.sample_id
JOIN datum_type ON datum_type.id = datum.type
WHERE sample.lab_id = '22-00001'
ORDER BY unit DESC

This query yields the following results: