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TSAL — Time Series Analytics Language

TSAL is the expression language at the core of Impulse. It provides a Pythonic, Matlab-style syntax for selecting physical channels, defining virtual signals, and expressing event conditions. All TSAL expressions are lazy — no computation happens until a solver executes the query.

The flow​

Working with Impulse follows one path, and this section is organized around it:

  1. Define expressions. Select channels by their metadata tags and combine them with operators and signal methods to describe signals and events — see Defining Expressions.
  2. Evaluate. A query is solved per container: each expression's lazy tree is resolved against the silver-layer data and evaluated bottom-up — see Evaluation.
  3. Get core-model results. Each expression resolves to one of a few in-memory result classes (SampleSeries, Intervals, PointsInTime, PointsInTimeSeries) — the Core Data Model.
db = my_report.get_db()

# 1. select physical channels by tags
eng_rpm = db.query.channel(channel_name='Engine RPM', brand='Seat', model='Leon')
amb_air_temp = db.query.channel(channel_name='Ambient Air Temperature')
intake_air_temp = db.query.channel(channel_name='Intake Air Temperature')

# 2. derive a virtual signal
avg_temp = (amb_air_temp + intake_air_temp) / 2

# 3. define an event as a boolean expression over signals
high_rpm = (eng_rpm > 2000) & (eng_rpm < 5000)

Nothing above touches Spark or runs a computation — the expressions are evaluated only when the query is solved.

In this section​

  • Defining Expressions — channel selection, operators, signal methods, and virtual signals.
  • Core Data Model — the in-memory classes expressions resolve to, and how they interact.
  • Evaluation — the expression tree and how solve() turns it into core-model results.