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Tutorials

Hands-on walkthroughs for common timeseries-qc workflows. Each tutorial uses the real v0.5.0 API (tsqc.check, YAML rules, and QCResult methods).

TL;DR

Start with the Solar Farm CSV walkthrough if you are new. Use YAML Rules From Scratch when you need per-tag configuration, OSIsoft PI Export for historian quality columns, and CI Gate to fail builds on bad data.

Solar Farm CSV Walkthrough

Load a multi-tag solar CSV, run tsqc.check with assume_tz, then inspect summary(), plot(), and export_report().

YAML Rules From Scratch

Author default_rules and tag_rules covering null, flatline, delta, range, and outlier — plus optional quality_map.

OSIsoft PI Export

Wire a historian export with external_quality_col and quality_mode (combined / exclusive).

CI Gate on Data Quality

Fail a GitHub Actions job when result.summary() pct_bad exceeds a threshold.

Prerequisites

  • Python 3.10+
  • pip install timeseries-qc (v0.5.0)
  • A CSV or DataFrame with timestamp, value, and optionally tag_name

Suggested order

  1. Solar Farm CSV — end-to-end happy path
  2. YAML Rules From Scratch — configuration depth
  3. OSIsoft PI Export — external quality columns
  4. CI Gate — automation