# timeseries-qc

> Open-source Python library for quality control of SCADA, DCS, IoT, and historian time-series data. Tags every row as good, suspect, or bad with built-in rules, YAML configuration, and Plotly timeline charts. Compatible with pandas DataFrames. MIT licensed.

## Getting Started

- [Quickstart](https://nagusubra.github.io/timeseries-qc/quickstart/): Run your first quality check in 5 lines of Python.
- [Installation](https://nagusubra.github.io/timeseries-qc/installation/): System requirements, pip install, and dev setup.
- [User Guide](https://nagusubra.github.io/timeseries-qc/user-guide/): Complete walkthrough with loading data, running checks, interpreting results, and generating reports.

## API Reference

- [API Reference](https://nagusubra.github.io/timeseries-qc/api-reference/): Full documentation for `tsqc.check()`, `QCResult` methods (summary, plot, issue_summary, check_timestamps, export_report), and all rule classes.
- [Rule Engine](https://nagusubra.github.io/timeseries-qc/rules/): How built-in rules work — NullRule, FlatlineRule (with min_duration), DeltaRule (min_delta/max_delta), RangeRule, OutlierRule (zscore/mad/iqr), CustomRule. Severity levels and rule ordering.
- [YAML Configuration](https://nagusubra.github.io/timeseries-qc/yaml-configuration/): Define rules in YAML files. Supports default_rules, tag_rules with glob patterns, and quality_map for external quality columns.

## Visualization & Reports

- [Visualization](https://nagusubra.github.io/timeseries-qc/visualization/): Interactive multi-tag quality timeline charts with Plotly. Hover tooltips, range selector, timezone handling, filtering by tag/time.
- [Report Generation](https://nagusubra.github.io/timeseries-qc/report-generation/): Self-contained HTML reports with embedded Plotly chart, per-tag summary, issue breakdown, and timestamp health.

## Timestamp Validation

- [Timestamp Validation](https://nagusubra.github.io/timeseries-qc/timestamp-validation/): Detect gaps, duplicates, non-monotonic timestamps, frequency drift, and DST ambiguities.

## Architecture & Design

- [Architecture](https://nagusubra.github.io/timeseries-qc/architecture/): Package structure, data flow, design decisions (pandas-native, vectorized, 3-level classification, worst-level-wins).
- [Design System](https://nagusubra.github.io/timeseries-qc/design/): Brand logo, color tokens, typography, asset locations, and rules for docs / reports / social cards.

## Integration Guides

- [SCADA & Historian Integration](https://nagusubra.github.io/timeseries-qc/scada-integration/): Using timeseries-qc with OSIsoft PI, OPC UA, and other historian systems. External quality column support.
- [Industrial Use Cases](https://nagusubra.github.io/timeseries-qc/industry-use-cases/): Solar farm, oil well pad, and hydro power plant examples with synthetic data generators.

## Reference

- [FAQ](https://nagusubra.github.io/timeseries-qc/faq/): Frequently asked questions about time series QC and library usage.
- [Glossary](https://nagusubra.github.io/timeseries-qc/glossary/): Terms and definitions (SCADA, DCS, historian, flatline, OPC UA, etc.).
- [Why timeseries-qc?](https://nagusubra.github.io/timeseries-qc/why-timeseries-qc/): Comparison with Pecos, SaQC, and Great Expectations.
- [Troubleshooting](https://nagusubra.github.io/timeseries-qc/troubleshooting/): Common issues and solutions.

## Project

- [Changelog](https://nagusubra.github.io/timeseries-qc/changelog/): Version history (0.1.0 through 0.5.0).
- [Roadmap](https://nagusubra.github.io/timeseries-qc/roadmap/): Planned features — CLI tool, Parquet support, Spark/Dask backend.
- [Contributing](https://nagusubra.github.io/timeseries-qc/contributing/): Development setup, testing, adding new rules, and PR process.

## AI Agent Skills

- [AI Skill File](https://nagusubra.github.io/timeseries-qc/timeseries-qc/): Structured instructions for AI coding agents (OpenCode, Cursor, Claude, Copilot).

## External Resources

- [GitHub Repository](https://github.com/nagusubra/timeseries-qc): Source code, issues, discussions.
- [PyPI Package](https://pypi.org/project/timeseries-qc/): Python package index page.
- [Brand logo (512×512)](https://nagusubra.github.io/timeseries-qc/assets/brand/logo-master-512.png): Official pictorial mark (navy bars + green check).
- [Social preview (1200×630)](https://nagusubra.github.io/timeseries-qc/assets/images/social-preview.png): Canonical Open Graph / GitHub social card.

## Specifications

- [Sitemap](https://nagusubra.github.io/timeseries-qc/sitemap.xml): XML sitemap for full site indexing.
- [llms-full.txt](https://nagusubra.github.io/timeseries-qc/llms-full.txt): Expanded LLM context for this documentation site.