74 lines
3.7 KiB
Markdown
74 lines
3.7 KiB
Markdown
# Project knowledge
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## Purpose
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This service is a calculation and persistence layer between ENLYZE and
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Grafana. It must not become a second historian: raw process data stays in
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ENLYZE and is retrieved again when historical calculations need reproduction.
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## Core architectural decisions
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- Python with a `src/` package layout.
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- FastAPI is the preferred future service layer; no HTTP API is needed now.
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- PostgreSQL with TimescaleDB is the target derived-data store.
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- Grafana reads derived data from that database as an additional datasource.
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- Secrets are environment variables only; no credentials or site-specific
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configuration are committed.
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- Calculations are modular, testable Python implementations. Configuration
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declares instances; it is not a generic low-code language.
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## Peak-cycle detector
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The first generic calculation is a timestamp-based `PeakCycleDetector` under
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`calculations`. It is transport-independent and works for roll length, roll
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weight, and similar cyclic signals. It maintains the maximum in an open cycle;
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after a value falls below `drop_ratio * current_peak`, it emits that maximum
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only after actual subsequent below-threshold observations support a continuous
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`hold_seconds` interval. `min_peak` is a generic detector setting, along with
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`drop_ratio` (0 < ratio < 1), `hold_seconds` (>= 0), and an explicit positive
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`max_sample_gap_seconds`. Elapsed time, never a sample count, determines
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confirmation. An interval longer than the configured maximum sample gap
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restarts a reset candidate, so a timestamp gap is not
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continuous-below evidence. Reset values can be negative rather than zero,
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equal timestamps add no elapsed time, and backwards timestamps are rejected.
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The maximum gap is required rather than inferred or defaulted, making each
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source's continuity assumption explicit.
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K7 roll length, variable `bf81c547-dccf-4709-aee2-0f78366d1dfc` (m), is the
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first real-world validation case. The tested capture is continuous on a
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10-second grid through its latest available sample; its shorter-than-requested
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result was caused by an end time queried in the future, not observed time-series
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gaps. The raw capture remains ignored and deterministic tests use synthetic
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fixtures only.
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## Central domain context
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Production orders connect machine, article/material, source interval, and
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derived results. Metrics and events must retain calculation type/version and
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source-time-range provenance.
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## Known versus unknown
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Known from the ENLYZE UI: machine identity, operational/downtime state,
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current production order, article/material number, and process signals exist.
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It is **not yet verified** how, or whether, each is exposed by the ENLYZE API.
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Do not infer endpoint paths, authentication mechanisms, identifiers, paging,
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timestamp semantics, or signal payloads. The active open-question list is in
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[docs/enlyze-api.md](docs/enlyze-api.md).
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## Exploration workflow
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A deliberately generic, read-only CLI is available as
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`production-analytics enlyze raw PATH`. It only makes GET requests, never
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guesses endpoint schemas, and emits sanitized response metadata/body. The
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operator must first obtain an authorized base URL, a verified safe path, and
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the authentication method. Credentials reside in ignored `secrets/enlyze.env`;
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the CLI parses simple assignments without sourcing/executing the file. Public
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ENLYZE documentation verifies an `Authorization: Bearer <ENLYZE_API_KEY>`
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authentication header; its value is never printed.
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Unmodified captures are local in ignored `data/raw/enlyze/`. Fixtures written
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to `fixtures/enlyze/` are sanitized, but must still be reviewed before commit.
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The deliverable is verified API observations and sanitized fixtures, not
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production calculations or database ingestion.
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