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* Add Unstructured engagement report as uv workspace member Three-tier Plotly Dash app (Executive Brief, Engineering Team, Full Detail) with data in JSON, theme constants in theme.py, and Dash production improvements (Google Fonts, clientside callbacks, meta tags). Also: add .playwright-mcp/ to .gitignore, add reports/* ruff overrides, remove tracked .codeflash/observability/read-tracker. * Rewrite statusline to derive context from git state Detects active area from changed files (reports, packages, plugin, .codeflash, case-studies, evals), falls back to branch name convention (perf/*, feat/*, fix/*), shows dirty indicator. Uses whoami for cross-platform user detection. * Add pre-push lint rule to commit guidelines * Exclude .codeflash/ from ruff linting Benchmark and profiling scripts in .codeflash/ are scratch work, not package source. Excluding them prevents CI failures from ad-hoc scripts. * Run ruff format across packages, scripts, evals, and plugin refs * Fix github-app async test failures in CI Add asyncio_mode = "auto" to root pytest config so async tests are detected when running from the repo root via uv run pytest packages/.
76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
from pipeline.core import process_readings
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def test_basic():
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raw = [
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{
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"id": 1,
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"sensor_type": "temp",
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"timestamp": "2024-01-01T00:00:00",
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"value": 22.5,
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"metadata": {"location": {"lat": 0, "lng": 0}},
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},
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{
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"id": 2,
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"sensor_type": "temp",
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"timestamp": "2024-01-01T01:00:00",
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"value": 23.0,
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"metadata": {"location": {"lat": 0, "lng": 0}},
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},
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{
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"id": 3,
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"sensor_type": "humidity",
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"timestamp": "2024-01-01T00:00:00",
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"value": 45.0,
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"metadata": {"location": {"lat": 0, "lng": 0}},
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},
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]
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result = process_readings(raw)
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assert len(result) == 2
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assert result["temp"]["count"] == 2
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assert result["humidity"]["count"] == 1
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def test_large_dataset():
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"""Production-scale dataset — process_readings uses too much memory.
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With 50k readings, peak memory is far higher than the input data size.
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The goal is to reduce memory usage while preserving correctness.
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"""
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sensor_types = [f"sensor-{i}" for i in range(50)]
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raw = []
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for i in range(50_000):
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raw.append(
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{
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"id": i,
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"sensor_type": sensor_types[i % len(sensor_types)],
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"timestamp": f"2024-{(i % 12) + 1:02d}-{(i % 28) + 1:02d}T{(i % 24):02d}:00:00",
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"value": round(20.0 + (i % 100) * 0.1, 2),
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"unit": "celsius",
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"metadata": {
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"location": {
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"lat": round(37.0 + (i % 10) * 0.1, 4),
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"lng": round(-122.0 + (i % 10) * 0.1, 4),
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"altitude": float(i % 50),
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},
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"firmware": f"v{1 + i % 3}.{i % 10}.0",
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"calibration_date": "2024-01-15",
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"sensor_config": {
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"sample_rate": 100,
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"precision": "high",
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"filter": "kalman",
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},
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"deployment": {
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"site": f"site-{i % 10}",
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"rack": f"rack-{chr(65 + i % 8)}",
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"position": i % 20,
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},
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"tags": [f"tag-{i % 5}", f"tag-{(i + 1) % 5}"],
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"history": [f"event-{j}" for j in range(3)],
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},
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}
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)
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result = process_readings(raw)
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assert len(result) == 50
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assert all(r["count"] == 1000 for r in result.values())
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assert all(r["mean"] > 0 for r in result.values())
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