DEMO DATAActual Exampulse terminal capture · demo data
OVERVIEW
A Python command-line tool connecting wearable recovery metrics with exam schedules for personal, time-aware analysis.
MY ROLE
Personal software project · Python integration, data modeling, CLI experience, and test-focused analysis.
STATUS
Working local CLI · No hosted dashboard
TECH STACK
PythonWHOOP APIOAuthSQLModelSQLitepytest
01 — CONTEXT
The problem
Wearable data and exam schedules sit in different places, making it hard to inspect personal recovery and stress patterns around demanding days.
02 — APPROACH
The system
A Python CLI syncs WHOOP data via OAuth/API or imports official exports, stores records locally in SQLite, and generates readiness reports relative to a personal 14-day baseline.
ArchitectureSimplified system flow
01
WHOOP API / Exports
OAuth · Import
→↓02
Python CLI
Sync · Commands
→↓03
Local Analysis
14-day baseline
↓Connected from Local Analysis↳
Reports / Output
Terminal reports
→↓04
SQLite
Local storage
03 — IMPLEMENTATION
Engineering highlights
Excluded the target exam night from its own baseline to avoid contaminating comparisons.
Implemented resilient ingestion and missing-data handling without inventing unavailable measurements.
Designed local-first storage, idempotent import behavior, and privacy-conscious diagnostics.
Added focused tests across API integration, time handling, analysis, reports, and error cases.
04 — REALITY CHECK
Scope & transparency
Readiness scores are personal exploratory indicators, not a validated medical or academic performance model. Some WHOOP streams may be unavailable; fine-grained heart-rate analysis requires separately supplied data. No public deployment or package-registry release was verified.
DEMO DATA