urban-analytics-core

Five TDX clients collapse into one; five settings/logging systems and three cache and geo/time utilities converge to a single source of truth — with reliability built in: auto token refresh, exponential backoff with jitter, Retry-After handling and forced refresh on 401.

• TDX OAuth2 client-credentials auth with in-memory token caching and safety-margin auto-refresh
• HTTP client with exponential backoff + jitter retries, 429 Retry-After parsing and minimum request-interval throttling
• Layered config: Pydantic v2 settings with YAML deep-merge and environment-variable overrides
• FastAPI app factory with built-in CORS, TTL response cache and per-IP sliding-window rate-limit middleware
• Dual storage backends: SQLite (WAL mode) for reference/metadata, DuckDB+Parquet for large time-series analytics
• Lightweight geo helpers (haversine) with no GIS dependency and Asia/Taipei timezone normalization

• Reconciling inconsistent interfaces and behavior across five existing projects into abstractions usable by all of them
• Isolating heavy dependencies (DuckDB/SciPy) behind optional-dependencies so lightweight consumers avoid the install burden

• Portfolio reviewers and interviewers
• Technical readers who need a quick view of purpose, stack, and maturity

• Detected technical signals: Python, FastAPI, Pydantic v2, httpx, DuckDB, SQLite, pandas, NumPy, Starlette, Uvicorn, PyYAML, SciPy
• README evidence exists and can support a fuller reviewed case study
• A public GitHub repository is not verified yet; the portfolio marks it as pending

This case study is generated from the portfolio catalog pipeline using README, Git metadata, package/build configuration, and media signals. The final architecture narrative still needs source-level review. Current detected technology signals include: Python, FastAPI, Pydantic v2, httpx, DuckDB, SQLite, pandas, NumPy, Starlette, Uvicorn, PyYAML, SciPy.

A public data-flow narrative is not fully reviewed yet. If the project includes data processing, AI pipelines, or backend APIs, the next pass should document input, processing, storage, and UI/output flow end to end.

urban-analytics-core/
  README.md              # project documentation, when available
  source files           # implementation reviewed by local audit
  package/build config   # detected capability signals

This project does not expose a verified runnable web command yet. Review the README/source tree and add exact install, run, test, and build commands before interview use.
No verified build command was detected. Treat the current portfolio page as a case-study placeholder until build steps are reviewed.

• Complete the production-quality README, screenshots, and demo recording
• Add architecture diagrams, data-flow notes, and key technical decisions
• Verify build/test status and update the portfolio release report

• State the current maturity and demonstrable scope first
• Focus on verified stack, source structure, and completed behavior
• Do not claim unverified deployment, video, or test coverage as finished

• Build out test coverage (pytest/pytest-asyncio already configured) and add CI
• Publish as a versioned internal package and migrate the five downstream projects to consume it