Reports

matten includes a local development tool, matten-report, that renders small fixed demonstrations of shape reasoning, dynamic-tensor readiness, and preprocessing over a handful of built-in scenarios. The five pages under this section are its Markdown output, generated once and committed so they are readable here without a checkout.

What these are

• Fixed demos, not a live tool. Each page is the output of one matten-report --demo <kind> invocation against data baked into the tool. Nothing here runs in your browser, and nothing here reads data you provide — see the Playground for the page that does compute live, on shapes you enter.
• Not automatic expression tracing. Every page says so in its own ## Input section. These demonstrate specific, hand-chosen operations — they do not observe or replay arbitrary code.

What matten-report is — and is not

matten-report is a local development tool: workspace-excluded, publish = false, and never published to crates.io. It is not a matten public API, and using it does not require depending on anything beyond the crates you already use.

The tool can also render HTML and JSON, and can run against a CSV file you supply (--input <path> --kind data-readiness). Neither the HTML/JSON output nor that live-input mode is published here, or anywhere public — RFC-070 declined a public reporting or visualization surface, and generating these five Markdown pages does not reopen that decision (RFC-097 §3). What you are reading is rendered output, not an interface anything can build against.

Running it yourself

# Any of the five fixed demos, Markdown to stdout:
cargo run --manifest-path tools/matten-report/Cargo.toml -- --demo shape-flow

# Against your own CSV:
cargo run --manifest-path tools/matten-report/Cargo.toml -- \
  --input your-data.csv --kind data-readiness --select column_a,column_b

The five demos

• shape-flow — broadcasting, reshape, axis reductions, and matmul, the same operations as the Playground, shown as fixed output.
• educational-path — a longer walk through shape reasoning, dynamic readiness, and standardization in one report.
• mlprep-standardization — before/after column standardization.
• data-readiness — CSV column selection, missing-value counts, and strict numeric conversion.
• dynamic-readiness — a mixed-type dynamic tensor, its readiness masks, and two conversion policies.

Staying accurate

These pages are generated, not hand-maintained — regenerating them from the commands above must reproduce them byte for byte. scripts/check-report-demos.sh enforces that in CI; if the tool’s output ever changes, these pages are regenerated and recommitted in the same change, never edited by hand.