iints-sdk-python35 1.4.0

A pre-clinical Edge-AI SDK for diabetes management validation.

IINTS-AF SDK

IINTS-AF is a safety-first SDK for insulin-algorithm research. It lets you simulate, validate, and report results with reproducible artifacts.

Docs (GitHub Pages): python35.github.io/IINTS-SDK

What You Can Do

• Run virtual patient simulations.
• Test algorithm safety gates (deterministic supervisor).
• Add optional AI glucose forecasting.
• Validate datasets before training/evaluation.
• Generate audit-ready CSV/JSON/PDF/HTML outputs.

Quick Start

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -U pip
python -m pip install -U "iints-sdk-python35[mdmp]"

iints doctor --smoke-run
iints quickstart --project-name iints_quickstart
cd iints_quickstart
iints presets run --name baseline_t1d --algo algorithms/example_algorithm.py

If you want the clearest install/path rules first, read:

• docs/INSTALLATION.md
• docs/AI_ASSISTANT.md for the small Ollama setup and SDK linking flow
• docs/EVIDENCE_BASE.md for the full medical + technical source legend

Short rule:

• installed iints ... commands can run from any folder
• python -m pip install -e ".[mdmp]" only works from the SDK repo root
• after iints quickstart, switch into the generated project folder

CareLink Import

The SDK can now ingest Medtronic CareLink / MiniMed CSV exports and convert them into the standard IINTS schema:

iints import-carelink \
  --input-csv "/path/to/CareLink export.csv" \
  --output-dir results/imported_carelink

This writes:

• cgm_standard.csv
• scenario.json
• carelink_summary.json

It extracts glucose, carb, and insulin events from the CareLink event log and aligns them onto an IINTS-ready timeline.

If you want a reusable personal-data workspace in one command, build a CareLink workbench:

iints carelink-workbench \
  --input-csv "/path/to/CareLink export.csv" \
  --output-dir results/personal_carelink

This adds:

• carelink_timeline.csv
• carelink_metrics.json
• carelink_dashboard.png
• carelink_poster.png
• carelink_dashboard.html
• ai/report_payload.json
• ai/trends_payload.json
• ai/anomalies_payload.json
• ai/step_riskiest.json

That workbench is designed for three things:

• inspect your own data visually
• reuse the generated scenario.json inside IINTS experiments
• let the local AI assistant explain what the imported patterns mean

AI Assistant (Ministral 3 Open-Weight via Ollama)

The SDK now includes a research-only AI assistant layer for explanations and run summaries. It is gated by MDMP verification before any LLM call is allowed.

Use an active virtual environment for the full flow.

If you installed the released SDK from PyPI, run:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -U pip
python -m pip install -U "iints-sdk-python35[mdmp]"

If you are developing from source instead, first move into the SDK repo root and then run:

cd /path/to/IINTS-SDK
python -m pip install -U -e ".[mdmp]"

Run the open local Mistral model locally with Ollama:

ollama pull ministral-3:8b
iints ai models

If you want the short "install Ollama + link it to the SDK" guide, read:

• docs/AI_ASSISTANT.md

If you want the full legend of medical, dataset, runtime, and emulator references used across the project docs:

• docs/EVIDENCE_BASE.md

Recommended first-time setup:

ollama pull ministral-3:8b
iints ai local-check --model ministral-3:8b

local-check now performs a tiny generation smoke-test by default, so it verifies both model presence and real inference readiness.

Recommended flow:

iints quickstart --project-name iints_quickstart
cd iints_quickstart
iints presets run --name baseline_t1d --algo algorithms/example_algorithm.py
iints ai prepare results/<run_id>
iints ai report results/<run_id>

For imported CareLink data, the matching flow is:

iints carelink-workbench \
  --input-csv "/path/to/CareLink export.csv" \
  --output-dir results/personal_carelink

iints ai report results/personal_carelink --model ministral-3:3b
iints ai trends results/personal_carelink --model ministral-3:3b
iints ai explain results/personal_carelink --model ministral-3:3b

Direct JSON mode still works if you already have your own payloads and signed MDMP artifact:

iints ai explain results/step.json \
  --mdmp-cert results/report.signed.mdmp

Notes:

• AI analysis is blocked if the MDMP artifact is invalid.
• Minimum required MDMP grade defaults to research_grade.
• The SDK now targets the open local Ministral 3 Ollama model by default.
• Users can choose a larger or smaller local Mistral-family model with --model ....
• Large JSON payloads are clipped automatically before prompt generation to keep local inference stable.
• iints ai prepare <run_dir> now creates AI-ready JSON payloads and, when MDMP is installed, a local development certificate plus keypair in <run_dir>/ai/.
• iints carelink-workbench now does the same kind of AI preparation for imported personal CareLink data and also generates a dashboard PNG/HTML pair.
• If Ollama closes the connection during generation, the SDK now surfaces an explicit recovery hint and points users toward ministral-3:3b for lower-memory systems.
• After iints ai prepare, you can point iints ai explain|trends|anomalies|report directly at the run directory.
• After iints carelink-workbench, you can point those same AI commands directly at the generated CareLink workspace directory.
• Output is research-only and not medical advice.

Jury Poster / Demo Graphic

You can now generate a poster-style PNG directly from one to three real run bundles:

iints poster \
  --run-dir results/normal_run \
  --run-dir results/meal_stress \
  --run-dir results/supervisor_override \
  --label "Normal Run" \
  --label "Meal Stress Test" \
  --label "Supervisor Override" \
  --output-path results/posters/iints_results_poster.png

The poster shows:

• glucose curves with the target range highlighted
• meal events
• supervisor interventions
• panel summaries with TIR, hypo time, meal count, and intervention count

If you omit --run-dir, the CLI auto-discovers the latest run bundles under ./results.

Fair / Jury Demo

If you want one clean live demo for a booth, jury, or pitch session, use the built-in booth flow:

./scripts/run_booth_demo.sh

This generates:

• three run bundles (Normal Run, Meal Stress Test, Supervisor Override)
• a ready-to-show poster PNG
• a markdown jury talk track
• a plain-text live demo script for the stand
• optional AI-ready artifacts for the safety case

You can also run it through the CLI:

iints demo-booth --output-dir results/booth_demo

For a cleaner live explanation, show this source file first:

examples/demos/07_live_stage_demo.py

That file is deliberately small and readable, so you can point to:

• PATIENT_CONFIG
• OUTPUT_DIR
• DURATION_MINUTES
• TIME_STEP_MINUTES
• SEED

Then run:

./scripts/run_live_stage_demo.sh

That shell wrapper resolves the SDK repo root automatically, so it still works if you launch it from another working directory via its full path.

And open:

• results/booth_demo_live/booth_demo_poster.png
• results/booth_demo_live/JURY_TALK_TRACK.md
• results/booth_demo_live/BEURS_LIVE_DEMO_SCRIPT.txt

What makes this script good for a booth:

• it visibly calls run_full(...)
• it visibly calls generate_results_poster(...)
• it visibly calls prepare_ai_ready_artifacts(...)
• you can point to one patient setting and explain how the same pipeline reruns for another patient

If you installed the SDK on another machine and do not have the repository checkout there, export the same demo code with:

iints demo-export --output-dir iints_demo
cd iints_demo
python 07_live_stage_demo.py

Updating The SDK

If another machine is missing newer commands like iints ai ... or iints demo-booth, upgrade inside the active virtual environment to the latest release:

source .venv/bin/activate
python -m pip install -U pip
python -m pip install -U "iints-sdk-python35[mdmp]"
hash -r
python -c "import iints; print(iints.__version__)"

If that machine still behaves like an old install, run:

iints-sdk-doctor

Full guide:

• docs/UPDATING.md

If you specifically need to reproduce a known environment, you can pin an exact release number instead of using the unpinned upgrade command above.

Troubleshooting:

• If iints ai ... says No such command 'ai', your environment usually still has a legacy iints package installed alongside iints-sdk-python35.
• Run iints-sdk-doctor first.
• If it reports a conflict, repair the environment with:

python -m pip uninstall -y iints iints-sdk-python35
python -m pip install -U "iints-sdk-python35[mdmp]"
hash -r

MDMP (Short)

MDMP is the data-quality protocol used by IINTS.

• Contract: defines expected columns, types, units, and bounds.
• Validation: checks a dataset against the contract.
• Fingerprint + Grade: writes deterministic hashes and a grade (draft, research_grade, clinical_grade).
• Visualizer: builds a single-file HTML report for audits.

Use the dedicated namespace:

iints mdmp template --output-path mdmp_contract.yaml
iints mdmp validate mdmp_contract.yaml data/my_cgm.csv --output-json results/mdmp_report.json
iints mdmp visualizer results/mdmp_report.json --output-html results/mdmp_dashboard.html

Use bundled mdmp_core backend (optional):

export IINTS_MDMP_BACKEND=mdmp_core

Staleness / lineage checks via the bundled mdmp CLI:

mdmp fingerprint-record data/my_cgm.csv --output-json results/fingerprint.json --expires-days 365
mdmp fingerprint-check results/fingerprint.json data/my_cgm.csv
mdmp lineage-card-refresh results/mdmp_model_card.yaml
mdmp registry init --registry registry/mdmp_registry.json
mdmp registry push --registry registry/mdmp_registry.json --report results/mdmp_report.json

Bundled MDMP

MDMP now ships inside the SDK, so the SDK no longer depends on a separate public MDMP repository checkout.

That means:

• iints mdmp ... stays available
• the bundled mdmp CLI can still be used
• local AI signing and verification can still use mdmp_core

Reference:

• docs/DUAL_REPO_WORKFLOW.md

Tools Layout

Repository helpers are now grouped by purpose:

• scripts/: simple user-facing shortcuts like test, lint, and demo entrypoints
• tools/ci/: CI gates and policy checks
• tools/dev/: maintainer workflows and multi-repo helpers
• tools/docs/: manual and documentation builders
• tools/data/: dataset import and conversion utilities
• tools/analysis/: plotting, diagnostics, and report helpers
• tools/assets/: branding and asset generation helpers

Reference: tools/README.md

Typical Workflow

1. Prepare or import data.
2. Validate data with MDMP.
3. Run simulation or forecast evaluation.
4. Review report artifacts and metrics.

Key Commands

iints run-full --algo algorithms/example_algorithm.py --scenario-path scenarios/clinic_safe_baseline.json --output-dir results/run_full
iints scorecard --algo algorithms/example_algorithm.py --profile research_default --output-dir results/scorecard
iints study-ready --algo algorithms/example_algorithm.py --output-dir results/study_ready
iints sources --output-json results/source_manifest.json

Documentation

• Docs site: python35.github.io/IINTS-SDK
• Plain guide: docs/PLAIN_LANGUAGE_GUIDE.md
• Comprehensive guide: docs/COMPREHENSIVE_GUIDE.md
• MDMP draft: docs/MDMP.md
• Demos: examples/demos
• Notebooks: examples/notebooks

Safety Notice

For research use only. Not a medical device. No clinical dosing advice.