iints-sdk-python35 0.1.22

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

IINTS-AF SDK

IINTS-AF is a safety-first simulation SDK for insulin algorithm research. It helps you test controllers, validate safety behavior, and generate audit-ready artifacts.

New here? Start with: docs/PLAIN_LANGUAGE_GUIDE.md

What You Get

• Virtual patient simulation for insulin strategies.
• Deterministic safety supervision (hard rules).
• Optional AI glucose predictor as advisory signal.
• Audit outputs: CSV, JSON, PDF reports.
• Validation and scorecard tooling for repeatable benchmarks.
• Transparent evidence manifest linked to peer-reviewed sources (iints sources).

Open Logic (Dual-Guard)

IINTS-AF is built as layered logic, not black-box control:

1. InputValidator
   Filters biologically implausible glucose input and applies fail-soft fallback.
2. LSTM Predictor (optional)
   Forecasts future glucose; advisory only.
3. IndependentSupervisor
   Deterministically validates every dose request and overrides unsafe actions.

Install

pip install iints-sdk-python35

Research extras:

pip install iints-sdk-python35[research]

Quick Start (CLI)

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

Clinical-trial scaffold (MDMP-ready):

iints init --project-name iints_trial --template clinical-trial
cd iints_trial
iints data contract-run contracts/clinical_mdmp_contract.yaml data/demo/diabetes_cgm.csv \
  --output-json audit/contract_data_report.json \
  --min-mdmp-grade research_grade --fail-on-noncompliant
iints data mdmp-visualizer audit/contract_data_report.json --output-html audit/mdmp_dashboard.html

One full run with artifacts:

iints run-full \
  --algo algorithms/example_algorithm.py \
  --scenario-path scenarios/clinic_safe_baseline.json \
  --output-dir results/run_full

Dual-Guard run with predictor:

iints run-full \
  --algo algorithms/example_algorithm.py \
  --predictor models/hupa_finetuned_v2/predictor.pt \
  --scenario-path scenarios/clinic_safe_baseline.json \
  --output-dir results/dual_guard

Quick Start (Python)

import iints
from iints.core.algorithms.pid_controller import PIDController

outputs = iints.run_simulation(
    algorithm=PIDController(),
    scenario="scenarios/example_scenario.json",
    patient_config="default_patient",
    duration_minutes=720,
    seed=42,
    output_dir="results/quick_run",
)

Most Used Commands

# Environment and validation checks
iints doctor --smoke-run
iints study-ready --algo algorithms/example_algorithm.py --output-dir results/study_ready
iints validation-profiles
iints validate-run --results-csv results/run_full/results.csv --profile research_default
iints contract-verify --output-json results/contract_report.json
iints data contract-template --output-path data_contract.yaml
iints data contract-run data_contract.yaml data/my_cgm.csv --output-json results/contract_data_report.json
iints data contract-run data_contract.yaml data/my_cgm.csv --min-mdmp-grade research_grade --fail-on-noncompliant
iints data synthetic-mirror data/my_cgm.csv data_contract.yaml --output-csv data/synthetic_mirror.csv --output-json results/synthetic_mirror_report.json
iints data mdmp-visualizer results/contract_data_report.json --output-html results/mdmp_dashboard.html
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 synthetic-mirror data/my_cgm.csv mdmp_contract.yaml --output-csv data/synthetic_mirror.csv --output-json results/synthetic_mirror_report.json
iints mdmp visualizer results/mdmp_report.json --output-html results/mdmp_dashboard.html
iints replay-check --algo algorithms/example_algorithm.py --output-json results/replay_check.json
iints golden-benchmark --algo algorithms/example_algorithm.py --output-json results/golden_benchmark.json

# Scenario bank benchmark
iints scorecard --algo algorithms/example_algorithm.py --profile research_default --output-dir results/scorecard

# Research diagnostics
iints research audit-split --data data_packs/public/OhioT1DM/processed/ohio_merged.csv
iints research evaluate-forecast --input-csv results/dual_guard/results.csv
iints research evaluate-forecast --input-csv results/dual_guard/results.csv --gate-profile research_default
iints research registry-list --registry models/registry.json
iints research registry-promote --registry models/registry.json --run-id <run-id> --stage validated

iints study-ready produces a complete bundle by default:

• simulation outputs
• validation_report.json
• sources_manifest.json
• SUMMARY.md (human-readable quick review)

iints data contract-run writes deterministic fingerprints plus an MDMP grade:

• draft
• research_grade
• clinical_grade

iints data mdmp-visualizer turns a contract report JSON into a shareable single-file HTML dashboard for audit review.

iints data synthetic-mirror generates a privacy-safe synthetic dataset that keeps schema and broad statistical behavior, then validates it with the same MDMP gates.

MDMP as Separate Protocol Surface

MDMP now has a dedicated namespace:

• Python imports: iints.mdmp
• CLI commands: iints mdmp ...

The older iints data ... MDMP commands remain supported for backward compatibility.

MDMP Auto-Guardians enforce quality gates directly in Python pipelines:

import pandas as pd
from iints import mdmp_gate

@mdmp_gate("contracts/clinical_mdmp_contract.yaml", min_grade="clinical_grade")
def process(df: pd.DataFrame) -> int:
    return len(df)

Demos and Notebooks

• Demo scripts: examples/demos
• Notebook tutorials: examples/notebooks

Documentation

• Docs site (GitHub Pages): https://python35.github.io/IINTS-SDK/
• Manual (PDF): docs/manuals/IINTS-AF_SDK_Manual.pdf
• Comprehensive guide: docs/COMPREHENSIVE_GUIDE.md
• Technical README: docs/TECHNICAL_README.md
• Plain-language guide: docs/PLAIN_LANGUAGE_GUIDE.md
• MDMP (draft): docs/MDMP.md
• Evidence base (peer-reviewed sources): docs/EVIDENCE_BASE.md
• Research track: research/README.md

Evidence and Provenance

The SDK ships a source manifest grounded in peer-reviewed diabetes literature and standards.

iints sources
iints sources --category trial --output-json results/source_manifest.json

Safety Notice

This SDK is for research and validation only.
It is not a medical device and does not provide clinical dosing advice.