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v1.5.1

v1.5.1 packages a more scientific public SDK workflow: stronger study tooling, clearer protocol bundles, and a more reproducible evidence path.

Highlights

  • Added a scientific workflow layer for hypothesis-driven studies:
  • iints study-protocol
  • docs/SCIENTIFIC_WORKFLOW.md
  • protocol bundles with STUDY_PROTOCOL.md, study_design.json, and study_matrix.csv
  • Added controlled corruption tooling for clean-vs-corrupted experiments:
  • iints data corrupt-for-study
  • timestamp shifts, missing blocks, duplicated rows, glucose spikes, dropped meal annotations, and unit-scale errors
  • Added a fixed study-preset path for repeatable benchmarking
  • Extended study analysis:
  • descriptive statistics with standard deviation and 95% confidence intervals
  • failure analysis for severe hypo, early terminations, and safety-heavy runs
  • optional external plausibility comparison against imported CareLink metrics
  • Extended study comparison:
  • confidence intervals on differences in means
  • Cohen's d effect estimates for key metrics
  • Improved study posters:
  • failure-analysis visibility
  • external plausibility verdicts

New Scientific Commands

iints scenarios export-study-pack --output-dir scenarios/study_pack

iints study-protocol --output-dir results/study_protocol

iints data corrupt-for-study data/demo/diabetes_cgm.csv \
  --output-csv data/demo/diabetes_cgm_corrupted.csv \
  --mode timestamp_shift \
  --mode missing_block \
  --mode glucose_spikes

iints analyze results/study \
  --output-json results/study_summary.json \
  --output-markdown results/study_summary.md \
  --output-csv results/evidence_table.csv \
  --output-evidence-markdown results/evidence_table.md \
  --carelink-metrics results/personal_carelink/carelink_metrics.json

iints compare-study results/study_clean results/study_corrupted \
  --output-json results/study_comparison.json \
  --output-markdown results/study_comparison.md

iints run-study \
  --algo algorithms/example_algorithm.py \
  --output-dir results/study_bundle

Why This Release Matters

Before v1.5.1, the SDK already had simulation, certification, AI review, and poster tooling, but the scientific story still depended too much on manually assembled study folders and ad-hoc experiment design.

With v1.5.1, the SDK can now:

  • define the study before running it
  • document the intended corruption operators
  • keep a fixed clean/corrupted/supervisor-off matrix
  • quantify uncertainty and effect size
  • compare simulation behavior against imported real-world glucose metrics

That makes the project much easier to defend in front of a science jury, a public audience, or a technical reviewer.

Installation

Latest release:

python -m pip install -U "iints-sdk-python35[mdmp]"

Pinned:

python -m pip install -U "iints-sdk-python35[mdmp]==1.5.1"