criticality-spectrometer 0.1.1

A domain-agnostic instrument for measuring how node-removal impact changes as alternatives become available over an adaptation horizon in systems with AND/OR dependencies.

Criticality Spectrometer

Measure node criticality as a curve across adaptation horizons in AND/OR dependency systems.

Most network rankings assign one importance score per node. Criticality Spectrometer instead removes each node, sweeps the time at which substitutes become available, and records mission loss at every horizon. The result distinguishes a node that must be protected now and later from one whose risk can be reduced by enabling alternatives.

Run it in 60 seconds

git clone https://github.com/AMBRA7592/criticality-spectrometer.git
cd criticality-spectrometer
python -m pip install -e .
criticality-spectrometer run examples/canonical/model.json

The canonical model is a seven-node, hand-verifiable fixture. Its bottleneck has impact 1 at tau=0 and 0 after its backup activates at tau=12:

node                     impact               shape                OR gap
bottleneck               [1, 0, 0]            fully_adaptable      [1, 0, 0]

Use JSON output for a reproducible artifact:

criticality-spectrometer run examples/canonical/model.json --format json > result.json

JSON reports identify the instrument and schema versions, model SHA-256, horizons, and run configuration. The contract is schema/result.schema.json.

Prefer the Python API?

from criticality_spectrometer import load_model, run_sweep

model = load_model("examples/tutorial/model.json")
result = run_sweep(model)
print(result.curves["runner_primary"].impact)
# [1, 0]

The narrated model-your-first-system tutorial builds that ten-node CI pipeline from scratch.

CLI exit codes are stable: 0 means success, 2 means invalid CLI input or an invalid model, and 3 means the model failed the positive, constant-baseline requirement. criticality-spectrometer --version prints the installed version.

What the instrument returns

For every node, the sweep reports an impact curve and a conservative shape class:

shape	curve behavior	interpretation
persistent	positive and unchanged	alternatives do not reduce measured loss
fully_adaptable	falls to zero	available alternatives eventually restore the mission
partially_adaptable	declines but stays positive	adaptation helps without eliminating loss
none	zero at every horizon	removal does not reduce the selected mission outcome

These labels describe model output. They are not policy recommendations or empirical claims by themselves.

Model contract

A model contains:

• nodes;
• identified requirement groups with AND or OR logic;
• substitutes targeted to a specific requirement group and activation time;
• one mission outcome: served_sinks or ordered_served_sinks.

The machine-readable contract is in schema/model.schema.json. The formal cascade, outcome, baseline, and comparison semantics are in docs/method.md.

Worked example: AI compute supply chain

The repository includes a 52-node worked example expressed entirely as model data; the engine contains no semiconductor-specific entities. Three missions separate topology, an advanced-fab path, and the primary ordered frontier stack.

The primary stack reproduces the prior case study's seven named acceptance tests at the shape level. For example, the modeled EUV corridor is persistent, TSMC is fully adaptable over the specified horizons, and germanium has no impact on that mission. The example is an application, not cross-domain validation.

• Parity analysis and divergences
• Evidence ledger
• Frozen source manifest
• Non-claims and empirical boundaries

Rebuild and verify the example:

python examples/ai_compute/build_ai_case.py
pytest -q tests/test_ai_case.py

How it differs from common network measures

method	represents	answer type
Centrality	position in a graph	scalar score
Critical-node detection	disconnection caused by removal	scalar or set
Criticality Spectrometer	mission loss across adaptation horizons under explicit requirements	curve and shape class

Scope of v0.1

This is an alpha research instrument with one canonical fixture and one empirical domain. It does not infer dependencies, estimate activation times, prove causal claims, or turn shape classes into policy prescriptions. The current model contract also lacks connectivity-only edges; the worked example logs where that boundary matters.

See docs/nonclaims.md for the full boundary and CHANGELOG.md for release history.

Development

python -m pip install -e ".[test]"
pytest -q

Contributions are welcome, especially independent examples that exercise the frozen model contract without adding domain logic to the engine. See CONTRIBUTING.md.

Citation and license

Citation metadata is provided in CITATION.cff. Released under the MIT License.