research/tools/simulators/blend/pd/pyproject.toml

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[project]
name = "pd"
version = "0.1.0"
description = "Peering-degree Monte-Carlo graph simulator for the Blend network"
requires-python = ">=3.11"
dependencies = [
    "numpy",
    "pandas",
    "pyarrow",
    "matplotlib",
    "scipy",
    "pyyaml",
    "tqdm",
    "joblib",
]

[project.optional-dependencies]
dev = ["pytest", "pytest-xdist", "ruff", "mypy"]

[project.scripts]
pd-sweep = "pd.sweep:main"
pd-verify = "pd.verify:main"
pd-figures = "pd.plotting.make_figures:main"

[tool.hatch.build.targets.wheel]
packages = ["src/pd"]

[tool.ruff]
line-length = 100
target-version = "py311"

[tool.ruff.lint]
select = ["E", "F", "I", "UP", "B", "NPY"]
ignore = ["E741"]

[tool.pytest.ini_options]
testpaths = ["tests"]
addopts = "-q"
markers = ["slow: marks slow tests (deselect with -m 'not slow')"]
Add pd: peering-degree Blend Monte-Carlo graph simulator Static-graph simulator quantifying how a node's peering degree trades off propagation speed, adversary exposure, deanonymization, and reliability in the Blend network. Scales to 1e6 nodes (sparse CSR + sampled Dijkstra); the adversary and deanonymization metrics are exact at every N. Model (ms): seeded d-regular peer graph (matching-union), Blend cascade (sender -> blend_hops timed-release mix relays -> final flood), geographic link base + exponential transport jitter, per-node processing lag, free-running release-clock mixing. Metrics: - propagation: full-delay mean/p50/p90/p99, path/broadcast split, coverage times - reliability: message success-delivery-rate ~ (1-unresponsive_frac)^blend_hops and flood coverage, with unresponsive nodes modelled as routing holes - adversary (exact): observed/eclipsed fractions, random + worst-case placement - deanonymization (exact): P(whole blend path adversarial) ~ f_adv^blend_hops, and full deanonymization (path adversarial AND honest sender peered with an adversary) = deanon_rate * observed_frac Deterministic blake2b seed streams, three parquet tables, joblib parallelism, memguard, an analytic verify harness, 50 unit tests, and an auto-installing Makefile. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> 2026-08-03 16:47:46 +02:00			`[build-system]`
			`requires = ["hatchling"]`
			`build-backend = "hatchling.build"`

			`[project]`
			`name = "pd"`
			`version = "0.1.0"`
			`description = "Peering-degree Monte-Carlo graph simulator for the Blend network"`
			`requires-python = ">=3.11"`
			`dependencies = [`
			`"numpy",`
			`"pandas",`
			`"pyarrow",`
			`"matplotlib",`
			`"scipy",`
			`"pyyaml",`
			`"tqdm",`
			`"joblib",`
			`]`

			`[project.optional-dependencies]`
			`dev = ["pytest", "pytest-xdist", "ruff", "mypy"]`

			`[project.scripts]`
			`pd-sweep = "pd.sweep:main"`
			`pd-verify = "pd.verify:main"`
			`pd-figures = "pd.plotting.make_figures:main"`

			`[tool.hatch.build.targets.wheel]`
			`packages = ["src/pd"]`

			`[tool.ruff]`
			`line-length = 100`
			`target-version = "py311"`

			`[tool.ruff.lint]`
			`select = ["E", "F", "I", "UP", "B", "NPY"]`
			`ignore = ["E741"]`

			`[tool.pytest.ini_options]`
			`testpaths = ["tests"]`
			`addopts = "-q"`
			`markers = ["slow: marks slow tests (deselect with -m 'not slow')"]`