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90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
import numpy as np
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from tsi_sim.blocktree import BlockTree
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from tsi_sim.tsi import density_m, referenced_uncle_ids, slot_stats, update_D
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def make_tree(slots, parents, heights, uncles):
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n = len(slots)
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return BlockTree(
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slot=np.array(slots, np.int64),
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parent=np.array(parents, np.int64),
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height=np.array(heights, np.int64),
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leader=np.zeros(n, np.int64),
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uncles=uncles,
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)
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def test_density_counts_honest_plus_deduped_uncles_in_window():
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# canonical 1(slot0),4(slot3); orphans 2(slot1),3(slot2). block4 refs uncles 2 and 3.
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tree = make_tree(
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slots=[-1, 0, 1, 2, 3],
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parents=[-1, 0, 0, 0, 1],
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heights=[0, 1, 1, 1, 2],
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uncles=[(), (), (), (), (2, 3)],
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)
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canonical = [4, 1] # tip-first
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# window T=10 includes all slots
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assert density_m(tree, canonical, T=10) == 4 # 2 honest (slots 0,3) + 2 uncles (1,2)
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# window T=2 excludes slots 2,3 -> honest {slot0}=1, uncle slot1=1 (slot2 excluded)
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assert density_m(tree, canonical, T=2) == 2
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def test_uncle_counted_by_own_slot_and_deduped():
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tree = make_tree(
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slots=[-1, 0, 5, 1],
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parents=[-1, 0, 1, 0],
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heights=[0, 1, 2, 1],
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uncles=[(), (), (3,), ()], # block2 (slot5) references orphan 3 (slot1)
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)
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canonical = [2, 1]
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assert referenced_uncle_ids(tree, canonical) == {3}
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# uncle counted by its OWN slot (1), so window T=2 includes it
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assert density_m(tree, canonical, T=2) == 2 # honest slot0 + uncle slot1
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# window that excludes the uncle's own slot
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assert density_m(tree, canonical, T=1) == 1 # only honest slot0
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def test_slot_stats_q_and_qeff():
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# active slots 0,1,2 in window; honest occupies 0,2; orphan at slot1 recovered by uncle
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tree = make_tree(
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slots=[-1, 0, 1, 2],
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parents=[-1, 0, 0, 1],
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heights=[0, 1, 1, 2],
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uncles=[(), (), (), (2,)], # block3 refs orphan 2 (slot1)
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)
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canonical = [3, 1]
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active = np.array([0, 1, 2], np.int64)
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ref = referenced_uncle_ids(tree, canonical)
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ss = slot_stats(tree, canonical, ref, active, T=10)
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assert ss.n_active == 3
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assert ss.n_honest == 2 # slots 0 and 2
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assert ss.n_recovered == 1 # slot 1 recovered via uncle
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assert abs(ss.q - 2 / 3) < 1e-9
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assert abs(ss.q_eff - 1.0) < 1e-9
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def test_update_D_fixed_point():
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f, T = 1 / 30, 3000
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m = int(round(T * f)) # measured density == f -> D unchanged
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assert abs(update_D(1000.0, m, T, f, beta=1.0) - 1000.0) < 1e-6
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# measured below f -> estimate drops
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assert update_D(1000.0, m - 20, T, f, 1.0) < 1000.0
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# clamp at 1
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assert update_D(1.0, 0, T, f, 1.0) == 1.0
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def test_update_D_fixed_point_mode_targets_truncated_f():
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# In fixed-point mode the target rate is int(f*PRECISION)/PRECISION, slightly below f.
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from tsi_sim.tsi import PRECISION
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f, T = 1 / 30, 1_000_000
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f_p = int(f * PRECISION) / PRECISION
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m = 34000
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# For the same measured density, fixed-point (lower target f_p) raises the estimate
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# more than exact-f, i.e. it is systematically higher (now only ~1e-5 at PRECISION=1e6).
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assert (update_D(1000.0, m, T, f, 1.0, fixed_point=True)
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>= update_D(1000.0, m, T, f, 1.0, fixed_point=False))
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# A density of exactly f_p is the fixed-point fixed point (estimate unchanged).
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m_trunc = int(round(f_p * T)) # exact when f_p*T is integral (T=1e6)
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assert abs(update_D(1000.0, m_trunc, T, f, 1.0, fixed_point=True) - 1000.0) < 1e-6
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