mirror of
https://github.com/logos-co/nomos-specs.git
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2d3f463bb7
* feat(cryptarchia/epoch-schedule): switch to 334 schedule (from 433) * factor out common test config building code * feat(cryptarchia): test_leader uses common test config object * cryptarchia: update test_ledger_state_is_properly_updated_on_reorg * cryptarchia: update test_epoch_transition test * move to .tip() in tests instead of tip_id() * cryptarchia: wrap long comments * cryptarchia: move mk_block to test_common * cryptarchia: move mk_genesis_state to test_common * cryptarchia: refactor fork test to use mk_chain * cryptarchia: fork choice rules tests use mk_chain helper * cryptarchia: rename fork choice test suite to TestForkChoice * cryptarchia: config.s is always 3k/f or 3*base_period_length * cryptarchia: hardcode epoch schedule in specification * un-hard code epoch sched. params + provide a v0.0.1 spec for params
53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
from unittest import TestCase
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import numpy as np
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from .cryptarchia import (
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Leader,
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Config,
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EpochState,
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LedgerState,
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Coin,
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phi,
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TimeConfig,
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Slot,
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)
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from .test_common import mk_config
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class TestLeader(TestCase):
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def test_slot_leader_statistics(self):
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epoch = EpochState(
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stake_distribution_snapshot=LedgerState(
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total_stake=1000,
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),
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nonce_snapshot=LedgerState(nonce=b"1010101010"),
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)
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f = 0.05
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l = Leader(
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config=mk_config().replace(active_slot_coeff=f),
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coin=Coin(sk=0, value=10),
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)
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# We'll use the Margin of Error equation to decide how many samples we need.
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# https://en.wikipedia.org/wiki/Margin_of_error
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margin_of_error = 1e-4
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p = phi(f=f, alpha=10 / 1000)
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std = np.sqrt(p * (1 - p))
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Z = 3 # we want 3 std from the mean to be within the margin of error
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N = int((Z * std / margin_of_error) ** 2)
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# After N slots, the measured leader rate should be within the
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# interval `p +- margin_of_error` with high probabiltiy
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leader_rate = (
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sum(
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l.try_prove_slot_leader(epoch, Slot(slot), bytes(32)) is not None
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for slot in range(N)
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)
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/ N
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)
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assert (
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abs(leader_rate - p) < margin_of_error
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), f"{leader_rate} != {p}, err={abs(leader_rate - p)} > {margin_of_error}"
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