150 lines
7.0 KiB
Plaintext
150 lines
7.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import plyvel\n",
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"from itertools import islice\n",
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"import iavltree"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# db = plyvel.DB('../node/nodedir/data/application.db')\n",
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"height = iavltree.max_height(db)\n",
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"height"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"[k for k, v in iavltree.iterate(db, 's/k:mint/', height)]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"dict(iavltree.parse_pb(next(iavltree.iterate(db, 's/k:mint/', height, key = [138]))[1]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"it = iavltree.iterate(db, 's/k:emissions/', height, key = [62, 64], format = 'Qss')\n",
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"ooiiregrets = [(k[2],k[3],value[1],float(value[2])) for k,v in it for value in (dict(iavltree.parse_pb(v)),)]\n",
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"\n",
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"len(ooiiregrets), len(it.inner.lookups)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"keynames = {0: \"Params\", 1: \"TotalStake\", 2: \"TopicStake\", 3: \"Rewards\", 4: \"NextTopicId\", 5: \"Topics\", 6: \"TopicWorkers\", 7: \"TopicReputers\", 8: \"DelegatorStake\", 9: \"DelegateStakePlacement\", 10: \"TargetStake\", 11: \"Inferences\", 12: \"Forecasts\", 13: \"WorkerNodes\", 14: \"ReputerNodes\", 15: \"LatestInferencesTs\", 16: \"ActiveTopics\", 17: \"AllInferences\", 18: \"AllForecasts\", 19: \"AllLossBundles\", 20: \"StakeRemoval\", 21: \"StakeByReputerAndTopicId\", 22: \"DelegateStakeRemoval\", 23: \"AllTopicStakeSum\", 24: \"AddressTopics\", 24: \"WhitelistAdmins\", 25: \"ChurnableTopics\", 26: \"RewardableTopics\", 27: \"NetworkLossBundles\", 28: \"NetworkRegrets\", 29: \"StakeByReputerAndTopicId\", 30: \"ReputerScores\", 31: \"InferenceScores\", 32: \"ForecastScores\", 33: \"ReputerListeningCoefficient\", 34: \"InfererNetworkRegrets\", 35: \"ForecasterNetworkRegrets\", 36: \"OneInForecasterNetworkRegrets\", 37: \"OneInForecasterSelfNetworkRegrets\", 38: \"UnfulfilledWorkerNonces\", 39: \"UnfulfilledReputerNonces\", 40: \"FeeRevenueEpoch\", 41: \"TopicFeeRevenue\", 42: \"PreviousTopicWeight\", 43: \"PreviousReputerRewardFraction\", 44: \"PreviousInferenceRewardFraction\", 45: \"PreviousForecastRewardFraction\", 46: \"InfererScoreEmas\", 47: \"ForecasterScoreEmas\", 48: \"ReputerScoreEmas\", 49: \"TopicRewardNonce\", 50: \"DelegateRewardPerShare\", 51: \"PreviousPercentageRewardToStakedReputers\", 52: \"StakeRemovalsByBlock\", 53: \"DelegateStakeRemovalsByBlock\", 54: \"StakeRemovalsByActor\", 55: \"DelegateStakeRemovalsByActor\", 56: \"TopicLastWorkerCommit\", 57: \"TopicLastReputerCommit\", 58: \"TopicLastWorkerPayload\", 59: \"TopicLastReputerPayload\", 60: \"OpenWorkerWindows\", 61: \"LatestNaiveInfererNetworkRegrets\", 62: \"LatestOneOutInfererInfererNetworkRegrets\", 63: \"LatestOneOutInfererForecasterNetworkRegrets\", 64: \"LatestOneOutForecasterInfererNetworkRegrets\", 65: \"LatestOneOutForecasterForecasterNetworkRegrets\", 66: \"PreviousForecasterScoreRatio\", 67: \"LastDripBlock\", 68: \"TopicToNextPossibleChurningBlock\", 69: \"BlockToActiveTopics\", 70: \"BlockToLowestActiveTopicWeight\", 71: \"PreviousTopicQuantileInfererScoreEma\", 72: \"PreviousTopicQuantileForecasterScoreEma\", 73: \"PreviousTopicQuantileReputerScoreEma\", 74: \"CountInfererInclusionsInTopic\", 75: \"CountForecasterInclusionsInTopic\", 76: \"ActiveInferers\", 77: \"ActiveForecasters\", 78: \"ActiveReputers\", 79: \"LowestInfererScoreEma\", 80: \"LowestForecasterScoreEma\", 81: \"LowestReputerScoreEma\", 82: \"LossBundles\", 83: \"TotalSumPreviousTopicWeights\", 84: \"RewardCurrentBlockEmission\", 85: \"GlobalWhitelist\", 86: \"TopicCreatorWhitelist\", 87: \"TopicWorkerWhitelist\", 88: \"TopicReputerWhitelist\", 89: \"TopicWorkerWhitelistEnabled\", 90: \"TopicReputerWhitelistEnabled\", 91: \"LastMedianInferences\", 92: \"MadInferences\", 93: \"InitialInfererEmaScore\", 94: \"InitialForecasterEmaScore\", 95: \"InitialReputerEmaScore\", 96: \"GlobalWorkerWhitelist\", 97: \"GlobalReputerWhitelist\", 98: \"GlobalAdminWhitelist\", 99: \"LatestRegretStdNorm\", 100: \"LatestInfererWeights\", 101: \"LatestForecasterWeights\", 102: \"NetworkInferences\", 103: \"OutlierResistantNetworkInferences\", 104: \"MonthlyReputerRewards\", 105: \"MonthlyTopicRewards\",}\n",
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"lens = np.zeros(256, dtype = int)\n",
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"\n",
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"for field in range(255):\n",
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" lens[field] = iavltree.count(db, 's/k:emissions/', height, key = [field])\n",
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"\n",
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"order = np.lexsort((np.arange(256)[::-1], lens))[::-1]\n",
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"\n",
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"for i in range(len(order)):\n",
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" if lens[order[i]] == 0 and order[i] not in keynames:\n",
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" break\n",
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"\n",
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" print(f'{keynames[order[i]]:50} {lens[order[i]]:9d}')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# def has_prefix(db, prefix: bytes) -> bool:\n",
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"# it = db.iterator(start = prefix)\n",
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"# try:\n",
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"# first_key, _ = next(it)\n",
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"# return first_key.startswith(prefix)\n",
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"# except StopIteration:\n",
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"# return False\n",
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"# finally:\n",
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"# it.close()\n",
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"\n",
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"# found = []\n",
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"# prefixes = [b's/']\n",
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"\n",
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"# for i in range(20):\n",
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"# new_prefixes = []\n",
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"\n",
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"# for p in prefixes:\n",
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"# for i in range(256):\n",
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"# b = p + bytes([i])\n",
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"# if has_prefix(db, b):\n",
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"# if b.endswith(b'/'):\n",
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"# found.append((b, db.approximate_size(b, b + b'\\xff')))\n",
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"# else:\n",
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"# new_prefixes.append(b)\n",
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"# # new_prefixes.append(b)\n",
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"\n",
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"# prefixes = new_prefixes\n",
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"\n",
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"# found"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"# allora testnet module addresses\n",
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"# mod allorapendingrewards 54C6D62FF29ECFEE9A5F0366DEC0F9CB44C10BB4\n",
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"# mod allorarewards F3CA54C42E5B7DC7CB2A347B21E77AC248D914D2\n",
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"# mod allorastaking 3C19B4642DA1C2DBB7E44679FA48F72FD9A97E5E\n",
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"# mod ecosystem 570DD38DC5BAF3112A7C83A420ED399A8E59C5FC"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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