103 lines
4.4 KiB
Python
103 lines
4.4 KiB
Python
#!/bin/python3
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import numpy as np
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from DAS.block import *
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class Observer:
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"""This class gathers global data from the simulation, like an 'all-seen god'."""
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def __init__(self, logger, config):
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"""It initializes the observer with a logger and given configuration."""
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self.config = config
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self.format = {"entity": "Observer"}
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self.logger = logger
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self.block = [0] * self.config.blockSize * self.config.blockSize
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self.rows = [0] * self.config.blockSize
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self.columns = [0] * self.config.blockSize
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self.broadcasted = Block(self.config.blockSize)
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def checkRowsColumns(self, validators):
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"""It checks how many validators have been assigned to each row and column."""
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for val in validators:
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if val.amIproposer == 0:
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for r in val.rowIDs:
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self.rows[r] += 1
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for c in val.columnIDs:
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self.columns[c] += 1
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for i in range(self.config.blockSize):
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self.logger.debug("Row/Column %d have %d and %d validators assigned." % (i, self.rows[i], self.columns[i]), extra=self.format)
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if self.rows[i] == 0 or self.columns[i] == 0:
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self.logger.warning("There is a row/column that has not been assigned", extra=self.format)
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def checkBroadcasted(self):
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"""It checks how many broadcasted samples are still missing in the network."""
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zeros = 0
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for i in range(self.blockSize * self.blockSize):
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if self.broadcasted.data[i] == 0:
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zeros += 1
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if zeros > 0:
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self.logger.debug("There are %d missing samples in the network" % zeros, extra=self.format)
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return zeros
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def checkStatus(self, validators):
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"""It checks the status of how many expected and arrived samples globally."""
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arrived = 0
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expected = 0
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ready = 0
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validatedall = 0
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validated = 0
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for val in validators:
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if val.amIproposer == 0:
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(a, e, v) = val.checkStatus()
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arrived += a
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expected += e
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if a == e:
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ready += 1
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validatedall += val.vpn
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validated += v
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return (arrived, expected, ready, validatedall, validated)
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def getProgress(self, validators):
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"""Calculate current simulation progress with different metrics.
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Returns:
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- missingSamples: overall number of sample instances missing in nodes.
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Sample are counted on both rows and columns, so intersections of interest are counted twice.
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- sampleProgress: previous expressed as progress ratio
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- nodeProgress: ratio of nodes having all segments interested in
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- validatorProgress: same as above, but vpn weighted average. I.e. it counts per validator,
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but counts a validator only if its support node's all validators see all interesting segments
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TODO: add real per validator progress counter
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"""
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arrived, expected, ready, validatedall, validated = self.checkStatus(validators)
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missingSamples = expected - arrived
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sampleProgress = arrived / expected
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nodeProgress = ready / (len(validators)-1)
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validatorCnt = sum([v.vpn for v in validators[1:]])
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validatorAllProgress = validatedall / validatorCnt
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validatorProgress = validated / validatorCnt
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return missingSamples, sampleProgress, nodeProgress, validatorAllProgress, validatorProgress
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def getTrafficStats(self, validators):
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"""Summary statistics of traffic measurements in a timestep."""
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def maxOrNan(l):
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return np.max(l) if l else np.NaN
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def meanOrNan(l):
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return np.mean(l) if l else np.NaN
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trafficStats = {}
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for cl in range(0,3):
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Tx = [v.statsTxInSlot for v in validators if v.nodeClass == cl]
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Rx = [v.statsRxInSlot for v in validators if v.nodeClass == cl]
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RxDup = [v.statsRxDupInSlot for v in validators if v.nodeClass == cl]
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trafficStats[cl] = {
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"Tx": {"mean": meanOrNan(Tx), "max": maxOrNan(Tx)},
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"Rx": {"mean": meanOrNan(Rx), "max": maxOrNan(Rx)},
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"RxDup": {"mean": meanOrNan(RxDup), "max": maxOrNan(RxDup)},
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}
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return trafficStats
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