def runSimulation():
totalResults = []
for file in files:
print "Working on: " + file.split('/')[-2]
# Initial data ---------------------------------------------------------------
importer = Importer()
importedData = importer.getInputData(file, samplingInterval)
#PredictedData ---------------------------------------------------------------
predictor = DRPredictor()
predictedData = predictor.getPredictedData(importedData, predictionInterval, \
samplingInterval)
for threshold in thresholds:
#Transmitted packets and data ---------------------------------------------
transmitter = DRTransmitter(heartbeat)
transmittedPackets = transmitter.getTransmittedPackets(threshold, predictedData)
reconstructor = Reconstructor()
transmittedData = reconstructor.getReconstructedData(transmittedPackets)
result = PartAResult()
result.movementType = findMovementType(file)
result.threshold = threshold
result.txRate = calculateTransmissionRate(importedData, transmittedPackets)
result.txError = calculateError(importedData, transmittedData)
totalResults.append(result)
return totalResults
def simulateSynch():
totalResults = []
for file in files:
print "Working on: " + file.split('/')[-2]
# Initial data ---------------------------------------------------------------
importer = Importer()
importedData = importer.getInputData(file, samplingInterval)
#PredictedData ---------------------------------------------------------------
predictor = DRPredictor()
predictedData = predictor.getPredictedData(importedData, predictionInterval, \
samplingInterval)
for txRate in txRates:
#Transmitted packets and data ---------------------------------------------
transmitter = SynchTransmitter()
transmittedPackets = transmitter.getTransmittedPackets(int(txRate), predictedData)
reconstructor = Reconstructor()
transmittedData = reconstructor.getReconstructedData(transmittedPackets)
result = PartBResult()
result.movementType = findMovementType(file)
result.threshold = 0
result.txRate = txRate
result.txError = calculateError(importedData, transmittedData)
totalResults.append(result)
return totalResults
def transmitData(inputFile, logDir, predictionInterval, samplingInterval,
heartbeat, drThreshold, delay, jitter, packetLoss):
# Import data
print "Importing data..."
importer = Importer()
rawInputData = importer.getInputData(inputFile, samplingInterval)
exportData(logDir + "RawInputData.txt", rawInputData)
# Filtering input data
print "Filtering data..."
samplingFreq = int(1e3 / samplingInterval)
taps = 80
bands = [0.0, 10, 11, 50.0]
weights = [1, 0]
coefficients = scipy.signal.remez(taps, bands, weights, Hz=samplingFreq)
gain = 1.0 / sum(coefficients)
filteredInputData = filterData(rawInputData, logDir, "cc",
samplingInterval, coefficients)[0]
filteredInputData = amplifyData(filteredInputData, gain)
exportData(logDir + "FilteredInputData.txt", filteredInputData)
# Create the prediction vectors
print "Creating the prediction vectors..."
predictor = DRPredictor()
predictedData = predictor.getPredictedData(filteredInputData,
predictionInterval,
samplingInterval)
exportData(logDir + "PredictionData.txt", predictedData)
# Run the transmission algorithm
print "Simulating the transmission algorithm..."
transmitter = DRTransmitter(heartbeat)
drTxPackets = transmitter.getTransmittedPackets(drThreshold, predictedData)
exportData(logDir + "DRTxPackets.txt", drTxPackets)
# Simulate the transmission of the packets
print "Simulating the network..."
network = Network()
drRxPackets = network.getReceivedPackets(drTxPackets, delay, jitter,
packetLoss)
exportData(logDir + "DRRxPackets.txt", drRxPackets)
# Receive the packets
print "Receiving the packets..."
receiver = Receiver()
drRxFilteredPackets = receiver.getFilteredData(drRxPackets)
exportData(logDir + "DRRxData.txt", drRxFilteredPackets)
return [
rawInputData, filteredInputData, predictedData, drTxPackets,
drRxPackets, drRxFilteredPackets
]
def transmitData(inputFile, logDir, predictionInterval, samplingInterval,
heartbeat, drThreshold, delay, jitter, packetLoss):
# Import data
print "Importing data..."
importer = Importer()
rawInputData = importer.getInputData(inputFile, samplingInterval)
exportData(logDir + "RawInputData.txt", rawInputData)
# Filtering input data
print "Filtering data..."
samplingFreq = int(1e3/samplingInterval)
taps = 80
bands = [0.0, 10, 11, 50.0]
weights = [1, 0]
coefficients = scipy.signal.remez(taps, bands, weights, Hz=samplingFreq)
gain = 1.0 / sum(coefficients)
filteredInputData = filterData(rawInputData, logDir, "cc", samplingInterval, coefficients)[0]
filteredInputData = amplifyData(filteredInputData, gain)
exportData(logDir + "FilteredInputData.txt", filteredInputData)
# Create the prediction vectors
print "Creating the prediction vectors..."
predictor = DRPredictor()
predictedData = predictor.getPredictedData(filteredInputData, predictionInterval, samplingInterval)
exportData(logDir + "PredictionData.txt", predictedData)
# Run the transmission algorithm
print "Simulating the transmission algorithm..."
transmitter = DRTransmitter(heartbeat)
drTxPackets = transmitter.getTransmittedPackets(drThreshold, predictedData)
exportData(logDir + "DRTxPackets.txt", drTxPackets)
# Simulate the transmission of the packets
print "Simulating the network..."
network = Network()
drRxPackets = network.getReceivedPackets(drTxPackets, delay, jitter, packetLoss)
exportData(logDir + "DRRxPackets.txt", drRxPackets)
# Receive the packets
print "Receiving the packets..."
receiver = Receiver()
drRxFilteredPackets = receiver.getFilteredData(drRxPackets)
exportData(logDir + "DRRxData.txt", drRxFilteredPackets)
return [rawInputData, filteredInputData, predictedData, drTxPackets,
drRxPackets, drRxFilteredPackets]
def simulateADR():
totalResults = []
for file in files:
print "Working on: " + file.split('/')[-2]
# Initial data ---------------------------------------------------------------
importer = Importer()
importedData = importer.getInputData(file, samplingInterval)
#PredictedData ---------------------------------------------------------------
predictor = DRPredictor()
predictedData = predictor.getPredictedData(importedData, predictionInterval, \
samplingInterval)
for index, delay in enumerate(delays):
delayList = [delay] * len(predictedData)
jitter = jitters[index]
jitterList = [jitter] * len(predictedData)
transmitter = ADRTransmitter(heartbeat)
transmittedPackets = transmitter.getTransmittedPackets(minThreshold, maxThreshold,
maxDelay, maxJitter, \
delayList, jitterList, \
predictedData)
reconstructor = Reconstructor()
transmittedData = reconstructor.getReconstructedData(transmittedPackets)
result = PartBResult()
result.movementType = findMovementType(file)
result.threshold = 0
result.txRate = calculateTransmissionRate(importedData, transmittedPackets)
result.txError = calculateError(importedData, transmittedData)
result.delay = delay
result.jitter = jitter
result.packetLoss = 0
totalResults.append(result)
return totalResults
simulationNumber = 1 inputFile = dataRoot + movement + "/Simulation" + str(simulationNumber) + "/positionLog.txt" # Test parameters samplingInterval = 1 # Viewing parameters plotTimeDomain = True plotFreqDomain = True plotPhaseResponse = True lowerFreqBound = -30 upperFreqBound = 30 # Import data importer = Importer() data = importer.getInputData(inputFile, samplingInterval) # Split data into components time = range(0, len(data) * samplingInterval, samplingInterval) x = [] y = [] z = [] for sample in data: x.append(sample.position.x) y.append(sample.position.y) z.append(sample.position.z) # Do the fft on the signals -> output is complex number fftX = scipy.fft(x) fftY = scipy.fft(y)
delay = 150 jitter = 80 packetLoss = 0 # Viewing parameters plotTimeDomain = True plotFreqDomain = True plotPhaseResponse = True useDb = True lowerBoundTime = 17000 #17000 upperBoundTime = 22000 #22000 lowerBoundFreq = 0 upperBoundFreq = 400 for fileName in files: # Import data print "Importing data..." importer = Importer() inputData = importer.getInputData(fileName, samplingInterval) print "First: " + str(inputData[0]) print "Last: " + str(inputData[-1]) print # ---Code unfinsihed--- # Not sure how to accomplish this task # a) Stitching together different data sets seems like it would cause high frequency noise # TODO-> Look into this problem and solve if applicable to the paper
dataRoot = "/Users/fstakem/Data/Movements_5_1_08/"
root = "/Users/fstakem/Research/PhD/2010_Research/OptimalFiltering/"
logDir = root + "code/log/"
outputDir = root + "code/output/"
movement = "Stacking"
inputFile = inputFile = dataRoot + movement + "/Simulation" + str(
1) + "/positionLog.txt"
# Parameters for all algorithms
samplingInterval = 10
numPoints = 131072
# Importing the raw data
print "Importing data..."
importer = Importer()
rawInputData = importer.getInputData(inputFile, samplingInterval)
s.exportData(logDir + "RawInputData.txt", rawInputData)
# Filtering input data
print "Filtering data..."
samplingFreq = int(1e3 / samplingInterval)
taps = 80
bands = [0.0, 10, 11, 50.0]
weights = [1, 0]
coefficients = scipy.signal.remez(taps, bands, weights, Hz=samplingFreq)
gain = 1.0 / sum(coefficients)
filteredInputData = s.filterData(rawInputData, logDir, "cc", samplingInterval,
coefficients)[0]
filteredInputData = s.amplifyData(filteredInputData, gain)
s.exportData(logDir + "FilteredInputData.txt", filteredInputData)
