def makeFiles(read, write):
for s in bbdata.allSensors:
d = bbdata.Data()
print "Parsing sensor " + str(s)
try:
sString = read + "sensor" + str(s) + ".txt"
d = bbparser.rawToCompressedRaw(sString, f="2010-01-01 00:00:00")
d.sensor = s
except:
pass
oString = write + "sensor" + str(s) + ".dat"
dataio.saveData(oString, d)
def makeFiles(read, write):
for s in bbdata.allSensors:
d = bbdata.Data()
print "Parsing sensor " + str(s)
try:
sString = read + "sensor" + str(s) + ".txt"
d = bbparser.rawToCompressedRaw(sString, f = "2010-01-01 00:00:00")
d.sensor = s
except:
pass
oString = write + "sensor" + str(s) + ".dat"
dataio.saveData(oString, d)
origList += dvec
timeVec += tvec
tmpP = analysis.projectList(dvec, lsaData.pwz)
projList += tmpP
classList += projections.classify(tmpP, 0)
print "Half Way"
"""
splits = bbdata.makeSplits(40, st, et, valid = [0, 2, 4], \
splitLen = datetime.timedelta(minutes = splitLength), \
sPeriod = "18:00:00", \
ePeriod = "18:50:00")
dvec, tvec = projections.makeModelCounts(splits, modelDirectory, dataDirectory, \
neighborhoodLocation, minBehavior)
origList += dvec
timeVec += tvec
tmpP = analysis.projectList(dvec, lsaData.pwz)
projList += tmpP
classList += projections.classify(tmpP, 1)
"""
d = bbdata.Dataset([])
d.projList = projList
d.origList = origList
d.classList = classList
d.timeVec = timeVec
dataio.saveData(writeLocation, d)
while (up - low) > 1:
if l[dex] == i:
return dex
if l[dex] > i:
up = dex
dex = (up - low) / 2 + low
if l[dex] < i:
low = dex
dex = (up - low) / 2 + low
return dex + 1
if __name__ == "__main__":
a = []
b = []
for i in range(100000):
b.append(i)
#make a file of 100000 dates
for i in range(100000):
a.append(datetime.datetime.now())
dataio.saveData("data2.dat", b)
d = datetime.datetime.strptime(tmp, "%Y-%m-%d %H:%M:%S")
foo = calc.datetonumber(d)
if foo >= startTime and foo <= endTime:
data.append(calc.datetonumber(d))
if d.toordinal() != oldD:
#Add to database
db.insert(s, d.toordinal(), d.weekday(), len(data) - 1)
oldD = d.toordinal()
print " " + str(d)
except Exception, e:
print "Except:" + str(e)
pass
allData[s] = data
allData['db'] = db
dataio.saveData(write, allData)
if __name__ == "__main__":
startTime = "2008-03-09 00:00:00"
endTime = "2008-04-13 23:59:59"
#startTime = "2010-01-01 00:00:00"
#endTime = "2010-01-01 00:14:00"
#makeFiles(readLocation, writeLocation)
makeDB(readLocation, writeDB, startTime, endTime)
#stripFiles(readLocation, writeLocation, startTime, endTime)
bestModels = bm
bestData = bd
bestOut = out
bestStates = states
bestInter = f
sigma = IntegerRange(0, obs)
bd2 = []
for j in bestData:
bd2 += j
s = hmmextra.hmmSilhoutte(bd2, bestModels, sigma)
f = markov_anneal._fitness(bestModels, bestData, sigma)
print "best models: " + str(len(bestModels)) + " best states:" + str(bestStates) + \
" best Silhouette:" + str(bestSil) + " best inter-distance:" + str(bestInter)
oData = bbdata.Dataset(None)
oData.sData = sData
oData.out = bestOut
oData.models = bestModels
oData.obs = obs
oData.states = bestStates
oData.assignedData = bestData
oData.sensors = sensors[i]
oData.modelToMatrix(True)
wl = writeLocation + str(sensors[i][0]) + "_" + \
str(sensors[i][-1]) + ".dat"
dataio.saveData(wl, oData)
if foo >= startTime and foo <= endTime:
data.append(calc.datetonumber(d))
if d.toordinal() != oldD:
#Add to database
db.insert(s, d.toordinal(), d.weekday(), len(data) - 1)
oldD = d.toordinal()
print " " + str(d)
except Exception, e:
print "Except:" + str(e)
pass
allData[s] = data
allData['db'] = db
dataio.saveData(write, allData)
if __name__ == "__main__":
startTime = "2008-03-09 00:00:00"
endTime = "2008-04-13 23:59:59"
#startTime = "2010-01-01 00:00:00"
#endTime = "2010-01-01 00:14:00"
#makeFiles(readLocation, writeLocation)
makeDB(readLocation, writeDB, startTime, endTime)
#stripFiles(readLocation, writeLocation, startTime, endTime)
if s > bestSil:
bestSil = s
bestModels = bm
bestData = bd
bestOut = out
bestStates = states
bestInter = f
sigma = IntegerRange(0, obs)
bd2 = []
for j in bestData:
bd2 += j
s = hmmextra.hmmSilhoutte(bd2, bestModels, sigma)
f = markov_anneal._fitness(bestModels, bestData, sigma)
print "best models: " + str(len(bestModels)) + " best states:" + str(bestStates) + \
" best Silhouette:" + str(bestSil) + " best inter-distance:" + str(bestInter)
oData = bbdata.Dataset(None)
oData.sData = sData
oData.out = bestOut
oData.models = bestModels
oData.obs = obs
oData.states = bestStates
oData.assignedData = bestData
oData.sensors = sensors[i]
oData.modelToMatrix(True)
wl = writeLocation + str(sensors[i][0]) + "_" + \
str(sensors[i][-1]) + ".dat"
dataio.saveData(wl, oData)
