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)