if __name__ == "__main__":
st = datetime.datetime.strptime(st, "%Y-%m-%d %H:%M:%S")
et = datetime.datetime.strptime(et, "%Y-%m-%d %H:%M:%S")
files = os.listdir(modelLocation)
#Get the sensor blocks
for f in files:
print f
#It is a data file.
if f.split('.')[-1] == 'dat':
#Open it and grab the models and sensor list
fn = dataio.loadData(modelLocation + str(f))
fn.matrixToModel(fn.modelList)
print "Sensors:" + str(fn.sensors)
cd, td = bbdata.comp(st, et, \
comp = compress, \
sens = fn.sensors,
readLocation = dataLocation)
sData = markov_anneal.splitLocalMax(cd, td, splitLen)
outFile = writeLocation + str(f.split('.')[0]) + '.txt'
#Make the file.
detections.write_detections(sData, fn.models, fileName=outFile)
print i
i+=1
oldSplit = datetime.datetime.strptime(s[0], "%Y-%m-%d %H:%M:%S")
newSplit = datetime.datetime.strptime(s[1], "%Y-%m-%d %H:%M:%S")
tmpDoc = []
suppress.suppress(2)
#Get the sensor blocks
for f in files:
#It is a data file.
if f.split('.')[-1] == 'dat':
#Open it and grab the models and sensor list
fn = dataio.loadData(modelDirectory + str(f))
fn.matrixToModel(fn.modelList)
cd, td = bbdata.comp(oldSplit, newSplit, \
comp = compress, \
sens = fn.sensors,
readLocation = dataDirectory)
sData = markov_anneal.splitLocalMax(cd, td, splitLen)
#for each split, make a document matrix and append it to the
#ongoing tdmatrix
try:
val, counts = analysis.ratio(sData.values(), fn.models)
except:
counts = [0] * len(fn.models)
val = [0] * len(fn.models)
suppress.restore(2)
print i
i += 1
oldSplit = datetime.datetime.strptime(s[0], "%Y-%m-%d %H:%M:%S")
newSplit = datetime.datetime.strptime(s[1], "%Y-%m-%d %H:%M:%S")
tmpDoc = []
suppress.suppress(2)
#Get the sensor blocks
for f in files:
#It is a data file.
if f.split('.')[-1] == 'dat':
#Open it and grab the models and sensor list
fn = dataio.loadData(modelDirectory + str(f))
fn.matrixToModel(fn.modelList)
cd, td = bbdata.comp(oldSplit, newSplit, \
comp = compress, \
sens = fn.sensors,
readLocation = dataDirectory)
sData = markov_anneal.splitLocalMax(cd, td, splitLen)
#for each split, make a document matrix and append it to the
#ongoing tdmatrix
try:
val, counts = analysis.ratio(sData.values(), fn.models)
except:
counts = [0] * len(fn.models)
val = [0] * len(fn.models)
suppress.restore(2)
if __name__ == "__main__":
st = datetime.datetime.strptime(st, "%Y-%m-%d %H:%M:%S")
et = datetime.datetime.strptime(et, "%Y-%m-%d %H:%M:%S")
files = os.listdir(modelLocation)
#Get the sensor blocks
for f in files:
print f
#It is a data file.
if f.split('.')[-1] == 'dat':
#Open it and grab the models and sensor list
fn = dataio.loadData(modelLocation + str(f))
fn.matrixToModel(fn.modelList)
print "Sensors:" + str(fn.sensors)
cd, td = bbdata.comp(st, et, \
comp = compress, \
sens = fn.sensors,
readLocation = dataLocation)
sData = markov_anneal.splitLocalMax(cd, td, splitLen)
outFile = writeLocation + str(f.split('.')[0]) + '.txt'
#Make the file.
detections.write_detections(sData, fn.models, fileName = outFile)