dt_nugenLE = ReadData(f_nugen, m_sname_E2, "!"+opts.sigcut)
# Load Corsika and low enegy corsika
print "Loading Corsika..."
dt_corsika = ReadData(f_corsika, m_sname_corsika, "")
dt_corsikaLE = ReadData(f_corsikaLE, m_sname_corsikaLE, "")
# combine
dt_corsika = Data(np.concatenate((dt_corsika.data,dt_corsikaLE.data),axis=0),
np.concatenate((dt_corsika.targets,dt_corsikaLE.targets),axis=0),
"totalCorsika",
1)
print "Loading data..."
dt_data = ReadData(f_data, m_sname_data, "")
#dt_data = None
#print dt_nugen.data
#print dt_nugenLE.data
#print dt_corsika.data
dt_total = Data(np.concatenate((dt_nugen.data,dt_corsika.data),axis=0),
np.concatenate((dt_nugen.targets,dt_corsika.targets),axis=0),
"total",
1)
print "Saving..."
savedata(dt_total, dt_nugenLE, "total_withweights", None, dt_data)
# Check if model is set
if len(opts.modelinput) == 0:
print "Please specify the input model to run"
print "Otherwise this process of saving scores"
print "will take too long."
sys.exit()
# Read in the low energy data as well
d_LE = ReadData(opts.fsig, m_sname_E2, opts.cuts+"&&!"+opts.sigcut)
# Load classifier
clf = joblib.load(opts.modelinput)
# save data
savedata(d_eval, d_LE, options.savename, clf)
#**********************************************#
# Run over the evaluation data set
#**********************************************#
if options.evaluate:
evaluate(d_eval,d_dev,opts)
#**********************************************#
# Plot effective area
#**********************************************#
if options.ploteffarea:
# Add in the low energy data as well
d_LE = ReadData(opts.fsig, m_sname_E2, opts.cuts+"&&!"+opts.sigcut)
# Load Low Energy NuGen
print "Loading NuGen LE..."
dt_nugenLE = ReadData(f_nugen, m_sname_E2, "!" + opts.sigcut)
# Load Corsika and low enegy corsika
print "Loading Corsika..."
dt_corsika = ReadData(f_corsika, m_sname_corsika, "")
dt_corsikaLE = ReadData(f_corsikaLE, m_sname_corsikaLE, "")
# combine
dt_corsika = Data(
np.concatenate((dt_corsika.data, dt_corsikaLE.data), axis=0),
np.concatenate((dt_corsika.targets, dt_corsikaLE.targets), axis=0),
"totalCorsika", 1)
print "Loading data..."
dt_data = ReadData(f_data, m_sname_data, "")
#dt_data = None
#print dt_nugen.data
#print dt_nugenLE.data
#print dt_corsika.data
dt_total = Data(np.concatenate((dt_nugen.data, dt_corsika.data), axis=0),
np.concatenate((dt_nugen.targets, dt_corsika.targets), axis=0),
"total", 1)
print "Saving..."
savedata(dt_total, dt_nugenLE, "total_withweights", None, dt_data)
# Check if model is set
if len(opts.modelinput) == 0:
print "Please specify the input model to run"
print "Otherwise this process of saving scores"
print "will take too long."
sys.exit()
# Read in the low energy data as well
d_LE = ReadData(opts.fsig, m_sname_E2, opts.cuts + "&&!" + opts.sigcut)
# Load classifier
clf = joblib.load(opts.modelinput)
# save data
savedata(d_eval, d_LE, options.savename, clf)
#**********************************************#
# Run over the evaluation data set
#**********************************************#
if options.evaluate:
evaluate(d_eval, d_dev, opts)
#**********************************************#
# Plot effective area
#**********************************************#
if options.ploteffarea:
# Add in the low energy data as well
d_LE = ReadData(opts.fsig, m_sname_E2, opts.cuts + "&&!" + opts.sigcut)
