10 from keras.models
import load_model
14 kVeto =
Cut(
lambda tables: tables[
'rec.sel.veto'][
'keep'] == 1)
17 classes = [
'numuid',
'nueid',
'nutauid',
'ncid',
'cosmicid']
20 pms = tables[
'rec.training.cvnmaps'][
'cvnmap']
21 df = pms.apply(
lambda x: model.predict(np.array([x]))[0])
22 return pd.DataFrame(df.values.tolist(), columns=classes, index=df.index)
26 withid = df.reset_index()
28 for col
in list(withid):
29 ret[col] = withid[col].values[..., np.newaxis].astype(np.float32)
32 if __name__ ==
'__main__':
35 stride =
int(sys.argv[2])
36 offset =
int(sys.argv[3])
37 print(
'Adding new cvns to files in '+d)
38 print(
'Stride: '+
str(stride)+
'; Offset: '+
str(offset))
39 files = [f
for f
in os.listdir(d)
if 'h5caf.h5' in f][offset::stride]
40 print(
'There are '+
str(len(files))+
' files.')
44 modelBase = load_model(
'models/model_mynet_cos_best.h5')
46 modelPTP = load_model(
'models/model_mynet_ptp_best.h5')
48 modellist = [modelBase, modelPTP]
49 namelist = [
'veto',
'ptpcut']
66 h5 = h5py.File(os.path.join(outdir,f),
'a')
68 for i,s
in enumerate(specs):
70 for dataset, vals
in thedict.items():
71 datastr =
'rec.sel.cvn2020'+namelist[i]+
'/'+dataset
75 h5.create_dataset(datastr, data=vals)
78 print(
'File '+f+
' processed at '+
str(time.time()-t0))
80 print(
'Finished in '+
str(time.time()-t0))