def worker(i):
td = TDOld()
tdnew = TrainData()
print("converting",dcold.samples[i])
td.readIn(dir + dcold.samples[i])
x = td.x
y = td.y
w = td.w
tdnew.tdnew._store(x,y,w)
tdnew.writeToFile(dcnew.samples[i])
td.clear()
tdnew.clear()
del x,y,w
return True
td.skim(int(args.e))
#td=td.split(int(args.e)+1)#get the first e+1 elements
#if int(args.e)>0:
# td.split(1) #reduce to the last element (the e'th one)
model = load_model(args.inputModel, custom_objects=get_custom_objects())
predicted = predict_from_TrainData(model, td, batchsize=100000)
pred = predicted[0]
feat = td.transferFeatureListToNumpy()
rs = feat[1]
feat = feat[0]
#weights = td.transferWeightListToNumpy()
truth = td.transferTruthListToNumpy()[0]
td.clear()
print(feat.shape)
print(truth.shape)
fig = plt.figure(figsize=(10, 4))
ax = [fig.add_subplot(1, 2, 1, projection='3d'), fig.add_subplot(1, 2, 2)]
data = create_index_dict(truth, pred, usetf=False)
feats = create_feature_dict(feat)
make_cluster_coordinates_plot(
plt,
ax[1],
data['truthHitAssignementIdx'], #[ V ]
data['predBeta'], #[ V ]