Using two pipelines to split more evenly the preprocessing steps.

README.md

@@ -32,12 +32,11 @@ model.fit(low_resolution_raw_data,
           high_resolution_photon_energy)
 
 # save it for later usage:
-model.save("model.h5")
+model.save("model.joblib")
 
 # when performing inference:
 # load a model:
-model = Model()
-model.load("model.h5")
+model = Model.load("model.joblib")
 
 # and use it to map a low-resolution spectrum to a high-resolution one
 # as before, the low_resolution_raw_data refers to a dictionary mapping the channel name

pes_to_spec/test/offline_analysis.py

@@ -139,18 +139,17 @@ def main():
               spec_raw_pe[train_idx, :])
     t += [time_ns() - start]
     t_names += ["Fit"]
-    spec_smooth = model.preprocess_high_res(spec_raw_int, spec_raw_pe)
+    spec_smooth = model.preprocess_high_res(spec_raw_int)
 
     print("Saving the model")
     start = time_ns()
-    model.save("model.h5")
+    model.save("model.joblib")
     t += [time_ns() - start]
     t_names += ["Save"]
 
     print("Loading the model")
     start = time_ns()
-    model = Model()
-    model.load("model.h5")
+    model = Model.load("model.joblib")
     t += [time_ns() - start]
     t_names += ["Load"]