Improve fit

• Estimate peak using a simple Gaussian filter and np.argmax to speed up fitting.
• Calculate outliers with IsolationForest and per channel.
• Add possibility of a polynomial fit on pes and sqrt(pes).
• Add possibility of fitting with weights and function to get weights based on the XGM intensity to get a flatter response a as a function of intensity.

pes_to_spec/test/offline_analysis.py

@@ -186,7 +186,7 @@ def main():
                for ch in channels}
     pes_raw_t = {ch: run[pes_name, f"digitizers.{ch}.raw.samples"].select_trains(by_id[test_tids]).ndarray()
                for ch in channels}
-    
+
     print("Data in memory.")
 
     # read the XGM information
@@ -202,7 +202,7 @@ def main():
 
     train_idx = np.isin(tids, train_tids)
 
-    #model.debug_peak_finding(pes_raw, os.path.join(args.directory, "test_peak_finding.png"))
+    model.debug_peak_finding(pes_raw, os.path.join(args.directory, "test_peak_finding.png"))
     if len(args.model) == 0:
         print("Fitting")
         start = time_ns()