diff --git a/multiple_experiment_fitter.py b/multiple_experiment_fitter.py
index 16810b9..85f725b 100644
--- a/multiple_experiment_fitter.py
+++ b/multiple_experiment_fitter.py
@@ -1,5 +1,6 @@
 import h5py
 import pandas as pd
+import numpy as np
 import re
 
 import matplotlib as plt
@@ -10,6 +11,8 @@ from Data import Data
 
 file = "ltcc_current.h5"
 
+dfs_by_sex_tag_spid = {}
+
 
 def print_attrs(name, obj):
     #  print(f"\nAttributes for {name}:")
@@ -18,12 +21,6 @@ def print_attrs(name, obj):
     pass
 
 
-with h5py.File(file, "r") as h5_file:
-    h5_file.visititems(print_attrs)
-
-
-dfs_by_sex_tag_spid = {}
-
 with h5py.File(file, "r") as h5_file:
     for eid in h5_file.keys():
         attributes = h5_file[eid].attrs
@@ -53,30 +50,45 @@ def fit_and_plot_dataframes(dfs_by_sex_tag_spid):
     for key, df in dfs_by_sex_tag_spid.items():
         print(f"Fitting and plotting data for {key}...")
 
-        combined_data = []
+        combined_current = []
+        combined_time = []
 
         for eid in df["experiment_id"].tolist():
             data = Data(file, group_key=eid)
-            combined_data.append(data)
+            combined_current.append(data.current)
+            combined_time.append(data.current_t)
 
-        collective_data = Data.combine(combined_data)
+        combined_current = np.concatenate(combined_current)
+        combined_time = np.concatenate(combined_time)
 
-        fit = Fitter(Model, collective_data)
+        # Sort by time for consistency - inspired by data
+        sorted_indices = np.argsort(combined_time)
+        combined_time = combined_time[sorted_indices]
+        combined_current = combined_current[sorted_indices]
+
+        combined_data = Data(file, group_key=eid)
+        combined_data.current = combined_current
+        combined_data.current_t = combined_time
+
+        fit = Fitter(Model, combined_data)
         fit.optimize()
         res, fig = fit.optimize()
 
         plt.figure()
         plt.title(f"Fit results for {key}")
 
-        for single_data in combined_data:
-            plt.plot(single_data.x, single_data.y,
-                     label=f"Experiment {single_data.eid}")
+        for i, eid in enumerate(df["experiment_id"].tolist()):
+            plt.plot(combined_time, combined_current,
+                     label=f"Experiment {eid}")
+
+        plt.plot(
+            combined_data.current_t, combined_data.current, "k-",
+            label="Combined Fit"
+        )
 
-        plt.plot(collective_data.x, collective_data.y, 'k-',
-                 label="Combined Fit")
         plt.legend()
-        plt.xlabel("X")
-        plt.ylabel("Y")
+        plt.xlabel("Time")
+        plt.ylabel("Current")
 
         key_cleaned = re.sub(r"[^\w.-]", "", key)
         plt.savefig(f"combined_plot_{key_cleaned}.png")
@@ -90,7 +102,7 @@ def fit_and_plot_dataframes(dfs_by_sex_tag_spid):
         res_filename = res_filename.replace(" ", "_").replace(":", "-")
         fit_hist.to_csv(res_filename, index=True)
 
-        print(f"Finished fitting for {key}. Results saved.")
+        print(f"Finished fitting for {key}.")
 
 
 fit_and_plot_dataframes(dfs_by_sex_tag_spid)