diff --git a/.gitignore b/.gitignore
index 8e77355..d393d71 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,3 +3,6 @@ fit_results_1c5ca4b12ae2ddffc3960c1fe39a3cce35967ce23dbac57c010f450e796d01fd_201
 plot_1c5ca4b12ae2ddffc3960c1fe39a3cce35967ce23dbac57c010f450e796d01fd_2017.11.27140704.pdf
 plot_1c5ca4b12ae2ddffc3960c1fe39a3cce35967ce23dbac57c010f450e796d01fd_2017.11.27140704.png
 naidis_fit.pdf
+__pycache__/Data.cpython-312.pyc
+__pycache__/Model.cpython-312.pyc
+__pycache__/fitter.cpython-312.pyc
diff --git a/experiment_fitter.py b/experiment_fitter.py
index 1f2a5fd..d67a0a4 100644
--- a/experiment_fitter.py
+++ b/experiment_fitter.py
@@ -6,6 +6,45 @@ from Model import Model
 from fitter import Fitter
 from Data import Data
 
+file = "ltcc_current.h5"
+
+
+def print_attrs(name, obj):
+    print(f"\nAttributes for {name}:")
+    for key, val in obj.attrs.items():
+        print(f"  {key}: {val}")
+
+
+with h5py.File(file, "r") as h5_file:
+    h5_file.visititems(print_attrs)
+
+# Dict to hold DFs 'sex', 'tag' & 'spid'
+dfs_by_sex_tag_spid = {}
+
+with h5py.File(file, "r") as h5_file:
+    for eid in h5_file.keys():
+        attributes = h5_file[eid].attrs
+        sex = attributes.get("sex")
+        tag = attributes.get("tag")
+        spid = attributes.get("spid")
+
+        key = f"{sex}_{tag}_{spid}"
+
+        if key not in dfs_by_sex_tag_spid:
+            dfs_by_sex_tag_spid[key] = pd.DataFrame()
+
+        row_data = {"experiment_id": eid, "sex": sex, "tag": tag, "spid": spid}
+        temp_df = pd.DataFrame([row_data])
+
+        dfs_by_sex_tag_spid[key] = pd.concat(
+            [dfs_by_sex_tag_spid[key], temp_df], ignore_index=True
+        )
+
+for key, df in dfs_by_sex_tag_spid.items():
+    print(f"DataFrame for {key}:")
+    print(df)
+    print()
+
 
 def fit_data():
     filename = "ltcc_current.h5"
diff --git a/multiple_experiment_fitter.py b/multiple_experiment_fitter.py
new file mode 100644
index 0000000..16810b9
--- /dev/null
+++ b/multiple_experiment_fitter.py
@@ -0,0 +1,124 @@
+import h5py
+import pandas as pd
+import re
+
+import matplotlib as plt
+
+from Model import Model
+from fitter import Fitter
+from Data import Data
+
+file = "ltcc_current.h5"
+
+
+def print_attrs(name, obj):
+    #  print(f"\nAttributes for {name}:")
+    #  for key, val in obj.attrs.items():
+    #  print(f"  {key}: {val}")
+    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
+        sex = attributes.get("sex")
+        tag = attributes.get("tag")
+        spid = attributes.get("spid")
+
+        key = f"{sex}_{tag}_{spid}"
+
+        if key not in dfs_by_sex_tag_spid:
+            dfs_by_sex_tag_spid[key] = pd.DataFrame()
+
+        row_data = {"experiment_id": eid, "sex": sex, "tag": tag, "spid": spid}
+        temp_df = pd.DataFrame([row_data])
+
+        dfs_by_sex_tag_spid[key] = pd.concat(
+            [dfs_by_sex_tag_spid[key], temp_df], ignore_index=True
+        )
+
+#  for key, df in dfs_by_sex_tag_spid.items():
+#  print(f"DataFrame for {key}:")
+#  print(df)
+#  print()
+
+
+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 = []
+
+        for eid in df["experiment_id"].tolist():
+            data = Data(file, group_key=eid)
+            combined_data.append(data)
+
+        collective_data = Data.combine(combined_data)
+
+        fit = Fitter(Model, collective_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}")
+
+        plt.plot(collective_data.x, collective_data.y, 'k-',
+                 label="Combined Fit")
+        plt.legend()
+        plt.xlabel("X")
+        plt.ylabel("Y")
+
+        key_cleaned = re.sub(r"[^\w.-]", "", key)
+        plt.savefig(f"combined_plot_{key_cleaned}.png")
+        plt.savefig(f"combined_plot_{key_cleaned}.pdf")
+        plt.close()
+
+        fit_hist = pd.DataFrame.from_dict(fit.fit_results, orient="index").T
+        fit_hist.index.name = "Iterations"
+
+        res_filename = f"combined_fit_results_{key}.csv"
+        res_filename = res_filename.replace(" ", "_").replace(":", "-")
+        fit_hist.to_csv(res_filename, index=True)
+
+        print(f"Finished fitting for {key}. Results saved.")
+
+
+fit_and_plot_dataframes(dfs_by_sex_tag_spid)
+
+"""
+def fit_data():
+    filename = "ltcc_current.h5"
+    with h5py.File(filename, "r") as h5:
+        eids = list(h5.keys())
+
+    for eid in eids:
+        data = Data(filename, group_key=eid)
+        fit = Fitter(Model, data)
+        fit.optimize()
+        res, fig = fit.optimize()
+
+        fit_hist = pd.DataFrame.from_dict(fit.fit_results, orient="index").T
+        fit_hist.index.name = "Iterations"
+
+        res_filename = f"fit_results_{eid}.csv"
+        res_filename = res_filename.replace(" ", "_").replace(":", "-")
+        fit_hist.to_csv(res_filename, index=True)
+
+        eid_cleaned = re.sub(r"[^w.-]", "", eid)  # Eemaldab kõik eritähed
+        fig.savefig(f"plot_{eid_cleaned}.png")
+        fig.savefig(f"plot_{eid_cleaned}.pdf")
+
+
+fit_data()
+
+"""