diff --git a/save_model_fits_as_csv.py b/save_model_fits_as_csv.py
index 36fca43..0a87f73 100644
--- a/save_model_fits_as_csv.py
+++ b/save_model_fits_as_csv.py
@@ -1,33 +1,105 @@
 
 from ModelFit import get_best_fit, ModelFit
 import os
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from Baseline import BaselineCellData
+
+
+SAVE_DIR = "results/lab_rotation/"
 
 
 def main():
 
-    dir = "results/final_1/"
+    res_folder = "results/final_2/"
+
+    # save_model_parameters(res_folder)
+    # save_cell_info(res_folder)
+
+    # test_save_cell_info()
+
 
+def save_model_parameters(res_folder):
     cells = []
     eod_freqs = []
     parameters = []
 
-    for cell in sorted(os.listdir(dir)):
-        cell_dir = dir + cell
+    for cell in sorted(os.listdir(res_folder)):
+        cell_dir = res_folder + cell
 
         model = get_best_fit(cell_dir, use_comparable_error=False)
         cells.append(cell)
         eod_freqs.append(model.get_cell_data().get_eod_frequency())
         parameters.append(model.get_final_parameters())
 
-    save_csv(dir + "models.csv", cells, eod_freqs, parameters)
+    save_csv(SAVE_DIR + "models.csv", cells, eod_freqs, parameters)
+
+
+def test_save_cell_info():
+    for cell in sorted(os.listdir(SAVE_DIR)):
+        cell_dir = SAVE_DIR + cell + "/"
+        if not os.path.isdir(cell_dir):
+            continue
+
+        fi_frame = pd.read_csv(cell_dir + "fi_curve_info.csv")
+
+        plt.plot(fi_frame["contrast"], fi_frame["f_inf"], 'o')
+        plt.plot(fi_frame["contrast"], fi_frame["f_zero"], '+')
+        plt.show()
+        plt.close()
+
+        count = 1
+        spike_file = "baseline_spikes_trial_{}.npy".format(count)
+        while os.path.exists(cell_dir + spike_file):
+            spiketimes = np.load(cell_dir + spike_file) * 1000
+
+            plt.hist(np.diff(spiketimes), bins=np.arange(0, 50, 0.1))
+            plt.show()
+            plt.close()
+
+            count += 1
+            spike_file = "baseline_spikes_trial_{}.npy".format(count)
+
+
+def save_cell_info(res_folder):
+    for cell in sorted(os.listdir(res_folder)):
+        cell_dir = res_folder + cell
+
+        fit = get_best_fit(cell_dir, use_comparable_error=False)
+
+        save_path = SAVE_DIR + cell + "/"
+
+        if not os.path.exists(save_path):
+            os.mkdir(save_path)
+
+        # fi-curve
+        cell_data = fit.get_cell_data()
+        f_zeros = fit.get_cell_f_zero_values()
+        f_infs = fit.get_cell_f_inf_values()
+        contrasts = cell_data.get_fi_contrasts()
+
+        data_array = np.array([contrasts, f_infs, f_zeros]).T
+        fi_frame = pd.DataFrame(data_array, columns=["contrast", "f_inf", "f_zero"])
+        fi_frame.to_csv(save_path + "fi_curve_info.csv")
+
+        spikes = cell_data.get_base_spikes()
+        for i, spike_list in enumerate(spikes):
+            spike_array = np.array(spike_list)
+            np.save(save_path + "baseline_spikes_trial_{}.npy".format(i+1), spike_array)
 
 
 def save_csv(file, cells, eod_freqs, parameters):
     keys = sorted(parameters[0].keys())
     with open(file, "w") as file:
-        header = "cell,eod_frequency"
+        header = "cell,EODf"
         for k in keys:
-            header += ",{}".format(k)
+            if k == "refractory_period":
+                header += ",ref_period"
+            elif k == "step_size":
+                header += ",deltat"
+            else:
+                header += ",{}".format(k)
         file.write(header + "\n")
 
         for i in range(len(cells)):