diff --git a/Data.py b/Data.py
index 49ec8b9..b2f8c6c 100644
--- a/Data.py
+++ b/Data.py
@@ -9,17 +9,16 @@ class Data:
         self.datasets = []
 
         with h5py.File(self.filename, "r") as h5:
-            
             if 1:
                 print(f"Atribuudid grupis '{group_key}'")
                 grp = h5[group_key]
-                
                 self.current = grp["current_raw"][()] / grp.attrs["c_mem"]
                 self.current_t = grp["current_t"][()]
                 self.ECal = grp.attrs["vrev"]
                 self.gGaL = grp.attrs["gmax"]
                 self.eid = group_key
-  
+
     def get_current_slice(self, times: np.ndarray):
-        interpolator = interp1d(self.current_t, self.current, fill_value="extrapolate")
+        interpolator = interp1d(self.current_t, self.current,
+                                fill_value="extrapolate")
         return interpolator(times)
diff --git a/fitter.py b/fitter.py
index 7104892..0e337e2 100644
--- a/fitter.py
+++ b/fitter.py
@@ -37,7 +37,7 @@ class Fitter:
             return current
 
         k = np.zeros(current.size)
-        k[k.size // 2 :] = np.exp(-np.arange(k.size // 2) / np.abs(tau))
+        k[k.size // 2:] = np.exp(-np.arange(k.size // 2) / np.abs(tau))
         k /= k.sum()
 
         if tau > 0:
@@ -46,7 +46,7 @@ class Fitter:
             return np.convolve(current, k[::-1], mode="same")
 
     def cost_func(self, parameters: np.ndarray):
-        
+
         model = self.model()
 
         gGaL, ECal, K_pc_half, tau_xfer, tau_RC, offset = parameters
@@ -65,7 +65,6 @@ class Fitter:
         err = np.mean(res**2)  # mean squared error
         self.iteration += 1
         print(self.iteration, parameters.tolist(), "err", err)
-        
         # measured_fluo = self.data.fluo
         # fluo_interplolator = interp1d(self.time_domain, model.calculated_fluo)
         # calculated_fluo = fluo_interplolator(self.data.fluo_time)
@@ -120,9 +119,8 @@ class Fitter:
             'tau_xfer': tau_xfer,
             'tau_RC': tau_RC,
             'offset': offset,
-            'mean_squared_error': err })
+            'mean_squared_error': err})
             
-    
             model = self.model()
     
             model.ECaL = ECal
@@ -168,15 +166,16 @@ class Fitter:
         return cov, cor
 
     def plot_correlation_matrix(cor):
-    plt.imshow(cor, cmap='viridis', interpolation='nearest')
-    plt.colorbar(label='Correlation')
-    plt.title('Correlation Matrix')
-    plt.xlabel('Variables')
-    plt.ylabel('Variables')
-    plt.show()
+        plt.imshow(cor, cmap='viridis', interpolation='nearest')
+        plt.colorbar(label='Correlation')
+        plt.title('Correlation Matrix')
+        plt.xlabel('Variables')
+        plt.ylabel('Variables')
+        plt.show()
 
 if __name__ == "__main__":
 
+
     filename = "ltcc_current.h5"
     eid = "0033635a51b096dc449eb9964e70443a67fc16b9587ae3ff6564eea1fa0e3437_2018.06.18 14:48:40"
 
@@ -191,7 +190,6 @@ if __name__ == "__main__":
     res_filename = res_filename.replace(" ", "_").replace(":", "-")
     fit_hist.to_csv(res_filename, index=True)
     
-    
     eid_cleaned = re.sub(r'[^\w.-]', '', eid)  # Eemalda kõik eritähed ja jääb alles alphanumbrilised tähed, sidekriipsud ja punktid
     fig.savefig(f"plot_{eid_cleaned}.png")
     fig.savefig(f"plot_{eid_cleaned}.pdf")