diff --git a/statistiline_filter.py b/statistiline_filter.py
index 7b7e9ee..a68cbd7 100644
--- a/statistiline_filter.py
+++ b/statistiline_filter.py
@@ -1,15 +1,6 @@
-# -*- coding: utf-8 -*-
-
-#%% import
-
 import h5py
 import os
 import pandas as pd
-import statsmodels.api as sm
-
-from statsmodels.formula.api import ols
-from scipy.stats import f
-from scipy.stats import shapiro
 
 h5_file = 'ltcc_current.h5'
 
@@ -23,9 +14,8 @@ with h5py.File(h5_file, 'r') as h5_file:
         if 'tag' in h5_file[eid].attrs:
             tag_val = h5_file[eid].attrs['tag']
 
-            # pean tõdema, et siin aitas chatgpt oma soovitusega :/ vaga halb debug oli
-             if isinstance(tag_val, bytes):
-            tag_val = tag_val.decode('utf-8')
+            if isinstance(tag_val, bytes):
+                tag_val = tag_val.decode('utf-8')
 
         puhas_eid = eid.replace(" ", "_").replace(":", "-")
         fit_result_eid = "fit_results_" + puhas_eid
@@ -36,30 +26,25 @@ with h5py.File(h5_file, 'r') as h5_file:
             ttx_eid_list.append(fit_result_eid)
         else:
             teised_eid_list.append(fit_result_eid)
-                #for attr_key, attr_value in h5_file[eid].attrs.items():
-                #print(f"Atribute: {attr_key}, Value: {attr_value}")
-
-#%% 
 
 file = 'ltcc_current.h5'
 
 with h5py.File(file, 'r') as h5_file:
     for eid in h5_file.keys():
         puhastatud_eid = eid.replace(" ", "_").replace(":", "-")
-        
+
         atribuudid = h5_file[eid].attrs
         sex = atribuudid.get('sex')
         spid = atribuudid.get('spid')
-        
+
         csv_file_name = f"fit_results_{puhastatud_eid}.csv"
-        
+
         if os.path.exists(csv_file_name):
             df = pd.read_csv(csv_file_name)
-            
+
             df['sex'] = sex
-            df['spid'] = spid.replace("Mouse AGAT","")
+            df['spid'] = spid.replace("Mouse AGAT", "")
             df['eid'] = eid
-            
             df.to_csv(csv_file_name, index=False)
 
 for fail in os.listdir():
@@ -68,25 +53,23 @@ for fail in os.listdir():
         eksperiment_id = fail.replace(".csv", "")
 
         if eksperiment_id in sobiv_eid_list:
-            
+
             df = pd.read_csv(fail)
             df['tag'] = 'iso'
             df.to_csv(fail, index=False)
 
         elif eksperiment_id in ttx_eid_list:
-            
+
             df = pd.read_csv(fail)
             df['tag'] = 'ttx'
             df.to_csv(fail, index=False)
 
         else:
-            
+
             df = pd.read_csv(fail)
             df['tag'] = 'teised'
             df.to_csv(fail, index=False)
 
-#%%
-
 comb_df = pd.DataFrame()
 
 for filename in os.listdir():
@@ -95,98 +78,7 @@ for filename in os.listdir():
         df = pd.read_csv(filename)
 
         if 'tag' in df.columns and df['tag'].isin(['iso', 'ttx']).all():
-        
+
             comb_df = pd.concat([comb_df, df], ignore_index=True)
 
 print(comb_df)
-
-#%% lugemine, et teha kindlaks mis tüüpi ANOVA teha kasutades statsmodels packetit, 
-
-sex_counts = comb_df['sex'].value_counts()
-
-spid_counts = comb_df['spid'].value_counts()
-
-tag_counts = comb_df['tag'].value_counts()
-
-
-print(sex_counts,spid_counts,tag_counts)
-
-#%% normaalsuse kontroll
-tau_xfer = comb_df['tau_xfer']
-
-stat, p = shapiro(tau_xfer)
-
-alpha = 0.05
-if p > alpha:
-    print("Andmed on normaalselt jaotunud (ei lükka tagasi nullhüpoteesi)")
-else:
-    print("Andmed ei ole normaalselt jaotunud (lükata tagasi nullhüpotees)")
-
-
-
-#%% ANOVA 2 WAY
-
-comb_df['spid'] = comb_df['spid'].astype('category')
-comb_df['sex'] = comb_df['sex'].astype('category')
-comb_df['tag'] = comb_df['tag'].astype('category')
-
-model = ols('tau_xfer ~ C(sex) + C(spid)+ C(tag)', data=comb_df).fit()
-
-anova_table = sm.stats.anova_lm(model, typ=2)
-print(anova_table)
-
-#%% kriitiline vaartus
-
-df_between_groups = 2  # Vabadusastmed gruppide vahel
-df_within_groups = 67  # Vabadusastmed rühmades (Residual)
-
-alpha = 0.05
-
-critical_f = f.ppf(1 - alpha, df_between_groups, df_within_groups)
-
-print("Kriitiline F-väärtus:", critical_f)
-
-
-#%% kriitiline vaartus
-
-
-df_between_groups = 2  # Vabadusastmed gruppide vahel (sex ja spid)
-df_within_groups = 36  # Vabadusastmed rühmades (Residual)
-
-alpha = 0.05
-
-critical_f = f.ppf(1 - alpha, df_between_groups, df_within_groups)
-
-print("Kriitiline F-väärtus:", critical_f)
-
-"""
-#%%grupeeringud
-
-groups = comb_df.groupby('tag')
-
-iso_group = groups.get_group('iso')
-ttx_group = groups.get_group('ttx')
-
-print("ISO grupp:", iso_group)
-print("nTTX grupp:", ttx_group)
-
-#%% ANOVA jaoks on grupid piisavalt suured, 
-#allikas https://support.minitab.com/en-us/minitab/help-and-how-to/statistical-modeling/anova/how-to/one-way-anova/before-you-start/data-considerations/
-
-f_statistic, p_value = f_oneway(iso_group['tau_xfer'], ttx_group['tau_xfer'])
-
-#%% f_crit control
-
-dfn = 2-1 #2 gruppi, miinus 1, hetkel ei lahendanud seda vaga automatiseeritult
-dfd = len(iso_group) + len(ttx_group) - 2 #ka ei lahendanud automatiseeritult
-
-alpha = 0.05
-
-f_crit = f.ppf(1 - alpha, dfn, dfd)
-
-#%% Väljastame tulemused
-
-print("F-statistika:", f_statistic)
-print("P-väärtus:", p_value)
-print("F-kriitiline:", f_crit)
-"""