Calcium_Model/statistiline_filter.py

# -*- 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'

sobiv_eid_list = []
ttx_eid_list = []
teised_eid_list = []

with h5py.File(h5_file, 'r') as h5_file:

    for eid in h5_file.keys():
        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')

        puhas_eid = eid.replace(" ", "_").replace(":", "-")
        fit_result_eid = "fit_results_" + puhas_eid

        if tag_val == 'iso':
            sobiv_eid_list.append(fit_result_eid)
        elif tag_val == 'ttx':
            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['eid'] = eid
            
            df.to_csv(csv_file_name, index=False)

for fail in os.listdir():
    if fail.endswith('.csv'):

        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():

    if filename.endswith('.csv'):
        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)
"""