from matplotlib.pyplot import *
from numpy import average,mean
from scipy import interpolate
from scipy.stats import pearsonr, spearmanr, sem, ks_2samp,wilcoxon,mannwhitneyu
import pandas as pd
import numpy as np

protein_data = pd.read_csv('martin2012_ostreo_diurnal_N15_proteomics.csv')
timepoints = protein_data.columns[5:]
nC = len(protein_data.columns)

for idx in range(4):
    t0 = timepoints[idx]
    t1 = timepoints[idx+1]
    col_name = t1 + '/' + t0
    protein_data[col_name] = (protein_data[t1]/protein_data[t0])

ratio_labels = protein_data.columns[nC:nC+4]


protein_data['12-0']=protein_data['12']
for idx in range(4):
    t0 = timepoints[idx]
    t1 = timepoints[idx+1]
    col_name = t1 + '-' + t0
    protein_data[col_name] = (protein_data[t1]-protein_data[t0])/protein_data[timepoints[-1]]

diff_labels = protein_data.columns[nC+4:nC+9]

#estimate L/D ratio of protein synthesis, using 36-24 (L) vs 24-12 (D)
light_mean = mean(protein_data['36-24'].dropna())
dark_mean = mean(protein_data['24-12'].dropna())
light_sem = sem(protein_data['36-24'].dropna())
dark_sem = sem(protein_data['24-12'].dropna())

out_data = protein_data[['Ot gene Idb','36-24','24-12']]
out_data.columns = ['Ot gene Idb','Incorporation in the light','Incorporation in the dark']
out_data.to_csv('Otauri_dark_and_light_protein_synthesis.csv',index=False)