import numpy as np

import pandas as pd

import seaborn as sns

sns.set_style('ticks')

sns.set_context('paper')

import matplotlib.pyplot as plt

from glob import glob

with open('conditions_list','r') as f:

conditions = [x.strip() for x in f.readlines()]

global_data = pd.DataFrame()

for condition in conditions:

green_imgs = glob('%s/*.tif' % condition)

red_imgs = glob('%s/*.tif' % condition.replace('Green','Red'))

for img in green_imgs:

I = plt.imread(img).flatten()

tile_id = int(img.split('_tile')[1].split('_')[0])

global_data = global_data.append({'Condition':condition, 'TileID': tile_id,'Color':'Green', 'MedianIntensity':np.median(I),'StdDevIntensity': np.std(I)}, ignore_index=True)

for img in red_imgs:

I = plt.imread(img).flatten()

tile_id = int(img.split('_tile')[1].split('_')[0])

global_data = global_data.append({'Condition':condition.replace('Green','Red'), 'TileID': tile_id,'Color':'Red', 'MedianIntensity':np.median(I),'StdDevIntensity': np.std(I)}, ignore_index=True)

print("Processed", condition)

global_data.to_csv('quicklook_Tiles.csv',index=False)

plt.figure(figsize=(12,8))

plt.subplot(1,2,1)

sns.stripplot(y='Condition', x='MedianIntensity', data=global_data.loc[global_data.Color=='Green'])

plt.title('Green Median Intensity')

plt.subplot(1,2,2)

sns.stripplot(y='Condition', x='MedianIntensity', data=global_data.loc[global_data.Color=='Red'])

plt.title('Red Median Intensity')

plt.tight_layout()

plt.savefig('quicklook_Tiles.pdf',bbox_inches='tight')