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//paip/script/unused/therImageAvgMax.py

# -*- coding: utf-8 -*-
"""
Created on Fri Jun 18 11:53:29 2021

@author: lbk
"""

import os
import warnings
warnings.filterwarnings("ignore")
from itertools import count
import sys
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from glob import glob
import matplotlib.patches as patches
from collections import Counter
import pandas as pd
#%%

def readpgm(name):
    with open(name) as f:
        lines = f.readlines()

# Ignores commented lines
    for l in list(lines):
        if l[0] == '#':
            lines.remove(l)

# Makes sure it is ASCII format (P2)
    assert lines[0].strip() == 'P2'

# Converts data to a list of integers
    data = []
    for line in lines[1:]:
        data.extend([int(c) for c in line.split()])

return (np.array(data[3:]), (data[1], data[0]), data[2])

# different shades of gray from black (0) to white (up to 65,536)

#%%
pgm_path = glob(sys.argv[1])
data = readpgm(pgm_path[0])
#plt.imshow(np.reshape(data[0], data[1]),cmap='gray')

#%%
p_data = np.reshape(data[0], data[1])
p_data = p_data*150/4096 - 10
#print(p_data)

#plt.imshow(p_data,cmap='gray')

#%%
p_data = pd.DataFrame(p_data)

test = pd.melt(p_data,value_vars=p_data.columns, ignore_index=False)
test.columns = ['x','value']
test['y'] = test.index
test = test.reset_index(drop=True)

#test0 = test[test.value < 10]
test1 = test[test.value >= 10]

#%%
df = test1.drop(['value'],axis=1)

from sklearn.cluster import DBSCAN
model = DBSCAN(eps=np.sqrt(2), min_samples=5)
pred = model.fit_predict(df)
test1['group'] = pred

result = test1['value'].groupby(test1['group'])
size = result.size()
size[size > size.median()*1.5] = 0
size[size < size.median()*0.5] = 0
size = size[size !=0 ]
valid = size.index

temp = []
for i in valid:
    sub = test1[test1.group == i]
    sub = sub.sort_values(by=['value'], axis=0, ascending=False)
    temp.append(sub[:9].value.mean())

avg_temp = np.round(np.median(temp),1)
#max_temp = np.round(test1.value.max(),1)
max_temp = np.round(np.max(temp),1)

print(max_temp,avg_temp)