from turtle import heading


def find_study_area_values2(field_name,satellite, imageType,bounds, from_date, to_date,CLIENT_ID,CLIENT_SECRET,INSTANCE_ID, max_num):
	from sentinelhub import WebFeatureService, BBox, CRS, DataSource, MimeType, CRS, BBox, WmsRequest,DataCollection
	from sentinelhub import SHConfig
	from firebase_admin import credentials
	from firebase_admin import db
	from statistics import mean
	from PIL import Image, ImageFilter
	import os.path
	import PIL
	import numpy as np
	import time
	from sentinelhub import BBoxSplitter
	from pathlib import Path
	from osgeo import gdal
	import os
	from osgeo import gdal
	from osgeo import osr
	import numpy as np
	import os,sys
	import cv2
	import csv
	import json
	from make_geotiff import make_geotiff

	#from scipy import ndimag
	config = SHConfig()
	print(imageType)
	img_values_arr = []
	cred = credentials.Certificate('servicekey2.json')
	access_key = 'AKIAIPCM5ZR7FRHMY3MA'
	secret_key = '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'
	if CLIENT_ID and CLIENT_SECRET:
		config.sh_client_id = CLIENT_ID
		config.sh_client_secret = CLIENT_SECRET
		config.instance_id = INSTANCE_ID
	lat_len = (bounds[3]-bounds[1])
	lng_len = bounds[2] - bounds[0]
	#if width > height

	img_width = 500
	search_bbox = BBox(bbox=bounds, crs=CRS.WGS84)
	search_time_interval = (from_date, to_date)

		# Here we are splitting you bbox that was transformed to WGS84 into 5x5 areas
	bbox_splitter = BBoxSplitter([search_bbox.geometry], CRS.WGS84, (2,2)) 

	# Get a list of bboxes from the splitter
	bbox_list = bbox_splitter.get_bbox_list()
	date_list = []
	iter_num = 0
	for small_bbox in bbox_list:
		#print(small_bbox)
		iterator_wms = WebFeatureService(small_bbox,search_time_interval,data_collection=satellite,maxcc=1.0,config=config)
		images_arr = []
		prev_date = '90101010'
		for tile_info in iterator_wms:
			s1_request = WmsRequest(data_collection=satellite,layer=imageType,bbox=small_bbox,time=tile_info["properties"]["date"],width=img_width,image_format=MimeType.TIFF,config=config)
			s1_data = s1_request.get_data()
			new_date = tile_info["properties"]["date"]
			prev_day = prev_date.replace("-","")
			new_day = new_date.replace("-","")
			file_date = new_date
			if int(prev_day) - int(new_day) <6:
				file_date = prev_date
			else:
				prev_date = new_date
			
			file_name= file_date + '/' +str(tile_info["properties"]["date"]) + str(iter_num)+'.tiff'
			print(file_name)
			isdir = os.path.isdir(file_date)
			if isdir != True:
				os.mkdir(file_date)
			aqa = 1
			z_num = 0

			im = s1_data[-1]
			w,h = len(im[0]), len(im)
			for p in range(0,h-1):
				for q in range(0,w-1):
					pixel_value = im[p,q,0]
					if im[p,q,0] == 0:
						z_num = z_num + 1
			if z_num < int(w*h*0.6):
				if file_date not in date_list:
					date_list.append(file_date)
				im = PIL.Image.fromarray(s1_data[-1])
				im.save(file_name)

				make_geotiff(file_name, small_bbox)
				iter_num = iter_num + 1
			# Save the data to disk
			#wms_bands_img = wms_request.save_data()
		# List the downloaded Tiffs in the different subfolders with pathlib (native library)
	for single_date in date_list:
		file_list = [f"{x}" for x in Path(single_date).iterdir()]
		print(file_list)

		# Create a virtual raster
		folder_name = 'tif_' + single_date
		isdir = os.path.isdir(folder_name)
		if isdir != True:
			os.mkdir(folder_name)
		
		gdal.BuildVRT((folder_name+'/merged.vrt'), file_list)
		# time.sleep(10)
		# folder_name = 'tif_2022-09-10'
		# Convert to JPEG
		try:
			gdal.Translate((folder_name+'/out.jpeg'),(folder_name + '/merged.vrt'))
		except Exception as e:
			print(e)
			gdal.Translate((folder_name+'/out.jpeg'),(folder_name + '/merged.vrt'))
      
        # im = s1_data[-1]
	im = PIL.Image.open((folder_name+'/out.jpeg'))
	im = np.array(im)
	w,h = len(im[0]), len(im)
	images_arr.append(im[:,:,0])
        
	new_arr = []
	try:
		for i in range(0, len(images_arr[0])):
			for j in range(0, len(images_arr[0][0])):
				temp_arr = []
				for k in range(0, max_num):
					#print(k,i,j)
					temp_arr.append(round(100*images_arr[k][i][j])/100)
				new_arr.append(temp_arr)
	except:
		aaa = 1
	#print(new_arr)
	#np.savetxt('new_arr.csv',new_arr, delimiter=",")
	time.sleep(0.25)
	return new_arr,w,h