# -*- coding: utf-8 -*-

#==========================================================#
# Download L8 images of a given area between given dates
#==========================================================#

# Initial settings
import os
import numpy as np
import matplotlib.pyplot as plt
import pdb
import ee

# other modules
from osgeo import gdal, ogr, osr
from urllib.request import urlretrieve
import zipfile
from datetime import datetime
import pytz
import pickle

# image processing modules
import skimage.filters as filters 
import skimage.exposure as exposure
import skimage.transform as transform
import sklearn.decomposition as decomposition
import skimage.measure as measure

# import own modules
import functions.utils as utils

np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
ee.Initialize()

def download_tif(image, polygon, bandsId, filepath):
    """downloads tif image (region and bands) from the ee server and stores it in a temp file"""
    url = ee.data.makeDownloadUrl(ee.data.getDownloadId({
        'image': image.serialize(),
        'region': polygon,
        'bands': bandsId,
        'filePerBand': 'false',
        'name': 'data',
        }))
    local_zip, headers = urlretrieve(url)
    with zipfile.ZipFile(local_zip) as local_zipfile:
        return local_zipfile.extract('data.tif', filepath)
    
# select collection
input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
# Location (Narrabeen all)
#polygon = [[[151.3473129272461,-33.69035274454718],
#            [151.2820816040039,-33.68206818063878], 
#            [151.27281188964844,-33.74775138989556],
#            [151.3425064086914,-33.75231878701767],
#            [151.3473129272461,-33.69035274454718]]];
# location (Narrabeen-Collaroy beach)
#polygon = [[[151.301454, -33.700754],
#               [151.311453, -33.702075], 
#               [151.307237, -33.739761],
#               [151.294220, -33.736329],
#               [151.301454, -33.700754]]];
# location (Oldbar beach)
#polygon = [[[152.664508, -31.896163],
#               [152.665827, -31.897112], 
#               [152.631516, -31.924846],
#               [152.629285, -31.923362],
#               [152.664508, -31.896163]]]
# location (Oldbar inlet)
#polygon = [[[152.676283, -31.866784],
#   [152.709174, -31.869993], 
#   [152.678229, -31.892082],
#   [152.670366, -31.886360],
#   [152.676283, -31.866784]]];
# Location (Sand Engine)
#polygon = [[[4.171742, 52.070455], 
#            [4.223708, 52.069576],
#            [4.220808, 52.025293],
#            [4.147749, 52.028861],
#            [4.171742, 52.070455]]];
# Location (Tairua)
#polygon = [[[175.852115, -36.985414], 
#            [175.872797, -36.985145],
#            [175.873738, -37.000039],
#            [175.853956, -36.998749],
#            [175.852115, -36.985414]]];  
# Location (Duck)
polygon = [[[-75.766220, 36.195928], 
            [-75.748282, 36.196401],
            [-75.738851, 36.173974],
            [-75.763546, 36.174249],
            [-75.766220, 36.195928]]];
            
# dates
start_date = '2013-01-01'
end_date = '2019-01-01'
# filter by location
flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(start_date, end_date)

n_img = flt_col.size().getInfo()
print('Number of images covering the area:', n_img)
im_all = flt_col.getInfo().get('features')

satname = 'L8'
#sitename = 'NARRA_all'
#sitename = 'NARRA'
#sitename = 'OLDBAR'
#sitename = 'SANDMOTOR'
#sitename = 'TAIRUA'
sitename = 'DUCK'

suffix = '.tif'
filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
filepath_pan = os.path.join(filepath, 'pan')
filepath_ms = os.path.join(filepath, 'ms')

all_names_pan = []
all_names_ms = []
timestamps = []
acc_georef = []
# loop through all images
for i in range(n_img):
    # find each image in ee database
    im = ee.Image(im_all[i].get('id'))
    im_dic = im.getInfo()
    im_bands = im_dic.get('bands')
    im_date = im_dic['properties']['DATE_ACQUIRED']
    t = im_dic['properties']['system:time_start']
    im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
    timestamps.append(im_timestamp)
    im_epsg = int(im_dic['bands'][0]['crs'][5:])
    try:
        acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL'])
    except:
        acc_georef.append(10)   
        print('No geometric rmse model property')
    
    # delete dimensions key from dictionnary, otherwise the entire image is extracted
    for j in range(len(im_bands)): del im_bands[j]['dimensions']
    pan_band = [im_bands[7]]
    ms_bands = [im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[5], im_bands[11]]

    filename_pan = satname + '_' + sitename + '_' + im_date + '_pan' + suffix
    filename_ms = satname + '_' + sitename + '_' + im_date +  '_ms' + suffix

    print(i)
    if any(filename_pan in _ for _ in all_names_pan):
        filename_pan = satname + '_' + sitename + '_' + im_date + '_pan' + '_r' + suffix 
        filename_ms = satname + '_' + sitename + '_' + im_date + '_ms' + '_r' + suffix 
    all_names_pan.append(filename_pan)
    
    local_data_pan = download_tif(im, polygon, pan_band, filepath_pan)
    os.rename(local_data_pan, os.path.join(filepath_pan, filename_pan))
    local_data_ms = download_tif(im, polygon, ms_bands, filepath_ms)
    os.rename(local_data_ms, os.path.join(filepath_ms, filename_ms))
    
    
with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'wb') as f:
    pickle.dump(timestamps, f)  
with open(os.path.join(filepath, sitename + '_epsgcode' + '.pkl'), 'wb') as f:
    pickle.dump(im_epsg, f)      
with open(os.path.join(filepath, sitename + '_accuracy_georef' + '.pkl'), 'wb') as f:
    pickle.dump(acc_georef, f)