from tempfile import tempdir
import firebase_admin
from firebase_admin import credentials
from firebase_admin import db
from find_s1_image import find_img_value
import pandas as pd
import statsmodels.api as sm
import seaborn as sns
sns.set()
import json
import pandas as pdf
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
import statsmodels.api as sm
import seaborn as sns
sns.set()
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
import numpy as np
from sklearn.cluster import KMeans
from find_study_area_values import find_study_area_values
from make_area_estimate_image import make_area_estimate_image
from sentinelhub import WebFeatureService, BBox, CRS, DataSource, MimeType, CRS, BBox, WmsRequest,DataCollection
cred = credentials.Certificate('servicekey.json')
x = 0.0003
y = 0.0003
fromdate = '20221025'
todate = '20221205'

s1_images = ['IW-VH-DB']
s2_images = []
# s1_images = ["B02", "B03", "B04", "B05"]
#s2_images = ["RVI-NEW"]
try:
    firebase_admin.initialize_app(cred, {'databaseURL': 'https://farmbase-b2f7e-31c0c.firebaseio.com/'})
except:
    print('fire running')
study_uid = "MBFshQgimkS6AwSDtqAyOpRXZW12"
study_field_uid = '6HjLvQdSnUQAWPWL5ck4rVnpdfT2'
gt_uids = ['rF4qo7DGoxd3AeEC1Ux4vBGbsbl1', 'fSRlyKzCxxMJHJqDzpGhQImyyxf2']
different_gt_uid = 'joCrKyhfobbzgCxCQPHJfOOKKPF3'
gt_fields = {}
for gt_uid in gt_uids:
    temp_fields = db.reference('PaidMonitoredFields').child('PMF').child(gt_uid).get()
    for (fieldid, fieldobj) in temp_fields.items():
        gt_fields[fieldid] = fieldobj

different_gt_fields = db.reference('PaidMonitoredFields').child('PMF').child(different_gt_uid).get()
study_fields = db.reference('PaidMonitoredFields').child('PMF').child(study_uid).get()
for (key, val) in study_fields.items():
    study_field = val
    study_field_id = key
sentinelSettings = db.reference('SentinelSettings').get()
clientID = sentinelSettings["ClientID"]
clientSecret = sentinelSettings["ClientSecret"]
wmsID = sentinelSettings["WMSID"]
rviID = sentinelSettings["RVIID"]
demID = sentinelSettings["DEMID"]
field_num = 0
from_date = '2022-10-20T00:00:00'
to_date = '2022-12-05T23:59:59'
max_num = 4

def append_img_vals(temp_arr, img_values):
    if temp_arr is not None:
        for k in temp_arr:
            img_values.append(k)
    return img_values

all_fields_img_values = []
gt_fields_img_values = []

i = 0
X = []
for (fieldid, fieldobj) in gt_fields.items():
    if i < 500:
        #gt_field_points = fieldobj["geometry"]["coordinates"]
        #print(gt_field_points)
        img_values = []
        # midlat = gt_field_points[1]
        # midlng = gt_field_points[0]
        midlat = (float(fieldobj["FieldMinLat"]) + float(fieldobj["FieldMaxLat"]))/2
        midlng = (float(fieldobj["FieldMinLong"]) + float(fieldobj["FieldMaxLong"]))/2
        bounds = [(midlng-y),(midlat-x),(midlng+y),(midlat+x)]
        #print(bounds)
        image_num = 0
        for image in s2_images:
            temp_arr = find_img_value(fieldid,DataCollection.SENTINEL2_L1C,image, bounds, from_date, to_date, clientID, clientSecret, wmsID, max_num)
            img_values = append_img_vals(temp_arr, img_values)
        for image in s1_images:
            temp_arr = find_img_value(fieldid,DataCollection.SENTINEL1_IW, image, bounds, from_date, to_date, clientID, clientSecret, rviID, max_num)
            img_values = append_img_vals(temp_arr, img_values)
        if len(img_values) > 0:
            all_fields_img_values.append(img_values)
            gt_fields_img_values.append(img_values)
            X.append(1)
        i = i+1
    else:
        break

i = 0

for (fieldid, fieldobj) in different_gt_fields.items():
    if i < 100:
        img_values = []
        midlat = (float(fieldobj["FieldMinLat"]) + float(fieldobj["FieldMaxLat"]))/2
        midlng = (float(fieldobj["FieldMinLong"]) + float(fieldobj["FieldMaxLong"]))/2
        bounds = [(midlng-y),(midlat-x),(midlng+y),(midlat+x)]
        print(bounds)
        image_num = 0
        for image in s2_images:
            temp_arr = find_img_value(fieldid,DataCollection.SENTINEL2_L1C,image, bounds, from_date, to_date, clientID, clientSecret, wmsID, max_num)
            img_values = append_img_vals(temp_arr, img_values)
        for image in s1_images:
            temp_arr = find_img_value(fieldid,DataCollection.SENTINEL1_IW, image, bounds, from_date, to_date, clientID, clientSecret, rviID, max_num)
            img_values = append_img_vals(temp_arr, img_values)
        if len(img_values) > 0:
            all_fields_img_values.append(img_values)
            X.append(0)
        i = i+1
    else:
        break


print(all_fields_img_values)
print(gt_fields_img_values)
np.savetxt("wheat_all_field_img_values.csv", all_fields_img_values, delimiter = ",")
np.savetxt("wheat_gt_field_img_values.csv", gt_fields_img_values, delimiter = ",")

midlat = (float(study_field["FieldMinLat"]) + float(study_field["FieldMaxLat"]))/2
midlng = (float(study_field["FieldMinLong"]) + float(study_field["FieldMaxLong"]))/2
bounds = [float(study_field["FieldMinLong"]),float(study_field["FieldMinLat"]),float(study_field["FieldMaxLong"]),float(study_field["FieldMaxLat"])]
print(bounds)
# image_num = 0
# for image in s2_images:
#     temp_arr,w,h = find_study_area_values(DataCollection.SENTINEL2_L1C,image, bounds, from_date, to_date, clientID, clientSecret, wmsID, max_num)
# for image in s1_images:
#     temp_arr,w,h = find_study_area_values(DataCollection.SENTINEL1_IW, image, bounds, from_date, to_date, clientID, clientSecret, rviID, max_num)

X_train, X_test, y_train, y_test = train_test_split(X, all_fields_img_values, test_size=0.25, random_state=0)
ii = 0
regressor = RandomForestRegressor(n_estimators=200,random_state=0)
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X)
#make_area_estimate_image(Identified_clusters, w, h, study_uid, study_field_id, pred_vals)