import os
import time
import smbus
import csv
import numpy as np
import pandas as pd
import warnings

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

from imusensor.MPU9250 import MPU9250
from imusensor.filters import kalman
#from gpiozero import Buzzer

warnings.filterwarnings('ignore')
#buzzer = Buzzer(17)

dfn = pd.read_csv("/home/pi/Desktop/sarsinti/data_normal.csv")
dfn['result'] = 0

dfs = pd.read_csv("/home/pi/Desktop/sarsinti/data_sarsinti.csv")
dfs['result'] = 1

df = dfn.merge(dfs, how='outer')
dforj = df.copy()

y = df['result']
df.drop('result', axis=1, inplace=True)

X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.3, random_state=100)

logreg = LogisticRegression(solver='liblinear', random_state = 0)
logreg.fit(X_train, y_train)
y_pred = logreg.predict(X_test)
startscore = accuracy_score(y_test, y_pred)
print("Start AccScore: {0:0.4f}".format(startscore))

address = 0x68
bus = smbus.SMBus(1)
imu = MPU9250.MPU9250(bus, address)
imu.begin()

sensorfusion = kalman.Kalman()

imu.readSensor()
imu.computeOrientation()
sensorfusion.roll = imu.roll
sensorfusion.pitch = imu.pitch
sensorfusion.yaw = imu.yaw

count = 0
currTime = time.time()

#dfnew = pd.DataFrame(columns=['roll','pitch','result'])
while True:
	imu.readSensor()
	imu.computeOrientation()
	newTime = time.time()
	dt = newTime - currTime
	currTime = newTime
	sensorfusion.computeAndUpdateRollPitchYaw(imu.AccelVals[0], imu.AccelVals[1], imu.AccelVals[2], imu.GyroVals[0], imu.GyroVals[1], imu.GyroVals[2],\
												imu.MagVals[0], imu.MagVals[1], imu.MagVals[2], dt)	
	row = [sensorfusion.roll, sensorfusion.pitch]
	nprow = np.array([row])
	rowresult = int(logreg.predict(nprow))
	#if rowresult == 1:
		#buzzer.on()
		#time.sleep(0.01)
		#buzzer.off()
	print("Sarsıntı Var/Yok: ", rowresult)
	
	#dfrow = {'roll': sensorfusion.roll, 'pitch': sensorfusion.pitch, 'result': rowresult}
	#dfnew = pd.concat([dfnew, pd.DataFrame([dfrow])], ignore_index = True)
	#lendfnew = len(dfnew)
	
	#if lendfnew == 1000:
		#df = dforj.merge(dfnew, how='outer')
		#dfcsv = df.copy()
		#y = df['result']
		#y = y.astype('int')
		#df.drop('result', axis=1, inplace=True)
		#X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.3, random_state=100)
		#logreg.fit(X_train, y_train)
		#y_pred = logreg.predict(X_test)
		#newscore = accuracy_score(y_test, y_pred)
		#if newscore > startscore:
			#print("New AccScore: {0:0.4f}".format(newscore))
			#dfcsv.to_csv('/home/pi/Desktop/sarsinti/data.csv')
			#startscore = newscore
		#dfnew = pd.DataFrame(columns=['roll','pitch','result'])
	time.sleep(0.01)