import numpy as np
import matplotlib.pyplot as plt
from math import sin, cos, pi
import csv

myX = np.loadtxt("x_test.csv", delimiter=",", dtype=np.float32, encoding='UTF8', skiprows=1)
myY = np.loadtxt("Y_test.csv", delimiter=",", dtype=np.float32, encoding='UTF8', skiprows=1)
myX = np.expand_dims(myX, axis=0)
print(myX.shape, myY.shape)


# #### Hyperparameters :  sigma = STD of the zoom-in/out factor
sigma = 0.1
def DA_Scaling(X, sigma=0.1):
    scalingFactor = np.random.normal(loc=1.0, scale=sigma, size=(1,X.shape[1])) # shape=(1,3)
    myNoise = np.matmul(np.ones((X.shape[0],1)), scalingFactor)
    return X*myNoise

sin_function = [sin(0.04 * pi * x) for x in range(0,10000)]
y = [i for i in range(10000)]

plt.plot(list(myX)[0])

for i in range(10000):
    if myY[i] == 1:
        plt.vlines(x=i, ymin=-1.5, ymax=myX[0][i], color='red')

# plt.plot(sin_function)
# plt.plot(list(myY))
# plt.plot(list(DA_Scaling(myX, sigma))[0])
# plt.legend(["original", "scaling"])
plt.xlabel("Time")
plt.ylabel("Data")

plt.show()