diff --git a/12_EulerEKF_pitch.png b/12_EulerEKF_pitch.png
index f30a2f6..a5b0d02 100644
Binary files a/12_EulerEKF_pitch.png and b/12_EulerEKF_pitch.png differ
diff --git a/12_EulerEKF_roll.png b/12_EulerEKF_roll.png
index 5b713e2..1cb3207 100644
Binary files a/12_EulerEKF_roll.png and b/12_EulerEKF_roll.png differ
diff --git a/euler_ekf.py b/euler_ekf.py
index 0ba7eb8..e9ea1fb 100644
--- a/euler_ekf.py
+++ b/euler_ekf.py
@@ -3,6 +3,7 @@ from numpy.linalg import inv
 import matplotlib.pyplot as plt
 from math import cos, sin, tan, asin, pi
 import pandas as pd
+from scipy.signal import butter, filtfilt
 
 def get_data(i, data):
     x = data[0][i]  # (41500, 1)
@@ -87,23 +88,54 @@ def Euler_EKF(z, rates, dt):
     psi   = x[2]
     return phi, theta, psi
 
+def butter_lowpass_filter(data, cutoff, fs, order):
+    nyq = (0.5 * fs) # Nyquist Frequency
+    normal_cutoff = cutoff / nyq
+    # Get the filter coefficients 
+    b, a = butter(order, normal_cutoff, btype='low', analog=False)
+    y = filtfilt(b, a, data)
+    return y
+
 def normalize_data_vector(data, division):
     return [i/division for i in list(data)]
 
 H, Q, R = None, None, None
 x, P = None, None
 firstRun = True
+
 division_param = 32768
 
+# Filter requirements.
+T = 5.0         # Sample Period
+fs = 30.0       # sample rate, Hz
+cutoff = 2      # desired cutoff frequency of the filter, Hz, slightly higher than actual 1.2 Hz
+order = 2       # sin wave can be approx represented as quadratic
+
 df = pd.read_csv('raw_data_6d.xls', sep='\t')
 
-gyroX = normalize_data_vector(df['%GyroX'].values, division_param)
-gyroY = normalize_data_vector(df['%GyroX'].values, division_param)
-gyroZ = normalize_data_vector(df['%GyroX'].values, division_param)
+gyroX = df['%GyroX'].values
+gyroY = df['GyroY'].values
+gyroZ = df['GyroZ'].values
 
-acceX = normalize_data_vector(df['AcceX'].values, division_param)
-acceY = normalize_data_vector(df['AcceY'].values, division_param)
-acceZ = normalize_data_vector(df['AcceZ'].values, division_param)
+acceX = df['AcceX'].values
+acceY = df['AcceY'].values
+acceZ = df['AcceZ'].values
+
+gyroX = normalize_data_vector(gyroX, division_param)
+gyroY = normalize_data_vector(gyroY, division_param)
+gyroZ = normalize_data_vector(gyroZ, division_param)
+
+acceX = normalize_data_vector(acceX, division_param)
+acceY = normalize_data_vector(acceY, division_param)
+acceZ = normalize_data_vector(acceZ, division_param)
+
+# gyroX = butter_lowpass_filter(gyroX, cutoff, fs, order)
+# gyroY = butter_lowpass_filter(gyroY, cutoff, fs, order)
+# gyroZ = butter_lowpass_filter(gyroZ, cutoff, fs, order)
+
+# acceX = butter_lowpass_filter(acceX, cutoff, fs, order)
+# acceY = butter_lowpass_filter(acceY, cutoff, fs, order)
+# acceZ = butter_lowpass_filter(acceZ, cutoff, fs, order)
 
 gyro_data = [gyroX, gyroY, gyroZ]
 acce_data = [acceX, acceY, acceZ]
@@ -146,4 +178,12 @@ plt.subplot(133)
 plt.plot(t, PsiSaved)
 plt.xlabel('Time [Sec]')
 plt.ylabel('Psi angle [deg]')
-'''
\ No newline at end of file
+'''
+
+n =int(T * fs) 
+# sin wave
+sig = np.sin(1.2*2*np.pi*n)# Lets add some noise
+noise = 1.5*np.cos(9*2*np.pi*n) + 0.5*np.sin(12.0*2*np.pi*n)
+data = sig + noise
+
+print(data)