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'''
Here we take the average of 3 terms x0, A, B where,
x0 = The point to be estimated
A = weighted average of n terms previous to x0
B = weighted avreage of n terms ahead of x0
n = window size
'''
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
from mpl_toolkits.mplot3d import Axes3D
from util import read_data as rd
def weighted_average(params):
'''
Calculates the weighted average of terms in the input
Args:
params (list): a list of numbers
Returns:
list: weighted average of the terms in the list
'''
weighted_sum = 0
weight = len(params)
weight_sum = weight * (weight+1) / 2
for num in params:
weighted_sum += weight*num
weight -= 1
return weighted_sum / weight_sum
def triple_moving_average(signal_array, window_size):
'''
Apply triple moving average to a signal
Args:
signal_array (numpy array): the array of values on which the filter is to be applied
window_size (int): the no. of points before and after x0 which should be considered for calculating A and B
Returns:
numpy array: a filtered array of size same as that of signal_array
'''
filtered_signal = []
arr_len = len(signal_array)
for index, point in enumerate(signal_array):
if (index < window_size or index > arr_len - window_size ):
filtered_signal.append(point)
else:
A, B = [], []
for i in range(0, window_size):
A.append(signal_array[index + i])
B.append(signal_array[index - i])
wa_A = weighted_average(A)
wa_B = weighted_average(B)
filtered_signal.append((point + wa_B + wa_A ) / 3)
return filtered_signal
def generate_filtered_data(in_data, window):
'''
Apply the filter and generate the filtered data
Args:
in_data (string): numpy array containing the positional data
window (int): window size applied into the filter
Returns:
numpy array: the final filtered array
'''
averaged_x = (triple_moving_average(list(in_data[:,1]), window))
averaged_y = triple_moving_average(list(in_data[:,2]), window)
averaged_z = triple_moving_average(list(in_data[:,3]), window)
output = np.hstack(((in_data[:,0])[:, np.newaxis], (np.array(averaged_x))[:, np.newaxis],
(np.array(averaged_y))[:, np.newaxis], (np.array(averaged_z))[:, np.newaxis] ))
return output
if __name__ == "__main__":
signal = rd.load_data(os.getcwd() + '/' + sys.argv[1])
import time
time_start = time.clock()
output = generate_filtered_data(signal, 2)
time_stop = time.clock()
print("File {} processed in {} seconds.".format(
sys.argv[1],
time_stop - time_start
)
)
np.savetxt("filtered.csv", output, delimiter=",")
print("Filtered output saved as filtered.csv")
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot(output[:,1], output[:,2], output[:,3], 'b', label='filtered')
ax.plot(list(signal[:,1]), list(signal[:,2]), list(signal[:,3]), 'r', label='noisy')
ax.legend(['Filtered Orbit', 'Noisy Orbit'])
plt.show()
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