def write_data(sample_size):
if sample_size < 0:
raise ValueError("Number should be positive")
if type(sample_size) != int:
raise TypeError("Argument should be a number")
# Write Sensor Data
data = []
file = open("sensor_data.txt", "w")
for d in generate_sensor_data(sample_size):
data.append(d)
for n in data:
file.write(str(n) + '\n')
file.close()
# Convert Sensor Data to Null Data
null_data = apply_null_filter(data)
file2 = open("null_data.txt", "w")
for n in null_data:
file2.write(str(n) + '\n')
file2.close()
for number in range(final_start):
start = number
subset = data[start:end]
new_datum = mean(subset)
filtered.append(new_datum)
end += 1
if w > len(data) or w < 0 or w % 2 == 0:
raise ValueError
return filtered
if __name__ == '__main__':
sensor_data = generate_sensor_data(1000)
amp_filter_data = apply_amp_filter(sensor_data)
mean_data = mean_filter(amp_filter_data)
all_data = zip(sensor_data[1:1000], amp_filter_data[1:1000], mean_data)
with open('mean_filter.csv', 'w') as f:
csv_file = csv.writer(f)
csv_file.writerows(all_data)
test_data = (0, 1, 2, 3, 4, 5, 6, 7, 8)
for i in range(-3, 11):
try:
new_data = mean_filter(test_data, i)
print('For test_data and w =', i, 'output:', new_data)
except ValueError:
from sensor import generate_sensor_data
from null_filter import apply_null_fil
def write(data, file_name):
with open(file_name, 'w') as file:
for d in data:
file.write(str(d) + '\n')
write(generate_sensor_data(50), 'sensor_data')
data = []
with open('sensor_data', 'r') as f:
for line in f.readlines():
line = line.strip('\n')
data.extend([line])
filter_data = apply_null_filter(data)
write(filter_data, 'filted_data')
import matplotlib.pyplot as plt
import numpy as np
from sensor import generate_sensor_data
from null_filter import apply_null_filter
from filters import mean_filter
from writefile import write_file
n = 256
x = np.linspace(1, n - 1, n)
sigma = 0.1
data = []
# use generator function to acquire noisy data
for d in generate_sensor_data(n, sigma):
data = data + [d]
# apply mean filter with a few different widths
data_null = apply_null_filter(data)
data_filt_3 = mean_filter(data, 3)
data_filt_7 = mean_filter(data, 7)
data_filt_11 = mean_filter(data, 11)
# write unfiltered and filtered data to file
filepath_noisy = 'C:\\Users\\jmich\\.spyder-py3\\ME 599\\lab 2\\noisy_data.txt'
filepath_null = 'C:\\Users\\jmich\\.spyder-py3\\ME 599\\lab 2\\null_filt_data.txt'
filepath_mean = 'C:\\Users\\jmich\\.spyder-py3\\ME 599\\lab 2\\mean_filt_data.txt'
write_file(data, filepath_noisy)
write_file(data_null, filepath_null)