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)