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Code: Select all
Frequency,Magnitude
0.015873015873015872,7.693787725565066
0.031746031746031744,15.307929229099642
0.047619047619047616,6.827003020802145
0.06349206349206349,6.916880805035763
0.07936507936507936,6.02594460167426
0.09523809523809523,3.1312430504094233
0.1111111111111111,6.094026874631805
0.12698412698412698,4.255716096788426
0.14285714285714285,4.83208936877447Code: Select all
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Load the data from the CSV file
input_file = 'data_RAW_TP9.csv'
df = pd.read_csv(input_file)
# Preprocess the data by filling missing values with zeros
df['RAW_TP9'] = df['RAW_TP9'].fillna(0)
# Create a time array for plotting (assuming a sampling rate of 0.5 seconds)
time = np.arange(len(df)) * 0.5
# Apply FFT to 'RAW_TP9' data
fft_result = np.fft.fft(df['RAW_TP9'])
# Calculate the frequency values
n = len(fft_result)
freq = np.fft.fftfreq(n, d=0.5) # Relative frequencies
# Extract the magnitude of the FFT result
magnitude = np.abs(fft_result)
# Filter out positive frequencies
positive_freq_mask = freq > 0
positive_freq = freq[positive_freq_mask]
positive_magnitude = magnitude[positive_freq_mask]
# Plot the FFT result for positive frequencies
plt.figure(figsize=(12, 6))
plt.title('FFT for RAW_TP9 (Positive Frequencies)')
plt.plot(positive_freq, positive_magnitude)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Magnitude')
plt.grid()
# Save the graph as a JPG file
plt.savefig('FFT_RAW_TP9.jpg')
plt.close()
# Create a DataFrame for the positive frequency and magnitude data
fft_data = pd.DataFrame({'Frequency (Hz)': positive_freq, 'Magnitude': positive_magnitude})
# Save the data as a new CSV file
output_file = 'output_RAW_TP9.csv'
fft_data.to_csv(output_file, index=False)
Code: Select all
Frequency (Hz) Magnitude
0.014388489208633100 3883.7829057527200
0.02877697841726620 1763.875806532500
0.04316546762589930 2364.7548375790900
0.05755395683453240 907.6062576131200
0.07194244604316550 2669.2160446270400
0.08633093525179860 1248.871647181520
0.10071942446043200 3759.879066465700
0.11510791366906500 1772.5406775104700
0.12949640287769800 2900.538053568630
0.14388489208633100 630.6437474856770
0.15827338129496400 2604.040351348210
0.17266187050359700 904.9073505367600
0.18705035971223000 526.1452294559570
0.20143884892086300 3102.666058392090
0.21582733812949600 533.7863481259360
0.2302158273381300 3965.0983632988300
0.2446043165467630 1481.8775183763900
0.2589928057553960 1077.8259170570900
0.2733812949640290 1009.3464059089800
0.28776978417266200 4131.412146691210
0.302158273381295 3851.888931564770
0.3165467625899280 1843.7622520145600
0.33093525179856100 5030.344442277420
0.34532374100719400 4124.8800392147200
0.3597122302158270 2100.5685677079000
0.37410071942446000 3252.958097644980
0.38848920863309400 1726.4378511252200
0.40287769784172700 5113.858435326000
0.4172661870503600 2251.3356583357300
0.43165467625899300 4425.053235589330
0.4460431654676260 1586.4498150133700
0.460431654676259 2849.2559453142100
0.47482014388489200 2893.008732903440
0.4892086330935250 1614.9546763683000
0.5035971223021580 1846.2720175788300
0.5179856115107910 1765.780963653180
0.5323741007194250 2990.4000684758700
0.5467625899280580 3019.9402003919300
0.5611510791366910 1280.4490461331500
0.5755395683453240 216.0353139727510
0.5899280575539570 2367.487101991650
0.60431654676259 3073.866656329910
0.6187050359712230 4069.594041778200
0.6330935251798560 7772.182198705500
0.6474820143884890 899.2311906069560
0.6618705035971220 1982.365662897000
0.6762589928057550 4448.110015312240
0.6906474820143890 3261.2028389329300
0.7050359712230220 1055.8389715596900
0.7194244604316550 2999.2154585418200
0.7338129496402880 4019.7373480294400
0.7482014388489210 1648.0264095310800
0.762589928057554 3760.209760886390
0.7769784172661870 2981.56771239279
0.7913669064748200 1975.2381301532000
0.8057553956834530 377.6915705888330
0.8201438848920860 3186.086116124280
0.8345323741007200 5073.995335057610
0.8489208633093530 3072.168550462750
0.8633093525179860 1070.9828307775100
0.8776978417266190 218.77773879881100
0.8920863309352520 836.8906042412640
0.9064748201438850 3212.120376041330
0.920863309352518 3784.0798211796000
0.9352517985611510 2440.153846550750
0.9496402877697840 2872.140113724980
0.9640287769784170 3988.246796237670
0.9784172661870500 3886.6931154881700
0.9928057553956840 2509.0556476818800