質問編集履歴
6
画像の修正
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###結果
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5
dc成分を除去したときのコードと結果を追加
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[電圧データ](https://www.dropbox.com/s/cnr26ac09seqw0j/398Hz.csv?dl=0)
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## DC成分を引いた場合
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```remove_dc.ipynb
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test_data_middle = pd.read_csv("CSVファイルへのパス")
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x = test_data_middle["time"]
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y = test_data_middle["volt"]
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N = len(y)
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dt = x[1]-x[0]
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t = np.arange(0,N*dt,dt)
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#DCを削除する為に元データの平均を引く
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Y = np.array(y)
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Y_block_offset = Y - Y.mean()
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F = np.fft.fft(Y_block_offset)
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Amp = np.abs(F)
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freq = np.fft.fftfreq(N,d=dt)
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#PLot
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plt.figure(figsize=(18,3),facecolor="white")
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plt.plot(x,Y_block_offset,'b-', linewidth=1)
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plt.xlabel('Time')
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plt.ylabel('Volt')
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plt.grid(True)
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#FFT graph
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fig, ax = plt.subplots(nrows=4, sharex=True, figsize=(6,6))
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ax[0].plot(F.real[0:20], label="Real part")
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ax[0].legend()
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ax[1].plot(F.imag[0:20], label="Imaginary part")
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ax[1].legend()
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ax[2].plot(freq[0:20], label="Frequency")
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ax[2].legend()
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ax[3].plot(Amp[0:20], label="Amp")
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ax[3].legend()
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ax[3].set_xlabel("Number of data")
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plt.show()
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```
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###結果
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4
1枚目のグラフを間違えていたので修正
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3
Ampのプロットと全範囲のプロット、それに伴う画像と#FFT graph以下のコード変更
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#FFT graph
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fig, ax = plt.subplots(nrows=
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fig, ax = plt.subplots(nrows=4, sharex=True, figsize=(6,6))
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ax[0].plot(F.real
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ax[0].plot(F.real, label="Real part")
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ax[0].legend()
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ax[1].plot(F.imag
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ax[1].plot(F.imag, label="Imaginary part")
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ax[1].legend()
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ax[2].plot(freq
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ax[2].plot(freq, label="Frequency")
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ax[2].legend()
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ax[3].plot(Amp, label="Amp")
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ax[3].legend()
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ax[
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ax[3].set_xlabel("Number of data")
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plt.show()
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2
タイトル修正
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Python
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Pythonのnumpyで高速フーリエ変換(FFT)をしたい
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1
タイトル修正
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Python(Jupyter notebook)でFFTをしたい
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Python(Jupyter notebook)で高速フーリエ変換FFTをしたい
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