Add fft frequency transformation #62
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…t be at least twice the signal bandwidth
…t at least twice the signal bandwidth
sarahmish
reviewed
Nov 14, 2024
| @@ -16,6 +16,7 @@ def fft_freq(amplitude_values, sampling_frequency): | |||
| * `amplitude_values (numpy.ndarray)` | |||
| * `frequency_values (numpy.ndarray)` | |||
| """ | |||
| frequency_values = np.fft.fftfreq(len(amplitude_values), 1 / sampling_frequency) | |||
| # frequency_values = np.fft.fftfreq(len(amplitude_values), 1 / sampling_frequency) | |||
| frequency_values = np.arange(0, len(amplitude_values)) * sampling_frequency | |||
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Shouldn't it be divided by len(amplitude_values)?
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Theoretically the end time is:
End Time = Timestamp + (Length of Values * sampling frequency )
So the frequencies values are
np.arange(0, N)* sampling frequency
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Fft frequency transformation when data sampling rate is not at least twice the signal's bandwidth.
Previous solution:
- Designed for cases where amplitude values include both positive and negative frequency components.
- Designed for data where sampling rate is at least twice the signal's bandwidth.
- Not suitable for our use case, which assumes amplitude values represent only the positive side of the FFT result.
- The formula was using the Numpy fftfreq function: frequency_values = np.fft.fftfreq(len(amplitude_values), 1 / sampling_frequency)
Current solution:
- Addresses the limitations of
numpy.fft.fftfreqandnumpy.fft.rfftfreq, which do not directly align with our assumptions about positive-only FFT amplitudes.- Assumes a uniformly spaced frequency axis starting at zero and progressing in increments based on the sampling frequency, ensuring compatibility with our assumptions.
- Now, the function is: frequency_values = np.arange(0, len(amplitude_values)) * sampling_frequency