I originally posted this in physics stack exchange but they requested it be posted here as well….

I am trying to create a known signal with a known wavelength, amplitude, and phase. I then want to break this signal apart into all of its frequencies, find amplitudes, phases, and wavelengths for each frequency, then create equations for each frequency based on these new wavelengths, amplitudes, and phases. In theory, the equations should be identical to the individual signals. However, they are not. I am almost positive it is an issue with phase but I cannot figure out how to resolve it. I will post the exact code to reproduce this below. Please help as my phase, wavelength, and amplitudes will vary once I get more complicated signals so it need to work for any combination of these.

```
import numpy as np
from matplotlib import pyplot as plt
from scipy import fftpack
# create signal
time_vec = np.arange(1, 11, 1)
wavelength = 1/.1
phase = 0
amp = 10
created_signal = amp * np.sin((2 * np.pi / wavelength * time_vec) + phase)
# plot it
fig, axs = plt.subplots(2, 1, figsize=(10,6))
axs(0).plot(time_vec, created_signal, label='exact_data')
# get fft and freq array
sig_fft = fftpack.fft(created_signal)
sample_freq = fftpack.fftfreq(created_signal.size, d=1)
# do inverse fft and verify same curve as original signal. This is fine!
filtered_signal = fftpack.ifft(sig_fft)
filtered_signal += np.mean(created_signal)
# create individual signals for each frequency
filtered_signals = ()
for i in range(len(sample_freq)):
high_freq_fft = sig_fft.copy()
high_freq_fft(np.abs(sample_freq) < np.nanmin(sample_freq(i))) = 0
high_freq_fft(np.abs(sample_freq) > np.nanmax(sample_freq(i))) = 0
filtered_sig = fftpack.ifft(high_freq_fft)
filtered_sig += np.mean(created_signal)
filtered_signals.append(filtered_sig)
# get phase, amplitude, and wavelength for each individual frequency
sig_size = len(created_signal)
wavelength = ()
ph = ()
amp = ()
indices = ()
for j in range(len(sample_freq)):
wavelength.append(1 / sample_freq(j))
indices.append(int(sig_size * sample_freq(j)))
for j in indices:
phase = np.angle(sig_fft(j))
phase = np.arctan2(sig_fft(j).imag, sig_fft(j).real)
ph.append((phase))
amp.append((np.sqrt((sig_fft(j).real * sig_fft(j).real) + (sig_fft(j).imag * sig_fft(j).imag)) / (sig_size / 2)))
# create an equation for each frequency based on each phase, amp, and wavelength found from above.
def eqn(filtered_si, wavelength, time_vec, phase, amp):
return amp * np.sin((2 * np.pi / wavelength * time_vec) + phase)
def find_equations(filtered_signals_mean, high_freq_fft, wavelength, filtered_signals, time_vec, ph, amp):
equations = ()
for i in range(len(wavelength)):
temp = eqn(filtered_signals(i), wavelength(i), time_vec, ph(i), amp(i))
equations.append(temp + filtered_signals_mean)
return equations
filtered_signals_mean = np.abs(np.mean(filtered_signals))
equations = find_equations(filtered_signals_mean, sig_fft, wavelength,
filtered_signals, time_vec, ph, amp)
# at this point each equation, for each frequency should match identically each signal from each frequency,
# however, the phase seems wrong and they do not match!!??
axs(0).plot(time_vec, filtered_signal, '--', linewidth=3, label='filtered_sig_combined')
axs(1).plot(time_vec, filtered_signals(1), label='filtered_sig(-1)')
axs(1).plot(time_vec, equations(1), label='equations(-1)')
axs(0).legend()
axs(1).legend()
fig.tight_layout()
plt.show()
```