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[Undescribed Horrific Abuse, One Victim & Survivor of Many]

here's current file. i think i'd like to change the test data to be a
low frequency square wave. then it is easy to debug by inspection; and
it may already work fine, the issues stemming only from high-magnitude
high-frequency data that is not recorded for long enough.

import numpy as np

signal_bufsize = 1024 * 1024
recording_bufsize = signal_bufsize
original_signal = np.random.random(signal_bufsize)
recording = np.empty(recording_bufsize)
period_length = len(original_signal) // recording_bufsize ** 0.5  *
np.random.random()

for out_idx in range(recording_bufsize): # using a loop because i am confused
    in_idx = int(out_idx * signal_bufsize / period_length) % signal_bufsize
    recording[out_idx] = original_signal[in_idx]

recording_fft = np.fft.rfft(recording)
# now take fft with np.fft.rfft
# then select only upper portion of 1 + bandwidth equal to period_length
    # note: am guessing that period_in_recording = recording_bufsize / rfft_idx
    # so, solving for rfft_idx
    # rfft_idx = recording_bufsize / period_in_recording
    # and, if right, that would be the index in the rfft output of the
period's peak
    # so further data on it would be to the right, at higher frequencies
import pdb; pdb.set_trace()
period_fft_idx = recording_bufsize / period_length
fft_subregion =
np.concatenate((recording_fft[:1],recording_fft[int(period_fft_idx):]))
# then reconstruct using irfft [curious: is this complex output? what
do the angles look like?]
# then downsample original_signal to the bandwidth using averaging
# then compare real values
# NOTE: failure is expected AT FIRST. then, identify the mistake, and
improve it.

reconstructed_fft = fft_subregion
original_fft = np.fft.rfft(original_signal)

reconstructed_rfftsize = min(len(reconstructed_fft), len(original_fft)) // 2
reconstructed_from_reconstructed =
np.fft.irfft(abs(reconstructed_fft[:reconstructed_rfftsize]))
reconstructed_from_original =
np.fft.irfft(abs(original_fft[:reconstructed_rfftsize]))