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# This is a sample Python script.
import struct
# Press ⌃R to execute it or replace it with your code.
# Press Double ⇧ to search everywhere for classes, files, tool windows, actions, and settings.
from scipy.fftpack import fft
import numpy as np
import pyaudio
import matplotlib.pyplot as plt
class Test(object):
def __init__(self):
# stream constants
self.CHUNK = 2048 * 2
self.FORMAT = pyaudio.paInt32
self.CHANNELS = 1
self.RATE = 44100
self.pause = False
# stream object
self.p = pyaudio.PyAudio()
self.stream = self.p.open(
format=self.FORMAT,
channels=self.CHANNELS,
rate=self.RATE,
input=True,
output=True,
frames_per_buffer=self.CHUNK,
)
self.seenvalues = set()
self.sample_divs = 4
# goal: returns the max frequency of waveform
def get_fundamental_frequency(self, audio_waveform):
spectrum = fft(audio_waveform)
# scale and normalize the spectrum, some are imaginary
scaled_spectrum = np.abs(spectrum)
scaled_spectrum = scaled_spectrum / (np.linalg.norm(scaled_spectrum) + 1e-16)
# FIXME: update to self values, given if ur a sender or receiver
starting_freq = 18000
end_freq = 20000
freq_to_index_ratio = (self.CHUNK - 1) / self.RATE
# only accept the scaled spectrum from our starting range to 20000 Hz
starting_range_index = int(starting_freq * freq_to_index_ratio)
ending_range_index = int(end_freq * freq_to_index_ratio)
print(starting_freq, end_freq, starting_range_index, ending_range_index)
restricted_spectrum = scaled_spectrum[starting_range_index:ending_range_index + 1]
# get the n indices of the max peaks, within our confined spectrum
# FIXME: update to self values
bytes = 1
num_bits = bytes * 8 + 2
top8_indices = np.argpartition(restricted_spectrum, -num_bits)[-num_bits:]
# map the top 8 to indices to frequencies
top8_freqs = [int((top8_indices[i] + starting_freq) / freq_to_index_ratio) for i in range(len(top8_indices))]
# print(index_to_freq[max_index], max_index, max_index * self.RATE / (self.CHUNK - 1))
return top8_freqs[0], scaled_spectrum
def read_audio_stream(self):
data = self.stream.read(self.CHUNK)
data_int = struct.unpack(str(self.CHUNK) + 'i', data)
return data_int
def open_loop(self, graphics=True):
self.init_plots()
while not self.pause:
waveform = self.read_audio_stream()
freq_max, scaled_spectrum = self.get_fundamental_frequency(waveform)
if freq_max not in self.seenvalues:
print(freq_max)
# update figure canvas if wanted
if graphics:
# set top graph
data_np = np.array(waveform) / (np.linalg.norm(waveform) + 1e-16) * 10
self.line.set_ydata(data_np[0:len(data_np)// self.sample_divs])
# set bottom graph
self.line_fft.set_ydata(scaled_spectrum)
# update figures
self.fig.canvas.draw()
self.fig.canvas.flush_events()
def init_plots(self):
# x variables for plotting
x = np.arange(0, self.CHUNK * 2 // self.sample_divs, 2)
xf = np.linspace(0, self.RATE, self.CHUNK)
# create matplotlib figure and axes
self.fig, (ax1, ax2) = plt.subplots(2, figsize=(15, 7))
# create a line object with random data
self.line, = ax1.plot(x, np.random.rand(self.CHUNK // self.sample_divs), '-', lw=2)
# create semilogx line for spectrum
self.line_fft, = ax2.semilogx(
xf, np.random.rand(self.CHUNK), '-', lw=2)
# format waveform axes
ax1.set_title('AUDIO WAVEFORM')
ax1.set_xlabel('samples')
ax1.set_ylabel('volume')
ax1.set_ylim(-1, 1)
ax1.set_xlim(0, self.CHUNK // self.sample_divs)
plt.setp(
ax1,
xticks=[0, self.CHUNK // self.sample_divs, self.CHUNK * 2 // self.sample_divs],
)
plt.setp(ax2, yticks=[0, 1], )
# format spectrum axes
ax2.set_xlim(20, self.RATE / 2)
# show axes
thismanager = plt.get_current_fig_manager()
# thismanager.window.setGeometry(5, 120, 1910, 1070)
plt.show(block=False)
if __name__ == '__main__':
t = Test()
t.open_loop()
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