I am trying yo write a python script to act as a violin tuner / real time spectral display. So far I got pyaudio to record blocks of data from the microphone and can compute the frequency spectrum for short times series of audio. I would like to plot those in real time using matplotlib, but my figure window is blank while the data is been recorded and only the last plot is updated on the screen, after the script ends. What am I doing wrong?
# -*- coding: utf-8 -*-
"""
Created on Mon May 1 00:03:55 2017
@author: Hugo.
"""
import pyaudio
import struct
import numpy as np
import matplotlib.pyplot as plt
from time import sleep
CHUNK = 2**14 #2**15 #4096
WIDTH = 2
FORMAT = pyaudio.paInt16
CHANNELS = 2
RATE = 44100
dt = 1.0/RATE
### frequencies of the strings for the violin (tunned in A), in Hz
f4 = 195.998 ## G3
f3 = 293.665 ## D4
f2 = 440.000 ## A4
f1 = 659.255 ## E5
n = CHUNK
freqs = np.fft.rfftfreq(n, d = dt)
def Frequency_of_position(position):
""" Returns the frequency (Hz) of the note in from its position (halftones)
relative to A4 in an equal tempered scale. Ex: 0 -> 440 Hz (A4),
12 -> 880 Hz (A5)."""
return 440.0*(2**(1.0/12.0))**position
def Position_to_note(position):
"A A# B C C# D D# E F F# G G#"
SCALE = ["A", "A#", "B", "C", "C#", "D", "D#", "E", "F", "F#", "G", "G#"]
LETTER = SCALE[position % 12]
NUMBER = str(int((position+48) / 12))
return LETTER+NUMBER
pos = np.array(range(-36,48))
vnote_freqs = np.vectorize(Frequency_of_position)
note_freqs = vnote_freqs(pos)
def get_frequency( spectrum ):
return freqs[np.argmax(spectrum)]
class Freq_analysis(object):
def __init__(self):
self.pa = pyaudio.PyAudio()
self.stream = self.open_mic_stream()
self.plots = self.prepare_figure()
#self.fig_and_axes = self.prepare_figure()
#self.first_plot = self.plot_first_figure()
def stop(self):
self.stream.close()
def open_mic_stream( self ):
device_index = self.find_input_device()
stream = self.pa.open( format = FORMAT,
channels = CHANNELS,
rate = RATE,
input = True,
input_device_index = device_index,
frames_per_buffer = CHUNK)
return stream
def find_input_device(self):
device_index = None
for i in range( self.pa.get_device_count() ):
devinfo = self.pa.get_device_info_by_index(i)
print( "Device %d: %s"%(i,devinfo["name"]) )
for keyword in ["mic","input"]:
if keyword in devinfo["name"].lower():
print( "Found an input: device %d - %s"% (i,devinfo["name"]) )
device_index = i
return device_index
if device_index == None:
print( "No preferred input found; using default input device." )
return device_index
def prepare_figure(self):
fig1 = plt.figure(1, figsize = (16,6))
wide_plot = plt.subplot(2,1,1)
plt.vlines([f1,f2,f3,f4],1,1e17, linestyles = 'dashed')
plt.xlabel("freq (Hz)")
plt.ylabel("S^2 (u. arb.)")
plt.xscale('log')
plt.yscale('log')
plt.xlim([80,4000])
#plt.xlim([600,700])
#plt.xlim([400,500])
plt.ylim([1e0,1e17])
spec_w, = plt.plot([1,1],[1,1], '-',c = 'blue')
f4_plot = plt.subplot(2,4,5)
plt.vlines(f4,1,1e17, linestyles = 'dashed')
plt.xlabel("freq (Hz)")
plt.ylabel("S^2 (u. arb.)")
plt.yscale('log')
plt.xlim([140,260])
plt.ylim([1e0,1e17])
spec_f4, = plt.plot([1,1],[1,1], '-',c = 'blue')
f3_plot = plt.subplot(2,4,6)
plt.vlines(f3,1,1e17, linestyles = 'dashed')
plt.xlabel("freq (Hz)")
plt.yscale('log')
plt.xlim([220,380])
plt.ylim([1e0,1e17])
spec_f3, = plt.plot([1,1],[1,1], '-',c = 'blue')
f2_plot = plt.subplot(2,4,7)
plt.vlines(f2,1,1e17, linestyles = 'dashed')
plt.xlabel("freq (Hz)")
plt.yscale('log')
plt.xlim([400,500])
plt.ylim([1e0,1e17])
spec_f2, = plt.plot([1,1],[1,1], '-',c = 'blue')
f1_plot = plt.subplot(2,4,8)
plt.vlines(f1,1,1e17, linestyles = 'dashed')
plt.xlabel("freq (Hz)")
plt.yscale('log')
plt.xlim([600,700])
plt.ylim([1e0,1e17])
spec_f1, = plt.plot([1,1],[1,1], '-',c = 'blue')
plt.show()
#return fig1, wide_plot, f1_plot, f2_plot, f3_plot, f4_plot
return spec_w, spec_f1, spec_f2, spec_f3, spec_f4
def PrintFreq(self, S2):
dominant = get_frequency( S2 )
dist = np.abs(note_freqs-dominant)
closest_pos = pos[np.argmin(dist)]
closest_note = Position_to_note(closest_pos)
print(dominant, "(",closest_note, "=",Frequency_of_position(closest_pos),")")
def listen(self):
try:
block = self.stream.read(CHUNK)
except IOError:
# An error occurred.
print( "Error recording.")
return
indata = np.array(struct.unpack("%dh"%(len(block)/2),block))
n = indata.size
freqs = np.fft.rfftfreq(n, d = dt)
data_rfft = np.fft.rfft(indata)
S2 = np.abs(data_rfft)**2
#self.PrintFreq(block)
#self.update_fig(block)
self.PrintFreq(S2)
self.update_fig(freqs, S2)
def update_fig(self, freqs, S2):
self.plots[0].set_xdata(freqs)
self.plots[1].set_xdata(freqs)
self.plots[2].set_xdata(freqs)
self.plots[3].set_xdata(freqs)
self.plots[4].set_xdata(freqs)
self.plots[0].set_ydata(S2)
self.plots[1].set_ydata(S2)
self.plots[2].set_ydata(S2)
self.plots[3].set_ydata(S2)
self.plots[4].set_ydata(S2)
#plt.draw()
#plt.show()
if __name__ == "__main__":
Tuner = Freq_analysis()
for i in range(1000):
Tuner.listen()
plt.show()
Since I cannot run the code I can only guess. But it seems you never actually redraw the canvas.
Try adding
self.plots[0].figure.canvas.draw_idle()
at the end of the update_fig function.
This might or might not work. So you might also want to try interactive mode. Turn plt.ion() and add
plt.draw()
plt.pause(0.0001)
at the end of the update_fig function. At the end you might turn plt.ioff() and call plt.show() to keep the figure open.
The following code runs fine for me:
import pyaudio
import struct
import numpy as np
import matplotlib.pyplot as plt
from time import sleep
CHUNK = 2**14 #2**15 #4096
WIDTH = 2
FORMAT = pyaudio.paInt16
CHANNELS = 2
RATE = 44100
dt = 1.0/RATE
### frequencies of the strings for the violin (tunned in A), in Hz
f4 = 195.998 ## G3
f3 = 293.665 ## D4
f2 = 440.000 ## A4
f1 = 659.255 ## E5
n = CHUNK
freqs = np.fft.rfftfreq(n, d = dt)
def Frequency_of_position(position):
""" Returns the frequency (Hz) of the note in from its position (halftones)
relative to A4 in an equal tempered scale. Ex: 0 -> 440 Hz (A4),
12 -> 880 Hz (A5)."""
return 440.0*(2**(1.0/12.0))**position
def Position_to_note(position):
"A A# B C C# D D# E F F# G G#"
SCALE = ["A", "A#", "B", "C", "C#", "D", "D#", "E", "F", "F#", "G", "G#"]
LETTER = SCALE[position % 12]
NUMBER = str(int((position+57) / 12))
return LETTER+NUMBER
pos = np.array(range(-36,48))
vnote_freqs = np.vectorize(Frequency_of_position)
note_freqs = vnote_freqs(pos)
def get_frequency( spectrum ):
return freqs[np.argmax(spectrum)]
class Freq_analysis(object):
def __init__(self):
self.pa = pyaudio.PyAudio()
self.stream = self.open_mic_stream()
self.plots = self.prepare_figure()
#self.fig_and_axes = self.prepare_figure()
#self.first_plot = self.plot_first_figure()
def stop(self):
self.stream.close()
def open_mic_stream( self ):
device_index = self.find_input_device()
stream = self.pa.open( format = FORMAT,
channels = CHANNELS,
rate = RATE,
input = True,
input_device_index = device_index,
frames_per_buffer = CHUNK)
return stream
def find_input_device(self):
device_index = None
for i in range( self.pa.get_device_count() ):
devinfo = self.pa.get_device_info_by_index(i)
print( "Device %d: %s"%(i,devinfo["name"]) )
for keyword in ["mic","input"]:
if keyword in devinfo["name"].lower():
print( "Found an input: device %d - %s"% (i,devinfo["name"]) )
device_index = i
return device_index
if device_index == None:
print( "No preferred input found; using default input device." )
return device_index
def prepare_figure(self):
plt.ion()
fig1 = plt.figure(1, figsize = (16,6))
wide_plot = plt.subplot(2,1,1)
plt.vlines([f1,f2,f3,f4],1,1e17, linestyles = 'dashed')
plt.xlabel("freq (Hz)")
plt.ylabel("S^2 (u. arb.)")
plt.xscale('log')
plt.yscale('log')
plt.xlim([80,4000])
#plt.xlim([600,700])
#plt.xlim([400,500])
plt.ylim([1e0,1e17])
spec_w, = plt.plot([1,1],[1,1], '-',c = 'blue')
f4_plot = plt.subplot(2,4,5)
plt.vlines(f4,1,1e17, linestyles = 'dashed')
plt.xlabel("freq (Hz)")
plt.ylabel("S^2 (u. arb.)")
plt.yscale('log')
plt.xlim([140,260])
plt.ylim([1e0,1e17])
spec_f4, = plt.plot([1,1],[1,1], '-',c = 'blue')
f3_plot = plt.subplot(2,4,6)
plt.vlines(f3,1,1e17, linestyles = 'dashed')
plt.xlabel("freq (Hz)")
plt.yscale('log')
plt.xlim([220,380])
plt.ylim([1e0,1e17])
spec_f3, = plt.plot([1,1],[1,1], '-',c = 'blue')
f2_plot = plt.subplot(2,4,7)
plt.vlines(f2,1,1e17, linestyles = 'dashed')
plt.xlabel("freq (Hz)")
plt.yscale('log')
plt.xlim([400,500])
plt.ylim([1e0,1e17])
spec_f2, = plt.plot([1,1],[1,1], '-',c = 'blue')
f1_plot = plt.subplot(2,4,8)
plt.vlines(f1,1,1e17, linestyles = 'dashed')
plt.xlabel("freq (Hz)")
plt.yscale('log')
plt.xlim([600,700])
plt.ylim([1e0,1e17])
spec_f1, = plt.plot([1,1],[1,1], '-',c = 'blue')
plt.draw()
#return fig1, wide_plot, f1_plot, f2_plot, f3_plot, f4_plot
return spec_w, spec_f1, spec_f2, spec_f3, spec_f4
def PrintFreq(self, S2):
dominant = get_frequency( S2 )
dist = np.abs(note_freqs-dominant)
closest_pos = pos[np.argmin(dist)]
closest_note = Position_to_note(closest_pos)
print(dominant, "(",closest_note, "=",Frequency_of_position(closest_pos),")")
def listen(self):
try:
block = self.stream.read(CHUNK)
except IOError:
# An error occurred.
print( "Error recording.")
return
indata = np.array(struct.unpack("%dh"%(len(block)/2),block))
n = indata.size
freqs = np.fft.rfftfreq(n, d = dt)
data_rfft = np.fft.rfft(indata)
S2 = np.abs(data_rfft)**2
#self.PrintFreq(block)
#self.update_fig(block)
self.PrintFreq(S2)
self.update_fig(freqs, S2)
def update_fig(self, freqs, S2):
self.plots[0].set_xdata(freqs)
self.plots[1].set_xdata(freqs)
self.plots[2].set_xdata(freqs)
self.plots[3].set_xdata(freqs)
self.plots[4].set_xdata(freqs)
self.plots[0].set_ydata(S2)
self.plots[1].set_ydata(S2)
self.plots[2].set_ydata(S2)
self.plots[3].set_ydata(S2)
self.plots[4].set_ydata(S2)
plt.draw()
plt.pause(0.001)
if __name__ == "__main__":
Tuner = Freq_analysis()
for i in range(100):
Tuner.listen()
plt.ioff()
plt.show()