使用 python 和 matplotlib 的小提琴调音器
Violin tuner using python and matplotlib
我正在尝试编写一个 python 脚本来充当小提琴调音器/实时频谱显示。到目前为止，我让 pyaudio 记录了来自麦克风的数据块，并且可以计算短时间序列音频的频谱。我想使用 matplotlib 实时绘制这些图，但是在记录数据时我的图 window 是空白的，并且在脚本结束后只有最后一个图在屏幕上更新。我究竟做错了什么？
# -*- 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()
我认为你只需要在剧情更新之间增加一些睡眠时间。您可能打算这样做，因为您已经导入了 sleep.
from time import sleep
...
for i in range(1000):
sleep(10)
Tuner.listen()
plt.show()
但是，更好的做法是使用 matplotlib.animation 模块，查看 official example!
因为我无法 运行 代码，所以我只能猜测。但似乎您从未真正重绘 canvas。
尝试添加
self.plots[0].figure.canvas.draw_idle()
在 update_fig 函数的末尾。
这可能有效也可能无效。所以您可能还想尝试交互模式。转plt.ion()加
plt.draw()
plt.pause(0.0001)
在 update_fig 函数的末尾。最后你可能会转动 plt.ioff() 并调用 plt.show() 以保持图形打开。
下面的代码运行对我来说很好：
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()
我正在尝试编写一个 python 脚本来充当小提琴调音器/实时频谱显示。到目前为止，我让 pyaudio 记录了来自麦克风的数据块，并且可以计算短时间序列音频的频谱。我想使用 matplotlib 实时绘制这些图，但是在记录数据时我的图 window 是空白的，并且在脚本结束后只有最后一个图在屏幕上更新。我究竟做错了什么？
# -*- 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()
我认为你只需要在剧情更新之间增加一些睡眠时间。您可能打算这样做，因为您已经导入了 sleep.
from time import sleep
...
for i in range(1000):
sleep(10)
Tuner.listen()
plt.show()
但是，更好的做法是使用 matplotlib.animation 模块，查看 official example!
因为我无法 运行 代码，所以我只能猜测。但似乎您从未真正重绘 canvas。
尝试添加
self.plots[0].figure.canvas.draw_idle()
在 update_fig 函数的末尾。
这可能有效也可能无效。所以您可能还想尝试交互模式。转plt.ion()加
plt.draw()
plt.pause(0.0001)
在 update_fig 函数的末尾。最后你可能会转动 plt.ioff() 并调用 plt.show() 以保持图形打开。
下面的代码运行对我来说很好：
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()