def Euclidean(m,n):
if m < n:
m,n = n,m
if n == 0:
return m
elif m % n == 0:
return n
else:return Euclidean(n,m%n)
def deciToFrac(deci):
i = 0
while deci % 1 != 0:
deci = deci * 10
i += 1
numer,denomi = int(deci),int(10 ** i)
gcd = Euclidean(numer,denomi)
return numer / gcd,denomi / gcd
>>> deciToFrac(2.6)
(13, 5)
>>> deciToFrac(1.2)
(6, 5)
def delay(inp,rate):
outp = []
for i in range(len(inp)):
for j in range(rate):
outp.append(inp[i])
return array(outp)
def fast(inp,rate):
outp = []
for i in range(len(inp) / rate):
outp.append(inp[i * rate])
return array(outp)
from scipy import *
import pyaudio
import wave
import sys
def changePlaySpeed(inp,rate):
outp = []
for i in range(int(len(inp) / rate)):
outp.append(inp[int(i * float(rate))])
return array(outp)
chunk = 1024
wf = wave.open("hoge.wav", 'rb')
p = pyaudio.PyAudio()
# open stream
stream = p.open(format =
p.get_format_from_width(wf.getsampwidth()),
channels = wf.getnchannels(),
rate = wf.getframerate(),
output = True)
# read data
data = wf.readframes(chunk)
# play stream
while data != '':
stream.write(data)
data = wf.readframes(chunk)
data = frombuffer(data,dtype = "int16")
if data != '':
data = changePlaySpeed(data,1.8)
data = int16(data).tostring()
stream.close()
p.terminate()
>>> 20 * log10(f[i] / fmax)
#coding:utf-8
from pylab import *
from scipy import *
def LPF(array):
output = []
R = 1000.
C = 0.0000002
Ts = 1 / 50000.
for i in range(len(array)):
if i == 0:
output.append(1/(R*C)*Ts*array[0])
else:
output.append(1/(R*C)*Ts*array[i] + exp(-1/(R*C)*Ts)*output[i-1])
return output
Fs = 50000.
Ts = 1 / Fs
t = arange(0,1,Ts)
f = 500
s1 = sin(2 * pi * f * t)
s2 = sin(2 * pi * 2 * f * t)
s3 = sin(2 * pi * 5 * f * t)
plot(t[0:200],LPF(s1)[0:200])
plot(t[0:200],LPF(s2)[0:200])
plot(t[0:200],LPF(s3)[0:200])
xlabel("time(s)")
legend(("500Hz","1000Hz","2500Hz"))
show()
#coding:utf-8
from pylab import *
from matplotlib.widgets import Slider, Button
#FIR filter
def FIR(array,FIRCoefficient):
output = []
for i in range(len(array)):
temp = 0
for j in range(len(FIRCoefficient)):
temp += array[i - j] * FIRCoefficient[j]
output.append(temp)
return output
#Time axis
t = arange(0.0,1.0,0.0001)
f = 10
s = []
#Noise cofficient
nc = 1
#Noisy sine wave
for i in range(len(t)):
s.append(sin(2 * pi * f * t[i]) + nc * randn())
ax = subplot(121)
l, = plot(t,s)
axis([0,1,-10,10])
subplots_adjust(left=0.25, bottom=0.25)
xlabel("time(s)")
ax.set_position([0.1,0.5,0.35,0.3],"original")
#Filtered wave
FIRCoefficient = []
FIRlength = 10
for i in range(FIRlength):
FIRCoefficient.append(1/ float(FIRlength))
s2 = FIR(s,FIRCoefficient)
ax2 = subplot(122)
m, = plot(t[:len(s2)],s2)
axis([0,1,-10,10])
ax2.set_position([0.55,0.5,0.35,0.3],"original")
#Noise controll
axcolor = 'lightgoldenrodyellow'
axnoise = axes([0.1,0.3,0.32,0.03], axisbg = axcolor)
snoise = Slider(axnoise,"Noise",0.0,10.0,valinit = 1)
def update(val):
global s
nc = snoise.val
s = []
for i in range(len(t)):
s.append(sin(2 * pi * f * t[i]) + nc * randn())
s = array(s)
l.set_ydata(s)
FIRCoefficient = []
for i in range(FIRlength):
FIRCoefficient.append(1/ float(FIRlength))
s2 = FIR(s,FIRCoefficient)
m.set_ydata(s2)
draw()
snoise.on_changed(update)
#Filter length controll
axfilter = axes([0.55,0.3,0.32,0.03], axisbg = axcolor)
sfilter = Slider(axfilter,"Filter",5,30,valinit = 10)
def filtering(val):
global FIRlength
FIRlength = int(sfilter.val)
FIRCoefficient = []
#Moving average filter
for i in range(FIRlength):
FIRCoefficient.append(1/ float(FIRlength))
s2 = FIR(s,FIRCoefficient)
m.set_ydata(s2)
draw()
sfilter.on_changed(filtering)
show()
#coding:utf-8
from scipy import *
from matplotlib.pylab import *
def IIR(array):
output = []
#係数格納行列
FIRCoefficient = [1,0,0]
IIRCoefficient = [0.7,0.2,0.1]
for i in range(0,len(array)):
temp = 0
for i in range(len(array)):
temp = 0
for j in range(len(FIRCoefficient)):
temp += array[i - j] * FIRCoefficient[j]
if len(output) > j:
temp += output[i - j - 2] * IIRCoefficient[j]
output.append(temp)
return output
Fs = 100.
t = arange(0,1,1/Fs)
f = 5
inp = []
#入力信号生成(正弦波+ノイズ)
for i in range(len(t)):
inp.append(sin(2 * pi * f * t[i]) + rand())
outp = IIR(inp)
plot(t,inp,"b")
plot(t,outp,"r")
xlabel("time(s)")
show()
#coding:utf-8
from scipy import *
from matplotlib.pylab import *
#FIRフィルタ
def FIR(array):
output = []
for i in range(2,len(array)):
output.append(array[i] * 0.7 + array[i-1] * 0.2 + array[i-2] * 0.1)
return output
Fs = 100.
t = arange(0,1,1/Fs)
f = 5
inp = []
#入力信号生成(正弦波+ノイズ)
for i in range(len(t)):
inp.append(sin(2 * pi * f * t[i]) + rand())
outp = FIR(inp)
plot(t,inp,"b")
plot(t[2:len(t)],outp,"r")
xlabel("time(s)")
show()
def AMDF(array,frameLength):
amdf = []
length = len(array)
for i in range(frameLength):
temp = 0
for j in range(length):
temp += abs(array[j] - array[j - i])
amdf.append(temp / length)
return amdf
>>> correlate(signal,signal,"same")
def CCF(array1,array2):
if len(array1) < len(array2):
array1,array2 = array2,array1
ccf = []
for i in range(len(array1) - len(array2)):
temp = 0
for j in range(len(array2)):
temp += array2[j] * array1[j - i]
ccf.append(temp / len(array2))
return ccf
FORMAT = pyaudio.paInt16
p = pyaudio.PyAudio()
stream = p.open(format = FORMAT,
channels = CHANNELS,
rate = RATE,
input = True,
output = True,
frames_per_buffer = chunk)
>>> a = frombuffer(data,dtype = "int16")
>>> a
array([ 1, -4, -4, ..., 0, 0, 0], dtype=int16)
from pylab import *
from numpy import *
import pyaudio
import sys
chunk = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 44100
RECORD_SECONDS = 10
p = pyaudio.PyAudio()
stream = p.open(format = FORMAT,
channels = CHANNELS,
rate = RATE,
input = True,
output = True,
frames_per_buffer = chunk)
inp = []
print "* recording"
for i in range(0, 44100 / chunk * RECORD_SECONDS):
data = stream.read(chunk)
inp.extend(frombuffer(data,dtype = "int16"))
print "* done"
plot(inp)
show()
>>> w = wave.open("filePath","r")
>>> w.readframes(5)
'\x00\x00\x00\x00\xdb\xff\xdb\xff\xb5\xff\xb5\xff\x8f\xff\x8f\xffj\xffj\xff'
>>> r = w.readframes(5)
>>> len(r)
20
from pylab import *
import pyaudio
import sys
chunk = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 44100
RECORD_SECONDS = 5
p = pyaudio.PyAudio()
stream = p.open(format = FORMAT,
channels = CHANNELS,
rate = RATE,
input = True,
output = True,
frames_per_buffer = chunk)
inp = []
print "* recording"
for i in range(0, 44100 / chunk * RECORD_SECONDS):
data = stream.read(chunk)
for j in range(len(data)):
for k in range(len(data[j])):
inp.append(ord(data[j][k]))
print "* done"
plot(inp)
show()
>>> data[0]
'\x00'
