昨日の続き。
Python&matplotlibで移動平均フィルタリングアプリを作ってみた。
…とは言っても、かなりやっつけ仕事。
一応形にはなったので、画像とソースを張っておく。
左側がフィルタリング前の波形、右側がフィルタリング後の波形。
左下のスライダーがノイズ成分の強度調整、右下のスライダーが移動平均フィルタの強度調整。
以下が、ソースコード。
#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()
昨日作ったアプリと早いところ統合したいなぁ。
でも、その前にどっちのアプリも個別にブラッシュアップしとかないと。
