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--- fastdev:python_signals [2009/11/11 10:54] – memeruiz
+++ fastdev:python_signals [2009/11/11 11:33] (current) – memeruiz
@@ Line 1: / Line 1: @@
+====== Python + Filters + FFT + Gnuplot ======
+==== Noise ====
+{{:fastdev:white-noise.png?300|white noise}}
+==== Filters ====
+=== Linear filters ===
+There are five basic parameters to design a filter: wp (pass frequency), ws (stop frequency), gpass (pass gain), gstop (stop gain), filter type
+{{:fastdev:filter_design.jpg?500|filter design parameters}}
+{{:fastdev:filtspec.png|filter design parameters2}}
+{{:fastdev:fir_filter_df1.png?500|fir filter}}
+{{:fastdev:600px-iir_filter_direct_form_1.svg.png?400|iir filter}}
+{{:fastdev:750px-electronic_linear_filters.svg.png|types of filters}}
+=== Non linear filters ===
+== Median filter ==
+{{:fastdev:medianalgorithm.gif|median algorithm}}
+{{:fastdev:medfilt1_001.png|median filter}}
+{{:fastdev:545px-median_filter_example.jpg?600|median filter}}
+== Sharpness: high pass filter ==
+{{:fastdev:high_pass.png|high pass}}
+==== Fast Fourier Transform ====
+{{:fastdev:fft-partial.png?600|fft}}
+==== Example ====
+The following example code takes data from a phidget analog input and filters this signal using first a IIR filter, then a median and then it calculates the FFT of the whole signal.
+  import scipy.signal as filters
+  class filter:
+      def __init__(self,wp,ws,gpass,gstop):
+          self.b,self.a=filters.iirdesign(wp,ws,gpass,gstop,ftype='ellip')
+          self.z=filters.lfiltic(self.b,self.a,[])
+          self.M=len(self.b)-1
+          self.N=len(self.a)-1
+          self.K=max(self.M,self.N)
+      def get_output(self,input):
+          '''input must be of size N'''
+          self.output,self.z=filters.lfilter(self.b,self.a,input,zi=self.z)
+          return(self.output)
+      def get_N(self):
+          return self.N
+      def get_ab(self):
+          return([self.a,self.b])
+      N=property(get_N)
+  from Phidgets.PhidgetException import *
+  from Phidgets.Events.Events import *
+  from Phidgets.Devices import *
+  import numpy as n
+  ik=InterfaceKit.InterfaceKit()
+  ik.openPhidget()
+  ik.waitForAttach(10000)
+  my_filter=filter(0.1,0.3,1,60)
+  N=my_filter.N
+  time_array=[]
+  total_input=[]
+  total_output=[]
+  import time
+  inittime=time.time()
+  while True:
+    t=[]
+    input=[]
+    for i in range(N):
+        t.append(time.time()-inittime)
+        input.append(ik.getSensorValue(0))
+        time.sleep(0.05)
+    output=my_filter.get_output(input)
+    time_array=n.concatenate((time_array,t))
+    total_input=n.concatenate((total_input,input))
+    total_output=n.concatenate((total_output,output))
+    if t[-1]>10:
+        break
+  total_output_median=filters.medfilt(total_input,kernel_size=11)
+  import Gnuplot
+  g=Gnuplot.Gnuplot()
+  g.title("filter")
+  g('set data style linespoints')
+  g.plot(zip(time_array,total_input),zip(time_array,total_output))
+  raw_input()
+  import scipy
+  from math import pi
+  fft=scipy.fft(total_input)
+  g.plot(zip(n.arange(0,pi,pi/len(fft)),fft[0:len(fft)/2]))
+  raw_input()
+  g.plot(zip(time_array,total_input),zip(time_array,total_output_median))
+  a,b=my_filter.get_ab()
+  w,h=filters.freqz(b,a)
+  from numpy import log10
+  h_db=20*log10(abs(h))
+  raw_input()
+  g.plot(zip(w/max(w),h_db))
+  while True:
+    time.sleep(0.1)