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PyLibrary/EwenLib/Filters.py

+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.signal import *
+
+
+
+
+#####
+#ALL Filters applies along first dimension if multiple dimensions are given
+
+
+####
+#%%
+def Box(signal1, tau1,  verbose = False):
+    print(np.size(np.shape(signal1)))
+    signal1=np.squeeze(signal1)
+    Shape=list(np.shape(signal1))
+    Result1=np.zeros(signal1.shape)
+    #Result1[tau1:]=signal1[tau1:]-signal1[:-tau1]
+    Shape[0]=2*tau1+1
+    kernel = np.ones(Shape)/(2*tau1+1)
+    #kernel[tau1]=1
+    Result1 =  fftconvolve(signal1,kernel,mode='full',axes=0)[tau1:-tau1]
+    fp=tau1
+    lp=len(signal1)-tau1
+    if verbose:
+        plt.figure(1)
+        plt.clf()
+        #plt.subplot(121)
+        plt.plot(signal1,'k.-')
+        plt.plot(Result1,'r-')
+        
+    
+    return Result1, fp, lp
+#%%
+def CausalBox(signal1, tau1,  verbose = False, order=[]):
+    
+    #Result1=np.zeros(signal1.shape)
+    #Result1[tau1:]=signal1[tau1:]-signal1[:-tau1]
+    signal1=np.squeeze(signal1)
+
+    #Result1[tau1:]=signal1[tau1:]-signal1[:-tau1]
+
+    Shape=list(np.shape(signal1))
+    Shape[0]=tau1+1
+    kernel = np.ones(Shape)/(tau1+1)
+    #kernel[0]=1
+    #Result1[tau1//2:] = fftconvolve(signal1,kernel,mode='full',axes=0)[tau1//2:len(signal1)]
+    Result1 = fftconvolve(signal1,kernel,mode='full',axes=0)[:len(signal1)]
+    fp=tau1
+    lp=len(signal1)
+        
+    if verbose:
+        plt.figure(1)
+        #plt.clf()
+        #plt.subplot(121)
+        plt.plot(signal1,'k-')
+        plt.plot(Result1,'r-')
+        
+    
+    return Result1, fp, lp
+#%%
+def AntiCausalBox(signal1, tau1,  verbose = False):
+    
+    signal1=np.squeeze(signal1)
+
+    Shape=list(np.shape(signal1))
+    Shape[0]=tau1+1
+    kernel = np.ones(Shape)/(tau1+1)
+    #kernel[-1]=1
+    Result1 = fftconvolve(signal1,kernel,mode='full',axes=0)[tau1:]
+    fp=0
+    lp=len(signal1)-tau1
+    
+    if verbose:
+        plt.figure(1)
+        plt.clf()
+        #plt.subplot(121)
+        plt.plot(signal1,'k-')
+        plt.plot(Result1[:-tau1],'r-')
+        
+
+
+    return Result1, fp, lp
+#%%
+def Poor(signal1, tau1,  verbose = False):
+    
+    signal1=np.squeeze(signal1)
+    Result1=np.zeros(signal1.shape)
+    #Result1[tau1:]=signal1[tau1:]-signal1[:-tau1]
+
+    Shape=list(np.shape(signal1))
+    Shape[0]=2*tau1+1
+    kernel = -np.ones(Shape)/(2*tau1)
+    kernel[tau1]=1
+    Result1 =  fftconvolve(signal1,kernel,mode='full',axes=0)[tau1:-tau1]
+    fp=tau1
+    lp=len(signal1)-tau1
+    if verbose:
+        plt.figure(1)
+        plt.clf()
+        #plt.subplot(121)
+        plt.plot(signal1,'k.-')
+        plt.plot(Result1,'r-')
+        
+    
+    return Result1, fp, lp
+
+#%%
+def CausalPoor(signal1, tau1,  verbose = False, order=[]):
+    
+    #Result1=np.zeros(signal1.shape)
+    #Result1[tau1:]=signal1[tau1:]-signal1[:-tau1]
+    Shape=list(np.shape(signal1))
+    Shape[0]=tau1+1
+    kernel = -np.ones(Shape)/tau1
+    kernel[0]=1
+    #Result1[tau1//2:] = fftconvolve(signal1,kernel,mode='full',axes=0)[tau1//2:len(signal1)]
+    Result1 = fftconvolve(signal1,kernel,mode='full',axes=0)[:len(signal1)]
+    fp=tau1
+    lp=len(signal1)
+        
+    if verbose:
+        plt.figure(1)
+        #plt.clf()
+        #plt.subplot(121)
+        plt.plot(signal1,'k-')
+        plt.plot(Result1,'r-')
+        
+    
+    return Result1, fp, lp
+#%%
+def AntiCausalPoor(signal1, tau1,  verbose = False):
+    
+    Shape=list(np.shape(signal1))
+    Shape[0]=tau1+1
+    kernel = -np.ones(Shape)/tau1
+    kernel[-1]=1
+    Result1 = fftconvolve(signal1,kernel,mode='full',axes=0)[tau1:]
+    fp=0
+    lp=len(signal1)-tau1
+    
+    if verbose:
+        plt.figure(1)
+        plt.clf()
+        #plt.subplot(121)
+        #plt.plot(signal1,'k-')
+        plt.plot(Result1[:-tau1],'r-')
+        
+
+
+    return Result1, fp, lp
+
+#%%
+def Incr(signal1, tau1,  verbose = False):
+    signal1=np.squeeze(signal1)
+    N=np.shape(signal1)[0]
+    Result1=np.zeros(signal1.shape)
+    Result1[tau1:] = signal1.take(indices=range(tau1,N),axis=0)-signal1.take(indices=range(0,N-tau1),axis=0)
+    fp=tau1
+    lp=len(signal1)
+    if verbose:
+        plt.figure(1)
+        plt.clf()
+        #plt.subplot(121)
+        plt.plot(signal1,'k.-')
+        plt.plot(Result1,'r-')
+        
+
+
+    return Result1, fp, lp
+
+
+def AntiIncr(signal1, tau1,  verbose = False):
+    signal1=np.squeeze(signal1)
+    N=np.shape(signal1)[0]
+    Result1=np.zeros(signal1.shape)
+    Result1[:-tau1] = -signal1.take(indices=range(tau1,N),axis=0)+signal1.take(indices=range(0,N-tau1),axis=0)
+    fp=0
+    lp=len(signal1)-tau1
+    if verbose:
+        plt.figure(1)
+        plt.clf()
+        #plt.subplot(121)
+        plt.plot(signal1,'k.-')
+        plt.plot(Result1,'r-')
+        
+
+
+    return Result1, fp, lp
+
+#%%
+def SymIncr(signal1, tau1,  verbose = False):
+    signal1=np.squeeze(signal1)
+    N=np.shape(signal1)[0]
+    Result1=np.zeros(signal1.shape)
+    Result1[tau1:-tau1] = 2*signal1.take(indices=range(tau1,N-tau1),axis=0)-signal1.take(indices=range(0,N-2*tau1),axis=0)-signal1.take(indices=range(2*tau1,N),axis=0)
+    
+    Result1[tau1:-tau1] = 2*signal1[tau1:-tau1]-signal1[:-2*tau1]-signal1[2*tau1:]
+    
+    fp=tau1
+    lp=len(signal1)-tau1
+    if verbose:
+        plt.figure(1)
+        plt.clf()
+        #plt.subplot(121)
+        plt.plot(signal1,'k.-')
+        plt.plot(Result1,'r.-')
+    
+
+    return Result1, fp, lp
+
+def LPT(signal1, tau1,  verbose = False, order=[]): #Same as Box
+    
+    #Result1=np.zeros(signal1.shape)
+    #Result1[tau1:]=signal1[tau1:]-signal1[:-tau1]
+    signal1=np.squeeze(signal1)
+
+    #Result1[tau1:]=signal1[tau1:]-signal1[:-tau1]
+
+    Shape=list(np.shape(signal1))
+    Shape[0]=tau1+1
+    kernel = np.ones(Shape)/(tau1+1)
+    #kernel[0]=1
+    #Result1[tau1//2:] = fftconvolve(signal1,kernel,mode='full',axes=0)[tau1//2:len(signal1)]
+    Result1 = fftconvolve(signal1,kernel,mode='full',axes=0)[:len(signal1)]
+    fp=tau1
+    lp=len(signal1)
+        
+    if verbose:
+        plt.figure(1)
+        #plt.clf()
+        #plt.subplot(121)
+        plt.plot(signal1,'k-')
+        plt.plot(Result1,'r-')
+        
+    
+    return Result1, fp, lp
+
+def HPT(signal1, tau1,  verbose = False, order=[]):
+    fp=tau1
+    lp=len(signal1)
+    Result1=signal1-LPT(signal1,tau1)[0]
+    if verbose:
+        plt.figure(1)
+        #plt.clf()
+        #plt.subplot(121)
+        plt.plot(signal1,'k-')
+        plt.plot(Result1,'r-')
+        
+    return Result1, fp, lp
+
+def BPT(signal1, tau1, tau2, verbose = False, order=[]):
+    fp=tau1
+    lp=len(signal1)
+    Result1=LPT(signal1,tau1)[0]-LPT(signal1,tau2)[0]
+    if verbose:
+        plt.figure(1)
+        #plt.clf()
+        #plt.subplot(121)
+        plt.plot(signal1,'k-')
+        plt.plot(Result1,'r-')
+        
+    return Result1, fp, lp
+
+
+def BPFreq(signal1,tau1,tau2,verbose=False):
+    signal1=np.squeeze(signal1)
+    Result1=np.zeros(signal1.shape)
+    
+    fp=tau1
+    lp=len(signal1)-tau1
+
+    minfreq=1/tau2
+    maxfreq=1/tau1
+    fftsignal = np.fft.fft(signal1,axis=0)
+    freq = np.fft.fftfreq(np.size(signal1,axis=0))
+    Mask=(np.abs(freq)>=minfreq).astype(int)*(np.abs(freq)<=maxfreq).astype(int)
+    
+    fftfiltered=(fftsignal.T*Mask).T
+    Result1 = np.fft.ifft(fftfiltered,axis=0)
+    if verbose:
+        plt.figure(1)
+        plt.clf()
+        #plt.subplot(121)
+        plt.plot(signal1,'k.-')
+        plt.plot(Result1,'r-')
+    
+
+
+    return Result1, fp, lp
+
+def BPOrder(signal1,tau1,tau2,order=2,verbose=False):
+    signal1=np.squeeze(signal1)
+    Result1=np.zeros(signal1.shape)
+    
+    fp=tau1
+    lp=len(signal1)-tau1
+
+    minfreq=1/tau2
+    maxfreq=1/tau1
+    fftsignal = np.fft.fft(signal1,axis=0)
+    freq = np.fft.fftfreq(np.size(signal1,axis=0))
+    MaskHP=(1j*(freq/minfreq)**order)/(1+1j*(freq/minfreq)**order) 
+    MaskLP=1/(1+1j*(freq/maxfreq)**order) 
+    Mask=MaskLP*MaskHP
+    fftfiltered=(fftsignal.T*Mask).T
+    Result1 = np.fft.ifft(fftfiltered,axis=0)
+    if verbose:
+        plt.figure(1)
+        plt.clf()
+        #plt.subplot(121)
+        plt.plot(signal1,'k.-')
+        plt.plot(Result1,'r-')
+
+
+    return Result1, fp, lp
+