Filtering</H2>



In [59]:

    
%pylab inline
from scipy import signal
# check this https://stackoverflow.com/questions/25191620/creating-lowpass-filter-in-scipy-understanding-methods-and-units









    



Populating the interactive namespace from numpy and matplotlib



In [68]:

    
trace = np.loadtxt('LFP.txt') # 10 seconds at 200 Hz?
fs = 200 
#time = np.linspace(0, len(trace)/fs, 1/fs ,endpoint=0)
fig = plt.figure(figsize=(12,3))
ax = fig.add_subplot(111)

ax.plot(trace, lw=.5, c='gray')









    Out[68]:





[<matplotlib.lines.Line2D at 0x7f3742a217d0>]

Low-pass butter filter



In [30]:

    
def butter_lowpass_filter(data, cutoff, fs, order=5):
    """
    Performs the low pass filtering of the data
    
    Arguments:
    
    data -- Numpy array with the trace
    cutoff -- frequency (e.g., 10 Hz)
    
    """
    nyq = 0.5* fs # Nysquid frequency
    normal_cutoff = cutoff/ nyq
    b, a = signal.butter(order, normal_cutoff, btype='low', analog=False)
    y = signal.lfilter(b,a, data)
    return(y)



In [52]:

    
order = 8
fs = 1000 # sample rate 100 Hz
cutoff = 20


fig = plt.figure(figsize=(12,3))
ax = fig.add_subplot(111)

LFP = butter_lowpass_filter(trace, cutoff, fs, order)

#ax.plot(trace, lw=0.5)
ax.plot(LFP)









    Out[52]:





[<matplotlib.lines.Line2D at 0x7f374344a990>]

Down-sampling



In [61]:

    
downsample = signal.decimate(trace,2)
fig = plt.figure(figsize=(12,3))
ax = fig.add_subplot(111)
ax.plot(downsample)









    Out[61]:





[<matplotlib.lines.Line2D at 0x7f3742f94510>]



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