# Determine Nyquist and set generator frequency fmax=1.0/(2*dt) # nyquist fg = 2.0 # Hz # Generate f0 Hz sine wave pg = 1.0/fg #in seconds time = np.linspace(0,npts*dt,npts) # in seconds y = np.sin(2*PI/pg * time + PI/5) # Hanning window win_han = np.hanning(npts) y_han = y * win_han # Filter the trace f0 = 3.0 #cut off frequency freq, H, y_1 = filter_bc(y,dt,3.0) freq, H, y_2 = filter_bc(y_han,dt,3.0) # Just for plotting the frequency domain y_f = np.fft.rfft(y) y_f2 = np.fft.rfft(y_han) # Remove offset freq = freq[1:] y_f = y_f[1:] y_f2 = y_f2[1:] # Print rms (root-mean-square) print "Orig data/filtered RMS", np.sqrt(np.mean((y - y_1)**2)) print "Tapered data/filtered RMS", np.sqrt(np.mean((y_han - y_2)**2))
# Determine Nyquist and set generator frequency fmax = 1.0 / (2 * dt) # nyquist fg = 2.0 # Hz # Generate f0 Hz sine wave pg = 1.0 / fg #in seconds time = np.linspace(0, npts * dt, npts) # in seconds y = np.sin(2 * PI / pg * time + PI / 5) # Hanning window win_han = np.hanning(npts) y_han = y * win_han # Filter the trace f0 = 3.0 #cut off frequency freq, H, y_1 = filter_bc(y, dt, 3.0) freq, H, y_2 = filter_bc(y_han, dt, 3.0) # Just for plotting the frequency domain y_f = np.fft.rfft(y) y_f2 = np.fft.rfft(y_han) # Remove offset freq = freq[1:] y_f = y_f[1:] y_f2 = y_f2[1:] # Print rms (root-mean-square) print "Orig data/filtered RMS", np.sqrt(np.mean((y - y_1)**2)) print "Tapered data/filtered RMS", np.sqrt(np.mean((y_han - y_2)**2))
dt = 0.05
# Determining nyquist frequency and asking for cut off frequency f0
fmax = 1.0 / (2 * dt) # nyquist
f0 = float(raw_input('Give cut-off below Nyquist: fmax = %4.1f Hz ' % fmax))
print 'f0 =', f0
# Uncomment from random points
#y = random.rand(npts) - .5 # uniform random numbers, zero mean
# Spike at npts/2
y = np.zeros(npts, dtype='float')
y[npts / 2] = 1
# Filter with filter_bc
freq, H, y_filt = filter_bc(y, dt, f0)
# Just for the plot, frequency domain representaion of y
y_f = np.fft.rfft(y)
# Remove offset
y_f = y_f[1:]
freq = freq[1:]
H = H[1:]
# For convenience
time = np.arange(0, npts) * dt
#
# Plot the whole filtering process
#
dt=0.05
# Determining nyquist frequency and asking for cut off frequency f0
fmax=1.0/(2*dt) # nyquist
f0 = float(raw_input('Give cut-off below Nyquist: fmax = %4.1f Hz ' % fmax))
print 'f0 =', f0
# Uncomment from random points
#y = random.rand(npts) - .5 # uniform random numbers, zero mean
# Spike at npts/2
y = np.zeros(npts,dtype='float')
y[npts/2] = 1
# Filter with filter_bc
freq, H, y_filt = filter_bc(y,dt,f0)
# Just for the plot, frequency domain representaion of y
y_f = np.fft.rfft(y)
# Remove offset
y_f = y_f[1:]
freq = freq[1:]
H = H[1:]
# For convenience
time = np.arange(0,npts)*dt
#
