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New class DCfc of object Magnitude for calculating source spectrum and to derive DC value and corner frequency.

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This commit is contained in:
Ludger Küperkoch 2015-09-23 16:31:48 +02:00
parent 9d5b7ad5ae
commit 30ee81a39d
1 changed files with 51 additions and 5 deletions
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56
pylot/core/analysis/magnitude.py
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@ -25,7 +25,8 @@ class Magnitude(object):
:type: float
:param: pwin, pick window [To To+pwin] to get maximum
peak-to-peak amplitude
peak-to-peak amplitude (WApp) or to calculate
source spectrum (DCfc)
:type: float
:param: iplot, no. of figure window for plotting interims results
@ -40,6 +41,7 @@ class Magnitude(object):
self.setpwin(pwin)
self.setiplot(iplot)
self.calcwapp()
self.calcsourcespec()
def getwfstream(self):
@ -68,18 +70,23 @@ class Magnitude(object):
def getwapp(self):
return self.wapp
def getw0(self):
return self.w0
def getfc(self):
return self.fc
def calcwapp(self):
self.wapp = None
def calcsourcespec(self):
self.sourcespek = None
class WApp(Magnitude):
'''
Method to derive peak-to-peak amplitude as seen on a Wood-Anderson-
seismograph. Has to be derived from corrected traces!
seismograph. Has to be derived from instrument corrected traces!
'''
def calcwapp(self):
@ -110,6 +117,7 @@ class WApp(Magnitude):
iwin = getsignalwin(th, self.getTo(), self.getpwin())
self.wapp = np.max(sqH[iwin])
print ("Determined Wood-Anderson peak-to-peak amplitude: %f mm") % self.wapp
if self.getiplot() > 1:
stream.plot()
f = plt.figure(2)
@ -128,10 +136,48 @@ class WApp(Magnitude):
class DCfc(Magnitude):
'''
Method to calculate the source spectrum and to derive from that the plateau
(the so-called DC-value) and the corner frequency assuming Aki's omega-square
source model. Has to be derived from corrected traces!
(so-called DC-value) and the corner frequency assuming Aki's omega-square
source model. Has to be derived from instrument corrected displacement traces!
'''
def calcsourcespec(self):
print ("Calculating source spectrum ....")
self.w0 = None # DC-value
self.fc = None # corner frequency
stream = self.getwfstream()
tr = stream[0]
# get time array
t = np.arange(0, len(tr) * tr.stats.delta, tr.stats.delta)
iwin = getsignalwin(t, self.getTo(), self.getpwin())
xdat = tr.data[iwin]
# fft
fny = tr.stats.sampling_rate / 2
N = 1024
y = tr.stats.delta * np.fft.fft(xdat, N)
Y = abs(y[: N/2])
L = (N - 1) / tr.stats.sampling_rate
f = np.arange(0, fny, 1 / L)
#if self.getiplot() > 1:
iplot=2
if iplot > 1:
f1 = plt.figure(1)
plt.subplot(2,1,1)
plt.plot(t, np.multiply(tr, 1000), 'k') # show displacement in mm
plt.plot(t[iwin], np.multiply(xdat, 1000), 'g') # show displacement in mm
plt.title('Seismogram and P pulse, station %s' % tr.stats.station)
plt.xlabel('Time since %s' % tr.stats.starttime)
plt.ylabel('Displacement [mm]')
plt.subplot(2,1,2)
plt.semilogy(f, Y.real)
plt.title('Source Spectrum from P Pulse')
plt.xlabel('Frequency [Hz]')
plt.ylabel('Amplitude [m/Hz]')
plt.show()
raw_input()
plt.close(f1)