def run_analysis(filename,mode,method):
click.echo('Reading file : %s'%filename)
data = IOfile.parsing_input_file(filename)
click.echo('Creating class...')
theclass = TFC(data)
click.echo('Calculating transfer function using %s method'%method)
if method=='tf_kramer286_sh':
theclass.tf_kramer286_sh()
elif method=='tf_knopoff_sh':
theclass.tf_knopoff_sh()
elif method=='tf_knopoff_sh_adv':
theclass.tf_knopoff_sh_adv()
plt.plot(theclass.freq,np.abs(theclass.tf[0]),label=method)
plt.xlabel('frequency (Hz)')
plt.ylabel('Amplification')
plt.yscale('log')
plt.xscale('log')
plt.grid(True,which='both')
plt.legend(loc='best',fancybox=True,framealpha=0.5)
#plt.axis('tight')
plt.autoscale(True,axis='x',tight=True)
plt.tight_layout()
plt.savefig('test.png', format='png')
click.echo(click.style('Calculation has been finished!',fg='green'))
def sensitivityTools(fname,sensitivity='incidence angle',senslinspace=[0.,90.,91],method='tf_kramer286_sh',yscale='lin'):
import IOfile
import numpy as np
import pylab as plt
from TFCalculator import TFCalculator as TFC
from TFDisplayTools import SpectroPlot
# timing module
import time
data = IOfile.parsing_input_file(fname)
data['sensitivity'] = True
elapsed = []
x = np.array([])
y = np.array([])
z = np.array([])
if yscale == 'log':
ianglist = np.logspace(np.log10(senslinspace[0]),np.log10(senslinspace[1]),senslinspace[2])
else:
ianglist = np.linspace(senslinspace[0],senslinspace[1],senslinspace[2])
if sensitivity=='incidence angle':
for i in range(len(ianglist)):
data['iang'] = np.deg2rad(ianglist[i])
theclass = TFC(data)
start = time.clock()
tf = eval('theclass.'+method+'()')
elapsed.append(time.clock()-start)
x = np.concatenate((x,theclass.freq+theclass.freq[1]))
z = np.concatenate((z,np.abs(tf[0])))
y = np.concatenate((y,np.zeros_like(theclass.freq)+ianglist[i]))
ylabel = 'Incidence angle'
#print np.min(theclass.freq),np.min(np.abs(tf[0]))
data['iang']=ianglist
elif sensitivity=='incidence angle phase':
for i in range(len(ianglist)):
data['iang'] = np.deg2rad(ianglist[i])
theclass = TFC(data)
start = time.clock()
tf = eval('theclass.'+method+'()')
elapsed.append(time.clock()-start)
x = np.concatenate((x,theclass.freq+theclass.freq[1]))
z = np.concatenate((z,np.rad2deg(np.angle(tf[0]))))
y = np.concatenate((y,np.zeros_like(theclass.freq)+ianglist[i]))
ylabel = 'Incidence angle'
data['iang']=ianglist
elif sensitivity=='incidence angle vectorial':
for i in range(len(ianglist)):
data['iang'] = np.deg2rad(ianglist[i])
theclass = TFC(data)
start = time.clock()
tf = eval('theclass.'+method+'()')
elapsed.append(time.clock()-start)
x = np.concatenate((x,theclass.freq+theclass.freq[1]))
z = np.concatenate((z,np.sqrt(np.abs(tf[0])**2+np.abs(tf[1])**2)))
y = np.concatenate((y,np.zeros_like(theclass.freq)+ianglist[i]))
ylabel = 'Incidence angle'
data['iang']=ianglist
elif sensitivity=='thickness':
for i in range(len(ianglist)):
data['hl'] = [ianglist[i],0.]
theclass = TFC(data)
start = time.clock()
tf = eval('theclass.'+method+'()')
elapsed.append(time.clock()-start)
x = np.concatenate((x,theclass.freq+theclass.freq[1]))
z = np.concatenate((z,np.abs(tf[0])))
y = np.concatenate((y,np.zeros_like(theclass.freq)+ianglist[i]))
ylabel = 'Thickness (m)'
data['hl'][0]=ianglist.tolist()
elif sensitivity[:2]=='vp':
tmp = sensitivity.split()
for i in range(len(ianglist)):
data['vp'][int(tmp[1])-1] = ianglist[i]
data['comp']='p'
theclass = TFC(data)
start = time.clock()
tf = eval('theclass.'+method+'()')
elapsed.append(time.clock()-start)
x = np.concatenate((x,theclass.freq+theclass.freq[1]))
z = np.concatenate((z,np.abs(tf[1])))
y = np.concatenate((y,np.zeros_like(theclass.freq)+ianglist[i]))
ylabel = 'Vp (m/s)'
data['vp'][int(tmp[1])-1]=ianglist.tolist()
elif sensitivity[:2]=='vs':
tmp = sensitivity.split()
for i in range(len(ianglist)):
data['vs'][int(tmp[1])-1] = ianglist[i]
theclass = TFC(data)
start = time.clock()
tf = eval('theclass.'+method+'()')
elapsed.append(time.clock()-start)
x = np.concatenate((x,theclass.freq+theclass.freq[1]))
z = np.concatenate((z,np.abs(tf[0])))
y = np.concatenate((y,np.zeros_like(theclass.freq)+ianglist[i]))
ylabel = 'Vs (m/s)'
data['vs'][int(tmp[1])-1]=ianglist.tolist()
elif sensitivity[:2]=='qp':
tmp = sensitivity.split()
for i in range(len(ianglist)):
data['qp'][int(tmp[1])-1] = ianglist[i]
data['comp']='p'
theclass = TFC(data)
start = time.clock()
tf = eval('theclass.'+method+'()')
elapsed.append(time.clock()-start)
x = np.concatenate((x,theclass.freq+theclass.freq[1]))
z = np.concatenate((z,np.abs(tf[1])))
y = np.concatenate((y,np.zeros_like(theclass.freq)+ianglist[i]))
ylabel = '$Q_{P}$'
data['qp'][int(tmp[1])-1]=ianglist.tolist()
elif sensitivity[:2]=='qs':
tmp = sensitivity.split()
for i in range(len(ianglist)):
data['qs'][int(tmp[1])-1] = ianglist[i]
theclass = TFC(data)
start = time.clock()
tf = eval('theclass.'+method+'()')
elapsed.append(time.clock()-start)
x = np.concatenate((x,theclass.freq+theclass.freq[1]))
z = np.concatenate((z,np.abs(tf[0])))
y = np.concatenate((y,np.zeros_like(theclass.freq)+ianglist[i]))
ylabel = '$Q_{S}$'
data['qs'][int(tmp[1])-1]=ianglist.tolist()
elif sensitivity[:2]=='dn':
tmp = sensitivity.split()
for i in range(len(ianglist)):
data['dn'][int(tmp[1])-1] = ianglist[i]
theclass = TFC(data)
start = time.clock()
tf = eval('theclass.'+method+'()')
elapsed.append(time.clock()-start)
x = np.concatenate((x,theclass.freq+theclass.freq[1]))
z = np.concatenate((z,np.abs(tf[0])))
y = np.concatenate((y,np.zeros_like(theclass.freq)+ianglist[i]))
ylabel = '$\\rho (kg/m^3)$'
data['dn'][int(tmp[1])-1]=ianglist.tolist()
data['x'] = x
data['y'] = y
data['z'] = z
print('method : %s; average elapsed time : %.6f with standard deviation : %.6f'%(method,np.mean(elapsed),np.std(elapsed)))
if yscale=='log':
SpectroPlot(data,nx=100,ny=100,ylabel=ylabel,cmap='rainbow',yscale='log')
else:
SpectroPlot(data,nx=100,ny=100,ylabel=ylabel,cmap='rainbow')
import IOfile
from TFCalculator import TFCalculator as TFC
import TFDisplayTools
from TSCalculator import TSCalculator as TSC
# single layer test case
# filename
basedir = 'Example/Input'
fname = os.path.join(basedir,'sampleinput_linear_elastic_1layer_halfspace.dat')
fname2 = os.path.join(basedir,'sampleinput_psv_s_linear_elastic_1layer_halfspace.dat')
fname3 = os.path.join(basedir,'sampleinput_psv_p_linear_elastic_1layer_halfspace.dat')
fname4 = os.path.join(basedir,'GoverGmax.dat')
# input file reading
datash = IOfile.parsing_input_file(fname)
datapsvs = IOfile.parsing_input_file(fname2)
# datapsvp = IOfile.parsing_input_file(fname3)
datanonlin = IOfile.parsing_nonlinear_parameter(fname4,True)
def modnlayer(data,nlayer):
nl = nlayer
data['sourceloc']=nl
data['nlayer']=nl+1
data['tfPair'][0][1]=nl
newhl = []; newvs = []; newdn = []; newqs = []; newvp = []; newqp = []
newsoiltype = []
for j in range(nl):
newhl.append(data['hl'][0]/nl)
newvs.append(data['vs'][0])
newqs.append(data['qs'][0])
def __init__(self,parfile,method='auto',sublayercriteria = 5.,numiter = 10,conv_level = 0.01,verbose=False):
# parameter file initialization
self.parfile = parfile
# read file parameter
self.parameters = IOfile.parsing_input_file(parfile)
# method is automatically defined
# if method=='auto':
# if self.parameters['type']=='PSV':
# self.method = 'knopoff_psv_adv'
# else:
# if self.parameters['nlayer']<=5:
# if self.parameters['iang']==0.:
# self.method='knopoff_sh'
# else:
# self.method='knopoff_sh_adv'
# else:
# self.method = 'kennet_sh'
# checking input file
if self.parameters['inputmotion'][1]=='ascii':
self.inp_time,self.inp_signal = IOfile.read_ascii_seismogram(self.parameters['inputmotion'][0])
else:
raise KeyError('Input motion other than ascii format is not yet supported! Please convert it to displacement on another software first!')
self.inp_signal = [i/100. for i in self.inp_signal]
self.fs = 1/(self.inp_time[1]-self.inp_time[0])
self.dt = self.inp_time[1]-self.inp_time[0]
self.df = 1./((len(self.inp_signal)-1)*self.dt)
# baseline correction for input signal
self.inp_signal = self.inp_signal-np.mean(self.inp_signal)
# if self.parameters['inputmotion'][2]=='vel':
# self.inp_signal = GT.vel2disp(self.inp_signal,self.dt)
# self.inp_signal = self.cosine_tapering(self.inp_signal)
# self.inp_signal = self.butter_highpass_filter(self.inp_signal,2.*self.df,self.fs)
# elif self.parameters['inputmotion'][2]=='acc':
# self.inp_signal = GT.acc2disp(self.inp_signal,self.dt)
# self.inp_signal = self.cosine_tapering(self.inp_signal)
# self.inp_signal = self.butter_highpass_filter(self.inp_signal,2.*self.df,self.fs)
if self.parameters['modeID']==11 or self.parameters['modeID']==12:
# method is automatically defined
if method=='auto':
if self.parameters['type']=='PSV':
self.method = 'knopoff_psv_adv'
else:
if self.parameters['iang']==0.:
self.method = 'kramer286_sh'
else:
self.method = 'knopoff_sh_adv'
else:
self.method = method
self.linear_equivalent_TF2TS(sublayercriteria,numiter,conv_level,verbose)
else:
# method is automatically defined
if method=='auto':
if self.parameters['type']=='PSV':
self.method = 'knopoff_psv_adv'
else:
if self.parameters['iang']==0.:
self.method = 'knopoff_sh'
else:
self.method = 'knopoff_sh_adv'
else:
self.method = method
self.linear_TF2TS()
self.lastiter = 1
if self.parameters['inputmotion'][2]=='acc':
for i in range(len(self.time_series)):
self.time_series[i] = GT.disp2acc(self.time_series[i],self.dt)
elif self.parameters['inputmotion'][2]=='vel':
for i in range(len(self.time_series)):
self.time_series[i] = GT.disp2vel(self.time_series[i],self.dt)
