def get_age_arr(self,renew=False): """Get age array for the grids of the tomography result """ dset = dispDBase.dispASDF(self.attrs['age_h5']) minlat = self.attrs['minlat'] maxlat = self.attrs['maxlat'] minlon = self.attrs['minlon'] maxlon = self.attrs['maxlon'] dset.set_poly(self.poly_lst,minlon,minlat,maxlon,maxlat) dset.read_age_mdl() self.create_dataset(name='age_nc_Arr', data=dset.age_data) self.create_dataset(name='age_lon_Vec', data=dset.age_lon) self.create_dataset(name='age_lat_Vec', data=dset.age_lat) for period in self.attrs['prd_arr']: group = self['%g_sec'%( period )] lons = group['lonArr'].value lats = group['latArr'].value age_Arr = dset.get_ages(lons.reshape(lons.size),lats.reshape(lats.size)) age_Arr = age_Arr.reshape(lats.shape) mask_age = age_Arr > 180. if renew: del group['age_Arr'] del group['age_Arr_msk'] group.create_dataset(name='age_Arr', data=age_Arr) group.create_dataset(name='age_Arr_msk', data=mask_age) return
def get_c(self,wave='phase'):
"""Ger c0, c1, c2 for the given period
"""
dset = dispDBase.dispASDF(self.attrs['age_h5'])
# dset = dispDBase.dispASDF('age_model_new.h5')
for period in self.attrs['prd_arr']:
str_per = str(int(period)).zfill(2)
group = self['%g_sec'%( period )]
c_dic = dset.auxiliary_data.FitResult[str_per][wave].parameters
c0 = c_dic['c0']
c1 = c_dic['c1']
c2 = c_dic['c2']
age_vel = c0 + c1*np.sqrt(group['age_Arr'].value) + c2*group['age_Arr'].value
group.create_dataset(name='age_vel', data=age_vel)
group.create_dataset(name='age_vel_msk', data=group['age_Arr_msk'].value)
group.attrs.create(name = 'c0', data = c0, dtype='f')
group.attrs.create(name = 'c1', data = c1, dtype='f')
group.attrs.create(name = 'c2', data = c2, dtype='f')
return
import dispDBase
import numpy as np
import matplotlib.pyplot as plt
dset = dispDBase.dispASDF('./age_model_180321.h5')
# lst = [(360.-131.108, 47.443),(360.-127.51, 49.382),(360-124.01, 49.282),(360.-124.1, 43.2),(360.-126.76, 43.77),(360.-130.91,44.57),(360.-133.57,45.57)]
lst = [(360.-126.729,49.292),(360.-125.021,48.105),(360.-124.299,44.963),(360.-124.7,43.203),(360.-126.545,43.586),(360.-131.406,44.8),(360.-130.97,47.),(360.-129.821,49.12)]
dset.set_poly(lst,228,43,237,49)
dset.read_age_mdl()
dset.read_stations(stafile='/work3/wang/JdF/station_7D.lst',source='CIEI',chans=['BHZ', 'BHE', 'BHN'])
dset.read_paths(disp_dir='/work3/wang/JdF/FTAN_4_All',res=3.5)
dset.intp_disp(pers = np.append( np.arange(7.)*2.+6., np.arange(4.)*3.+20.))
pers = np.append( np.arange(7.)*2.+6., np.arange(4.)*3.+20.)
dset.write_2_sta_in(outdir='/work3/wang/JdF/Input_4_Ray', pers=pers, cut=0.8)
# for per in pers:
# if per < 20:
# continue
# dset.fit_Harmon(int(per),vel_type='phase')
# dset.fit_Harmon(int(per),vel_type='group')
# dset.plot_stations(ppoly=False)
# dset.plot_age_topo(10,vel_type='phase')
# dset.plot_vel_topo(10,vel_type='phase')
# dset.plot_vel_age(10,vel_type='phase')
# dset.plot_paths(6,vel_type='phase')
# dset.plot_all_vel(pers=np.array([6,8,10,12,18]),vel_type='group')
#dset.get_vel_age(3.9,-0.15,0.05,3.75,3.9)
for i, ages in enumerate(ages_lst):
age_inc = (ages[0:-1] + ages[1:])/2
d_d = np.repeat(d_dist[i], age_inc.size)
v_ages = c0+c1*np.sqrt(age_inc)+c2*age_inc
v_ages[v_ages==0] = d_dist[i]/9999. # if model gives a 0 velocity change it to a small number to avoid 0 division
t_path[i] = (d_d/v_ages).sum()
sq_misfit = ((dist/t_path - V) **2).sum()
return sq_misfit
T = 10.
UC = 'phase' # flag for chosing group or phase velocity measurement
c0,c1,c2 = 3.9, -0.15, 0.05
if __name__ == '__main__':
dset = dispDBase.dispASDF('./age_model.h5')
lst = [(360.-131.108, 47.443),(360.-127.51, 49.382),(360-124.01, 49.282),(360.-124.1, 43.2),(360.-126.76, 43.77),(360.-130.91,44.57),(360.-133.57,45.57)]
dset.set_poly(lst,228,43,237,49)
dset.read_age_mdl()
per = str(int(T)).zfill(2) # reformat the period to a 2-character string
# outname = "path_list_"+per+"_"+UC
# outfile = open(outname,'w')
path_list = dset.auxiliary_data.FinalStas[per][UC].data.value # path list every row contains netcode and stacode for the two stations
age_out = np.array([])
vel_out = np.array([])
dist_arr = np.array([])
d_dist_arr = np.array([])
vel_arr = np.array([])
ages_lst = []
for path in path_list:
lat1 = dset.waveforms[path[0].decode("ASCII")+'.'+path[1].decode("ASCII")].coordinates['latitude']
import dispDBase
import numpy as np
import matplotlib.pyplot as plt
dset = dispDBase.dispASDF('./age_model_Gorda_new.h5')
lst = [(360. - 130.155, 44.05), (360. - 128.48, 43.703),
(360. - 126.224, 43.046), (360. - 124.9, 42.745),
(360. - 123.65, 42.611), (360. - 124.33, 40.52), (360. - 127.56, 40.57),
(360. - 130.96, 40.68)]
dset.set_poly(lst, 230, 40, 237, 44)
dset.read_age_mdl()
dset.read_stations(
stafile='/scratch/summit/howa1663/CC_JdF/XCORR/station_7D.lst',
source='CIEI',
chans=['BHZ', 'BHE', 'BHN'])
dset.read_paths(disp_dir='/scratch/summit/howa1663/CC_JdF/XCORR/FTAN_4_All',
res=3.5)
dset.intp_disp(
pers=np.append(np.arange(6.) * 2. + 6.,
np.arange(4.) * 3. + 18.))
pers = np.append(np.arange(6.) * 2. + 6., np.arange(4.) * 3. + 18.)
for per in pers:
dset.fit_Harmon(int(per), vel_type='phase')
dset.fit_Harmon(int(per), vel_type='group')
# dset.fit_Harmon(10,vel_type='group')
# dset.plot_vel_age(10,vel_type='group')
# dset.plot_paths(10,vel_type='group')
# dset.plot_all_vel(pers=np.array([6,8,10,14]),vel_type='group')