def main():
## Post - Processing [processing the results from inversion]
result = Post_processing_sdr()
directory = '/home/nienke/Documents/Applied_geophysics/Thesis/anaconda/Euler_data'
path_to_file = '/home/nienke/Documents/Applied_geophysics/Thesis/anaconda/Euler_data/Trials/Trial_close_GOOD/close_small_spread.txt'
# path_to_stream = '/home/nienke/Documents/Applied_geophysics/Thesis/anaconda/Blindtest/bw_reject.mseed'
# path= '/home/nienke/Documents/Applied_geophysics/Thesis/anaconda/Additional_scripts/Iteration_runs/Together'
savename = 'Trials'
show = False # Choose True for direct show, choose False for saving
skiprows = 70
column_names = [
"Epi", "Depth", "Strike", "Dip", "Rake", "Total_misfit", "S_z", "S_r",
"S_t", "P_z", "P_r", "BW_misfit", "Rtot", "Ltot"
]
# column_names= ["Epi", "Depth", "Strike", "Dip", "Rake","Total_misfit","S_z","S_r","S_t"]
# path_to_file, save = result.convert_txt_folder_to_yaml(path, savename)
# result.get_stereonets(filepath=path_to_file, savename=savename, directory=directory, show=show)
# result.plot_streams(stream_filepath=path_to_stream,filepath=path_to_file,savename=savename, directory=directory,skiprows=skiprows ,column_names=column_names)
par = Get_Paramters()
REAL = par.get_unkown()
PRIOR = par.get_prior()
result.event_plot(PRIOR['la_r'], PRIOR['lo_r'], REAL['la_s'], REAL['lo_s'])
def Normal_check():
# Initiate Parameters:
get_parameters = Get_Paramters()
PARAMETERS = get_parameters.get_unkown()
PRIOR = get_parameters.get_prior()
VALUES = get_parameters.specifications()
VALUES['blind'] = False
VALUES[
'directory'] = '/home/nienke/Documents/Applied_geophysics/Thesis/anaconda/Final/check_waveforms'
## DISCUSS THIS!!!!
PRIOR['az'] = PARAMETERS['az']
PRIOR['baz'] = PARAMETERS['baz']
# Initiate the databases from instaseis:
db = instaseis.open_db(PRIOR['VELOC'])
create = Create_observed(PRIOR, db)
d_obs, tr_obs, source = create.get_seis_automatic(
parameters=PARAMETERS,
prior=PRIOR,
noise_model=VALUES['noise'],
sdr=VALUES['sdr'])
traces_obs, p_obs, s_obs, p_time_obs, s_time_obs = create.get_window_obspy(
tr_obs, PARAMETERS['epi'], PARAMETERS['depth_s'],
PARAMETERS['origin_time'], VALUES['npts'])
time_at_receiver = create.get_receiver_time(PARAMETERS['epi'],
PARAMETERS['depth_s'],
PARAMETERS['origin_time'])
PRIOR['var_est'] = create.get_var_data(p_time_obs, tr_obs)
sw = Surface_waves(PRIOR)
R_env_obs = sw.rayleigh_pick(tr_obs.traces[0],
PARAMETERS['la_s'],
PARAMETERS['lo_s'],
PARAMETERS['depth_s'],
VALUES['directory'],
PARAMETERS['origin_time'],
VALUES['npts'],
plot_modus=False)
L_env_obs = sw.love_pick(tr_obs.traces[2],
PARAMETERS['la_s'],
PARAMETERS['lo_s'],
PARAMETERS['depth_s'],
VALUES['directory'],
PARAMETERS['origin_time'],
VALUES['npts'],
plot_modus=False)
# tr_obs.plot()
# ------------------------------------------------------------------
seis = Seismogram(PRIOR, db)
window_code = Source_code(PRIOR['VELOC_taup'])
misfit = Misfit(VALUES['directory'])
epi = PARAMETERS['epi']
depth = PARAMETERS['depth_s']
strike = PARAMETERS['strike']
dip = PARAMETERS['dip']
rake = PARAMETERS['rake']
time = PARAMETERS['origin_time']
# --------------------------------------------------------------------------------------------------------------- #
dict = geo.Geodesic(a=PRIOR['radius'], f=0).ArcDirect(lat1=PRIOR['la_r'],
lon1=PRIOR['lo_r'],
azi1=PRIOR['baz'],
a12=epi,
outmask=1929)
d_syn, traces_syn, sources = seis.get_seis_manual(la_s=dict['lat2'],
lo_s=dict['lon2'],
depth=depth,
strike=strike,
dip=dip,
rake=rake,
time=time,
M0=PRIOR['M0'],
sdr=VALUES['sdr'])
R_env_syn = sw.rayleigh_pick(Z_trace=traces_syn.traces[0],
la_s=dict['lat2'],
lo_s=dict['lon2'],
depth=depth,
save_directory=VALUES['directory'],
time_at_rec=time,
npts=VALUES['npts'],
plot_modus=True)
L_env_syn = sw.love_pick(T_trace=traces_syn.traces[2],
la_s=dict['lat2'],
lo_s=dict['lon2'],
depth=depth,
save_directory=VALUES['directory'],
time_at_rec=time,
npts=VALUES['npts'],
plot_modus=False)
traces_syn.plot(outfile=VALUES['directory'] + '/syntethic')
total_syn, p_syn, s_syn, p_time_syn, s_time_syn = window_code.get_window_obspy(
traces_syn, epi, depth, time, VALUES['npts'])
ax1 = plt.subplot2grid((5, 1), (0, 0))
ax1.plot(zero_to_nan(p_syn.traces[0].data), c='r', linewidth=0.3)
ax1.plot(zero_to_nan(p_obs.traces[0].data),
c='k',
linestyle=':',
linewidth=0.3)
plt.tight_layout()
ax2 = plt.subplot2grid((5, 1), (1, 0))
ax2.plot(zero_to_nan(p_syn.traces[1].data), c='r', linewidth=0.3)
ax2.plot(zero_to_nan(p_obs.traces[1].data),
c='k',
linestyle=':',
linewidth=0.3)
plt.tight_layout()
ax3 = plt.subplot2grid((5, 1), (2, 0))
ax3.plot(zero_to_nan(s_syn.traces[0].data), c='r', linewidth=0.3)
ax3.plot(zero_to_nan(s_obs.traces[0].data), c='k', linewidth=0.3)
plt.tight_layout()
ax4 = plt.subplot2grid((5, 1), (3, 0))
ax4.plot(zero_to_nan(s_syn.traces[1].data), c='r', linewidth=0.3)
ax4.plot(zero_to_nan(s_obs.traces[1].data), c='k', linewidth=0.3)
plt.tight_layout()
ax5 = plt.subplot2grid((5, 1), (4, 0))
ax5.plot(zero_to_nan(s_syn.traces[2].data), c='r', linewidth=0.3)
ax5.plot(zero_to_nan(s_obs.traces[2].data), c='k', linewidth=0.3)
plt.tight_layout()
plt.savefig(VALUES['directory'] + '/%.2f_%.2f.pdf' % (epi, depth))
plt.close()
ax1 = plt.subplot2grid((3, 1), (0, 0))
ax1.plot(zero_to_nan(total_syn.traces[0].data), c='r', linewidth=0.5)
ax1.plot(zero_to_nan(traces_obs.traces[0].data),
c='k',
linestyle=':',
linewidth=0.5)
ax1.set_title('SYNTHETIC: = epi: %.2f REAL: epi = %.2f (depth fixed' %
(epi, epi))
plt.tight_layout()
ax2 = plt.subplot2grid((3, 1), (1, 0))
ax2.plot(zero_to_nan(total_syn.traces[1].data), c='r', linewidth=0.5)
ax2.plot(zero_to_nan(traces_obs.traces[1].data),
c='k',
linestyle=':',
linewidth=0.5)
plt.tight_layout()
ax3 = plt.subplot2grid((3, 1), (2, 0))
ax3.plot(zero_to_nan(total_syn.traces[2].data), c='r', linewidth=0.5)
ax3.plot(zero_to_nan(traces_obs.traces[2].data),
c='k',
linestyle=':',
linewidth=0.5)
plt.tight_layout()
plt.savefig(VALUES['directory'] + '/PS_%.2f_%.2f.pdf' % (epi, depth))
plt.close()
Xi_bw_new, time_shift_new, amplitude = misfit.CC_stream(
p_obs, p_syn, s_obs, s_syn, p_time_obs, p_time_syn)
s_z_new = 0.1 * Xi_bw_new[0]
s_r_new = 0.1 * Xi_bw_new[1]
s_t_new = 1 * Xi_bw_new[2]
p_z_new = 5 * Xi_bw_new[3]
p_r_new = 5 * Xi_bw_new[4]
bw_new = s_z_new + s_r_new + s_t_new + p_z_new + p_r_new
Xi_R_new = misfit.SW_L2(R_env_obs, R_env_syn, PRIOR['var_est'], amplitude)
Xi_L_new = misfit.SW_L2(L_env_obs, L_env_syn, PRIOR['var_est'], amplitude)
R_dict_new = {}
rw_new = 0
for j, v in enumerate(Xi_R_new):
R_dict_new.update({'R_%i_new' % j: v})
rw_new += v
L_dict_new = {}
lw_new = 0
for j, v in enumerate(Xi_L_new):
L_dict_new.update({'L_%i_new' % j: v})
lw_new += v
Xi_new = bw_new + rw_new + lw_new
a = 1
def Acces_Normal():
# Initiate Parameters:
get_parameters = Get_Paramters()
PARAMETERS = get_parameters.get_unkown()
PRIOR = get_parameters.get_prior()
VALUES = get_parameters.specifications()
VALUES[
'directory'] = '/home/nienke/Documents/Applied_geophysics/Thesis/anaconda/Final'
## DISCUSS THIS!!!!
PRIOR['az'] = PARAMETERS['az']
PRIOR['baz'] = PARAMETERS['baz']
# Initiate the databases from instaseis:
db = instaseis.open_db(PRIOR['VELOC'])
create = Create_observed(PRIOR, db)
d_obs, tr_obs, source = create.get_seis_automatic(
parameters=PARAMETERS,
prior=PRIOR,
noise_model=VALUES['noise'],
sdr=VALUES['sdr'])
time_at_receiver = create.get_receiver_time(PARAMETERS['epi'],
PARAMETERS['depth_s'],
PARAMETERS['origin_time'])
traces_obs, p_obs, s_obs, start_time_p, start_time_s = create.get_window_obspy(
tr_obs, PARAMETERS['epi'], PARAMETERS['depth_s'],
PARAMETERS['origin_time'], VALUES['npts'])
PRIOR['var_est'] = create.get_var_data(start_time_p, tr_obs)
# PRIOR['var_est'] =1
sw = Surface_waves(PRIOR)
R_env_obs = sw.rayleigh_pick(tr_obs.traces[0],
PARAMETERS['la_s'],
PARAMETERS['lo_s'],
PARAMETERS['depth_s'],
VALUES['directory'],
PARAMETERS['origin_time'],
VALUES['npts'],
plot_modus=False)
L_env_obs = sw.love_pick(tr_obs.traces[2],
PARAMETERS['la_s'],
PARAMETERS['lo_s'],
PARAMETERS['depth_s'],
VALUES['directory'],
PARAMETERS['origin_time'],
VALUES['npts'],
plot_modus=False)
# tr_obs.plot()
# ------------------------------------------------------------------
seis = Seismogram(PRIOR, db)
window_code = Source_code(PRIOR['VELOC_taup'])
misfit = Misfit(VALUES['directory'])
# start_sample_path = '/home/nienke/Documents/Applied_geophysics/Thesis/anaconda/Final/close_sample.txt'
start_sample_path = None
m = MCMC_stream(R_env_obs,
L_env_obs,
traces_obs,
p_obs,
s_obs,
PRIOR,
db,
VALUES,
PARAMETERS['origin_time'],
start_time_p,
start_time_s,
start_sample_path,
None,
full_obs_trace=tr_obs)
m.start_MCMC(VALUES['directory'] + '/Exploring.txt')
def Acces_Blindtest_check():
BLINDTEST_MSEED = '/home/nienke/Documents/Applied_geophysics/Thesis/anaconda/Database/data_Nienke/M5.0_3914855_deg_2019-09-22.mseed'
BLINDTEST_XML = BLINDTEST_MSEED.replace(".mseed", ".xml")
# Initiate Parameters:
get_parameters = Get_Paramters()
PRIOR = get_parameters.get_prior()
VALUES = get_parameters.specifications()
VALUES['npts'] = 2000
VALUES[
'directory'] = '/home/nienke/Documents/Applied_geophysics/Thesis/anaconda/Blindtest/check_waveforms'
VALUES['blind'] = True
# st = read(VALUES['directory'] + '/bw.mseed')
# st_reject = read(VALUES['directory'] + '/bw_reject.mseed')
# Initiate the databases from instaseis:
db = instaseis.open_db(PRIOR['VELOC'])
tr_obs = obspy.read(BLINDTEST_MSEED)
# tr_obs.plot(outfile=VALUES['directory'] + '/Observed')
tr_obs.integrate()
tr_obs.plot(outfile=VALUES['directory'] + '/Observed_integrated')
source = instaseis.Source.parse(BLINDTEST_XML)
blindtest = Blindtest()
events = blindtest.get_events(BLINDTEST_XML)
# get_parameters.get_prior_blindtest(events[0])
time, depth, la_s, lo_s = blindtest.get_pref_origin(events[0])
dist, az, baz = gps2dist_azimuth(lat1=la_s,
lon1=lo_s,
lat2=PRIOR['la_r'],
lon2=PRIOR['lo_r'],
a=PRIOR['radius'],
f=0)
epi = kilometer2degrees(dist, radius=PRIOR['radius'])
PRIOR['az'] = az
PRIOR['baz'] = baz
PRIOR['epi']['range_min'] = epi - 5
PRIOR['epi']['range_max'] = epi + 5
PRIOR['epi']['spread'] = 1
PRIOR['depth']['range_min'] = depth - 10000
PRIOR['depth']['range_max'] = depth + 10000
PRIOR['network'] = tr_obs.traces[0].meta.network
PRIOR['location'] = tr_obs.traces[0].meta.location
PRIOR['station'] = tr_obs.traces[0].meta.station
est_noise = Create_observed(PRIOR, db)
create = Source_code(PRIOR['VELOC_taup'])
traces_obs, p_obs, s_obs, p_time_obs, s_time_obs = create.get_window_obspy(
tr_obs, epi, depth, time, VALUES['npts'])
PRIOR['var_est'] = est_noise.get_var_data(p_time_obs, tr_obs)
obs_time = Create_observed(PRIOR, db)
time_at_receiver = obs_time.get_receiver_time(epi, depth, time)
plt.figure()
catalog_path = '/home/nienke/Documents/Applied_geophysics/Thesis/anaconda/Additional_scripts/MQScatalog_withFrequencies/MQS_absolute_withFrequencyInfo.xml'
events_catalog = blindtest.get_events(catalog_path)
for v in events_catalog:
t, d, lat_ev, lo_ev = blindtest.get_pref_origin(v)
if time.date == t.date:
Pick_event = v
break
PRIOR['M0'] = blindtest.get_pref_scalarmoment(Pick_event)
picks_surface = get_phase_picks(Pick_event, pick_type='surface')
R_env_obs, L_env_obs = blindtest.pick_sw(tr_obs,
picks_surface,
epi,
PRIOR,
VALUES['npts'],
VALUES['directory'],
plot_modus=True)
start_sample = create_starting_sample()
strike = 243.423396191
dip = 34.436087773
rake = 164.912874159
from Seismogram import Seismogram
from Misfit import Misfit
misfit = Misfit(VALUES['directory'])
seis = Seismogram(PRIOR, db)
epi = epi - 3
depth = depth
# --------------------------------------------------------------------------------------------------------------- #
dict = geo.Geodesic(a=PRIOR['radius'], f=0).ArcDirect(lat1=PRIOR['la_r'],
lon1=PRIOR['lo_r'],
azi1=PRIOR['baz'],
a12=epi,
outmask=1929)
d_syn, traces_syn, sources = seis.get_seis_manual(la_s=dict['lat2'],
lo_s=dict['lon2'],
depth=depth,
strike=strike,
dip=dip,
rake=rake,
time=time,
M0=PRIOR['M0'],
sdr=VALUES['sdr'])
R_env_syn, L_env_syn = blindtest.pick_sw(traces_syn,
picks_surface,
epi,
PRIOR,
VALUES['npts'],
VALUES['directory'],
plot_modus=False)
traces_syn.plot(outfile=VALUES['directory'] + '/syntethic')
total_syn, p_syn, s_syn, p_time_syn, s_time_syn = create.get_window_obspy(
traces_syn, epi, depth, time, VALUES['npts'])
ax1 = plt.subplot2grid((5, 1), (0, 0))
ax1.plot(zero_to_nan(p_syn.traces[0].data), c='r', linewidth=0.3)
ax1.plot(zero_to_nan(p_obs.traces[0].data),
c='k',
linestyle=':',
linewidth=0.3)
plt.tight_layout()
ax2 = plt.subplot2grid((5, 1), (1, 0))
ax2.plot(zero_to_nan(p_syn.traces[1].data), c='r', linewidth=0.3)
ax2.plot(zero_to_nan(p_obs.traces[1].data),
c='k',
linestyle=':',
linewidth=0.3)
plt.tight_layout()
ax3 = plt.subplot2grid((5, 1), (2, 0))
ax3.plot(zero_to_nan(s_syn.traces[0].data), c='r', linewidth=0.3)
ax3.plot(zero_to_nan(s_obs.traces[0].data), c='k', linewidth=0.3)
plt.tight_layout()
ax4 = plt.subplot2grid((5, 1), (3, 0))
ax4.plot(zero_to_nan(s_syn.traces[1].data), c='r', linewidth=0.3)
ax4.plot(zero_to_nan(s_obs.traces[1].data), c='k', linewidth=0.3)
plt.tight_layout()
ax5 = plt.subplot2grid((5, 1), (4, 0))
ax5.plot(zero_to_nan(s_syn.traces[2].data), c='r', linewidth=0.3)
ax5.plot(zero_to_nan(s_obs.traces[2].data), c='k', linewidth=0.3)
plt.tight_layout()
plt.savefig(VALUES['directory'] + '/%.2f_%.2f.pdf' % (epi, depth))
plt.close()
# time =
ax1 = plt.subplot2grid((3, 1), (0, 0))
ax1.plot(zero_to_nan(total_syn.traces[0].data), c='r', linewidth=0.5)
ax1.plot(zero_to_nan(traces_obs.traces[0].data),
c='k',
linestyle=':',
linewidth=0.5)
ax1.set_title('SYNTHETIC: = epi: %.2f REAL: epi = %.2f (depth fixed' %
(epi, epi + 3))
plt.tight_layout()
ax2 = plt.subplot2grid((3, 1), (1, 0))
ax2.plot(zero_to_nan(total_syn.traces[1].data), c='r', linewidth=0.5)
ax2.plot(zero_to_nan(traces_obs.traces[1].data),
c='k',
linestyle=':',
linewidth=0.5)
plt.tight_layout()
ax3 = plt.subplot2grid((3, 1), (2, 0))
ax3.plot(zero_to_nan(total_syn.traces[2].data), c='r', linewidth=0.5)
ax3.plot(zero_to_nan(traces_obs.traces[2].data),
c='k',
linestyle=':',
linewidth=0.5)
plt.tight_layout()
plt.savefig(VALUES['directory'] + '/PS_%.2f_%.2f.pdf' % (epi, depth))
plt.close()
Xi_bw_new, time_shift_new, amplitude = misfit.CC_stream(
p_obs, p_syn, s_obs, s_syn, p_time_obs, p_time_syn)
s_z_new = 0.1 * Xi_bw_new[0]
s_r_new = 0.1 * Xi_bw_new[1]
s_t_new = 1 * Xi_bw_new[2]
p_z_new = 5 * Xi_bw_new[3]
p_r_new = 5 * Xi_bw_new[4]
bw_new = s_z_new + s_r_new + s_t_new + p_z_new + p_r_new
Xi_R_new = misfit.SW_L2(R_env_obs, R_env_syn, PRIOR['var_est'], amplitude)
Xi_L_new = misfit.SW_L2(L_env_obs, L_env_syn, PRIOR['var_est'], amplitude)
R_dict_new = {}
rw_new = 0
for j, v in enumerate(Xi_R_new):
R_dict_new.update({'R_%i_new' % j: v})
rw_new += v
L_dict_new = {}
lw_new = 0
for j, v in enumerate(Xi_L_new):
L_dict_new.update({'L_%i_new' % j: v})
lw_new += v
Xi_new = bw_new + rw_new + lw_new
a = 1
def Acces_Blindtest():
# BLINDTEST_MSEED = '/home/nienke/Documents/Applied_geophysics/Thesis/anaconda/Database/data_Nienke/M3.5_8213363_deg_2019-02-15.mseed'
BLINDTEST_MSEED = '/home/nienke/Documents/Applied_geophysics/Thesis/anaconda/Database/data_Nienke/M5.0_3914855_deg_2019-09-22.mseed'
BLINDTEST_XML = BLINDTEST_MSEED.replace(".mseed", ".xml")
# Initiate Parameters:
get_parameters = Get_Paramters()
PRIOR = get_parameters.get_prior()
VALUES = get_parameters.specifications()
VALUES['npts'] = 30000
VALUES[
'directory'] = '/home/nienke/Documents/Applied_geophysics/Thesis/anaconda/Blindtest'
# st = read(VALUES['directory'] + '/bw.mseed')
# st_reject = read(VALUES['directory'] + '/bw_reject.mseed')
# Initiate the databases from instaseis:
db = instaseis.open_db(PRIOR['VELOC'])
tr_obs = obspy.read(BLINDTEST_MSEED)
# tr_obs.plot(outfile=VALUES['directory'] + '/Observed')
tr_obs.integrate()
# tr_obs.plot(outfile=VALUES['directory'] + '/Observed_integrated')
# source = instaseis.Source.parse(BLINDTEST_XML)
blindtest = Blindtest()
events = blindtest.get_events(BLINDTEST_XML)
# get_parameters.get_prior_blindtest(events[0])
time, depth, la_s, lo_s = blindtest.get_pref_origin(events[0])
dist, az, baz = gps2dist_azimuth(lat1=la_s,
lon1=lo_s,
lat2=PRIOR['la_r'],
lon2=PRIOR['lo_r'],
a=PRIOR['radius'],
f=0)
epi = kilometer2degrees(dist, radius=PRIOR['radius'])
PRIOR['az'] = az
PRIOR['baz'] = baz
PRIOR['epi']['range_min'] = epi - 5
PRIOR['epi']['range_max'] = epi + 5
PRIOR['epi']['spread'] = 1
PRIOR['depth']['range_min'] = depth - 10000
PRIOR['depth']['range_max'] = depth + 10000
PRIOR['network'] = tr_obs.traces[0].meta.network
PRIOR['location'] = tr_obs.traces[0].meta.location
PRIOR['station'] = tr_obs.traces[0].meta.station
est_noise = Create_observed(PRIOR, db)
create = Source_code(PRIOR['VELOC_taup'])
traces_obs, p_obs, s_obs, start_time_p, start_time_s = create.get_window_obspy(
tr_obs, epi, depth, time, VALUES['npts'])
PRIOR['var_est'] = est_noise.get_var_data(start_time_p, tr_obs)
# time_at_receiver = create.get_receiver_time(epi,depth, time)
plt.figure()
catalog_path = '/home/nienke/Documents/Applied_geophysics/Thesis/anaconda/Additional_scripts/MQScatalog_withFrequencies/MQS_absolute_withFrequencyInfo.xml'
catalog = Blindtest()
events_catalog = catalog.get_events(catalog_path)
for v in events_catalog:
t, d, lat_ev, lo_ev = catalog.get_pref_origin(v)
if time.date == t.date:
Pick_event = v
break
PRIOR['M0'] = catalog.get_pref_scalarmoment(Pick_event)
picks_surface = get_phase_picks(Pick_event, pick_type='surface')
R_env_obs, L_env_obs = blindtest.pick_sw(tr_obs,
picks_surface,
epi,
PRIOR,
30000,
VALUES['directory'],
plot_modus=False)
start_sample = create_starting_sample()
strike = np.random.uniform(PRIOR['strike']['range_min'],
PRIOR['strike']['range_max'])
dip = np.random.uniform(PRIOR['dip']['range_min'],
PRIOR['dip']['range_max'])
rake = np.random.uniform(PRIOR['rake']['range_min'],
PRIOR['rake']['range_max'])
sample_path = start_sample.get_sample_manual(
epi, depth, strike, dip, rake,
VALUES['directory'] + '/Blindtest_trialrun_sample.txt')
# sample_path = '/home/nienke/Documents/Applied_geophysics/Thesis/anaconda/Blindtest/Blindtest_trialrun_sample.txt'
mcmc = MCMC_stream(R_env_obs=R_env_obs,
L_env_obs=L_env_obs,
total_traces_obs=traces_obs,
P_traces_obs=p_obs,
S_traces_obs=s_obs,
PRIOR=PRIOR,
db=db,
specification_values=VALUES,
time_at_receiver=time,
start_sample_path=sample_path,
picked_events=picks_surface,
full_obs_trace=tr_obs,
P_start=start_time_p,
S_start=start_time_s)
mcmc.start_MCMC(VALUES['directory'] + '/Blindtest_trialrun.txt')
# IMPORTANT: in Get_Parameters --> sdr = False !!!!!!! import instaseis import numpy as np import matplotlib.pylab as plt ## All different classes: from Create_observed import Create_observed from Get_Parameters import Get_Paramters from Green_functions import Green_functions from Inversion_problems import Inversion_problem from Forward_problem import Forward_problem from Misfit import Misfit # Getting parameters to create the observed data: get_parameters = Get_Paramters() PARAMETERS = get_parameters.get_unkown( ) # These values can only be used to create d_obs PRIOR = get_parameters.get_prior(PARAMETERS['epi']) VALUES = get_parameters.specifications() PRIOR['az'] = PARAMETERS['az'] PRIOR['baz'] = PARAMETERS['baz'] # Initiate the databases from instaseis: db = instaseis.open_db(PRIOR['VELOC']) ## Step 1 - Create the observed data: create = Create_observed(PRIOR, PARAMETERS, db) # FUll seismogram: # d_obs = stacked numpy array of the seismogram # traces = obspy stream with 3 traces [Z,R,T]
def trace_density(self,
filepath,
savename,
directory,
skiprows,
column_names,
show=True):
dir = directory + '/%s' % (savename.strip('.yaml'))
if not os.path.exists(dir):
os.makedirs(dir)
if filepath.endswith('.yaml') == True:
with open(filepath, 'r') as stream:
data_file = yaml.load(stream)
stream.close()
data = data_file['data']
# parameters = data_file['parameters']
else:
# parameters = open(filepath, "r").readlines()[:33]
data = np.loadtxt(filepath, delimiter=',', skiprows=skiprows)
length = len(data[0]) - (len(column_names) + 3)
L_length = 4 #int(data[0][-1])
R_length = 4 #int(data[0][-2])
for i in range(R_length):
column_names = np.append(column_names, "R_%i" % (i + 1))
for i in range(L_length):
column_names = np.append(column_names, "L_%i" % (i + 1))
column_names = np.append(column_names, "Accepted")
# column_names = np.append(column_names,"Rayleigh_length")
# column_names = np.append(column_names,"Love_length")
params = {
'legend.fontsize': 'x-large',
'figure.figsize': (15, 15),
'axes.labelsize': 25,
'axes.titlesize': 'x-large',
'xtick.labelsize': 25,
'ytick.labelsize': 25
}
pylab.rcParams.update(params)
#
df = pd.DataFrame(data, columns=column_names)
df_select = df[["Epi", "Depth", "Strike", "Dip", "Rake"]]
par = Get_Paramters()
REAL = par.get_unkown()
real_v = np.array([
REAL['epi'], REAL['depth_s'], REAL['strike'], REAL['dip'],
REAL['rake']
])
# real_v=np.array([35.2068855191,16848.1405882,99.88000245897199, 77.02994881296266,68.80551508147109])
# real_v=np.array([73.8059395565,26247.7456326,158.92744919732135, 47.46909146946276,-45.69139707143311])
strike, dip, rake = aux_plane(REAL['strike'], REAL['dip'],
REAL['rake'])
# strike,dip,rake = aux_plane(99.88000245897199,77.02994881296266,68.80551508147109)
# strike,dip,rake = aux_plane(158.92744919732135, 47.46909146946276,-45.69139707143311)
fig = plt.figure(figsize=(20, 20))
row = 0
for i in df_select:
ax1 = plt.subplot2grid((5, 2), (row, 0))
ax1.plot(df_select[i], label=i)
# ax1.axhline(y=REAL[df_select], linewidth=0.3, linestyle=':')
ax1.set_title("Trace %s" % i, color='b', fontsize=20)
ax1.set_xlabel("Iteration")
# ax1.set_ylabel("Epicentral ")
plt.tight_layout()
ax2 = plt.subplot2grid((5, 2), (row, 1))
plt.hist(df_select[i], bins=100)
ymin, ymax = ax2.get_ylim()
plt.vlines(df_select[i][1],
ymin=ymin,
ymax=ymax,
colors='r',
linewidth=3)
plt.vlines(real_v[row],
ymin=ymin,
ymax=ymax,
colors='k',
linewidth=3)
if i == 'Strike':
plt.vlines(strike,
ymin=ymin,
ymax=ymax,
colors='g',
linewidth=3)
if i == 'Dip':
plt.vlines(dip, ymin=ymin, ymax=ymax, colors='g', linewidth=3)
if i == 'Rake':
plt.vlines(rake, ymin=ymin, ymax=ymax, colors='g', linewidth=3)
# y, binEdges = np.histogram(df_select[i], bins=100)
# bincenters = 0.5 * (binEdges[1:] + binEdges[:-1])
# pylab.plot(bincenters, y, '-',label = "%s" % i)
ax2.set_title("Density %s" % i, color='b', fontsize=20)
ax2.set_xlabel("N=%i" % (len(df_select[i])))
plt.tight_layout()
row += 1
plt.savefig(dir + '/Trace_density.pdf')
def combine_all(self,
filepath,
savename,
directory,
skiprows,
column_names,
show=True):
dir = directory + '/%s' % (savename.strip('.yaml'))
if not os.path.exists(dir):
os.makedirs(dir)
if filepath.endswith('.yaml') == True:
with open(filepath, 'r') as stream:
data_file = yaml.load(stream)
stream.close()
data = data_file['data']
# parameters = data_file['parameters']
else:
# parameters = open(filepath, "r").readlines()[:33]
data = np.loadtxt(filepath, delimiter=',', skiprows=skiprows)
length = len(data[0]) - (len(column_names) + 3)
L_length = int(data[0][-1])
R_length = int(data[0][-2])
for i in range(R_length):
column_names = np.append(column_names, "R_%i" % (i + 1))
for i in range(L_length):
column_names = np.append(column_names, "L_%i" % (i + 1))
column_names = np.append(column_names, "Accepted")
column_names = np.append(column_names, "Rayleigh_length")
column_names = np.append(column_names, "Love_length")
par = Get_Paramters()
REAL = par.get_unkown()
df = pd.DataFrame(data, columns=column_names)
df_select = df[["Epi", "Depth", "Strike", "Dip", "Rake", "M0"]]
fig = plt.figure(figsize=(20, 20))
ax1 = plt.subplot2grid((5, 2), (0, 0), colspan=2)
# ax1.axhline(y = REAL['epi'] ,linewidth = 0.3 , linestyle =':', color = 'b')
ax1.plot(df_select['Epi'], label="Epi", c='b')
ax1.tick_params("y", colors='b')
ax1.set_ylabel('Epicentral distance [degree]', color='b')
ax2 = ax1.twinx()
ax2.plot(df_select['Depth'], label="Depth", c='r')
# ax2.axhline(y=REAL['depth_s'], linewidth=0.3, linestyle=':', color='r')
ax2.tick_params('y', colors='r')
ax2.set_ylabel('Depth [m]', color='r')
plt.tight_layout()
ax3 = plt.subplot2grid((5, 2), (1, 0), colspan=2)
ax3.plot(df_select['Strike'], label="Strike", color="b")
# ax3.axhline(y=REAL['strike'], linewidth=0.3, linestyle=':', color='b')
# ax3.axhline(y=REAL['dip'], linewidth=0.3, linestyle=':', color='g')
# ax3.axhline(y=REAL['rake'], linewidth=0.3, linestyle=':', color='r')
ax3.plot(df_select['Dip'], label="Dip", color='g')
ax3.plot(df_select['Rake'], label="Rake", color='r')
ax3.set_ylabel('Moment tensor angle [degree]', color='k')
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.tight_layout()
axes = ax3.twinx()
axes.plot(df_select['M0'], label="M0", c='m')
# ax2.axhline(y=REAL['depth_s'], linewidth=0.3, linestyle=':', color='r')
axes.tick_params('y', colors='m')
axes.set_ylabel('M0', color='r')
plt.yscale('log')
plt.tight_layout()
R_select = df.filter(like='R_')
L_select = df.filter(like='L_')
df_select_xi = df[[
"Total_misfit", "S_z", "S_r", "S_t", "P_z", "P_r", "BW_misfit",
"Rtot", "Ltot"
]]
ax4 = plt.subplot2grid((5, 2), (2, 0), colspan=2)
ax4.plot(df_select_xi['Total_misfit'], label="Total_misfit", c='r')
# ax4.plot(df_select_xi['S_z'], label = "S_z")
# ax4.plot(df_select_xi['S_r'], label = "S_r")
# ax4.plot(df_select_xi['S_t'], label = "S_t")
# ax4.plot(df_select_xi['P_z'], label = "P_z")
# ax4.plot(df_select_xi['P_r'], label = "P_r")
ax4.plot(df_select_xi['BW_misfit'], label="BW_tot")
ymin, ymax = ax4.get_ylim()
plt.yscale('log')
# plt.ylim((pow(10, 0), pow(10, 3)))
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.tight_layout()
ax5 = plt.subplot2grid((5, 2), (3, 0), colspan=2)
# for i in R_select:
# ax5.plot(R_select[i],label = i)
ax5.plot(df_select_xi['Rtot'], label="Rtot")
plt.yscale('log')
# plt.ylim((pow(10, 0), pow(10, 4)))
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.tight_layout()
ax6 = plt.subplot2grid((5, 2), (4, 0), colspan=2)
# for i in L_select:
# ax6.plot(L_select[i],label = i)
ax6.plot(df_select_xi['Ltot'], label="Ltot")
plt.yscale('log')
# plt.ylim((pow(10, 0), pow(10, 4)))
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.tight_layout()
plt.savefig(dir + '/combined_all_par.pdf')
plt.close()
# This code will run the MCMC algorithm
# IMPORTANT: in Get_Parameters --> MCMC = 'M' or MCMC = 'MH' (see Get_Parameters for further explanation)
import instaseis
## All different classes:
from Get_Parameters import Get_Paramters
from MCMC_stream import MCMC_stream
from Create_observed import Create_observed
# Initiate Parameters:
get_parameters = Get_Paramters()
PARAMETERS = get_parameters.get_unkown()
PRIOR = get_parameters.get_prior()
VALUES = get_parameters.specifications()
## DISCUSS THIS!!!!
PRIOR['az'] = PARAMETERS['az']
PRIOR['baz'] = PARAMETERS['baz']
# Initiate the databases from instaseis:
db = instaseis.open_db(PRIOR['VELOC'])
create = Create_observed(PRIOR, PARAMETERS, db)
d_obs, traces, source = create.get_seis_automatic(prior=PRIOR, noise_model=VALUES['noise'], sdr=VALUES['sdr'])
traces_obs, p_obs, s_obs = create.get_window_obspy(traces, PARAMETERS['epi'], PARAMETERS['depth_s'],
PARAMETERS['origin_time'], VALUES['npts'])
time_at_receiver = create.get_receiver_time(PARAMETERS['epi'], PARAMETERS['depth_s'], traces)
time_between_windows = create.time_between_windows(PARAMETERS['epi'], PARAMETERS['depth_s'], traces[0].meta.delta)