def measurement(source_config, mtype, step, ignore_net, ix_bandpass, bandpass,
step_test, taper_perc, **options):
"""
Get measurements on noise correlation data and synthetics.
options: g_speed,window_params (only needed if
mtype is ln_energy_ratio or enery_diff)
"""
verbose = yaml.safe_load(
open(os.path.join(source_config['project_path'],
'config.yml')))['verbose']
step_n = 'iteration_{}'.format(int(step))
step_dir = os.path.join(source_config['source_path'], step_n)
if step_test:
corr_dir = os.path.join(step_dir, 'obs_slt')
else:
corr_dir = os.path.join(source_config['source_path'],
'observed_correlations')
files = [f for f in os.listdir(corr_dir)]
files = [os.path.join(corr_dir, f) for f in files]
synth_dir = os.path.join(step_dir, 'corr')
columns = [
'sta1', 'sta2', 'lat1', 'lon1', 'lat2', 'lon2', 'dist', 'az', 'baz',
'syn', 'syn_a', 'obs', 'obs_a', 'l2_norm', 'snr', 'snr_a', 'nstack'
]
measurements = pd.DataFrame(columns=columns)
options['window_params']['causal_side'] = True # relic for signal to noise
_options_ac = copy.deepcopy(options)
_options_ac['window_params']['causal_side'] = (
not (options['window_params']['causal_side']))
if files == []:
msg = 'No input found!'
raise ValueError(msg)
for i, f in enumerate(files):
# Read data
try:
tr_o = read(f)[0]
except IOError:
if verbose:
print('\nCould not read data: ' + os.path.basename(f))
continue
# Read synthetics
synth_filename = get_synthetics_filename(os.path.basename(f),
synth_dir,
ignore_network=ignore_net)
if synth_filename is None:
continue
try:
tr_s = read(synth_filename)[0]
except IOError:
if verbose:
print('\nCould not read synthetics: ' + synth_filename)
continue
# Assigning stats to synthetics, cutting them to right length
tr_s.stats.sac = tr_o.stats.sac.copy()
tr_s.data = my_centered(tr_s.data, tr_o.stats.npts)
# Get all the necessary information
info = get_station_info(tr_o.stats)
# Collect the adjoint source
adjoint_source = Stream()
adjoint_source += Trace()
adjoint_source[0].stats.sampling_rate = tr_s.stats.sampling_rate
adjoint_source[0].stats.sac = tr_s.stats.sac.copy()
# Filter
if bandpass is not None:
tr_o.taper(taper_perc / 100.)
tr_o.filter('bandpass',
freqmin=bandpass[0],
freqmax=bandpass[1],
corners=bandpass[2],
zerophase=True)
tr_s.taper(taper_perc / 100.)
tr_s.filter('bandpass',
freqmin=bandpass[0],
freqmax=bandpass[1],
corners=bandpass[2],
zerophase=True)
# Weight observed stack by nstack
tr_o.data /= tr_o.stats.sac.user0
# Take the measurement
func = rm.get_measure_func(mtype)
msr_o = func(tr_o, **options)
msr_s = func(tr_s, **options)
# Get the adjoint source
adjt_func = am.get_adj_func(mtype)
adjt, success = adjt_func(tr_o, tr_s, **options)
if not success:
continue
# timeseries-like measurements:
if mtype in ['square_envelope', 'full_waveform', 'windowed_waveform']:
l2_so = 0.5 * np.sum(np.power((msr_s - msr_o), 2))
snr = snratio(tr_o, **options)
snr_a = snratio(tr_o, **_options_ac)
info.extend([
np.nan, np.nan, np.nan, np.nan, l2_so, snr, snr_a,
tr_o.stats.sac.user0
])
adjoint_source[0].data = adjt
# single value measurements:
else:
if mtype == 'energy_diff':
l2_so = 0.5 * (msr_s - msr_o)**2 / (msr_o)**2
msr = msr_o[0]
msr_a = msr_o[1]
snr = snratio(tr_o, **options)
snr_a = snratio(tr_o, **_options_ac)
l2 = l2_so.sum()
info.extend([
msr_s[0], msr_s[1], msr, msr_a, l2, snr, snr_a,
tr_o.stats.sac.user0
])
adjoint_source += adjoint_source[0].copy()
for ix_branch in range(2):
adjoint_source[ix_branch].data = adjt[ix_branch]
adjoint_source[ix_branch].data *= (
msr_s[ix_branch] -
msr_o[ix_branch]) / msr_o[ix_branch]**2
elif mtype == 'ln_energy_ratio':
l2_so = 0.5 * (msr_s - msr_o)**2
msr = msr_o
snr = snratio(tr_o, **options)
snr_a = snratio(tr_o, **_options_ac)
info.extend([
msr_s, np.nan, msr, np.nan, l2_so, snr, snr_a,
tr_o.stats.sac.user0
])
adjt *= (msr_s - msr_o)
adjoint_source[0].data = adjt
measurements.loc[i] = info
# save the adjoint source
if len(adjoint_source) == 1:
adjt_filename = os.path.basename(synth_filename).rstrip('sac') +\
'{}.sac'.format(ix_bandpass)
adjoint_source[0].write(os.path.join(step_dir, 'adjt',
adjt_filename),
format='SAC')
elif len(adjoint_source) == 2:
for ix_branch, branch in enumerate(['c', 'a']):
adjt_filename = os.path.basename(synth_filename).\
rstrip('sac') + '{}.{}.sac'.format(branch, ix_bandpass)
adjoint_source[ix_branch].write(os.path.join(
step_dir, 'adjt', adjt_filename),
format='SAC')
else:
raise ValueError("Some problem with adjoint sources.")
return measurements
window_params['sep_noise'] = 1.
window_params['win_overlap'] = False
window_params['wtype'] = 'hann'
window_params['causal_side'] = False
window_params['plot'] = False
# *********************************************
# *********************************************
# only for testing the test:
# def l2_simple(tr_1,tr_2):
# mf = np.sum(0.5 * (tr_1.data - tr_2.data) **2)
# adstf = (tr_1.data - tr_2.data)
# return mf,adstf
m_a_options = {'g_speed':g_speed,'window_params':window_params}
m_func = rm.get_measure_func(mtype)
a_func = af.get_adj_func(mtype)
# create observed data, synthetics and perturbation
c_obs = 2 * (np.random.rand(2401,) - 0.5)
c_ini = 2 * (np.random.rand(2401,) - 0.5)
d_c = 2 * (np.random.rand(2401,) - 0.5)
# form traces (measurement script works with obspy trace objects, not pure arrays)
c_obs = Trace(data=c_obs)
c_obs.stats.sampling_rate = 1.0
c_obs.stats.sac = sacdict
c_syn = Trace(data=c_ini)
c_syn.stats.sampling_rate = 1.0
c_syn.stats.sac = sacdict
def measurement(source_config,mtype,step,ignore_network,bandpass,step_test,**options):
"""
Get measurements on noise correlation data and synthetics.
options: g_speed,window_params (only needed if mtype is ln_energy_ratio or enery_diff)
"""
step_n = 'step_{}'.format(int(step))
step_dir = os.path.join(source_config['source_path'],
step_n)
if step_test:
corr_dir = os.path.join(step_dir,'obs_slt')
else:
corr_dir = os.path.join(source_config['source_path'],
'observed_correlations')
files = [f for f in os.listdir(corr_dir) ]
files = [os.path.join(corr_dir,f) for f in files]
synth_dir = os.path.join(step_dir,'corr')
columns = ['sta1','sta2','lat1','lon1','lat2','lon2','dist','az','baz',
'syn','syn_a','obs','obs_a','l2_norm','snr','snr_a','nstack']
measurements = pd.DataFrame(columns=columns)
_options_ac = copy.deepcopy(options)
_options_ac['window_params']['causal_side'] = not(options['window_params']['causal_side'])
# ToDo
if mtype == 'inst_phase':
_opt_inst = copy.deepcopy(options)
if files == []:
msg = 'No input found!'
raise ValueError(msg)
i = 0
with click.progressbar(files,label='Taking measurements...') as bar:
for f in bar:
#======================================================
# Reading
#======================================================
try:
tr_o = read(f)[0]
except:
print('\nCould not read data: '+os.path.basename(f))
i+=1
continue
try:
synth_filename = get_synthetics_filename(os.path.basename(f),
synth_dir,ignore_network=ignore_network)
if synth_filename is None:
continue
#sfile = glob(os.path.join(synth_dir,synth_filename))[0]
#print(synth_filename)
tr_s = read(synth_filename)[0]
except:
print('\nCould not read synthetics: ' + synth_filename)
i+=1
continue
#======================================================
# Filtering
#======================================================
if bandpass is not None:
tr_o.taper(0.05)
tr_o.filter('bandpass',freqmin=bandpass[0],
freqmax=bandpass[1],corners=bandpass[2],
zerophase=True)
tr_s.taper(0.05)
tr_s.filter('bandpass',freqmin=bandpass[0],
freqmax=bandpass[1],corners=bandpass[2],
zerophase=True)
#======================================================
# Assigning stats to synthetics, cutting them to right length
#======================================================
tr_s.stats.sac = tr_o.stats.sac.copy() #ToDo: Give the stats to this thing before!
tr_s.data = my_centered(tr_s.data,tr_o.stats.npts)
# Get all the necessary information
info = get_station_info(tr_o.stats)
#======================================================
# Weight observed stack by nstack
#======================================================
tr_o.data /= tr_o.stats.sac.user0
#======================================================
# Measurement
#======================================================
# Take the measurement
func = rm.get_measure_func(mtype)
# ToDo Change this!!!
if mtype == 'inst_phase':
_opt_inst['corr_syn'] = tr_s
try:
msr = func(tr_o,**_opt_inst)
except:
print("** Could not take measurement")
print(f)
continue
else:
try:
msr_o = func(tr_o,**options)
msr_s = func(tr_s,**options)
except:
print("** Could not take measurement")
print(f)
continue
# timeseries-like measurements:
if mtype in ['envelope','windowed_envelope','waveform',\
'windowed_waveform']:
l2_so = np.trapz(0.5*(msr_s-msr_o)**2) * tr_o.stats.delta
msr = np.nan
snr = np.nan
snr_a = np.nan
# single value measurements:
else:
if mtype == 'energy_diff':
l2_so = 0.5*(msr_s-msr_o)**2
msr = msr_o[0]
msr_a = msr_o[1]
snr = snratio(tr_o,**options)
snr_a = snratio(tr_o,**_options_ac)
l2 = l2_so.sum()/2.
info.extend([msr_s[0],msr_s[1],msr,msr_a,
l2,snr,snr_a,tr_o.stats.sac.user0])
elif mtype == 'ln_energy_ratio':
l2_so = 0.5*(msr_s-msr_o)**2
msr = msr_o
snr = snratio(tr_o,**options)
snr_a = snratio(tr_o,**_options_ac)
info.extend([msr_s,np.nan,msr,np.nan,
l2_so,snr,snr_a,tr_o.stats.sac.user0])
elif mtype == 'inst_phase':
snr = snratio(tr_o,**options)
snr_a = snratio(tr_o,**_options_ac)
info.extend([np.nan,np.nan,np.nan,np.nan,
msr,snr,snr_a,tr_o.stats.sac.user0])
measurements.loc[i] = info
# step index
i+=1
filename = '{}.measurement.csv'.format(mtype)
measurements.to_csv(os.path.join(step_dir,filename),index=None)
# *********************************************
class args(object):
def __init__(self):
self.source_model = os.path.join('test', 'testdata_v1',
'testsource_v1')
self.step = 0
self.steplengthrun = False,
self.ignore_network = True
args = args()
all_config = config_params(args, comm, size, rank)
m_a_options = {'g_speed': g_speed, 'window_params': window_params}
m_func = rm.get_measure_func(mtype)
all_ns = get_ns(all_config)
ntime, n, n_corr, Fs = all_ns
taper = cosine_taper(ntime, p=0.01)
taper[0:ntime // 2] = 1.0
with NoiseSource(source_file) as n:
surf_area = n.surf_area
# sum the dimension that relates to filters
grad = grad.sum(axis=1)
# apply surface elements for integration
for j in range(grad.shape[0]):
grad[j, :, 0] *= surf_area
def measurement(source_config,mtype,step,ignore_network,
bandpass,step_test,taper_perc,**options):
"""
Get measurements on noise correlation data and synthetics.
options: g_speed,window_params (only needed if
mtype is ln_energy_ratio or enery_diff)
"""
step_n = 'step_{}'.format(int(step))
step_dir = os.path.join(source_config['source_path'],
step_n)
if step_test:
corr_dir = os.path.join(step_dir,'obs_slt')
else:
corr_dir = os.path.join(source_config['source_path'],
'observed_correlations')
files = [f for f in os.listdir(corr_dir) ]
files = [os.path.join(corr_dir,f) for f in files]
synth_dir = os.path.join(step_dir,'corr')
columns = ['sta1','sta2','lat1','lon1','lat2','lon2','dist','az','baz',
'syn','syn_a','obs','obs_a','l2_norm','snr','snr_a','nstack']
measurements = pd.DataFrame(columns=columns)
_options_ac = copy.deepcopy(options)
_options_ac['window_params']['causal_side'] = not(options['window_params']['causal_side'])
# ToDo
if mtype == 'inst_phase':
_opt_inst = copy.deepcopy(options)
if files == []:
msg = 'No input found!'
raise ValueError(msg)
i = 0
with click.progressbar(files,label='Taking measurements...') as bar:
for f in bar:
#======================================================
# Reading
#======================================================
try:
tr_o = read(f)[0]
except:
print('\nCould not read data: '+os.path.basename(f))
i+=1
continue
try:
synth_filename = get_synthetics_filename(os.path.basename(f),
synth_dir,ignore_network=ignore_network)
except:
print('\nCould not obtain synthetics filename: ' + \
os.path.basename(f))
i+=1
continue
if synth_filename is None:
continue
#sfile = glob(os.path.join(synth_dir,synth_filename))[0]
#print(synth_filename)
try:
tr_s = read(synth_filename)[0]
except:
print('\nCould not read synthetics: ' + \
synth_filename)
i+=1
continue
#======================================================
# Assigning stats to synthetics, cutting them to right length
#======================================================
tr_s.stats.sac = tr_o.stats.sac.copy() #ToDo: Give the stats to this thing before!
tr_s.data = my_centered(tr_s.data,tr_o.stats.npts)
# Get all the necessary information
info = get_station_info(tr_o.stats)
#======================================================
# Filtering
#======================================================
print(bandpass)
if bandpass != None:
tr_o.taper(taper_perc)
tr_o.filter('bandpass',freqmin=bandpass[0],
freqmax=bandpass[1],corners=bandpass[2],
zerophase=True)
tr_s.taper(taper_perc)
tr_s.filter('bandpass',freqmin=bandpass[0],
freqmax=bandpass[1],corners=bandpass[2],
zerophase=True)
#======================================================
# Weight observed stack by nstack
#======================================================
tr_o.data /= tr_o.stats.sac.user0
#======================================================
# Measurement
#======================================================
# Take the measurement
func = rm.get_measure_func(mtype)
# ToDo Change this!!!
if mtype == 'inst_phase':
_opt_inst['corr_syn'] = tr_s
try:
msr = func(tr_o,**_opt_inst)
except:
print("** Could not take measurement")
print(f)
continue
else:
try:
msr_o = func(tr_o,**options)
msr_s = func(tr_s,**options)
except:
print("** Could not take measurement")
print(f)
continue
# timeseries-like measurements:
if mtype in ['square_envelope',
'waveform','windowed_waveform']:
# l2_so = np.trapz(0.5*(msr_s-msr_o)**2) * tr_o.stats.delta
l2_so = 0.5 * np.sum(np.power((msr_s-msr_o),2))#0.5*np.dot((msr_s-msr_o),(msr_s-msr_o))
snr = snratio(tr_o,**options)
snr_a = snratio(tr_o,**_options_ac)
info.extend([np.nan,np.nan,np.nan,np.nan,
l2_so,snr,snr_a,tr_o.stats.sac.user0])
# single value measurements:
else:
if mtype == 'energy_diff':
l2_so = 0.5*(msr_s-msr_o)**2
msr = msr_o[0]
msr_a = msr_o[1]
snr = snratio(tr_o,**options)
snr_a = snratio(tr_o,**_options_ac)
l2 = l2_so.sum()
info.extend([msr_s[0],msr_s[1],msr,msr_a,
l2,snr,snr_a,tr_o.stats.sac.user0])
elif mtype == 'ln_energy_ratio':
l2_so = 0.5*(msr_s-msr_o)**2
msr = msr_o
snr = snratio(tr_o,**options)
snr_a = snratio(tr_o,**_options_ac)
info.extend([msr_s,np.nan,msr,np.nan,
l2_so,snr,snr_a,tr_o.stats.sac.user0])
elif mtype == 'inst_phase':
snr = snratio(tr_o,**options)
snr_a = snratio(tr_o,**_options_ac)
info.extend([np.nan,np.nan,np.nan,np.nan,
msr,snr,snr_a,tr_o.stats.sac.user0])
measurements.loc[i] = info
# step index
i+=1
return measurements