def compute_A_b(self, window, win_idx, dsyn):
"""
Calculate the matrix A and vector b based on one pair of
observed data and synthetic data on a given window.
:param window: data and window information
:type window: :class:`pycmt3d.Window`
:param win_idx: window index(a specific window)
:type win_idx: integer
:param dsyn: derivative synthetic data matrix
:type dsyn: numpy.array
:return:
"""
npar = self.config.npar
datalist = window.datalist
obsd = datalist['obsd']
synt = datalist['synt']
npts = min(obsd.stats.npts, synt.stats.npts)
win = [window.win_time[win_idx, 0], window.win_time[win_idx, 1]]
istart = int(max(math.floor(win[0] / obsd.stats.delta), 1))
iend = int(min(math.ceil(win[1] / obsd.stats.delta), npts))
if istart > iend:
raise ValueError("Check window for %s.%s.%s.%s" %
(window.station, window.network,
window.location, window.component))
# station correction
istart_d, iend_d, istart_s, iend_s, nshift, cc, dlnA, cc_amp_value = \
self.apply_station_correction(obsd, synt, istart, iend)
dt_synt = datalist['synt'].stats.delta
dt_obsd = datalist['obsd'].stats.delta
if abs(dt_synt - dt_obsd) > 0.0001:
raise ValueError("Delta in synthetic and observed no the same")
dt = dt_synt
# hanning taper
taper = construct_taper(iend_s - istart_s, taper_type=const.taper_type)
A1 = np.zeros((npar, npar))
b1 = np.zeros(npar)
# compute A and b
for j in range(npar):
for i in range(0, j + 1):
A1[i, j] = np.sum(taper * dsyn[i, istart_s:iend_s] *
dsyn[j, istart_s:iend_s]) * dt
b1[j] = np.sum(
taper * (obsd.data[istart_d:iend_d] -
synt.data[istart_s:iend_s]) *
dsyn[j, istart_s:iend_s]) * dt
for j in range(npar):
for i in range(j + 1, npar):
A1[i, j] = A1[j, i]
return [A1, b1]
def calculate_var_one_trace(self, obsd, synt, win_time):
"""
Calculate the variance reduction on a pair of obsd and
synt and windows
:param obsd: observed data trace
:type obsd: :class:`obspy.core.trace.Trace`
:param synt: synthetic data trace
:type synt: :class:`obspy.core.trace.Trace`
:param win_time: [win_start, win_end]
:type win_time: :class:`list` or :class:`numpy.array`
:return: waveform misfit reduction and observed data energy [v1, d1]
:rtype: [float, float]
"""
num_wins = win_time.shape[0]
v1 = np.zeros(num_wins)
d1 = np.zeros(num_wins)
nshift_array = np.zeros(num_wins)
cc_array = np.zeros(num_wins)
dlnA_array = np.zeros(num_wins)
cc_amp_value_array = np.zeros(num_wins)
npts = min(obsd.stats.npts, synt.stats.npts)
for _win_idx in range(win_time.shape[0]):
tstart = win_time[_win_idx, 0]
tend = win_time[_win_idx, 1]
idx_start = int(max(math.floor(tstart / obsd.stats.delta), 1))
idx_end = int(min(math.ceil(tend / obsd.stats.delta),
obsd.stats.npts))
istart_d, iend_d, istart, iend, nshift, cc, dlnA, cc_amp_value = \
self.apply_station_correction(obsd, synt, idx_start, idx_end)
taper = construct_taper(iend - istart)
v1[_win_idx] = \
np.sum(taper * (synt.data[istart:iend] -
obsd.data[istart_d:iend_d]) ** 2)
d1[_win_idx] = np.sum(taper * obsd.data[istart_d:iend_d] ** 2)
nshift_array[_win_idx] = nshift
cc_array[_win_idx] = cc
dlnA_array[_win_idx] = dlnA
cc_amp_value_array[_win_idx] = cc_amp_value
#print "v1, idx:", v1[_win_idx], istart, iend, istart_d, \
#iend_d, _win_idx, nshift
return [v1, d1, nshift_array, cc_array, dlnA_array,
cc_amp_value_array]