def make_subwindows(nr, nwin, st, st0, st1, nsamp):
import numpy as np
from subroutine_CLEAN_3c import tap
xt = np.zeros((3, nr, nwin, nsamp))
for i in range(nr):
for j in range(nwin):
xt[0, i, j] = st[i][j * nsamp / 2:(j + 2) * nsamp / 2]
xt[1, i, j] = st0[i][j * nsamp / 2:(j + 2) * nsamp / 2]
xt[2, i, j] = st1[i][j * nsamp / 2:(j + 2) * nsamp / 2]
xt[0, i, j] -= np.mean(xt[0, i, j])
xt[1, i, j] -= np.mean(xt[1, i, j])
xt[2, i, j] -= np.mean(xt[2, i, j])
xt[0, i, j] = tap(xt[0, i, j], nsamp)
xt[1, i, j] = tap(xt[1, i, j], nsamp)
xt[2, i, j] = tap(xt[2, i, j], nsamp)
return xt
def make_subwindows(nr, nwin, st, st0, st1, nsamp):
import numpy as np
from subroutine_CLEAN_3c import tap
xt = np.zeros((3, nr, nwin, nsamp))
for i in range(nr):
for j in range(nwin):
xt[0, i, j] = st[i][j * nsamp / 2 : (j + 2) * nsamp / 2]
xt[1, i, j] = st0[i][j * nsamp / 2 : (j + 2) * nsamp / 2]
xt[2, i, j] = st1[i][j * nsamp / 2 : (j + 2) * nsamp / 2]
xt[0, i, j] -= np.mean(xt[0, i, j])
xt[1, i, j] -= np.mean(xt[1, i, j])
xt[2, i, j] -= np.mean(xt[2, i, j])
xt[0, i, j] = tap(xt[0, i, j], nsamp)
xt[1, i, j] = tap(xt[1, i, j], nsamp)
xt[2, i, j] = tap(xt[2, i, j], nsamp)
return xt
def make_subwindows_PSAR(nr, nwin, pdic, st, st0, st1, nsamp):
import numpy as np
from subroutine_CLEAN_3c import tap
xt = np.zeros((3, nr, nwin, nsamp))
for i in range(nr):
for j in range(nwin):
theta = pdic[st0[i].stats.station] / 180. * np.pi
xt[0, i, j] = st[i][j * nsamp / 2:(j + 2) * nsamp / 2]
xt[1, i, j] = st0[i][j * nsamp / 2:(j + 2) * nsamp / 2] * np.cos(
theta) - st1[i][j * nsamp / 2:
(j + 2) * nsamp / 2] * np.sin(theta)
xt[2, i, j] = st0[i][j * nsamp / 2:(j + 2) * nsamp / 2] * np.sin(
theta) + st1[i][j * nsamp / 2:
(j + 2) * nsamp / 2] * np.cos(theta)
xt[0, i, j] -= np.mean(xt[0, i, j])
xt[1, i, j] -= np.mean(xt[1, i, j])
xt[2, i, j] -= np.mean(xt[2, i, j])
xt[0, i, j] = tap(xt[0, i, j], nsamp)
xt[1, i, j] = tap(xt[1, i, j], nsamp)
xt[2, i, j] = tap(xt[2, i, j], nsamp)
return xt
def make_subwindows_PSAR(nr, nwin, pdic, st, st0, st1, nsamp):
import numpy as np
from subroutine_CLEAN_3c import tap
xt = np.zeros((3, nr, nwin, nsamp))
for i in range(nr):
for j in range(nwin):
theta = pdic[st0[i].stats.station] / 180.0 * np.pi
xt[0, i, j] = st[i][j * nsamp / 2 : (j + 2) * nsamp / 2]
xt[1, i, j] = st0[i][j * nsamp / 2 : (j + 2) * nsamp / 2] * np.cos(theta) - st1[i][
j * nsamp / 2 : (j + 2) * nsamp / 2
] * np.sin(theta)
xt[2, i, j] = st0[i][j * nsamp / 2 : (j + 2) * nsamp / 2] * np.sin(theta) + st1[i][
j * nsamp / 2 : (j + 2) * nsamp / 2
] * np.cos(theta)
xt[0, i, j] -= np.mean(xt[0, i, j])
xt[1, i, j] -= np.mean(xt[1, i, j])
xt[2, i, j] -= np.mean(xt[2, i, j])
xt[0, i, j] = tap(xt[0, i, j], nsamp)
xt[1, i, j] = tap(xt[1, i, j], nsamp)
xt[2, i, j] = tap(xt[2, i, j], nsamp)
return xt
