def test_get_spline_values(): # 9. get_spline_values
# Check one example
filt = filters.key_81_CosSin_2009()
out, new_inp = transform.get_spline_values(filt, np.arange(1, 6), -1)
# Expected values
oout = np.array([[6.70925256e-05, 8.19469958e-05, 1.00090287e-04,
1.22250552e-04, 1.49317162e-04, 1.82376393e-04,
2.22755030e-04, 2.72073608e-04, 3.32311455e-04,
4.05886127e-04, 4.95750435e-04, 6.05510949e-04,
7.39572743e-04, 9.03316189e-04, 1.10331288e-03,
1.34758940e-03, 1.64594941e-03, 2.01036715e-03,
2.45546798e-03, 2.99911536e-03, 3.66312778e-03,
4.47415437e-03, 5.46474449e-03, 6.67465399e-03,
8.15244080e-03, 9.95741367e-03, 1.21620125e-02,
1.48547156e-02, 1.81435907e-02, 2.21606317e-02,
2.70670566e-02, 3.30597776e-02, 4.03793036e-02,
4.93193928e-02, 6.02388424e-02, 7.35758882e-02,
8.98657928e-02, 1.09762327e-01, 1.34064009e-01,
1.63746151e-01, 2.00000000e-01, 2.44280552e-01,
2.98364940e-01, 3.64423760e-01, 4.45108186e-01,
5.43656366e-01, 6.64023385e-01, 8.11039993e-01,
9.90606485e-01, 1.20992949e+00, 1.47781122e+00,
1.80500270e+00, 2.20463528e+00, 2.69274761e+00,
3.28892935e+00, 4.01710738e+00, 4.90650604e+00,
5.99282001e+00, 7.31964689e+00, 8.94023690e+00,
1.09196300e+01, 1.33372662e+01, 1.62901737e+01,
1.98968631e+01, 2.43020835e+01, 2.96826318e+01,
3.62544484e+01, 4.42812832e+01, 5.40852815e+01,
6.60599120e+01, 8.06857587e+01, 9.85498082e+01,
1.20369008e+02, 1.47019038e+02, 1.79569458e+02,
2.19326632e+02, 2.67886153e+02, 3.27196886e+02,
3.99639179e+02, 4.88120396e+02, 5.96191597e+02,
7.28190061e+02, 8.89413350e+02, 1.08633192e+03,
1.32684880e+03, 1.62061679e+03, 1.97942581e+03,
2.41767615e+03, 2.95295631e+03, 3.60674899e+03]])
onew_inp = np.array([5., 4.09365377, 3.35160023, 2.74405818, 2.24664482,
1.83939721, 1.50597106, 1.23298482, 1.00948259,
0.82649444])
# Comparison
assert_allclose(out, oout)
assert_allclose(new_inp, onew_inp)
# Ensure output dimension
hfilt = filters.anderson_801_1982()
out, _ = transform.get_spline_values(hfilt, np.array([1, 1.1]), -1)
assert_allclose(out.size, 804)
# Check a hypothetical short filter, with small pts_per_dec, and ensure
# at least four points are returned
filt = filters.DigitalFilter('shortest')
filt.base = np.array([1., 1.1])
out, new_inp = transform.get_spline_values(filt, np.array([1.]), 1)
assert_allclose(out.size, 4)
# Check standard example
ffilt = filters.key_81_CosSin_2009()
inp = np.arange(1, 6)
out, new_inp = transform.get_spline_values(ffilt, inp, 0)
assert_allclose(inp, new_inp)
assert_allclose(out, ffilt.base/inp[:, None])
def test_dlf(): # 10. dlf
# DLF is integral of fht and ffht, and therefore tested a lot through
# those. Here we just ensure status quo. And if a problem arises in fht or
# ffht, it would make it obvious if the problem arises from dlf or not.
# Check DLF for Fourier
t = DATA['t'][()]
for i in [0, 1, 2]:
dat = DATA['ffht' + str(i)][()]
tres = DATA['tEM' + str(i)][()]
finp = dat['fEM']
ftarg = dat['ftarg']
if i > 0:
finp /= 2j * np.pi * dat['f']
if i > 1:
finp *= -1
if ftarg[1] == 0:
finp = finp.reshape(t.size, -1)
tEM = transform.dlf(finp,
2 * np.pi * dat['f'],
t,
ftarg[0],
ftarg[1],
kind=ftarg[2])
assert_allclose(tEM * 2 / np.pi, tres, rtol=1e-3)
# Check DLF for Hankel
for ab in [12, 22, 13, 33]:
model = utils.check_model([], 10, 2, 2, 5, 1, 10, True, 0)
depth, res, aniso, epermH, epermV, mpermH, mpermV, isfullspace = model
frequency = utils.check_frequency(1, res, aniso, epermH, epermV,
mpermH, mpermV, 0)
freq, etaH, etaV, zetaH, zetaV = frequency
src = [0, 0, 0]
src, nsrc = utils.check_dipole(src, 'src', 0)
ab, msrc, mrec = utils.check_ab(ab, 0)
ht, htarg = utils.check_hankel('fht', None, 0)
options = utils.check_opt(None, None, ht, htarg, 0)
use_ne_eval, loop_freq, loop_off = options
xdirect = False # Important, as we want to comp. wavenumber-frequency!
rec = [np.arange(1, 11) * 500, np.zeros(10), 300]
rec, nrec = utils.check_dipole(rec, 'rec', 0)
off, angle = utils.get_off_ang(src, rec, nsrc, nrec, 0)
lsrc, zsrc = utils.get_layer_nr(src, depth)
lrec, zrec = utils.get_layer_nr(rec, depth)
fhtfilt = htarg[0]
pts_per_dec = htarg[1]
# # # 0. No Spline # # #
# fht calculation
lambd = fhtfilt.base / off[:, None]
PJ = kernel.wavenumber(zsrc, zrec, lsrc, lrec, depth, etaH, etaV,
zetaH, zetaV, lambd, ab, xdirect, msrc, mrec,
use_ne_eval)
factAng = kernel.angle_factor(angle, ab, msrc, mrec)
# dlf calculation
fEM0 = transform.dlf(PJ,
lambd,
off,
fhtfilt,
0,
factAng=factAng,
ab=ab)
# Analytical frequency-domain solution
freq1 = kernel.fullspace(off, angle, zsrc, zrec, etaH, etaV, zetaH,
zetaV, ab, msrc, mrec)
# Compare
assert_allclose(np.squeeze(fEM0), np.squeeze(freq1))
# # # 1. Spline; One angle # # #
options = utils.check_opt('spline', None, ht, htarg, 0)
use_ne_eval, loop_freq, loop_off = options
# fht calculation
lambd, _ = transform.get_spline_values(fhtfilt, off, pts_per_dec)
PJ1 = kernel.wavenumber(zsrc, zrec, lsrc, lrec, depth, etaH, etaV,
zetaH, zetaV, lambd, ab, xdirect, msrc, mrec,
use_ne_eval)
# dlf calculation
fEM1 = transform.dlf(PJ1,
lambd,
off,
fhtfilt,
pts_per_dec,
factAng=factAng,
ab=ab)
# Compare
assert_allclose(np.squeeze(fEM1), np.squeeze(freq1), rtol=1e-4)
# # # 2.a Lagged; One angle # # #
rec = [np.arange(1, 11) * 500, np.arange(-5, 5) * 0, 300]
rec, nrec = utils.check_dipole(rec, 'rec', 0)
off, angle = utils.get_off_ang(src, rec, nsrc, nrec, 0)
# fht calculation
lambd, _ = transform.get_spline_values(fhtfilt, off, -1)
PJ2 = kernel.wavenumber(zsrc, zrec, lsrc, lrec, depth, etaH, etaV,
zetaH, zetaV, lambd, ab, xdirect, msrc, mrec,
use_ne_eval)
factAng = kernel.angle_factor(angle, ab, msrc, mrec)
# dlf calculation
fEM2 = transform.dlf(PJ2,
lambd,
off,
fhtfilt,
-1,
factAng=factAng,
ab=ab)
# Analytical frequency-domain solution
freq2 = kernel.fullspace(off, angle, zsrc, zrec, etaH, etaV, zetaH,
zetaV, ab, msrc, mrec)
# Compare
assert_allclose(np.squeeze(fEM2), np.squeeze(freq2), rtol=1e-4)
# # # 2.b Lagged; Multi angle # # #
rec = [np.arange(1, 11) * 500, np.arange(-5, 5) * 200, 300]
rec, nrec = utils.check_dipole(rec, 'rec', 0)
off, angle = utils.get_off_ang(src, rec, nsrc, nrec, 0)
# fht calculation
lambd, _ = transform.get_spline_values(fhtfilt, off, -1)
PJ2 = kernel.wavenumber(zsrc, zrec, lsrc, lrec, depth, etaH, etaV,
zetaH, zetaV, lambd, ab, xdirect, msrc, mrec,
use_ne_eval)
factAng = kernel.angle_factor(angle, ab, msrc, mrec)
# dlf calculation
fEM2 = transform.dlf(PJ2,
lambd,
off,
fhtfilt,
-1,
factAng=factAng,
ab=ab)
# Analytical frequency-domain solution
freq2 = kernel.fullspace(off, angle, zsrc, zrec, etaH, etaV, zetaH,
zetaV, ab, msrc, mrec)
# Compare
assert_allclose(np.squeeze(fEM2), np.squeeze(freq2), rtol=1e-4)
# # # 3. Spline; Multi angle # # #
lambd, _ = transform.get_spline_values(fhtfilt, off, 10)
# fht calculation
PJ3 = kernel.wavenumber(zsrc, zrec, lsrc, lrec, depth, etaH, etaV,
zetaH, zetaV, lambd, ab, xdirect, msrc, mrec,
use_ne_eval)
# dlf calculation
fEM3 = transform.dlf(PJ3,
lambd,
off,
fhtfilt,
10,
factAng=factAng,
ab=ab)
# Compare
assert_allclose(np.squeeze(fEM3), np.squeeze(freq2), rtol=1e-3)
def forward(self, m, output_type='response'):
"""
Return Bz or dBzdt
"""
self.model = m
n_frequency = self.survey.n_frequency
flag = self.survey.field_type
n_layer = self.survey.n_layer
depth = self.survey.depth
I = self.survey.I
n_filter = self.n_filter
# Get lambd and offset, will depend on pts_per_dec
if self.survey.src_type == "VMD":
r = self.survey.offset
else:
# a is the radius of the loop
r = self.survey.a * np.ones(n_frequency)
# Use function from empymod
# size of lambd is (n_frequency x n_filter)
lambd = np.empty([self.survey.frequency.size, n_filter], order='F')
lambd[:, :], _ = get_spline_values(self.fhtfilt, r, self.hankel_pts_per_dec)
# lambd, _ = get_spline_values(self.fhtfilt, r, self.hankel_pts_per_dec)
# TODO: potentially store
f = np.empty([self.survey.frequency.size, n_filter], order='F')
f[:,:] = np.tile(self.survey.frequency.reshape([-1, 1]), (1, n_filter))
# h is an inversion parameter
if self.hMap is not None:
h = self.h
else:
h = self.survey.h
z = h + self.survey.dz
chi = self.chi
if np.isscalar(self.chi):
chi = np.ones_like(self.sigma) * self.chi
# TODO: potentially store
sig = self.sigma_cole()
if output_type == 'response':
# for simulation
if self.survey.src_type == 'VMD':
hz = self.hz_kernel_vertical_magnetic_dipole(
lambd, f, n_layer,
sig, chi, depth, h, z,
flag, output_type=output_type
)
# kernels for each bessel function
# (j0, j1, j2)
PJ = (hz, None, None) # PJ0
elif self.survey.src_type == 'CircularLoop':
hz = self.hz_kernel_circular_loop(
lambd, f, n_layer,
sig, chi, depth, h, z, I, r,
flag, output_type=output_type
)
# kernels for each bessel function
# (j0, j1, j2)
PJ = (None, hz, None) # PJ1
# TODO: This has not implemented yet!
elif self.survey.src_type == "piecewise_line":
# Need to compute y
hz = self.hz_kernel_horizontal_electric_dipole(
lambd, f, n_layer,
sig, chi, depth, h, z, I, r,
flag, output_type=output_type
)
# kernels for each bessel function
# (j0, j1, j2)
PJ = (None, hz, None) # PJ1
else:
raise Exception("Src options are only VMD or CircularLoop!!")
elif output_type == 'sensitivity_sigma':
# for simulation
if self.survey.src_type == 'VMD':
hz = self.hz_kernel_vertical_magnetic_dipole(
lambd, f, n_layer,
sig, chi, depth, h, z,
flag, output_type=output_type
)
PJ = (hz, None, None) # PJ0
elif self.survey.src_type == 'CircularLoop':
hz = self.hz_kernel_circular_loop(
lambd, f, n_layer,
sig, chi, depth, h, z, I, r,
flag, output_type=output_type
)
PJ = (None, hz, None) # PJ1
else:
raise Exception("Src options are only VMD or CircularLoop!!")
r = np.tile(r, (n_layer, 1))
elif output_type == 'sensitivity_height':
# for simulation
if self.survey.src_type == 'VMD':
hz = self.hz_kernel_vertical_magnetic_dipole(
lambd, f, n_layer,
sig, chi, depth, h, z,
flag, output_type=output_type
)
PJ = (hz, None, None) # PJ0
elif self.survey.src_type == 'CircularLoop':
hz = self.hz_kernel_circular_loop(
lambd, f, n_layer,
sig, chi, depth, h, z, I, r,
flag, output_type=output_type
)
PJ = (None, hz, None) # PJ1
else:
raise Exception("Src options are only VMD or CircularLoop!!")
# Carry out Hankel DLF
# ab=66 => 33 (vertical magnetic src and rec)
# For response
# HzFHT size = (n_frequency,)
# For sensitivity
# HzFHT size = (n_layer, n_frequency)
HzFHT = dlf(PJ, lambd, r, self.fhtfilt, self.hankel_pts_per_dec,
factAng=None, ab=33)
if output_type == "sensitivity_sigma":
return HzFHT.T
return HzFHT
def forward(self, m, output_type='response'):
"""
Return Bz or dBzdt
"""
self.model = m
n_frequency = self.survey.n_frequency
flag = self.survey.field_type
n_layer = self.survey.n_layer
depth = self.survey.depth
I = self.survey.I
n_filter = self.n_filter
# Get lambd and offset, will depend on pts_per_dec
if self.survey.src_type == "VMD":
r = self.survey.offset
else:
# a is the radius of the loop
r = self.survey.a * np.ones(n_frequency)
# Use function from empymod
# size of lambd is (n_frequency x n_filter)
lambd = np.empty([self.survey.frequency.size, n_filter], order='F')
lambd[:, :], _ = get_spline_values(
self.fhtfilt, r, self.hankel_pts_per_dec
)
# TODO: potentially store
f = np.empty([self.survey.frequency.size, n_filter], order='F')
f[:, :] = np.tile(
self.survey.frequency.reshape([-1, 1]), (1, n_filter)
)
# h is an inversion parameter
if self.hMap is not None:
h = self.h
else:
h = self.survey.h
z = h + self.survey.dz
chi = self.chi
if np.isscalar(self.chi):
chi = np.ones_like(self.sigma) * self.chi
# TODO: potentially store
sig = self.sigma_cole()
if output_type == 'response':
# for simulation
if self.survey.src_type == 'VMD':
hz = self.hz_kernel_vertical_magnetic_dipole(
lambd, f, n_layer,
sig, chi, depth, h, z,
flag, output_type=output_type
)
# kernels for each bessel function
# (j0, j1, j2)
PJ = (hz, None, None) # PJ0
elif self.survey.src_type == 'CircularLoop':
hz = self.hz_kernel_circular_loop(
lambd, f, n_layer,
sig, chi, depth, h, z, I, r,
flag, output_type=output_type
)
# kernels for each bessel function
# (j0, j1, j2)
PJ = (None, hz, None) # PJ1
# TODO: This has not implemented yet!
elif self.survey.src_type == "piecewise_line":
# Need to compute y
hz = self.hz_kernel_horizontal_electric_dipole(
lambd, f, n_layer,
sig, chi, depth, h, z, I, r,
flag, output_type=output_type
)
# kernels for each bessel function
# (j0, j1, j2)
PJ = (None, hz, None) # PJ1
else:
raise Exception("Src options are only VMD or CircularLoop!!")
elif output_type == 'sensitivity_sigma':
# for simulation
if self.survey.src_type == 'VMD':
hz = self.hz_kernel_vertical_magnetic_dipole(
lambd, f, n_layer,
sig, chi, depth, h, z,
flag, output_type=output_type
)
PJ = (hz, None, None) # PJ0
elif self.survey.src_type == 'CircularLoop':
hz = self.hz_kernel_circular_loop(
lambd, f, n_layer,
sig, chi, depth, h, z, I, r,
flag, output_type=output_type
)
PJ = (None, hz, None) # PJ1
else:
raise Exception("Src options are only VMD or CircularLoop!!")
r = np.tile(r, (n_layer, 1))
elif output_type == 'sensitivity_height':
# for simulation
if self.survey.src_type == 'VMD':
hz = self.hz_kernel_vertical_magnetic_dipole(
lambd, f, n_layer,
sig, chi, depth, h, z,
flag, output_type=output_type
)
PJ = (hz, None, None) # PJ0
elif self.survey.src_type == 'CircularLoop':
hz = self.hz_kernel_circular_loop(
lambd, f, n_layer,
sig, chi, depth, h, z, I, r,
flag, output_type=output_type
)
PJ = (None, hz, None) # PJ1
else:
raise Exception("Src options are only VMD or CircularLoop!!")
# Carry out Hankel DLF
# ab=66 => 33 (vertical magnetic src and rec)
# For response
# HzFHT size = (n_frequency,)
# For sensitivity
# HzFHT size = (n_layer, n_frequency)
HzFHT = dlf(PJ, lambd, r, self.fhtfilt, self.hankel_pts_per_dec,
factAng=None, ab=33)
if output_type == "sensitivity_sigma":
return HzFHT.T
return HzFHT
def test_hankel(htype): # 1. fht / 2. hqwe / 3. hquad
# Compare wavenumber-domain calculation / FHT with analytical
# frequency-domain fullspace solution
calc = getattr(transform, htype)
model = utils.check_model([], 10, 2, 2, 5, 1, 10, True, 0)
depth, res, aniso, epermH, epermV, mpermH, mpermV, _ = model
frequency = utils.check_frequency(1, res, aniso, epermH, epermV, mpermH,
mpermV, 0)
_, etaH, etaV, zetaH, zetaV = frequency
src = [0, 0, 0]
src, nsrc = utils.check_dipole(src, 'src', 0)
for ab_inp in [11, 12, 13, 33]:
ab, msrc, mrec = utils.check_ab(ab_inp, 0)
_, htarg = utils.check_hankel(htype, None, 0)
xdirect = False # Important, as we want to compare wavenr-frequency!
rec = [np.arange(1, 11)*500, np.zeros(10), 300]
rec, nrec = utils.check_dipole(rec, 'rec', 0)
off, angle = utils.get_off_ang(src, rec, nsrc, nrec, 0)
factAng = kernel.angle_factor(angle, ab, msrc, mrec)
lsrc, zsrc = utils.get_layer_nr(src, depth)
lrec, zrec = utils.get_layer_nr(rec, depth)
# # # 0. No Spline # # #
if htype != 'hquad': # hquad is always using spline
# Wavenumber solution plus transform
# Adjust htarg for fht
if htype == 'fht':
lambd, int_pts = transform.get_spline_values(htarg[0], off,
htarg[1])
htarg = (htarg[0], htarg[1], lambd, int_pts)
wvnr0, _, conv = calc(zsrc, zrec, lsrc, lrec, off, factAng, depth,
ab, etaH, etaV, zetaH, zetaV, xdirect, htarg,
False, msrc, mrec)
# Analytical frequency-domain solution
freq0 = kernel.fullspace(off, angle, zsrc, zrec, etaH, etaV, zetaH,
zetaV, ab, msrc, mrec)
# Compare
assert_allclose(conv, True)
assert_allclose(np.squeeze(wvnr0), np.squeeze(freq0))
# # # 1. Spline; One angle # # #
htarg, _ = utils.spline_backwards_hankel(htype, None, 'spline')
_, htarg = utils.check_hankel(htype, htarg, 0)
if htype == 'hquad': # Lower atol to ensure convergence
_, htarg = utils.check_hankel('quad', [1e-8], 0)
elif htype == 'fht': # Adjust htarg for fht
lambd, int_pts = transform.get_spline_values(htarg[0], off,
htarg[1])
htarg = (htarg[0], htarg[1], lambd, int_pts)
# Wavenumber solution plus transform
wvnr1, _, conv = calc(zsrc, zrec, lsrc, lrec, off, factAng, depth, ab,
etaH, etaV, zetaH, zetaV, xdirect, htarg, False,
msrc, mrec)
# Analytical frequency-domain solution
freq1 = kernel.fullspace(off, angle, zsrc, zrec, etaH, etaV, zetaH,
zetaV, ab, msrc, mrec)
# Compare
if htype == 'hqwe' and ab in [13, 33]:
assert_allclose(conv, False)
else:
assert_allclose(conv, True)
assert_allclose(np.squeeze(wvnr1), np.squeeze(freq1), rtol=1e-4)
# # # 2. Spline; Multi angle # # #
rec = [np.arange(1, 11)*500, np.arange(-5, 5)*200, 300]
rec, nrec = utils.check_dipole(rec, 'rec', 0)
off, angle = utils.get_off_ang(src, rec, nsrc, nrec, 0)
factAng = kernel.angle_factor(angle, ab, msrc, mrec)
if htype == 'hqwe': # Put a very low diff_quad, to test it.; lower err
_, htarg = utils.check_hankel('qwe', [1e-8, '', '', 200, 80, .1,
1e-6, .1, 1000], 0)
elif htype == 'fht': # Adjust htarg for fht
lambd, int_pts = transform.get_spline_values(htarg[0], off,
htarg[1])
htarg = (htarg[0], htarg[1], lambd, int_pts)
# Analytical frequency-domain solution
wvnr2, _, conv = calc(zsrc, zrec, lsrc, lrec, off, factAng, depth, ab,
etaH, etaV, zetaH, zetaV, xdirect, htarg, False,
msrc, mrec)
# Analytical frequency-domain solution
freq2 = kernel.fullspace(off, angle, zsrc, zrec, etaH, etaV, zetaH,
zetaV, ab, msrc, mrec)
# Compare
assert_allclose(conv, True)
assert_allclose(np.squeeze(wvnr2), np.squeeze(freq2), rtol=1e-4)
# # # 3. Spline; pts_per_dec # # #
if htype == 'fht':
_, htarg = utils.check_hankel('fht', ['key_201_2012', 20], 0)
lambd, int_pts = transform.get_spline_values(htarg[0], off,
htarg[1])
htarg = (htarg[0], htarg[1], lambd, int_pts)
elif htype == 'hqwe':
_, htarg = utils.check_hankel('qwe', ['', '', '', 80, 100], 0)
if htype != 'hquad': # hquad is always pts_per_dec
# Analytical frequency-domain solution
wvnr3, _, conv = calc(zsrc, zrec, lsrc, lrec, off, factAng, depth,
ab, etaH, etaV, zetaH, zetaV, xdirect, htarg,
False, msrc, mrec)
# Analytical frequency-domain solution
freq3 = kernel.fullspace(off, angle, zsrc, zrec, etaH, etaV, zetaH,
zetaV, ab, msrc, mrec)
# Compare
assert_allclose(conv, True)
assert_allclose(np.squeeze(wvnr3), np.squeeze(freq3), rtol=1e-4)
# # # 4. Spline; Only one offset # # #
rec = [5000, 0, 300]
rec, nrec = utils.check_dipole(rec, 'rec', 0)
off, angle = utils.get_off_ang(src, rec, nsrc, nrec, 0)
factAng = kernel.angle_factor(angle, ab, msrc, mrec)
if htype == 'hqwe':
_, htarg = utils.check_hankel('qwe', ['', '', '', 200, 80], 0)
elif htype == 'hquad':
_, htarg = utils.check_hankel('quad', None, 0)
elif htype == 'fht':
lambd, int_pts = transform.get_spline_values(htarg[0], off,
htarg[1])
htarg = (htarg[0], htarg[1], lambd, int_pts)
# Analytical frequency-domain solution
wvnr4, _, conv = calc(zsrc, zrec, lsrc, lrec, off, factAng, depth, ab,
etaH, etaV, zetaH, zetaV, xdirect, htarg, False,
msrc, mrec)
# Analytical frequency-domain solution
freq4 = kernel.fullspace(off, angle, zsrc, zrec, etaH, etaV, zetaH,
zetaV, ab, msrc, mrec)
# Compare
assert_allclose(conv, True)
assert_allclose(np.squeeze(wvnr4), np.squeeze(freq4), rtol=1e-4)
def setup(self, size):
# One big, one small model
if size == 'Small': # Total size: 5*1*1*1 = 5
off = np.array([500., 1000.])
else: # Total size: 5*100*1*201 = 100'500
off = np.arange(1, 101)*200.
# Define survey
freq = np.array([1])
lsrc = 1
lrec = 1
angle = np.zeros(off.shape)
ab = 11
msrc = False
mrec = False
if VERSION2:
zsrc = 250.
zrec = 300.
else:
zsrc = np.array([250.]) # Not sure if this distinction
zrec = np.array([300.]) # is actually needed
use_ne_eval = False
# Define model
depth = np.array([-np.infty, 0, 300, 2000, 2100])
res = np.array([2e14, .3, 1, 50, 1])
aniso = np.ones(res.shape)
epermH = np.ones(res.shape)
epermV = np.ones(res.shape)
mpermH = np.ones(res.shape)
mpermV = np.ones(res.shape)
# Other parameters
xdirect = False
verb = 0
# Compute eta, zeta
etaH = 1/res + np.outer(2j*np.pi*freq, epermH*epsilon_0)
etaV = 1/(res*aniso*aniso) + np.outer(2j*np.pi*freq, epermV*epsilon_0)
zetaH = np.outer(2j*np.pi*freq, mpermH*mu_0)
zetaV = np.outer(2j*np.pi*freq, mpermV*mu_0)
# Collect input
self.hankel = {'zsrc': zsrc, 'zrec': zrec, 'lsrc': lsrc, 'lrec': lrec,
'off': off, 'depth': depth, 'ab': ab, 'etaH': etaH,
'etaV': etaV, 'zetaH': zetaH, 'zetaV': zetaV, 'xdirect':
xdirect, 'msrc': msrc, 'mrec': mrec}
if not VERSION2:
self.hankel['use_ne_eval'] = use_ne_eval
# Before c73d6647; you had to give `ab` to `check_hankel`;
# check_opt didn't exist then.
if VERSION2:
charg = (verb, )
new_version = True
else:
try:
opt = utils.check_opt(None, None, 'fht', ['', 0], verb)
charg = (verb, )
if np.size(opt) == 4:
new_version = False
else:
new_version = True
except VariableCatch:
new_version = False
charg = (ab, verb)
# From 9bed72b0 onwards, there is no `use_spline`; `htarg` input
# changed (29/04/2018; before v1.4.1).
if new_version:
if VERSION2:
htarg = {'dlf': 'key_201_2009', 'pts_per_dec': -1}
else:
htarg = ['key_201_2009', -1]
else:
htarg = ['key_201_2009', None]
# HT arguments
if VERSION2:
dlfargname = 'htarg'
qweargname = 'htarg'
quadargname = 'htarg'
htarg1 = {'dlf': 'key_201_2009', 'pts_per_dec': 0}
htarg2 = {'dlf': 'key_201_2009', 'pts_per_dec': 10}
name = 'dlf'
else:
dlfargname = 'fhtarg'
qweargname = 'qweargs'
quadargname = 'quadargs'
htarg1 = ['key_201_2009', 0]
htarg2 = ['key_201_2009', 10]
name = 'fht'
_, fhtarg_st = utils.check_hankel(name, htarg1, *charg)
self.fhtarg_st = {dlfargname: fhtarg_st}
_, fhtarg_sp = utils.check_hankel(name, htarg2, *charg)
self.fhtarg_sp = {dlfargname: fhtarg_sp}
_, fhtarg_la = utils.check_hankel(name, htarg, *charg)
self.fhtarg_la = {dlfargname: fhtarg_la}
# QWE: We lower the requirements here, otherwise it takes too long
# ['rtol', 'atol', 'nquad', 'maxint', 'pts_per_dec', 'diff_quad', 'a',
# 'b', 'limit']
# Args depend if QUAD included into QWE or not
try:
if VERSION2:
args_sp = {'atol': 1e-6, 'rtol': 1e-10, 'nquad': 51,
'maxint': 100, 'pts_per_dec': 10,
'diff_quad': np.inf}
args_st = {'atol': 1e-6, 'rtol': 1e-10, 'nquad': 51,
'maxint': 100, 'pts_per_dec': 0,
'diff_quad': np.inf}
else:
args_sp = [1e-6, 1e-10, 51, 100, 10, np.inf]
args_st = [1e-6, 1e-10, 51, 100, 0, np.inf]
_, qwearg_sp = utils.check_hankel('qwe', args_sp, *charg)
_, qwearg_st = utils.check_hankel('qwe', args_st, *charg)
except VariableCatch:
args_sp = [1e-6, 1e-10, 51, 100, 10]
args_st = [1e-6, 1e-10, 51, 100, 0]
_, qwearg_sp = utils.check_hankel('qwe', args_sp, *charg)
_, qwearg_st = utils.check_hankel('qwe', args_st, *charg)
self.qwearg_st = {qweargname: qwearg_st}
self.qwearg_sp = {qweargname: qwearg_sp}
# QUAD: We lower the requirements here, otherwise it takes too long
# ['rtol', 'atol', 'limit', 'a', 'b', 'pts_per_dec']
if VERSION2:
args = {'atol': 1e-6, 'rtol': 1e-10, 'limit': 100, 'a': 1e-6,
'b': 0.1, 'pts_per_dec': 10}
else:
args = [1e-6, 1e-10, 100, 1e-6, 0.1, 10]
try: # QUAD only included since 6104614e (before v1.3.0)
_, quadargs = utils.check_hankel('quad', args, *charg)
self.quadargs = {quadargname: quadargs}
except VariableCatch:
self.quadargs = {}
if not new_version and not VERSION2:
self.fhtarg_la.update({'use_spline': True})
self.fhtarg_sp.update({'use_spline': True})
self.fhtarg_st.update({'use_spline': False})
self.qwearg_sp.update({'use_spline': True})
self.qwearg_st.update({'use_spline': False})
self.quadargs.update({'use_spline': True})
if VERSION2:
self.hankel['ang_fact'] = kernel.angle_factor(
angle, ab, msrc, mrec)
else:
# From bb6447a onwards ht-transforms take `factAng`, not `angle`,
# to avoid re-calculation in loops.
try:
transform.fht(angle=angle, **self.fhtarg_la, **self.hankel)
self.hankel['angle'] = angle
except VariableCatch:
self.hankel['factAng'] = kernel.angle_factor(
angle, ab, msrc, mrec)
if not VERSION2:
# From b6f6872 onwards fht-transforms calculates lambd/int_pts in
# model.fem, not in transform.fht, to avoid re-calculation in
# loops.
try:
transform.fht(**self.fhtarg_la, **self.hankel)
except VariableCatch:
lambd, int_pts = transform.get_spline_values(
fhtarg_st[0], off, fhtarg_st[1])
self.fhtarg_st.update({'fhtarg': (
fhtarg_st[0], fhtarg_st[1], lambd, int_pts)})
lambd, int_pts = transform.get_spline_values(
fhtarg_la[0], off, fhtarg_la[1])
self.fhtarg_la.update(
{'fhtarg':
(fhtarg_la[0], fhtarg_la[1], lambd, int_pts)})
lambd, int_pts = transform.get_spline_values(
fhtarg_sp[0], off, fhtarg_sp[1])
self.fhtarg_sp.update(
{'fhtarg':
(fhtarg_sp[0], fhtarg_sp[1], lambd, int_pts)})
def setup_cache(self):
"""setup_cache is not parametrized, so we do it manually. """
data = {}
for size in self.params[0]: # size
data[size] = {}
# One big, one small model
if size == 'Small': # Small; Total size: 5*1*1*1 = 5
x = np.array([500., 1000.])
else: # Big; Total size: 5*100*100*201 = 10'050'000
x = np.arange(1, 101)*200.
# Define model parameters
freq = np.array([1])
src = [0, 0, 250]
rec = [x, np.zeros(x.shape), 300]
depth = np.array([-np.infty, 0, 300, 2000, 2100])
res = np.array([2e14, .3, 1, 50, 1])
ab = 11
xdirect = False
verb = 0
if not VERSION2:
use_ne_eval = False
# Checks (since DLF exists the `utils`-checks haven't changed, so
# we just use them here.
model = utils.check_model(depth, res, None, None, None, None, None,
xdirect, verb)
depth, res, aniso, epermH, epermV, mpermH, mpermV, _ = model
frequency = utils.check_frequency(freq, res, aniso, epermH, epermV,
mpermH, mpermV, verb)
freq, etaH, etaV, zetaH, zetaV = frequency
ab, msrc, mrec = utils.check_ab(ab, verb)
src, nsrc = utils.check_dipole(src, 'src', verb)
rec, nrec = utils.check_dipole(rec, 'rec', verb)
off, angle = utils.get_off_ang(src, rec, nsrc, nrec, verb)
lsrc, zsrc = utils.get_layer_nr(src, depth)
lrec, zrec = utils.get_layer_nr(rec, depth)
for htype in self.params[1]: # htype
# pts_per_dec depending on htype
if htype == 'Standard':
pts_per_dec = 0
elif htype == 'Lagged':
pts_per_dec = -1
else:
pts_per_dec = 10
# Compute kernels for dlf
if VERSION2:
# HT arguments
_, fhtarg = utils.check_hankel(
'dlf',
{'dlf': 'key_201_2009',
'pts_per_dec': pts_per_dec},
0)
inp = (fhtarg['dlf'], off, fhtarg['pts_per_dec'])
lambd, _ = transform.get_dlf_points(*inp)
else:
# HT arguments
_, fhtarg = utils.check_hankel(
'fht', ['key_201_2009', pts_per_dec], 0)
inp = (fhtarg[0], off, fhtarg[1])
lambd, _ = transform.get_spline_values(*inp)
if VERSION2:
inp = (zsrc, zrec, lsrc, lrec, depth, etaH, etaV, zetaH,
zetaV, lambd, ab, xdirect, msrc, mrec)
else:
inp = (zsrc, zrec, lsrc, lrec, depth, etaH,
etaV, zetaH, zetaV, lambd, ab, xdirect,
msrc, mrec, use_ne_eval)
PJ = kernel.wavenumber(*inp)
factAng = kernel.angle_factor(angle, ab, msrc, mrec)
# Signature changed at commit a15af07 (20/05/2018; before
# v1.6.2)
try:
dlf = {'signal': PJ, 'points': lambd, 'out_pts': off,
'ab': ab}
if VERSION2:
dlf['ang_fact'] = factAng
dlf['filt'] = fhtarg['dlf']
dlf['pts_per_dec'] = fhtarg['pts_per_dec']
else:
dlf['factAng'] = factAng
dlf['filt'] = fhtarg[0]
dlf['pts_per_dec'] = fhtarg[1]
transform.dlf(**dlf)
except VariableCatch:
dlf = {'signal': PJ, 'points': lambd, 'out_pts': off,
'targ': fhtarg, 'factAng': factAng}
data[size][htype] = dlf
return data
