def test_tem():
# Just ensure functionality stays the same, with one example.
for i in ['6', '7', '8']: # Signal = 0, 1, -1
res = DATAFEMTEM['out'+i][()]
tEM, _ = tem(**res['inp'])
assert_allclose(tEM, res['EM'])
# Test `xdirect=None` through analytical/dipole-comparison with a simple
# model
# Fullspace model
inp = {'src': [[0, -100], [0, -200], 200],
'rec': [np.arange(1, 11)*500, np.arange(1, 11)*100, 300],
'freqtime': [0.1, 1, 10], 'res': 1}
fEM_fs = analytical(**inp)
# Add two layers
inp['depth'] = [0, 500]
inp['res'] = [10, 1, 30]
fEM_tot1 = dipole(xdirect=False, **inp)
fEM_tot2 = dipole(xdirect=True, **inp)
fEM_secondary = dipole(xdirect=None, **inp)
# `xdirect=False` and `xdirect=True` have to agree
assert_allclose(fEM_tot2, fEM_tot1)
# Total field minus minus direct field equals secondary field
assert_allclose(fEM_tot1 - fEM_fs, fEM_secondary)
def test_analytical():
# 1. fullspace
model = {'src': [500, -100, -200],
'rec': [0, 1000, 200],
'res': 6.71,
'aniso': 1.2,
'freqtime': 40,
'ab': 42,
'verb': 0}
dip_res = dipole(depth=[], **model)
ana_res = analytical(**model)
assert_allclose(dip_res, ana_res)
# \= Check 36/63
model['ab'] = 63
ana_res2 = analytical(**model)
assert_allclose(ana_res.shape, ana_res.shape)
assert np.count_nonzero(ana_res2) == 0
# 2. halfspace
for signal in [0, 1]: # Frequency, Time
model = {'src': [500, -100, 5],
'rec': [0, 1000, 20],
'res': 6.71,
'aniso': 1.2,
'freqtime': 1,
'signal': signal,
'ab': 12,
'verb': 0}
# Check dhs, dsplit, and dtetm
ana_res = analytical(solution='dhs', **model)
res1, res2, res3 = analytical(solution='dsplit', **model)
dTE, dTM, rTE, rTM, air = analytical(solution='dtetm', **model)
model['res'] = [2e14, model['res']]
model['aniso'] = [1, model['aniso']]
dip_res = dipole(depth=0, **model)
# Check dhs, dsplit
assert_allclose(dip_res, ana_res, rtol=1e-3)
assert_allclose(ana_res, res1+res2+res3)
# Check dsplit and dtetm
assert_allclose(res1, dTE+dTM)
assert_allclose(res2, rTE+rTM)
assert_allclose(res3, air)
def test_analytical():
# 1. fullspace
model = {
'src': [500, -100, -200],
'rec': [0, 1000, 200],
'res': 6.71,
'aniso': 1.2,
'freqtime': 40,
'ab': 42,
'verb': 0
}
dip_res = dipole(depth=[], **model)
ana_res = analytical(**model)
assert_allclose(dip_res, ana_res)
# 2. halfspace
model = {
'src': [500, -100, 5],
'rec': [0, 1000, 20],
'res': 6.71,
'aniso': 1.2,
'freqtime': 10,
'signal': 1,
'ab': 12,
'verb': 0
}
# Check dhs, dsplit, and dtetm
ana_res = analytical(solution='dhs', **model)
res1, res2, res3 = analytical(solution='dsplit', **model)
dTE, dTM, rTE, rTM, air = analytical(solution='dtetm', **model)
model['res'] = [2e14, model['res']]
model['aniso'] = [1, model['aniso']]
dip_res = dipole(depth=0, **model)
# Check dhs, dsplit
assert_allclose(dip_res, ana_res, rtol=1e-4)
assert_allclose(ana_res, res1 + res2 + res3)
# Check dsplit and dtetm
assert_allclose(res1, dTE + dTM)
assert_allclose(res2, rTE + rTM)
assert_allclose(res3, air)
'res': [2e14, 1],
'freqtime': 1,
'ab': 11,
'aniso': [1, 2],
'xdirect': False,
'verb': 0,
}
# Used Hankel filter
hfilt = empymod.filters.key_201_2009()
# Calculate analytical solution
resp = empymod.analytical(params['src'],
params['rec'],
params['res'][1],
params['freqtime'],
solution='dhs',
aniso=params['aniso'][1],
ab=params['ab'],
verb=params['verb'])
###############################################################################
# Calculate numerically the model using different Hankel options
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
standard = empymod.dipole(**params, htarg=[hfilt, 0])
laggedco = empymod.dipole(**params, htarg=[hfilt, -1])
spline10 = empymod.dipole(**params, htarg=[hfilt, 10])
spline30 = empymod.dipole(**params, htarg=[hfilt, 30])
splin100 = empymod.dipole(**params, htarg=[hfilt, 100])
###############################################################################
def test_analytical():
# 1. fullspace
model = {'src': [500, -100, -200],
'rec': [0, 1000, 200],
'res': 6.71,
'aniso': 1.2,
'freqtime': 40,
'ab': 42,
'verb': 0}
dip_res = dipole(depth=[], **model)
ana_res = analytical(**model)
assert_allclose(dip_res, ana_res)
# \= Check 36/63
model['ab'] = 63
ana_res2 = analytical(**model)
assert_allclose(ana_res.shape, ana_res.shape)
assert np.count_nonzero(ana_res2) == 0
# 2. halfspace
for signal in [None, 0, 1]: # Frequency, Time
model = {'src': [500, -100, 5],
'rec': [0, 1000, 20],
'res': 6.71,
'aniso': 1.2,
'freqtime': 1,
'signal': signal,
'ab': 12,
'verb': 0}
# Check dhs, dsplit, and dtetm
ana_res = analytical(solution='dhs', **model)
res1, res2, res3 = analytical(solution='dsplit', **model)
dTE, dTM, rTE, rTM, air = analytical(solution='dtetm', **model)
model['res'] = [2e14, model['res']]
model['aniso'] = [1, model['aniso']]
dip_res = dipole(depth=0, **model)
# Check dhs, dsplit
assert_allclose(dip_res, ana_res, rtol=1e-3)
assert_allclose(ana_res, res1+res2+res3)
# Check dsplit and dtetm
assert_allclose(res1, dTE+dTM)
assert_allclose(res2, rTE+rTM)
assert_allclose(res3, air)
# As above, but Laplace domain.
model = {'src': [500, -100, 5],
'rec': [0, 1000, 20],
'res': 6.71,
'aniso': 1.2,
'freqtime': -1,
'signal': None,
'ab': 12,
'verb': 0}
# Check dhs, dsplit, and dtetm
ana_res = analytical(solution='dhs', **model)
res1, res2, res3 = analytical(solution='dsplit', **model)
dTE, dTM, rTE, rTM, air = analytical(solution='dtetm', **model)
model['res'] = [2e14, model['res']]
model['aniso'] = [1, model['aniso']]
dip_res = dipole(depth=0, **model)
# Check dhs, dsplit
assert_allclose(dip_res, ana_res, rtol=1e-3)
assert_allclose(ana_res, res1+res2+res3)
# Check dsplit and dtetm
assert_allclose(res1, dTE+dTM)
assert_allclose(res2, rTE+rTM)
assert_allclose(res3, air)
# 3. Check user-hook for eta/zeta
def func_eta(inp, pdict):
# Dummy function to check if it works.
etaH = pdict['etaH'].real*inp['fact'] + 1j*pdict['etaH'].imag
etaV = pdict['etaV'].real*inp['fact'] + 1j*pdict['etaV'].imag
return etaH, etaV
def func_zeta(inp, pdict):
# Dummy function to check if it works.
etaH = pdict['zetaH']/inp['fact']
etaV = pdict['zetaV']/inp['fact']
return etaH, etaV
model = {'src': [0, 0, 500], 'rec': [500, 0, 600],
'freqtime': [0.1, 1, 10]}
res = 10
fact = 2
eta = {'res': fact*res, 'fact': fact, 'func_eta': func_eta}
zeta = {'res': res, 'fact': fact, 'func_zeta': func_zeta}
# Frequency domain fs
standard = analytical(res=res, **model)
outeta = analytical(res=eta, **model)
assert_allclose(standard, outeta)
outzeta = analytical(res=zeta, mpermH=fact, mpermV=fact, **model)
assert_allclose(standard, outzeta)
# Time domain dhs
standard = analytical(res=res, solution='dhs', signal=0, **model)
outeta = analytical(res=eta, solution='dhs', signal=0, **model)
assert_allclose(standard, outeta)
outzeta = analytical(res=zeta, solution='dhs',
signal=0, mpermH=fact, mpermV=fact, **model)
assert_allclose(standard, outzeta)
'freqtime': freq,
'aniso': aniso,
'ab': ab,
'epermH': perm,
'epermV': perm,
'mpermH': perm,
'mpermV': perm,
'verb': 0
}
###############################################################################
# Calculation
# -----------
# Halfspace
hs = empymod.analytical(**inp, solution='dfs')
hs = hs.reshape(np.shape(rx))
# Fullspace
fs = empymod.analytical(**inp)
fs = fs.reshape(np.shape(rx))
# Relative error (%)
amperr = np.abs((fs.amp - hs.amp)/fs.amp)*100
phaerr = np.abs((np.angle(fs) - np.angle(hs))/np.angle(fs))*100
###############################################################################
# Plot
# ----
fig, axs = plt.subplots(figsize=(10, 4.2), nrows=1, ncols=2)
def plot_t(EM, HS, title, i):
plt.figure(title, figsize=(10, 8))
plt.subplot(i)
plt.semilogx(t, EM)
plt.semilogx(t, HS, '--')
###############################################################################
# Impulse HS
plt.figure('Impulse HS')
i = 330
for ab in all_abs:
i += 1
EM = empymod.dipole(**inpEM, **modHS, ab=ab, signal=0, depth=0)
HS = empymod.analytical(**inpEM, **modFS, solution='dhs', ab=ab, signal=0)
plot_t(EM, HS, 'Impulse HS', i)
plt.suptitle('Impulse HS')
plt.show()
###############################################################################
# Switch-on HS
plt.figure('Switch-on HS')
i = 330
for ab in all_abs:
i += 1
EM = empymod.dipole(**inpEM, **modHS, ab=ab, signal=1, depth=0)
HS = empymod.analytical(**inpEM, **modFS, solution='dhs', ab=ab, signal=1)
plot_t(EM, HS, 'Switch-on HS', i)
plt.suptitle('Switch-on HS')
