def test_empy_hankel():
# 1. Simple test to compare ['j0', 'j1'] with 'j0' and 'j1'
out1 = fdesign.empy_hankel(['j0', 'j1'], 50, 100, [2e14, 1], 1, 0)
out2 = fdesign.empy_hankel('j0', 50, 100, [2e14, 1], 1, 0)
out3 = fdesign.empy_hankel('j1', 50, 100, [2e14, 1], 1, 0)
assert out1[0].name == out2.name
assert out1[1].name == out3.name
# 2. Check J0, J1 with analytical, wavenumber
zsrc = -50
zrec = 0
r = np.arange(1, 101)
f = 100
model1 = {'res': 100,
'aniso': 2,
'epermH': 15,
'epermV': 30,
'mpermH': 1,
'mpermV': 5}
out4a = fdesign.empy_hankel(['j0', 'j1'], zsrc, zrec, freqtime=f, depth=[],
**model1)
out4b = model.analytical([0, 0, zsrc], [r/np.sqrt(2), r/np.sqrt(2), zrec],
freqtime=f, verb=0, **model1)
out4c = model.analytical([0, 0, zsrc], [r, r*0, zrec], freqtime=f, verb=0,
ab=31, **model1)
out4d, _ = model.dipole_k(src=[0, 0, zsrc],
rec=[1/np.sqrt(2), 1/np.sqrt(2), zrec],
freq=f, depth=[], wavenumber=1/r, **model1)
_, out4e = model.dipole_k(src=[0, 0, zsrc], ab=31, rec=[1, 0, zrec],
freq=f, depth=[], wavenumber=1/r, **model1)
assert_allclose(out4a[0].rhs(r), out4b)
assert_allclose(out4a[1].rhs(r), out4c)
assert_allclose(out4a[0].lhs(1/r), out4d)
assert_allclose(out4a[1].lhs(1/r), out4e)
# 2. Check J2 with dipole, wavenumber
zsrc = 950
zrec = 1000
r = np.arange(1, 101)*20
f = 0.1
model2 = {'depth': [0, 1000],
'res': [2e14, 0.3, 1],
'aniso': [1, 1, 1.5],
'epermH': [1, 15, 1],
'epermV': [1, 1, 30],
'mpermH': [1, 1, 10],
'mpermV': [1, 1, 5]}
out5a = fdesign.empy_hankel('j2', zsrc, zrec, freqtime=f, **model2)
out5b = model.dipole([0, 0, zsrc], [r/np.sqrt(2), r/np.sqrt(2), zrec],
freqtime=f, verb=0, ab=12, **model2)
out5c, out5d = model.dipole_k(src=[0, 0, zsrc],
rec=[1/np.sqrt(2), 1/np.sqrt(2), zrec],
ab=12, freq=f, wavenumber=1/r, **model2)
assert_allclose(out5a.rhs(r), out5b)
assert_allclose(out5a.lhs(1/r)[0], out5c)
assert_allclose(out5a.lhs(1/r)[1], out5d)
def test_call_qc_transform_pairs2(self):
# plot_transform_pair J2
r = np.logspace(1, 2, 50)
fI = (fdesign.j0_1(5), fdesign.j1_1(5))
fC = fdesign.empy_hankel('j2', 950, 1000, 1, 1)
fC.rhs = fC.rhs(r)
fdesign._call_qc_transform_pairs(101, (0.06, 0.07, 0.01), (-1, 1, 0.3),
fI, [fC, ], r, (0, 0, 2), np.imag)
return plt.gcf()
def test_get_min_val(capsys):
# Some parameters
fI0 = fdesign.j0_1(5)
fI1 = fdesign.j1_1(5)
rdef = (1, 1, 2)
error = 0.01
# 1. "Normal" case
r = np.logspace(0, 2, 10)
fC = fdesign.j0_1(5)
fC.rhs = fC.rhs(r)
out = fdesign._get_min_val((0.05, -1.0), 201, [fI0, ], [fC, ], r, rdef,
error, np.real, 'amp', 0, 0, [])
assert_allclose(out, 2.386523e-05, rtol=1e-5)
# 2. "Normal" case j0 and j1; J0 is better than J1
fC1 = fdesign.j1_1(5)
fC1.rhs = fC1.rhs(r)
out = fdesign._get_min_val((0.05, -1.0), 201, [fI1, fI0], [fC1, fC], r,
rdef, error, np.real, 'amp', 0, 0, [])
assert_allclose(out, 2.386523e-05, rtol=1e-5)
# 3. f2
fC2 = fdesign.empy_hankel('j2', 950, 1000, 1, 1)
fC2.rhs = fC2.rhs(r)
out = fdesign._get_min_val((0.05, -1.0), 201, [fI0, fI1], [fC2, ], r, rdef,
error, np.real, 'amp', 0, 0, [])
assert_allclose(out, 6.831394e-08, rtol=1e-5)
# 4. No solution below error
out = fdesign._get_min_val((0.05, -10.0), 201, [fI0, ], [fC, ], r, rdef,
error, np.real, 'amp', 0, 0, [])
assert_allclose(out, np.inf)
# 5. All nan's; with max r
out = fdesign._get_min_val((0.05, 10.0), 201, [fI0, ], [fC, ], r, rdef,
error, np.real, 'r', 0, 0, [])
assert_allclose(out, np.inf)
# 6. r too small, with verbosity
log = {'cnt1': 1, 'cnt2': 9, 'totnr': 10, 'time': default_timer(),
'warn-r': 0}
r = np.logspace(0, 1.1, 10)
fC = fdesign.j0_1(5)
fC.rhs = fC.rhs(r)
out, _ = capsys.readouterr() # empty
fdesign._get_min_val((0.058, -1.26), 201, [fI0, ], [fC, ], r, rdef, error,
np.real, 'amp', 3, 0, log)
out, _ = capsys.readouterr()
assert "* WARNING :: all data have error < "+str(error)+";" in out
assert "brute fct calls : 10" in out
def test_plot_inversion2(self):
# plot_inversion maximum r
f = fdesign.empy_hankel('j2', 50, 100, 1, 1)
filt = filters.key_201_2009()
n = filt.base.size
a = filt.base[-1]
b = filt.base[-2]
spacing = np.log(a) - np.log(b)
shift = np.log(a) - spacing * (n // 2)
cvar = 'r'
r = np.logspace(1, 4.5, 100)
f.rhs = f.rhs(r)
k = filt.base / r[:, None]
lhs = f.lhs(k)
rhs0 = np.dot(lhs[0], filt.j0) / r
rhs1 = np.dot(lhs[1], filt.j1) / r**2
rhs = rhs0 + rhs1
rel_error = np.abs((rhs - f.rhs) / f.rhs)
imin = np.where(rel_error > 0.01)[0][0]
fdesign._plot_inversion(f, rhs, r, k, imin, spacing, shift, cvar)
return plt.gcf()
def test_design():
# 1. General case with various spacing and shifts
fI = (fdesign.j0_1(5), fdesign.j1_1(5))
dat1 = DATA['case1'][()]
_, out1 = fdesign.design(fI=fI, verb=0, plot=0, **dat1[0])
# First value is always the same.
# Second value jumps btw -2.722222 and -0.777778, so we don't check it.
assert_allclose(out1[0][0], dat1[2][0][0])
assert_allclose(out1[1], dat1[2][1], rtol=1e-3)
assert_allclose(out1[2], dat1[2][2])
# np.linalg(.qr) can have roundoff errors which are not deterministic,
# which can yield different results for badly conditioned matrices. This
# only affects the edge-cases, not the best result we are looking for.
# However, we have to limit the following comparison; we check that at
# least 50% are within a relative error of 0.1%.
rate = np.sum(np.abs((out1[3] - dat1[2][3]) / dat1[2][3]) < 1e-3)
assert rate > out1[3].size / 2
# 2. Specific model with only one spacing/shift
dat2 = DATA['case2'][()]
_, out2 = fdesign.design(fI=fI, verb=0, plot=0, **dat2[0])
assert_allclose(out2[0], dat2[2][0])
assert_allclose(out2[1], dat2[2][1], rtol=1e-3)
assert_allclose(out2[2], dat2[2][2])
assert_allclose(out2[3], dat2[2][3], rtol=1e-3)
# 3. Same, with only one transform
dat2b = DATA['case3'][()]
_, out2b = fdesign.design(fI=fI[0], verb=0, plot=0, **dat2b[0])
assert_allclose(out2b[0], dat2b[2][0])
assert_allclose(out2b[1], dat2b[2][1], rtol=1e-3)
assert_allclose(out2b[2], dat2b[2][2])
assert_allclose(out2b[3], dat2b[2][3], rtol=1e-3)
# 4.a Maximize r
dat4 = DATA['case4'][()]
dat4[0]['save'] = True
dat4[0]['name'] = 'tmpfilter'
_, out4 = fdesign.design(fI=fI, verb=0, plot=0, **dat4[0])
assert_allclose(out4[0], dat4[2][0])
assert_allclose(out4[1], dat4[2][1], rtol=1e-3)
assert_allclose(out4[2], dat4[2][2])
assert_allclose(out4[3], dat4[2][3], rtol=1e-3)
# Clean-up # Should be replaced eventually by tmpdir
os.remove('./filters/tmpfilter_base.txt')
os.remove('./filters/tmpfilter_j0.txt')
os.remove('./filters/tmpfilter_j1.txt')
os.remove('./filters/tmpfilter_full.txt')
# 4.b Without full output and all the other default inputs
dat4[0]['full_output'] = False
del dat4[0]['name']
dat4[0]['finish'] = 'Wrong input'
del dat4[0]['r']
dat4[0]['reim'] = np.imag # Set once to imag
fdesign.design(fI=fI, verb=2, plot=0, **dat4[0])
# Clean-up # Should be replaced eventually by tmpdir
os.remove('./filters/dlf_201_base.txt')
os.remove('./filters/dlf_201_j0.txt')
os.remove('./filters/dlf_201_j1.txt')
# 5. j2 for fI
with pytest.raises(ValueError, match="is only implemented for fC"):
fI2 = fdesign.empy_hankel('j2', 0, 50, 100, 1)
fdesign.design(fI=fI2, verb=0, plot=0, **dat4[0])
