def test_plot_result1(self):
# Quick run `design` with all verb/plot on, just to check that no
# errors occur. Actually plots are checked in test below and the other
# tests.
dat2 = DATA['case2'][()]
fdesign.design(fI=fdesign.j0_1(5), verb=2, plot=2, **dat2[0])
# plot_result for min amplitude
dat1 = DATA['case1'][()]
fdesign.plot_result(dat1[1], dat1[2], prntres=True)
return plt.gcf()
def test_design(self, capsys):
# Check it doesn't fail, message is correct, and input doesn't matter
# Same test as first in test_design
fI = (fdesign.j0_1(5), fdesign.j1_1(5))
dat1 = DATA['case1'][()]
_, _ = fdesign.design(fI=fI, verb=1, plot=2, **dat1[0])
out, _ = capsys.readouterr()
assert "* WARNING :: `matplotlib` is not installed, no " in out
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])
# Define main model
inp1 = {'spacing': (0.04, 0.1, 10),
'shift': (-3, -0.5, 10),
'n': 201,
'cvar': 'amp',
'save': False,
'full_output': True,
'r': np.logspace(0, 3, 10),
'r_def': (1, 1, 2),
'name': 'test',
'finish': None,
}
# 1. General case with various spacing and shifts
filt1, out1 = fdesign.design(verb=0, plot=0, fI=fI, **inp1)
case1 = (inp1, filt1, out1)
# 2. Specific model with only one spacing/shift
inp2 = dc(inp1)
inp2['spacing'] = 0.0641
inp2['shift'] = -1.2847
filt2, out2 = fdesign.design(verb=0, plot=0, fI=fI, **inp2)
case2 = (inp2, filt2, out2)
# 3 Same, with only one transform
filt3, out3 = fdesign.design(verb=0, plot=0, fI=fI[0], **inp2)
case3 = (inp2, filt3, out3)
# 4. Maximize r
inp4 = dc(inp2)
