def test_autocal_spec3():
"""Test AutoCalibrator on spectrum 3."""
kernel = bq.GaussianPeakFilter(700, 50, 10)
finder = bq.PeakFinder(spec3, kernel)
cal = bq.AutoCalibrator(finder)
cal.peakfinder.find_peaks(min_snr=3, xmin=100)
assert len(cal.peakfinder.centroids) == 8
# this fit succeeds but misidentifies the lines
cal.fit(
[609.32, 1460.82],
optional=[],
gain_range=[0.1, 5.0],
de_max=50.0,
)
assert len(cal.fit_channels) == 2
assert np.isclose(cal.gain, 2.59, rtol=1e-2)
# this fit correctly identifies the lines
cal.fit(
[609.32, 1460.82],
optional=OPTIONAL,
gain_range=[0.1, 5.0],
de_max=50.0,
)
assert len(cal.fit_channels) == 7
assert np.isclose(cal.gain, 2.02, rtol=1e-2)
def test_autocal_no_fit():
"""Test AutoCalibrator with no valid fit found."""
kernel = bq.GaussianPeakFilter(500, 50, fwhm_at_0=10)
finder = bq.PeakFinder(spec1, kernel)
cal = bq.AutoCalibrator(finder)
finder.find_peaks(min_snr=2, xmin=50)
assert len(cal.peakfinder.centroids) == 8
with pytest.raises(bq.AutoCalibratorError):
cal.fit(
REQUIRED,
optional=OPTIONAL,
gain_range=[1, 2.0],
de_max=20.0,
)
def test_autocal_one_line():
"""Test AutoCalibrator with one line."""
kernel = bq.GaussianPeakFilter(500, 50, fwhm_at_0=10)
finder = bq.PeakFinder(spec1, kernel)
cal = bq.AutoCalibrator(finder)
finder.find_peaks(min_snr=8, xmin=50)
assert len(cal.peakfinder.centroids) == 1
cal.fit(
[1460.82],
gain_range=[2.5, 4.0],
de_max=20.0,
)
assert len(cal.fit_channels) == 1
assert np.isclose(cal.gain, 3.01, rtol=1e-2)
def test_autocal_exceptions():
"""Test AutoCalibrator exceptions."""
kernel = bq.GaussianPeakFilter(500, 50, fwhm_at_0=10)
finder = bq.PeakFinder(spec1, kernel)
# init error
with pytest.raises(bq.AutoCalibratorError):
cal = bq.AutoCalibrator(None)
cal = bq.AutoCalibrator(finder)
# only one peak found, but multiple energies required
finder.find_peaks(min_snr=10, xmin=50)
assert len(cal.peakfinder.centroids) == 1
with pytest.raises(bq.AutoCalibratorError):
cal.fit(
REQUIRED,
gain_range=[2.5, 4.0],
de_max=20.0,
)
# multiple peaks found, but only one energy given
finder.reset()
finder.find_peaks(min_snr=1, xmin=50)
assert len(cal.peakfinder.centroids) == 10
with pytest.raises(bq.AutoCalibratorError):
cal.fit(
[1460.82],
gain_range=[2.5, 4.0],
de_max=20.0,
)
# more energies required than peaks found
finder.reset()
finder.find_peaks(min_snr=4, xmin=50)
assert len(cal.peakfinder.centroids) == 4
with pytest.raises(bq.AutoCalibratorError):
cal.fit(
[238.63, 351.93, 609.32, 1460.82, 2614.3],
gain_range=[2.5, 4.0],
de_max=20.0,
)
def test_autocal_spec4():
"""Test AutoCalibrator on spectrum 4."""
kernel = bq.GaussianPeakFilter(2400, 120, 30)
finder = bq.PeakFinder(spec4, kernel)
cal = bq.AutoCalibrator(finder)
cal.peakfinder.find_peaks(min_snr=3, xmin=100)
assert len(cal.peakfinder.centroids) == 7
cal.fit(
[356.0129, 661.657, 1460.82],
optional=[911.20, 1120.294, 1620.50, 1764.49, 2118.514, 2614.3],
gain_range=[0.3, 0.7],
de_max=100.0,
)
assert len(cal.fit_channels) == 4
assert np.isclose(cal.gain, 0.6133, rtol=1e-2)
def test_autocal_spec2():
"""Test AutoCalibrator on spectrum 2."""
kernel = bq.GaussianPeakFilter(3700, 10, 5)
finder = bq.PeakFinder(spec2, kernel)
cal = bq.AutoCalibrator(finder)
cal.peakfinder.find_peaks(min_snr=20, xmin=1000)
assert len(cal.peakfinder.centroids) == 9
cal.fit(
REQUIRED,
optional=OPTIONAL,
gain_range=[0.1, 0.6],
de_max=5.0,
)
assert len(cal.fit_channels) == 3
assert np.isclose(cal.gain, 0.3785, rtol=1e-2)
def test_autocal_plot():
"""Test AutoCalibrator.plot."""
kernel = bq.GaussianPeakFilter(500, 50, fwhm_at_0=10)
finder = bq.PeakFinder(spec1, kernel)
cal = bq.AutoCalibrator(finder)
finder.find_peaks(min_snr=1, xmin=50)
assert len(cal.peakfinder.centroids) == 10
cal.fit(
REQUIRED,
optional=OPTIONAL,
gain_range=[2.5, 4.0],
de_max=20.0,
)
plt.figure()
cal.plot()
plt.show()
def test_autocal_spec1():
"""Test basic functionality of AutoCalibrator."""
kernel = bq.GaussianPeakFilter(500, 50, fwhm_at_0=10)
finder = bq.PeakFinder(spec1, kernel)
cal = bq.AutoCalibrator(finder)
finder.find_peaks(min_snr=1, xmin=50)
assert len(cal.peakfinder.centroids) == 10
cal.fit(
REQUIRED,
optional=OPTIONAL,
gain_range=[2.5, 4.0],
de_max=25.0,
verbose=True,
)
assert len(cal.fit_channels) == 7
assert np.isclose(cal.gain, 3.01, rtol=1e-2)