def test_compensate(self):
"""Test applying efficiency compensation"""
# Spectrum
data = numpy.ones((251, 1, 1, 200, 300), dtype="uint16") + 1
wld = 433e-9 + numpy.arange(data.shape[0]) * 0.1e-9
spec = model.DataArray(data, metadata={model.MD_WL_LIST: wld})
# Background data
dbckg = numpy.ones(data.shape, dtype=numpy.uint16)
wl_bckg = 400e-9 + numpy.arange(dbckg.shape[0]) * 10e-9
obckg = model.DataArray(dbckg, metadata={model.MD_WL_LIST: wl_bckg})
bckg = calibration.get_spectrum_data([obckg])
# Compensation data
dcalib = numpy.array([1, 1.3, 2, 3.5, 4, 5, 0.1, 6, 9.1],
dtype=numpy.float)
dcalib.shape = (dcalib.shape[0], 1, 1, 1, 1)
wl_calib = 400e-9 + numpy.arange(dcalib.shape[0]) * 10e-9
calib = model.DataArray(dcalib, metadata={model.MD_WL_LIST: wl_calib})
compensated = calibration.apply_spectrum_corrections(spec, bckg, calib)
self.assertEqual(spec.shape, compensated.shape)
numpy.testing.assert_equal(spec.metadata[model.MD_WL_LIST],
compensated.metadata[model.MD_WL_LIST])
for i in range(dcalib.shape[0] - 1):
ca, cb = calib[i], calib[i + 1]
wla, wlb = wl_calib[i], wl_calib[i + 1]
# All the values between the 2 wavelengths should be compensated
# between the 2 factors
for vo, vb, vc, wl in zip(spec[..., 3, 3], bckg[..., 0, 0],
compensated[..., 3, 3], wld):
if wla <= wl <= wlb:
expa, expb = (vo - vb) * ca, (vo - vb) * cb
minc, maxc = min(expa, expb), max(expa, expb)
self.assertTrue(minc <= vc <= maxc)
def test_compensate_out(self):
"""Test applying efficiency compensation on an edge of calibration"""
# Spectrum
data = numpy.ones((251, 1, 1, 200, 300), dtype="uint16")
wld = 333e-9 + numpy.arange(data.shape[0]) * 0.1e-9
spec = model.DataArray(data, metadata={model.MD_WL_LIST: wld})
# Only from 400 nm => need to use the border (=1) for everything below
dcalib = numpy.array([1, 1, 2, 3, 4, 5, 1, 6, 9], dtype=numpy.float)
dcalib.shape = (dcalib.shape[0], 1, 1, 1, 1)
wl_calib = 400e-9 + numpy.arange(dcalib.shape[0]) * 10e-9
calib = model.DataArray(dcalib, metadata={model.MD_WL_LIST: wl_calib})
compensated = calibration.apply_spectrum_corrections(spec, coef=calib)
self.assertEqual(spec.shape, compensated.shape)
numpy.testing.assert_equal(spec.metadata[model.MD_WL_LIST],
compensated.metadata[model.MD_WL_LIST])
# Value before the first calibration wavelength must be estimated
for vo, vc, wl in zip(spec[..., 3, 3], compensated[..., 3, 3], wld):
if wl <= wl_calib[0]:
self.assertEqual(vo * dcalib[0], vc)
def _updateCalibratedData(self, bckg=None, coef=None):
"""
Try to update the data with a new calibration. The two parameters are
the same as apply_spectrum_corrections(). The input data comes from
.raw and the calibrated data is saved in .calibrated
:param bckg: (DataArray or None) The background image.
:param coef: (DataArray or None) The spectrum efficiency correction data.
:raise ValueError: If the data and calibration data are not valid or
compatible. In that case the current calibrated data is unchanged.
"""
data = self.raw[0] # only one image in .raw for spectrum, temporal spectrum and chronograph
if data is None:
self.calibrated.value = None
return
if bckg is None and coef is None:
# make sure to not display any other error
self.calibrated.value = data
return
calibrated = calibration.apply_spectrum_corrections(data, bckg, coef)
self.calibrated.value = calibrated