def test_load_background(self):
# Background data
dcalib = numpy.array([1, 2, 2, 3, 4, 5, 4, 6, 9], dtype=numpy.uint16)
dcalib.shape += (1, 1, 1, 1)
wl_calib = 400e-9 + numpy.array(range(dcalib.shape[0])) * 10e-9
calib = model.DataArray(dcalib, metadata={model.MD_WL_LIST: wl_calib})
# Give one DA, the correct one, so expect to get it back
out = calibration.get_spectrum_data([calib])
numpy.testing.assert_equal(out, calib)
numpy.testing.assert_almost_equal(out.metadata[model.MD_WL_LIST],
calib.metadata[model.MD_WL_LIST])
# More DataArrays, just to make it slightly harder to find the data
data1 = model.DataArray(numpy.ones((1, 1, 1, 520, 230), dtype=numpy.uint16))
out = calibration.get_spectrum_data([data1, calib])
numpy.testing.assert_equal(out, calib)
numpy.testing.assert_almost_equal(out.metadata[model.MD_WL_LIST],
calib.metadata[model.MD_WL_LIST])
# should also load spectra with more than one points (then return just
# the first point)
dcalibxy = numpy.ones((128, 1, 1, 24, 25), dtype=numpy.uint8)
dcalibxy[:, :, :, :] = range(dcalibxy.shape[-1])
dcalibxy[0, 0, 0, 0, 0] = 0
wl_calibxy = 400e-9 + numpy.array(range(dcalibxy.shape[0])) * 10e-9
calibxy = model.DataArray(dcalibxy, metadata={model.MD_WL_LIST: wl_calibxy})
out = calibration.get_spectrum_data([data1, calibxy])
eout = calibxy[:, 0:1, 0:1, 0:1, (dcalibxy.shape[-1] - 1) // 2] # middle should contain average
eout.shape += (1,)
numpy.testing.assert_equal(out, eout)
numpy.testing.assert_almost_equal(out.metadata[model.MD_WL_LIST],
calibxy.metadata[model.MD_WL_LIST])
def test_spec_calib(self):
"""Test StaticSpectrumStream calibration"""
spec = self._create_spec_data()
specs = stream.StaticSpectrumStream("test", spec)
# Check efficiency compensation
prev_im2d = specs.image.value
dbckg = numpy.ones(spec.shape, dtype=numpy.uint16)
wl_bckg = 400e-9 + numpy.array(range(dbckg.shape[0])) * 10e-9
obckg = model.DataArray(dbckg, metadata={model.MD_WL_LIST: wl_bckg})
bckg = calibration.get_spectrum_data([obckg])
dcalib = numpy.array([1, 1.3, 2, 3.5, 4, 5, 1.3, 6, 9.1],
dtype=numpy.float)
dcalib.shape = (dcalib.shape[0], 1, 1, 1, 1)
wl_calib = 400e-9 + numpy.array(range(dcalib.shape[0])) * 10e-9
calib = model.DataArray(dcalib, metadata={model.MD_WL_LIST: wl_calib})
specs.efficiencyCompensation.value = calib
specs.background.value = bckg
# Control spatial spectrum
im2d = specs.image.value
# Check it's a RGB DataArray
self.assertEqual(im2d.shape, spec.shape[-2:] + (3, ))
self.assertTrue(numpy.any(im2d != prev_im2d))
def test_spec_calib(self):
"""Test StaticSpectrumStream calibration"""
spec = self._create_spec_data()
specs = stream.StaticSpectrumStream("test", spec)
# Check efficiency compensation
prev_im2d = specs.image.value
dbckg = numpy.ones(spec.shape, dtype=numpy.uint16)
wl_bckg = 400e-9 + numpy.array(range(dbckg.shape[0])) * 10e-9
obckg = model.DataArray(dbckg, metadata={model.MD_WL_LIST: wl_bckg})
bckg = calibration.get_spectrum_data([obckg])
dcalib = numpy.array([1, 1.3, 2, 3.5, 4, 5, 1.3, 6, 9.1], dtype=numpy.float)
dcalib.shape = (dcalib.shape[0], 1, 1, 1, 1)
wl_calib = 400e-9 + numpy.array(range(dcalib.shape[0])) * 10e-9
calib = model.DataArray(dcalib, metadata={model.MD_WL_LIST: wl_calib})
specs.efficiencyCompensation.value = calib
specs.background.value = bckg
# Control spatial spectrum
im2d = specs.image.value
# Check it's a RGB DataArray
self.assertEqual(im2d.shape, spec.shape[-2:] + (3,))
self.assertTrue(numpy.any(im2d != prev_im2d))
def test_load_full(self):
"""
Check the whole sequence: saving calibration data to file, loading it
back from file, finding it.
"""
# Background data
dbckg = numpy.array([1, 2, 2, 3, 4, 5, 4, 6, 9], dtype=numpy.uint16)
dbckg.shape += (1, 1, 1, 1)
wl_calib = 400e-9 + numpy.array(range(dbckg.shape[0])) * 10e-9
bckg = model.DataArray(dbckg, metadata={model.MD_WL_LIST: wl_calib})
# Give one DA, the correct one, so expect to get it back
# 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.array(range(dcalib.shape[0])) * 10e-9
calib = model.DataArray(dcalib, metadata={model.MD_WL_LIST: wl_calib})
# More DataArrays, just to make it slightly harder to find the data
data1 = model.DataArray(numpy.ones((1, 1, 1, 520, 230), dtype=numpy.uint16))
data2 = model.DataArray(numpy.zeros((3, 1, 1, 520, 230), dtype=numpy.uint16))
# RGB image
thumb = model.DataArray(numpy.ones((520, 230, 3), dtype=numpy.uint8))
full_coef = [data1, calib, data2]
full_bckg = [data1, bckg, data2]
for fmt in dataio.get_available_formats(os.O_WRONLY):
exporter = dataio.get_converter(fmt)
logging.info("Trying to export/import with %s", fmt)
fn_coef = u"test_spec" + exporter.EXTENSIONS[0]
exporter.export(fn_coef, full_coef, thumb)
fn_bckg = u"test_bckg" + exporter.EXTENSIONS[0]
exporter.export(fn_bckg, full_bckg, thumb)
if fmt in dataio.get_available_formats(os.O_RDONLY):
data_bckg = exporter.read_data(fn_bckg)
ibckg = calibration.get_spectrum_data(data_bckg)
data_coef = exporter.read_data(fn_coef)
icoef = calibration.get_spectrum_efficiency(data_coef)
numpy.testing.assert_equal(icoef, calib)
numpy.testing.assert_almost_equal(icoef.metadata[model.MD_WL_LIST],
calib.metadata[model.MD_WL_LIST])
numpy.testing.assert_equal(ibckg, bckg)
numpy.testing.assert_almost_equal(ibckg.metadata[model.MD_WL_LIST],
bckg.metadata[model.MD_WL_LIST])
try:
os.remove(fn_coef)
except OSError:
logging.exception("Failed to delete the file %s", fn_coef)
try:
os.remove(fn_bckg)
except OSError:
logging.exception("Failed to delete the file %s", fn_bckg)
def test_load_full(self):
"""
Check the whole sequence: saving calibration data to file, loading it
back from file, finding it.
"""
# Background data
dbckg = numpy.array([1, 2, 2, 3, 4, 5, 4, 6, 9], dtype=numpy.uint16)
dbckg.shape += (1, 1, 1, 1)
wl_calib = 400e-9 + numpy.array(range(dbckg.shape[0])) * 10e-9
bckg = model.DataArray(dbckg, metadata={model.MD_WL_LIST: wl_calib})
# Give one DA, the correct one, so expect to get it back
# 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.array(range(dcalib.shape[0])) * 10e-9
calib = model.DataArray(dcalib, metadata={model.MD_WL_LIST: wl_calib})
# More DataArrays, just to make it slightly harder to find the data
data1 = model.DataArray(numpy.ones((1, 1, 1, 520, 230), dtype=numpy.uint16))
data2 = model.DataArray(numpy.zeros((3, 1, 1, 520, 230), dtype=numpy.uint16))
# RGB image
thumb = model.DataArray(numpy.ones((520, 230, 3), dtype=numpy.uint8))
full_coef = [data1, calib, data2]
full_bckg = [data1, bckg, data2]
for fmt in dataio.get_available_formats(os.O_WRONLY):
exporter = dataio.get_converter(fmt)
logging.info("Trying to export/import with %s", fmt)
fn_coef = u"test_spec" + exporter.EXTENSIONS[0]
exporter.export(fn_coef, full_coef, thumb)
fn_bckg = u"test_bckg" + exporter.EXTENSIONS[0]
exporter.export(fn_bckg, full_bckg, thumb)
if fmt in dataio.get_available_formats(os.O_RDONLY):
data_bckg = exporter.read_data(fn_bckg)
ibckg = calibration.get_spectrum_data(data_bckg)
data_coef = exporter.read_data(fn_coef)
icoef = calibration.get_spectrum_efficiency(data_coef)
numpy.testing.assert_equal(icoef, calib)
numpy.testing.assert_almost_equal(icoef.metadata[model.MD_WL_LIST],
calib.metadata[model.MD_WL_LIST])
numpy.testing.assert_equal(ibckg, bckg)
numpy.testing.assert_almost_equal(ibckg.metadata[model.MD_WL_LIST],
bckg.metadata[model.MD_WL_LIST])
try:
os.remove(fn_coef)
except OSError:
logging.exception("Failed to delete the file %s", fn_coef)
try:
os.remove(fn_bckg)
except OSError:
logging.exception("Failed to delete the file %s", fn_bckg)
def test_compensate(self):
"""Test applying efficiency compensation"""
# Spectrum
data = numpy.ones((251, 1, 1, 200, 300), dtype="uint16") + 1
wld = 433e-9 + numpy.array(range(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.array(range(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.array(range(dcalib.shape[0])) * 10e-9
calib = model.DataArray(dcalib, metadata={model.MD_WL_LIST: wl_calib})
compensated = calibration.compensate_spectrum_efficiency(
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(self):
"""Test applying efficiency compensation"""
# Spectrum
data = numpy.ones((251, 1, 1, 200, 300), dtype="uint16") + 1
wld = 433e-9 + numpy.array(range(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.array(range(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.array(range(dcalib.shape[0])) * 10e-9
calib = model.DataArray(dcalib, metadata={model.MD_WL_LIST: wl_calib})
compensated = calibration.compensate_spectrum_efficiency(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)
