def test_signal_and_background_shape_are_consistent_1d(self):
calibration = Calibration.Calibration(418.92, 0.97, 'eV')
data_and_metadata = DataAndMetadata.DataAndMetadata.from_data(
numpy.ones((2048, ), numpy.float),
dimensional_calibrations=[calibration])
fit_range = 0.2, 0.3
signal_range = 0.4, 0.5
signal = eels_analysis.extract_original_signal(data_and_metadata,
[fit_range],
signal_range)
background = eels_analysis.calculate_background_signal(
data_and_metadata, [fit_range], signal_range)
self.assertEqual(signal.data_shape, signal.data.shape)
self.assertEqual(background.data_shape, background.data.shape)
def execute(self, eels_xdata, region, fit_interval, signal_interval):
eels_spectrum_xdata = xd.sum_region(
eels_xdata,
region.mask_xdata_with_shape(eels_xdata.data_shape[0:2]))
signal = eels_analysis.make_signal_like(
eels_analysis.extract_original_signal(eels_spectrum_xdata,
[fit_interval],
signal_interval),
eels_spectrum_xdata)
background_xdata = eels_analysis.make_signal_like(
eels_analysis.calculate_background_signal(eels_spectrum_xdata,
[fit_interval],
signal_interval),
eels_spectrum_xdata)
subtracted_xdata = signal - background_xdata
self.__xdata = xd.vstack(
(eels_spectrum_xdata, background_xdata, subtracted_xdata))
def test_make_signal_like_puts_data_in_correct_place(self):
calibration = Calibration.Calibration(200.0, 2.0, 'eV')
spectrum_length = 1000
data_and_metadata = DataAndMetadata.DataAndMetadata.from_data(
numpy.ones((spectrum_length, ), numpy.float),
dimensional_calibrations=[calibration])
signal = eels_analysis.extract_original_signal(data_and_metadata,
[(0.2, 0.3)],
(0.4, 0.5))
signal = DataAndMetadata.DataAndMetadata.from_data(
numpy.ones(300, ), signal.intensity_calibration,
signal.dimensional_calibrations)
expanded = eels_analysis.make_signal_like(signal, data_and_metadata)
self.assertEqual(expanded.dimensional_calibrations[0], calibration)
self.assertTrue(
numpy.array_equal(expanded.data[0:200], numpy.zeros((200, ))))
self.assertTrue(
numpy.array_equal(expanded.data[200:500], numpy.ones((300, ))))
self.assertTrue(
numpy.array_equal(expanded.data[500:1000], numpy.zeros((500, ))))
def test_extracted_signal_has_correct_calibration_and_data(self):
calibration = Calibration.Calibration(200.0, 2.0, 'eV')
spectrum_length = 1000
data_and_metadata = DataAndMetadata.DataAndMetadata.from_data(
(numpy.random.randn(spectrum_length) * 100).astype(numpy.int32),
dimensional_calibrations=[calibration])
signal = eels_analysis.extract_original_signal(data_and_metadata,
[(0.2, 0.3)],
(0.4, 0.5))
self.assertEqual(data_and_metadata.dimensional_calibrations[0],
calibration) # dummy check
self.assertAlmostEqual(
signal.dimensional_calibrations[0].offset,
0.2 * spectrum_length * calibration.scale + calibration.offset)
self.assertAlmostEqual(signal.dimensional_calibrations[0].scale,
calibration.scale)
self.assertEqual(signal.dimensional_calibrations[0].units,
calibration.units)
self.assertTrue(
numpy.array_equal(signal.data, data_and_metadata.data[200:500]))
def execute(self, eels_spectrum_data_item, fit_interval_graphics,
signal_interval_graphic):
eels_spectrum_xdata = eels_spectrum_data_item.xdata
fit_intervals = [
fit_interval_graphic.interval
for fit_interval_graphic in fit_interval_graphics
]
signal_interval = signal_interval_graphic.interval
signal_xdata = eels_analysis.extract_original_signal(
eels_spectrum_xdata, fit_intervals, signal_interval)
self.__background_xdata = eels_analysis.calculate_background_signal(
eels_spectrum_xdata, fit_intervals, signal_interval)
subtracted_xdata = signal_xdata - self.__background_xdata
offset = int(
round((signal_interval[0] - fit_intervals[0][0]) *
eels_spectrum_xdata.data_shape[0]))
length = int(
round((signal_interval[1] - signal_interval[0]) *
eels_spectrum_xdata.data_shape[0]))
self.__subtracted_xdata = subtracted_xdata[offset:offset + length]
def execute(self, eels_xdata, region, fit_interval, signal_interval):
eels_spectrum_xdata = xd.sum_region(
eels_xdata,
region.mask_xdata_with_shape(eels_xdata.data_shape[0:2]))
signal = eels_analysis.make_signal_like(
eels_analysis.extract_original_signal(eels_spectrum_xdata,
[fit_interval],
signal_interval),
eels_spectrum_xdata)
background_xdata = eels_analysis.make_signal_like(
eels_analysis.calculate_background_signal(eels_spectrum_xdata,
[fit_interval],
signal_interval),
eels_spectrum_xdata)
subtracted_xdata = signal - background_xdata
# vstack will return a sequence; convert the sequence to an image
self.__xdata = xd.redimension(
xd.vstack(
(eels_spectrum_xdata, background_xdata, subtracted_xdata)),
DataAndMetadata.DataDescriptor(False, 0, 2))
