def integrate_signal(self, *,
spectrum_xdata: DataAndMetadata.DataAndMetadata,
fit_intervals: typing.Sequence[BackgroundInterval],
signal_interval: BackgroundInterval,
**kwargs) -> typing.Dict:
# set up initial values
subtracted_xdata = Core.calibrated_subtract_spectrum(
spectrum_xdata,
self.__fit_background(spectrum_xdata, fit_intervals,
signal_interval))
assert subtracted_xdata
subtracted_data = subtracted_xdata.data
assert subtracted_data is not None
if spectrum_xdata.is_navigable:
return {
"integrated":
DataAndMetadata.new_data_and_metadata(
numpy.trapz(subtracted_data),
dimensional_calibrations=spectrum_xdata.
navigation_dimensional_calibrations)
}
else:
return {
"integrated_value": numpy.trapz(subtracted_data),
}
def execute(self, eels_spectrum_data_item, background_model,
fit_interval_graphics, **kwargs) -> None:
try:
spectrum_xdata = eels_spectrum_data_item.xdata
assert spectrum_xdata.is_datum_1d
assert spectrum_xdata.datum_dimensional_calibrations[
0].units == "eV"
eels_spectrum_xdata = spectrum_xdata
# fit_interval_graphics.interval returns normalized coordinates. create calibrated intervals.
fit_intervals: typing.List[
BackgroundModel.BackgroundInterval] = list()
for fit_interval_graphic in fit_interval_graphics:
fit_intervals.append(fit_interval_graphic.interval)
fit_minimum = min(
[fit_interval[0] for fit_interval in fit_intervals])
signal_interval = fit_minimum, 1.0
signal_xdata = BackgroundModel.get_calibrated_interval_slice(
eels_spectrum_xdata, signal_interval)
background_xdata = None
subtracted_xdata = None
if background_model._data_structure.entity:
entity_id = background_model._data_structure.entity.entity_type.entity_id
for component in Registry.get_components_by_type(
"background-model"):
if entity_id == component.background_model_id:
fit_result = component.fit_background(
spectrum_xdata=spectrum_xdata,
fit_intervals=fit_intervals,
background_interval=signal_interval)
background_xdata = fit_result["background_model"]
# use 'or' to avoid doing subtraction if subtracted_spectrum already present
subtracted_xdata = fit_result.get(
"subtracted_spectrum",
None) or Core.calibrated_subtract_spectrum(
spectrum_xdata, background_xdata)
if background_xdata is None:
background_xdata = DataAndMetadata.new_data_and_metadata(
numpy.zeros_like(signal_xdata.data),
intensity_calibration=signal_xdata.intensity_calibration,
dimensional_calibrations=signal_xdata.
dimensional_calibrations)
if subtracted_xdata is None:
subtracted_xdata = DataAndMetadata.new_data_and_metadata(
signal_xdata.data,
intensity_calibration=signal_xdata.intensity_calibration,
dimensional_calibrations=signal_xdata.
dimensional_calibrations)
self.__background_xdata = background_xdata
self.__subtracted_xdata = subtracted_xdata
except Exception as e:
import traceback
print(traceback.format_exc())
print(e)
raise
def subtract_background(self, *,
spectrum_xdata: DataAndMetadata.DataAndMetadata,
fit_intervals: typing.Sequence[BackgroundInterval],
**kwargs) -> typing.Dict:
# set up initial values
fit_minimum = min([fit_interval[0] for fit_interval in fit_intervals])
signal_interval = fit_minimum, 1.0
subtracted_xdata = Core.calibrated_subtract_spectrum(
spectrum_xdata,
self.__fit_background(spectrum_xdata, fit_intervals,
signal_interval))
assert subtracted_xdata
return {"subtracted": subtracted_xdata}
def execute(self, eels_spectrum_data_item, zlp_model, **kwargs) -> None:
try:
spectrum_xdata = eels_spectrum_data_item.xdata
assert spectrum_xdata.is_datum_1d
assert spectrum_xdata.datum_dimensional_calibrations[
0].units == "eV"
eels_spectrum_xdata = spectrum_xdata
model_xdata = None
subtracted_xdata = None
if zlp_model._data_structure.entity:
entity_id = zlp_model._data_structure.entity.entity_type.entity_id
for component in Registry.get_components_by_type("zlp-model"):
# print(f"{entity_id=} {component.zero_loss_peak_model_id=}")
if entity_id == component.zero_loss_peak_model_id:
fit_result = component.fit_zero_loss_peak(
spectrum_xdata=spectrum_xdata)
model_xdata = fit_result["zero_loss_peak_model"]
# use 'or' to avoid doing subtraction if subtracted_spectrum already present
subtracted_xdata = fit_result.get(
"subtracted_spectrum",
None) or Core.calibrated_subtract_spectrum(
spectrum_xdata, model_xdata)
if model_xdata is None:
model_xdata = DataAndMetadata.new_data_and_metadata(
numpy.zeros_like(eels_spectrum_xdata.data),
intensity_calibration=eels_spectrum_xdata.
intensity_calibration,
dimensional_calibrations=eels_spectrum_xdata.
dimensional_calibrations)
if subtracted_xdata is None:
subtracted_xdata = DataAndMetadata.new_data_and_metadata(
eels_spectrum_xdata.data,
intensity_calibration=eels_spectrum_xdata.
intensity_calibration,
dimensional_calibrations=eels_spectrum_xdata.
dimensional_calibrations)
self.__model_xdata = model_xdata
self.__subtracted_xdata = subtracted_xdata
except Exception as e:
import traceback
print(traceback.format_exc())
print(e)
raise
