def calculate_statistics(display_data_and_metadata_func,
display_data_range, region,
displayed_intensity_calibration):
display_data_and_metadata = display_data_and_metadata_func()
data = display_data_and_metadata.data if display_data_and_metadata else None
data_range = display_data_range
if data is not None and data.size > 0 and displayed_intensity_calibration:
mean = numpy.mean(data)
std = numpy.std(data)
rms = numpy.sqrt(numpy.mean(numpy.square(
numpy.absolute(data))))
sum_data = mean * functools.reduce(
operator.mul,
Image.dimensional_shape_from_shape_and_dtype(
data.shape, data.dtype))
if region is None:
data_min, data_max = data_range if data_range is not None else (
None, None)
else:
data_min, data_max = numpy.amin(data), numpy.amax(data)
mean_str = displayed_intensity_calibration.convert_to_calibrated_value_str(
mean)
std_str = displayed_intensity_calibration.convert_to_calibrated_value_str(
std)
data_min_str = displayed_intensity_calibration.convert_to_calibrated_value_str(
data_min)
data_max_str = displayed_intensity_calibration.convert_to_calibrated_value_str(
data_max)
rms_str = displayed_intensity_calibration.convert_to_calibrated_value_str(
rms)
sum_data_str = displayed_intensity_calibration.convert_to_calibrated_value_str(
sum_data)
return {
"mean": mean_str,
"std": std_str,
"min": data_min_str,
"max": data_max_str,
"rms": rms_str,
"sum": sum_data_str
}
return dict()
def calculate_statistics(display_data_and_metadata_func: typing.Callable[[], typing.Optional[DataAndMetadata.DataAndMetadata]], display_data_range: typing.Optional[typing.Tuple[float, float]], region: typing.Optional[Graphics.Graphic], displayed_intensity_calibration: typing.Optional[Calibration.Calibration]) -> typing.Dict[str, str]:
display_data_and_metadata = display_data_and_metadata_func()
data = display_data_and_metadata.data if display_data_and_metadata else None
data_range = display_data_range
if data is not None and data.size > 0 and displayed_intensity_calibration:
mean = numpy.mean(data)
std = numpy.std(data)
rms = numpy.sqrt(numpy.mean(numpy.square(numpy.absolute(data))))
dimensional_shape = Image.dimensional_shape_from_shape_and_dtype(data.shape, data.dtype) or (1, 1)
sum_data = mean * functools.reduce(operator.mul, dimensional_shape)
if region is None:
data_min, data_max = data_range if data_range is not None else (None, None)
else:
data_min, data_max = numpy.amin(data), numpy.amax(data)
mean_str = displayed_intensity_calibration.convert_to_calibrated_value_str(mean)
std_str = displayed_intensity_calibration.convert_to_calibrated_value_str(std)
data_min_str = displayed_intensity_calibration.convert_to_calibrated_value_str(data_min) if data_min is not None else str()
data_max_str = displayed_intensity_calibration.convert_to_calibrated_value_str(data_max) if data_max is not None else str()
rms_str = displayed_intensity_calibration.convert_to_calibrated_value_str(rms)
sum_data_str = displayed_intensity_calibration.convert_to_calibrated_value_str(sum_data)
return { "mean": mean_str, "std": std_str, "min": data_min_str, "max": data_max_str, "rms": rms_str, "sum": sum_data_str }
return dict()
