def test_compare_hdf5_result_with_raw_result(lt_ctx, hdf5_same_data_4d,
raw_same_dataset_4d):
ds_hdf5 = lt_ctx.load(
"hdf5",
path=hdf5_same_data_4d.filename,
ds_path="data",
)
analysis_hdf5 = DiskMaskAnalysis(dataset=ds_hdf5,
parameters={
"cx": 13,
"cy": 13,
"r": 13
})
result_hdf5 = lt_ctx.run(analysis_hdf5)
result_hdf5 = result_hdf5['intensity'].raw_data
analysis_raw = DiskMaskAnalysis(dataset=raw_same_dataset_4d,
parameters={
"cx": 13,
"cy": 13,
"r": 13
})
result_raw = lt_ctx.run(analysis_raw)
result_raw = result_raw['intensity'].raw_data
assert not np.any(np.abs(result_hdf5 - result_raw))
def create_disk_analysis(self,
dataset: DataSet,
cx: int = None,
cy: int = None,
r: int = None) -> DiskMaskAnalysis:
"""
Create an Analysis that integrates over a disk (i.e. filled circle).
Parameters
----------
dataset
the dataset to work on
cx
center x value
cy
center y value
r
radius of the disk
Returns
-------
DiskMaskAnalysis : libertem.analysis.base.Analysis
When run by the Context, this Analysis generates a
:class:`libertem.analysis.masks.SingleMaskResultSet`.
"""
if dataset.shape.sig.dims != 2:
raise ValueError(
"incompatible dataset: need two signal dimensions")
loc = locals()
parameters = {
name: loc[name]
for name in ['cx', 'cy', 'r'] if loc[name] is not None
}
return DiskMaskAnalysis(dataset=dataset, parameters=parameters)
def create_disk_analysis(self,
dataset,
cx: int = None,
cy: int = None,
r: int = None):
"""
Integrate over a disk (i.e. filled circle)
Parameters
----------
dataset
the dataset to work on
cx
center x value
cy
center y value
r
radius of the disk
"""
if dataset.shape.sig.dims != 2:
raise ValueError(
"incompatible dataset: need two signal dimensions")
loc = locals()
parameters = {
name: loc[name]
for name in ['cx', 'cy', 'r'] if loc[name] is not None
}
return DiskMaskAnalysis(dataset=dataset, parameters=parameters)
