def test_roi_2(random_hdf5, lt_ctx, mnp):
ds = H5DataSet(
path=random_hdf5.filename,
ds_path="data",
tileshape=(1, 4, 16, 16),
min_num_partitions=mnp,
)
ds = ds.initialize(lt_ctx.executor)
roi = {
"shape": "disk",
"cx": 2,
"cy": 2,
"r": 1,
}
analysis = SumAnalysis(dataset=ds, parameters={
"roi": roi,
})
print(analysis.get_roi())
results = lt_ctx.run(analysis)
# let's draw a circle!
mask = np.full((5, 5), False)
mask[1, 2] = True
mask[2, 1:4] = True
mask[3, 2] = True
print(mask)
assert mask.shape == (5, 5)
assert mask.dtype == np.bool
reader = ds.get_reader()
with reader.get_h5ds() as h5ds:
data = np.array(h5ds)
# applying the mask flattens the first two dimensions, so we
# only sum over axis 0 here:
expected = data[mask, ...].sum(axis=(0, ))
assert expected.shape == (16, 16)
assert results.intensity.raw_data.shape == (16, 16)
# is not equal to results without mask:
assert not np.allclose(results.intensity.raw_data,
data.sum(axis=(0, 1)))
# ... but rather like `expected`:
assert np.allclose(results.intensity.raw_data, expected)
def test_sum_zero_roi(lt_ctx):
data = _mk_random(size=(16, 16, 16, 16), dtype='<u2')
dataset = MemoryDataSet(data=data, tileshape=(2, 16, 16), num_partitions=32)
roi = {
"shape": "disk",
"cx": -1,
"cy": -1,
"r": 0,
}
analysis = SumAnalysis(dataset=dataset, parameters={
"roi": roi,
})
results = lt_ctx.run(analysis)
mask = masks.circular(roi["cx"], roi["cy"], 16, 16, roi["r"])
assert mask.shape == (16, 16)
assert np.count_nonzero(mask) == 0
assert mask.dtype == np.bool
# applying the mask flattens the first two dimensions, so we
# only sum over axis 0 here:
expected = data[mask, ...].sum(axis=(0,))
assert expected.shape == (16, 16)
assert results['intensity'].raw_data.shape == (16, 16)
# is not equal to results without mask:
assert not np.allclose(results['intensity'].raw_data, data.sum(axis=(0, 1)))
# ... but rather like `expected`:
assert np.allclose(results['intensity'].raw_data, expected)
def test_dist_process(default_frms6, dist_ctx):
roi = {
"shape": "disk",
"cx": 5,
"cy": 6,
"r": 7,
}
analysis = SumAnalysis(dataset=default_frms6, parameters={"roi": roi})
dist_ctx.run(analysis)
def create_sum_analysis(self, dataset):
"""
Sum of all signal elements
Parameters
----------
dataset
the dataset to work on
"""
return SumAnalysis(dataset=dataset, parameters={})
def test_sum_analysis(default_frms6, lt_ctx):
roi = {
"shape": "disk",
"cx": 5,
"cy": 6,
"r": 7,
}
analysis = SumAnalysis(dataset=default_frms6, parameters={
"roi": roi,
})
# not checking result yet, just making sure it doesn't crash:
lt_ctx.run(analysis)
def create_sum_analysis(self, dataset) -> SumAnalysis:
"""
Create an Analysis that sums all signal elements along the navigation
dimension, preserving the signal dimension.
Parameters
----------
dataset
the dataset to work on
Returns
-------
SumAnalysis : libertem.analysis.base.Analysis
When run by the Context, this Analysis generates a
:class:`libertem.analysis.sum.SumResultSet`.
"""
return SumAnalysis(dataset=dataset, parameters={})
