def test_pick_analysis_via_api_3_3d_ds_fail_4(lt_ctx):
data = _mk_random(size=(16, 16, 16, 16, 16, 16))
dataset = MemoryDataSet(data=data,
tileshape=(1, 16, 16),
num_partitions=2,
sig_dims=2)
analysis = PickFrameAnalysis(dataset=dataset, parameters={})
with pytest.raises(AssertionError):
lt_ctx.run(analysis)
analysis = PickFrameAnalysis(dataset=dataset, parameters={"x": 7})
with pytest.raises(AssertionError):
lt_ctx.run(analysis)
analysis = PickFrameAnalysis(dataset=dataset, parameters={"x": 7, "y": 8})
with pytest.raises(AssertionError):
lt_ctx.run(analysis)
analysis = PickFrameAnalysis(dataset=dataset,
parameters={
"x": 7,
"y": 8,
"z": 11
})
with pytest.raises(AssertionError):
lt_ctx.run(analysis)
def test_pick_analysis(default_k2is, lt_ctx, TYPE):
analysis = PickFrameAnalysis(dataset=default_k2is,
parameters={
"x": 16,
"y": 16
})
analysis.TYPE = TYPE
results = lt_ctx.run(analysis)
assert results[0].raw_data.shape == (1860, 2048)
def test_pick_analysis(default_empad, lt_ctx, TYPE):
analysis = PickFrameAnalysis(dataset=default_empad,
parameters={
"x": 2,
"y": 2
})
analysis.TYPE = TYPE
results = lt_ctx.run(analysis)
assert results[0].raw_data.shape == (128, 128)
assert np.count_nonzero(results[0].raw_data) > 0
def test_pick_from_3d_ds(lt_ctx, TYPE):
data = _mk_random(size=(16 * 16, 16, 16))
dataset = MemoryDataSet(data=data,
tileshape=(1, 16, 16),
num_partitions=2,
sig_dims=2)
analysis = PickFrameAnalysis(dataset=dataset, parameters={"x": 5})
analysis.TYPE = TYPE
result = lt_ctx.run(analysis)
assert result.intensity.raw_data.shape == (16, 16)
assert np.allclose(result.intensity.raw_data, data[5])
def test_pick_analysis_via_api_3_3d_ds_fail_3(lt_ctx):
data = _mk_random(size=(16, 16, 16, 16, 16))
dataset = MemoryDataSet(data=data,
tileshape=(1, 16, 16, 16),
partition_shape=(16, 16, 16),
effective_shape=(16, 16, 16, 16, 16),
sig_dims=2)
analysis = PickFrameAnalysis(dataset=dataset, parameters={"x": 7})
with pytest.raises(ValueError):
lt_ctx.run(analysis)
analysis = PickFrameAnalysis(dataset=dataset, parameters={"x": 7, "y": 8})
with pytest.raises(ValueError):
lt_ctx.run(analysis)
def test_pick_analysis(lt_ctx, TYPE):
"""
the other tests cover the pick job, this one uses the analysis
"""
data = _mk_random(size=(16, 16, 16, 16))
dataset = MemoryDataSet(data=data,
tileshape=(1, 16, 16),
num_partitions=2,
sig_dims=2)
analysis = PickFrameAnalysis(dataset=dataset, parameters={"x": 5, "y": 5})
analysis.TYPE = TYPE
result = lt_ctx.run(analysis)
assert result.intensity.raw_data.shape == (16, 16)
assert np.allclose(result.intensity.raw_data, data[5, 5])
def test_pick_analysis(default_raw, lt_ctx):
analysis = PickFrameAnalysis(dataset=default_raw,
parameters={
"x": 16,
"y": 16
})
results = lt_ctx.run(analysis)
assert results[0].raw_data.shape == (128, 128)
def test_pick_analysis(default_raw, lt_ctx):
analysis = PickFrameAnalysis(dataset=default_raw,
parameters={
"x": 15,
"y": 15
})
results = lt_ctx.run(analysis)
assert results[0].raw_data.shape == (128, 128)
assert np.count_nonzero(results[0].raw_data) > 0
def test_pick_from_3d_ds(lt_ctx):
data = _mk_random(size=(16 * 16, 16, 16))
dataset = MemoryDataSet(data=data,
tileshape=(1, 16, 16),
partition_shape=(16, 16, 16),
effective_shape=(16, 16, 16, 16),
sig_dims=2)
analysis = PickFrameAnalysis(dataset=dataset, parameters={"x": 5, "y": 5})
result = lt_ctx.run(analysis)
assert result.intensity.raw_data.shape == (16, 16)
assert np.allclose(result.intensity.raw_data,
data.reshape(dataset._effective_shape)[5, 5])
def create_pick_analysis(self,
dataset: DataSet,
x: int,
y: int = None,
z: int = None) -> PickFrameAnalysis:
"""
Create an Analysis that picks a single frame / signal element from (z, y, x).
The number of parameters must match number of navigation dimensions in the dataset,
for example if you have a 4D dataset with two signal dimensions and two navigation
dimensions, you need to specify x and y.
Parameters
----------
dataset
The dataset to work on
x
x coordinate
y
y coordinate
z
z coordinate
Returns
-------
PickFrameAnalysis : libertem.analysis.base.Analysis
When run by the Context, this Analysis generates a
:class:`libertem.analysis.raw.PickResultSet`.
Examples
--------
>>> dataset = ctx.load(
... filetype="memory",
... data=np.zeros([16, 16, 16, 16, 16], dtype=np.float32),
... sig_dims=2
... )
>>> analysis = ctx.create_pick_analysis(dataset=dataset, x=9, y=8, z=7)
>>> result = ctx.run(analysis)
>>> assert result.intensity.raw_data.shape == tuple(dataset.shape.sig)
"""
loc = locals()
parameters = {
name: loc[name]
for name in ['x', 'y', 'z'] if loc[name] is not None
}
return PickFrameAnalysis(dataset=dataset, parameters=parameters)
def test_pick_analysis_via_api_3_3d_ds_fail_5(lt_ctx):
data = _mk_random(size=(16, 256, 16, 16))
dataset = MemoryDataSet(data=data,
tileshape=(1, 1, 16, 16),
partition_shape=(16, 16, 16, 16),
effective_shape=(16, 16, 16, 16, 16),
sig_dims=2)
analysis = PickFrameAnalysis(dataset=dataset,
parameters={
"x": 7,
"y": 8,
"z": 11
})
with pytest.raises(ValueError) as e:
lt_ctx.run(analysis)
assert "can only reduce origin from N dims to 1D" in str(e)
def create_pick_analysis(self,
dataset,
x: int,
y: int = None,
z: int = None):
"""
Pick a single frame / signal element from (z, y, x). Number of parameters
must match number of navigation dimensions in the dataset, for example if
you have a 4D dataset with two signal dimensions and two navigation dimensions,
you need to specify x and y.
Parameters
----------
dataset
The dataset to work on
x
x coordinate
y
y coordinate
z
z coordinate
Returns
-------
:py:class:`numpy.ndarray`
the frame as numpy array
Examples
--------
>>> dataset = ctx.load(
... filetype="memory",
... data=np.zeros([16, 16, 16, 16, 16], dtype=np.float32),
... sig_dims=2
... )
>>> job = ctx.create_pick_analysis(dataset=dataset, x=9, y=8, z=7)
>>> result = ctx.run(job)
>>> assert result.intensity.raw_data.shape == tuple(dataset.shape.sig)
"""
loc = locals()
parameters = {
name: loc[name]
for name in ['x', 'y', 'z'] if loc[name] is not None
}
return PickFrameAnalysis(dataset=dataset, parameters=parameters)
def create_pick_analysis(self,
dataset,
x: int,
y: int = None,
z: int = None):
"""
Pick a single frame / signal element from (z, y, x). Number of parameters
must match number of navigation dimensions in the dataset, for example if
you have a 4D dataset with two signal dimensions and two navigation dimensions,
you need to specify x and y.
Parameters
----------
dataset
The dataset to work on
x
x coordinate
y
y coordinate
z
z coordinate
Returns
-------
:py:class:`numpy.ndarray`
the frame as numpy array
Examples
--------
>>> from libertem.api import Context
>>> ctx = Context()
>>> ds = ctx.load("...")
>>> origin = (7, 8, 9)
>>> job = create_pick_analysis(dataset=ds, x=9, y=8, z=7)
>>> result = ctx.run(job)
>>> assert result.intensity.raw_data.shape == ds.shape.sig
"""
loc = locals()
parameters = {
name: loc[name]
for name in ['x', 'y', 'z'] if loc[name] is not None
}
return PickFrameAnalysis(dataset=dataset, parameters=parameters)