def test_sima(self):
"""
(BlockMethod) with SIMA strategy
"""
# NOTE: this test was brittle and failed non-deterministically with any
# more than one source
import sima.segment
# construct the SIMA strategy
simaStrategy = sima.segment.STICA(components=1)
simaStrategy.append(sima.segment.SparseROIsFromMasks(min_size=20))
simaStrategy.append(sima.segment.SmoothROIBoundaries())
simaStrategy.append(sima.segment.MergeOverlapping(threshold=0.5))
tsc = ThunderContext(self.sc)
data = tsc.makeExample('sources', dims=(60, 60), centers=[[20, 15]], noise=0.5, seed=42)
# create and fit the thunder extraction strategy
strategy = SourceExtraction('sima', simaStrategy=simaStrategy)
model = strategy.fit(data, size=(30, 30))
assert(model.count == 1)
# check that the one center is recovered
ep = 1.5
assert(model[0].distance([20, 15]) < ep)
def test_sima(self):
"""
(BlockMethod) with SIMA strategy
"""
# NOTE: this test was brittle and failed non-deterministically with any
# more than one source
import sima.segment
# construct the SIMA strategy
simaStrategy = sima.segment.STICA(components=1)
simaStrategy.append(sima.segment.SparseROIsFromMasks(min_size=20))
simaStrategy.append(sima.segment.SmoothROIBoundaries())
simaStrategy.append(sima.segment.MergeOverlapping(threshold=0.5))
tsc = ThunderContext(self.sc)
data = tsc.makeExample('sources',
dims=(60, 60),
centers=[[20, 15]],
noise=0.5,
seed=42)
# create and fit the thunder extraction strategy
strategy = SourceExtraction('sima', simaStrategy=simaStrategy)
model = strategy.fit(data, size=(30, 30))
assert (model.count == 1)
# check that the one center is recovered
ep = 1.5
assert (model[0].distance([20, 15]) < ep)
def run(data):
"""
Run a source extraction algorithm on an Images object
Parameters
----------
data : thunder.rdds.images.Images
The data the algorithm will be run on
Returns
-------
result : thunder.extraction.source.SourceModel
Sources found by the algorithm as a SourceModel object
"""
from thunder import SourceExtraction
method = SourceExtraction('localmax')
result = method.fit(data)
return result
def test_local_max(self):
"""
(FeatureMethod) localmax with defaults
"""
tsc = ThunderContext(self.sc)
data = tsc.makeExample('sources', dims=[60, 60], centers=[[10, 10], [40, 40]], noise=0.0, seed=42)
model = SourceExtraction('localmax').fit(data)
# order is irrelevant, but one of these must be true
cond1 = (model[0].distance([10, 10]) == 0) and (model[1].distance([40, 40]) == 0)
cond2 = (model[0].distance([40, 40]) == 0) and (model[1].distance([10, 10]) == 0)
assert(cond1 or cond2)
def test_nmf(self):
"""
(BlockMethod) nmf with defaults
"""
tsc = ThunderContext(self.sc)
data = tsc.makeExample('sources', dims=(60, 60), centers=[[20, 20], [40, 40]], noise=0.1, seed=42)
model = SourceExtraction('nmf', componentsPerBlock=1).fit(data, size=(30, 30))
# order is irrelevant, but one of these must be true
ep = 0.50
cond1 = (model[0].distance([20, 20]) < ep) and (model[1].distance([40, 40]) < ep)
cond2 = (model[0].distance([40, 40]) < ep) and (model[1].distance([20, 20]) < ep)
assert(cond1 or cond2)
def test_sima(self):
"""
(BlockMethod) with SIMA strategy
"""
import sima.segment
# construct the SIMA strategy
simaStrategy = sima.segment.STICA(components=2)
simaStrategy.append(sima.segment.SparseROIsFromMasks(min_size=20))
simaStrategy.append(sima.segment.SmoothROIBoundaries())
simaStrategy.append(sima.segment.MergeOverlapping(threshold=0.5))
tsc = ThunderContext(self.sc)
data = tsc.makeExample('sources', dims=(60, 60), centers=[[20, 15], [40, 45]], noise=0.1, seed=42)
# create and fit the thunder extraction strategy
strategy = SourceExtraction('sima', simaStrategy=simaStrategy)
model = strategy.fit(data, size=(30, 30))
# order is irrelevant, but one of these must be true
ep = 1.5
cond1 = (model[0].distance([20, 15]) < ep) and (model[1].distance([40, 45]) < ep)
cond2 = (model[1].distance([20, 15]) < ep) and (model[0].distance([40, 45]) < ep)
assert(cond1 or cond2)
def loadSources(self, path):
"""
Load a file with sources from a local file system or S3.
Parameters
----------
path : str
Path to file, can be on a local file system or an S3 bucket
Returns
-------
A SourceModel
See also
--------
thunder.SourceExtraction
"""
from thunder import SourceExtraction
blob = self.loadJSON(path)
return SourceExtraction.deserialize(blob)
