Coverage for tests / test_adaptiveThresholdDetection.py: 33%

1import unittest 

2 

3import lsst.utils.tests 

4import numpy as np 

5from lsst.afw.geom import makeCdMatrix, makeSkyWcs 

6from lsst.afw.table import SourceTable 

7from lsst.geom import Box2I, Extent2I, Point2D, Point2I, SpherePoint, degrees 

8from lsst.meas.algorithms.adaptive_thresholds import AdaptiveThresholdDetectionTask 

9from lsst.meas.algorithms.testUtils import plantSources 

10 

11 

12class AdaptiveThresholdDetectionTest(lsst.utils.tests.TestCase): 

13 def setUp(self): 

14 xy0 = Point2I(12345, 67890) # xy0 for image 

15 dims = Extent2I(2345, 2345) # Dimensions of image 

16 self.box = Box2I(xy0, dims) # Bounding box of image 

17 self.sigma = 3.21 # PSF sigma 

18 self.nSigmaForKernel = 5.0 # Number of PSF sigmas for kernel 

19 self.sky = 12345.6 # Sky level 

20 noise = np.sqrt(self.sky)*np.pi*self.sigma**2 # Poisson noise per PSF 

21 self.faint = 1.0*noise # Faintest level for star fluxes 

22 self.bright = 100.0*noise # Brightest level for star fluxes 

23 self.starBox = Box2I(self.box) # Area on image in which we can put star centers 

24 starBoxBuffer = 4.0 # Buffer for star centers around edge 

25 self.starBox.grow(-int(starBoxBuffer*self.sigma)) 

26 self.scale = 1.0e-5*degrees # Pixel scale 

27 self.config = AdaptiveThresholdDetectionTask.ConfigClass() 

28 self.rtol = 1e-4 

29 

30 def makeMockExposure(self, numStars=300): 

31 rng = np.random.Generator(np.random.MT19937(12345)) 

32 stars = [(xx, yy, ff, self.sigma) for xx, yy, ff in 

33 zip(rng.uniform(self.starBox.getMinX(), self.starBox.getMaxX(), numStars), 

34 rng.uniform(self.starBox.getMinY(), self.starBox.getMaxY(), numStars), 

35 np.linspace(self.faint, self.bright, numStars))] 

36 exposure = plantSources(self.box, 2*int(self.nSigmaForKernel*self.sigma) + 1, self.sky, stars, True) 

37 exposure.setWcs(makeSkyWcs(crpix=Point2D(0, 0), 

38 crval=SpherePoint(0, 0, degrees), 

39 cdMatrix=makeCdMatrix(scale=self.scale))) 

40 return exposure 

41 

42 def tearDown(self): 

43 # del self.exposure 

44 del self.config 

45 

46 def check(self, expectFactor, initialThreshold=None): 

47 schema = SourceTable.makeMinimalSchema() 

48 table = SourceTable.make(schema) 

49 task = AdaptiveThresholdDetectionTask(config=self.config) 

50 results = task.run(table, self.exposure, initialThreshold=initialThreshold) 

51 self.assertFloatsAlmostEqual(results.thresholdValue, expectFactor, rtol=self.rtol) 

52 

53 def testUncrowded(self): 

54 """Add sparse-ish numbers of stars. 

55 """ 

56 self.exposure = self.makeMockExposure() 

57 self.check(5.0) 

58 self.exposure = self.makeMockExposure(numStars=100) 

59 self.check(5.0) 

60 

61 def testCrowded(self): 

62 """Add enough stars to be considered crowded and test adjusting 

63 initial detection threshold. 

64 """ 

65 self.exposure = self.makeMockExposure(numStars=8000) 

66 self.check(23.8) 

67 self.exposure = self.makeMockExposure(numStars=8000) 

68 self.check(50.0, initialThreshold=50.0) 

69 self.exposure = self.makeMockExposure(numStars=25000) 

70 self.check(173.4, initialThreshold=20.0) 

71 

72 

73class TestMemory(lsst.utils.tests.MemoryTestCase): 

74 pass 

75 

76 

77def setup_module(module): 

78 lsst.utils.tests.init() 

79 

80 

81if __name__ == "__main__": 81 ↛ 82line 81 didn't jump to line 82 because the condition on line 81 was never true

82 import sys 

83 setup_module(sys.modules['__main__']) 

84 unittest.main()