def test_create_asciixyc_frame(self):
self.assertEqual(1, 1)
## The dataset wrapper.
ds = Dataset("testdata/ASCIIxyC")
## The frames from the test dataset.
frames = ds.getFrames((51.509915, -0.142515, 34.02))
# The tests
#-----------
## The cluster finder from the first frame.
kf = KlusterFinder(frames[0].getPixelMap(), frames[0].getWidth(), frames[0].getHeight(), frames[0].isMC())
# The number of clusters in the first frame.
#
# This frame has 34 clusters.
self.assertEqual(kf.getNumberOfKlusters(), 34)
self.assertEqual(kf.getNumberOfGammas(), 12)
self.assertEqual(kf.getNumberOfMonopixels(), 4)
self.assertEqual(kf.getNumberOfBipixels(), 2)
self.assertEqual(kf.getNumberOfTripixelGammas(), 4)
self.assertEqual(kf.getNumberOfTetrapixelGammas(), 2)
## The list of clusters.
ks = kf.getListOfKlusters()
# Double check the number of clusters found.
self.assertEqual(len(ks), 34)
# The first - and largest - cluster.
# Cluster size (number of pixels).
self.assertEqual(ks[0].getNumberOfPixels(), 104)
# Cluster location (raw pixels).
self.assertEqual(ks[0].getXMin(), 141)
self.assertEqual(ks[0].getXMax(), 153)
self.assertEqual(ks[0].getYMin(), 230)
self.assertEqual(ks[0].getYMax(), 244)
# Cluster width and height.
self.assertEqual(ks[0].getWidth(), 13)
self.assertEqual(ks[0].getHeight(), 15)
# Cluster properties based on the unweighted (UW) mean.
# * Location.
self.assertAlmostEqual(ks[0].getXUW(), 146.673077, places=6)
self.assertAlmostEqual(ks[0].getYUW(), 237.375000, places=6)
# * Radius and density.
self.assertAlmostEqual(ks[0].getRadiusUW(), 8.550607, places=6)
self.assertAlmostEqual(ks[0].getDensityUW(), 0.452782, places=6)
# Counts.
self.assertEqual(ks[0].getTotalCounts(), 8854)
self.assertEqual(ks[0].getMaxCountValue(), 577)
# Energy.
self.assertAlmostEqual(ks[0].getTotalEnergy(), 0.0, places=6)
self.assertAlmostEqual(ks[0].getMaxEnergy(), 0.0, places=6)
# Linearity.
m, c, sumR = ks[0].getLineOfBestFitValues()
self.assertAlmostEqual(m, -0.827317, places=6)
self.assertAlmostEqual(c, 358.720154, places=6)
self.assertAlmostEqual(sumR, 211.088562, places=6)
# Edge pixels.
self.assertEqual(ks[0].getNumberOfEdgePixels(), 57)
self.assertAlmostEqual(ks[0].getInnerPixelFraction(), 0.451923, places=6)
self.assertAlmostEqual(ks[0].getOuterPixelFraction(), 0.548077, places=6)
# Is it a Monte Carlo cluster?
self.assertEqual(ks[0].isMC(), False)
# Is it an edge cluster?
self.assertEqual(ks[0].isEdgeCluster(), False)
# A mid-range cluster.
# Cluster size (number of pixels).
self.assertEqual(ks[10].getNumberOfPixels(), 19)
# Cluster location (raw pixels).
self.assertEqual(ks[10].getXMin(), 167.0)
self.assertEqual(ks[10].getXMax(), 173.0)
self.assertEqual(ks[10].getYMin(), 196.0)
self.assertEqual(ks[10].getYMax(), 201.0)
# Cluster width and height.
self.assertEqual(ks[10].getWidth(), 7.0)
self.assertEqual(ks[10].getHeight(), 6.0)
# Cluster properties based on the unweighted (UW) mean.
# * Location.
self.assertAlmostEqual(ks[10].getXUW(), 170.315789, places=6)
self.assertAlmostEqual(ks[10].getYUW(), 198.526316, places=6)
# * Radius and density.
self.assertAlmostEqual(ks[10].getRadiusUW(), 3.357301, places=6)
self.assertAlmostEqual(ks[10].getDensityUW(), 0.536566, places=6)
# Counts.
self.assertEqual(ks[10].getTotalCounts(), 612)
self.assertEqual(ks[10].getMaxCountValue(), 69)
# Energy.
self.assertAlmostEqual(ks[10].getTotalEnergy(), 0.0, places=6)
self.assertAlmostEqual(ks[10].getMaxEnergy(), 0.0, places=6)
# Linearity.
m, c, sumR = ks[10].getLineOfBestFitValues()
self.assertAlmostEqual(m, 0.180385, places=6)
self.assertAlmostEqual(c, 167.803853, places=6)
self.assertAlmostEqual(sumR, 20.854321, places=6)
# Edge pixels.
self.assertEqual(ks[10].getNumberOfEdgePixels(), 19)
self.assertAlmostEqual(ks[10].getInnerPixelFraction(), 0.000000, places=6)
self.assertAlmostEqual(ks[10].getOuterPixelFraction(), 1.000000, places=6)
# Is it a Monte Carlo cluster?
self.assertEqual(ks[10].isMC(), False)
# Is it an edge cluster?
self.assertEqual(ks[10].isEdgeCluster(), False)
#print "JSON entry:", ks[10].getKlusterPropertiesJson()
# The last - and small - cluster.
# Cluster size (number of pixels).
self.assertEqual(ks[33].getNumberOfPixels(), 1)
# Cluster location (raw pixels).
self.assertEqual(ks[33].getXMin(), 97.0)
self.assertEqual(ks[33].getXMax(), 97.0)
self.assertEqual(ks[33].getYMin(), 135.0)
self.assertEqual(ks[33].getYMax(), 135.0)
# Cluster width and height.
self.assertEqual(ks[33].getWidth(), 1)
self.assertEqual(ks[33].getHeight(), 1)
# Cluster properties based on the unweighted (UW) mean.
# * Location.
self.assertAlmostEqual(ks[33].getXUW(), 97.000000, places=6)
self.assertAlmostEqual(ks[33].getYUW(), 135.000000, places=6)
# * Radius and density.
self.assertAlmostEqual(ks[33].getRadiusUW(), 0.000000, places=6)
self.assertAlmostEqual(ks[33].getDensityUW(), 0.000000, places=6)
# Counts.
self.assertEqual(ks[33].getTotalCounts(), 18)
self.assertEqual(ks[33].getMaxCountValue(), 18)
# Energy.
self.assertAlmostEqual(ks[33].getTotalEnergy(), 0.0, places=6)
self.assertAlmostEqual(ks[33].getMaxEnergy(), 0.0, places=6)
# Linearity.
m, c, sumR = ks[33].getLineOfBestFitValues()
self.assertAlmostEqual(m, 0.000000, places=6)
self.assertAlmostEqual(c, 97.000000, places=6)
self.assertAlmostEqual(sumR, 0.000000, places=6)
# Edge pixels.
self.assertEqual(ks[33].getNumberOfEdgePixels(), 1)
self.assertAlmostEqual(ks[33].getInnerPixelFraction(), 0.000000, places=6)
self.assertAlmostEqual(ks[33].getOuterPixelFraction(), 1.000000, places=6)
# Is it a Monte Carlo cluster?
self.assertEqual(ks[33].isMC(), False)
# Is it an edge cluster?
self.assertEqual(ks[33].isEdgeCluster(), False)
def test_create_asciixyc_frame(self):
## The dataset wrapper.
ds = Dataset("testdata/B06-W0212/2014-04-02-150255/RAW/ASCIIxyC/")
## The frame metadata.
fmd = None
#
with open("testdata/B06-W0212/2014-04-02-150255/geo.json", "r") as fmdf:
fmd = json.load(fmdf, fmd)
#
lat, lon, alt = fmd['lat'], fmd['lon'], fmd['alt']
## The pixel mask.
pixel_mask = {}
with open("testdata/B06-W0212/2014-04-02-150255/masked_pixels.txt", "r") as mpf:
rows = mpf.readlines()
for row in rows:
vals = [int(val) for val in row.strip().split("\t")]
x = vals[0]; y = vals[1]; X = (256*y) + x; C = 1
pixel_mask[X] = C
## The frames from the dataset.
frames = ds.getFrames((lat, lon, alt), pixelmask=pixel_mask)
# The tests
#-----------
## The cluster finder from the first frame.
kf = KlusterFinder(frames[0].getPixelMap(), frames[0].getWidth(), frames[0].getHeight(), frames[0].isMC())
# The number of clusters in the first frame.
#
# This frame has 34 clusters.
self.assertEqual(kf.getNumberOfKlusters(), 9)
self.assertEqual(kf.getNumberOfGammas(), 2)
self.assertEqual(kf.getNumberOfMonopixels(), 1)
self.assertEqual(kf.getNumberOfBipixels(), 0)
self.assertEqual(kf.getNumberOfTripixelGammas(), 0)
self.assertEqual(kf.getNumberOfTetrapixelGammas(), 1)
## The list of clusters.
ks = kf.getListOfKlusters()
# Double check the number of clusters found.
self.assertEqual(len(ks), 9)
# The first - and largest - cluster.
# Cluster size (number of pixels).
self.assertEqual(ks[0].getNumberOfPixels(), 25)
# Cluster location (raw pixels).
self.assertEqual(ks[0].getXMin(), 99)
self.assertEqual(ks[0].getXMax(), 110)
self.assertEqual(ks[0].getYMin(), 179)
self.assertEqual(ks[0].getYMax(), 193)
# Cluster width and height.
self.assertEqual(ks[0].getWidth(), 12)
self.assertEqual(ks[0].getHeight(), 15)
# Cluster properties based on the unweighted (UW) mean.
# * Location.
self.assertAlmostEqual(ks[0].getXUW(), 104.16, places=6)
self.assertAlmostEqual(ks[0].getYUW(), 185.320, places=6)
# * Radius and density.
self.assertAlmostEqual(ks[0].getRadiusUW(), 8.73430, places=6)
self.assertAlmostEqual(ks[0].getDensityUW(), 0.104312, places=6)
# Counts.
self.assertEqual(ks[0].getTotalCounts(), 879)
self.assertEqual(ks[0].getMaxCountValue(), 88)
# Energy.
self.assertAlmostEqual(ks[0].getTotalEnergy(), 0.0, places=6)
self.assertAlmostEqual(ks[0].getMaxEnergy(), 0.0, places=6)
# Linearity.
m, c, sumR = ks[0].getLineOfBestFitValues()
self.assertAlmostEqual(m, -1.127456, places=6)
self.assertAlmostEqual(c, 302.755829, places=6)
self.assertAlmostEqual(sumR, 16.283415, places=6)
# Edge pixels.
self.assertEqual(ks[0].getNumberOfEdgePixels(), 25)
self.assertAlmostEqual(ks[0].getInnerPixelFraction(), 0.0, places=6)
self.assertAlmostEqual(ks[0].getOuterPixelFraction(), 1.0, places=6)
# Is it a Monte Carlo cluster?
self.assertEqual(ks[0].isMC(), False)
# Is it an edge cluster?
self.assertEqual(ks[0].isEdgeCluster(), False)
