Modernize & clean tests

The unittests in meas_extensions_shapeHSM have been modernized to make them runnable under the py.test framework. They have been autoformatted by autopep8 to be compliant with the LSST PEP8 standard and have been checked for deprecated usages of assert_, assert<equals etc...
lsst · Aug 23, 2016 · 59f2e8b · 59f2e8b
1 parent 84a11d9
commit 59f2e8b
Showing 1 changed file with 75 additions and 76 deletions.
diff --git a/tests/testHsm.py b/tests/testHsm.py
@@ -1,6 +1,8 @@
 #!/usr/bin/env python
+#
 # LSST Data Management System
-# Copyright 2008-2015 AURA/LSST
+#
+# Copyright 2008-2016  AURA/LSST.
 #
 # This product includes software developed by the
 # LSST Project (http://www.lsst.org/).
@@ -17,10 +19,11 @@
 #
 # You should have received a copy of the LSST License Statement and
 # the GNU General Public License along with this program.  If not,
-# see <http://www.lsstcorp.org/LegalNotices/>.
+# see <https://www.lsstcorp.org/LegalNotices/>.
 #
-
-import re, os, sys
+import re
+import os
+import sys
 import glob
 import math
 import numpy as np
@@ -39,7 +42,7 @@
 import lsst.afw.geom.ellipses as afwEll
 import lsst.afw.coord as afwCoord
 import lsst.afw.display.ds9 as ds9
-
+import lsst.utils.tests
 import lsst.meas.extensions.shapeHSM
 
 try:
@@ -48,10 +51,10 @@
     verbose = 0
     display = False
 
-SIZE_DECIMALS = 2 # Number of decimals for equality in sizes
-SHAPE_DECIMALS = 3 # Number of decimals for equality in shapes
+SIZE_DECIMALS = 2  # Number of decimals for equality in sizes
+SHAPE_DECIMALS = 3  # Number of decimals for equality in shapes
 
-### The following values are pulled directly from GalSim's test_hsm.py:
+# The following values are pulled directly from GalSim's test_hsm.py:
 file_indices = [0, 2, 4, 6, 8]
 x_centroid = [35.888, 19.44, 8.74, 20.193, 57.94]
 y_centroid = [19.845, 25.047, 11.92, 38.93, 27.73]
@@ -60,80 +63,81 @@
 # Note: expected results give shear for KSB and distortion for others, but the results below have
 # converted KSB expected results to distortion for the sake of consistency
 e1_expected = np.array([
-        [0.467603106752, 0.381211727, 0.398856937, 0.401755571],
-        [0.28618443944, 0.199222784, 0.233883543, 0.234257525],
-        [0.271533794146, 0.158049396, 0.183517068, 0.184893412],
-        [-0.293754156071, -0.457024541, 0.123946584, -0.609233462],
-        [0.557720893779, 0.374143023, 0.714147448, 0.435404409] ])
+    [0.467603106752, 0.381211727, 0.398856937, 0.401755571],
+    [0.28618443944, 0.199222784, 0.233883543, 0.234257525],
+    [0.271533794146, 0.158049396, 0.183517068, 0.184893412],
+    [-0.293754156071, -0.457024541, 0.123946584, -0.609233462],
+    [0.557720893779, 0.374143023, 0.714147448, 0.435404409]])
 e2_expected = np.array([
-        [-0.867225166489, -0.734855778, -0.777027588, -0.774684891],
-        [-0.469354341577, -0.395520479, -0.502540961, -0.464466257],
-        [-0.519775291311, -0.471589061, -0.574750641, -0.529664935],
-        [0.345688365839, -0.342047099, 0.120603755, -0.44609129428863525],
-        [0.525728304099, 0.370691830, 0.702724807, 0.433999442] ])
+    [-0.867225166489, -0.734855778, -0.777027588, -0.774684891],
+    [-0.469354341577, -0.395520479, -0.502540961, -0.464466257],
+    [-0.519775291311, -0.471589061, -0.574750641, -0.529664935],
+    [0.345688365839, -0.342047099, 0.120603755, -0.44609129428863525],
+    [0.525728304099, 0.370691830, 0.702724807, 0.433999442]])
 resolution_expected = np.array([
-        [0.796144249, 0.835624917, 0.835624917, 0.827796187],
-        [0.685023735, 0.699602704, 0.699602704, 0.659457638],
-        [0.634736458, 0.651040481, 0.651040481, 0.614663396],
-        [0.477027015, 0.477210752, 0.477210752, 0.423157447],
-        [0.595205998, 0.611824797, 0.611824797, 0.563582092] ])
+    [0.796144249, 0.835624917, 0.835624917, 0.827796187],
+    [0.685023735, 0.699602704, 0.699602704, 0.659457638],
+    [0.634736458, 0.651040481, 0.651040481, 0.614663396],
+    [0.477027015, 0.477210752, 0.477210752, 0.423157447],
+    [0.595205998, 0.611824797, 0.611824797, 0.563582092]])
 sigma_e_expected = np.array([
-        [0.016924826, 0.014637648, 0.014637648, 0.014465546],
-        [0.075769504, 0.073602324, 0.073602324, 0.064414520],
-        [0.110253112, 0.106222900, 0.106222900, 0.099357106],
-        [0.185276702, 0.184300955, 0.184300955, 0.173478300],
-        [0.073020065, 0.070270966, 0.070270966, 0.061856263] ])
-### End of GalSim's values
+    [0.016924826, 0.014637648, 0.014637648, 0.014465546],
+    [0.075769504, 0.073602324, 0.073602324, 0.064414520],
+    [0.110253112, 0.106222900, 0.106222900, 0.099357106],
+    [0.185276702, 0.184300955, 0.184300955, 0.173478300],
+    [0.073020065, 0.070270966, 0.070270966, 0.061856263]])
+# End of GalSim's values
 
 # These values calculated using GalSim's HSM as part of GalSim
 galsim_e1 = np.array([
-    [0.399292618036, 0.381213068962, 0.398856908083,  0.401749581099],
-    [0.155929282308, 0.199228107929, 0.233882278204,  0.234371587634],
-    [0.150018423796, 0.158052951097, 0.183515056968,  0.184561833739],
+    [0.399292618036, 0.381213068962, 0.398856908083, 0.401749581099],
+    [0.155929282308, 0.199228107929, 0.233882278204, 0.234371587634],
+    [0.150018423796, 0.158052951097, 0.183515056968, 0.184561833739],
     [-2.6984937191, -0.457033962011, 0.123932465911, -0.60886412859],
-    [0.33959621191,  0.374140143394, 0.713756918907,  0.43560180068],
-    ])
+    [0.33959621191, 0.374140143394, 0.713756918907, 0.43560180068],
+])
 galsim_e2 = np.array([
-    [-0.74053555727,  -0.734855830669, -0.777024209499, -0.774700462818],
-    [-0.25573053956,  -0.395517915487, -0.50251352787,  -0.464388132095],
+    [-0.74053555727, -0.734855830669, -0.777024209499, -0.774700462818],
+    [-0.25573053956, -0.395517915487, -0.50251352787, -0.464388132095],
     [-0.287168383598, -0.471584022045, -0.574719130993, -0.5296921134],
-    [3.1754450798,    -0.342054128647,  0.120592080057, -0.446093201637],
-    [0.320115834475,   0.370669454336,  0.702303349972,  0.433968126774],
-    ])
+    [3.1754450798, -0.342054128647, 0.120592080057, -0.446093201637],
+    [0.320115834475, 0.370669454336, 0.702303349972, 0.433968126774],
+])
 galsim_resolution = np.array([
-    [0.79614430666,  0.835625052452, 0.835625052452, 0.827822327614],
+    [0.79614430666, 0.835625052452, 0.835625052452, 0.827822327614],
     [0.685023903847, 0.699601829052, 0.699601829052, 0.659438848495],
     [0.634736537933, 0.651039719582, 0.651039719582, 0.614759743214],
-    [0.477026551962, 0.47721144557,  0.47721144557,  0.423227936029],
+    [0.477026551962, 0.47721144557, 0.47721144557, 0.423227936029],
     [0.595205545425, 0.611821532249, 0.611821532249, 0.563564240932],
-    ])
+])
 galsim_err = np.array([
     [0.0169247947633, 0.0146376201883, 0.0146376201883, 0.0144661813974],
     [0.0757696777582, 0.0736026018858, 0.0736026018858, 0.0644160583615],
-    [0.110252402723,  0.106222368777,  0.106222368777,  0.0993555411696],
-    [0.185278102756,  0.184301897883,  0.184301897883,  0.17346136272],
+    [0.110252402723, 0.106222368777, 0.106222368777, 0.0993555411696],
+    [0.185278102756, 0.184301897883, 0.184301897883, 0.17346136272],
     [0.0730196461082, 0.0702708885074, 0.0702708885074, 0.0618583671749],
-    ])
+])
 
-moments_expected = np.array([ # sigma, e1, e2
+moments_expected = np.array([  # sigma, e1, e2
     [2.24490427971, 0.336240686301, -0.627372910656],
     [1.9031778574, 0.150566105384, -0.245272792302],
     [1.77790760994, 0.112286123389, -0.286203939641],
     [1.45464873314, -0.155597168978, -0.102008266223],
     [1.63144648075, 0.22886961923, 0.228813588897],
-    ])
-centroid_expected = np.array([ # x, y
+])
+centroid_expected = np.array([  # x, y
     [36.218247328, 20.5678722157],
     [20.325744838, 25.4176650386],
     [9.54257706283, 12.6134786199],
     [20.6407850048, 39.5864802706],
     [58.5008586442, 28.2850942049],
-    ])
+])
+
 
 def makePluginAndCat(alg, name, control=None, metadata=False, centroid=None):
     print "Making plugin ", alg, name
     if control == None:
-        control=alg.ConfigClass()
+        control = alg.ConfigClass()
     schema = afwTable.SourceTable.makeMinimalSchema()
     if centroid:
         schema.addField(centroid + "_x", type=float)
@@ -151,14 +155,15 @@ def makePluginAndCat(alg, name, control=None, metadata=False, centroid=None):
 
 #-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
 
+
 class ShapeTestCase(unittest.TestCase):
     """A test case for shape measurement"""
 
     def setUp(self):
 
         # load the known values
         self.dataDir = os.path.join(os.getenv('MEAS_EXTENSIONS_SHAPEHSM_DIR'), "tests", "data")
-        self.bkgd = 1000.0 # standard for atlas image
+        self.bkgd = 1000.0  # standard for atlas image
         self.offset = afwGeom.Extent2I(1234, 1234)
         self.xy0 = afwGeom.Point2I(5678, 9876)
 
@@ -190,7 +195,7 @@ def runMeasurement(self, algorithmName, imageid, x, y, v):
 
         exposure = afwImage.makeExposure(mimg)
         exposure.setWcs(afwImage.makeWcs(afwCoord.makeCoord(afwCoord.ICRS, 0. * afwGeom.degrees, 0. * afwGeom.degrees),
-                                         afwGeom.Point2D(1.0,1.0),
+                                         afwGeom.Point2D(1.0, 1.0),
                                          1.0/(2.53*3600.0), 0.0, 0.0, 1.0/(2.53*3600.0)))
 
         # load the corresponding test psf
@@ -228,7 +233,7 @@ def testHsmShape(self):
 
         for (algNum, algName), (i, imageid) in itertools.product(enumerate(correction_methods),
                                                                  enumerate(file_indices)):
-            algorithmName = "ext_shapeHSM_HsmShape" + algName[0:1].upper() +algName[1:].lower()
+            algorithmName = "ext_shapeHSM_HsmShape" + algName[0:1].upper() + algName[1:].lower()
 
             source = self.runMeasurement(algorithmName, imageid, x_centroid[i], y_centroid[i], sky_var[i])
 
@@ -252,15 +257,15 @@ def testHsmShape(self):
 
             tests = [
                 # label        known-value                            measured              tolerance
-                ["e1",         float(e1_expected[i][algNum]),         e1,             0.5*10**-SHAPE_DECIMALS],
-                ["e2",         float(e2_expected[i][algNum]),         e2,             0.5*10**-SHAPE_DECIMALS],
-                ["resolution", float(resolution_expected[i][algNum]), resolution,     0.5*10**-SIZE_DECIMALS],
+                ["e1", float(e1_expected[i][algNum]), e1, 0.5*10**-SHAPE_DECIMALS],
+                ["e2", float(e2_expected[i][algNum]), e2, 0.5*10**-SHAPE_DECIMALS],
+                ["resolution", float(resolution_expected[i][algNum]), resolution, 0.5*10**-SIZE_DECIMALS],
 
                 # sigma won't match exactly because
                 # we're using skyvar=mean(var) instead of measured value ... expected a difference
-                ["sigma",      float(sigma_e_expected[i][algNum]),    sigma,             0.07],
-                ["shapeStatus", 0,                          flags,             0],
-                ]
+                ["sigma", float(sigma_e_expected[i][algNum]), sigma, 0.07],
+                ["shapeStatus", 0, flags, 0],
+            ]
 
             for test in tests:
                 label, know, hsm, limit = test
@@ -281,11 +286,11 @@ def testHsmShape(self):
     def testHsmSourceMoments(self):
         for (i, imageid) in enumerate(file_indices):
             source = self.runMeasurement("ext_shapeHSM_HsmSourceMoments", imageid, x_centroid[i], y_centroid[i],
-                                        sky_var[i])
+                                         sky_var[i])
             x = source.get("ext_shapeHSM_HsmSourceMoments_x")
             y = source.get("ext_shapeHSM_HsmSourceMoments_y")
             xx = source.get("ext_shapeHSM_HsmSourceMoments_xx")
-            yy= source.get("ext_shapeHSM_HsmSourceMoments_yy")
+            yy = source.get("ext_shapeHSM_HsmSourceMoments_yy")
             xy = source.get("ext_shapeHSM_HsmSourceMoments_xy")
 
             # Centroids from GalSim use the FITS lower-left corner of 1,1
@@ -311,8 +316,8 @@ def testHsmPsfMoments(self):
             msConfig = base.SingleFrameMeasurementConfig()
             msConfig.algorithms.names = ["ext_shapeHSM_HsmPsfMoments"]
             plugin, cat = makePluginAndCat(lsst.meas.extensions.shapeHSM.HsmPsfMomentsAlgorithm,
-                "ext_shapeHSM_HsmPsfMoments", centroid="centroid",
-                control=lsst.meas.extensions.shapeHSM.HsmPsfMomentsControl())
+                                           "ext_shapeHSM_HsmPsfMoments", centroid="centroid",
+                                           control=lsst.meas.extensions.shapeHSM.HsmPsfMomentsControl())
             source = cat.addNew()
             source.set("centroid_x", 23)
             source.set("centroid_y", 34)
@@ -321,7 +326,7 @@ def testHsmPsfMoments(self):
             x = source.get("ext_shapeHSM_HsmPsfMoments_x")
             y = source.get("ext_shapeHSM_HsmPsfMoments_y")
             xx = source.get("ext_shapeHSM_HsmPsfMoments_xx")
-            yy= source.get("ext_shapeHSM_HsmPsfMoments_yy")
+            yy = source.get("ext_shapeHSM_HsmPsfMoments_yy")
             xy = source.get("ext_shapeHSM_HsmPsfMoments_xy")
 
             self.assertAlmostEqual(x, 0.0, 3)
@@ -335,19 +340,13 @@ def testHsmPsfMoments(self):
 
 #-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
 
-def suite():
-    """Returns a suite containing all the test cases in this module."""
-    utilsTests.init()
-
-    suites = []
-    suites += unittest.makeSuite(ShapeTestCase)
-    suites += unittest.makeSuite(utilsTests.MemoryTestCase)
+class TestMemory(lsst.utils.tests.MemoryTestCase):
+    pass
 
-    return unittest.TestSuite(suites)
 
-def run(exit = False):
-    """Run the tests"""
-    utilsTests.run(suite(), exit)
+def setup_module(module):
+    lsst.utils.tests.init()
 
 if __name__ == "__main__":
-    run(True)
+    lsst.utils.tests.init()
+    unittest.main()