DM-12656: Fix some new flake8 warnings

python/lsst/meas/base/noiseReplacer.py

@@ -127,7 +127,7 @@ def __init__(self, config, exposure, footprints, noiseImage=None, exposureId=Non
                 plane = mask.getMaskPlane(maskname)
                 if self.log:
                     self.log.debug('Mask plane "%s" already existed', maskname)
-            except:
+            except Exception:
                 # if not, add it; we should delete it when done.
                 plane = mask.addMaskPlane(maskname)
                 self.removeplanes.append(maskname)
@@ -155,11 +155,11 @@ def __init__(self, config, exposure, footprints, noiseImage=None, exposureId=Non
             elif fp[0] == 0:
                 self.heavies[id] = afwDet.makeHeavyFootprint(fp[1], mi)
 
-        ### FIXME: the heavy footprint includes the mask
-        ### and variance planes, which we shouldn't need
-        ### (I don't think we ever want to modify them in
-        ### the input image).  Copying them around is
-        ### wasteful.
+        # ## FIXME: the heavy footprint includes the mask
+        # ## and variance planes, which we shouldn't need
+        # ## (I don't think we ever want to modify them in
+        # ## the input image).  Copying them around is
+        # ## wasteful.
 
         # We now create a noise HeavyFootprint for each source with has a heavy footprint.
         # We'll put the noise footprints in a dict heavyNoise = {id:heavyNoiseFootprint}
@@ -281,7 +281,7 @@ def getNoiseGenerator(self, exposure, noiseImage, noiseMeanVar, exposureId=None)
                     self.log.debug('Using passed-in noise mean = %g, variance = %g -> stdev %g',
                                    noiseMean, noiseVar, noiseStd)
                 return FixedGaussianNoiseGenerator(noiseMean, noiseStd, rand=rand)
-            except:
+            except Exception:
                 if self.log:
                     self.log.debug('Failed to cast passed-in noiseMeanVar to floats: %s',
                                    str(noiseMeanVar))
@@ -300,7 +300,7 @@ def getNoiseGenerator(self, exposure, noiseImage, noiseMeanVar, exposureId=None)
                     self.log.debug('Using noise variance = (BGMEAN = %g) from exposure metadata',
                                    bgMean)
                 return FixedGaussianNoiseGenerator(offset, noiseStd, rand=rand)
-            except:
+            except Exception:
                 if self.log:
                     self.log.debug('Failed to get BGMEAN from exposure metadata')
 

tests/test_PluginLogs.py

@@ -239,7 +239,7 @@ def testLoggingCppPlugin(self):
             # This call throws an error, so be prepared for it
             try:
                 task.run(cat, exposure)
-            except:
+            except Exception:
                 pass
             directLog(log, None)
             # direct back to console, closing log files

tests/test_ShapeUtilities.py

@@ -42,29 +42,29 @@ def testIdentity(self):
     def testVsTransform(self):
         # Transforming an ellipse by multiplying by the matrix should be
         # equivalent to calling its transform() method.
-        l = lsst.afw.geom.LinearTransform.makeRotation(lsst.afw.geom.Angle(numpy.random.random()))
+        lt = lsst.afw.geom.LinearTransform.makeRotation(lsst.afw.geom.Angle(numpy.random.random()))
         e = lsst.afw.geom.ellipses.Quadrupole(numpy.random.random(), numpy.random.random(),
                                               numpy.random.random())
-        numpy.testing.assert_array_almost_equal(numpy.dot(lsst.meas.base.makeShapeTransformMatrix(l),
+        numpy.testing.assert_array_almost_equal(numpy.dot(lsst.meas.base.makeShapeTransformMatrix(lt),
                                                           e.getParameterVector()),
-                                                e.transform(l).getParameterVector())
+                                                e.transform(lt).getParameterVector())
 
     def testVales(self):
         # Test that the analytically-derived correct values are computed
-        l = lsst.afw.geom.LinearTransform(numpy.random.random((2, 2)))
-        m = lsst.meas.base.makeShapeTransformMatrix(l)
+        lt = lsst.afw.geom.LinearTransform(numpy.random.random((2, 2)))
+        m = lsst.meas.base.makeShapeTransformMatrix(lt)
 
-        self.assertEqual(m[0, 0], l[0, 0]*l[0, 0])
-        self.assertEqual(m[0, 1], l[0, 1]*l[0, 1])
-        self.assertEqual(m[0, 2], 2*l[0, 0]*l[0, 1])
+        self.assertEqual(m[0, 0], lt[0, 0]*lt[0, 0])
+        self.assertEqual(m[0, 1], lt[0, 1]*lt[0, 1])
+        self.assertEqual(m[0, 2], 2*lt[0, 0]*lt[0, 1])
 
-        self.assertEqual(m[1, 0], l[1, 0]*l[1, 0])
-        self.assertEqual(m[1, 1], l[1, 1]*l[1, 1])
-        self.assertEqual(m[1, 2], 2*l[1, 0]*l[1, 1])
+        self.assertEqual(m[1, 0], lt[1, 0]*lt[1, 0])
+        self.assertEqual(m[1, 1], lt[1, 1]*lt[1, 1])
+        self.assertEqual(m[1, 2], 2*lt[1, 0]*lt[1, 1])
 
-        self.assertEqual(m[2, 0], l[0, 0]*l[1, 0])
-        self.assertEqual(m[2, 1], l[0, 1]*l[1, 1])
-        self.assertEqual(m[2, 2], l[0, 0]*l[1, 1] + l[0, 1]*l[1, 0])
+        self.assertEqual(m[2, 0], lt[0, 0]*lt[1, 0])
+        self.assertEqual(m[2, 1], lt[0, 1]*lt[1, 1])
+        self.assertEqual(m[2, 2], lt[0, 0]*lt[1, 1] + lt[0, 1]*lt[1, 0])
 
 
 class TestMemory(lsst.utils.tests.MemoryTestCase):