Merge pull request #379 from lsst/u/lynnej/fix_test

tests/maf/test_3x2fom.py

@@ -21,8 +21,8 @@ def setUp(self):
         "Skipping 3x3 metric test because no dust maps.",
     )
     def test_3x2(self):
-        # Only testing that the metric successfully runs, not checking that the
-        # output values are valid.
+        # Only testing that the metric successfully runs, not checking that
+        # the output values are valid.
         bundle_list = []
         nside = 64
         colmap = maf.batches.col_map_dict("fbs")

tests/maf/test_archive.py

@@ -1,5 +1,4 @@
 # imports
-import os
 import sys
 import unittest
 from os import path
@@ -128,12 +127,12 @@ def test_get_family_descriptions(self):
 
     def test_describe_families(self):
         if "IPython" in sys.modules:
-            with patch("IPython.display.display_markdown") as display_markdown, patch(
-                "IPython.display.HTML"
-            ) as display_html, patch("IPython.display") as ip_display:
+            with patch("IPython.display.display_markdown") as _, patch("IPython.display.HTML") as _, patch(
+                "IPython.display"
+            ) as _:
                 self.perform_describe_families_test()
         else:
-            with patch("builtins.print") as print_mock:
+            with patch("builtins.print") as _:
                 self.perform_describe_families_test()
 
     def perform_describe_families_test(self):

tests/maf/test_basemetrics.py

@@ -1,6 +1,3 @@
-import matplotlib
-
-matplotlib.use("Agg")
 import unittest
 
 import rubin_sim.maf.metrics as metrics
@@ -13,7 +10,9 @@ def test_reduce_dict(self):
         self.assertEqual(list(testmetric.reduce_funcs.keys()), [])
 
     def test_metric_name(self):
-        """Test that metric name is set appropriately automatically and explicitly"""
+        """Test that metric name is set appropriately automatically
+        and when explicitly passed.
+        """
         # Test automatic setting of metric name
         testmetric = metrics.BaseMetric("testcol")
         self.assertEqual(testmetric.name, "Base testcol")
@@ -33,7 +32,8 @@ def test_col_registry(self):
         colset = set()
         colset.add(cols)
         testmetric = metrics.BaseMetric(cols)
-        # Class registry should have dictionary with values = set of columns for metric class
+        # Class registry should have dictionary with values =
+        # set of columns for metric class
         for item in colset:
             self.assertIn(item, testmetric.col_registry.col_set)
         cols = ["onecolumn", "twocolumn"]

tests/maf/test_batches.py

@@ -27,18 +27,31 @@ def setUp(self):
     )
     def testload_them_all(self):
         ack = batches.altazHealpix()
+        assert ack is not None
         ack = batches.altazLambert()
+        assert ack is not None
         ack = batches.standard_summary()
+        assert ack is not None
         ack = batches.standard_metrics("night")
+        assert ack is not None
         ack = batches.filtersPerNight()
+        assert ack is not None
         ack = batches.filtersWholeSurvey()
+        assert ack is not None
         ack = batches.glanceBatch()
+        assert ack is not None
         ack = batches.hourglassPlots()
+        assert ack is not None
         ack = batches.slewBasics()
+        assert ack is not None
         ack = batches.timeGaps()
+        assert ack is not None
         ack = batches.metadataBasics("airmass")
+        assert ack is not None
         ack = batches.metadataBasicsAngle("rotskyPos")
+        assert ack is not None
         ack = batches.metadataMaps("fiveSigmaDepth")
+        assert ack is not None
 
     @unittest.skipUnless(
         os.path.isdir(os.path.join(get_data_dir(), "maf")),
@@ -47,9 +60,13 @@ def testload_them_all(self):
     def test_moving_objects_batches(self):
         slicer = MoObjSlicer()
         ack = batches.quick_discovery_batch(slicer)
+        assert ack is not None
         ack = batches.discovery_batch(slicer)
+        assert ack is not None
         ack = batches.characterization_inner_batch(slicer)
+        assert ack is not None
         ack = batches.characterization_outer_batch(slicer)
+        assert ack is not None
 
     def test_moving_fractions(self):
         bdict = {}
@@ -58,24 +75,27 @@ def test_moving_fractions(self):
         bdict["days"] = bundle
         bdict["days"].slicer = MoObjSlicer()
         bdict["days"].slicer.slice_points["H"] = np.arange(50)
-        # Should probably fill in some data as well to make sure
-        # more parts of the code get exercised, but this is a start.
-        ack = batches.run_completeness_summary(bdict, 0.0, [10, 20], None, None)
-        ack = batches.run_fraction_summary(bdict, 0.0, None, None)
-        ack = batches.plot_fractions(bdict, out_dir=self.out_dir)
-        # A mystery why this batch takes a bundle while the rest take dicts.
-        ack = batches.plot_single(bundle, out_dir=self.out_dir)
-        ack = batches.plot_activity(bdict)
+        # This is a start, but the methods below aren't really doing anything
+        # without appropriately named/values bundles in the bdict.
+        batches.run_completeness_summary(bdict, 0.0, [10, 20], None, None)
+        batches.run_fraction_summary(bdict, 0.0, None, None)
+        batches.plot_fractions(bdict, out_dir=self.out_dir)
+        # This batch takes a single bundle because it plots that single
+        # bundle with multiple summary interpretations of the metric
+        batches.plot_single(bundle, out_dir=self.out_dir)
+        batches.plot_activity(bdict)
 
     @unittest.skipUnless(
         os.path.isdir(os.path.join(get_data_dir(), "maf")),
         "Skipping scienceRadarBatch test because operating without full MAF test data",
     )
     def test_science_radar(self):
-        # Loading the science radar batch requires reading a significant set of input files
+        # Loading the science radar batch requires reading a significant
+        # set of input files
         # This test is skipped if running with the lighter set of test data.
         # batch requires reading a lot of input files for lightcurves
         ack = batches.science_radar_batch()
+        assert ack is not None
 
     @unittest.skipUnless(
         os.path.isdir(os.path.join(get_data_dir(), "maf")),

tests/maf/test_cadencemetrics.py

@@ -1,6 +1,3 @@
-import matplotlib
-
-matplotlib.use("Agg")
 import unittest
 
 import numpy as np
@@ -101,7 +98,8 @@ def test_general_uniformity_metric(self):
         np.testing.assert_almost_equal(result3, 0.0)
         # Test setup with max_value > 1
         metric = metrics.GeneralUniformityMetric(col="observationStartMJD", min_value=0, max_value=10)
-        # Make a perfectly uniform dist covering limited range (0-1 out of 0-10 range)
+        # Make a perfectly uniform dist covering limited range
+        # (0-1 out of 0-10 range)
         data["observationStartMJD"] = np.arange(0.0, 1 + step / 2, step)
         result5 = metric.run(data, slice_point)
         self.assertEqual(result5, 0.9)
@@ -163,7 +161,8 @@ def test_t_gaps_percent_metric(self):
         data["observationStartMJD"] = [1, 2, 3, 4]
         metric = metrics.TgapsPercentMetric(min_time=0.5, max_time=1.5, all_gaps=True)
         result3 = metric.run(data)
-        # This should be 50% -- 3 gaps of 1 day, 2 gaps of 2 days, 1 gap of 3 days
+        # This should be 50% -- 3 gaps of 1 day, 2 gaps of 2 days, and
+        # 1 gap of 3 days
         self.assertEqual(result3, 50)
 
     def test_night_gap_metric(self):
@@ -248,7 +247,8 @@ def test_rapid_revisit_uniformity_metric(self):
         for i in range(10000):
             dtimes = rng.rand(100)
             data['observationStartMJD'] = dtimes.cumsum()
-            metric = metrics.RapidRevisitUniformityMetric(d_tmin=0.1, d_tmax=0.8, min_nvisits=50)
+            metric = metrics.RapidRevisitUniformityMetric(d_tmin=0.1,
+                                d_tmax=0.8, min_nvisits=50)
             result = metric.run(data)
             resmin = np.min([resmin, result])
             resmax = np.max([resmax, result])
@@ -314,7 +314,8 @@ def test_transient_metric(self):
         metric = metrics.TransientMetric(survey_duration=ndata / 365.25 * 2)
         self.assertEqual(metric.run(data_slice), 0.5)
 
-        # Set half of the m5 of the observations very bright, so kill another half.
+        # Set half of the m5 of the observations very bright,
+        # so kill another half.
         data_slice["fiveSigmaDepth"][0:50] = 20
         self.assertEqual(metric.run(data_slice), 0.25)
 

tests/maf/test_calibrationmetrics.py

@@ -1,6 +1,3 @@
-import matplotlib
-
-matplotlib.use("Agg")
 import unittest
 
 import numpy as np
@@ -50,8 +47,9 @@ def test_parallax_metric(self):
             worse4 = metrics.ParallaxMetric(normalize=flag, rmag=22.0, seeing_col="finSeeing").run(
                 data[0:300], slice_point
             )
-            # Make sure the RMS increases as seeing increases, the star gets fainter,
-            #    the background gets brighter, or the baseline decreases.
+            # Make sure the RMS increases as seeing increases,
+            # the star gets fainter, the background gets brighter,
+            # or the baseline decreases.
             if flag:
                 pass
             else:
@@ -102,10 +100,12 @@ def test_proper_motion_metric(self):
             worse4 = metrics.ProperMotionMetric(normalize=flag, rmag=22.0, seeing_col="finSeeing").run(
                 data[0:300], slice_point
             )
-            # Make sure the RMS increases as seeing increases, the star gets fainter,
-            # the background gets brighter, or the baseline decreases.
+            # Make sure the RMS increases as seeing increases,
+            # the star gets fainter, the background gets brighter,
+            # or the baseline decreases.
             if flag:
-                # When normalized, mag of star and m5 don't matter (just scheduling).
+                # When normalized, mag of star and m5 don't matter
+                # (just scheduling).
                 self.assertAlmostEqual(worse2, worse1)
                 self.assertAlmostEqual(worse4, worse3)
                 # But using fewer points should make proper motion worse.
@@ -204,7 +204,8 @@ def test_parallax_dcr_degen_metric(self):
         val = metric.run(data)
         np.testing.assert_almost_equal(val, 0.0, decimal=2)
 
-        # Generate a random distribution that should have little or no correlation
+        # Generate a random distribution that should have
+        # little or no correlation
         rng = np.random.RandomState(42)
 
         data["ra_pi_amp"] = rng.rand(100) * 2 - 1.0

tests/maf/test_healpixslicer.py

@@ -1,6 +1,3 @@
-import matplotlib
-
-matplotlib.use("Agg")
 import os
 import unittest
 
@@ -37,7 +34,8 @@ def make_data_values(
     datavalues = datavalues[randind]
     datavalues = np.array(list(zip(datavalues)), dtype=[("testdata", "float")])
     data.append(datavalues)
-    # Generate RA/Dec values equally spaces on sphere between ramin/max, decmin/max.
+    # Generate RA/Dec values equally spaces on sphere between
+    # ramin/max, decmin/max.
     ra = np.arange(0, size, dtype="float")
     ra *= (float(ramax) - float(ramin)) / (ra.max() - ra.min())
     randorder = rng.rand(size)
@@ -65,7 +63,8 @@ def make_data_values(
 
 def calc_dist_vincenty(ra1, dec1, ra2, dec2):
     """Calculates distance on a sphere using the Vincenty formula.
-    Give this function RA/Dec values in radians. Returns angular distance(s), in radians.
+    Give this function RA/Dec values in radians.
+    Returns angular distance(s), in radians.
     Note that since this is all numpy, you could input arrays of RA/Decs."""
     d1 = (np.cos(dec2) * np.sin(ra2 - ra1)) ** 2 + (
         np.cos(dec1) * np.sin(dec2) - np.sin(dec1) * np.cos(dec2) * np.cos(ra2 - ra1)
@@ -87,7 +86,8 @@ def test_slicertype(self):
         self.assertEqual(testslicer.slicer_name, "HealpixSlicer")
 
     def test_nsides_nbins(self):
-        """Test that number of sides passed to slicer produces expected number of bins."""
+        """Test that number of sides passed to slicer produces expected
+        number of bins."""
         nsides = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]
         npixx = [12, 48, 192, 768, 3072, 12288, 49152, 196608, 786432, 3145728]
         for nside, npix in zip(nsides, npixx):
@@ -129,8 +129,10 @@ def tearDown(self):
         self.testslicer = None
 
     def test_slicer_equivalence(self):
-        """Test that slicers are marked equal when appropriate, and unequal when appropriate."""
-        # Note that they are judged equal based on nsides (not on data in ra/dec spatial tree).
+        """Test that slicers are marked equal when appropriate,
+        and unequal when appropriate."""
+        # Note that they are judged equal based on nsides
+        # (not on data in ra/dec spatial tree).
         testslicer2 = HealpixSlicer(
             nside=self.nside,
             verbose=False,
@@ -180,7 +182,8 @@ def tearDown(self):
         self.testslicer = None
 
     def test_iteration(self):
-        """Test iteration goes through expected range and ra/dec are in expected range (radians)."""
+        """Test iteration goes through expected range and ra/dec
+        are in expected range (radians)."""
         npix = hp.nside2npix(self.nside)
         for i, s in enumerate(self.testslicer):
             self.assertEqual(i, s["slice_point"]["sid"])
@@ -201,7 +204,8 @@ def test_get_item(self):
 
 
 class TestHealpixSlicerSlicing(unittest.TestCase):
-    # Note that this is really testing baseSpatialSlicer, as slicing is done there for healpix grid
+    # Note that this is really testing baseSpatialSlicer,
+    # as slicing is done there for healpix grid
 
     def setUp(self):
         self.camera_footprint_file = os.path.join(get_data_dir(), "tests", "fov_map.npz")
@@ -233,7 +237,8 @@ def tearDown(self):
         self.testslicer = None
 
     def test_slicing(self):
-        """Test slicing returns (all) data points which are within 'radius' of bin point."""
+        """Test slicing returns (all) data points which are within
+        'radius' of bin point."""
         # Test that slicing fails before setup_slicer
         self.assertRaises(NotImplementedError, self.testslicer._slice_sim_data, 0)
         # Set up and test actual slicing.
@@ -252,7 +257,8 @@ def test_slicing(self):
 
 
 class TestHealpixChipGap(unittest.TestCase):
-    # Note that this is really testing baseSpatialSlicer, as slicing is done there for healpix grid
+    # Note that this is really testing baseSpatialSlicer,
+    # as slicing is done there for healpix grid
 
     def setUp(self):
         self.camera_footprint_file = os.path.join(get_data_dir(), "tests", "fov_map.npz")
@@ -285,18 +291,21 @@ def tearDown(self):
         self.testslicer = None
 
     def test_slicing(self):
-        """Test slicing returns (most) data points which are within 'radius' of bin point."""
+        """Test slicing returns (most) data points which are
+        within 'radius' of bin point."""
         # Test that slicing fails before setup_slicer
         self.assertRaises(NotImplementedError, self.testslicer._slice_sim_data, 0)
         # Set up and test actual slicing.
         self.testslicer.setup_slicer(self.dv)
         for s in self.testslicer:
             ra = s["slice_point"]["ra"]
             dec = s["slice_point"]["dec"]
-            # Find the points of 'dv' which are within self.radius of this slice_point
+            # Find the points of 'dv' which are within self.radius of
+            # this slice_point
             distances = calc_dist_vincenty(ra, dec, self.dv["ra"], self.dv["dec"])
             didxs = np.where(distances <= np.radians(self.radius))
-            # find the indexes of dv which the slicer says are in the camera footprint
+            # find the indexes of dv which the slicer says are in the
+            # camera footprint
             sidxs = s["idxs"]
             self.assertLessEqual(len(sidxs), len(didxs[0]))
             if len(sidxs) > 0: