Merge pull request #402 from lsst/bugfix/OPSIM-1141

Bugfix/opsim 1141

rubin_sim/maf/plots/spatial_plotters.py

@@ -574,26 +574,29 @@ def _plot_tissot_ellipse(self, lon, lat, radius, **kwargs):
 
         Parameters
         ----------
-        lon : float or array_like
-        longitude-like of ellipse centers (radians)
-        lat : float or array_like
-        latitude-like of ellipse centers (radians)
-        radius : float or array_like
-        radius of ellipses (radians)
-        matplotlib axes instance on which to draw ellipses.
-
-        Other Parameters
-        ----------------
-        other keyword arguments will be passed to matplotlib.patches.Ellipse.
+        lon : `float` or array_like
+            longitude-like of ellipse centers (radians)
+        lat : `float` or array_like
+            latitude-like of ellipse centers (radians)
+        radius : `float` or array_like
+            radius of ellipses (radians)
+        **kwargs : `dict`
+            Keyword argument which will be passed to
+            `matplotlib.patches.Ellipse`.
+
+        Returns
+        -------
+        ellipses : `list` [ `matplotlib.patches.Ellipse` ]
+            List of ellipses to add to the plot.
 
         # The code in this method adapted from astroML, which is BSD-licensed.
         # See http: //github.com/astroML/astroML for details.
         """
         # Code adapted from astroML, which is BSD-licensed.
         # See http: //github.com/astroML/astroML for details.
         ellipses = []
-        for lon, b, diam in np.broadcast(lon, lat, radius * 2.0):
-            el = Ellipse((lon, b), diam / np.cos(b), diam, **kwargs)
+        for ll, bb, diam in np.broadcast(lon, lat, radius * 2.0):
+            el = Ellipse((ll, bb), diam / np.cos(bb), diam, **kwargs)
             ellipses.append(el)
         return ellipses
 
@@ -674,7 +677,6 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fig=None):
             slicer.slice_points["dec"][good],
             plot_dict["radius"],
             rasterized=True,
-            ax=ax,
         )
         if plot_dict["metricIsColor"]:
             current = None

tests/maf/test_plotters.py

@@ -0,0 +1,68 @@
+# imports
+import unittest
+
+import numpy as np
+from matplotlib.figure import Figure
+
+import rubin_sim.maf as maf
+
+
+class TestPlotters(unittest.TestCase):
+    def setUp(self):
+        self.rng = np.random.default_rng()
+
+    def test_healpix_plotters(self):
+        # Set up a metric bundle to send to plotters
+        bundle1 = maf.create_empty_metric_bundle()
+        nside = 64
+        bundle1.slicer = maf.HealpixSlicer(nside=nside)
+        bundle1._setup_metric_values()
+        bundle1.metric_values += self.rng.uniform(size=len(bundle1.slicer))
+        # First test healpix sky map - just that it runs.
+        bundle1.set_plot_funcs([maf.HealpixSkyMap()])
+        figs = bundle1.plot()
+        self.assertTrue(isinstance(figs["SkyMap"], Figure))
+        # Test healpix histogram - just that it runs
+        bundle1.set_plot_funcs([maf.HealpixHistogram()])
+        figs = bundle1.plot()
+        self.assertTrue(isinstance(figs["Histogram"], Figure))
+        # Test power spectrum
+        bundle1.set_plot_funcs([maf.HealpixPowerSpectrum()])
+        figs = bundle1.plot()
+        self.assertTrue(isinstance(figs["PowerSpectrum"], Figure))
+
+    def test_base_skymap(self):
+        bundle1 = maf.create_empty_metric_bundle()
+        npoints = 1000
+        ra = self.rng.uniform(low=0, high=360, size=npoints)
+        dec = self.rng.uniform(low=-90, high=90, size=npoints)
+        bundle1.slicer = maf.UserPointsSlicer(ra, dec)
+        bundle1._setup_metric_values()
+        bundle1.metric_values += self.rng.uniform(size=len(bundle1.slicer))
+        # Test skymap
+        bundle1.set_plot_funcs([maf.BaseSkyMap()])
+        figs = bundle1.plot()
+        self.assertTrue(isinstance(figs["SkyMap"], Figure))
+        # Test healpix histogram - just that it runs
+        bundle1.set_plot_funcs([maf.BaseHistogram()])
+        figs = bundle1.plot()
+        self.assertTrue(isinstance(figs["Histogram"], Figure))
+
+    def test_oned_plotter(self):
+        bundle1 = maf.create_empty_metric_bundle()
+        npoints = 100
+        bins = np.arange(0, npoints, 1)
+        bundle1.slicer = maf.OneDSlicer(slice_col_name="test", bins=bins)
+        bundle1.slicer.slice_points = {"bins": bins}
+        bundle1.slicer.nslice = len(bins) - 1
+        bundle1.slicer.shape = len(bins) - 1
+        bundle1._setup_metric_values()
+        bundle1.metric_values += self.rng.uniform(size=len(bundle1.slicer))
+        # Test plotter
+        bundle1.set_plot_funcs([maf.OneDBinnedData()])
+        figs = bundle1.plot()
+        self.assertTrue(isinstance(figs["BinnedData"], Figure))
+
+
+if __name__ == "__main__":
+    unittest.main()