Change ts_map default MOC refinement strategy

cosipy/ts_map/fast_norm_fit.py

@@ -16,8 +16,8 @@ class FastNormFit:
 
         TS(N) = 2 \\sum_i \\left( \\frac{\\log P(d_i; b_i+N e_i)}{\\log P(d_i; b_i)} \\right)
 
-    where :math:`P(d; \lambda)` is the Poisson probability of
-    obtaining :math:`d` count where :math:`\lambda` is expected on
+    where :math:`P(d; \\lambda)` is the Poisson probability of
+    obtaining :math:`d` count where :math:`\\lambda` is expected on
     average; :math:`b` is the estimated number of background counts;
     :math:`N` is the normalization; and :math:`e` is the expected
     excess -i.e. signal- per normalization unit -i.e. the number of
@@ -37,7 +37,7 @@ class FastNormFit:
     .. note::
 
         Because of the Poisson probability, :math:`TS(N)` is only
-        well-defined for :math:`N \geq 1`. By default,
+        well-defined for :math:`N \\geq 1`. By default,
         underfluctuations are set to :math:`TS(N=0) = 0`. For cases
         when there is benefit in letting the normalization float to
         negative values, you can use `allow_negative`, but in that

cosipy/ts_map/moc_ts_fit.py

@@ -169,7 +169,7 @@ def fit(self, max_nside, spectrum, energy_channel = None,
           lowest nside used in map
         strategy : MOCTSMap.Strategy subclass, optional
           strategy to use in selecting pixels to refine.  If None,
-          default to TopKStrategy with k=8
+          default to PaddingStrategy(ContainmentStrategy(0.999)).
 
         Returns
         -------
@@ -197,7 +197,7 @@ def refine(pix):
             return res
 
         if strategy is None:
-            self.strategy = self.TopKStrategy(k=8)
+            self.strategy = self.PaddingStrategy(self.ContainmentStrategy(0.999))
         else:
             self.strategy = strategy
 

tests/ts_map/test_fast_ts_map.py

@@ -136,6 +136,48 @@ def test_moc_ts_fit():
     ts_results = ts.fit(max_nside = 2, energy_channel = [2,3],
                         spectrum = spectrum, cpu_cores = 1)
 
+    ts_values, pixels = ts_results
+    assert all(pixels == [
+        16, 20, 24, 28, 32,
+        36, 40, 44, 48, 52,
+        56, 60, 17, 21, 25,
+        29, 33, 37, 41, 45,
+        49, 53, 57, 61, 18,
+        22, 26, 30, 34, 38,
+        42, 46, 50, 54, 58,
+        62, 19, 23, 27, 31,
+        35, 39, 43, 47, 51,
+        55, 59, 63
+    ])
+
+    assert np.allclose(ts_values, [
+        40.31750179, 39.40582836, 37.39229509, 39.78630473, 40.39347596,
+        40.10805455, 38.79974817, 38.86985166, 40.14551663, 39.92706709,
+        39.19653532, 40.07420192, 40.07720833, 38.76345776, 37.46243839,
+        40.19657905, 40.41047825, 39.9701239,  37.23577505, 39.28060583,
+        40.2803205,  39.83138091, 39.25707762, 39.90376762, 40.20425492,
+        39.93311807, 37.35958207, 39.07431591, 40.3217545,  40.1935334,
+        38.98305396, 37.24284051, 40.41652632, 39.61022258, 39.22641583,
+        40.09740223, 39.81314166, 39.49561083, 37.43747447, 39.65452285,
+        39.940159,   39.61014066, 37.24223294, 37.35636565, 40.65108076,
+        39.92533792, 37.24385822, 40.2989865
+    ])
+
+    ts.plot_ts(*ts_results,
+               skycoord = SkyCoord(l=0, b=0, unit=u.deg, frame="galactic"))
+
+    ts.plot_ts(*ts_results, containment = 0.9, save_plot = True,
+               save_dir = "", save_name = "ts_map.png")
+
+    assert Path("ts_map.png").exists()
+
+    os.remove("ts_map.png")
+
+    # test top-k strategy
+    ts_results = ts.fit(max_nside = 2, energy_channel = [2,3],
+                        spectrum = spectrum, cpu_cores = 1,
+                        strategy=MOCTSMap.TopKStrategy(k=8))
+
     ts_values, pixels = ts_results
     assert all(pixels == [
         6,  10, 11, 14, 16,
@@ -159,22 +201,15 @@ def test_moc_ts_fit():
         40.2989865
     ])
 
-    ts.plot_ts(*ts_results,
-               skycoord = SkyCoord(l=0, b=0, unit=u.deg, frame="galactic"))
-
-    ts.plot_ts(*ts_results, containment = 0.9, save_plot = True,
-               save_dir = "", save_name = "ts_map.png")
-
-    assert Path("ts_map.png").exists()
-
-    os.remove("ts_map.png")
-
     # test containment strategy
     ts_results = ts.fit(max_nside = 2, energy_channel = [2,3],
                         spectrum = spectrum,
                         strategy=MOCTSMap.ContainmentStrategy(0.9))
 
     ts_values, pixels = ts_results
+    print(pixels)
+    print(ts_values)
+
     assert all(pixels == [
         16, 20, 24, 28, 32,
         36, 40, 44, 48, 52,