Fixing ordering and old sacc format.

firecrown/app/examples/_des_y1_3x2pt_pt_template.py

@@ -57,7 +57,7 @@ def _build_two_point_statistics(
         TwoPoint(source0=src0, source1=src0, sacc_data_type="galaxy_shear_xi_plus"),
         TwoPoint(source0=src0, source1=src0, sacc_data_type="galaxy_shear_xi_minus"),
         TwoPoint(
-            source0=lens0, source1=src0, sacc_data_type="galaxy_shearDensity_xi_t"
+            source0=src0, source1=lens0, sacc_data_type="galaxy_shearDensity_xi_t"
         ),
         TwoPoint(source0=lens0, source1=lens0, sacc_data_type="galaxy_density_xi"),
     ]

firecrown/app/examples/_des_y1_3x2pt_template.py

@@ -78,8 +78,8 @@ def build_likelihood(params: NamedParameters) -> tuple[ConstGaussian, ModelingTo
     for j in range(5):
         for i in range(4):
             stats[f"gammat_lens{j}_src{i}"] = TwoPoint(
-                source0=sources[f"lens{j}"],
-                source1=sources[f"src{i}"],
+                source0=sources[f"src{i}"],
+                source1=sources[f"lens{j}"],
                 sacc_data_type="galaxy_shearDensity_xi_t",
             )
 

firecrown/app/sacc/_load.py

@@ -1,6 +1,9 @@
 """Load command for SACC files."""
 
 import dataclasses
+import contextlib
+import io
+import warnings
 from pathlib import Path
 from typing import Annotated
 
@@ -41,7 +44,14 @@ def _load_sacc_file(self) -> None:
         try:
             if not self.sacc_file.exists():
                 raise typer.BadParameter(f"SACC file not found: {self.sacc_file}")
-            self.sacc_data = factories.load_sacc_data(self.sacc_file.as_posix())
+
+            with (
+                contextlib.redirect_stdout(io.StringIO()),
+                contextlib.redirect_stderr(io.StringIO()),
+                warnings.catch_warnings(),
+            ):
+                warnings.simplefilter("ignore")
+                self.sacc_data = factories.load_sacc_data(self.sacc_file.as_posix())
         except Exception as e:
             self.console.print(f"[bold red]Failed to load SACC file:[/bold red] {e}")
             raise

tests/likelihood/gauss_family/test_const_gaussianPM.py

@@ -214,9 +214,11 @@ def test_compute_chisq_fails_before_read(trivial_stats):
     """Note that the error message from the direct call to compute_chisq notes
     that update() must be called; this can only be called after read()."""
     likelihood = ConstGaussianPM(statistics=trivial_stats)
-    with pytest.raises(
-        AssertionError,
-        match=re.escape("update() must be called before compute_chisq()"),
+    with (
+        pytest.raises(
+            AssertionError,
+            match=re.escape("update() must be called before compute_chisq()"),
+        ),
     ):
         _ = likelihood.compute_chisq(ModelingTools())
 
@@ -225,7 +227,11 @@ def test_compute_chisq(trivial_stats, sacc_data_for_trivial_stat, trivial_params
     likelihood = ConstGaussianPM(statistics=trivial_stats)
     likelihood.read(sacc_data_for_trivial_stat)
     likelihood.update(trivial_params)
-    assert likelihood.compute_chisq(ModelingTools()) == 2.0
+    with pytest.warns(
+        UserWarning,
+        match=re.escape("inverse covariance correction has not yet been computed."),
+    ):
+        assert likelihood.compute_chisq(ModelingTools()) == 2.0
 
 
 def test_deprecated_compute(trivial_stats, sacc_data_for_trivial_stat, trivial_params):
@@ -256,7 +262,11 @@ def compute(
     likelihood.compute_theory_vector = compute_theory_vector  # type: ignore
     likelihood.compute = compute  # type: ignore
 
-    assert likelihood.compute_chisq(ModelingTools()) == 2.0
+    with pytest.warns(
+        UserWarning,
+        match=re.escape("inverse covariance correction has not yet been computed."),
+    ):
+        assert likelihood.compute_chisq(ModelingTools()) == 2.0
 
 
 def test_required_parameters(trivial_stats, sacc_data_for_trivial_stat, trivial_params):
@@ -282,7 +292,11 @@ def test_reset(trivial_stats, sacc_data_for_trivial_stat, trivial_params):
     likelihood.read(sacc_data_for_trivial_stat)
     likelihood.update(trivial_params)
     assert not trivial_stats[0].computed_theory_vector
-    assert likelihood.compute_loglike(ModelingTools()) == -1.0
+    with pytest.warns(
+        UserWarning,
+        match=re.escape("inverse covariance correction has not yet been computed."),
+    ):
+        assert likelihood.compute_loglike(ModelingTools()) == -1.0
     assert trivial_stats[0].computed_theory_vector
     likelihood.reset()
     assert not trivial_stats[0].computed_theory_vector
@@ -307,7 +321,11 @@ def test_using_good_sacc(
     likelihood.read(sacc_data_for_trivial_stat)
     params = firecrown.parameters.ParamsMap(mean=10.5)
     likelihood.update(params)
-    chisq = likelihood.compute_chisq(tools_with_vanilla_cosmology)
+    with pytest.warns(
+        UserWarning,
+        match=re.escape("inverse covariance correction has not yet been computed."),
+    ):
+        chisq = likelihood.compute_chisq(tools_with_vanilla_cosmology)
     assert isinstance(chisq, float)
     assert chisq > 0.0
 
@@ -331,15 +349,23 @@ def test_make_realization_chisq(
     likelihood.read(sacc_data_for_trivial_stat)
     params = firecrown.parameters.ParamsMap(mean=10.5)
     likelihood.update(params)
-    likelihood.compute_chisq(tools_with_vanilla_cosmology)
+    with pytest.warns(
+        UserWarning,
+        match=re.escape("inverse covariance correction has not yet been computed."),
+    ):
+        likelihood.compute_chisq(tools_with_vanilla_cosmology)
 
     new_sacc = likelihood.make_realization(sacc_data_for_trivial_stat)
 
     new_likelihood = ConstGaussianPM(statistics=[TrivialStatistic()])
     new_likelihood.read(new_sacc)
     params = firecrown.parameters.ParamsMap(mean=10.5)
     new_likelihood.update(params)
-    chisq = new_likelihood.compute_chisq(tools_with_vanilla_cosmology)
+    with pytest.warns(
+        UserWarning,
+        match=re.escape("inverse covariance correction has not yet been computed."),
+    ):
+        chisq = new_likelihood.compute_chisq(tools_with_vanilla_cosmology)
 
     # The new likelihood chisq is distributed as a chi-squared with 3 degrees of
     # freedom. We want to check that the new chisq is within the 1-10^-6 quantile
@@ -358,7 +384,11 @@ def test_make_realization_chisq_mean(
     likelihood.read(sacc_data_for_trivial_stat)
     params = firecrown.parameters.ParamsMap(mean=10.5)
     likelihood.update(params)
-    likelihood.compute_chisq(tools_with_vanilla_cosmology)
+    with pytest.warns(
+        UserWarning,
+        match=re.escape("inverse covariance correction has not yet been computed."),
+    ):
+        likelihood.compute_chisq(tools_with_vanilla_cosmology)
 
     chisq_list = []
     for _ in range(1000):
@@ -368,7 +398,11 @@ def test_make_realization_chisq_mean(
         new_likelihood.read(new_sacc)
         params = firecrown.parameters.ParamsMap(mean=10.5)
         new_likelihood.update(params)
-        chisq = new_likelihood.compute_chisq(tools_with_vanilla_cosmology)
+        with pytest.warns(
+            UserWarning,
+            match=re.escape("inverse covariance correction has not yet been computed."),
+        ):
+            chisq = new_likelihood.compute_chisq(tools_with_vanilla_cosmology)
         chisq_list.append(chisq)
 
     # The new likelihood chisq is distributed as a chi-squared with 3 degrees of
@@ -387,7 +421,11 @@ def test_make_realization_data_vector(
     likelihood.read(sacc_data_for_trivial_stat)
     params = firecrown.parameters.ParamsMap(mean=10.5)
     likelihood.update(params)
-    likelihood.compute_chisq(tools_with_vanilla_cosmology)
+    with pytest.warns(
+        UserWarning,
+        match=re.escape("inverse covariance correction has not yet been computed."),
+    ):
+        likelihood.compute_chisq(tools_with_vanilla_cosmology)
 
     data_vector_list = []
     for _ in range(1000):
@@ -430,7 +468,11 @@ def test_make_realization_no_noise(
     likelihood.read(sacc_data_for_trivial_stat)
     params = firecrown.parameters.ParamsMap(mean=10.5)
     likelihood.update(params)
-    likelihood.compute_chisq(tools_with_vanilla_cosmology)
+    with pytest.warns(
+        UserWarning,
+        match=re.escape("inverse covariance correction has not yet been computed."),
+    ):
+        likelihood.compute_chisq(tools_with_vanilla_cosmology)
 
     new_sacc = likelihood.make_realization(sacc_data_for_trivial_stat, add_noise=False)
 
@@ -561,8 +603,8 @@ def __init__(self, statistic):
         self.statistic = statistic
 
 
-@pytest.fixture
-def minimal_const_gaussian_PM():
+@pytest.fixture(name="minimal_const_gaussian_PM")
+def fixture_minimal_const_gaussian_PM() -> ConstGaussianPM:
     # Create minimal valid statistics for the class.
     z = np.array([0.1, 0.2, 0.3])
     dndz = np.array([1.0, 2.0, 3.0])
@@ -601,6 +643,9 @@ def minimal_const_gaussian_PM():
     return likelihood
 
 
+# pylint: disable=protected-access
+
+
 def test_precomputed_warning(minimal_const_gaussian_PM):
     # Check that running the precomputation twice gives a warning.
     minimal_const_gaussian_PM._generate_maps()
@@ -705,8 +750,8 @@ def test_PM_correction_matrix(sacc_data):
         sacc_data_type="galaxy_shear_xi_plus",
     )
     stats["gammat_lens0_src0"] = TwoPoint(
-        source0=nc.NumberCounts(sacc_tracer="lens0"),
-        source1=wl.WeakLensing(sacc_tracer="src0"),
+        source0=wl.WeakLensing(sacc_tracer="src0"),
+        source1=nc.NumberCounts(sacc_tracer="lens0"),
         sacc_data_type="galaxy_shearDensity_xi_t",
     )
     stats["wtheta_lens0_lens0"] = TwoPoint(
@@ -731,8 +776,8 @@ def test_compute_chisq_with_correction(sacc_data):
     # This tests the truthy branch of the if statement at line 50
     stats = {}
     stats["gammat_lens0_src0"] = TwoPoint(
-        source0=nc.NumberCounts(sacc_tracer="lens0"),
-        source1=wl.WeakLensing(sacc_tracer="src0"),
+        source0=wl.WeakLensing(sacc_tracer="src0"),
+        source1=nc.NumberCounts(sacc_tracer="lens0"),
         sacc_data_type="galaxy_shearDensity_xi_t",
     )
     likelihood = ConstGaussianPM(statistics=list(stats.values()))
@@ -759,8 +804,8 @@ def test_get_lens_statistic_not_found(sacc_data):
     # Test that _get_lens_statistic raises StopIteration when lens tracer not found
     stats = {}
     stats["gammat_lens0_src0"] = TwoPoint(
-        source0=nc.NumberCounts(sacc_tracer="lens0"),
-        source1=wl.WeakLensing(sacc_tracer="src0"),
+        source0=wl.WeakLensing(sacc_tracer="src0"),
+        source1=nc.NumberCounts(sacc_tracer="lens0"),
         sacc_data_type="galaxy_shearDensity_xi_t",
     )
     likelihood = ConstGaussianPM(statistics=list(stats.values()))
@@ -775,8 +820,8 @@ def test_get_src_statistic_not_found(sacc_data):
     # Test that _get_src_statistic raises StopIteration when source tracer not found
     stats = {}
     stats["gammat_lens0_src0"] = TwoPoint(
-        source0=nc.NumberCounts(sacc_tracer="lens0"),
-        source1=wl.WeakLensing(sacc_tracer="src0"),
+        source0=wl.WeakLensing(sacc_tracer="src0"),
+        source1=nc.NumberCounts(sacc_tracer="lens0"),
         sacc_data_type="galaxy_shearDensity_xi_t",
     )
     likelihood = ConstGaussianPM(statistics=list(stats.values()))
@@ -818,3 +863,6 @@ def __init__(self):
 
     with pytest.raises(StopIteration, match="missing attributes"):
         likelihood._collect_data_vectors()
+
+
+# pylint: enable=protected-access

tests/test_pt_systematics.py

@@ -64,7 +64,7 @@ def test_pt_systematics(weak_lensing_source, number_counts_source, sacc_data):
     stats = [
         TwoPoint("galaxy_shear_xi_plus", weak_lensing_source, weak_lensing_source),
         TwoPoint("galaxy_shear_xi_minus", weak_lensing_source, weak_lensing_source),
-        TwoPoint("galaxy_shearDensity_xi_t", number_counts_source, weak_lensing_source),
+        TwoPoint("galaxy_shearDensity_xi_t", weak_lensing_source, number_counts_source),
         TwoPoint("galaxy_density_xi", number_counts_source, number_counts_source),
     ]
 
@@ -179,9 +179,9 @@ def test_pt_systematics(weak_lensing_source, number_counts_source, sacc_data):
     # print(list(likelihood.statistics[2].cells.keys()))
     s2 = likelihood.statistics[2].statistic
     assert isinstance(s2, TwoPoint)
-    cells_gG = s2.cells[TracerNames("galaxies", "shear")]
-    cells_gI = s2.cells[TracerNames("galaxies", "intrinsic_pt")]
-    cells_mI = s2.cells[TracerNames("magnification+rsd", "intrinsic_pt")]
+    cells_gG = s2.cells[TracerNames("shear", "galaxies")]
+    cells_gI = s2.cells[TracerNames("intrinsic_pt", "galaxies")]
+    cells_mI = s2.cells[TracerNames("intrinsic_pt", "magnification+rsd")]
 
     # print(list(likelihood.statistics[3].cells.keys()))
     s3 = likelihood.statistics[3].statistic
@@ -267,8 +267,8 @@ def test_pt_mixed_systematics(sacc_data):
     )
 
     stat = TwoPoint(
-        source0=nc_source,
-        source1=wl_source,
+        source0=wl_source,
+        source1=nc_source,
         sacc_data_type="galaxy_shearDensity_xi_t",
     )
 
@@ -354,8 +354,8 @@ def test_pt_mixed_systematics(sacc_data):
     ells = s0.ells_for_xi
 
     # print(list(likelihood.statistics[2].cells.keys()))
-    cells_gG = s0.cells[TracerNames("galaxies+magnification+rsd", "shear")]
-    cells_gI = s0.cells[TracerNames("galaxies+magnification+rsd", "intrinsic_pt")]
+    cells_gG = s0.cells[TracerNames("shear", "galaxies+magnification+rsd")]
+    cells_gI = s0.cells[TracerNames("intrinsic_pt", "galaxies+magnification+rsd")]
     # pylint: enable=no-member
 
     # Code that computes effect from IA using that Pk2D object
@@ -399,8 +399,8 @@ def test_pt_mixed_systematics_zdep(sacc_data):
     )
 
     stat = TwoPoint(
-        source0=nc_source,
-        source1=wl_source,
+        source0=wl_source,
+        source1=nc_source,
         sacc_data_type="galaxy_shearDensity_xi_t",
     )
 
@@ -494,8 +494,8 @@ def test_pt_mixed_systematics_zdep(sacc_data):
     ells = s0.ells_for_xi
 
     # print(list(likelihood.statistics[2].cells.keys()))
-    cells_gG = s0.cells[TracerNames("galaxies+magnification+rsd", "shear")]
-    cells_gI = s0.cells[TracerNames("galaxies+magnification+rsd", "intrinsic_pt")]
+    cells_gG = s0.cells[TracerNames("shear", "galaxies+magnification+rsd")]
+    cells_gI = s0.cells[TracerNames("intrinsic_pt", "galaxies+magnification+rsd")]
     # pylint: enable=no-member
 
     # Code that computes effect from IA using that Pk2D object
@@ -533,7 +533,7 @@ def test_pt_systematics_zdep(weak_lensing_source, number_counts_source, sacc_dat
     stats = [
         TwoPoint("galaxy_shear_xi_plus", weak_lensing_source, weak_lensing_source),
         TwoPoint("galaxy_shear_xi_minus", weak_lensing_source, weak_lensing_source),
-        TwoPoint("galaxy_shearDensity_xi_t", number_counts_source, weak_lensing_source),
+        TwoPoint("galaxy_shearDensity_xi_t", weak_lensing_source, number_counts_source),
         TwoPoint("galaxy_density_xi", number_counts_source, number_counts_source),
     ]
 
@@ -656,9 +656,9 @@ def test_pt_systematics_zdep(weak_lensing_source, number_counts_source, sacc_dat
     # print(list(likelihood.statistics[2].cells.keys()))
     s2 = likelihood.statistics[2].statistic
     assert isinstance(s2, TwoPoint)
-    cells_gG = s2.cells[TracerNames("galaxies", "shear")]
-    cells_gI = s2.cells[TracerNames("galaxies", "intrinsic_pt")]
-    cells_mI = s2.cells[TracerNames("magnification+rsd", "intrinsic_pt")]
+    cells_gG = s2.cells[TracerNames("shear", "galaxies")]
+    cells_gI = s2.cells[TracerNames("intrinsic_pt", "galaxies")]
+    cells_mI = s2.cells[TracerNames("intrinsic_pt", "magnification+rsd")]
 
     # print(list(likelihood.statistics[3].cells.keys()))
     s3 = likelihood.statistics[3].statistic