V1.11 (#523)

* Add type coercion to satisfy mypy
* Change version number to 1.11.0

Author: marcpaterno

.github/workflows/ci.yml

@@ -27,14 +27,13 @@ jobs:
       - name: Setting up Miniforge
         uses: conda-incubator/setup-miniconda@v3
         with:
-          mamba-version: "*"
           miniforge-version: latest
-          auto-update-conda: true
-          activate-environment: firecrown_developer
           python-version: ${{ matrix.python-version }}
           show-channel-urls: true
           conda-remove-defaults: true
           environment-file: environment.yml
+          activate-environment: firecrown_developer
+
       - name: Cache date
         id: get-date
         run: echo "today=$(/bin/date -u '+%Y%m%d')" >> $GITHUB_OUTPUT
@@ -124,14 +123,12 @@ jobs:
       - name: Setting up Miniforge
         uses: conda-incubator/setup-miniconda@v3
         with:
-          mamba-version: "*"
           miniforge-version: latest
-          auto-update-conda: true
-          activate-environment: firecrown_developer
           python-version: ${{ matrix.python-version }}
           show-channel-urls: true
           conda-remove-defaults: true
           environment-file: environment.yml
+          activate-environment: firecrown_developer
       - name: Cache date
         id: get-date
         run: echo "today=$(/bin/date -u '+%Y%m%d')" >> $GITHUB_OUTPUT

docs/conf.py

@@ -9,7 +9,7 @@
 project = "Firecrown"
 copyright = "2025, LSST DESC Firecrown Contributors"
 author = "LSST DESC Firecrown Contributors"
-release = "1.11.0a0"
+release = "1.11.0"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

firecrown/likelihood/gaussfamily.py

@@ -316,7 +316,7 @@ def get_cov(
         indices: list[int] = []
         for stat in statistic_list:
             assert stat.sacc_indices is not None
-            temp = [self.cov_index_map[idx] for idx in stat.sacc_indices]
+            temp = [self.cov_index_map[int(idx)] for idx in stat.sacc_indices]
             indices += temp
         ixgrid = np.ix_(indices, indices)
 

firecrown/version.py

@@ -8,5 +8,5 @@
 
 FIRECROWN_MAJOR = 1
 FIRECROWN_MINOR = 11
-FIRECROWN_PATCH = "0a0"
+FIRECROWN_PATCH = 0
 __version__ = f"{FIRECROWN_MAJOR}.{FIRECROWN_MINOR}.{FIRECROWN_PATCH}"

tests/conftest.py

@@ -659,7 +659,7 @@ def fixture_sacc_galaxy_cells() -> tuple[sacc.Sacc, dict, dict]:
     dv = []
 
     for i, j in upper_triangle_indices(len(src_bins_centers)):
-        Cells = np.random.normal(size=ells.shape[0])
+        Cells = np.array(np.random.normal(size=ells.shape[0]))
         sacc_data.add_ell_cl("galaxy_shear_cl_ee", f"src{i}", f"src{j}", ells, Cells)
         tracer_pairs[(TracerNames(f"src{i}", f"src{j}"), "galaxy_shear_cl_ee")] = (
             ells,
@@ -668,7 +668,7 @@ def fixture_sacc_galaxy_cells() -> tuple[sacc.Sacc, dict, dict]:
         dv.append(Cells)
 
     for i, j in upper_triangle_indices(len(lens_bins_centers)):
-        Cells = np.random.normal(size=ells.shape[0])
+        Cells = np.array(np.random.normal(size=ells.shape[0]))
         sacc_data.add_ell_cl("galaxy_density_cl", f"lens{i}", f"lens{j}", ells, Cells)
         tracer_pairs[(TracerNames(f"lens{i}", f"lens{j}"), "galaxy_density_cl")] = (
             ells,
@@ -677,7 +677,7 @@ def fixture_sacc_galaxy_cells() -> tuple[sacc.Sacc, dict, dict]:
         dv.append(Cells)
 
     for i, j in product(range(len(src_bins_centers)), range(len(lens_bins_centers))):
-        Cells = np.random.normal(size=ells.shape[0])
+        Cells = np.array(np.random.normal(size=ells.shape[0]))
         sacc_data.add_ell_cl(
             "galaxy_shearDensity_cl_e", f"src{i}", f"lens{j}", ells, Cells
         )
@@ -828,7 +828,7 @@ def fixture_sacc_galaxy_xis():
     dv = []
 
     for i, j in upper_triangle_indices(len(lens_bins_centers)):
-        xis = np.random.normal(size=thetas.shape[0])
+        xis = np.array(np.random.normal(size=thetas.shape[0]))
         sacc_data.add_theta_xi("galaxy_density_xi", f"lens{i}", f"lens{j}", thetas, xis)
         tracer_pairs[(TracerNames(f"lens{i}", f"lens{j}"), "galaxy_density_xi")] = (
             thetas,
@@ -837,7 +837,7 @@ def fixture_sacc_galaxy_xis():
         dv.append(xis)
 
     for i, j in product(range(len(src_bins_centers)), range(len(lens_bins_centers))):
-        xis = np.random.normal(size=thetas.shape[0])
+        xis = np.array(np.random.normal(size=thetas.shape[0]))
         sacc_data.add_theta_xi(
             "galaxy_shearDensity_xi_t", f"src{i}", f"lens{j}", thetas, xis
         )
@@ -850,7 +850,7 @@ def fixture_sacc_galaxy_xis():
         dv.append(xis)
 
     for i, j in upper_triangle_indices(len(src_bins_centers)):
-        xis = np.random.normal(size=thetas.shape[0])
+        xis = np.array(np.random.normal(size=thetas.shape[0]))
         sacc_data.add_theta_xi(
             "galaxy_shear_xi_minus", f"src{i}", f"src{j}", thetas, xis
         )
@@ -860,7 +860,7 @@ def fixture_sacc_galaxy_xis():
         )
         dv.append(xis)
     for i, j in upper_triangle_indices(len(src_bins_centers)):
-        xis = np.random.normal(size=thetas.shape[0])
+        xis = np.array(np.random.normal(size=thetas.shape[0]))
         sacc_data.add_theta_xi(
             "galaxy_shear_xi_plus", f"src{i}", f"src{j}", thetas, xis
         )
@@ -908,7 +908,7 @@ def fixture_sacc_galaxy_xis_inverted():
     dv = []
 
     for i, j in upper_triangle_indices(len(lens_bins_centers)):
-        xis = np.random.normal(size=thetas.shape[0])
+        xis = np.array(np.random.normal(size=thetas.shape[0]))
         sacc_data.add_theta_xi("galaxy_density_xi", f"lens{j}", f"lens{i}", thetas, xis)
         tracer_pairs[(TracerNames(f"lens{j}", f"lens{i}"), "galaxy_density_xi")] = (
             thetas,
@@ -917,7 +917,7 @@ def fixture_sacc_galaxy_xis_inverted():
         dv.append(xis)
 
     for i, j in product(range(len(src_bins_centers)), range(len(lens_bins_centers))):
-        xis = np.random.normal(size=thetas.shape[0])
+        xis = np.array(np.random.normal(size=thetas.shape[0]))
         sacc_data.add_theta_xi(
             "galaxy_shearDensity_xi_t", f"lens{i}", f"src{j}", thetas, xis
         )
@@ -930,7 +930,7 @@ def fixture_sacc_galaxy_xis_inverted():
         dv.append(xis)
 
     for i, j in upper_triangle_indices(len(src_bins_centers)):
-        xis = np.random.normal(size=thetas.shape[0])
+        xis = np.array(np.random.normal(size=thetas.shape[0]))
         sacc_data.add_theta_xi(
             "galaxy_shear_xi_minus", f"src{j}", f"src{i}", thetas, xis
         )
@@ -940,7 +940,7 @@ def fixture_sacc_galaxy_xis_inverted():
         )
         dv.append(xis)
     for i, j in upper_triangle_indices(len(src_bins_centers)):
-        xis = np.random.normal(size=thetas.shape[0])
+        xis = np.array(np.random.normal(size=thetas.shape[0]))
         sacc_data.add_theta_xi(
             "galaxy_shear_xi_plus", f"src{j}", f"src{i}", thetas, xis
         )
@@ -969,7 +969,7 @@ def fixture_sacc_galaxy_cells_ambiguous() -> sacc.Sacc:
     dndz = np.exp(-0.5 * (z - 0.5) ** 2 / 0.05 / 0.05)
     sacc_data.add_tracer("NZ", "bin0", z, dndz)
     sacc_data.add_tracer("NZ", "bin1", z, dndz)
-    Cells = np.random.normal(size=ells.shape[0])
+    Cells = np.array(np.random.normal(size=ells.shape[0]))
     sacc_data.add_ell_cl("galaxy_shearDensity_cl_e", "bin0", "bin1", ells, Cells)
     cov = np.diag(np.zeros(len(ells)) + 0.01)
 
@@ -1052,7 +1052,7 @@ def fixture_sacc_galaxy_cells_src0_src0_no_window() -> (
     dndz = np.exp(-0.5 * (z - 0.5) ** 2 / 0.05 / 0.05)
     sacc_data.add_tracer("NZ", "src0", z, dndz)
 
-    Cells = np.random.normal(size=ells.shape[0])
+    Cells = np.array(np.random.normal(size=ells.shape[0]))
     sacc_data.add_ell_cl("galaxy_shear_cl_ee", "src0", "src0", ells, Cells)
 
     cov = np.diag(np.ones_like(Cells) * 0.01)

tests/metadata/test_metadata_two_point_sacc.py

@@ -190,7 +190,9 @@ def fixture_sacc_galaxy_xis_three_tracers() -> (
     dndz2 = np.exp(-0.5 * (z - 0.9) ** 2 / 0.05 / 0.05)
     sacc_data.add_tracer("NZ", "lens1", z, dndz2)
 
-    xis = np.random.normal(size=thetas.shape[0])
+    # We have to coerce the return type of np.random.normal to np.array,
+    # because mypy 1.16 is unable to infer the type of the return value.
+    xis = np.array(np.random.normal(size=thetas.shape[0]))
     for theta, xi in zip(thetas, xis):
         sacc_data.add_data_point(
             "galaxy_density_xi", ("src0", "lens0", "lens1"), xi, theta=theta
@@ -221,7 +223,9 @@ def fixture_sacc_galaxy_cells_three_tracers() -> (
     dndz2 = np.exp(-0.5 * (z - 0.9) ** 2 / 0.05 / 0.05)
     sacc_data.add_tracer("NZ", "lens1", z, dndz2)
 
-    Cells = np.random.normal(size=ells.shape[0])
+    # We have to coerce the return type of np.random.normal to np.array,
+    # because mypy 1.16 is unable to infer the type of the return value.
+    Cells = np.array(np.random.normal(size=ells.shape[0]))
     for ell, cell in zip(ells, Cells):
         sacc_data.add_data_point(
             "galaxy_shear_cl_ee", ("src0", "lens0", "lens1"), cell, ell=ell

tests/test_cluster_mass_richness.py

@@ -66,17 +66,19 @@ def test_create_musigma_kernel():
 
 def test_cluster_observed_z():
     for z in np.geomspace(1.0e-18, 2.0, 20):
-        z = np.atleast_1d(z)
+        zarray = np.atleast_1d(z)
         mass = np.atleast_1d(0)
-        f_z = MassRichnessGaussian.observed_value((0.0, 0.0, 1.0), mass, z, 0, 0)
-        assert f_z == pytest.approx(np.log1p(z), 1.0e-7, 0.0)
+        f_z = MassRichnessGaussian.observed_value((0.0, 0.0, 1.0), mass, zarray, 0, 0)
+        assert f_z == pytest.approx(np.log1p(zarray), 1.0e-7, 0.0)
 
 
 def test_cluster_observed_mass():
     for mass in np.linspace(10.0, 16.0, 20):
         z = np.atleast_1d(0)
-        mass = np.atleast_1d(mass)
-        f_logM = MassRichnessGaussian.observed_value((0.0, 1.0, 0.0), mass, z, 0, 0)
+        massarray = np.atleast_1d(mass)
+        f_logM = MassRichnessGaussian.observed_value(
+            (0.0, 1.0, 0.0), massarray, z, 0, 0
+        )
 
         assert f_logM == pytest.approx(mass * np.log(10.0), 1.0e-7, 0.0)
 
@@ -90,13 +92,13 @@ def test_cluster_murata_binned_distribution(murata_binned_relation: MurataBinned
         for mass1, mass2 in zip(mass_array[:-1], mass_array[1:]):
             mass1_a = np.atleast_1d(mass1)
             mass2_a = np.atleast_1d(mass2)
-            z = np.atleast_1d(z)
+            zarray = np.atleast_1d(z)
 
             probability_0 = murata_binned_relation.distribution(
-                mass1_a, z, mass_proxy_limits
+                mass1_a, zarray, mass_proxy_limits
             )
             probability_1 = murata_binned_relation.distribution(
-                mass2_a, z, mass_proxy_limits
+                mass2_a, zarray, mass_proxy_limits
             )
 
             assert probability_0 >= 0
@@ -122,14 +124,14 @@ def test_cluster_murata_binned_distribution(murata_binned_relation: MurataBinned
 def test_cluster_murata_binned_mean(murata_binned_relation: MurataBinned):
     for mass in np.linspace(7.0, 26.0, 20):
         for z in np.geomspace(1.0e-18, 2.0, 20):
-            mass = np.atleast_1d(mass)
-            z = np.atleast_1d(z)
-            test = murata_binned_relation.get_proxy_mean(mass, z)
+            massarray = np.atleast_1d(mass)
+            zarray = np.atleast_1d(z)
+            test = murata_binned_relation.get_proxy_mean(massarray, zarray)
 
             true = MassRichnessGaussian.observed_value(
                 (3.00, 0.086, 0.01),
-                mass,
-                z,
+                massarray,
+                zarray,
                 PIVOT_MASS * np.log(10.0),
                 np.log1p(PIVOT_Z),
             )
@@ -140,14 +142,14 @@ def test_cluster_murata_binned_mean(murata_binned_relation: MurataBinned):
 def test_cluster_murata_binned_variance(murata_binned_relation: MurataBinned):
     for mass in np.linspace(7.0, 26.0, 20):
         for z in np.geomspace(1.0e-18, 2.0, 20):
-            mass = np.atleast_1d(mass)
-            z = np.atleast_1d(z)
-            test = murata_binned_relation.get_proxy_sigma(mass, z)
+            massarray = np.atleast_1d(mass)
+            zarray = np.atleast_1d(z)
+            test = murata_binned_relation.get_proxy_sigma(massarray, zarray)
 
             true = MassRichnessGaussian.observed_value(
                 (3.00, 0.07, 0.01),
-                mass,
-                z,
+                massarray,
+                zarray,
                 PIVOT_MASS * np.log(10.0),
                 np.log1p(PIVOT_Z),
             )
@@ -163,14 +165,14 @@ def test_cluster_murata_unbinned_distribution(murata_unbinned_relation: MurataUn
         for mass1, mass2 in zip(mass_array[:-1], mass_array[1:]):
             mass1_a = np.atleast_1d(mass1)
             mass2_a = np.atleast_1d(mass2)
-            z = np.atleast_1d(z)
+            zarray = np.atleast_1d(z)
             mass_proxy = np.atleast_1d(1)
 
             probability_0 = murata_unbinned_relation.distribution(
-                mass1_a, z, mass_proxy
+                mass1_a, zarray, mass_proxy
             )
             probability_1 = murata_unbinned_relation.distribution(
-                mass2_a, z, mass_proxy
+                mass2_a, zarray, mass_proxy
             )
 
             # Probability density should be initially monotonically increasing

tests/test_version.py

@@ -8,4 +8,4 @@
 
 
 def test_version():
-    assert firecrown.__version__ == "1.11.0a0"
+    assert firecrown.__version__ == "1.11.0"

tutorial/introduction_to_firecrown.qmd

@@ -1,6 +1,6 @@
 ---
 title: An Introduction to Firecrown
-subtitle: "Version 1.11.0a0"
+subtitle: "Version 1.11.0"
 authors:
   - Marc Paterno
   - Sandro Vitenti

tutorial/two_point_generators.qmd

@@ -93,7 +93,7 @@ wl_photoz = wl.PhotoZShiftFactory()
 wl_mult_bias = wl.MultiplicativeShearBiasFactory()
 
 # NumberCounts systematics -- global
-# As for Firecrown 1.11.0a0, we do not have any global systematics for number counts
+# As for Firecrown 1.11.0, we do not have any global systematics for number counts
 # NumberCounts systematics -- per-bin
 nc_photoz = nc.PhotoZShiftFactory()