Save predictions to sacc

codecov.yml

@@ -31,7 +31,7 @@ coverage:
     patch:
       default:
         # basic
-        target: auto
+        target: 100%
         threshold: 0%
 
         # advanced
@@ -63,4 +63,5 @@ coverage:
         removed_code_behavior: fully_covered_patch #off, removals_only, adjust_base",
 
 github_checks:
-	annotations: true #,false
+	annotations: true #,false
+

examples/des_y1_3x2pt/des_y1_3x2pt_PT.py

@@ -43,6 +43,9 @@ class CclSetup:
 
 @dataclass
 class CElls:
+    """A package of related C_ell values, to reduce the number of variables
+    used in the :meth:`run_likelihood` method."""
+
     GG: np.ndarray
     GI: np.ndarray
     II: np.ndarray
@@ -234,6 +237,7 @@ def run_likelihood() -> None:
     assert likelihood.cov is not None
 
     stat0 = likelihood.statistics[0].statistic
+    assert isinstance(stat0, TwoPoint)
 
     # x = likelihood.statistics[0].ell_or_theta_
     # y_data = likelihood.statistics[0].measured_statistic_
@@ -243,11 +247,12 @@ def run_likelihood() -> None:
 
     print(list(stat0.cells.keys()))
 
-    stat2 = likelihood.statistics[2].statistic
+    stat2 = likelihood.statistics[2].statistic  # pylint: disable=no-member
     assert isinstance(stat2, TwoPoint)
     print(list(stat2.cells.keys()))
 
-    stat3 = likelihood.statistics[3].statistic
+    stat3 = likelihood.statistics[3].statistic  # pylint: disable=no-member
+    assert isinstance(stat3, TwoPoint)
     print(list(stat3.cells.keys()))
 
     plot_predicted_and_measured_statistics(

examples/des_y1_3x2pt/des_y1_cosmic_shear_TATT.py

@@ -158,6 +158,7 @@ def run_likelihood() -> None:
     print(f"Log-like = {log_like:.1f}")
 
     # Plot the predicted and measured statistic
+    assert isinstance(likelihood, ConstGaussian)
     two_point_0 = likelihood.statistics[0].statistic
     assert isinstance(two_point_0, TwoPoint)
 

examples/des_y1_3x2pt/des_y1_cosmic_shear_pk_modifier.py

@@ -156,10 +156,9 @@ def run_likelihood() -> None:
     print(f"Log-like = {log_like:.1f}")
 
     # Plot the predicted and measured statistic
+    assert isinstance(likelihood, ConstGaussian)
     two_point_0 = likelihood.statistics[0].statistic
     assert isinstance(two_point_0, TwoPoint)
-
-    assert isinstance(likelihood, ConstGaussian)
     assert likelihood.cov is not None
 
     # Predict CCL Cl

firecrown/connector/cosmosis/likelihood.py

@@ -41,9 +41,6 @@ class FirecrownLikelihood:
     :param config: current CosmoSIS datablock
     """
 
-    likelihood: Likelihood
-    map: MappingCosmoSIS
-
     def __init__(self, config: cosmosis.datablock):
         """Create the FirecrownLikelihood object from the given configuration."""
         likelihood_source = config.get_string(option_section, "likelihood_source", "")
@@ -61,6 +58,7 @@ def __init__(self, config: cosmosis.datablock):
 
         self.firecrown_module_name = option_section
         self.sampling_sections = sections
+        self.likelihood: Likelihood
         try:
             self.likelihood, self.tools = load_likelihood(
                 likelihood_source, build_parameters
@@ -70,7 +68,7 @@ def __init__(self, config: cosmosis.datablock):
             print(f"The Firecrown likelihood needs a required parameter: {err}")
             print("*" * 30)
             raise
-        self.map = mapping_builder(
+        self.map: MappingCosmoSIS = mapping_builder(
             input_style="CosmoSIS", require_nonlinear_pk=require_nonlinear_pk
         )
 
@@ -127,23 +125,31 @@ def execute(self, sample: cosmosis.datablock) -> int:
         for section, name, val in derived_params_collection:
             sample.put(section, name, val)
 
-        self.likelihood.reset()
-        self.tools.reset()
+        if not isinstance(self.likelihood, GaussFamily):
+            self.likelihood.reset()
+            self.tools.reset()
+            return 0
 
-        # Save concatenated data vector and inverse covariance to enable support
+        # If we get here, we have a GaussFamily likelihood, and we need to
+        # save concatenated data vector and inverse covariance to enable support
         # for the CosmoSIS Fisher sampler. This can only work for likelihoods
         # that have these quantities. Currently, this is only GaussFamily.
 
-        if isinstance(self.likelihood, GaussFamily):
-            sample.put(
-                "data_vector", "firecrown_theory", self.likelihood.predicted_data_vector
-            )
-            sample.put(
-                "data_vector", "firecrown_data", self.likelihood.measured_data_vector
-            )
-            sample.put(
-                "data_vector", "firecrown_inverse_covariance", self.likelihood.inv_cov
-            )
+        sample.put(
+            "data_vector",
+            "firecrown_theory",
+            self.likelihood.get_theory_vector(),
+        )
+        sample.put(
+            "data_vector",
+            "firecrown_data",
+            self.likelihood.get_data_vector(),
+        )
+        sample.put(
+            "data_vector",
+            "firecrown_inverse_covariance",
+            self.likelihood.inv_cov,
+        )
 
         # Write out theory and data vectors to the data block the ease
         # debugging.
@@ -164,14 +170,16 @@ def execute(self, sample: cosmosis.datablock) -> int:
                 sample.put(
                     "data_vector",
                     f"theory_{stat.sacc_data_type}_{tracer}",
-                    stat.predicted_statistic_,
+                    stat.get_theory_vector(),
                 )
                 sample.put(
                     "data_vector",
                     f"data_{stat.sacc_data_type}_{tracer}",
-                    stat.measured_statistic_,
+                    stat.get_data_vector(),
                 )
 
+        self.likelihood.reset()
+        self.tools.reset()
         return 0
 
     def form_error_message(self, exc: MissingSamplerParameterError) -> str: