Factory function framework (#207)

* First version of the factory function framework for firecrown likelihoods.

* Added missing section name in the firecrown likelihood configuration section.

* Remove unused parameters

Co-authored-by: Marc Paterno <paterno@fnal.gov>

examples/cosmicshear/cosmicshear.ini

@@ -2,7 +2,7 @@
 sampler = test
 root = ${PWD}
 
-[default]
+[DEFAULT]
 fatal_errors = T
 
 [output]
@@ -48,6 +48,7 @@ file = ${FIRECROWN_DIR}/firecrown/connector/cosmosis/likelihood.py
 ;; code.
 firecrown_config = ${FIRECROWN_DIR}/examples/cosmicshear/cosmicshear.py
 require_nonlinear_pk = True
+sampling_parameters_sections = firecrown_two_point
 
 [test]
 fatal_errors = T

examples/cosmicshear/cosmicshear.py

@@ -9,73 +9,71 @@
 
 import sacc
 
-# Sources
 
+def build_likelihood(_):
+    """
+        Creating sources, each one maps to a specific section of a SACC file. In
+        this case trc0, trc1 describe weak-lensing probes. The sources are saved
+        in a dictionary since they will be used by one or more two-point function.
+    """
+    sources = {}
 
-"""
-    Creating sources, each one maps to a specific section of a SACC file. In
-    this case trc0, trc1 describe weak-lensing probes. The sources are saved
-    in a dictionary since they will be used by one or more two-point function.
-"""
-sources = {}
+    for i in range(2):
+        """
+        We include a photo-z shift bias (a constant shift in dndz). We also
+        have a different parameter for each bin, so here again we use the
+        src{i}_ prefix.
+        """
+        pzshift = wl.PhotoZShift(sacc_tracer=f"trc{i}")
+
+        """
+            Now we can finally create the weak-lensing source that will compute the
+            theoretical prediction for that section of the data, given the
+            systematics.
+        """
+        sources[f"trc{i}"] = wl.WeakLensing(sacc_tracer=f"trc{i}", systematics=[pzshift])
 
-for i in range(2):
     """
-    We include a photo-z shift bias (a constant shift in dndz). We also
-    have a different parameter for each bin, so here again we use the
-    src{i}_ prefix.
+        Now that we have all sources we can instantiate all the two-point
+        functions. For each one we create a new two-point function object.
     """
-    pzshift = wl.PhotoZShift(sacc_tracer=f"trc{i}")
+    stats = {}
 
     """
-        Now we can finally create the weak-lensing source that will compute the
-        theoretical prediction for that section of the data, given the
-        systematics.
+        Creating all auto/cross-correlations two-point function objects for
+        the weak-lensing probes.
     """
-    sources[f"trc{i}"] = wl.WeakLensing(sacc_tracer=f"trc{i}", systematics=[pzshift])
-
-"""
-    Now that we have all sources we can instantiate all the two-point
-    functions. For each one we create a new two-point function object.
-"""
-stats = {}
+    for i in range(2):
+        for j in range(i, 2):
+            stats[f"trc{i}_trc{j}"] = TwoPoint(
+                source0=sources[f"trc{i}"],
+                source1=sources[f"trc{j}"],
+                sacc_data_type="galaxy_shear_cl_ee",
+            )
 
-"""
-    Creating all auto/cross-correlations two-point function objects for
-    the weak-lensing probes.
-"""
-for i in range(2):
-    for j in range(i, 2):
-        stats[f"trc{i}_trc{j}"] = TwoPoint(
-            source0=sources[f"trc{i}"],
-            source1=sources[f"trc{j}"],
-            sacc_data_type="galaxy_shear_cl_ee",
-        )
-
-"""
-    Here we instantiate the actual likelihood. The statistics argument carry
-    the order of the data/theory vector.
-"""
-lk = ConstGaussian(statistics=list(stats.values()))
+    """
+        Here we instantiate the actual likelihood. The statistics argument carry
+        the order of the data/theory vector.
+    """
+    lk = ConstGaussian(statistics=list(stats.values()))
 
-"""
-    We load the correct SACC file.
-"""
-saccfile = os.path.expanduser(
-    os.path.expandvars("${FIRECROWN_DIR}/examples/cosmicshear/cosmicshear.fits")
-)
-sacc_data = sacc.Sacc.load_fits(saccfile)
+    """
+        We load the correct SACC file.
+    """
+    saccfile = os.path.expanduser(
+        os.path.expandvars("${FIRECROWN_DIR}/examples/cosmicshear/cosmicshear.fits")
+    )
+    sacc_data = sacc.Sacc.load_fits(saccfile)
 
-"""
-    The read likelihood method is called passing the loaded SACC file, the
-    two-point functions will receive the appropriated sections of the SACC
-    file and the sources their respective dndz.
-"""
-lk.read(sacc_data)
+    """
+        The read likelihood method is called passing the loaded SACC file, the
+        two-point functions will receive the appropriated sections of the SACC
+        file and the sources their respective dndz.
+    """
+    lk.read(sacc_data)
 
-"""
-    This script will be loaded by the appropriated connector. The framework
-    then looks for the `likelihood` variable to find the instance that will
-    be used to compute the likelihood.
-"""
-likelihood = lk
+    """
+        This script will be loaded by the appropriated connector. The framework
+        will call the factory function that should return a Likelihood instance.
+    """
+    return lk

examples/des_y1_3x2pt/des_y1_3x2pt.ini

@@ -2,7 +2,7 @@
 sampler = test
 root = ${PWD}
 
-[default]
+[DEFAULT]
 fatal_errors = T
 
 [output]
@@ -45,6 +45,7 @@ file = ${CSL_DIR}/utility/sample_sigma8/sigma8_rescale.py
 file = ${FIRECROWN_DIR}/firecrown/connector/cosmosis/likelihood.py
 likelihood_source = ${FIRECROWN_DIR}/examples/des_y1_3x2pt/des_y1_3x2pt.py
 require_nonlinear_pk = True
+sampling_parameters_sections = firecrown_two_point
 
 [test]
 fatal_errors = T

examples/des_y1_3x2pt/des_y1_3x2pt.py

@@ -14,123 +14,124 @@
 # Sources
 
 
-lai_systematic = wl.LinearAlignmentSystematic(sacc_tracer="", alphag=None)
+def build_likelihood(_):
 
-"""
-    Creating sources, each one maps to a specific section of a SACC file. In
-    this case src0, src1, src2 and src3 describe weak-lensing probes. The
-    sources are saved in a dictionary since they will be used by one or more
-    two-point function.
-"""
-sources = {}
+    lai_systematic = wl.LinearAlignmentSystematic(sacc_tracer="", alphag=None)
 
-for i in range(4):
     """
-    Each weak-lensing section has its own multiplicative bias. Parameters
-    reflect this by using src{i}_ prefix.
+        Creating sources, each one maps to a specific section of a SACC file. In
+        this case src0, src1, src2 and src3 describe weak-lensing probes. The
+        sources are saved in a dictionary since they will be used by one or more
+        two-point function.
     """
-    mbias = wl.MultiplicativeShearBias(sacc_tracer=f"src{i}")
+    sources = {}
 
-    """
-        We also include a photo-z shift bias (a constant shift in dndz). We
-        also have a different parameter for each bin, so here again we use the
-        src{i}_ prefix.
-    """
-    pzshift = wl.PhotoZShift(sacc_tracer=f"src{i}")
+    for i in range(4):
+        """
+        Each weak-lensing section has its own multiplicative bias. Parameters
+        reflect this by using src{i}_ prefix.
+        """
+        mbias = wl.MultiplicativeShearBias(sacc_tracer=f"src{i}")
+
+        """
+            We also include a photo-z shift bias (a constant shift in dndz). We
+            also have a different parameter for each bin, so here again we use the
+            src{i}_ prefix.
+        """
+        pzshift = wl.PhotoZShift(sacc_tracer=f"src{i}")
+
+        """
+            Now we can finally create the weak-lensing source that will compute the
+            theoretical prediction for that section of the data, given the
+            systematics.
+        """
+        sources[f"src{i}"] = wl.WeakLensing(
+            sacc_tracer=f"src{i}", systematics=[lai_systematic, mbias, pzshift]
+        )
 
     """
-        Now we can finally create the weak-lensing source that will compute the
-        theoretical prediction for that section of the data, given the
-        systematics.
-    """
-    sources[f"src{i}"] = wl.WeakLensing(
-        sacc_tracer=f"src{i}", systematics=[lai_systematic, mbias, pzshift]
-    )
+        Creating the number counting sources. There are five sources each one
+        labeled by lens{i}.
+    """
+    for i in range(5):
+        """
+        We also include a photo-z shift for the dndz.
+        """
+        pzshift = nc.PhotoZShift(sacc_tracer=f"lens{i}")
+
+        """
+            The source is created and saved (temporarely in the sources dict).
+        """
+        sources[f"lens{i}"] = nc.NumberCounts(sacc_tracer=f"lens{i}", systematics=[pzshift], derived_scale=True)
+
+    """
+        Now that we have all sources we can instantiate all the two-point
+        functions. The weak-lensing sources have two "data types", for each one we
+        create a new two-point function.
+    """
+    stats = {}
+    for stat, sacc_stat in [
+        ("xip", "galaxy_shear_xi_plus"),
+        ("xim", "galaxy_shear_xi_minus"),
+    ]:
+        """
+        Creating all auto/cross-correlations two-point function objects for the
+        weak-lensing probes.
+        """
+        for i in range(4):
+            for j in range(i, 4):
+                stats[f"{stat}_src{i}_src{j}"] = TwoPoint(
+                    source0=sources[f"src{i}"],
+                    source1=sources[f"src{j}"],
+                    sacc_data_type=sacc_stat,
+                )
+        """
+            The following two-point function objects compute the cross correlations
+            between the weak-lensing and number count sources.
+        """
+    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}"],
+                sacc_data_type="galaxy_shearDensity_xi_t",
+            )
 
-"""
-    Creating the number counting sources. There are five sources each one
-    labeled by lens{i}.
-"""
-for i in range(5):
-    """
-    We also include a photo-z shift for the dndz.
-    """
-    pzshift = nc.PhotoZShift(sacc_tracer=f"lens{i}")
+        """
+            Finally the instances for the lensing auto-correlations are created.
+        """
+    for i in range(5):
+        stats[f"wtheta_lens{i}_lens{i}"] = TwoPoint(
+            source0=sources[f"lens{i}"],
+            source1=sources[f"lens{i}"],
+            sacc_data_type="galaxy_density_xi",
+        )
 
     """
-        The source is created and saved (temporarely in the sources dict).
-    """
-    sources[f"lens{i}"] = nc.NumberCounts(sacc_tracer=f"lens{i}", systematics=[pzshift], derived_scale=True)
-
-"""
-    Now that we have all sources we can instantiate all the two-point
-    functions. The weak-lensing sources have two "data types", for each one we
-    create a new two-point function.
-"""
-stats = {}
-for stat, sacc_stat in [
-    ("xip", "galaxy_shear_xi_plus"),
-    ("xim", "galaxy_shear_xi_minus"),
-]:
+        Here we instantiate the actual likelihood. The statistics argument carry
+        the order of the data/theory vector.
     """
-    Creating all auto/cross-correlations two-point function objects for the
-    weak-lensing probes.
-    """
-    for i in range(4):
-        for j in range(i, 4):
-            stats[f"{stat}_src{i}_src{j}"] = TwoPoint(
-                source0=sources[f"src{i}"],
-                source1=sources[f"src{j}"],
-                sacc_data_type=sacc_stat,
-            )
+    lk = ConstGaussian(statistics=list(stats.values()))
+
     """
-        The following two-point function objects compute the cross correlations
-        between the weak-lensing and number count sources.
+        We load the correct SACC file.
     """
-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}"],
-            sacc_data_type="galaxy_shearDensity_xi_t",
+    saccfile = os.path.expanduser(
+        os.path.expandvars(
+            "${FIRECROWN_DIR}/examples/des_y1_3x2pt/des_y1_3x2pt_sacc_data.fits"
         )
+    )
+    sacc_data = sacc.Sacc.load_fits(saccfile)
 
     """
-        Finally the instances for the lensing auto-correlations are created.
+        The read likelihood method is called passing the loaded SACC file, the
+        two-point functions will receive the appropriated sections of the SACC
+        file and the sources their respective dndz.
     """
-for i in range(5):
-    stats[f"wtheta_lens{i}_lens{i}"] = TwoPoint(
-        source0=sources[f"lens{i}"],
-        source1=sources[f"lens{i}"],
-        sacc_data_type="galaxy_density_xi",
-    )
+    lk.read(sacc_data)
 
-"""
-    Here we instantiate the actual likelihood. The statistics argument carry
-    the order of the data/theory vector.
-"""
-lk = ConstGaussian(statistics=list(stats.values()))
-
-"""
-    We load the correct SACC file.
-"""
-saccfile = os.path.expanduser(
-    os.path.expandvars(
-        "${FIRECROWN_DIR}/examples/des_y1_3x2pt/des_y1_3x2pt_sacc_data.fits"
-    )
-)
-sacc_data = sacc.Sacc.load_fits(saccfile)
-
-"""
-    The read likelihood method is called passing the loaded SACC file, the
-    two-point functions will receive the appropriated sections of the SACC
-    file and the sources their respective dndz.
-"""
-lk.read(sacc_data)
-
-"""
-    This script will be loaded by the appropriated connector. The framework
-    then looks for the `likelihood` variable to find the instance that will
-    be used to compute the likelihood.
-"""
-likelihood = lk
+    """
+        This script will be loaded by the appropriated connector. The framework
+        will call the factory function that should return a Likelihood instance.
+    """
+    return lk