Removing cluster theory prediction from Firecrown

environment.yml

@@ -33,6 +33,7 @@ dependencies:
   - pip >= 20.1  # pip is needed as dependency
   - pip:
     - cobaya
+    - desc-crow
     - isitgr
     - pygobject-stubs
   - plotnine < 0.15

examples/cluster_number_counts/cluster_SDSS_redshift_richness.py

@@ -4,26 +4,65 @@
 
 import pyccl as ccl
 
+
+from crow import ClusterAbundance, kernel, mass_proxy
+from crow.properties import ClusterProperty
+from crow.recipes.binned_exact import ExactBinnedClusterRecipe
+
 from firecrown.likelihood.factories import load_sacc_data
 from firecrown.likelihood import (
     BinnedClusterNumberCounts,
     ConstGaussian,
     Likelihood,
     NamedParameters,
 )
+
 from firecrown.modeling_tools import ModelingTools
-from firecrown.models.cluster import ClusterAbundance, ClusterProperty
-from firecrown.models.cluster import MurataBinnedSpecZRecipe
 
 
 def get_cluster_abundance() -> ClusterAbundance:
-    """Creates and returns a ClusterAbundance object."""
-    hmf = ccl.halos.MassFuncBocquet16()
-    min_mass, max_mass = 13.0, 17.0
-    min_z, max_z = 0.2, 0.8
-    cluster_abundance = ClusterAbundance((min_mass, max_mass), (min_z, max_z), hmf)
+    """Creates and returns a ClusterShearProfile object."""
+    cluster_theory = ClusterAbundance(
+        cosmo=ccl.CosmologyVanillaLCDM(),
+        halo_mass_function=ccl.halos.MassFuncTinker08(mass_def="200c"),
+    )
+
+    return cluster_theory
+
+
+def get_cluster_recipe(
+    cluster_theory=None,
+    pivot_mass: float = 14.625862906,
+    pivot_redshift: float = 0.6,
+    mass_interval=(12, 17),
+    true_z_interval=(0.1, 2.0),
+):
+    """Creates and returns an ExactBinnedClusterRecipe.
+
+    Parameters
+    ----------
+    cluster_theory : ClusterShearProfile or None
+        If None, uses get_cluster_shear_profile()
+    Returns
+    -------
+    ExactBinnedClusterRecipe
+    """
+    if cluster_theory is None:
+        cluster_theory = get_cluster_abundance()
+    redshift_distribution = kernel.SpectroscopicRedshift()
+    mass_distribution = mass_proxy.MurataBinned(pivot_mass, pivot_redshift)
+
+    recipe = ExactBinnedClusterRecipe(
+        cluster_theory=cluster_theory,
+        redshift_distribution=redshift_distribution,
+        mass_distribution=mass_distribution,
+        completeness=None,
+        purity=None,
+        mass_interval=mass_interval,
+        true_z_interval=true_z_interval,
+    )
 
-    return cluster_abundance
+    return recipe
 
 
 def build_likelihood(
@@ -38,8 +77,9 @@ def build_likelihood(
         average_on |= ClusterProperty.MASS
 
     survey_name = "SDSSCluster_redshift_richness"
+    recipe = get_cluster_recipe()
     likelihood = ConstGaussian(
-        [BinnedClusterNumberCounts(average_on, survey_name, MurataBinnedSpecZRecipe())]
+        [BinnedClusterNumberCounts(average_on, survey_name, recipe)]
     )
 
     # Read in sacc data
@@ -50,7 +90,6 @@ def build_likelihood(
     sacc_data = load_sacc_data(os.path.join(sacc_path, sacc_file_nm))
     likelihood.read(sacc_data)
 
-    cluster_abundance = get_cluster_abundance()
-    modeling_tools = ModelingTools(cluster_abundance=cluster_abundance)
+    modeling_tools = ModelingTools()
 
     return likelihood, modeling_tools

examples/cluster_number_counts/cluster_counts_mean_mass_redshift_richness_delta_sigma.ini

@@ -0,0 +1,61 @@
+[runtime]
+sampler = test
+resume = T
+root = ${PWD}
+
+[default]
+fatal_errors = T
+
+[output]
+filename = output_rps_2/number_counts_samples.txt
+format = text
+verbosity = 0
+
+[pipeline]
+modules = consistency camb firecrown_likelihood
+values = ${FIRECROWN_DIR}/examples/cluster_number_counts/cluster_richness_values_deltasigma.ini
+likelihoods = firecrown
+quiet = T
+debug = T
+timing = T
+
+[consistency]
+file = ${CSL_DIR}/utility/consistency/consistency_interface.py
+
+[camb]
+file = ${CSL_DIR}/boltzmann/camb/camb_interface.py
+
+mode = all
+lmax = 2500
+feedback = 0
+zmin = 0.0
+zmax = 1.0
+nz = 100
+kmin = 1e-4
+kmax = 50.0
+nk = 1000
+
+[firecrown_likelihood]
+;; Fix this to use an environment variable to find the files.
+;; Set FIRECROWN_DIR to the base of the firecrown installation (or build, if you haven't
+;; installed it)
+file = ${FIRECROWN_DIR}/firecrown/connector/cosmosis/likelihood.py
+likelihood_source = ${FIRECROWN_DIR}/examples/cluster_number_counts/cluster_redshift_richness_deltasigma.py
+sampling_parameters_sections = firecrown_number_counts
+use_cluster_counts = True
+use_mean_log_mass = False
+use_mean_deltasigma = True
+use_mean_reduced_shear = False
+
+[test]
+fatal_errors = T
+save_dir = output_counts_mean_mass
+
+[metropolis]
+samples = 1000
+nsteps = 1
+
+[emcee]
+walkers = 20
+samples = 4000
+nsteps = 3

examples/cluster_number_counts/cluster_counts_mean_mass_redshift_richness_shear.ini

@@ -40,11 +40,12 @@ nk = 1000
 ;; Set FIRECROWN_DIR to the base of the firecrown installation (or build, if you haven't
 ;; installed it)
 file = ${FIRECROWN_DIR}/firecrown/connector/cosmosis/likelihood.py
-likelihood_source = ${FIRECROWN_DIR}/examples/cluster_number_counts/cluster_redshift_richness_deltasigma.py
+likelihood_source = ${FIRECROWN_DIR}/examples/cluster_number_counts/cluster_redshift_richness_reduced_shear.py
 sampling_parameters_sections = firecrown_number_counts
 use_cluster_counts = True
 use_mean_log_mass = False
-use_mean_deltasigma = True
+use_mean_deltasigma = False
+use_mean_reduced_shear = True
 
 [test]
 fatal_errors = T

examples/cluster_number_counts/cluster_redshift_richness.py

@@ -4,26 +4,65 @@
 
 import pyccl as ccl
 
+from crow import ClusterAbundance, kernel, mass_proxy
+from crow.properties import ClusterProperty
+from crow.recipes.binned_exact import ExactBinnedClusterRecipe
+
 from firecrown.likelihood.factories import load_sacc_data
 from firecrown.likelihood import (
     BinnedClusterNumberCounts,
     ConstGaussian,
     Likelihood,
     NamedParameters,
 )
+
 from firecrown.modeling_tools import ModelingTools
-from firecrown.models.cluster import ClusterAbundance, ClusterProperty
-from firecrown.models.cluster import MurataBinnedSpecZRecipe
 
 
 def get_cluster_abundance() -> ClusterAbundance:
-    """Creates and returns a ClusterAbundance object."""
-    hmf = ccl.halos.MassFuncBocquet16()
-    min_mass, max_mass = 13.0, 16.0
-    min_z, max_z = 0.2, 0.8
-    cluster_abundance = ClusterAbundance((min_mass, max_mass), (min_z, max_z), hmf)
+    """Creates and returns a ClusterShearProfile."""
+    cluster_theory = ClusterAbundance(
+        cosmo=ccl.CosmologyVanillaLCDM(),
+        halo_mass_function=ccl.halos.MassFuncTinker08(mass_def="200c"),
+    )
+
+    return cluster_theory
+
+
+def get_cluster_recipe(
+    cluster_theory=None,
+    pivot_mass: float = 14.625862906,
+    pivot_redshift: float = 0.6,
+    mass_interval=(12, 17),
+    true_z_interval=(0.1, 2.0),
+):
+    """Creates and returns an ExactBinnedClusterRecipe.
+
+    Parameters
+    ----------
+    cluster_theory : ClusterShearProfile or None
+        If None, uses get_cluster_shear_profile()
+
+    Returns
+    -------
+    ExactBinnedClusterRecipe
+    """
+    if cluster_theory is None:
+        cluster_theory = get_cluster_abundance()
+    redshift_distribution = kernel.SpectroscopicRedshift()
+    mass_distribution = mass_proxy.MurataBinned(pivot_mass, pivot_redshift)
+
+    recipe = ExactBinnedClusterRecipe(
+        cluster_theory=cluster_theory,
+        redshift_distribution=redshift_distribution,
+        mass_distribution=mass_distribution,
+        completeness=None,
+        purity=None,
+        mass_interval=mass_interval,
+        true_z_interval=true_z_interval,
+    )
 
-    return cluster_abundance
+    return recipe
 
 
 def build_likelihood(
@@ -38,8 +77,9 @@ def build_likelihood(
         average_on |= ClusterProperty.MASS
 
     survey_name = "numcosmo_simulated_redshift_richness"
+    recipe = get_cluster_recipe()
     likelihood = ConstGaussian(
-        [BinnedClusterNumberCounts(average_on, survey_name, MurataBinnedSpecZRecipe())]
+        [BinnedClusterNumberCounts(average_on, survey_name, recipe)]
     )
 
     # Read in sacc data
@@ -50,7 +90,6 @@ def build_likelihood(
     sacc_data = load_sacc_data(os.path.join(sacc_path, sacc_file_nm))
     likelihood.read(sacc_data)
 
-    cluster_abundance = get_cluster_abundance()
-    modeling_tools = ModelingTools(cluster_abundance=cluster_abundance)
+    modeling_tools = ModelingTools()
 
     return likelihood, modeling_tools