Issue/438/cluster wl simple

examples/cluster_number_counts/cluster_counts_mean_mass_redshift_richness_shear.ini

@@ -0,0 +1,60 @@
+[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 = True
+use_mean_deltasigma = True
+
+[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_redshift_richness_deltasigma.py

@@ -0,0 +1,87 @@
+"""Likelihood factory function for cluster number counts."""
+
+import os
+
+import pyccl as ccl
+import sacc
+
+from firecrown.likelihood.gaussian import ConstGaussian
+from firecrown.likelihood.binned_cluster_number_counts_deltasigma import (
+    BinnedClusterDeltaSigma,
+)
+from firecrown.likelihood.binned_cluster_number_counts import (
+    BinnedClusterNumberCounts,
+)
+
+from firecrown.likelihood.likelihood import Likelihood, NamedParameters
+from firecrown.modeling_tools import ModelingTools
+from firecrown.models.cluster.abundance import ClusterAbundance
+from firecrown.models.cluster.deltasigma import ClusterDeltaSigma
+from firecrown.models.cluster.properties import ClusterProperty
+from firecrown.models.cluster.recipes.murata_binned_spec_z_deltasigma import (
+    MurataBinnedSpecZDeltaSigmaRecipe,
+)
+from firecrown.models.cluster.recipes.murata_binned_spec_z import (
+    MurataBinnedSpecZRecipe,
+)
+
+
+def get_cluster_abundance() -> ClusterAbundance:
+    """Creates and returns a ClusterAbundance object."""
+    hmf = ccl.halos.MassFuncTinker08()
+    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)
+
+    return cluster_abundance
+
+
+def get_cluster_deltasigma() -> ClusterDeltaSigma:
+    """Creates and returns a ClusterAbundance object."""
+    hmf = ccl.halos.MassFuncTinker08()
+    min_mass, max_mass = 13.0, 16.0
+    min_z, max_z = 0.2, 0.8
+    cluster_deltasigma = ClusterDeltaSigma(min_mass, max_mass, min_z, max_z, hmf)
+
+    return cluster_deltasigma
+
+
+def build_likelihood(
+    build_parameters: NamedParameters,
+) -> tuple[Likelihood, ModelingTools]:
+    """Builds the likelihood for Firecrown."""
+    # Pull params for the likelihood from build_parameters
+    average_on = ClusterProperty.NONE
+    if build_parameters.get_bool("use_cluster_counts", True):
+        average_on |= ClusterProperty.COUNTS
+    if build_parameters.get_bool("use_mean_log_mass", True):
+        average_on |= ClusterProperty.MASS
+    if build_parameters.get_bool("use_mean_deltasigma", True):
+        average_on |= ClusterProperty.DELTASIGMA
+
+    survey_name = "numcosmo_simulated_redshift_richness_deltasigma"
+    likelihood = ConstGaussian(
+        [
+            BinnedClusterNumberCounts(
+                average_on, survey_name, MurataBinnedSpecZRecipe()
+            ),
+            BinnedClusterDeltaSigma(
+                average_on, survey_name, MurataBinnedSpecZDeltaSigmaRecipe()
+            ),
+        ]
+    )
+
+    # Read in sacc data
+    sacc_file_nm = "cluster_redshift_richness_deltasigma_sacc_data.fits"
+    sacc_path = os.path.expanduser(
+        os.path.expandvars("${FIRECROWN_DIR}/examples/cluster_number_counts/")
+    )
+    sacc_data = sacc.Sacc.load_fits(os.path.join(sacc_path, sacc_file_nm))
+    likelihood.read(sacc_data)
+    cluster_abundance = get_cluster_abundance()
+    cluster_deltasigma = get_cluster_deltasigma()
+    modeling_tools = ModelingTools(
+        cluster_abundance=cluster_abundance, cluster_deltasigma=cluster_deltasigma
+    )
+
+    return likelihood, modeling_tools

examples/cluster_number_counts/cluster_richness_values.ini

@@ -4,7 +4,7 @@
 [cosmological_parameters]
 
 ; These are the only cosmological parameters being varied.
-omega_c = 0.1552 0.2052 0.3552
+omega_c = 0.1552 0.22 0.3552
 # We are choosing to use a flat prior in sigma_8.
 # To choose a float prior in A_s, remove the specification
 # of a prior for sigma_8 and replace it with the

examples/cluster_number_counts/cluster_richness_values_deltasigma.ini

@@ -0,0 +1,37 @@
+; Parameters and data in CosmoSIS are organized into sections
+; so we can easily see what they mean.
+; There is only one section in this case, called cosmological_parameters
+[cosmological_parameters]
+
+; These are the only cosmological parameters being varied.
+omega_c = 0.1552 0.22 0.3552
+# We are choosing to use a flat prior in sigma_8.
+# To choose a float prior in A_s, remove the specification
+# of a prior for sigma_8 and replace it with the
+# desired range for A_s
+sigma_8 = 0.7 0.800 0.9
+; The following parameters are set, but not varied.
+;
+omega_k = 0.0
+omega_b = 0.0448
+tau = 0.08
+n_s = 0.963
+h0 = 0.71
+w = -1.0
+wa = 0.0
+
+[firecrown_number_counts]
+; These are the firecrown likelihood parameters.
+; These parameters are used to set the richness-mass
+; proxy relation using the data from cluster number counts.
+;
+; The following parameters can be fixed in the same way as the above
+; cosmological parameters if needed.
+mu_p0 = 3.19
+mu_p1 = 0.8685889638
+mu_p2 = -0.304
+sigma_p0 = 0.33
+sigma_p1 = -0.034 
+sigma_p2 = 0
+#cluster_conc = 4.
+

examples/cluster_number_counts/gen_sacc_aux.py

@@ -0,0 +1,96 @@
+#!/usr/bin/env python
+
+"""Defines a function to generate a SACC file for cluster number counts."""
+
+# # Cluster count-only SACC file creation
+#
+# This notebook examplifies the creation of a SACC file for cluster count, using
+# NumCosmo facilities to simulate cluster data.
+
+import itertools
+
+from numcosmo_py import Nc
+from numcosmo_py import Ncm
+
+from typing import Any
+import sacc
+
+
+def convert_binned_profile_to_sacc(
+    cluster_counts,
+    mean_DeltaSigma,
+    covariance,
+    z_edges,
+    richness_edges,
+    radius_edges,
+    radius_centers,
+    area,
+    survey_name="numcosmo_simulated_redshift_richness",
+) -> sacc.Sacc:
+    N_z = len(z_edges) - 1
+    N_richness = len(richness_edges) - 1
+    N_radius = len(radius_edges) - 1
+
+    # Prepare the SACC file
+    s_count = sacc.Sacc()
+    bin_z_labels = []
+    bin_richness_labels = []
+    bin_radius_labels = []
+
+    s_count.add_tracer("survey", survey_name, area)
+
+    for i, z_bin in enumerate(zip(z_edges[:-1], z_edges[1:])):
+        lower, upper = z_bin
+        bin_z_label = f"bin_z_{i}"
+        s_count.add_tracer("bin_z", bin_z_label, lower, upper)
+        bin_z_labels.append(bin_z_label)
+
+    for i, richness_bin in enumerate(zip(richness_edges[:-1], richness_edges[1:])):
+        lower, upper = richness_bin
+        bin_richness_label = f"rich_{i}"
+        s_count.add_tracer("bin_richness", bin_richness_label, lower, upper)
+        bin_richness_labels.append(bin_richness_label)
+
+    for i, (radius_lower, radius_upper, radius_center) in enumerate(
+        zip(radius_edges[:-1], radius_edges[1:], radius_centers)
+    ):
+        bin_radius_label = f"bin_radius_{i}"
+        s_count.add_tracer(
+            "bin_radius", bin_radius_label, radius_lower, radius_upper, radius_center
+        )
+        bin_radius_labels.append(bin_radius_label)
+
+    #  pylint: disable-next=no-member
+    type_cluster_counts = sacc.standard_types.cluster_counts
+    #  pylint: disable-next=no-member
+    type_cluster_DeltaSigma = sacc.standard_types.cluster_shear
+
+    for counts, (bin_z_label, bin_richness_label) in zip(
+        cluster_counts.flatten(), itertools.product(bin_z_labels, bin_richness_labels)
+    ):
+        s_count.add_data_point(
+            type_cluster_counts,
+            (survey_name, bin_z_label, bin_richness_label),
+            int(counts),
+        )
+
+    for profile, (bin_z_label, bin_richness_label) in zip(
+        mean_DeltaSigma.reshape(N_richness * N_z, N_radius),
+        itertools.product(bin_z_labels, bin_richness_labels),
+    ):
+        for profile_value, bin_radius_label in zip(profile, bin_radius_labels):
+            s_count.add_data_point(
+                type_cluster_DeltaSigma,
+                (survey_name, bin_z_label, bin_richness_label, bin_radius_label),
+                profile_value,
+            )
+
+    # ### Then the add the covariance and save the file
+
+    s_count.add_covariance(covariance)
+    s_count.to_canonical_order()
+    return s_count
+
+
+if __name__ == "__main__":
+    Ncm.cfg_init()