From fb31bae6362b9e493f077b0e18ba6268e2612b01 Mon Sep 17 00:00:00 2001
From: Jean Jacquemier <jean.jacquemier@lapp.in2p3.fr>
Date: Tue, 6 Oct 2026 09:12:20 +0200
Subject: [PATCH] Add the documentation with Sphinx and the corresponding
 mechanisms     - Add a logo to the README.     - Add clp_covariance and
 clp_firecrown confirguration options.     - Add an overview.     - Add an
 installation doc.     - Add doc generation (to test it)  with the CI at every
 Pull Request.     - Use github Pages withgithub actions to publish the docs. 
    - Trigger the documentation deployment when a branch is merged into the
 main branch.     - Add some github badges.     - Add LSST DESC software
 dependencie to doc.     - Add sphinx copy button extension.     - Add
 coveralls command.     - Add API doc.

---
 .github/workflows/ci.yml                      |  31 +-
 .github/workflows/docs.yml                    |  58 ++
 .gitignore                                    |   5 +
 README.md                                     |  31 +-
 clpipe/clp_covariance.py                      |   8 -
 clpipe/clp_firecrown.py                       |  31 +-
 clpipe/cluster_theory_pred.py                 |   1 +
 coverage.xml                                  | 952 ++++++++++++++++++
 docs/Makefile                                 |  31 +
 docs/clp_covariance.txt                       | 389 ++++---
 docs/clp_firecrown.txt                        | 541 +++++++---
 docs/conf.py                                  |  47 +
 docs/index.rst                                |  22 +
 docs/requirements.txt                         |   4 +
 .../LSST DESC software dependencies.rst       |  58 ++
 docs/source/api.rst                           |  19 +
 docs/source/clp_covariance.rst                | 410 ++++++++
 docs/source/clp_firecrown.rst                 | 520 ++++++++++
 docs/source/clpipe_logo.png                   | Bin 0 -> 292184 bytes
 docs/source/diagram.png                       | Bin 0 -> 121999 bytes
 docs/source/installation.rst                  |  41 +
 docs/source/overview.rst                      |  21 +
 firecrown_environment.yml                     |  11 +-
 tests/test_clp_covariance.py                  |   1 -
 tests/test_clp_firecrown.py                   |  32 +
 25 files changed, 2977 insertions(+), 287 deletions(-)
 create mode 100644 .github/workflows/docs.yml
 create mode 100644 coverage.xml
 create mode 100644 docs/Makefile
 create mode 100644 docs/conf.py
 create mode 100644 docs/index.rst
 create mode 100644 docs/requirements.txt
 create mode 100644 docs/source/LSST DESC software dependencies.rst
 create mode 100644 docs/source/api.rst
 create mode 100644 docs/source/clp_covariance.rst
 create mode 100644 docs/source/clp_firecrown.rst
 create mode 100644 docs/source/clpipe_logo.png
 create mode 100644 docs/source/diagram.png
 create mode 100644 docs/source/installation.rst
 create mode 100644 docs/source/overview.rst

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 066f855..5b65afe 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -213,8 +213,37 @@ jobs:
           # re-run here too -- cheap relative to the cosmosis run, and it
           # confirms they also pass with the full stack installed (not just
           # in the stripped-down fast env).
-          pytest tests -v
+          pytest tests -v --cov --cov-branch --cov-report=xml
+        env:
+          DISPLAY: test
 
+      - name: Upload coverage reports to Codecov
+        uses: codecov/codecov-action@v5
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}
+     
+
+  doc-valid:
+    name: Documentation Validation
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: conda-incubator/setup-miniconda@v3
+        with:
+          activate-environment: clpipe-full
+          environment-file: ${{ env.FULL_ENV_FILE }}
+      - uses: actions/cache@v4
+        with:
+          path: ${{ env.CONDA }}/pkgs
+          key: ${{ runner.os }}-conda-${{ env.CONDA_ENV_NAME }}
+      - name: Install the package
+        shell: bash -l {0}
+        run: |
+          pip install . -vv --no-deps --no-build-isolation
+      - name: Run the docs
+        shell: bash -l {0}
+        run: |
+          make -C docs/ html
   # ------------------------------------------------------------------ #
   # (Optional, disabled) lint. Enable in a separate formatting PR once  #
   # the pipeline files are actually black/isort-clean, so the           #
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
new file mode 100644
index 0000000..f613b27
--- /dev/null
+++ b/.github/workflows/docs.yml
@@ -0,0 +1,58 @@
+name: docs
+
+# Builds the Sphinx HTML docs on every PR/push touching them (as a check),
+# and publishes to GitHub Pages on pushes to main.
+# One-time setup: Settings -> Pages -> Source: "GitHub Actions".
+
+on:
+  push:
+    branches: [main]
+    paths: ["docs/**", "clpipe/**", ".github/workflows/docs.yml"]
+  pull_request:
+    paths: ["docs/**", "clpipe/**", ".github/workflows/docs.yml"]
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+concurrency:
+  group: pages-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+          cache: pip
+          cache-dependency-path: docs/requirements.txt
+
+      - name: Install doc dependencies
+        run: pip install -r docs/requirements.txt
+
+      - name: Build HTML docs
+        working-directory: docs
+        run: make html
+
+      - name: Upload artifact (for inspection and Pages)
+        uses: actions/upload-pages-artifact@v3
+        with:
+          path: docs/_build/html
+
+  deploy:
+    needs: build
+    if: github.event_name != 'pull_request' && (github.ref == 'refs/heads/main')
+    runs-on: ubuntu-latest
+    permissions:
+      pages: write
+      id-token: write
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    steps:
+      - id: deployment
+        uses: actions/deploy-pages@v4
diff --git a/.gitignore b/.gitignore
index 23ad31d..b83a2a5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,6 +4,7 @@
 __pycache__/
 *.py[cod]
 *.so
+.coverage
 
 # ─────────────────────────────────────────
 # Jupyter
@@ -28,3 +29,7 @@ slurm*
 *.tmp
 *.swp
 *~
+# ─────────────────────────────────────────
+# Documentation
+# ─────────────────────────────────────────
+docs/_build
diff --git a/README.md b/README.md
index 28bf6e9..8afefc5 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,31 @@
+<p align="center">
+<img src="./docs/source/clpipe_logo.png" alt="drawing" width="500" style="display: block; margin: auto; "/>
+</p>
+
+
+
 # CLPipe
 
-Repository dedicated to the Cluster Working Group of the DESC-LSST collaboration.
+[![Build and Check](https://github.com/LSSTDESC/CLPipe/actions/workflows/ci.yml/badge.svg)](https://github.com/LSSTDESC/CLPipe/actions/workflows/ci.yml)
+[![Coverage Status](https://codecov.io/gh/LSSTDESC/CLPipe/branch/main/graph/badge.svg)](https://codecov.io/gh/LSSTDESC/CLPipe)
+[![Documentation](https://github.com/LSSTDESC/CLPipe/actions/workflows/docs.yml/badge.svg)](https://lsstdesc.github.io/CLPipe/)
+
+CLPipe is the current implementation of the LSST DESC Cluster Pipeline,
+developed by the Cluster Working Group of the LSST Dark Energy Science
+Collaboration (DESC) to prepare cosmological analyses of galaxy clusters with
+Rubin LSST data. This repository does not reimplement the analysis tools: it
+runs the existing DESC tools together, in a single automated and reproducible
+chain, from galaxy and cluster catalogs to cosmological parameter constraints.
+TXPipe measures the cluster abundance and the stacked weak lensing profiles,
+TJPCov computes the theoretical covariance of the cluster counts, and
+Firecrown builds the likelihood, with theory predictions from CROW (built on
+CCL and CLMM), which CosmoSIS then samples.
+
+The stages are connected with [ceci](https://github.com/LSSTDESC/ceci) through
+their input and output files, so a stage can be replaced or extended without
+changing the rest of the pipeline. The current version runs a binned analysis
+of cluster counts and stacked weak lensing profiles, and has been validated on
+mock data and on the cosmoDC2 redMaPPer cluster catalog.
 
 ## Installation
 
@@ -19,7 +44,7 @@ Because some Python packages required for Firecrown are incompatible with those
 # IN2P3
 conda activate /sps/lsst/groups/clusters/cl_pipeline_project/conda_envs/firecrown_clp
 
-# NERSC
+# NERSCstyle="display: block; margin: auto; "
 conda activate /global/cfs/projectdirs/lsst/groups/CL/cl_pipeline_project/conda_envs/firecrown_clp
 ```
 
@@ -92,7 +117,7 @@ Firecrown → inference inputs
 A separate job must be run to execute the final Firecrown inference — it is
 not launched automatically as part of the ceci pipeline.
 
-> **Note:** due to missing components in TJPCov, the Firecrown stage
+> **Note:** due to missing components in TJPCov, the CLPCovariance stage
 > currently performs additional computations to rescale the covariance
 > matrix.
 
diff --git a/clpipe/clp_covariance.py b/clpipe/clp_covariance.py
index c3570e0..ee17a48 100644
--- a/clpipe/clp_covariance.py
+++ b/clpipe/clp_covariance.py
@@ -42,7 +42,6 @@ class CLPCovariance(PipelineStage):
         "replace_tjpcov_cov": StageParameter(bool, True, msg="Replace TJPCov cluster-count covariance with CROW covariance."),
         # Selection function
         "sel_func": StageParameter(bool, True, msg="Include purity and completeness selection functions."),
-        "wazp_catalog": StageParameter(bool, False, msg="Use WaZP completeness parameters instead of Aguena+16 defaults (extra, non-default)."),
         "diagonal_shear_covariance": StageParameter(
             bool, True,
             msg=(
@@ -255,7 +254,6 @@ def replace_crow_counts(self, config_dict, sacc_full, cov_terms, full_cov, cosmo
 
         # This function should not exist as it should be implemented in TJPCov.
         # This is temporary and so most of the options and configurations are fixed.
-        is_wazp = config_dict.get("wazp_catalog", False)
         sel_func = config_dict.get("sel_func", True)
         mor_params = config_dict["mor_parameters"]
         hmf = ccl.halos.MassFuncDespali16(mass_def="200c")
@@ -274,12 +272,6 @@ def replace_crow_counts(self, config_dict, sacc_full, cov_terms, full_cov, cosmo
         cl_abundance          = ClusterAbundance(cosmo, hmf)
         purity_aguena         = None#purity_models.PurityAguena16LnProxy()
         completeness_aguena   = completeness_models.CompletenessAguena16()
-        if is_wazp:
-            purity_aguena = None
-            completeness_aguena['a_n'] = 1.570597
-            completeness_aguena['b_n'] = -0.028690
-            completeness_aguena['a_logm_piv'] = 14.264386
-            completeness_aguena['b_logm_piv'] = 0.029814
         redshift_distribution = kernel.SpectroscopicRedshift()
         if not sel_func:
             completeness_aguena = None
diff --git a/clpipe/clp_firecrown.py b/clpipe/clp_firecrown.py
index edccffd..bcc1ea9 100644
--- a/clpipe/clp_firecrown.py
+++ b/clpipe/clp_firecrown.py
@@ -30,6 +30,7 @@ class CLPFirecrown(PipelineStage):
     """Firecrown pipeline stage for cluster cosmology analysis.
 
     This stage:
+
     - Builds a Firecrown likelihood from a SACC file
     - Generates the corresponding CosmoSIS configuration
     - Writes parameter files for sampling, defaulting the cosmological
@@ -38,6 +39,7 @@ class CLPFirecrown(PipelineStage):
       how a parameter gets sampled instead of held fixed
 
     Key configuration groups:
+    
     - Modeling options (hmf, mass range, redshift range)
     - Observable selection (cluster counts, shear)
     - Systematics (purity, completeness)
@@ -92,10 +94,10 @@ class CLPFirecrown(PipelineStage):
         "emcee_walkers": StageParameter(int, 100, msg="Number of emcee walkers."),
         "emcee_samples": StageParameter(int, 20000, msg="Number of emcee samples."),
         "emcee_nsteps": StageParameter(int, 20, msg="Number of emcee steps per sample."),
-        "polycord_live_points": StageParameter(int, 500, msg="Number of PolyChord live points."),
-        "polycord_num_repeats": StageParameter(int, 30, msg="Number of PolyChord repeats."),
-        "polycord_tolerance": StageParameter(float, 0.05, msg="PolyChord evidence tolerance."),
-        "polycord_feedback": StageParameter(int, 1, msg="PolyChord feedback level."),
+        "polychord_live_points": StageParameter(int, 500, msg="Number of PolyChord live points."),
+        "polychord_num_repeats": StageParameter(int, 30, msg="Number of PolyChord repeats."),
+        "polychord_tolerance": StageParameter(float, 0.05, msg="PolyChord evidence tolerance."),
+        "polychord_feedback": StageParameter(int, 1, msg="PolyChord feedback level."),
         "resume": StageParameter(bool, False, msg="If True, CosmoSIS appends to the existing chain at `filename` instead of starting fresh"),
         # Cosmology -- tau is a CAMB input (reionization optical depth), not
         # a CCL cosmology parameter, so it isn't in the fiducial cosmology
@@ -240,7 +242,10 @@ def generate_python_file(self, path_name):
                 # Core CROW imports
                 f.write("from crow import ClusterAbundance, ClusterShearProfile, kernel, mass_proxy\n")
                 f.write("from crow.properties import ClusterProperty\n")
-                f.write("from crow.recipes.binned_grid import GridBinnedClusterRecipe\n\n")
+                if use_grid:
+                    f.write("from crow.recipes.binned_grid import GridBinnedClusterRecipe\n\n")
+                else:
+                    f.write("from crow.recipes.binned_exact import ExactBinnedClusterRecipe\n\n")
                 f.write(
                     "from crow import purity_models, completeness_models\n"
                 )
@@ -411,10 +416,10 @@ def generate_ini_file(self, output_ini_path, likelihood_source_name, values_file
             emcee_samples = cfg["emcee_samples"]
             emcee_nsteps = cfg["emcee_nsteps"]
 
-            polycord_live_points = cfg["polycord_live_points"]
-            polycord_num_repeats = cfg["polycord_num_repeats"]
-            polycord_tolerance = cfg["polycord_tolerance"]
-            polycord_feedback = cfg["polycord_feedback"]
+            polychord_live_points = cfg["polychord_live_points"]
+            polychord_num_repeats = cfg["polychord_num_repeats"]
+            polychord_tolerance = cfg["polychord_tolerance"]
+            polychord_feedback = cfg["polychord_feedback"]
 
             beta_parameters = cfg["beta_parameters"]
             with open(output_ini_path, 'w') as f:
@@ -478,10 +483,10 @@ def generate_ini_file(self, output_ini_path, likelihood_source_name, values_file
                 f.write(f"nsteps = {emcee_nsteps}\n")
 
                 f.write("[polychord]\n")
-                f.write(f"live_points = {polycord_live_points}\n")
-                f.write(f"num_repeats = {polycord_num_repeats}\n")
-                f.write(f"tolerance = {polycord_tolerance}\n")
-                f.write(f"feedback = {polycord_feedback}\n")
+                f.write(f"live_points = {polychord_live_points}\n")
+                f.write(f"num_repeats = {polychord_num_repeats}\n")
+                f.write(f"tolerance = {polychord_tolerance}\n")
+                f.write(f"feedback = {polychord_feedback}\n")
             print(f"INI file written to {output_ini_path}")
             return True
 
diff --git a/clpipe/cluster_theory_pred.py b/clpipe/cluster_theory_pred.py
index 104b18e..25136a5 100644
--- a/clpipe/cluster_theory_pred.py
+++ b/clpipe/cluster_theory_pred.py
@@ -159,6 +159,7 @@ def build_cluster_recipes_from_config(yml_file: str, sacc_file: str, set_params:
     Build cluster counts and (optionally) shear recipes from a YAML configuration and return theory/data vectors.
 
     This function:
+    
     - Loads configuration from `yml_file` (supports a top-level CLPFirecrown key).
     - Builds a pyccl halo mass function and pyccl Cosmology (via build_ccl_cosmology_from_config).
     - Constructs completeness/purity models if requested.
diff --git a/coverage.xml b/coverage.xml
new file mode 100644
index 0000000..07034f1
--- /dev/null
+++ b/coverage.xml
@@ -0,0 +1,952 @@
+<?xml version="1.0" ?>
+<coverage version="7.16.2" timestamp="1791373996470" lines-valid="895" lines-covered="842" line-rate="0.9408" branches-valid="114" branches-covered="98" branch-rate="0.8596" complexity="0">
+	<!-- Generated by coverage.py: https://coverage.readthedocs.io/en/7.16.2 -->
+	<!-- Based on https://raw.githubusercontent.com/cobertura/web/master/htdocs/xml/coverage-04.dtd -->
+	<sources>
+		<source>/home/jacquemier/Working_dir/LSST/CLPipe</source>
+	</sources>
+	<packages>
+		<package name="clpipe" line-rate="0.9131" branch-rate="0.7917" complexity="0">
+			<classes>
+				<class name="__init__.py" filename="clpipe/__init__.py" complexity="0" line-rate="1" branch-rate="1">
+					<methods/>
+					<lines>
+						<line number="5" hits="1"/>
+						<line number="6" hits="1"/>
+						<line number="7" hits="1"/>
+					</lines>
+				</class>
+				<class name="clp_covariance.py" filename="clpipe/clp_covariance.py" complexity="0" line-rate="0.8571" branch-rate="0.6923">
+					<methods/>
+					<lines>
+						<line number="3" hits="1"/>
+						<line number="4" hits="1"/>
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+						<line number="233" hits="1"/>
+						<line number="235" hits="0"/>
+						<line number="236" hits="0"/>
+					</lines>
+				</class>
+				<class name="test_clp_firecrown.py" filename="tests/test_clp_firecrown.py" complexity="0" line-rate="0.9757" branch-rate="1">
+					<methods/>
+					<lines>
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+						<line number="195" hits="1" branch="true" condition-coverage="100% (2/2)"/>
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+						<line number="419" hits="1" branch="true" condition-coverage="100% (2/2)"/>
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+						<line number="426" hits="1"/>
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+						<line number="434" hits="1"/>
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+						<line number="444" hits="1"/>
+						<line number="446" hits="1"/>
+						<line number="447" hits="1"/>
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+						<line number="450" hits="1"/>
+						<line number="451" hits="1"/>
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+						<line number="468" hits="0"/>
+						<line number="469" hits="0"/>
+						<line number="471" hits="0"/>
+						<line number="472" hits="0"/>
+						<line number="473" hits="0"/>
+					</lines>
+				</class>
+			</classes>
+		</package>
+	</packages>
+</coverage>
diff --git a/docs/Makefile b/docs/Makefile
new file mode 100644
index 0000000..8015636
--- /dev/null
+++ b/docs/Makefile
@@ -0,0 +1,31 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SPHINXPROJ    = CLPipe
+SOURCEDIR     = .
+BUILDDIR      = _build
+RM	      = rm -rf
+MODULEDIR     = ../clpipe
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+.PHONY: html
+html: 
+	@$(SPHINXBUILD) -M html "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) -W
+
+.PHONY: clean
+clean:
+	@$(RM) _build/* api/ api.rst compiled-examples/
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) -W
+
diff --git a/docs/clp_covariance.txt b/docs/clp_covariance.txt
index 97bf26d..57367cb 100644
--- a/docs/clp_covariance.txt
+++ b/docs/clp_covariance.txt
@@ -1,217 +1,346 @@
 CLPCovariance Configuration Options
 ===================================
 
-This document describes the configuration options available for the
-CLPCovariance stage. This stage computes covariance matrices for cluster
-observables using TJPCov and outputs an updated SACC file.
+This document describes the configuration options of the CLPCovariance
+stage. The stage computes the theoretical covariance of the cluster number
+counts with TJPCov and writes it into an output SACC file.
 
 --------------------------------------------------
 Overview
 --------------------------------------------------
 
-The CLPCovariance:
+CLPCovariance runs the following steps:
 
-- Reads an input SACC file (data vector)
-- Computes covariance terms using TJPCov
-- Writes a new SACC file with covariance
-- Optionally merges existing covariance (for non-cluster observables)
-- Optionally replaces cluster-count covariance with a custom CROW-based model
+1. Read the input SACC file (data vector) and the fiducial cosmology.
+2. Compute the covariance terms listed in cov_type with TJPCov.
+3. If the input SACC file also contains other observables with a
+   covariance (e.g. cluster_delta_sigma), copy those blocks into the new
+   covariance.
+4. If replace_tjpcov_cov is True, replace the cluster-count block with a
+   CROW-based covariance.
+5. Write a single output SACC file with the final covariance.
+
+Inputs and outputs:
+
+    clusters_sacc_file       (input)    SACC data vector, from TXPipe
+    fiducial_cosmology       (input)    Fiducial cosmology, shared with TXPipe
+    clusters_sacc_file_cov   (output)   SACC file with the final covariance
+
+The cosmology is always read from the fiducial_cosmology input. It is not
+set in the stage configuration.
+
+Input SACC file:
+
+The cluster_counts data points must each have three tracers, in this order:
+
+- survey: survey tracer, with sky_area in deg^2
+- bin_richness: richness bin, with lower/upper edges in log10(richness)
+- bin_z: redshift bin, with lower/upper edges
 
 --------------------------------------------------
-Core Options
+Covariance Model
 --------------------------------------------------
 
-use_mpi (bool, default: False)
-    Enable MPI parallelization in TJPCov.
-    Must be consistent with the execution environment.
+The cluster-count covariance is the sum of a Poisson shot-noise term and a
+super-sample covariance (SSC) term (Lacasa et al. 2018):
 
-do_xi (bool, default: False)
-    Whether to compute real-space correlation functions (xi).
-    Typically False for cluster-count analyses.
+```math
+[\mathrm{Cov}_N]_{ijkl} = \delta^K_{ik} \, \delta^K_{jl} \, N_{ij} + [\mathrm{Cov}_{\rm SSC}]_{ijkl}
 
-cov_type (list of str)
-    List of covariance terms to compute.
+[\mathrm{Cov}_{\rm SSC}]_{ijkl} = N_{ij} \, N_{kl} \, \langle b \rangle_{ij} \, \langle b \rangle_{kl} \, S_{ik}
+```
 
-    Supported values include:
-        - ClusterCountsGaussian
-        - ClusterCountsSSC
-        - ClusterMass (optional, not always used)
+where i, k run over redshift bins and j, l over richness bins:
 
-    Example:
-        cov_type: [ClusterCountsGaussian, ClusterCountsSSC]
+- N_ij: predicted number of clusters in bin (i, j)
+- <b>_ij: halo bias averaged over the clusters in bin (i, j)
+  (model set by halo_bias)
+- S_ik: covariance of the matter density contrast, smoothed by the survey
+  window, between redshift bins i and k (partial-sky window computed from
+  the survey area)
+
+The SSC term scales as N^2, while the shot noise scales as N, so SSC
+becomes important for large cluster samples such as LSST (Fumagalli et al.
+2021).
+
+TJPCov computes the shot-noise term (ClusterCountsGaussian) and the SSC
+term (ClusterCountsSSC) separately. Both use the halo model, mass–richness
+relation and photo-z model set by mor_parameters and photo-z below.
 
 --------------------------------------------------
-Cosmology Options
+TJPCov Options
 --------------------------------------------------
 
-cosmo (str)
-    Defines how cosmology is provided.
+use_mpi (bool, default: False)
+    Enable MPI parallelization in TJPCov.
+    Must be consistent with the execution environment.
 
-    Options:
-        - "set": use parameters from config
-        - (future) external cosmology object or file
+do_xi (bool, default: False)
+    Compute real-space correlation function (xi) terms.
+    Typically False for cluster-count analyses.
 
-parameters (dict)
-    Cosmological parameters passed to CCL.
+cov_type (list of str, default: [ClusterCountsGaussian, ClusterCountsSSC])
+    Covariance terms to compute. Supported values:
 
-    Required fields:
-        Omega_c
-        Omega_b
-        h
-        n_s
-        sigma8
-        w0
-        wa
-        transfer_function
+    - ClusterCountsGaussian: Poisson shot-noise term (diagonal)
+    - ClusterCountsSSC: super-sample covariance term
+    - ClusterMass (optional, not always used)
 
     Example:
-        Omega_c: 0.22
-        Omega_b: 0.0448
-        h: 0.71
-        n_s: 0.963
-        sigma8: 0.8
 
-Note:
-    These parameters are currently only used in the custom covariance
-    replacement step (replace_crow_counts), not directly by TJPCov.
+    ```yaml
+    cov_type: [ClusterCountsGaussian, ClusterCountsSSC]
+    ```
 
 --------------------------------------------------
 Photo-z Options
 --------------------------------------------------
 
-photo-z (dict)
+photo-z (dict, required by TJPCov)
+    Photometric redshift model used by TJPCov. The observed redshift is
+    Gaussian around the true redshift (truncated at z_phot > 0), with a
+    scatter that grows with redshift:
 
-    sigma_0 (float)
-        Photometric redshift uncertainty parameter.
+    ```math
+    \sigma_z(z) = \sigma_0 \, (1 + z)
+    ```
+
+    - sigma_0 (float): photo-z scatter parameter, e.g. 0.05
+
+    The CROW replacement does not use it: it assumes exact (spectroscopic)
+    redshifts.
 
     Example:
+
+    ```yaml
+    photo-z:
         sigma_0: 0.05
+    ```
 
 --------------------------------------------------
 Mass–Observable Relation (MOR) Options
 --------------------------------------------------
 
-mor_parameters (dict)
+mor_parameters (dict, required by TJPCov)
+    Halo model and mass–richness relation.
+
+    Halo model (used by TJPCov only):
+
+    - mass_func (str): halo mass function, e.g. "Despali16".
+      Supported: Angulo12, Bocquet16, Bocquet20, Despali16, Jenkins01,
+      Nishimichi19, Press74, Sheth99, Tinker08, Tinker10, Watson13
+    - mass_def (str): mass definition, e.g. "200c"
+    - halo_bias (str): halo bias model, used for <b> in the SSC term,
+      e.g. "Tinker10".
+      Supported: Bhattacharya11, Sheth01, Sheth99, Tinker10
 
-    Defines the halo mass function and richness–mass relation.
+    Mass range (used by TJPCov and CROW):
 
-    Halo model:
-        mass_func (str)        : e.g. "Despali16"
-        mass_def (str)         : e.g. "200c"
-        halo_bias (str)        : e.g. "Tinker10"
+    - min_halo_mass (float): lower mass limit in M_sun, e.g. 1.0e12
+    - max_halo_mass (float): upper mass limit in M_sun, e.g. 3.16e15
 
-    Mass range:
-        min_halo_mass (float)
-        max_halo_mass (float)
+    These are linear masses, not log10.
 
-    Pivot values:
-        m_pivot (float)
-        z_pivot (float)
+    Mass–richness relation (used by TJPCov and CROW):
 
-    Richness–mass relation parameters:
-        mu_p0, mu_p1, mu_p2
-        sigma_p0, sigma_p1, sigma_p2
+    The richness follows the Murata et al. (2019) model. At fixed mass and
+    redshift, ln(richness) is Gaussian. Its mean and its scatter are each
+    linear in ln(M) and ln(1+z), with 3 parameters each:
 
-    These control the mapping between halo mass and observable richness.
+    ```math
+    P(\ln\lambda \mid M, z) = \frac{1}{\sqrt{2\pi} \, \sigma_{\ln\lambda}} \exp\left[ -\frac{(\ln\lambda - \mu_{\ln\lambda})^2}{2 \sigma_{\ln\lambda}^2} \right]
+
+    \mu_{\ln\lambda}(M, z) = \mu_0 + \mu_m \ln\frac{M}{M_{\rm piv}} + \mu_z \ln\frac{1+z}{1+z_{\rm piv}}
+
+    \sigma_{\ln\lambda}(M, z) = \sigma_0 + \sigma_m \ln\frac{M}{M_{\rm piv}} + \sigma_z \ln\frac{1+z}{1+z_{\rm piv}}
+    ```
+
+    - m_pivot (float): pivot mass, log10(M_piv / M_sun), e.g. 14.3
+    - z_pivot (float): pivot redshift z_piv, e.g. 0.5
+    - mu_p0, mu_p1, mu_p2 (float): mean parameters mu_0, mu_m, mu_z
+    - sigma_p0, sigma_p1, sigma_p2 (float): scatter parameters
+      sigma_0, sigma_m, sigma_z
 
 --------------------------------------------------
 Pipeline-Specific Options
 --------------------------------------------------
 
 replace_tjpcov_cov (bool, default: True)
-    If True, replaces the TJPCov cluster-count covariance with a
-    custom covariance computed using CROW.
+    Replace the TJPCov cluster-count covariance with a covariance computed
+    with CROW (see "Custom Covariance Replacement" below):
+
+    - Diagonal terms are recomputed from CROW theory predictions.
+    - SSC and Gaussian contributions are combined.
+
+    WARNING: this is a temporary workaround. It should eventually be
+    handled directly inside TJPCov.
+
+sel_func (bool, default: True)
+    Only used when replace_tjpcov_cov is True.
+
+    If True, the CROW counts are weighted by the Aguena & Lima (2018)
+    completeness model:
 
-    This includes:
-        - Recomputing diagonal terms using theory predictions
-        - Combining SSC and Gaussian contributions
+    ```math
+    c(M, z) = \frac{\left(M / M_0(z)\right)^{n_c(z)}}{1 + \left(M / M_0(z)\right)^{n_c(z)}}
 
-    WARNING:
-        This is a temporary workaround and should ideally be handled
-        directly inside TJPCov.
+    n_c(z) = a_n + b_n \, (1 + z), \qquad \log_{10} M_0(z) = a_{\rm piv} + b_{\rm piv} \, (1 + z)
+    ```
 
-wazp_catalog (bool, default: False)
-    Special handling for WaZP catalogs:
-        - Disables purity model
-        - Modifies completeness parameters
+    The completeness parameters are fixed to the CROW defaults, which are
+    the cosmoDC2 redMaPPer values (a_n = 1.1321, b_n = 0.7751,
+    a_piv = a_logm_piv = 13.31, b_piv = b_logm_piv = 0.2025). They cannot
+    be set from the configuration.
+
+    If False, no selection function is applied (c = 1).
+    The purity model is currently disabled in the code.
+
+diagonal_shear_covariance (bool, default: True)
+    Only used when the input SACC file contains other observables with a
+    covariance (e.g. cluster_delta_sigma).
+    If True, only the diagonal (per-bin variance) of those blocks is kept.
+    If False, the full blocks are copied.
 
 --------------------------------------------------
 Internal Behavior
 --------------------------------------------------
 
 1. Covariance Computation
-------------------------
+-------------------------
 
-TJPCov computes covariance terms based on cov_type and writes:
+TJPCov computes the terms listed in cov_type and writes:
 
     clusters_sacc_file_cov.sacc
 
-Additional intermediate files may include:
-    - clusters_sacc_file_cov_SSC.sacc
-    - clusters_sacc_file_cov_gauss.sacc
+Intermediate files with the individual terms may also be written:
+
+    clusters_sacc_file_cov_SSC.sacc
+    clusters_sacc_file_cov_gauss.sacc
 
 2. Mixed Data Handling
----------------------
+----------------------
 
-If the input SACC file contains multiple observables:
+If the input SACC file contains other observables besides cluster counts
+and already has a covariance:
 
-    - Cluster counts covariance is recomputed
-    - Other covariance blocks (e.g. lensing) are preserved
+- The cluster-count covariance is recomputed.
+- The other covariance blocks (e.g. lensing) are copied from the input
+  file, diagonal-only or in full (see diagonal_shear_covariance).
 
-This is handled by:
-    extract_data_covariance()
+This is handled by merge_data_covariance().
 
 3. Custom Covariance Replacement
 --------------------------------
 
-If replace_tjpcov_cov = True:
+If replace_tjpcov_cov is True, the cluster-count block is rebuilt with
+CROW. For each cluster-count data point a (one richness-redshift bin),
+CROW predicts the counts
+
+```math
+\begin{aligned}
+N_a = {} & \Omega_S \int_{z_a} dz \int_{\lambda_a} d\ln\lambda \int_{M_{\min}}^{M_{\max}} dM \\
+& \times \frac{d^2V}{dz \, d\Omega} \, \frac{dn}{dM}(M, z) \, P(\ln\lambda \mid M, z) \, c(M, z)
+\end{aligned}
+```
+
+where Omega_S is the survey area in steradians (from the SACC survey
+tracer), dn/dM is the Despali16 mass function, and c(M, z) is the
+completeness (see sel_func).
+
+The covariance is then
 
-    - The covariance is recomputed using CROW
-    - Theory predictions for counts are evaluated
-    - Covariance elements are replaced using:
+```math
+C_{aa} = N_a + \mathrm{SSC}_{aa} \left( \frac{N_a}{N^{\rm TJPCov}_a} \right)^2
 
-        C_ii = N_theory + SSC * (N_theory^2 / N_gauss^2)
-        C_ij = SSC_ij * (N_i * N_j) / (N_gauss_i * N_gauss_j)
+C_{ab} = \mathrm{SSC}_{ab} \, \frac{N_a \, N_b}{N^{\rm TJPCov}_a \, N^{\rm TJPCov}_b}, \qquad a \neq b
+```
 
-    This modifies only the cluster-count block.
+where:
+
+- N_a: CROW theory counts for data point a
+- N^TJPCov_a: TJPCov Gaussian (shot-noise) term for data point a, i.e. the
+  counts predicted by TJPCov
+- SSC_ab: TJPCov super-sample covariance term
+
+The CROW counts are used as the Poisson term on the diagonal. The SSC term
+scales as N_a N_b, so it is rescaled from the TJPCov counts to the CROW
+counts. Only the cluster-count block is modified.
 
 --------------------------------------------------
 Known Limitations
 --------------------------------------------------
 
-- replace_crow_counts() is a temporary workaround and should not exist
-  long-term (should be implemented in TJPCov directly).
+- replace_crow_counts() is a temporary workaround. It should be removed
+  once this is implemented in TJPCov.
+
+- The cosmology is passed to TJPCov both as a CCL object and as a
+  parameter dictionary. This duplication comes from TJPCov and should be
+  fixed there.
+
+- Hardcoded choices in the CROW replacement:
+    - Mass function: Despali16 (200c), whatever mass_func and mass_def are
+    - Redshifts: spectroscopic (photo-z sigma_0 is not used)
+    - Completeness parameters: CROW defaults
+    - Purity model: disabled
+    - Grid sizes: mass 80, redshift 40, proxy 40
+    - Recipe: GridBinnedClusterRecipe
 
-- Some configuration options (e.g. cosmology) are duplicated and not
-  consistently used across all steps.
+- The covariance is computed once, at the fiducial cosmology and MOR
+  parameters, and held fixed during inference.
 
-- Hardcoded choices exist:
-    - Mass function (Despali16)
-    - Grid sizes
-    - Recipe type (GridBinnedClusterRecipe)
+- The input configuration is not validated.
+
+--------------------------------------------------
+Pipeline Configuration
+--------------------------------------------------
 
-- No validation is performed on input configuration.
+The stage is wired into a ceci pipeline file. This example comes from
+examples/cosmodc2_redmapper/baseline/cosmodc2_redmapper_full_analysis/run_in2p3_both/TJPCov.yml:
+
+```yaml
+id: TJPCov
+modules: clpipe
+launcher:
+    name: mini
+    interval: 0.5
+site:
+    name: local
+    max_threads: 4
+stages:
+  - name: CLPCovariance
+    module_name: clpipe.clp_covariance
+    nprocess: 1
+inputs:
+    fiducial_cosmology: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/cosmodc2/fiducial_cosmology.yml
+    clusters_sacc_file: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/cosmodc2/outputs-full-2026//cluster_sacc_catalog.sacc
+config: ./config_in2p3_both.yml
+resume: false
+output_dir: ./outputs_both
+log_dir: ./logs_both
+```
+
+Run it with:
+
+```bash
+ceci TJPCov.yml
+```
+
+The stage options below go in the stage config file (config: above),
+under a CLPCovariance block.
 
 --------------------------------------------------
 Example Configuration
 --------------------------------------------------
 
+```yaml
 CLPCovariance:
     use_mpi: False
     do_xi: False
     cov_type: [ClusterCountsGaussian, ClusterCountsSSC]
 
-    cosmo: 'set'
-
-    parameters:
-        Omega_c: 0.22
-        Omega_b: 0.0448
-        h: 0.71
-        n_s: 0.963
-        sigma8: 0.8
-        w0: -1
-        wa: 0
-        transfer_function: 'boltzmann_camb'
+    replace_tjpcov_cov: True
+    sel_func: True
+    diagonal_shear_covariance: True
 
     photo-z:
         sigma_0: 0.05
@@ -232,9 +361,29 @@ CLPCovariance:
         sigma_p0: 0.5623
         sigma_p1: 0.0455
         sigma_p2: -0.0445
+```
 
 --------------------------------------------------
 
 Notes:
-- Additional TJPCov parameters can be added depending on the covariance model.
-- Ensure consistency between SACC input data and MOR configuration.
\ No newline at end of file
+
+- Additional TJPCov parameters can be added depending on the covariance
+  model. They are passed through to TJPCov.
+- Make sure the SACC input data and the MOR configuration are consistent.
+
+--------------------------------------------------
+References
+--------------------------------------------------
+
+- Murata et al. (2019), mass–richness relation:
+  https://doi.org/10.1093/pasj/psz092 (arXiv:1904.07524)
+- Lacasa, Lima & Aguena (2018), super-sample covariance:
+  https://arxiv.org/abs/1612.05958
+- Fumagalli et al. (2021), impact of sample covariance on cluster counts:
+  https://arxiv.org/abs/2102.08914
+- Aguena & Lima (2018), completeness and purity:
+  https://arxiv.org/abs/1611.05468
+- Despali et al. (2016), halo mass function:
+  https://arxiv.org/abs/1507.05627
+- Tinker et al. (2010), halo bias:
+  https://arxiv.org/abs/1001.3162
diff --git a/docs/clp_firecrown.txt b/docs/clp_firecrown.txt
index 9943756..133b057 100644
--- a/docs/clp_firecrown.txt
+++ b/docs/clp_firecrown.txt
@@ -1,236 +1,505 @@
 CLPFirecrown Configuration Options
-======================================
+==================================
 
-This document describes the configuration options available for the
-CLPFirecrown stage. These options control the construction of the
-Firecrown likelihood, the cluster modeling assumptions, and the sampling
-configuration.
+This document describes the configuration options of the CLPFirecrown
+stage. The stage prepares a cluster cosmology inference with Firecrown and
+CosmoSIS: it writes the Firecrown likelihood and the CosmoSIS configuration
+files. It does not run the sampler. Sampling is a separate CosmoSIS run
+(see "Running the Inference").
 
 --------------------------------------------------
-General Options
+Overview
 --------------------------------------------------
 
-hmf (str, default: "bocquet16")
-    Halo Mass Function used in the analysis.
-    Supported values:
-        - angulo12
-        - bocquet16
-        - bocquet20
-        - despali16
-        - jenkins01
-        - press74
-        - sheth99
-        - tinker08
-        - tinker10
-        - watson13
+CLPFirecrown runs the following steps:
 
-mass_def (str, default: "200c")
-    Mass definition used in the halo mass function (e.g. "200c", "500c").
+1. Read the SACC file with covariance (from CLPCovariance) and the fiducial
+   cosmology.
+2. Write the Firecrown likelihood file. It builds CROW recipes for the
+   cluster counts and/or the stacked shear profile, from the modeling
+   options below.
+3. Write the CosmoSIS pipeline file: consistency, CAMB and the Firecrown
+   likelihood modules, plus the sampler settings.
+4. Write the CosmoSIS values file: cosmological and Firecrown parameters,
+   with their priors.
+
+Inputs and outputs:
+
+    clusters_sacc_file_cov   (input)    SACC data vector with covariance, from CLPCovariance
+    fiducial_cosmology       (input)    Fiducial cosmology, shared with TXPipe and CLPCovariance
+    sampler_file             (output)   CosmoSIS pipeline file (sampler_file.ini)
+    likelihood_file          (output)   Firecrown likelihood (likelihood_file.py)
+    priors_file              (output)   CosmoSIS values file with priors (priors_file.ini)
+
+--------------------------------------------------
+Theory Model
+--------------------------------------------------
+
+The predictions are computed with CROW. For a redshift bin i and a richness
+bin j, the number counts and the stacked lensing profile are
+
+```math
+\begin{aligned}
+N_{ij} = {} & \Omega_S \int_{z_i}^{z_{i+1}} dz \int_{\ln\lambda_j}^{\ln\lambda_{j+1}} d\ln\lambda \int_{M_{\min}}^{M_{\max}} dM \\
+& \times \frac{d^2V}{dz \, d\Omega} \, \frac{dn}{dM}(M, z) \, P(\ln\lambda \mid M, z) \, \Phi(M, \lambda, z)
+\end{aligned}
+
+\begin{aligned}
+\Theta_{ij}(R) = {} & \frac{\Omega_S}{N_{ij}} \int_{z_i}^{z_{i+1}} dz \int_{\ln\lambda_j}^{\ln\lambda_{j+1}} d\ln\lambda \int_{M_{\min}}^{M_{\max}} dM \\
+& \times \frac{d^2V}{dz \, d\Omega} \, \frac{dn}{dM}(M, z) \, P(\ln\lambda \mid M, z) \, \Phi(M, \lambda, z) \, \Theta(R \mid M)
+\end{aligned}
+```
+
+where:
+
+- Omega_S: survey area in steradians, from the SACC file
+- dn/dM: halo mass function (hmf, mass_def)
+- P(ln lambda | M, z): mass–richness relation (see below)
+- Phi = c(M, z) / p(lambda, z): selection function, from the completeness
+  c and the purity p (see below). Each is 1 when disabled.
+- Theta(R | M): lensing profile of a halo of mass M, either the excess
+  surface density DeltaSigma or the reduced tangential shear g_t
+
+1. Mass–richness relation
+-------------------------
+
+The richness follows the Murata et al. (2019) model. At fixed mass and
+redshift, ln(richness) is Gaussian. Its mean and its scatter are each
+linear in ln(M) and ln(1+z), with 3 parameters each:
+
+```math
+P(\ln\lambda \mid M, z) = \frac{1}{\sqrt{2\pi} \, \sigma_{\ln\lambda}} \exp\left[ -\frac{(\ln\lambda - \mu_{\ln\lambda})^2}{2 \sigma_{\ln\lambda}^2} \right]
+
+\mu_{\ln\lambda}(M, z) = \mu_0 + \mu_m \ln\frac{M}{M_{\rm piv}} + \mu_z \ln\frac{1+z}{1+z_{\rm piv}}
+
+\sigma_{\ln\lambda}(M, z) = \sigma_0 + \sigma_m \ln\frac{M}{M_{\rm piv}} + \sigma_z \ln\frac{1+z}{1+z_{\rm piv}}
+```
+
+The pivots are set by pivot_mass and pivot_z. The 6 parameters are Firecrown
+parameters (see "Firecrown Parameters").
+
+2. Completeness
+---------------
+
+With use_completeness, the Aguena & Lima (2018) completeness model:
+
+```math
+c(M, z) = \frac{\left(M / M_0(z)\right)^{n_c(z)}}{1 + \left(M / M_0(z)\right)^{n_c(z)}}
+
+n_c(z) = a_n + b_n \, (1 + z), \qquad \log_{10} M_0(z) = a_{\rm piv} + b_{\rm piv} \, (1 + z)
+```
+
+3. Purity
+---------
+
+With use_purity, the Aguena & Lima (2018) purity model, written in ln(richness):
 
-min_mass, max_mass (float)
-    Log10 mass range of clusters used in the analysis.
+```math
+p(\lambda, z) = \frac{\left(\ln\lambda / \ln\lambda_0(z)\right)^{n_p(z)}}{1 + \left(\ln\lambda / \ln\lambda_0(z)\right)^{n_p(z)}}
 
-min_z, max_z (float)
-    Redshift range of clusters.
+n_p(z) = a_n + b_n \, (1 + z), \qquad \ln\lambda_0(z) = a_{\rm piv} + b_{\rm piv} \, (1 + z)
+```
 
-pivot_mass (float)
-    Pivot log10 mass used in the mass–observable relation.
+4. Lensing profile
+------------------
 
-pivot_z (float)
-    Pivot redshift used in the mass–observable relation.
+Theta(R | M) is computed with CLMM from an NFW profile (one-halo term). The
+concentration is the Firecrown parameter cluster_theory_cluster_concentration.
+A value below 1 uses the Bhattacharya et al. (2013) mass–concentration
+relation instead. The two-halo term and the boost-factor correction can be
+added (two_halo_term, boost_factor).
 
-survey_name (str)
-    Name of the survey used to match SACC data.
+For the reduced shear g_t, the lensing efficiency is averaged over the
+source redshift distribution (beta_parameters, use_beta_interp).
+
+5. Likelihood
+-------------
+
+The likelihood is a Gaussian (Firecrown ConstGaussian) over all the selected
+statistics:
+
+- BinnedClusterNumberCounts: counts N_ij (and optionally the mean log mass)
+- BinnedClusterShearProfile: stacked DeltaSigma or g_t profiles
+
+The covariance is read from the SACC file and held fixed during sampling.
 
 --------------------------------------------------
 Modeling Options
 --------------------------------------------------
 
+hmf (str, default: "despali16")
+    Halo mass function (case-insensitive). Supported values:
+
+    - angulo12
+    - bocquet16
+    - bocquet20
+    - despali16
+    - jenkins01
+    - press74
+    - sheth99
+    - tinker08
+    - tinker10
+    - watson13
+
+mass_def (str, default: "200c")
+    Mass definition used by the halo mass function (e.g. "200c", "500c").
+
+min_mass, max_mass (float, default: 12.0, 15.5)
+    Halo mass integration range, in log10(M / M_sun).
+    Note: CLPCovariance uses linear masses for the same range.
+
+min_z, max_z (float, default: 0.2, 0.8)
+    True redshift integration range.
+
+pivot_mass (float, default: 14.3)
+    Pivot mass of the mass–richness relation, log10(M_piv / M_sun).
+
+pivot_z (float, default: 0.5)
+    Pivot redshift of the mass–richness relation.
+
+survey_name (str, default: "cosmodc2_redmapper")
+    Survey tracer name in the SACC file. Must match it.
+
+--------------------------------------------------
+Observable Options
+--------------------------------------------------
+
 use_cluster_counts (bool, default: True)
-    Include cluster number counts in the likelihood.
+    Include the cluster number counts in the likelihood.
 
 use_shear_profile (bool, default: False)
-    Include cluster shear (or DeltaSigma) profile in the likelihood.
+    Include the stacked shear profiles in the likelihood.
 
-use_mean_log_mass (bool)
-    Include mean log-mass observable in the likelihood averaging.
+is_deltasigma (bool, default: False)
+    Theory side: predict DeltaSigma (True) or the reduced shear g_t (False).
 
-use_mean_deltasigma (bool)
-    Use DeltaSigma instead of reduced shear.
+use_mean_deltasigma (bool, default: False)
+    Data side: read DeltaSigma (True) or the reduced shear g_t (False) from
+    the SACC file. Set it to the same value as is_deltasigma.
 
-is_deltasigma (bool, default: False)
-    Controls whether the shear observable is DeltaSigma.
+use_mean_log_mass (bool, default: False)
+    Also use the mean log mass per bin, read from the SACC file.
 
 --------------------------------------------------
-Systematics and Corrections
+Selection Function Options
 --------------------------------------------------
 
-use_completeness (bool or None)
-    If True, applies a completeness model (Aguena16).
-    If False or None, no completeness correction is applied.
+use_completeness (bool, default: True)
+    Apply the completeness model c(M, z) (see "Theory Model").
 
-use_purity (bool or None)
-    If True, applies a purity model (Aguena16).
-    If False or None, no purity correction is applied.
+use_purity (bool, default: True)
+    Apply the purity model p(lambda, z) (see "Theory Model").
 
-cluster_concentration (float or None)
-    Fixed cluster concentration parameter.
-    If None, default modeling assumptions are used.
+--------------------------------------------------
+Lensing Options
+--------------------------------------------------
+
+Only used when use_shear_profile is True.
 
 two_halo_term (bool, default: False)
-    Include the two-halo term in the shear model.
+    Add the two-halo term to the lensing profile.
 
 boost_factor (bool, default: False)
-    Include boost factor corrections in shear modeling.
+    Apply the boost-factor correction to the lensing profile.
+
+set_concentration (bool)
+    Reserved for future use. The stage does not read it yet, so it
+    currently has no effect. The concentration is set with
+    cluster_theory_cluster_concentration in firecrown_parameters.
+
+use_beta_interp (bool, default: False)
+    Interpolate the mean lensing efficiency <beta_s> in redshift.
+    Only used for the reduced shear (is_deltasigma: False).
+
+beta_parameters (list of float, default: [10.0, 5.0])
+    Parameters of the mean lensing efficiency <beta_s>, passed to CROW as
+    [z_inf, zmax]: the redshift taken as infinity, and the maximum source
+    redshift. Only used for the reduced shear (is_deltasigma: False).
+
+    The first value (z_inf, 10 by default) is also always used as the
+    maximum redshift of CAMB (zmax in the [camb] section of
+    sampler_file.ini), even without shear profiles.
 
 --------------------------------------------------
-Grid and Interpolation Options
+Integration Options
 --------------------------------------------------
 
 use_grid (bool, default: True)
-    If True, uses GridBinnedClusterRecipe.
-    If False, uses ExactBinnedClusterRecipe.
+    If True, use GridBinnedClusterRecipe: integrals on precomputed grids,
+    about 100 times faster, recommended for inference.
+    If False, use ExactBinnedClusterRecipe (direct integration with
+    NumCosmo), slower, useful to validate the grid results.
 
 redshift_grid_size (int, default: 20)
-    Number of bins in redshift grid.
+    Number of redshift grid points.
 
 mass_grid_size (int, default: 60)
-    Number of bins in mass grid.
+    Number of mass grid points.
 
 proxy_grid_size (int, default: 20)
-    Number of bins in observable (richness) grid.
-
-use_beta_interp (bool, default: False)
-    Enables interpolation of beta_s in shear calculations.
-
-beta_parameters (list[float], default: [10.0, 5.0])
-    Parameters controlling the beta function in shear modeling.
+    Number of richness grid points.
 
-use_poisson_noise (bool, default: False)
-    Adds Poisson noise to the mass–observable relation.
+The grid sizes are only used when use_grid is True.
 
 --------------------------------------------------
 Sampler Options
 --------------------------------------------------
 
-sampler (str, default: "test")
-    Sampler used by CosmoSIS.
-    Supported examples:
-        - test
-        - emcee
-        - polychord
-        - metropolis
+sampler (str, default: "emcee")
+    CosmoSIS sampler. Supported values:
 
-emcee_walkers (int)
-    Number of walkers for emcee sampler.
+    - test: a single likelihood evaluation, useful to check the setup
+    - metropolis
+    - emcee
+    - polychord
 
-emcee_samples (int)
-    Total number of samples for emcee.
+emcee_walkers (int, default: 100)
+    Number of emcee walkers.
 
-emcee_nsteps (int)
-    Number of steps per iteration for emcee.
+emcee_samples (int, default: 20000)
+    Number of emcee samples per walker.
 
-polychord_live_points (int)
-    Number of live points for Polychord.
+emcee_nsteps (int, default: 20)
+    Number of emcee steps between chain outputs.
 
-polychord_num_repeats (int)
-    Number of slice sampling repeats.
+polychord_live_points (int, default: 500)
+    Number of PolyChord live points.
 
-polychord_tolerance (float)
-    Evidence tolerance for stopping criterion.
+polychord_num_repeats (int, default: 30)
+    Number of PolyChord slice-sampling repeats.
 
-polychord_feedback (int)
-    Verbosity level for Polychord.
+polychord_tolerance (float, default: 0.05)
+    PolyChord evidence tolerance (stopping criterion).
+
+polychord_feedback (int, default: 1)
+    PolyChord verbosity level.
+
+The polychord_* options fill the [polychord] section of sampler_file.ini
+(see https://cosmosis.readthedocs.io/en/latest/reference/samplers/polychord.html).
+
+resume (bool, default: False)
+    If True, CosmoSIS appends to the existing chain instead of starting a
+    new one.
+
+filename (str, optional, default: "output_rp/number_counts_samples.txt")
+    Path of the output chain file.
 
 --------------------------------------------------
 Cosmological Parameters
 --------------------------------------------------
 
-cosmological_parameters (dict)
-    Dictionary defining cosmological parameters.
+tau (float, default: 0.08)
+    Optical depth to reionization. It is a CAMB input, not part of the
+    fiducial cosmology file, so it is set here.
+
+cosmological_parameters (dict, default: {})
+    Overrides on top of the fiducial cosmology. By default, every
+    cosmological parameter is fixed to its value in fiducial_cosmology.
+    List a parameter here to sample it.
+
+    Parameter names (CosmoSIS naming):
 
-    Format:
-        parameter_name:
-            sample: True/False
-            values: [min, fiducial, max]   (if sampled)
-                    or scalar value       (if fixed)
+        omega_c, omega_b, h0, n_s, sigma_8, omega_k, w, wa, tau
+
+    A fixed entry (sample: False) must have the fiducial value, otherwise
+    the stage fails. To change the fiducial cosmology, edit the
+    fiducial_cosmology file instead, so that all stages use it.
 
     Example:
-        omega_c:
-            sample: True
-            values: [0.1552, 0.2052, 0.3552]
+
+    ```yaml
+    cosmological_parameters:
+        omega_c: {'sample': True, 'values': [0.10, 0.22, 0.5]}
+        sigma_8: {'sample': True, 'values': [0.5, 0.800, 1.1]}
+    ```
+
+--------------------------------------------------
+Firecrown Parameters
+--------------------------------------------------
+
+firecrown_parameters (dict, default: {})
+    Parameters of the CROW models, written to the [firecrown_number_counts]
+    section of the values file. Set all the parameters of the models you
+    enable, fixed or sampled.
+
+    Mass–richness relation:
+
+    - mass_distribution_mu0, mass_distribution_mu1, mass_distribution_mu2:
+      mu_0, mu_m, mu_z
+    - mass_distribution_sigma0, mass_distribution_sigma1,
+      mass_distribution_sigma2: sigma_0, sigma_m, sigma_z
+
+    Completeness (use_completeness):
+
+    - completeness_a_n, completeness_b_n: a_n, b_n
+    - completeness_a_logm_piv, completeness_b_logm_piv: a_piv, b_piv
+    - CROW defaults (cosmoDC2 redMaPPer): 1.1321, 0.7751, 13.31, 0.2025
+
+    Purity (use_purity):
+
+    - purity_a_n, purity_b_n: a_n, b_n
+    - purity_a_logm_piv, purity_b_logm_piv: a_piv, b_piv
+    - CROW defaults (cosmoDC2 redMaPPer): 1.9830, 0.8121, 2.2183, -0.6592
+
+    Lensing profile (use_shear_profile):
+
+    - cluster_theory_cluster_concentration: halo concentration
+      (below 1: Bhattacharya et al. 2013 relation)
 
 --------------------------------------------------
-Firecrown Likelihood Parameters
+Parameter Format
 --------------------------------------------------
 
-firecrown_parameters (dict)
-    Parameters controlling the cluster mass–observable relation and systematics.
+Entries in cosmological_parameters and firecrown_parameters use the same
+format:
 
-    Same format as cosmological parameters:
-        sample: True/False
-        values: [min, fiducial, max] or scalar
+```yaml
+# Sampled, with a flat prior between min and max:
+name: {'sample': True, 'values': [min, start, max]}
 
-    Typical parameters include:
-        - mass_distribution_mu*
-        - mass_distribution_sigma*
-        - completeness_*
-        - purity_*
-        - cluster_theory_cluster_concentration
+# Fixed:
+name: {'sample': False, 'values': value}
+```
+
+--------------------------------------------------
+Pipeline Configuration
+--------------------------------------------------
 
-    Additional parameters can be added freely as long as they are
-    recognized by the Firecrown likelihood.
+The stage is wired into a ceci pipeline file. This example comes from
+examples/cosmodc2_redmapper/baseline/cosmodc2_redmapper_full_analysis/run_in2p3_both/Firecrown.yml:
+
+```yaml
+id: Firecrown
+modules: clpipe
+launcher:
+    name: mini
+    interval: 0.5
+site:
+    name: local
+    max_threads: 4
+stages:
+  - name: CLPFirecrown
+    module_name: clpipe.clp_firecrown
+    nprocess: 1
+inputs:
+    fiducial_cosmology: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/cosmodc2/fiducial_cosmology.yml
+    clusters_sacc_file_cov: ./outputs_both/clusters_sacc_file_cov.sacc
+config: ./config_in2p3_both.yml
+resume: false
+output_dir: ./outputs_both
+log_dir: ./logs_both
+```
+
+The stage options go in the stage config file (config: above), under a
+CLPFirecrown block.
 
 --------------------------------------------------
-Notes and Caveats
+Running the Inference
 --------------------------------------------------
 
-- If both use_cluster_counts and use_shear_profile are True,
-  the likelihood combines both observables.
+Generate the files with ceci, then run CosmoSIS from the output directory:
 
-- If only one is enabled, only that observable is used.
+```bash
+ceci Firecrown.yml
+
+cd ./outputs_both
+cosmosis sampler_file.ini
+
+# or, with MPI:
+mpirun -n 30 cosmosis --mpi sampler_file.ini
+```
+
+The generated files refer to each other, and to the SACC file, by file
+name only. Run CosmoSIS from the output directory, with the SACC file in
+it (this is the case when CLPCovariance writes to the same output
+directory). CosmoSIS also needs the CSL_DIR environment variable (see the
+installation instructions in the README).
+
+--------------------------------------------------
+Known Limitations
+--------------------------------------------------
 
-- Purity can be forcibly disabled internally when using shear-only
-  configurations.
+- The test and metropolis sampler settings are fixed in the generated
+  file (metropolis: 1000 samples).
 
-- The SACC file provided as input must match the survey_name.
+- The covariance is held fixed during sampling.
 
-- Grid-based recipes are recommended for performance unless exact
-  integration is required.
+- Only the binned likelihood is supported.
 
-- Some parameters (e.g. cluster_concentration) are passed directly
-  to the underlying modeling code and are not validated here.
+- The names in firecrown_parameters are not validated by the stage.
 
 --------------------------------------------------
 Example Configuration
 --------------------------------------------------
 
+This is the CLPFirecrown block of the baseline cosmoDC2 redMaPPer analysis
+(counts + stacked DeltaSigma):
+
+```yaml
 CLPFirecrown:
     hmf: 'despali16'
+    mass_def: '200c'
     min_mass: 12.0
     max_mass: 15.5
     min_z: 0.2
     max_z: 0.8
-    mass_def: '200c'
-    use_shear_profile: True
-    use_completeness: True
-    use_purity: True
-    use_grid: True
-    is_deltasigma: True
-    use_beta_interp: False
-    beta_parameters: [10.0, 5.0]
     pivot_mass: 14.3
     pivot_z: 0.5
     survey_name: 'cosmodc2_redmapper'
+
+    use_cluster_counts: true
+    use_shear_profile: true
+    is_deltasigma: true
+    use_mean_deltasigma: true
+    use_mean_log_mass: false
+
+    use_completeness: true
+    use_purity: false
+
+    use_grid: true
+    use_beta_interp: false
+    beta_parameters: [10.0, 5.0]
+
     sampler: 'emcee'
-    use_cluster_counts: True
-    use_mean_log_mass: False
-    use_mean_deltasigma: True
-    emcee_walkers: 50
-    emcee_samples: 20000
+    emcee_walkers: 100
+    emcee_samples: 50000
     emcee_nsteps: 20
 
+    cosmological_parameters:
+        omega_c: {'sample': true, 'values': [0.10, 0.22, 0.5]}
+        sigma_8: {'sample': true, 'values': [0.5, 0.800, 1.1]}
+
+    firecrown_parameters:
+        mass_distribution_mu0: {'sample': true, 'values': [2.0, 3.3439, 10.0]}
+        mass_distribution_mu1: {'sample': true, 'values': [0.5, 0.958236982, 2.0]}
+        mass_distribution_mu2: {'sample': true, 'values': [-2.0, -0.0192802, 2.0]}
+        mass_distribution_sigma0: {'sample': true, 'values': [0.1, 0.562317194, 2.0]}
+        mass_distribution_sigma1: {'sample': true, 'values': [-0.6, 0.04552506, 0.3]}
+        mass_distribution_sigma2: {'sample': true, 'values': [-0.5, -0.0445, 2.0]}
+        completeness_a_n: {'sample': false, 'values': 1.1321}
+        completeness_b_n: {'sample': false, 'values': 0.7751}
+        completeness_a_logm_piv: {'sample': false, 'values': 13.31}
+        completeness_b_logm_piv: {'sample': false, 'values': 0.2025}
+        cluster_theory_cluster_concentration: {'sample': false, 'values': 3.8}
+```
+
+--------------------------------------------------
+References
 --------------------------------------------------
 
-Firecrown parameters can be extended as needed depending on the
-likelihood model.
\ No newline at end of file
+- Murata et al. (2019), mass–richness relation:
+  https://doi.org/10.1093/pasj/psz092 (arXiv:1904.07524)
+- Aguena & Lima (2018), completeness and purity:
+  https://arxiv.org/abs/1611.05468
+- Despali et al. (2016), halo mass function:
+  https://arxiv.org/abs/1507.05627
+- Navarro, Frenk & White (1997), NFW profile:
+  https://arxiv.org/abs/astro-ph/9611107
+- Bhattacharya et al. (2013), mass–concentration relation:
+  https://arxiv.org/abs/1112.5479
+- Zuntz et al. (2015), CosmoSIS:
+  https://arxiv.org/abs/1409.3409
+- Foreman-Mackey et al. (2013), emcee:
+  https://arxiv.org/abs/1202.3665
+- Firecrown: https://github.com/LSSTDESC/firecrown
+- CROW: https://github.com/LSSTDESC/crow
diff --git a/docs/conf.py b/docs/conf.py
new file mode 100644
index 0000000..af8915b
--- /dev/null
+++ b/docs/conf.py
@@ -0,0 +1,47 @@
+project = "CLPipe"
+author = "LSST DESC CLPipe Contributors"
+release = "0.0.1"
+
+import os
+import sys
+
+sys.path.insert(0, os.path.abspath(".."))
+
+root_doc = "index"
+extensions = [
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.viewcode",
+    "sphinx_autodoc_typehints",
+    "sphinx_copybutton",
+]
+
+autosummary_generate = True
+autodoc_member_order = "bysource"
+autodoc_default_options = {"members": True, "show-inheritance": True}
+# Heavy DESC dependencies are not installed in the standalone docs workflow.
+autodoc_mock_imports = [
+    "ceci",
+    "crow",
+    "firecrown",
+    "pyccl",
+    "sacc",
+    "tjpcov",
+    "cosmosis",
+    "yaml",
+    "numpy",
+]
+exclude_patterns = [
+	"_build",
+	"venv/**",
+	"Thumbs.db",
+	".DS_Store",
+]
+
+html_theme = "sphinx_rtd_theme"
+html_theme_options = {
+    "prev_next_buttons_location": None,
+    "collapse_navigation": False,
+    "titles_only": True,
+}
diff --git a/docs/index.rst b/docs/index.rst
new file mode 100644
index 0000000..ac8b809
--- /dev/null
+++ b/docs/index.rst
@@ -0,0 +1,22 @@
+********************
+CLPipe Documentation
+********************
+
+The LSST-DESC CLPipe Repository is dedicated to the Cluster Working Group of the DESC-LSST collaboration.
+
+
+The source code is publicly available at https://github.com/LSSTDESC/CLPipe.
+
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Getting Started
+
+   source/overview
+   source/installation
+   source/clp_covariance
+   source/clp_firecrown
+   source/LSST DESC software dependencies
+   source/api
+
+
diff --git a/docs/requirements.txt b/docs/requirements.txt
new file mode 100644
index 0000000..2c6d25e
--- /dev/null
+++ b/docs/requirements.txt
@@ -0,0 +1,4 @@
+sphinx
+sphinx-autodoc-typehints
+sphinx-rtd-theme
+sphinx_copybutton
\ No newline at end of file
diff --git a/docs/source/LSST DESC software dependencies.rst b/docs/source/LSST DESC software dependencies.rst
new file mode 100644
index 0000000..6d0defd
--- /dev/null
+++ b/docs/source/LSST DESC software dependencies.rst	
@@ -0,0 +1,58 @@
+********************************
+LSST DESC software dependencies
+********************************
+This pipeline relies on several Python packages developed by the LSST Dark Energy Science Collaboration (LSST DESC).
+It integrates these DESC tools into a unified analysis workflow.
+We encourage readers to consult the official documentation for each project; links are provided below.
+
+.. figure:: diagram.png
+   :scale: 100 %
+   :alt: CLPipe and its dependencies diagram
+   :align: center
+
+
+
+--------------------------------------------------
+ Package dependencies:
+--------------------------------------------------
+
+1. CLL
+------
+DESC Core Cosmology Library: cosmology routines with validated numerical accuracy  
+
+* https://github.com/LSSTDESC/CCL
+
+2. CLMM
+---------
+The LSST-DESC Cluster Lensing Mass Modeling
+
+* https://github.com/LSSTDESC/CLMM
+
+
+
+3. CROW
+-------
+The LSST-DESC Cluster Reconstruction of Observables Workbench (CROW)
+
+* https://github.com/LSSTDESC/CROW
+
+
+4. Firecrown
+------------
+Provides the DESC framework to implement likelihoods
+
+* https://github.com/LSSTDESC/Firecrown
+
+
+5. TJPCov
+---------
+A general covariance calculator interface to be used within LSST DESC.
+
+* https://github.com/LSSTDESC/TJPCov
+
+
+6. TXPipe
+---------
+ The DESC 3x2pt Pipeline implementation. It measures lensing and clustering 2pt quantities in real and Fourier space
+
+* https://github.com/LSSTDESC/TXPipe
diff --git a/docs/source/api.rst b/docs/source/api.rst
new file mode 100644
index 0000000..bbc776e
--- /dev/null
+++ b/docs/source/api.rst
@@ -0,0 +1,19 @@
+API Reference
+=============
+
+Pipeline stages
+---------------
+
+.. automodule:: clpipe.clp_covariance
+
+.. automodule:: clpipe.clp_firecrown
+
+File types
+----------
+
+.. automodule:: clpipe.file_types
+
+Cluster theory predictions
+--------------------------
+
+.. automodule:: clpipe.cluster_theory_pred
diff --git a/docs/source/clp_covariance.rst b/docs/source/clp_covariance.rst
new file mode 100644
index 0000000..5eebe55
--- /dev/null
+++ b/docs/source/clp_covariance.rst
@@ -0,0 +1,410 @@
+==================================================
+CLPCovariance Configuration Options
+==================================================
+
+This document describes the configuration options of the CLPCovariance
+stage. The stage computes the theoretical covariance of the cluster number
+counts with TJPCov and writes it into an output SACC file.
+
+Overview
+========
+
+CLPCovariance runs the following steps:
+
+1. Read the input SACC file (data vector) and the fiducial cosmology.
+2. Compute the covariance terms listed in ``cov_type`` with TJPCov.
+3. If the input SACC file also contains other observables with a
+   covariance (e.g. ``cluster_delta_sigma``), copy those blocks into the new
+   covariance.
+4. If ``replace_tjpcov_cov`` is True, replace the cluster-count block with a
+   CROW-based covariance.
+5. Write a single output SACC file with the final covariance.
+
+Inputs and outputs:
+
+- ``clusters_sacc_file`` (input): SACC data vector, from TXPipe
+- ``fiducial_cosmology`` (input): fiducial cosmology, shared with TXPipe
+- ``clusters_sacc_file_cov`` (output): SACC file with the final covariance
+
+The cosmology is always read from the ``fiducial_cosmology`` input. It is not
+set in the stage configuration.
+
+Input SACC file
+---------------
+
+The ``cluster_counts`` data points must each have three tracers, in this
+order:
+
+- ``survey``: survey tracer, with ``sky_area`` in deg\ :sup:`2`
+- ``bin_richness``: richness bin, with lower/upper edges in
+  :math:`\log_{10}\lambda`
+- ``bin_z``: redshift bin, with lower/upper edges
+
+Covariance Model
+================
+
+The cluster-count covariance is the sum of a Poisson shot-noise term and a
+super-sample covariance (SSC) term
+(`Lacasa et al. 2018 <https://arxiv.org/abs/1612.05958>`__):
+
+.. math::
+
+    [\mathrm{Cov}_N]_{ijkl} = \delta^K_{ik} \, \delta^K_{jl} \, N_{ij} + [\mathrm{Cov}_{\rm SSC}]_{ijkl}
+
+.. math::
+
+    [\mathrm{Cov}_{\rm SSC}]_{ijkl} = N_{ij} \, N_{kl} \, \langle b \rangle_{ij} \, \langle b \rangle_{kl} \, S_{ik}
+
+where :math:`i, k` run over redshift bins and :math:`j, l` over richness
+bins:
+
+- :math:`N_{ij}`: predicted number of clusters in bin :math:`(i, j)`
+- :math:`\langle b \rangle_{ij}`: halo bias averaged over the clusters in bin
+  :math:`(i, j)` (model set by ``halo_bias``)
+- :math:`S_{ik}`: covariance of the matter density contrast, smoothed by the
+  survey window, between redshift bins :math:`i` and :math:`k`
+  (partial-sky window computed from the survey area)
+
+The SSC term scales as :math:`N^2`, while the shot noise scales as
+:math:`N`, so SSC becomes important for large cluster samples such as LSST
+(`Fumagalli et al. 2021 <https://arxiv.org/abs/2102.08914>`__).
+
+TJPCov computes the shot-noise term (``ClusterCountsGaussian``) and the SSC
+term (``ClusterCountsSSC``) separately. Both use the halo model,
+mass–richness relation and photo-z model set by ``mor_parameters`` and
+``photo-z`` below.
+
+TJPCov Options
+==============
+
+``use_mpi`` (bool, default: False)
+    Enable MPI parallelization in TJPCov.
+    Must be consistent with the execution environment.
+
+``do_xi`` (bool, default: False)
+    Compute real-space correlation function (xi) terms.
+    Typically False for cluster-count analyses.
+
+``cov_type`` (list of str, default: [ClusterCountsGaussian, ClusterCountsSSC])
+    Covariance terms to compute. Supported values:
+
+    - ``ClusterCountsGaussian``: Poisson shot-noise term (diagonal)
+    - ``ClusterCountsSSC``: super-sample covariance term
+    - ``ClusterMass`` (optional, not always used)
+
+    Example:
+
+    .. code-block:: yaml
+
+        cov_type: [ClusterCountsGaussian, ClusterCountsSSC]
+
+Photo-z Options
+===============
+
+``photo-z`` (dict, required by TJPCov)
+    Photometric redshift model used by TJPCov. The observed redshift is
+    Gaussian around the true redshift (truncated at :math:`z_{\rm phot} > 0`),
+    with a scatter that grows with redshift:
+
+    .. math::
+
+        \sigma_z(z) = \sigma_0 \, (1 + z)
+
+    - ``sigma_0`` (float): photo-z scatter parameter :math:`\sigma_0`,
+      e.g. 0.05
+
+    The CROW replacement does not use it: it assumes exact (spectroscopic)
+    redshifts.
+
+    Example:
+
+    .. code-block:: yaml
+
+        photo-z:
+            sigma_0: 0.05
+
+Mass–Observable Relation (MOR) Options
+======================================
+
+``mor_parameters`` (dict, required by TJPCov)
+    Halo model and mass–richness relation.
+
+    **Halo model** (used by TJPCov only):
+
+    - ``mass_func`` (str): halo mass function, e.g. ``"Despali16"``.
+      Supported: Angulo12, Bocquet16, Bocquet20, Despali16, Jenkins01,
+      Nishimichi19, Press74, Sheth99, Tinker08, Tinker10, Watson13
+    - ``mass_def`` (str): mass definition, e.g. ``"200c"``
+    - ``halo_bias`` (str): halo bias model, used for
+      :math:`\langle b \rangle` in the SSC term, e.g. ``"Tinker10"``.
+      Supported: Bhattacharya11, Sheth01, Sheth99, Tinker10
+
+    **Mass range** (used by TJPCov and CROW):
+
+    - ``min_halo_mass`` (float): lower mass limit in
+      :math:`M_\odot`, e.g. 1.0e12
+    - ``max_halo_mass`` (float): upper mass limit in
+      :math:`M_\odot`, e.g. 3.16e15
+
+    These are linear masses, not log10.
+
+    **Mass–richness relation** (used by TJPCov and CROW):
+
+    The richness follows the
+    `Murata et al. (2019) <https://doi.org/10.1093/pasj/psz092>`__ model.
+    At fixed mass and redshift, :math:`\ln\lambda` is Gaussian. Its mean and
+    its scatter are each linear in :math:`\ln M` and :math:`\ln(1+z)`, with 3
+    parameters each:
+
+    .. math::
+
+        P(\ln\lambda \mid M, z) = \frac{1}{\sqrt{2\pi} \, \sigma_{\ln\lambda}} \exp\left[ -\frac{(\ln\lambda - \mu_{\ln\lambda})^2}{2 \sigma_{\ln\lambda}^2} \right]
+
+    .. math::
+
+        \mu_{\ln\lambda}(M, z) = \mu_0 + \mu_m \ln\frac{M}{M_{\rm piv}} + \mu_z \ln\frac{1+z}{1+z_{\rm piv}}
+
+    .. math::
+
+        \sigma_{\ln\lambda}(M, z) = \sigma_0 + \sigma_m \ln\frac{M}{M_{\rm piv}} + \sigma_z \ln\frac{1+z}{1+z_{\rm piv}}
+
+    - ``m_pivot`` (float): pivot mass
+      :math:`\log_{10}(M_{\rm piv} / M_\odot)`, e.g. 14.3
+    - ``z_pivot`` (float): pivot redshift :math:`z_{\rm piv}`, e.g. 0.5
+    - ``mu_p0``, ``mu_p1``, ``mu_p2`` (float): mean parameters
+      :math:`\mu_0, \mu_m, \mu_z`
+    - ``sigma_p0``, ``sigma_p1``, ``sigma_p2`` (float): scatter parameters
+      :math:`\sigma_0, \sigma_m, \sigma_z`
+
+Pipeline-Specific Options
+=========================
+
+``replace_tjpcov_cov`` (bool, default: True)
+    Replace the TJPCov cluster-count covariance with a covariance computed
+    with CROW (see `Custom Covariance Replacement`_ below):
+
+    - Diagonal terms are recomputed from CROW theory predictions.
+    - SSC and Gaussian contributions are combined.
+
+    .. warning::
+
+        This is a temporary workaround. It should eventually be handled
+        directly inside TJPCov.
+
+``sel_func`` (bool, default: True)
+    Only used when ``replace_tjpcov_cov`` is True.
+
+    If True, the CROW counts are weighted by the
+    `Aguena & Lima (2018) <https://arxiv.org/abs/1611.05468>`__ completeness
+    model:
+
+    .. math::
+
+        c(M, z) = \frac{\left(M / M_0(z)\right)^{n_c(z)}}{1 + \left(M / M_0(z)\right)^{n_c(z)}}
+
+    .. math::
+
+        n_c(z) = a_n + b_n \, (1 + z), \qquad \log_{10} M_0(z) = a_{\rm piv} + b_{\rm piv} \, (1 + z)
+
+    The completeness parameters are fixed to the CROW defaults, which are the
+    cosmoDC2 redMaPPer values (:math:`a_n` = 1.1321, :math:`b_n` = 0.7751,
+    :math:`a_{\rm piv}` = ``a_logm_piv`` = 13.31,
+    :math:`b_{\rm piv}` = ``b_logm_piv`` = 0.2025). They cannot be set from
+    the configuration.
+
+    If False, no selection function is applied (:math:`c = 1`).
+    The purity model is currently disabled in the code.
+
+``diagonal_shear_covariance`` (bool, default: True)
+    Only used when the input SACC file contains other observables with a
+    covariance (e.g. ``cluster_delta_sigma``).
+    If True, only the diagonal (per-bin variance) of those blocks is kept.
+    If False, the full blocks are copied.
+
+Internal Behavior
+=================
+
+Covariance Computation
+----------------------
+
+TJPCov computes the terms listed in ``cov_type`` and writes:
+
+.. code-block:: text
+
+    clusters_sacc_file_cov.sacc
+
+Intermediate files with the individual terms may also be written:
+
+.. code-block:: text
+
+    clusters_sacc_file_cov_SSC.sacc
+    clusters_sacc_file_cov_gauss.sacc
+
+Mixed Data Handling
+-------------------
+
+If the input SACC file contains other observables besides cluster counts
+and already has a covariance:
+
+- The cluster-count covariance is recomputed.
+- The other covariance blocks (e.g. lensing) are copied from the input
+  file, diagonal-only or in full (see ``diagonal_shear_covariance``).
+
+This is handled by ``merge_data_covariance()``.
+
+Custom Covariance Replacement
+-----------------------------
+
+If ``replace_tjpcov_cov`` is True, the cluster-count block is rebuilt with
+CROW. For each cluster-count data point :math:`a` (one richness-redshift
+bin), CROW predicts the counts
+
+.. math::
+
+    N_a = {} & \Omega_S \int_{z_a} dz \int_{\lambda_a} d\ln\lambda \int_{M_{\min}}^{M_{\max}} dM \\
+    & \times \frac{d^2V}{dz \, d\Omega} \, \frac{dn}{dM}(M, z) \, P(\ln\lambda \mid M, z) \, c(M, z)
+
+where :math:`\Omega_S` is the survey area in steradians (from the SACC
+survey tracer), :math:`dn/dM` is the Despali16 mass function, and
+:math:`c(M, z)` is the completeness (see ``sel_func``).
+
+The covariance is then
+
+.. math::
+
+    C_{aa} = N_a + \mathrm{SSC}_{aa} \left( \frac{N_a}{N^{\rm TJPCov}_a} \right)^2
+
+.. math::
+
+    C_{ab} = \mathrm{SSC}_{ab} \, \frac{N_a \, N_b}{N^{\rm TJPCov}_a \, N^{\rm TJPCov}_b}, \qquad a \neq b
+
+where:
+
+- :math:`N_a`: CROW theory counts for data point :math:`a`
+- :math:`N^{\rm TJPCov}_a`: TJPCov Gaussian (shot-noise) term for data point
+  :math:`a`, i.e. the counts predicted by TJPCov
+- :math:`\mathrm{SSC}_{ab}`: TJPCov super-sample covariance term
+
+The CROW counts are used as the Poisson term on the diagonal. The SSC term
+scales as :math:`N_a N_b`, so it is rescaled from the TJPCov counts to the
+CROW counts. Only the cluster-count block is modified.
+
+Known Limitations
+=================
+
+- ``replace_crow_counts()`` is a temporary workaround. It should be removed
+  once this is implemented in TJPCov.
+
+- The cosmology is passed to TJPCov both as a CCL object and as a
+  parameter dictionary. This duplication comes from TJPCov and should be
+  fixed there.
+
+- Hardcoded choices in the CROW replacement:
+
+  - Mass function: Despali16 (200c), whatever ``mass_func`` and
+    ``mass_def`` are
+  - Redshifts: spectroscopic (photo-z ``sigma_0`` is not used)
+  - Completeness parameters: CROW defaults
+  - Purity model: disabled
+  - Grid sizes: mass 80, redshift 40, proxy 40
+  - Recipe: ``GridBinnedClusterRecipe``
+
+- The covariance is computed once, at the fiducial cosmology and MOR
+  parameters, and held fixed during inference.
+
+- The input configuration is not validated.
+
+Pipeline Configuration
+======================
+
+The stage is wired into a ceci pipeline file. This example comes from
+``examples/cosmodc2_redmapper/baseline/cosmodc2_redmapper_full_analysis/run_in2p3_both/TJPCov.yml``:
+
+.. code-block:: yaml
+
+    id: TJPCov
+    modules: clpipe
+    launcher:
+        name: mini
+        interval: 0.5
+    site:
+        name: local
+        max_threads: 4
+    stages:
+      - name: CLPCovariance
+        module_name: clpipe.clp_covariance
+        nprocess: 1
+    inputs:
+        fiducial_cosmology: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/cosmodc2/fiducial_cosmology.yml
+        clusters_sacc_file: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/cosmodc2/outputs-full-2026//cluster_sacc_catalog.sacc
+    config: ./config_in2p3_both.yml
+    resume: false
+    output_dir: ./outputs_both
+    log_dir: ./logs_both
+
+Run it with:
+
+.. code-block:: bash
+
+    ceci TJPCov.yml
+
+The stage options below go in the stage config file (``config:`` above),
+under a ``CLPCovariance`` block.
+
+Example Configuration
+=====================
+
+.. code-block:: yaml
+
+    CLPCovariance:
+        use_mpi: False
+        do_xi: False
+        cov_type: [ClusterCountsGaussian, ClusterCountsSSC]
+
+        replace_tjpcov_cov: True
+        sel_func: True
+        diagonal_shear_covariance: True
+
+        photo-z:
+            sigma_0: 0.05
+
+        mor_parameters:
+            mass_func: 'Despali16'
+            mass_def: '200c'
+            halo_bias: 'Tinker10'
+            min_halo_mass: 1.0e12
+            max_halo_mass: 3.16e15
+
+            m_pivot: 14.3
+            z_pivot: 0.5
+
+            mu_p0: 3.3439
+            mu_p1: 0.9582
+            mu_p2: -0.0193
+            sigma_p0: 0.5623
+            sigma_p1: 0.0455
+            sigma_p2: -0.0445
+
+Notes
+=====
+
+- Additional TJPCov parameters can be added depending on the covariance
+  model. They are passed through to TJPCov.
+- Make sure the SACC input data and the MOR configuration are consistent.
+
+References
+==========
+
+- Murata et al. (2019), mass–richness relation:
+  `doi:10.1093/pasj/psz092 <https://doi.org/10.1093/pasj/psz092>`__
+  (`arXiv:1904.07524 <https://arxiv.org/abs/1904.07524>`__)
+- Lacasa, Lima & Aguena (2018), super-sample covariance:
+  `arXiv:1612.05958 <https://arxiv.org/abs/1612.05958>`__
+- Fumagalli et al. (2021), impact of sample covariance on cluster counts:
+  `arXiv:2102.08914 <https://arxiv.org/abs/2102.08914>`__
+- Aguena & Lima (2018), completeness and purity:
+  `arXiv:1611.05468 <https://arxiv.org/abs/1611.05468>`__
+- Despali et al. (2016), halo mass function:
+  `arXiv:1507.05627 <https://arxiv.org/abs/1507.05627>`__
+- Tinker et al. (2010), halo bias:
+  `arXiv:1001.3162 <https://arxiv.org/abs/1001.3162>`__
diff --git a/docs/source/clp_firecrown.rst b/docs/source/clp_firecrown.rst
new file mode 100644
index 0000000..2974924
--- /dev/null
+++ b/docs/source/clp_firecrown.rst
@@ -0,0 +1,520 @@
+==================================================
+CLPFirecrown Configuration Options
+==================================================
+
+This document describes the configuration options of the CLPFirecrown
+stage. The stage prepares a cluster cosmology inference with Firecrown and
+CosmoSIS: it writes the Firecrown likelihood and the CosmoSIS configuration
+files. It does not run the sampler. Sampling is a separate CosmoSIS run
+(see `Running the Inference`_).
+
+Overview
+========
+
+CLPFirecrown runs the following steps:
+
+1. Read the SACC file with covariance (from CLPCovariance) and the fiducial
+   cosmology.
+2. Write the Firecrown likelihood file. It builds CROW recipes for the
+   cluster counts and/or the stacked shear profile, from the modeling
+   options below.
+3. Write the CosmoSIS pipeline file: consistency, CAMB and the Firecrown
+   likelihood modules, plus the sampler settings.
+4. Write the CosmoSIS values file: cosmological and Firecrown parameters,
+   with their priors.
+
+Inputs and outputs:
+
+- ``clusters_sacc_file_cov`` (input): SACC data vector with covariance, from
+  CLPCovariance
+- ``fiducial_cosmology`` (input): fiducial cosmology, shared with TXPipe and
+  CLPCovariance
+- ``sampler_file`` (output): CosmoSIS pipeline file (``sampler_file.ini``)
+- ``likelihood_file`` (output): Firecrown likelihood (``likelihood_file.py``)
+- ``priors_file`` (output): CosmoSIS values file with priors
+  (``priors_file.ini``)
+
+Theory Model
+============
+
+The predictions are computed with CROW. For a redshift bin :math:`i` and a
+richness bin :math:`j`, the number counts and the stacked lensing profile
+are
+
+.. math::
+
+    N_{ij} = {} & \Omega_S \int_{z_i}^{z_{i+1}} dz \int_{\ln\lambda_j}^{\ln\lambda_{j+1}} d\ln\lambda \int_{M_{\min}}^{M_{\max}} dM \\
+    & \times \frac{d^2V}{dz \, d\Omega} \, \frac{dn}{dM}(M, z) \, P(\ln\lambda \mid M, z) \, \Phi(M, \lambda, z)
+
+.. math::
+
+    \Theta_{ij}(R) = {} & \frac{\Omega_S}{N_{ij}} \int_{z_i}^{z_{i+1}} dz \int_{\ln\lambda_j}^{\ln\lambda_{j+1}} d\ln\lambda \int_{M_{\min}}^{M_{\max}} dM \\
+    & \times \frac{d^2V}{dz \, d\Omega} \, \frac{dn}{dM}(M, z) \, P(\ln\lambda \mid M, z) \, \Phi(M, \lambda, z) \, \Theta(R \mid M)
+
+where:
+
+- :math:`\Omega_S`: survey area in steradians, from the SACC file
+- :math:`dn/dM`: halo mass function (``hmf``, ``mass_def``)
+- :math:`P(\ln\lambda \mid M, z)`: mass–richness relation (see below)
+- :math:`\Phi = c(M, z) / p(\lambda, z)`: selection function, from the
+  completeness :math:`c` and the purity :math:`p` (see below). Each is 1
+  when disabled.
+- :math:`\Theta(R \mid M)`: lensing profile of a halo of mass :math:`M`,
+  either the excess surface density :math:`\Delta\Sigma` or the reduced
+  tangential shear :math:`g_t`
+
+Mass–richness relation
+----------------------
+
+The richness follows the
+`Murata et al. (2019) <https://doi.org/10.1093/pasj/psz092>`__ model. At
+fixed mass and redshift, :math:`\ln\lambda` is Gaussian. Its mean and its
+scatter are each linear in :math:`\ln M` and :math:`\ln(1+z)`, with 3
+parameters each:
+
+.. math::
+
+    P(\ln\lambda \mid M, z) = \frac{1}{\sqrt{2\pi} \, \sigma_{\ln\lambda}} \exp\left[ -\frac{(\ln\lambda - \mu_{\ln\lambda})^2}{2 \sigma_{\ln\lambda}^2} \right]
+
+.. math::
+
+    \mu_{\ln\lambda}(M, z) = \mu_0 + \mu_m \ln\frac{M}{M_{\rm piv}} + \mu_z \ln\frac{1+z}{1+z_{\rm piv}}
+
+.. math::
+
+    \sigma_{\ln\lambda}(M, z) = \sigma_0 + \sigma_m \ln\frac{M}{M_{\rm piv}} + \sigma_z \ln\frac{1+z}{1+z_{\rm piv}}
+
+The pivots are set by ``pivot_mass`` and ``pivot_z``. The 6 parameters are
+Firecrown parameters (see `Firecrown Parameters`_).
+
+Completeness
+------------
+
+With ``use_completeness``, the
+`Aguena & Lima (2018) <https://arxiv.org/abs/1611.05468>`__ completeness
+model:
+
+.. math::
+
+    c(M, z) = \frac{\left(M / M_0(z)\right)^{n_c(z)}}{1 + \left(M / M_0(z)\right)^{n_c(z)}}
+
+.. math::
+
+    n_c(z) = a_n + b_n \, (1 + z), \qquad \log_{10} M_0(z) = a_{\rm piv} + b_{\rm piv} \, (1 + z)
+
+Purity
+------
+
+With ``use_purity``, the Aguena & Lima (2018) purity model, written in
+:math:`\ln\lambda`:
+
+.. math::
+
+    p(\lambda, z) = \frac{\left(\ln\lambda / \ln\lambda_0(z)\right)^{n_p(z)}}{1 + \left(\ln\lambda / \ln\lambda_0(z)\right)^{n_p(z)}}
+
+.. math::
+
+    n_p(z) = a_n + b_n \, (1 + z), \qquad \ln\lambda_0(z) = a_{\rm piv} + b_{\rm piv} \, (1 + z)
+
+Lensing profile
+---------------
+
+:math:`\Theta(R \mid M)` is computed with CLMM from an NFW profile (one-halo
+term). The concentration is the Firecrown parameter
+``cluster_theory_cluster_concentration``. A value below 1 uses the
+`Bhattacharya et al. (2013) <https://arxiv.org/abs/1112.5479>`__
+mass–concentration relation instead. The two-halo term and the boost-factor
+correction can be added (``two_halo_term``, ``boost_factor``).
+
+For the reduced shear :math:`g_t`, the lensing efficiency is averaged over
+the source redshift distribution (``beta_parameters``, ``use_beta_interp``).
+
+Likelihood
+----------
+
+The likelihood is a Gaussian (Firecrown ``ConstGaussian``) over all the
+selected statistics:
+
+- ``BinnedClusterNumberCounts``: counts :math:`N_{ij}` (and optionally the
+  mean log mass)
+- ``BinnedClusterShearProfile``: stacked :math:`\Delta\Sigma` or :math:`g_t`
+  profiles
+
+The covariance is read from the SACC file and held fixed during sampling.
+
+Modeling Options
+================
+
+``hmf`` (str, default: "despali16")
+    Halo mass function (case-insensitive). Supported values:
+
+    - angulo12
+    - bocquet16
+    - bocquet20
+    - despali16
+    - jenkins01
+    - press74
+    - sheth99
+    - tinker08
+    - tinker10
+    - watson13
+
+``mass_def`` (str, default: "200c")
+    Mass definition used by the halo mass function (e.g. ``"200c"``,
+    ``"500c"``).
+
+``min_mass``, ``max_mass`` (float, default: 12.0, 15.5)
+    Halo mass integration range, in :math:`\log_{10}(M / M_\odot)`.
+    Note: CLPCovariance uses linear masses for the same range.
+
+``min_z``, ``max_z`` (float, default: 0.2, 0.8)
+    True redshift integration range.
+
+``pivot_mass`` (float, default: 14.3)
+    Pivot mass of the mass–richness relation,
+    :math:`\log_{10}(M_{\rm piv} / M_\odot)`.
+
+``pivot_z`` (float, default: 0.5)
+    Pivot redshift of the mass–richness relation.
+
+``survey_name`` (str, default: "cosmodc2_redmapper")
+    Survey tracer name in the SACC file. Must match it.
+
+Observable Options
+==================
+
+``use_cluster_counts`` (bool, default: True)
+    Include the cluster number counts in the likelihood.
+
+``use_shear_profile`` (bool, default: False)
+    Include the stacked shear profiles in the likelihood.
+
+``is_deltasigma`` (bool, default: False)
+    Theory side: predict :math:`\Delta\Sigma` (True) or the reduced shear
+    :math:`g_t` (False).
+
+``use_mean_deltasigma`` (bool, default: False)
+    Data side: read :math:`\Delta\Sigma` (True) or the reduced shear
+    :math:`g_t` (False) from the SACC file. Set it to the same value as
+    ``is_deltasigma``.
+
+``use_mean_log_mass`` (bool, default: False)
+    Also use the mean log mass per bin, read from the SACC file.
+
+Selection Function Options
+==========================
+
+``use_completeness`` (bool, default: True)
+    Apply the completeness model :math:`c(M, z)` (see `Completeness`_).
+
+``use_purity`` (bool, default: True)
+    Apply the purity model :math:`p(\lambda, z)` (see `Purity`_).
+
+Lensing Options
+===============
+
+Only used when ``use_shear_profile`` is True.
+
+``two_halo_term`` (bool, default: False)
+    Add the two-halo term to the lensing profile.
+
+``boost_factor`` (bool, default: False)
+    Apply the boost-factor correction to the lensing profile.
+
+``set_concentration`` (bool)
+    Reserved for future use. The stage does not read it yet, so it
+    currently has no effect. The concentration is set with
+    ``cluster_theory_cluster_concentration`` in ``firecrown_parameters``.
+
+``use_beta_interp`` (bool, default: False)
+    Interpolate the mean lensing efficiency :math:`\langle\beta_s\rangle` in
+    redshift. Only used for the reduced shear (``is_deltasigma: False``).
+
+``beta_parameters`` (list of float, default: [10.0, 5.0])
+    Parameters of the mean lensing efficiency :math:`\langle\beta_s\rangle`,
+    passed to CROW as ``[z_inf, zmax]``: the redshift taken as infinity, and
+    the maximum source redshift. Only used for the reduced shear
+    (``is_deltasigma: False``).
+
+    The first value (``z_inf``, 10 by default) is also always used as the
+    maximum redshift of CAMB (``zmax`` in the ``[camb]`` section of
+    ``sampler_file.ini``), even without shear profiles.
+
+Integration Options
+===================
+
+``use_grid`` (bool, default: True)
+    If True, use ``GridBinnedClusterRecipe``: integrals on precomputed
+    grids, about 100 times faster, recommended for inference.
+    If False, use ``ExactBinnedClusterRecipe`` (direct integration with
+    NumCosmo), slower, useful to validate the grid results.
+
+``redshift_grid_size`` (int, default: 20)
+    Number of redshift grid points.
+
+``mass_grid_size`` (int, default: 60)
+    Number of mass grid points.
+
+``proxy_grid_size`` (int, default: 20)
+    Number of richness grid points.
+
+The grid sizes are only used when ``use_grid`` is True.
+
+Sampler Options
+===============
+
+``sampler`` (str, default: "emcee")
+    CosmoSIS sampler. Supported values:
+
+    - ``test``: a single likelihood evaluation, useful to check the setup
+    - ``metropolis``
+    - ``emcee``
+    - ``polychord``
+
+``emcee_walkers`` (int, default: 100)
+    Number of emcee walkers.
+
+``emcee_samples`` (int, default: 20000)
+    Number of emcee samples per walker.
+
+``emcee_nsteps`` (int, default: 20)
+    Number of emcee steps between chain outputs.
+
+``polychord_live_points`` (int, default: 500)
+    Number of PolyChord live points.
+
+``polychord_num_repeats`` (int, default: 30)
+    Number of PolyChord slice-sampling repeats.
+
+``polychord_tolerance`` (float, default: 0.05)
+    PolyChord evidence tolerance (stopping criterion).
+
+``polychord_feedback`` (int, default: 1)
+    PolyChord verbosity level.
+
+.. note::
+
+    The ``polychord_*`` options fill the ``[polychord]`` section of
+    ``sampler_file.ini`` (see the
+    `CosmoSIS PolyChord sampler <https://cosmosis.readthedocs.io/en/latest/reference/samplers/polychord.html>`__).
+
+``resume`` (bool, default: False)
+    If True, CosmoSIS appends to the existing chain instead of starting a
+    new one.
+
+``filename`` (str, optional, default: "output_rp/number_counts_samples.txt")
+    Path of the output chain file.
+
+Cosmological Parameters
+=======================
+
+``tau`` (float, default: 0.08)
+    Optical depth to reionization. It is a CAMB input, not part of the
+    fiducial cosmology file, so it is set here.
+
+``cosmological_parameters`` (dict, default: {})
+    Overrides on top of the fiducial cosmology. By default, every
+    cosmological parameter is fixed to its value in ``fiducial_cosmology``.
+    List a parameter here to sample it.
+
+    Parameter names (CosmoSIS naming): ``omega_c``, ``omega_b``, ``h0``,
+    ``n_s``, ``sigma_8``, ``omega_k``, ``w``, ``wa``, ``tau``.
+
+    A fixed entry (``sample: False``) must have the fiducial value, otherwise
+    the stage fails. To change the fiducial cosmology, edit the
+    ``fiducial_cosmology`` file instead, so that all stages use it.
+
+    Example:
+
+    .. code-block:: yaml
+
+        cosmological_parameters:
+            omega_c: {'sample': True, 'values': [0.10, 0.22, 0.5]}
+            sigma_8: {'sample': True, 'values': [0.5, 0.800, 1.1]}
+
+Firecrown Parameters
+====================
+
+``firecrown_parameters`` (dict, default: {})
+    Parameters of the CROW models, written to the
+    ``[firecrown_number_counts]`` section of the values file. Set all the
+    parameters of the models you enable, fixed or sampled.
+
+    **Mass–richness relation:**
+
+    - ``mass_distribution_mu0``, ``mass_distribution_mu1``,
+      ``mass_distribution_mu2``: :math:`\mu_0, \mu_m, \mu_z`
+    - ``mass_distribution_sigma0``, ``mass_distribution_sigma1``,
+      ``mass_distribution_sigma2``: :math:`\sigma_0, \sigma_m, \sigma_z`
+
+    **Completeness** (``use_completeness``):
+
+    - ``completeness_a_n``, ``completeness_b_n``: :math:`a_n, b_n`
+    - ``completeness_a_logm_piv``, ``completeness_b_logm_piv``:
+      :math:`a_{\rm piv}, b_{\rm piv}`
+    - CROW defaults (cosmoDC2 redMaPPer): 1.1321, 0.7751, 13.31, 0.2025
+
+    **Purity** (``use_purity``):
+
+    - ``purity_a_n``, ``purity_b_n``: :math:`a_n, b_n`
+    - ``purity_a_logm_piv``, ``purity_b_logm_piv``:
+      :math:`a_{\rm piv}, b_{\rm piv}`
+    - CROW defaults (cosmoDC2 redMaPPer): 1.9830, 0.8121, 2.2183, -0.6592
+
+    **Lensing profile** (``use_shear_profile``):
+
+    - ``cluster_theory_cluster_concentration``: halo concentration
+      (below 1: Bhattacharya et al. 2013 relation)
+
+Parameter Format
+================
+
+Entries in ``cosmological_parameters`` and ``firecrown_parameters`` use the
+same format:
+
+.. code-block:: yaml
+
+    # Sampled, with a flat prior between min and max:
+    name: {'sample': True, 'values': [min, start, max]}
+
+    # Fixed:
+    name: {'sample': False, 'values': value}
+
+Pipeline Configuration
+======================
+
+The stage is wired into a ceci pipeline file. This example comes from
+``examples/cosmodc2_redmapper/baseline/cosmodc2_redmapper_full_analysis/run_in2p3_both/Firecrown.yml``:
+
+.. code-block:: yaml
+
+    id: Firecrown
+    modules: clpipe
+    launcher:
+        name: mini
+        interval: 0.5
+    site:
+        name: local
+        max_threads: 4
+    stages:
+      - name: CLPFirecrown
+        module_name: clpipe.clp_firecrown
+        nprocess: 1
+    inputs:
+        fiducial_cosmology: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/cosmodc2/fiducial_cosmology.yml
+        clusters_sacc_file_cov: ./outputs_both/clusters_sacc_file_cov.sacc
+    config: ./config_in2p3_both.yml
+    resume: false
+    output_dir: ./outputs_both
+    log_dir: ./logs_both
+
+The stage options go in the stage config file (``config:`` above), under a
+``CLPFirecrown`` block.
+
+Running the Inference
+=====================
+
+Generate the files with ceci, then run CosmoSIS from the output directory:
+
+.. code-block:: bash
+
+    ceci Firecrown.yml
+
+    cd ./outputs_both
+    cosmosis sampler_file.ini
+
+    # or, with MPI:
+    mpirun -n 30 cosmosis --mpi sampler_file.ini
+
+The generated files refer to each other, and to the SACC file, by file name
+only. Run CosmoSIS from the output directory, with the SACC file in it (this
+is the case when CLPCovariance writes to the same output directory).
+CosmoSIS also needs the ``CSL_DIR`` environment variable (see the
+installation instructions in the README).
+
+Known Limitations
+=================
+
+- The ``test`` and ``metropolis`` sampler settings are fixed in the
+  generated file (``metropolis``: 1000 samples).
+
+- The covariance is held fixed during sampling.
+
+- Only the binned likelihood is supported.
+
+- The names in ``firecrown_parameters`` are not validated by the stage.
+
+Example Configuration
+=====================
+
+This is the ``CLPFirecrown`` block of the baseline cosmoDC2 redMaPPer
+analysis (counts + stacked :math:`\Delta\Sigma`):
+
+.. code-block:: yaml
+
+    CLPFirecrown:
+        hmf: 'despali16'
+        mass_def: '200c'
+        min_mass: 12.0
+        max_mass: 15.5
+        min_z: 0.2
+        max_z: 0.8
+        pivot_mass: 14.3
+        pivot_z: 0.5
+        survey_name: 'cosmodc2_redmapper'
+
+        use_cluster_counts: true
+        use_shear_profile: true
+        is_deltasigma: true
+        use_mean_deltasigma: true
+        use_mean_log_mass: false
+
+        use_completeness: true
+        use_purity: false
+
+        use_grid: true
+        use_beta_interp: false
+        beta_parameters: [10.0, 5.0]
+
+        sampler: 'emcee'
+        emcee_walkers: 100
+        emcee_samples: 50000
+        emcee_nsteps: 20
+
+        cosmological_parameters:
+            omega_c: {'sample': true, 'values': [0.10, 0.22, 0.5]}
+            sigma_8: {'sample': true, 'values': [0.5, 0.800, 1.1]}
+
+        firecrown_parameters:
+            mass_distribution_mu0: {'sample': true, 'values': [2.0, 3.3439, 10.0]}
+            mass_distribution_mu1: {'sample': true, 'values': [0.5, 0.958236982, 2.0]}
+            mass_distribution_mu2: {'sample': true, 'values': [-2.0, -0.0192802, 2.0]}
+            mass_distribution_sigma0: {'sample': true, 'values': [0.1, 0.562317194, 2.0]}
+            mass_distribution_sigma1: {'sample': true, 'values': [-0.6, 0.04552506, 0.3]}
+            mass_distribution_sigma2: {'sample': true, 'values': [-0.5, -0.0445, 2.0]}
+            completeness_a_n: {'sample': false, 'values': 1.1321}
+            completeness_b_n: {'sample': false, 'values': 0.7751}
+            completeness_a_logm_piv: {'sample': false, 'values': 13.31}
+            completeness_b_logm_piv: {'sample': false, 'values': 0.2025}
+            cluster_theory_cluster_concentration: {'sample': false, 'values': 3.8}
+
+References
+==========
+
+- Murata et al. (2019), mass–richness relation:
+  `doi:10.1093/pasj/psz092 <https://doi.org/10.1093/pasj/psz092>`__
+  (`arXiv:1904.07524 <https://arxiv.org/abs/1904.07524>`__)
+- Aguena & Lima (2018), completeness and purity:
+  `arXiv:1611.05468 <https://arxiv.org/abs/1611.05468>`__
+- Despali et al. (2016), halo mass function:
+  `arXiv:1507.05627 <https://arxiv.org/abs/1507.05627>`__
+- Navarro, Frenk & White (1997), NFW profile:
+  `arXiv:astro-ph/9611107 <https://arxiv.org/abs/astro-ph/9611107>`__
+- Bhattacharya et al. (2013), mass–concentration relation:
+  `arXiv:1112.5479 <https://arxiv.org/abs/1112.5479>`__
+- Zuntz et al. (2015), CosmoSIS:
+  `arXiv:1409.3409 <https://arxiv.org/abs/1409.3409>`__
+- Foreman-Mackey et al. (2013), emcee:
+  `arXiv:1202.3665 <https://arxiv.org/abs/1202.3665>`__
+- Firecrown: https://github.com/LSSTDESC/firecrown
+- CROW: https://github.com/LSSTDESC/crow
diff --git a/docs/source/clpipe_logo.png b/docs/source/clpipe_logo.png
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literal 0
HcmV?d00001

diff --git a/docs/source/installation.rst b/docs/source/installation.rst
new file mode 100644
index 0000000..b3c19ad
--- /dev/null
+++ b/docs/source/installation.rst
@@ -0,0 +1,41 @@
+**************
+Installation
+**************
+
+Requirements
+============
+
+
+Installation
+============
+To install CLPipe you currently need to build it from source.
+Note: Do not use pip directly to install the required packages,
+as not all dependencies can be installed this way. Instead, use conda, then pip to install clpipe.::
+
+
+  git clone https://github.com/LSSTDESC/CLPipe.git 
+  cd CLPipe
+  conda env update -f txpipe_environment.yml
+  conda activate txpipe_clp
+  pip install .
+  conda deactivate
+
+
+  conda env update -f firecrown_environment.yml
+  conda activate firecrown_clp
+  pip install .
+  conda env config vars set CSL_DIR=${CONDA_PREFIX}/cosmosis-standard-library
+  conda deactivate
+  conda activate firecrown_clp
+
+  cd ${CONDA_PREFIX}
+  source ${CONDA_PREFIX}/bin/cosmosis-configure
+  cosmosis-build-standard-library main
+  
+
+To run the tests you can do::
+
+  
+  pytest
+  
+  
diff --git a/docs/source/overview.rst b/docs/source/overview.rst
new file mode 100644
index 0000000..887c571
--- /dev/null
+++ b/docs/source/overview.rst
@@ -0,0 +1,21 @@
+******************
+Rapid overview
+******************
+
+clpipe is an automated framework developed within the Legacy
+Survey of Space and Time Dark Energy Science Collaboration (LSST DESC) for galaxy cluster cosmology,
+in preparation for the first Rubin LSST Data releases. This cluster cosmology pipeline
+integrates DESC-developed tools spanning galaxy/cluster catalog ingestion, cluster number count and
+stacked weak lensing measurements, modeling, and parameter inference through posterior sampling,
+enabling reproducible end-to-end cluster cosmology analyses. We test/validate the pipeline using mock
+datasets and the LSST DESC cosmoDC2 simulation, performing a joint analysis of redMaPPer cluster
+abundance and stacked weak lensing profiles measured in bins of redshift and richness. We constrain
+some cosmological parameters along with six cluster mass–observable relation parameters. Besides
+our baseline recovering the fiducial cosmoDC2 values within 2σ, we also showcase the flexibility of the
+overall pipeline by assessing the impact of fiducial cosmology, alternative modeling prescriptions, and
+cluster finder selection function on the parameter constraints.
+
+.. figure:: diagram.png
+   :scale: 100 %
+   :alt: CLPipe and its dependencies diagram
+   :align: center
\ No newline at end of file
diff --git a/firecrown_environment.yml b/firecrown_environment.yml
index 32ff9c9..d2bc67f 100644
--- a/firecrown_environment.yml
+++ b/firecrown_environment.yml
@@ -37,11 +37,6 @@ dependencies:
   - requests
   - sacc >= 2.1
   - scipy
-  - sphinx
-  - sphinx-autoapi
-  - sphinx-autodoc-typehints
-  - sphinx-rtd-theme
-  - sphinxcontrib-apidoc
   - tabulate
   - types-pyyaml
   - urllib3
@@ -55,6 +50,11 @@ dependencies:
   - types-requests
   - linkchecker
   - pytest-json-report
+  - sphinx
+  - sphinx-autoapi
+  - sphinx-autodoc-typehints
+  - sphinx-rtd-theme
+  - sphinxcontrib-apidoc
   - pip:
     - ceci >= 2.5
     - qp-prob
@@ -65,3 +65,4 @@ dependencies:
     - cobaya
     - isitgr
     - lsstdesc-crow
+    - sphinx_copybutton
diff --git a/tests/test_clp_covariance.py b/tests/test_clp_covariance.py
index 2a2e55f..d2341f5 100644
--- a/tests/test_clp_covariance.py
+++ b/tests/test_clp_covariance.py
@@ -15,7 +15,6 @@ def _base_tjpcov_config(mor_parameters, **overrides):
     cfg = {
         "replace_tjpcov_cov": True,
         "sel_func": True,
-        "wazp_catalog": False,
         "diagonal_shear_covariance": True,
         "use_mpi": False,
         "do_xi": False,
diff --git a/tests/test_clp_firecrown.py b/tests/test_clp_firecrown.py
index df87c6b..c090b54 100644
--- a/tests/test_clp_firecrown.py
+++ b/tests/test_clp_firecrown.py
@@ -189,6 +189,17 @@ def test_generated_python_is_syntactically_valid(ceci_stack, tmp_path, mock_fidu
     assert "build_likelihood" in func_names
     assert "get_cluster_recipe" in func_names
 
+    # ast.parse only checks syntax: a recipe class used without being
+    # imported would only fail when Firecrown loads the file.
+    imported_names = {
+        alias.asname or alias.name
+        for node in ast.walk(tree) if isinstance(node, ast.ImportFrom)
+        for alias in node.names
+    }
+    recipe_cls = "GridBinnedClusterRecipe" if cfg["use_grid"] else "ExactBinnedClusterRecipe"
+    assert f"{recipe_cls}(" in source
+    assert recipe_cls in imported_names
+
     if cfg["use_cluster_counts"]:
         assert "get_cluster_abundance" in func_names
         assert "BinnedClusterNumberCounts" in source
@@ -268,6 +279,27 @@ def test_filename_option_used_not_hardcoded_default(full_stack, tmp_path, mock_f
     assert parser["output"]["filename"] == custom_filename
 
 
+def test_polychord_options_written_to_polychord_section(full_stack, tmp_path, mock_fiducial_cosmology):
+    cfg = _base_firecrown_config(
+        polychord_live_points=123,
+        polychord_num_repeats=7,
+        polychord_tolerance=0.2,
+        polychord_feedback=2,
+    )
+    sacc_path = tmp_path / "clusters_sacc_file_cov.sacc"
+    stage = _make_stage(tmp_path, sacc_path, mock_fiducial_cosmology, cfg)
+
+    ini_path = tmp_path / "test_polychord.ini"
+    assert stage.generate_ini_file(str(ini_path), "likelihood_file.py", "priors_file.ini")
+
+    parser = configparser.ConfigParser()
+    parser.read(ini_path)
+    assert parser["polychord"]["live_points"] == "123"
+    assert parser["polychord"]["num_repeats"] == "7"
+    assert parser["polychord"]["tolerance"] == "0.2"
+    assert parser["polychord"]["feedback"] == "2"
+
+
 def test_only_cosmo_sampled_writes_correct_priors(full_stack, tmp_path, mock_fiducial_cosmology):
     cfg = _base_firecrown_config(
         cosmological_parameters=_as_sampled(COSMOLOGICAL_PARAMETERS),