diff --git a/examples/cosmodc2/Cluster_pipelines/20deg2-in2p3-reduced.sub b/examples/cosmodc2/Cluster_pipelines/20deg2-in2p3-reduced.sub
new file mode 100644
index 000000000..0617e6508
--- /dev/null
+++ b/examples/cosmodc2/Cluster_pipelines/20deg2-in2p3-reduced.sub
@@ -0,0 +1,17 @@
+#!/usr/bin/bash
+#SBATCH --time=02:00:00
+#SBATCH --partition=hpc,lsst
+#SBATCH --ntasks=20
+#SBATCH --cpus-per-task=1
+#SBATCH --mem=128000
+
+module load conda
+#source /pbs/throng/lsst/software/desc/common/miniconda/setup_current_python.sh
+conda activate /sps/lsst/groups/clusters/cl_pipeline_project/conda_envs/txpipe_clp
+export HDF5_DO_MPI_FILE_SYNC=0
+export PYTHONPATH=/sps/lsst/users/ebarroso/TXPipe:$PYTHONPATH
+ceci CLClusterReducedShear-20deg2-CL.yml 
+
+#source ./conda/bin/activate
+#export HDF5_DO_MPI_FILE_SYNC=0
+#ceci examples/cosmodc2/Cluster_pipelines/pipeline-20deg2-CL-in2p3.yml
diff --git a/examples/cosmodc2/Cluster_pipelines/CLClusterReducedShear-20deg2-CL.yml b/examples/cosmodc2/Cluster_pipelines/CLClusterReducedShear-20deg2-CL.yml
new file mode 100644
index 000000000..50553820e
--- /dev/null
+++ b/examples/cosmodc2/Cluster_pipelines/CLClusterReducedShear-20deg2-CL.yml
@@ -0,0 +1,66 @@
+#this step depends on where you run
+#for CCin2p3
+site:
+    name: cc-parallel
+    mpi_command: mpirun -n
+    max_threads: 20
+#for NERSC
+#site:
+#    name: cori-batch
+#    image: ghcr.io/lsstdesc/txpipe-dev
+
+
+#all the following steps should not depend on where you run 
+launcher:
+    name: mini
+    interval: 3.0
+modules: >
+    txpipe
+    rail.estimation.algos.bpz_lite
+
+python_paths: []
+
+stages:
+    - name: TXSourceSelectorMetadetect
+      nprocess: 20
+    - name: BPZliteInformer
+      nprocess: 1
+      aliases:
+        input: spectroscopic_catalog
+        model: photoz_model
+    - name: BPZliteEstimator
+      nprocess: 20
+      aliases:
+        model: photoz_model
+        input: shear_catalog
+        output: source_photoz_pdfs
+    - name: CLClusterBinningRedshiftRichness
+      nprocess: 1
+    - name: CLClusterShearCatalogs
+      nprocess: 1 #>1 does not work with mpi
+    - name: CLClusterEnsembleProfiles
+      nprocess: 10
+    - name: CLClusterSACC
+      nprocess: 1
+      aliases:
+          cluster_profiles: cluster_profiles
+
+output_dir: /sps/lsst/users/ebarroso/TXPipe/data/cosmodc2/outputs-20deg2-CL
+config: /sps/lsst/users/ebarroso/TXPipe/examples/cosmodc2/Cluster_pipelines/config-20deg2-CL.yml
+
+inputs:
+inputs:
+    # See README for paths to download these files
+    shear_catalog: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/cosmodc2/20deg2/shear_catalog.hdf5
+    #photometry_catalog:  /sps/lsst/users/mricci/TXPipe_data/data_link/cosmodc2/20deg2/photometry_catalog.hdf5
+    fiducial_cosmology: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/cosmodc2/fiducial_cosmology.yml
+    calibration_table: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/cosmodc2/20deg2/sample_cosmodc2_w10year_errors.dat
+    spectroscopic_catalog: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/cosmodc2/20deg2/spectroscopic_catalog.hdf5
+    cluster_catalog: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/cosmodc2/20deg2/cluster_catalog.hdf5
+    #shear_tomography_catalog: ./data/example/outputs_metadetect/shear_tomography_catalog.hdf5
+    #source_photoz_pdfs: ./data/example/inputs/photoz_pdfs.hdf5
+
+resume: true
+log_dir: /sps/lsst/users/ebarroso/TXPipe/data/cosmodc2/logs
+pipeline_log: /sps/lsst/users/ebarroso/TXPipe/data/cosmodc2/log_20deg2.txt
+
diff --git a/examples/cosmodc2/Cluster_pipelines/config-20deg2-CL.yml b/examples/cosmodc2/Cluster_pipelines/config-20deg2-CL.yml
index 102f42257..acb2a2c74 100644
--- a/examples/cosmodc2/Cluster_pipelines/config-20deg2-CL.yml
+++ b/examples/cosmodc2/Cluster_pipelines/config-20deg2-CL.yml
@@ -14,8 +14,8 @@ BPZliteInformer:
     zmin: 0.0
     zmax: 3.0
     nzbins: 301
-    columns_file: ./data/bpz_riz.columns
-    data_path: ./data/example/rail-bpz-inputs
+    columns_file: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/bpz_riz.columns
+    data_path: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/example/rail-bpz-inputs
     spectra_file: CWWSB4.list
     prior_band: i
     ref_band: i
@@ -37,12 +37,12 @@ BPZliteEstimator:
     zmax: 3.0
     dz: 0.01
     nzbins: 301
-    data_path: ./data/example/rail-bpz-inputs
+    data_path: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/example/rail-bpz-inputs
     bands: [mag_r, mag_i, mag_z]
     err_bands: [mag_err_r, mag_err_i, mag_err_z]
     hdf5_groupname: shear/00
     nondetect_val: .inf
-    columns_file: ./data/bpz_riz.columns
+    columns_file: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/bpz_riz.columns
     spectra_file: CWWSB4.list
     ref_band: mag_i
     prior_file: hdfn_gen
@@ -66,28 +66,27 @@ CLClusterShearCatalogs:
     chunk_rows: 100_000  # rows to read at once from source cat
     max_radius: 10.  # Mpc
     delta_z: 0.2   # redshift buffer
-    redshift_cut_criterion: ztrue
-    redshift_weight_criterion: ztrue
+    redshift_cut_criterion: zmode
+    redshift_weight_criterion: zmode
     redshift_cut_criterion_pdf_fraction: 0.9
     subtract_mean_shear: false
     coordinate_system: celestial #euclidean or celestial
     use_true_shear: false
-
+    delta_sigma: false
 
 CLClusterEnsembleProfiles:
     #radial bin definition
     r_min: 0.5  #in Mpc
     r_max: 10.  #in Mpc
-    nbins: 10  # number of bins
+    nbins: 3  # number of bins
     #type of profile
     delta_sigma_profile: true
     shear_profile: false
     magnification_profile: false
     coordinate_system: celestial #euclidean or celestial
-
+    units: 'arcmin'
+    angle_arcmin_min: 5
+    angle_arcmin_max: 30
 CLClusterSACC:
     survey_name: 'cosmodc2-20deg2-CL'
-    area: 440.0
-
-
-
+    area: 20.0
diff --git a/examples/cosmodc2/Cluster_pipelines/pipeline-20deg2-CL-in2p3.yml b/examples/cosmodc2/Cluster_pipelines/pipeline-20deg2-CL-in2p3.yml
index a10e3a3f1..4171a2852 100644
--- a/examples/cosmodc2/Cluster_pipelines/pipeline-20deg2-CL-in2p3.yml
+++ b/examples/cosmodc2/Cluster_pipelines/pipeline-20deg2-CL-in2p3.yml
@@ -49,20 +49,20 @@ stages:
 
 
 
-output_dir: ./data/cosmodc2/outputs-20deg2-CL
-config: ./examples/cosmodc2/Cluster_pipelines/config-20deg2-CL.yml
+output_dir: /sps/lsst/users/ebarroso/TXPipe/data/cosmodc2/outputs-20deg2-CL
+config: /sps/lsst/users/ebarroso/TXPipe/examples/cosmodc2/Cluster_pipelines/config-20deg2-CL.yml
 
 inputs:
     # See README for paths to download these files
-    shear_catalog: /sps/lsst/users/mricci/TXPipe_data/data_link/cosmodc2/20deg2/shear_catalog.hdf5
+    shear_catalog: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/cosmodc2/20deg2/shear_catalog.hdf5
     #photometry_catalog:  /sps/lsst/users/mricci/TXPipe_data/data_link/cosmodc2/20deg2/photometry_catalog.hdf5
-    fiducial_cosmology: ./data/fiducial_cosmology.yml
-    calibration_table: /sps/lsst/users/mricci/TXPipe_data/data_link/cosmodc2/20deg2/sample_cosmodc2_w10year_errors.dat
-    spectroscopic_catalog: /sps/lsst/users/mricci/TXPipe_data/data_link/cosmodc2/20deg2/spectroscopic_catalog.hdf5
-    cluster_catalog: /sps/lsst/users/mricci/TXPipe_data/data_link/cosmodc2/20deg2/cluster_catalog.hdf5
+    fiducial_cosmology: /sps/lsst/users/ebarroso/TXPipe/data/fiducial_cosmology.yml
+    calibration_table: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/cosmodc2/20deg2/sample_cosmodc2_w10year_errors.dat
+    spectroscopic_catalog: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/cosmodc2/20deg2/spectroscopic_catalog.hdf5
+    cluster_catalog: /sps/lsst/groups/clusters/cl_pipeline_project/TXPipe_data/cosmodc2/20deg2/cluster_catalog.hdf5
     #shear_tomography_catalog: ./data/example/outputs_metadetect/shear_tomography_catalog.hdf5
     #source_photoz_pdfs: ./data/example/inputs/photoz_pdfs.hdf5
 resume: true
-log_dir: ./data/cosmodc2/logs
-pipeline_log: ./data/cosmodc2/log_20deg2.txt
+log_dir: /sps/lsst/users/ebarroso/TXPipe/data/cosmodc2/logs
+pipeline_log: /sps/lsst/users/ebarroso/TXPipe/data/cosmodc2/log_20deg2.txt
 
diff --git a/notebooks/cluster_counts/tangential_shear_selection.ipynb b/notebooks/cluster_counts/tangential_shear_selection.ipynb
new file mode 100644
index 000000000..f3602bb87
--- /dev/null
+++ b/notebooks/cluster_counts/tangential_shear_selection.ipynb
@@ -0,0 +1,3740 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "35818ae3-26f3-48cf-b910-a22cae198f23",
+   "metadata": {},
+   "source": [
+    "# TXPipe - Reduced Shear\n",
+    "\n",
+    "This notebook illustrates the new option to compute **reduced shear** directly within TXPipe using CLMM.  \n",
+    "Instead of producing excess surface density (ΔΣ), the pipeline here outputs background galaxy shapes in terms of reduced shear, providing an alternative lensing observable for cluster analyses.  \n",
+    "\n",
+    "We will walk through:  \n",
+    "- Running the TXPipe stages needed to build per-cluster reduced shear catalogs  \n",
+    "- Inspecting the structure of the outputs  \n",
+    "- Demonstrating how these catalogs can be used as CLMM inputs\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4792903e-4b02-4f75-8d87-108f364062c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.14.6\n",
+      "3.2.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "import clmm\n",
+    "print(clmm.__version__)\n",
+    "import pyccl as ccl\n",
+    "print(ccl.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "13c35f16-03a7-4042-bc68-8858711d8974",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/sps/lsst/groups/clusters/cl_pipeline_project/conda_envs/txpipe_clp/lib/python3.12/site-packages/ceci/__init__.py:12: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
+      "  from pkg_resources import DistributionNotFound\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from pprint import pprint\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from IPython.display import Image\n",
+    "import ceci"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54d52caf-ba4f-4d87-98fc-36bbd275135e",
+   "metadata": {},
+   "source": [
+    "# 1 deg$^2$ Sample\n",
+    "\n",
+    "We will do some runs on the 1 deg^2 example data set with around 80k galaxies. This is small enough that we can do it all in jupyter.\n",
+    "\n",
+    "The data set, which is based on CosmoDC2, contains pre-computed photo-z and and contains a RedMapper cluster catalog for the field.\n",
+    "\n",
+    "We will clone our own copy of the TXPipe directory, and run this notebook from there.  **Please change `my_txpipe_dir`** to your own version of the path when running this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "32961686-f5c2-4873-b529-9fcaf22c54e7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "my_txpipe_dir = \"/sps/lsst/users/ebarroso/TXPipe\" # \"/sps/lsst/groups/clusters/cl_pipeline_project/TXPipe\"\n",
+    "import sys\n",
+    "sys.path.append(\"/sps/lsst/users/ebarroso/TXPipe\")#\"/sps/lsst/groups/clusters/cl_pipeline_project/TXPipe\")\n",
+    "sys.path.append(\"/sps/lsst/users/ebarroso/TXPipe/txpipe/extensions\")#\"/sps/lsst/groups/clusters/cl_pipeline_project/TXPipe/txpipe/extensions/\")\n",
+    "import importlib\n",
+    "import txpipe\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c7fd3ce-e568-4228-9788-436c36916d36",
+   "metadata": {},
+   "source": [
+    "Now we make an output directory for everything, if it doesn't exist already."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "aa17705a-f043-41f6-97a7-6bae04237781",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "os.makedirs(f\"{my_txpipe_dir}/data/example/outputs_metadetect\", exist_ok=True)\n",
+    "os.makedirs(f\"{my_txpipe_dir}/data/example/outputs_shear_catalogs\", exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a2123975-cf19-4f1c-bae4-6894ecd42d24",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(f\"{my_txpipe_dir}/data/example/inputs/metadetect_shear_catalog.hdf5\"):\n",
+    "    raise RuntimeError(\"Download and extract the sample data file to continue\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44a5c54a-c9f2-4301-ba88-29dff76f93a1",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## WL sample selection\n",
+    "\n",
+    "Our first step is the WL sample selection. This does both selection and tomography. The latter is not used here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ed600f7-4cf7-4eff-9ba6-2edb1ead85c8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "step1 = txpipe.TXSourceSelectorMetadetect.make_stage(\n",
+    "    # This file is the input metadetect shear catalog\n",
+    "    shear_catalog=f\"{my_txpipe_dir}/data/example/inputs/metadetect_shear_catalog.hdf5\",\n",
+    "    # This is an input training set for the tomographic selection\n",
+    "    calibration_table=f\"{my_txpipe_dir}/data/example/inputs/sample_cosmodc2_w10year_errors.dat\",\n",
+    "\n",
+    "    # This contains all the options for this stage. You can override them here\n",
+    "    # manually too.\n",
+    "    config=f\"{my_txpipe_dir}/examples/metadetect/config.yml\",\n",
+    "\n",
+    "    # This is the output file for this stage\n",
+    "    shear_tomography_catalog=f\"{my_txpipe_dir}/data/example/outputs_metadetect/shear_tomography_catalog.hdf5\",\n",
+    "    true_z=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b30b542-fe35-478d-9561-f4df45d83850",
+   "metadata": {},
+   "source": [
+    "This step will first train a classifier to select objects into tomographic bins, and then run it on the input data files\n",
+    "to produce the output file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6b9def04-7ae8-4f39-80b6-8f18d4faaf11",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 0 running selection for rows 0-82,200\n"
+     ]
+    }
+   ],
+   "source": [
+    "step1.run()\n",
+    "step1.finalize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36dfecbc-057a-46b2-acfb-cd5bc3bd22c6",
+   "metadata": {},
+   "source": [
+    "## Inspecting the output of the first stages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "40ad44da-205a-41ed-b4f4-7079dbf7d366",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Keys: <KeysViewHDF5 ['counts', 'provenance', 'response', 'tomography']>\n",
+      "[np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(0), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(0), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(2), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(2), np.int32(1), np.int32(1), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(2), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(2), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(2), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(2), np.int32(1), np.int32(2), np.int32(2), np.int32(2), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(2), np.int32(2), np.int32(2), np.int32(1), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(0), np.int32(0), np.int32(0), np.int32(1), np.int32(1), np.int32(2), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(0), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(1), np.int32(2), np.int32(1), np.int32(1), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(2), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(2), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3), np.int32(3)]\n",
+      "9099\n"
+     ]
+    }
+   ],
+   "source": [
+    "import h5py\n",
+    "with h5py.File(f\"{my_txpipe_dir}/data/example/outputs_metadetect/shear_tomography_catalog.hdf5\", \"r\") as f:\n",
+    "    # Print all root level object names (aka keys)\n",
+    "    print(\"Keys: %s\" % f.keys())\n",
+    "    # Access the 'tomography' group and then the 'bin' dataset\n",
+    "    bin_data = f['tomography']['bin'][()]\n",
+    "    print([bin_data[i] for i in range(len(bin_data)) if bin_data[i]!= -1 ])\n",
+    "    # Count how many entries are not -1\n",
+    "    count = len(bin_data[bin_data != -1])\n",
+    "    print(count)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ae049562-355f-4a7b-845f-2f2be598ff2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Keys: <KeysViewHDF5 ['shear']>\n",
+      "<KeysViewHDF5 ['T', 'T_err', 'dec', 'flags', 'g1', 'g2', 'id', 'mag_err_i', 'mag_err_r', 'mag_err_z', 'mag_i', 'mag_r', 'mag_z', 'mcal_psf_T_mean', 'mcal_psf_g1', 'mcal_psf_g2', 'psf_g1', 'psf_g2', 'ra', 'redshift_true', 's2n', 'true_g1', 'true_g2', 'weight']>\n"
+     ]
+    }
+   ],
+   "source": [
+    "import h5py\n",
+    "with h5py.File(f\"{my_txpipe_dir}/data/example/inputs/metadetect_shear_catalog.hdf5\", \"r\") as f:\n",
+    "    # Print all root level object names (aka keys) \n",
+    "    # these can be group or dataset names \n",
+    "    print(\"Keys: %s\" % f.keys())\n",
+    "    # get first object name/key; may or may NOT be a group\n",
+    "    print(f['shear']['00'].keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33844c37-b2c7-4400-9f6c-f0c5bb0c3ce4",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Cluster shear catalog indexing and weights\n",
+    "\n",
+    "Our second step runs the matching to find the shear catalog behind every cluster. Here we are already demonstrating that we can use several options, as for insteanse the reduced shear instead of delta sigma.\n",
+    "\n",
+    "This step saves a cluster shear catalog, which is actually just an index into the shear and cluster catalogs (to avoid making many copies of the data), with added weights from CLMM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "52cd1f06-b0ea-4afb-b398-0ef5404d8ae4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Options for this pipeline and their defaults:\n",
+      "{'chunk_rows': 100000, 'max_radius': 10.0, 'delta_z': 0.1, 'redshift_cut_criterion': 'zmode', 'redshift_weight_criterion': 'zmode', 'redshift_cut_criterion_pdf_fraction': 0.9, 'subtract_mean_shear': False, 'coordinate_system': 'celestial', 'use_true_shear': False, 'delta_sigma': False, 'use_shape_noise': False, 'use_radius': True, 'max_angle': 30}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Options for this pipeline and their defaults:\")\n",
+    "import txpipe.extensions as extensions\n",
+    "print(extensions.CLClusterShearCatalogs.config_options)\n",
+    "def run_different_options(delta_sigma, weight_criterion, cut_criterion, name, use_radius=False, shape=False):\n",
+    "    print(use_radius)\n",
+    "    step2 = extensions.CLClusterShearCatalogs.make_stage(\n",
+    "        # Shear catalog, as before\n",
+    "        shear_catalog=f\"{my_txpipe_dir}/data/example/inputs/metadetect_shear_catalog.hdf5\",\n",
+    "        # This is the initial cluster catalog - RAs, Decs, richess, redshift, etc.\n",
+    "        cluster_catalog=f\"{my_txpipe_dir}/data/example/inputs/cluster_catalog.hdf5\",\n",
+    "        # This fiducial cosmology is used to convert distance separations to redshifts\n",
+    "        fiducial_cosmology=f\"{my_txpipe_dir}/data/fiducial_cosmology.yml\",\n",
+    "        # The tomography catalog created in step 1 selects objects for the WL sample\n",
+    "        # and assigns them to tomographic bins. We don't need the tomography here, just the basic selection\n",
+    "        shear_tomography_catalog=f\"{my_txpipe_dir}/data/example/outputs_metadetect/shear_tomography_catalog.hdf5\",\n",
+    "        # This is a QP file created by RAIL to generate the photo-zs for this sample\n",
+    "        source_photoz_pdfs=f\"{my_txpipe_dir}/data/example/inputs/photoz_pdfs.hdf5\",\n",
+    "        # This is the output for this stage\n",
+    "        cluster_shear_catalogs=f\"{my_txpipe_dir}/data/example/outputs_shear_catalogs/my_cluster_shear_catalog_{name}.hdf5\",\n",
+    "        output_dir=f\"{my_txpipe_dir}/data/example/outputs_shear_catalogs/\",\n",
+    "        #redshift_cut_criterion = 'nz',\n",
+    "        # Let's override one of the configuration parameters for this stage:\n",
+    "        max_radius=10.0,\n",
+    "        use_radius = use_radius,\n",
+    "        max_angle= 40,\n",
+    "        delta_sigma = delta_sigma,\n",
+    "        redshift_weight_criterion = weight_criterion,\n",
+    "        redshift_cut_criterion = cut_criterion,\n",
+    "        use_shape_noise = shape,\n",
+    "    )\n",
+    "    step2.run()\n",
+    "    step2.finalize()\n",
+    "    final_name = f\"{my_txpipe_dir}/data/example/outputs_shear_catalogs/my_cluster_shear_catalog_{name}.hdf5\",\n",
+    "    return final_name\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7b228202-bebc-42fd-b604-2a185079b1cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.14.6\n",
+      "3.2.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "import clmm\n",
+    "print(clmm.__version__)\n",
+    "import pyccl as ccl\n",
+    "print(ccl.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "53847723-b024-4e66-bfad-6b71cf2845e4",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "Min search angle =  20.111239360034133 arcmin\n",
+      "Mean search angle =  25.964606752423457 arcmin\n",
+      "Max search angle =  45.0930880390025 arcmin\n",
+      "Max theta_max = 0.013117047602871116 radians = 45.0930880390025 arcmin\n",
+      "Using single 2D shear calibration!\n",
+      "Process 0 processing chunk 0 - 82,200\n",
+      "Process 0 done reading\n",
+      "Overall pair count =  215422\n",
+      "Collecting data for cluster 0\n",
+      "Found 2991 total galaxies in catalog for cluster 11\n",
+      "Found 2612 total galaxies in catalog for cluster 827\n",
+      "Found 2579 total galaxies in catalog for cluster 1985\n",
+      "Found 2522 total galaxies in catalog for cluster 1632\n",
+      "Found 1017 total galaxies in catalog for cluster 2453\n",
+      "Found 4021 total galaxies in catalog for cluster 2678\n",
+      "Found 1265 total galaxies in catalog for cluster 4643\n",
+      "Found 495 total galaxies in catalog for cluster 5084\n",
+      "Found 2298 total galaxies in catalog for cluster 4434\n",
+      "Found 2146 total galaxies in catalog for cluster 3939\n",
+      "Found 4680 total galaxies in catalog for cluster 6139\n",
+      "Found 1701 total galaxies in catalog for cluster 4709\n",
+      "Found 4205 total galaxies in catalog for cluster 7121\n",
+      "Found 3553 total galaxies in catalog for cluster 8547\n",
+      "Found 963 total galaxies in catalog for cluster 8685\n",
+      "Found 765 total galaxies in catalog for cluster 8995\n",
+      "Found 3888 total galaxies in catalog for cluster 7698\n",
+      "Found 6780 total galaxies in catalog for cluster 10999\n",
+      "Found 753 total galaxies in catalog for cluster 9029\n",
+      "Found 1352 total galaxies in catalog for cluster 9429\n",
+      "Found 2615 total galaxies in catalog for cluster 10146\n",
+      "Found 3499 total galaxies in catalog for cluster 14476\n",
+      "Found 306 total galaxies in catalog for cluster 8395\n",
+      "Found 4506 total galaxies in catalog for cluster 16657\n",
+      "Found 3474 total galaxies in catalog for cluster 13039\n",
+      "Found 3375 total galaxies in catalog for cluster 15382\n",
+      "Found 1637 total galaxies in catalog for cluster 17011\n",
+      "Found 7041 total galaxies in catalog for cluster 8523\n",
+      "Found 1487 total galaxies in catalog for cluster 11300\n",
+      "Found 6914 total galaxies in catalog for cluster 17462\n",
+      "Found 1911 total galaxies in catalog for cluster 13025\n",
+      "Found 3004 total galaxies in catalog for cluster 21385\n",
+      "Found 3279 total galaxies in catalog for cluster 20888\n",
+      "Found 4175 total galaxies in catalog for cluster 21815\n",
+      "Found 1167 total galaxies in catalog for cluster 7594\n",
+      "Found 748 total galaxies in catalog for cluster 21425\n",
+      "Found 8544 total galaxies in catalog for cluster 30553\n",
+      "Found 2346 total galaxies in catalog for cluster 11651\n",
+      "Found 3331 total galaxies in catalog for cluster 27728\n",
+      "Found 3555 total galaxies in catalog for cluster 30757\n",
+      "Found 5920 total galaxies in catalog for cluster 32634\n",
+      "Found 3916 total galaxies in catalog for cluster 33429\n",
+      "Found 2534 total galaxies in catalog for cluster 15656\n",
+      "Found 1140 total galaxies in catalog for cluster 29784\n",
+      "Found 2527 total galaxies in catalog for cluster 22630\n",
+      "Found 1443 total galaxies in catalog for cluster 17777\n",
+      "Found 2760 total galaxies in catalog for cluster 33569\n",
+      "Found 2483 total galaxies in catalog for cluster 39996\n",
+      "Found 4301 total galaxies in catalog for cluster 37007\n",
+      "Found 5029 total galaxies in catalog for cluster 25937\n",
+      "Found 1670 total galaxies in catalog for cluster 24386\n",
+      "Found 5535 total galaxies in catalog for cluster 44685\n",
+      "Found 2980 total galaxies in catalog for cluster 48725\n",
+      "Found 2547 total galaxies in catalog for cluster 27260\n",
+      "Found 1775 total galaxies in catalog for cluster 35316\n",
+      "Found 1463 total galaxies in catalog for cluster 59428\n",
+      "Found 2451 total galaxies in catalog for cluster 25976\n",
+      "Found 3392 total galaxies in catalog for cluster 31596\n",
+      "Found 6473 total galaxies in catalog for cluster 26346\n",
+      "Found 3239 total galaxies in catalog for cluster 36608\n",
+      "Found 4003 total galaxies in catalog for cluster 26938\n",
+      "Found 1837 total galaxies in catalog for cluster 74483\n",
+      "Found 3191 total galaxies in catalog for cluster 62882\n",
+      "Found 807 total galaxies in catalog for cluster 25770\n",
+      "Found 623 total galaxies in catalog for cluster 59302\n",
+      "Found 1313 total galaxies in catalog for cluster 53335\n",
+      "Found 674 total galaxies in catalog for cluster 23919\n",
+      "Found 3983 total galaxies in catalog for cluster 16567\n",
+      "Found 2756 total galaxies in catalog for cluster 41321\n",
+      "Found 634 total galaxies in catalog for cluster 52141\n",
+      "Found 3003 total galaxies in catalog for cluster 52451\n",
+      "Found 1669 total galaxies in catalog for cluster 51057\n",
+      "Found 2896 total galaxies in catalog for cluster 14210\n",
+      "Found 3094 total galaxies in catalog for cluster 33410\n",
+      "Found 3861 total galaxies in catalog for cluster 78132\n",
+      "True\n",
+      "Min search angle =  20.111239360034133 arcmin\n",
+      "Mean search angle =  25.964606752423457 arcmin\n",
+      "Max search angle =  45.0930880390025 arcmin\n",
+      "Max theta_max = 0.013117047602871116 radians = 45.0930880390025 arcmin\n",
+      "Using single 2D shear calibration!\n",
+      "Process 0 processing chunk 0 - 82,200\n",
+      "Process 0 done reading\n",
+      "Overall pair count =  200603\n",
+      "Collecting data for cluster 0\n",
+      "Found 2851 total galaxies in catalog for cluster 11\n",
+      "Found 2456 total galaxies in catalog for cluster 827\n",
+      "Found 2413 total galaxies in catalog for cluster 1985\n",
+      "Found 2346 total galaxies in catalog for cluster 1632\n",
+      "Found 891 total galaxies in catalog for cluster 2453\n",
+      "Found 3782 total galaxies in catalog for cluster 2678\n",
+      "Found 1069 total galaxies in catalog for cluster 4643\n",
+      "Found 432 total galaxies in catalog for cluster 5084\n",
+      "Found 2149 total galaxies in catalog for cluster 4434\n",
+      "Found 1830 total galaxies in catalog for cluster 3939\n",
+      "Found 4496 total galaxies in catalog for cluster 6139\n",
+      "Found 1511 total galaxies in catalog for cluster 4709\n",
+      "Found 3903 total galaxies in catalog for cluster 7121\n",
+      "Found 3294 total galaxies in catalog for cluster 8547\n",
+      "Found 819 total galaxies in catalog for cluster 8685\n",
+      "Found 626 total galaxies in catalog for cluster 8995\n",
+      "Found 3589 total galaxies in catalog for cluster 7698\n",
+      "Found 6508 total galaxies in catalog for cluster 10999\n",
+      "Found 645 total galaxies in catalog for cluster 9029\n",
+      "Found 1219 total galaxies in catalog for cluster 9429\n",
+      "Found 2477 total galaxies in catalog for cluster 10146\n",
+      "Found 3236 total galaxies in catalog for cluster 14476\n",
+      "Found 267 total galaxies in catalog for cluster 8395\n",
+      "Found 4237 total galaxies in catalog for cluster 16657\n",
+      "Found 3203 total galaxies in catalog for cluster 13039\n",
+      "Found 3217 total galaxies in catalog for cluster 15382\n",
+      "Found 1554 total galaxies in catalog for cluster 17011\n",
+      "Found 6645 total galaxies in catalog for cluster 8523\n",
+      "Found 1321 total galaxies in catalog for cluster 11300\n",
+      "Found 6793 total galaxies in catalog for cluster 17462\n",
+      "Found 1814 total galaxies in catalog for cluster 13025\n",
+      "Found 2637 total galaxies in catalog for cluster 21385\n",
+      "Found 3100 total galaxies in catalog for cluster 20888\n",
+      "Found 3863 total galaxies in catalog for cluster 21815\n",
+      "Found 956 total galaxies in catalog for cluster 7594\n",
+      "Found 632 total galaxies in catalog for cluster 21425\n",
+      "Found 8401 total galaxies in catalog for cluster 30553\n",
+      "Found 2161 total galaxies in catalog for cluster 11651\n",
+      "Found 3115 total galaxies in catalog for cluster 27728\n",
+      "Found 3282 total galaxies in catalog for cluster 30757\n",
+      "Found 5806 total galaxies in catalog for cluster 32634\n",
+      "Found 3646 total galaxies in catalog for cluster 33429\n",
+      "Found 2372 total galaxies in catalog for cluster 15656\n",
+      "Found 963 total galaxies in catalog for cluster 29784\n",
+      "Found 2313 total galaxies in catalog for cluster 22630\n",
+      "Found 1247 total galaxies in catalog for cluster 17777\n",
+      "Found 2529 total galaxies in catalog for cluster 33569\n",
+      "Found 2263 total galaxies in catalog for cluster 39996\n",
+      "Found 4094 total galaxies in catalog for cluster 37007\n",
+      "Found 4852 total galaxies in catalog for cluster 25937\n",
+      "Found 1586 total galaxies in catalog for cluster 24386\n",
+      "Found 5253 total galaxies in catalog for cluster 44685\n",
+      "Found 2779 total galaxies in catalog for cluster 48725\n",
+      "Found 2228 total galaxies in catalog for cluster 27260\n",
+      "Found 1532 total galaxies in catalog for cluster 35316\n",
+      "Found 1270 total galaxies in catalog for cluster 59428\n",
+      "Found 2305 total galaxies in catalog for cluster 25976\n",
+      "Found 3178 total galaxies in catalog for cluster 31596\n",
+      "Found 6358 total galaxies in catalog for cluster 26346\n",
+      "Found 3056 total galaxies in catalog for cluster 36608\n",
+      "Found 3770 total galaxies in catalog for cluster 26938\n",
+      "Found 1528 total galaxies in catalog for cluster 74483\n",
+      "Found 2946 total galaxies in catalog for cluster 62882\n",
+      "Found 626 total galaxies in catalog for cluster 25770\n",
+      "Found 514 total galaxies in catalog for cluster 59302\n",
+      "Found 1152 total galaxies in catalog for cluster 53335\n",
+      "Found 609 total galaxies in catalog for cluster 23919\n",
+      "Found 3765 total galaxies in catalog for cluster 16567\n",
+      "Found 2588 total galaxies in catalog for cluster 41321\n",
+      "Found 558 total galaxies in catalog for cluster 52141\n",
+      "Found 2760 total galaxies in catalog for cluster 52451\n",
+      "Found 1560 total galaxies in catalog for cluster 51057\n",
+      "Found 2515 total galaxies in catalog for cluster 14210\n",
+      "Found 2800 total galaxies in catalog for cluster 33410\n",
+      "Found 3542 total galaxies in catalog for cluster 78132\n",
+      "True\n",
+      "Min search angle =  20.111239360034133 arcmin\n",
+      "Mean search angle =  25.964606752423457 arcmin\n",
+      "Max search angle =  45.0930880390025 arcmin\n",
+      "Max theta_max = 0.013117047602871116 radians = 45.0930880390025 arcmin\n",
+      "Using single 2D shear calibration!\n",
+      "Process 0 processing chunk 0 - 82,200\n",
+      "Process 0 done reading\n",
+      "Overall pair count =  168710\n",
+      "Collecting data for cluster 0\n",
+      "Found 2416 total galaxies in catalog for cluster 11\n",
+      "Found 1982 total galaxies in catalog for cluster 827\n",
+      "Found 1929 total galaxies in catalog for cluster 1985\n",
+      "Found 1887 total galaxies in catalog for cluster 1632\n",
+      "Found 639 total galaxies in catalog for cluster 2453\n",
+      "Found 3068 total galaxies in catalog for cluster 2678\n",
+      "Found 722 total galaxies in catalog for cluster 4643\n",
+      "Found 244 total galaxies in catalog for cluster 5084\n",
+      "Found 1803 total galaxies in catalog for cluster 4434\n",
+      "Found 1408 total galaxies in catalog for cluster 3939\n",
+      "Found 4123 total galaxies in catalog for cluster 6139\n",
+      "Found 1196 total galaxies in catalog for cluster 4709\n",
+      "Found 3401 total galaxies in catalog for cluster 7121\n",
+      "Found 2874 total galaxies in catalog for cluster 8547\n",
+      "Found 448 total galaxies in catalog for cluster 8685\n",
+      "Found 354 total galaxies in catalog for cluster 8995\n",
+      "Found 3085 total galaxies in catalog for cluster 7698\n",
+      "Found 5985 total galaxies in catalog for cluster 10999\n",
+      "Found 444 total galaxies in catalog for cluster 9029\n",
+      "Found 996 total galaxies in catalog for cluster 9429\n",
+      "Found 2072 total galaxies in catalog for cluster 10146\n",
+      "Found 2695 total galaxies in catalog for cluster 14476\n",
+      "Found 119 total galaxies in catalog for cluster 8395\n",
+      "Found 3612 total galaxies in catalog for cluster 16657\n",
+      "Found 2732 total galaxies in catalog for cluster 13039\n",
+      "Found 2714 total galaxies in catalog for cluster 15382\n",
+      "Found 1343 total galaxies in catalog for cluster 17011\n",
+      "Found 6019 total galaxies in catalog for cluster 8523\n",
+      "Found 1038 total galaxies in catalog for cluster 11300\n",
+      "Found 6292 total galaxies in catalog for cluster 17462\n",
+      "Found 1520 total galaxies in catalog for cluster 13025\n",
+      "Found 2090 total galaxies in catalog for cluster 21385\n",
+      "Found 2591 total galaxies in catalog for cluster 20888\n",
+      "Found 3139 total galaxies in catalog for cluster 21815\n",
+      "Found 528 total galaxies in catalog for cluster 7594\n",
+      "Found 363 total galaxies in catalog for cluster 21425\n",
+      "Found 7786 total galaxies in catalog for cluster 30553\n",
+      "Found 1777 total galaxies in catalog for cluster 11651\n",
+      "Found 2526 total galaxies in catalog for cluster 27728\n",
+      "Found 2665 total galaxies in catalog for cluster 30757\n",
+      "Found 5370 total galaxies in catalog for cluster 32634\n",
+      "Found 3080 total galaxies in catalog for cluster 33429\n",
+      "Found 1942 total galaxies in catalog for cluster 15656\n",
+      "Found 662 total galaxies in catalog for cluster 29784\n",
+      "Found 1912 total galaxies in catalog for cluster 22630\n",
+      "Found 942 total galaxies in catalog for cluster 17777\n",
+      "Found 2126 total galaxies in catalog for cluster 33569\n",
+      "Found 1969 total galaxies in catalog for cluster 39996\n",
+      "Found 3464 total galaxies in catalog for cluster 37007\n",
+      "Found 4503 total galaxies in catalog for cluster 25937\n",
+      "Found 1258 total galaxies in catalog for cluster 24386\n",
+      "Found 4662 total galaxies in catalog for cluster 44685\n",
+      "Found 2258 total galaxies in catalog for cluster 48725\n",
+      "Found 1761 total galaxies in catalog for cluster 27260\n",
+      "Found 1011 total galaxies in catalog for cluster 35316\n",
+      "Found 872 total galaxies in catalog for cluster 59428\n",
+      "Found 1944 total galaxies in catalog for cluster 25976\n",
+      "Found 2708 total galaxies in catalog for cluster 31596\n",
+      "Found 5886 total galaxies in catalog for cluster 26346\n",
+      "Found 2469 total galaxies in catalog for cluster 36608\n",
+      "Found 3301 total galaxies in catalog for cluster 26938\n",
+      "Found 1179 total galaxies in catalog for cluster 74483\n",
+      "Found 2586 total galaxies in catalog for cluster 62882\n",
+      "Found 366 total galaxies in catalog for cluster 25770\n",
+      "Found 280 total galaxies in catalog for cluster 59302\n",
+      "Found 776 total galaxies in catalog for cluster 53335\n",
+      "Found 342 total galaxies in catalog for cluster 23919\n",
+      "Found 3197 total galaxies in catalog for cluster 16567\n",
+      "Found 2110 total galaxies in catalog for cluster 41321\n",
+      "Found 368 total galaxies in catalog for cluster 52141\n",
+      "Found 2288 total galaxies in catalog for cluster 52451\n",
+      "Found 1273 total galaxies in catalog for cluster 51057\n",
+      "Found 1986 total galaxies in catalog for cluster 14210\n",
+      "Found 2256 total galaxies in catalog for cluster 33410\n",
+      "Found 2978 total galaxies in catalog for cluster 78132\n",
+      "True\n",
+      "Min search angle =  20.111239360034133 arcmin\n",
+      "Mean search angle =  25.964606752423457 arcmin\n",
+      "Max search angle =  45.0930880390025 arcmin\n",
+      "Max theta_max = 0.013117047602871116 radians = 45.0930880390025 arcmin\n",
+      "Using single 2D shear calibration!\n",
+      "Process 0 processing chunk 0 - 82,200\n",
+      "Process 0 done reading\n",
+      "Overall pair count =  168710\n",
+      "Collecting data for cluster 0\n",
+      "Found 2416 total galaxies in catalog for cluster 11\n",
+      "Found 1982 total galaxies in catalog for cluster 827\n",
+      "Found 1929 total galaxies in catalog for cluster 1985\n",
+      "Found 1887 total galaxies in catalog for cluster 1632\n",
+      "Found 639 total galaxies in catalog for cluster 2453\n",
+      "Found 3068 total galaxies in catalog for cluster 2678\n",
+      "Found 722 total galaxies in catalog for cluster 4643\n",
+      "Found 244 total galaxies in catalog for cluster 5084\n",
+      "Found 1803 total galaxies in catalog for cluster 4434\n",
+      "Found 1408 total galaxies in catalog for cluster 3939\n",
+      "Found 4123 total galaxies in catalog for cluster 6139\n",
+      "Found 1196 total galaxies in catalog for cluster 4709\n",
+      "Found 3401 total galaxies in catalog for cluster 7121\n",
+      "Found 2874 total galaxies in catalog for cluster 8547\n",
+      "Found 448 total galaxies in catalog for cluster 8685\n",
+      "Found 354 total galaxies in catalog for cluster 8995\n",
+      "Found 3085 total galaxies in catalog for cluster 7698\n",
+      "Found 5985 total galaxies in catalog for cluster 10999\n",
+      "Found 444 total galaxies in catalog for cluster 9029\n",
+      "Found 996 total galaxies in catalog for cluster 9429\n",
+      "Found 2072 total galaxies in catalog for cluster 10146\n",
+      "Found 2695 total galaxies in catalog for cluster 14476\n",
+      "Found 119 total galaxies in catalog for cluster 8395\n",
+      "Found 3612 total galaxies in catalog for cluster 16657\n",
+      "Found 2732 total galaxies in catalog for cluster 13039\n",
+      "Found 2714 total galaxies in catalog for cluster 15382\n",
+      "Found 1343 total galaxies in catalog for cluster 17011\n",
+      "Found 6019 total galaxies in catalog for cluster 8523\n",
+      "Found 1038 total galaxies in catalog for cluster 11300\n",
+      "Found 6292 total galaxies in catalog for cluster 17462\n",
+      "Found 1520 total galaxies in catalog for cluster 13025\n",
+      "Found 2090 total galaxies in catalog for cluster 21385\n",
+      "Found 2591 total galaxies in catalog for cluster 20888\n",
+      "Found 3139 total galaxies in catalog for cluster 21815\n",
+      "Found 528 total galaxies in catalog for cluster 7594\n",
+      "Found 363 total galaxies in catalog for cluster 21425\n",
+      "Found 7786 total galaxies in catalog for cluster 30553\n",
+      "Found 1777 total galaxies in catalog for cluster 11651\n",
+      "Found 2526 total galaxies in catalog for cluster 27728\n",
+      "Found 2665 total galaxies in catalog for cluster 30757\n",
+      "Found 5370 total galaxies in catalog for cluster 32634\n",
+      "Found 3080 total galaxies in catalog for cluster 33429\n",
+      "Found 1942 total galaxies in catalog for cluster 15656\n",
+      "Found 662 total galaxies in catalog for cluster 29784\n",
+      "Found 1912 total galaxies in catalog for cluster 22630\n",
+      "Found 942 total galaxies in catalog for cluster 17777\n",
+      "Found 2126 total galaxies in catalog for cluster 33569\n",
+      "Found 1969 total galaxies in catalog for cluster 39996\n",
+      "Found 3464 total galaxies in catalog for cluster 37007\n",
+      "Found 4503 total galaxies in catalog for cluster 25937\n",
+      "Found 1258 total galaxies in catalog for cluster 24386\n",
+      "Found 4662 total galaxies in catalog for cluster 44685\n",
+      "Found 2258 total galaxies in catalog for cluster 48725\n",
+      "Found 1761 total galaxies in catalog for cluster 27260\n",
+      "Found 1011 total galaxies in catalog for cluster 35316\n",
+      "Found 872 total galaxies in catalog for cluster 59428\n",
+      "Found 1944 total galaxies in catalog for cluster 25976\n",
+      "Found 2708 total galaxies in catalog for cluster 31596\n",
+      "Found 5886 total galaxies in catalog for cluster 26346\n",
+      "Found 2469 total galaxies in catalog for cluster 36608\n",
+      "Found 3301 total galaxies in catalog for cluster 26938\n",
+      "Found 1179 total galaxies in catalog for cluster 74483\n",
+      "Found 2586 total galaxies in catalog for cluster 62882\n",
+      "Found 366 total galaxies in catalog for cluster 25770\n",
+      "Found 280 total galaxies in catalog for cluster 59302\n",
+      "Found 776 total galaxies in catalog for cluster 53335\n",
+      "Found 342 total galaxies in catalog for cluster 23919\n",
+      "Found 3197 total galaxies in catalog for cluster 16567\n",
+      "Found 2110 total galaxies in catalog for cluster 41321\n",
+      "Found 368 total galaxies in catalog for cluster 52141\n",
+      "Found 2288 total galaxies in catalog for cluster 52451\n",
+      "Found 1273 total galaxies in catalog for cluster 51057\n",
+      "Found 1986 total galaxies in catalog for cluster 14210\n",
+      "Found 2256 total galaxies in catalog for cluster 33410\n",
+      "Found 2978 total galaxies in catalog for cluster 78132\n",
+      "False\n",
+      "Max theta_max = 0.011635528346628864 radians = 40 arcmin\n",
+      "Using single 2D shear calibration!\n",
+      "Process 0 processing chunk 0 - 82,200\n",
+      "Process 0 done reading\n",
+      "Overall pair count =  374266\n",
+      "Collecting data for cluster 0\n",
+      "Found 5359 total galaxies in catalog for cluster 11\n",
+      "Found 5490 total galaxies in catalog for cluster 827\n",
+      "Found 5255 total galaxies in catalog for cluster 1985\n",
+      "Found 5355 total galaxies in catalog for cluster 1632\n",
+      "Found 3090 total galaxies in catalog for cluster 2453\n",
+      "Found 6907 total galaxies in catalog for cluster 2678\n",
+      "Found 3421 total galaxies in catalog for cluster 4643\n",
+      "Found 2124 total galaxies in catalog for cluster 5084\n",
+      "Found 4727 total galaxies in catalog for cluster 4434\n",
+      "Found 4933 total galaxies in catalog for cluster 3939\n",
+      "Found 5685 total galaxies in catalog for cluster 6139\n",
+      "Found 4120 total galaxies in catalog for cluster 4709\n",
+      "Found 6586 total galaxies in catalog for cluster 7121\n",
+      "Found 5807 total galaxies in catalog for cluster 8547\n",
+      "Found 3141 total galaxies in catalog for cluster 8685\n",
+      "Found 2588 total galaxies in catalog for cluster 8995\n",
+      "Found 6403 total galaxies in catalog for cluster 7698\n",
+      "Found 7812 total galaxies in catalog for cluster 10999\n",
+      "Found 2356 total galaxies in catalog for cluster 9029\n",
+      "Found 3434 total galaxies in catalog for cluster 9429\n",
+      "Found 5197 total galaxies in catalog for cluster 10146\n",
+      "Found 6185 total galaxies in catalog for cluster 14476\n",
+      "Found 1496 total galaxies in catalog for cluster 8395\n",
+      "Found 6562 total galaxies in catalog for cluster 16657\n",
+      "Found 5860 total galaxies in catalog for cluster 13039\n",
+      "Found 6154 total galaxies in catalog for cluster 15382\n",
+      "Found 3308 total galaxies in catalog for cluster 17011\n",
+      "Found 8175 total galaxies in catalog for cluster 8523\n",
+      "Found 3841 total galaxies in catalog for cluster 11300\n",
+      "Found 6259 total galaxies in catalog for cluster 17462\n",
+      "Found 4083 total galaxies in catalog for cluster 13025\n",
+      "Found 5637 total galaxies in catalog for cluster 21385\n",
+      "Found 6305 total galaxies in catalog for cluster 20888\n",
+      "Found 6975 total galaxies in catalog for cluster 21815\n",
+      "Found 3453 total galaxies in catalog for cluster 7594\n",
+      "Found 2706 total galaxies in catalog for cluster 21425\n",
+      "Found 7592 total galaxies in catalog for cluster 30553\n",
+      "Found 4675 total galaxies in catalog for cluster 11651\n",
+      "Found 5996 total galaxies in catalog for cluster 27728\n",
+      "Found 6669 total galaxies in catalog for cluster 30757\n",
+      "Found 6120 total galaxies in catalog for cluster 32634\n",
+      "Found 6214 total galaxies in catalog for cluster 33429\n",
+      "Found 5253 total galaxies in catalog for cluster 15656\n",
+      "Found 3371 total galaxies in catalog for cluster 29784\n",
+      "Found 4922 total galaxies in catalog for cluster 22630\n",
+      "Found 3705 total galaxies in catalog for cluster 17777\n",
+      "Found 5175 total galaxies in catalog for cluster 33569\n",
+      "Found 4048 total galaxies in catalog for cluster 39996\n",
+      "Found 7069 total galaxies in catalog for cluster 37007\n",
+      "Found 5873 total galaxies in catalog for cluster 25937\n",
+      "Found 4093 total galaxies in catalog for cluster 24386\n",
+      "Found 7755 total galaxies in catalog for cluster 44685\n",
+      "Found 5878 total galaxies in catalog for cluster 48725\n",
+      "Found 5174 total galaxies in catalog for cluster 27260\n",
+      "Found 4190 total galaxies in catalog for cluster 35316\n",
+      "Found 3708 total galaxies in catalog for cluster 59428\n",
+      "Found 5129 total galaxies in catalog for cluster 25976\n",
+      "Found 5900 total galaxies in catalog for cluster 31596\n",
+      "Found 5704 total galaxies in catalog for cluster 26346\n",
+      "Found 6009 total galaxies in catalog for cluster 36608\n",
+      "Found 6207 total galaxies in catalog for cluster 26938\n",
+      "Found 4202 total galaxies in catalog for cluster 74483\n",
+      "Found 4920 total galaxies in catalog for cluster 62882\n",
+      "Found 2616 total galaxies in catalog for cluster 25770\n",
+      "Found 2228 total galaxies in catalog for cluster 59302\n",
+      "Found 3532 total galaxies in catalog for cluster 53335\n",
+      "Found 2482 total galaxies in catalog for cluster 23919\n",
+      "Found 6625 total galaxies in catalog for cluster 16567\n",
+      "Found 5672 total galaxies in catalog for cluster 41321\n",
+      "Found 2354 total galaxies in catalog for cluster 52141\n",
+      "Found 5690 total galaxies in catalog for cluster 52451\n",
+      "Found 3696 total galaxies in catalog for cluster 51057\n",
+      "Found 5595 total galaxies in catalog for cluster 14210\n",
+      "Found 5616 total galaxies in catalog for cluster 33410\n",
+      "Found 5820 total galaxies in catalog for cluster 78132\n",
+      "False\n",
+      "Max theta_max = 0.011635528346628864 radians = 40 arcmin\n",
+      "Using single 2D shear calibration!\n",
+      "Process 0 processing chunk 0 - 82,200\n",
+      "Process 0 done reading\n",
+      "Overall pair count =  332293\n",
+      "Collecting data for cluster 0\n",
+      "Found 5057 total galaxies in catalog for cluster 11\n",
+      "Found 4822 total galaxies in catalog for cluster 827\n",
+      "Found 4598 total galaxies in catalog for cluster 1985\n",
+      "Found 4675 total galaxies in catalog for cluster 1632\n",
+      "Found 2446 total galaxies in catalog for cluster 2453\n",
+      "Found 6193 total galaxies in catalog for cluster 2678\n",
+      "Found 2525 total galaxies in catalog for cluster 4643\n",
+      "Found 1380 total galaxies in catalog for cluster 5084\n",
+      "Found 4282 total galaxies in catalog for cluster 4434\n",
+      "Found 3902 total galaxies in catalog for cluster 3939\n",
+      "Found 5571 total galaxies in catalog for cluster 6139\n",
+      "Found 3392 total galaxies in catalog for cluster 4709\n",
+      "Found 6228 total galaxies in catalog for cluster 7121\n",
+      "Found 5483 total galaxies in catalog for cluster 8547\n",
+      "Found 2021 total galaxies in catalog for cluster 8685\n",
+      "Found 1693 total galaxies in catalog for cluster 8995\n",
+      "Found 5988 total galaxies in catalog for cluster 7698\n",
+      "Found 7661 total galaxies in catalog for cluster 10999\n",
+      "Found 1848 total galaxies in catalog for cluster 9029\n",
+      "Found 2912 total galaxies in catalog for cluster 9429\n",
+      "Found 4754 total galaxies in catalog for cluster 10146\n",
+      "Found 5603 total galaxies in catalog for cluster 14476\n",
+      "Found 949 total galaxies in catalog for cluster 8395\n",
+      "Found 6156 total galaxies in catalog for cluster 16657\n",
+      "Found 5466 total galaxies in catalog for cluster 13039\n",
+      "Found 5689 total galaxies in catalog for cluster 15382\n",
+      "Found 3126 total galaxies in catalog for cluster 17011\n",
+      "Found 7947 total galaxies in catalog for cluster 8523\n",
+      "Found 3143 total galaxies in catalog for cluster 11300\n",
+      "Found 6189 total galaxies in catalog for cluster 17462\n",
+      "Found 3736 total galaxies in catalog for cluster 13025\n",
+      "Found 4721 total galaxies in catalog for cluster 21385\n",
+      "Found 5739 total galaxies in catalog for cluster 20888\n",
+      "Found 6280 total galaxies in catalog for cluster 21815\n",
+      "Found 2229 total galaxies in catalog for cluster 7594\n",
+      "Found 1711 total galaxies in catalog for cluster 21425\n",
+      "Found 7496 total galaxies in catalog for cluster 30553\n",
+      "Found 4229 total galaxies in catalog for cluster 11651\n",
+      "Found 5420 total galaxies in catalog for cluster 27728\n",
+      "Found 6011 total galaxies in catalog for cluster 30757\n",
+      "Found 6060 total galaxies in catalog for cluster 32634\n",
+      "Found 5813 total galaxies in catalog for cluster 33429\n",
+      "Found 4722 total galaxies in catalog for cluster 15656\n",
+      "Found 2484 total galaxies in catalog for cluster 29784\n",
+      "Found 4443 total galaxies in catalog for cluster 22630\n",
+      "Found 2969 total galaxies in catalog for cluster 17777\n",
+      "Found 4778 total galaxies in catalog for cluster 33569\n",
+      "Found 3844 total galaxies in catalog for cluster 39996\n",
+      "Found 6640 total galaxies in catalog for cluster 37007\n",
+      "Found 5765 total galaxies in catalog for cluster 25937\n",
+      "Found 3691 total galaxies in catalog for cluster 24386\n",
+      "Found 7471 total galaxies in catalog for cluster 44685\n",
+      "Found 5295 total galaxies in catalog for cluster 48725\n",
+      "Found 4325 total galaxies in catalog for cluster 27260\n",
+      "Found 3037 total galaxies in catalog for cluster 35316\n",
+      "Found 2793 total galaxies in catalog for cluster 59428\n",
+      "Found 4613 total galaxies in catalog for cluster 25976\n",
+      "Found 5515 total galaxies in catalog for cluster 31596\n",
+      "Found 5627 total galaxies in catalog for cluster 26346\n",
+      "Found 5352 total galaxies in catalog for cluster 36608\n",
+      "Found 5896 total galaxies in catalog for cluster 26938\n",
+      "Found 3355 total galaxies in catalog for cluster 74483\n",
+      "Found 4686 total galaxies in catalog for cluster 62882\n",
+      "Found 1660 total galaxies in catalog for cluster 25770\n",
+      "Found 1469 total galaxies in catalog for cluster 59302\n",
+      "Found 2676 total galaxies in catalog for cluster 53335\n",
+      "Found 1630 total galaxies in catalog for cluster 23919\n",
+      "Found 6208 total galaxies in catalog for cluster 16567\n",
+      "Found 5126 total galaxies in catalog for cluster 41321\n",
+      "Found 1636 total galaxies in catalog for cluster 52141\n",
+      "Found 5129 total galaxies in catalog for cluster 52451\n",
+      "Found 3352 total galaxies in catalog for cluster 51057\n",
+      "Found 4721 total galaxies in catalog for cluster 14210\n",
+      "Found 4882 total galaxies in catalog for cluster 33410\n",
+      "Found 5359 total galaxies in catalog for cluster 78132\n",
+      "True\n",
+      "Min search angle =  20.111239360034133 arcmin\n",
+      "Mean search angle =  25.964606752423457 arcmin\n",
+      "Max search angle =  45.0930880390025 arcmin\n",
+      "Max theta_max = 0.013117047602871116 radians = 45.0930880390025 arcmin\n",
+      "Using single 2D shear calibration!\n",
+      "Process 0 processing chunk 0 - 82,200\n",
+      "Process 0 done reading\n",
+      "Overall pair count =  168710\n",
+      "Collecting data for cluster 0\n",
+      "Found 2416 total galaxies in catalog for cluster 11\n",
+      "Found 1982 total galaxies in catalog for cluster 827\n",
+      "Found 1929 total galaxies in catalog for cluster 1985\n",
+      "Found 1887 total galaxies in catalog for cluster 1632\n",
+      "Found 639 total galaxies in catalog for cluster 2453\n",
+      "Found 3068 total galaxies in catalog for cluster 2678\n",
+      "Found 722 total galaxies in catalog for cluster 4643\n",
+      "Found 244 total galaxies in catalog for cluster 5084\n",
+      "Found 1803 total galaxies in catalog for cluster 4434\n",
+      "Found 1408 total galaxies in catalog for cluster 3939\n",
+      "Found 4123 total galaxies in catalog for cluster 6139\n",
+      "Found 1196 total galaxies in catalog for cluster 4709\n",
+      "Found 3401 total galaxies in catalog for cluster 7121\n",
+      "Found 2874 total galaxies in catalog for cluster 8547\n",
+      "Found 448 total galaxies in catalog for cluster 8685\n",
+      "Found 354 total galaxies in catalog for cluster 8995\n",
+      "Found 3085 total galaxies in catalog for cluster 7698\n",
+      "Found 5985 total galaxies in catalog for cluster 10999\n",
+      "Found 444 total galaxies in catalog for cluster 9029\n",
+      "Found 996 total galaxies in catalog for cluster 9429\n",
+      "Found 2072 total galaxies in catalog for cluster 10146\n",
+      "Found 2695 total galaxies in catalog for cluster 14476\n",
+      "Found 119 total galaxies in catalog for cluster 8395\n",
+      "Found 3612 total galaxies in catalog for cluster 16657\n",
+      "Found 2732 total galaxies in catalog for cluster 13039\n",
+      "Found 2714 total galaxies in catalog for cluster 15382\n",
+      "Found 1343 total galaxies in catalog for cluster 17011\n",
+      "Found 6019 total galaxies in catalog for cluster 8523\n",
+      "Found 1038 total galaxies in catalog for cluster 11300\n",
+      "Found 6292 total galaxies in catalog for cluster 17462\n",
+      "Found 1520 total galaxies in catalog for cluster 13025\n",
+      "Found 2090 total galaxies in catalog for cluster 21385\n",
+      "Found 2591 total galaxies in catalog for cluster 20888\n",
+      "Found 3139 total galaxies in catalog for cluster 21815\n",
+      "Found 528 total galaxies in catalog for cluster 7594\n",
+      "Found 363 total galaxies in catalog for cluster 21425\n",
+      "Found 7786 total galaxies in catalog for cluster 30553\n",
+      "Found 1777 total galaxies in catalog for cluster 11651\n",
+      "Found 2526 total galaxies in catalog for cluster 27728\n",
+      "Found 2665 total galaxies in catalog for cluster 30757\n",
+      "Found 5370 total galaxies in catalog for cluster 32634\n",
+      "Found 3080 total galaxies in catalog for cluster 33429\n",
+      "Found 1942 total galaxies in catalog for cluster 15656\n",
+      "Found 662 total galaxies in catalog for cluster 29784\n",
+      "Found 1912 total galaxies in catalog for cluster 22630\n",
+      "Found 942 total galaxies in catalog for cluster 17777\n",
+      "Found 2126 total galaxies in catalog for cluster 33569\n",
+      "Found 1969 total galaxies in catalog for cluster 39996\n",
+      "Found 3464 total galaxies in catalog for cluster 37007\n",
+      "Found 4503 total galaxies in catalog for cluster 25937\n",
+      "Found 1258 total galaxies in catalog for cluster 24386\n",
+      "Found 4662 total galaxies in catalog for cluster 44685\n",
+      "Found 2258 total galaxies in catalog for cluster 48725\n",
+      "Found 1761 total galaxies in catalog for cluster 27260\n",
+      "Found 1011 total galaxies in catalog for cluster 35316\n",
+      "Found 872 total galaxies in catalog for cluster 59428\n",
+      "Found 1944 total galaxies in catalog for cluster 25976\n",
+      "Found 2708 total galaxies in catalog for cluster 31596\n",
+      "Found 5886 total galaxies in catalog for cluster 26346\n",
+      "Found 2469 total galaxies in catalog for cluster 36608\n",
+      "Found 3301 total galaxies in catalog for cluster 26938\n",
+      "Found 1179 total galaxies in catalog for cluster 74483\n",
+      "Found 2586 total galaxies in catalog for cluster 62882\n",
+      "Found 366 total galaxies in catalog for cluster 25770\n",
+      "Found 280 total galaxies in catalog for cluster 59302\n",
+      "Found 776 total galaxies in catalog for cluster 53335\n",
+      "Found 342 total galaxies in catalog for cluster 23919\n",
+      "Found 3197 total galaxies in catalog for cluster 16567\n",
+      "Found 2110 total galaxies in catalog for cluster 41321\n",
+      "Found 368 total galaxies in catalog for cluster 52141\n",
+      "Found 2288 total galaxies in catalog for cluster 52451\n",
+      "Found 1273 total galaxies in catalog for cluster 51057\n",
+      "Found 1986 total galaxies in catalog for cluster 14210\n",
+      "Found 2256 total galaxies in catalog for cluster 33410\n",
+      "Found 2978 total galaxies in catalog for cluster 78132\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean_ds = run_different_options(True, 'ztrue', 'ztrue', 'mean_ds', True)\n",
+    "mode_ds = run_different_options(True, 'zmode', 'zmode', 'mode_ds', True)\n",
+    "pdf_ds = run_different_options(True, 'pdf', 'pdf', 'pdf_ds', True)\n",
+    "pdf_ds_shape = run_different_options(True, 'pdf', 'pdf', 'pdf_ds_shape',True, True)\n",
+    "mean_gamma = run_different_options(False, 'zmean', 'zmean', 'mean_gamma', False)\n",
+    "mode_gamma = run_different_options(False, 'zmode', 'zmode', 'mode_gamma', False)\n",
+    "pdf_gamma = run_different_options(False, 'pdf', 'pdf', 'pdf_gamma', True)\n",
+    "\n",
+    "run_options = ['mean_ds', 'mode_ds', 'pdf_ds', 'pdf_ds_shape', 'mean_gamma', 'mode_gamma', 'pdf_gamma']\n",
+    "all_runs_names = [mean_ds, mode_ds, pdf_ds,pdf_ds_shape, mean_gamma, mode_gamma, pdf_gamma]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "bfdc1eb8-4617-4848-b40d-85a3fdd2d6be",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# mean_ds = run_different_options(True, 'zmean', 'zmean', 'mean_ds', True)\n",
+    "# mode_ds = run_different_options(True, 'zmode', 'zmode', 'mode_ds', True)\n",
+    "# pdf_ds = run_different_options(True, 'pdf', 'pdf', 'pdf_ds', True)\n",
+    "# pdf_ds_shape = run_different_options(True, 'pdf', 'pdf', 'pdf_ds_shape',True, True)\n",
+    "# mean_gamma = run_different_options(False, 'zmean', 'zmean', 'mean_gamma', False)\n",
+    "# mode_gamma = run_different_options(False, 'zmode', 'zmode', 'mode_gamma', False)\n",
+    "# pdf_gamma = run_different_options(False, 'pdf', 'pdf', 'pdf_gamma', True)\n",
+    "\n",
+    "# run_options = ['mean_ds', 'mode_ds', 'pdf_ds', 'pdf_ds_shape', 'mean_gamma', 'mode_gamma', 'pdf_gamma']\n",
+    "# all_runs_names = [mean_ds, mode_ds, pdf_ds,pdf_ds_shape, mean_gamma, mode_gamma, pdf_gamma]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7eb7d1e3-186b-42b8-ad4f-d96b59781a36",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Exploring the index\n",
+    "\n",
+    "To avoid making lots and lots of copies of the data, this stage has not made a catalog, but instead made an index into the other catalogs, and stored only the relevant weight.\n",
+    "\n",
+    "We have a helper class which is designed to match up all the different catalogs that go into this and collect the results for each cluster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d14189ad-0523-4603-8ae2-e5cf44f2da34",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def run_combined_cluster_per_name(name):\n",
+    "    ccc = extensions.CombinedClusterCatalog(\n",
+    "        shear_catalog=f\"{my_txpipe_dir}/data/example/inputs/metadetect_shear_catalog.hdf5\",\n",
+    "        shear_tomography_catalog=f\"{my_txpipe_dir}/data/example/outputs_metadetect/shear_tomography_catalog.hdf5\",\n",
+    "        cluster_catalog=f\"{my_txpipe_dir}/data/example/inputs/cluster_catalog.hdf5\",\n",
+    "        cluster_shear_catalogs=name,\n",
+    "        source_photoz_pdfs=f\"{my_txpipe_dir}/data/example/inputs/photoz_pdfs.hdf5\",\n",
+    "    )\n",
+    "    print(name)\n",
+    "    return ccc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "9279a694-03c0-4222-b297-aef4c44823c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/my_cluster_shear_catalog_mean_ds.hdf5\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/my_cluster_shear_catalog_mode_ds.hdf5\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/my_cluster_shear_catalog_pdf_ds.hdf5\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/my_cluster_shear_catalog_pdf_ds_shape.hdf5\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/my_cluster_shear_catalog_mean_gamma.hdf5\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/my_cluster_shear_catalog_mode_gamma.hdf5\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/my_cluster_shear_catalog_pdf_gamma.hdf5\n",
+      "[<txpipe.extensions.cluster_counts.sources_select_compute.CombinedClusterCatalog object at 0x7f19b01e3e60>, <txpipe.extensions.cluster_counts.sources_select_compute.CombinedClusterCatalog object at 0x7f19ad0c5b50>, <txpipe.extensions.cluster_counts.sources_select_compute.CombinedClusterCatalog object at 0x7f19ad042de0>, <txpipe.extensions.cluster_counts.sources_select_compute.CombinedClusterCatalog object at 0x7f19ad042870>, <txpipe.extensions.cluster_counts.sources_select_compute.CombinedClusterCatalog object at 0x7f19a7dd4830>, <txpipe.extensions.cluster_counts.sources_select_compute.CombinedClusterCatalog object at 0x7f19a7dd42c0>, <txpipe.extensions.cluster_counts.sources_select_compute.CombinedClusterCatalog object at 0x7f19a7dc9d30>]\n"
+     ]
+    }
+   ],
+   "source": [
+    "combined_list = [run_combined_cluster_per_name(name[0]) for name in all_runs_names]\n",
+    "print(combined_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "dae202ed-1843-42c6-93fa-b2d486cbd4fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bg_cat_list = [ccc.get_background_shear_catalog(0) for ccc in combined_list]\n",
+    "all_runs_names = [mean_ds, mode_ds, pdf_ds, pdf_ds_shape, mean_gamma, mode_gamma, pdf_gamma]\n",
+    "labels = ['zmean_ds', 'zmode_ds', 'zpdf_ds', 'pdf_ds_shape','zmean_gamma', 'zmode_gamma', 'zpdf_gamma']\n",
+    "for i,bg_cat in enumerate(bg_cat_list):\n",
+    "    plt.scatter(bg_cat['distance_arcmin'] ,bg_cat['tangential_comp'], label = labels[i])\n",
+    "    plt.ylabel('Tangential Shear Component')\n",
+    "    plt.xlabel('Distance from Cluster (arcmin)')\n",
+    "    plt.legend()\n",
+    "    if i == 3:\n",
+    "        plt.show()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2616e035-a282-4f5e-8cda-85fa426bddd4",
+   "metadata": {},
+   "source": [
+    "A also the shear catalog associated with that cluster, again by index, in the CLMM data format:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75ffed91",
+   "metadata": {},
+   "source": [
+    "## Binning Cluster Catalog"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f999eeea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Options for this pipeline and their defaults:\n",
+      "{'zedge': [0.2, 0.4, 0.6, 0.8, 1.0], 'richedge': [5.0, 10.0, 20.0], 'initial_size': 100000, 'chunk_rows': 100000}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Options for this pipeline and their defaults:\")\n",
+    "print(extensions.CLClusterBinningRedshiftRichness.config_options)\n",
+    "step3 = extensions.CLClusterBinningRedshiftRichness.make_stage(\n",
+    "cluster_catalog=f\"{my_txpipe_dir}/data/example/inputs/cluster_catalog.hdf5\",\n",
+    "richedge = [1, 80],\n",
+    "cluster_catalog_tomography=f\"{my_txpipe_dir}/data/example/outputs_shear_catalogs/cluster_catalog_tomography.hdf5\",\n",
+    ")\n",
+    "step3.run()\n",
+    "step3.finalize()\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa3b0aca-43ff-45ce-8a64-ee95ade439b7",
+   "metadata": {},
+   "source": [
+    "## Making the Ensemble Profile\n",
+    "\n",
+    "We have also propagated the information about which profile we are computing, delta sigma or reduced shear in this stage. we see `('profile_type', 'reduced_shear')` in the metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "b452e198",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radius_bins = 10\n",
+    "def run_different_options_ensemble(delta_sigma, name, name2, units):\n",
+    "    print(name)\n",
+    "    step4 = extensions.CLClusterEnsembleProfiles.make_stage(\n",
+    "        shear_catalog=f\"{my_txpipe_dir}/data/example/inputs/metadetect_shear_catalog.hdf5\",\n",
+    "        fiducial_cosmology=f\"{my_txpipe_dir}/data/fiducial_cosmology.yml\",\n",
+    "        cluster_shear_catalogs=name,\n",
+    "        cluster_catalog_tomography = f\"{my_txpipe_dir}/data/example/outputs_shear_catalogs/cluster_catalog_tomography.hdf5\",\n",
+    "        r_max = 5.0,\n",
+    "        output_dir=f\"{my_txpipe_dir}/data/example/outputs_shear_catalogs/\",\n",
+    "        #config=\"examples/metadetect/config.yml\",  \n",
+    "        #max_radius=10.0,\n",
+    "        cluster_profiles = f\"{my_txpipe_dir}/data/example/outputs_shear_catalogs/cluster_profiles_{name2}.pkl\",\n",
+    "        units=units,\n",
+    "        angle_arcmin_min=5,\n",
+    "        angle_arcmin_max=30,\n",
+    "        nbins=radius_bins,\n",
+    "    )\n",
+    "    step4.run()\n",
+    "    step4.finalize()\n",
+    "    final_name = f\"{my_txpipe_dir}/data/example/outputs_shear_catalogs/cluster_profiles_{name2}.pkl\"\n",
+    "    return final_name\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "68294a04-66c5-4014-91b7-367142546f4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'bin_zbin_0_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.4), 'z_min': np.float64(0.2)}, 'n_cl': 8, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a77507a0>, 'profile_type': 'delta_sigma'}, 'bin_zbin_1_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.6), 'z_min': np.float64(0.4)}, 'n_cl': 28, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a7753380>, 'profile_type': 'delta_sigma'}, 'bin_zbin_2_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.8), 'z_min': np.float64(0.6)}, 'n_cl': 21, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a77537a0>, 'profile_type': 'delta_sigma'}, 'bin_zbin_3_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(1.0), 'z_min': np.float64(0.8)}, 'n_cl': 11, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a7750920>, 'profile_type': 'delta_sigma'}}\n",
+      "<class 'dict'>\n",
+      "dict_keys(['bin_zbin_0_richbin_0', 'bin_zbin_1_richbin_0', 'bin_zbin_2_richbin_0', 'bin_zbin_3_richbin_0'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<b>GCData</b>\n",
+       "<br> <b>defined by:</b> cosmo='CCLCosmology(H0=71.0, Omega_dm0=0.2199999, Omega_b0=0.0448, Omega_k0=0.0)', bin_units='mpc', radius_min=[0.2        0.27594593 ... 2.6265278  3.62389832], radius_max=[0.27594593 0.38073079 ... 3.62389832 5.        ]\n",
+       "<br> <b>with columns:</b> cluster_id, ra, dec, z, radius, tangential_comp, cross_comp, W_l\n",
+       "<br> 8 objects\n",
+       "<br> \n",
+       "<table id=\"table139748160548128\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>cluster_id</th><th>ra</th><th>dec</th><th>z</th><th>radius</th><th>tangential_comp</th><th>cross_comp</th><th>W_l</th></tr></thead>\n",
+       "<thead><tr><th>str5</th><th>float64</th><th>float64</th><th>float64</th><th>float64[10]</th><th>float64[10]</th><th>float64[10]</th><th>float64[10]</th></tr></thead>\n",
+       "<tr><td>6139</td><td>60.78565382938074</td><td>-30.88821992888556</td><td>0.33334264159202576</td><td>0.26613622041729446 .. 4.3238536847487845</td><td>-127382683091109.69 .. 14893298882430.727</td><td>-428539658268248.9 .. -33753536423602.83</td><td>3.0620647902832833e-31 .. 6.438473618718639e-29</td></tr>\n",
+       "<tr><td>10999</td><td>60.82553784920792</td><td>-30.51639368681256</td><td>0.3490130603313446</td><td>0.0 .. 4.290261960164885</td><td>0.0 .. -47994007048813.39</td><td>0.0 .. 15740953390154.602</td><td>0.0 .. 1.0968589954220527e-28</td></tr>\n",
+       "<tr><td>8523</td><td>60.48861752387501</td><td>-30.685130453859824</td><td>0.3723301589488983</td><td>0.24232813155900454 .. 4.358392244380749</td><td>83285341970697.34 .. -7586386351191.212</td><td>319420308457640.25 .. -23637792674743.32</td><td>3.8137836394076364e-31 .. 1.253997386405646e-28</td></tr>\n",
+       "<tr><td>17462</td><td>60.90715205643431</td><td>-30.24513872352905</td><td>0.2546227276325226</td><td>0.2489519314194748 .. 4.333180858932144</td><td>-58437991991323.17 .. 11934595817078.262</td><td>325196591247647.5 .. -39097583182945.11</td><td>7.166867884725944e-31 .. 1.0409666408158422e-28</td></tr>\n",
+       "<tr><td>30553</td><td>60.745430693132285</td><td>-30.722831995395694</td><td>0.23431362211704254</td><td>0.24085341216150016 .. 4.308890955136449</td><td>423947551992797.6 .. -20956913283666.07</td><td>-392021282972053.4 .. -28840829347100.742</td><td>1.1407795885039048e-30 .. 1.2867122936722198e-28</td></tr>\n",
+       "<tr><td>32634</td><td>60.13513592278265</td><td>-30.18630075837688</td><td>0.28674596548080444</td><td>0.238969451237102 .. 4.334249240929135</td><td>346677479789766.25 .. 37400388142697.46</td><td>616135024819341.0 .. -18907277869872.54</td><td>8.889566916615867e-31 .. 7.316669589242403e-29</td></tr>\n",
+       "<tr><td>25937</td><td>60.65087062021594</td><td>-30.035446420971837</td><td>0.344936341047287</td><td>0.2524364573214339 .. 4.344836997581819</td><td>-160001833659904.75 .. -66452687525606.88</td><td>176819019087057.03 .. 5091502654421.0</td><td>5.362465323809861e-31 .. 8.404860665340643e-29</td></tr>\n",
+       "<tr><td>26346</td><td>60.32661050735002</td><td>-30.939506455543473</td><td>0.24044087529182434</td><td>0.22143396975490834 .. 4.312027365104646</td><td>-1066956333723746.0 .. 52940670577613.97</td><td>125221986038851.45 .. 41431273626923.81</td><td>4.370825702158816e-31 .. 9.40226989251112e-29</td></tr>\n",
+       "</table></div>"
+      ],
+      "text/plain": [
+       "GCData(cosmo='CCLCosmology(H0=71.0, Omega_dm0=0.2199999, Omega_b0=0.0448, Omega_k0=0.0)', bin_units='mpc', radius_min=[0.2        0.27594593 ... 2.6265278  3.62389832], radius_max=[0.27594593 0.38073079 ... 3.62389832 5.        ], columns: cluster_id, ra, dec, z, radius, tangential_comp, cross_comp, W_l)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "file_path = \"/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/my_cluster_shear_catalog_mean_ds.hdf5\"\n",
+    "file_path = \"/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_profiles_0.pkl\"\n",
+    "\n",
+    "\n",
+    "import pickle\n",
+    "\n",
+    "with open(file_path, \"rb\") as f:\n",
+    "    data = pickle.load(f)\n",
+    "    \n",
+    "print(data)\n",
+    "print(type(data))\n",
+    "print(data.keys())\n",
+    "clmm_ensemble = data[\"bin_zbin_0_richbin_0\"][\"clmm_cluster_ensemble\"]\n",
+    "display(clmm_ensemble.data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "df5e4d5c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/my_cluster_shear_catalog_mean_ds.hdf5\n",
+      "[0.2        0.27594593 0.38073079 0.52530556 0.72477966 1.\n",
+      " 1.37972966 1.90365394 2.6265278  3.62389832 5.        ]\n",
+      "celestial\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.78565382938074  -30.88821992888556 ... 19.576345443725586  6139.0\n",
+      " 60.82553784920792  -30.51639368681256 ... 12.238702774047852 10999.0\n",
+      " 60.48861752387501 -30.685130453859824 ... 11.491209030151367  8523.0\n",
+      " 60.90715205643431  -30.24513872352905 ...  9.660612106323242 17462.0\n",
+      "60.745430693132285 -30.722831995395694 ...  6.640127182006836 30553.0\n",
+      " 60.13513592278265  -30.18630075837688 ...  6.533342361450195 32634.0\n",
+      " 60.65087062021594 -30.035446420971837 ...  6.716402053833008 25937.0\n",
+      " 60.32661050735002 -30.939506455543473 ...  6.350282669067383 26346.0\n",
+      "Ncluster 8\n",
+      "delta_sigma\n",
+      "For cluster 6139.0 at z= 0.33334264159202576 with n_source =  4680 theta_max is 35.03023710694247  arcmin = 9.998842434359426 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 10999.0 at z= 0.3490130603313446 with n_source =  6780 theta_max is 33.979436474340474  arcmin = 9.999808317244387 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 8523.0 at z= 0.3723301589488983 with n_source =  7041 theta_max is 32.58250761415833  arcmin = 9.999955820642535 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 17462.0 at z= 0.2546227276325226 with n_source =  6914 theta_max is 42.37073978575504  arcmin = 9.998979828146442 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 30553.0 at z= 0.23431362211704254 with n_source =  8544 theta_max is 45.089209135393354  arcmin = 9.999141202994123 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 32634.0 at z= 0.28674596548080444 with n_source =  5920 theta_max is 38.87511557016056  arcmin = 9.99920804366955 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 25937.0 at z= 0.344936341047287 with n_source =  5029 theta_max is 34.23641244979176  arcmin = 9.997569125090758 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 26346.0 at z= 0.24044087529182434 with n_source =  6473 theta_max is 44.19339532919497  arcmin = 9.99314759989732 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.15338004389374  -30.84990249476992 ... 18.844106674194336  4434.0\n",
+      "60.189275063181576 -30.261930359760694 ... 11.216619491577148  7121.0\n",
+      "60.162780713167805 -30.170345889294776 ... 10.896265029907227  8547.0\n",
+      " 60.12763083127777 -30.337487070312562 ... 14.877813339233398  7698.0\n",
+      "60.125099176098004  -30.74426694177359 ... 10.484380722045898 10146.0\n",
+      " 60.73839715022432  -30.24290857660279 ... 11.109834671020508 14476.0\n",
+      " 60.48457755028719 -30.793019321280497 ...  8.928373336791992 16657.0\n",
+      "60.054429554416735  -30.35999306037265 ...  8.974138259887695 13039.0\n",
+      " 60.74426729449158 -30.770085575179923 ...  8.791078567504883 15382.0\n",
+      " 60.98354871793877 -30.996867303772337 ...  7.753740310668945 17011.0\n",
+      "               ...                 ... ...                ...     ...\n",
+      " 60.77015429510177  -30.34177092134464 ...  6.640127182006836 37007.0\n",
+      " 60.77792158728647 -30.402391010530195 ...  5.953653335571289 44685.0\n",
+      " 60.03038636114792  -30.54048705270219 ...   8.50123405456543 25976.0\n",
+      "60.259817099939376  -30.82307688704441 ...  7.555425643920898 31596.0\n",
+      "60.881889055158645 -30.264969356070143 ...  5.312944412231445 26938.0\n",
+      " 60.19229962876435  -30.93397045748159 ...  5.785848617553711 62882.0\n",
+      " 60.71050096971124 -30.749563544549957 ...  5.175649642944336 16567.0\n",
+      "60.165975063232715 -30.665596607330865 ...  5.968908309936523 41321.0\n",
+      " 60.88425228697305 -30.334392706687627 ...  5.496004104614258 52451.0\n",
+      " 60.52944165719066 -30.104073019519454 ...  5.404474258422852 78132.0\n",
+      "Length = 28 rows\n",
+      "Ncluster 28\n",
+      "delta_sigma\n",
+      "For cluster 4434.0 at z= 0.5422797799110413 with n_source =  2298 theta_max is 26.1872535800939  arcmin = 9.994276984385321 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 7121.0 at z= 0.46572214365005493 with n_source =  4205 theta_max is 28.447758830156975  arcmin = 9.99733165689564 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 8547.0 at z= 0.4626827538013458 with n_source =  3553 theta_max is 28.55885796955221  arcmin = 9.998997052364723 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 7698.0 at z= 0.48154547810554504 with n_source =  3888 theta_max is 27.921476871660843  arcmin = 9.998709945548446 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 10146.0 at z= 0.548952579498291 with n_source =  2615 theta_max is 26.02386171468891  arcmin = 9.994331559874839 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 14476.0 at z= 0.5732821822166443 with n_source =  3499 theta_max is 25.47724399543762  arcmin = 9.999632475186491 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 16657.0 at z= 0.48545151948928833 with n_source =  4506 theta_max is 27.798966549407083  arcmin = 9.999640982054911 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 13039.0 at z= 0.48073717951774597 with n_source =  3474 theta_max is 27.94645753228624  arcmin = 9.998286473503612 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 15382.0 at z= 0.5205168724060059 with n_source =  3375 theta_max is 26.765613619605304  arcmin = 9.998894232587556 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 17011.0 at z= 0.4551392197608948 with n_source =  1637 theta_max is 28.827830627441873  arcmin = 9.99842353409287 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 13025.0 at z= 0.5495550036430359 with n_source =  1911 theta_max is 26.016429127595355  arcmin = 9.99706449320895 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 20888.0 at z= 0.5699324607849121 with n_source =  3279 theta_max is 25.547593749801187  arcmin = 9.99822198804875 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 11651.0 at z= 0.5927656888961792 with n_source =  2346 theta_max is 25.06481983291806  arcmin = 9.999202905782296 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 33429.0 at z= 0.5001161694526672 with n_source =  3916 theta_max is 27.346608524448584  arcmin = 9.999024627062909 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 15656.0 at z= 0.5939760208129883 with n_source =  2534 theta_max is 25.036518600320825  arcmin = 9.99769940289408 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 22630.0 at z= 0.5922470092773438 with n_source =  2527 theta_max is 25.075576371321915  arcmin = 9.999285256639787 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 33569.0 at z= 0.5305519104003906 with n_source =  2760 theta_max is 26.499531713839232  arcmin = 9.999442219558453 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 39996.0 at z= 0.43162813782691956 with n_source =  2483 theta_max is 29.738423940141566  arcmin = 9.998952770008419 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 37007.0 at z= 0.5187186598777771 with n_source =  4301 theta_max is 26.80268821810453  arcmin = 9.994389466739195 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 44685.0 at z= 0.4375304579734802 with n_source =  5535 theta_max is 29.495156722410957  arcmin = 9.997197969941768 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 25976.0 at z= 0.5719814300537109 with n_source =  2451 theta_max is 25.504033876695342  arcmin = 9.998922551853072 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 31596.0 at z= 0.48342546820640564 with n_source =  3392 theta_max is 27.86200006246653  arcmin = 9.999071823971107 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 26938.0 at z= 0.45206454396247864 with n_source =  4003 theta_max is 28.930166380733546  arcmin = 9.994704681462368 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 62882.0 at z= 0.41000840067863464 with n_source =  3191 theta_max is 30.670825674905267  arcmin = 9.998156030222507 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 16567.0 at z= 0.49412333965301514 with n_source =  3983 theta_max is 27.510533005748172  arcmin = 9.9929605554029 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 41321.0 at z= 0.5961660146713257 with n_source =  2756 theta_max is 24.980703463623314  arcmin = 9.993012947024907 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 52451.0 at z= 0.5932099223136902 with n_source =  3003 theta_max is 25.057414366942734  arcmin = 9.999846792667018 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 78132.0 at z= 0.5301636457443237 with n_source =  3861 theta_max is 26.5061278533721  arcmin = 9.99810456066382 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.24684072721323 -30.234968978217807 ...  50.26935577392578   827.0\n",
+      " 60.33037603819723 -30.155166403646447 ... 31.856599807739258  1985.0\n",
+      " 60.19798610694177 -30.288558532393417 ... 13.748945236206055  1632.0\n",
+      " 60.46305004484695   -30.4771453673221 ... 26.303789138793945  2678.0\n",
+      " 60.31124209468067 -30.644137931021007 ...   21.7120418548584  3939.0\n",
+      " 60.40601451137022 -30.889199256471997 ...  21.16286277770996  4709.0\n",
+      " 60.07871737759909  -30.87961443163394 ... 13.886240005493164  9429.0\n",
+      "60.326126517158364  -30.91291736913871 ...  7.997819900512695 11300.0\n",
+      "  60.3906485346257  -30.32504676069136 ...  8.272409439086914 21385.0\n",
+      " 60.56281963421085  -30.52347796810624 ...  7.341856002807617 21815.0\n",
+      " 60.42104325395154  -30.74695605874455 ...   8.36393928527832 27728.0\n",
+      " 60.29059774704672 -30.590332518294673 ...  7.936800003051758 30757.0\n",
+      " 60.18626761923588 -30.167117729820564 ... 10.881010055541992 17777.0\n",
+      " 60.13800305274047 -30.072928539498086 ...  8.074094772338867 24386.0\n",
+      " 60.29901520853626  -30.73384245958468 ...  6.045183181762695 48725.0\n",
+      " 60.32954746480673  -30.24791761027524 ...  6.502832412719727 27260.0\n",
+      " 60.47484833897218 -30.719415767017235 ...  6.197732925415039 36608.0\n",
+      "60.898622679672854 -30.329981860471364 ...  7.997819900512695 74483.0\n",
+      " 60.87221651027329  -30.00554561545366 ...  6.075693130493164 51057.0\n",
+      "60.702276976193374  -30.33353933696586 ...  7.067266464233398 14210.0\n",
+      " 60.63490889446615  -30.21708448572835 ...   6.65538215637207 33410.0\n",
+      "Ncluster 21\n",
+      "delta_sigma\n",
+      "For cluster 827.0 at z= 0.6460187435150146 with n_source =  2612 theta_max is 24.083637522549868  arcmin = 9.998637368826127 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 1985.0 at z= 0.641009509563446 with n_source =  2579 theta_max is 24.166811437147476  arcmin = 9.998254303130016 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 1632.0 at z= 0.6587467193603516 with n_source =  2522 theta_max is 23.877451698934042  arcmin = 9.998940401799496 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 2678.0 at z= 0.6254668831825256 with n_source =  4021 theta_max is 24.438905116690172  arcmin = 9.99868811505508 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 3939.0 at z= 0.7766682505607605 with n_source =  2146 theta_max is 22.34393749348601  arcmin = 9.999597122513025 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 4709.0 at z= 0.7267194390296936 with n_source =  1701 theta_max is 22.910794444281233  arcmin = 9.995890587192537 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 9429.0 at z= 0.6608030200004578 with n_source =  1352 theta_max is 23.847046730197572  arcmin = 9.999836750321784 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 11300.0 at z= 0.7256391644477844 with n_source =  1487 theta_max is 22.919482517107213  arcmin = 9.993773840158706 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 21385.0 at z= 0.7168400287628174 with n_source =  3004 theta_max is 23.045229477686867  arcmin = 9.999666115556877 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 21815.0 at z= 0.6154448986053467 with n_source =  4175 theta_max is 24.624993736016656  arcmin = 9.999830831727236 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 27728.0 at z= 0.608765184879303 with n_source =  3331 theta_max is 24.74517607120174  arcmin = 9.997453996283218 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 30757.0 at z= 0.6046680808067322 with n_source =  3555 theta_max is 24.82604391962198  arcmin = 9.998245594930532 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 17777.0 at z= 0.7892001271247864 with n_source =  1443 theta_max is 22.21288881987206  arcmin = 9.999094384000774 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 24386.0 at z= 0.6216957569122314 with n_source =  1670 theta_max is 24.504429051067234  arcmin = 9.997613796221609 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 48725.0 at z= 0.6061629056930542 with n_source =  2980 theta_max is 24.775944353521716  arcmin = 9.989710937194452 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 27260.0 at z= 0.7166086435317993 with n_source =  2547 theta_max is 23.03283824094242  arcmin = 9.99298964682421 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 36608.0 at z= 0.6384199261665344 with n_source =  3239 theta_max is 24.21108286930276  arcmin = 9.998331913323113 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 74483.0 at z= 0.7653682231903076 with n_source =  1837 theta_max is 22.452432935925334  arcmin = 9.99364403510921 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 51057.0 at z= 0.6163065433502197 with n_source =  1669 theta_max is 24.573151826851365  arcmin = 9.985279322137343 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 14210.0 at z= 0.7193936705589294 with n_source =  2896 theta_max is 23.013038083654518  arcmin = 9.999983513558279 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 33410.0 at z= 0.6530510187149048 with n_source =  3094 theta_max is 23.96410763697689  arcmin = 9.9969556650315 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      "60.486600234305534 -30.987929801464272 ... 25.281705856323242  2453.0\n",
+      " 60.86101302191349 -30.208113720652296 ... 19.820425033569336  4643.0\n",
+      " 60.93930110680046 -30.233165835592462 ... 11.323404312133789  8995.0\n",
+      " 60.92817627490327 -30.000803300701442 ... 12.421762466430664  9029.0\n",
+      " 60.23010184176201 -30.827430134550788 ...  6.579107284545898 29784.0\n",
+      " 60.55483814763497 -30.280744807372972 ...  6.716402053833008 35316.0\n",
+      "   60.598383979704  -30.83078557952782 ...   5.08411979675293 59428.0\n",
+      " 60.68142437164906 -30.915916676909713 ...  5.221414566040039 25770.0\n",
+      " 60.96024036136846  -30.14627416305006 ...  5.938398361206055 59302.0\n",
+      "60.719160516887946  -30.12308648557638 ...  5.557024002075195 53335.0\n",
+      " 60.01938474788308  -30.82153353230635 ...  5.602788925170898 52141.0\n",
+      "Ncluster 11\n",
+      "delta_sigma\n",
+      "For cluster 2453.0 at z= 0.8156213760375977 with n_source =  1017 theta_max is 21.932662763401424  arcmin = 9.98853154548119 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 4643.0 at z= 0.8727886080741882 with n_source =  1265 theta_max is 21.453607753366903  arcmin = 9.991678261250547 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 8995.0 at z= 0.9868596792221069 with n_source =  765 theta_max is 20.728695264261653  arcmin = 9.999708646378417 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 9029.0 at z= 0.8443910479545593 with n_source =  753 theta_max is 21.64701404063508  arcmin = 9.974700496018222 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 29784.0 at z= 0.8731144070625305 with n_source =  1140 theta_max is 21.458435834852434  arcmin = 9.995101965186793 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 35316.0 at z= 0.8984475135803223 with n_source =  1775 theta_max is 21.272049270663832  arcmin = 9.99604914606074 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 59428.0 at z= 0.8610619306564331 with n_source =  1463 theta_max is 21.557310864614582  arcmin = 9.996848270780752 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 25770.0 at z= 0.9812018275260925 with n_source =  807 theta_max is 20.749777509917138  arcmin = 9.995003783198838 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 59302.0 at z= 0.997758150100708 with n_source =  623 theta_max is 20.665568664621528  arcmin = 9.997175532563869 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 53335.0 at z= 0.8671668767929077 with n_source =  1313 theta_max is 21.493491388302335  arcmin = 9.989793703716519 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 52141.0 at z= 0.9101417064666748 with n_source =  634 theta_max is 21.164682764073333  arcmin = 9.98408272181414 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/my_cluster_shear_catalog_mode_ds.hdf5\n",
+      "[0.2        0.27594593 0.38073079 0.52530556 0.72477966 1.\n",
+      " 1.37972966 1.90365394 2.6265278  3.62389832 5.        ]\n",
+      "celestial\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.78565382938074  -30.88821992888556 ... 19.576345443725586  6139.0\n",
+      " 60.82553784920792  -30.51639368681256 ... 12.238702774047852 10999.0\n",
+      " 60.48861752387501 -30.685130453859824 ... 11.491209030151367  8523.0\n",
+      " 60.90715205643431  -30.24513872352905 ...  9.660612106323242 17462.0\n",
+      "60.745430693132285 -30.722831995395694 ...  6.640127182006836 30553.0\n",
+      " 60.13513592278265  -30.18630075837688 ...  6.533342361450195 32634.0\n",
+      " 60.65087062021594 -30.035446420971837 ...  6.716402053833008 25937.0\n",
+      " 60.32661050735002 -30.939506455543473 ...  6.350282669067383 26346.0\n",
+      "Ncluster 8\n",
+      "delta_sigma\n",
+      "For cluster 6139.0 at z= 0.33334264159202576 with n_source =  4496 theta_max is 35.03023710694247  arcmin = 9.998842434359426 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 10999.0 at z= 0.3490130603313446 with n_source =  6508 theta_max is 33.979436474340474  arcmin = 9.999808317244387 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 8523.0 at z= 0.3723301589488983 with n_source =  6645 theta_max is 32.58250761415833  arcmin = 9.999955820642535 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 17462.0 at z= 0.2546227276325226 with n_source =  6793 theta_max is 42.37073978575504  arcmin = 9.998979828146442 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 30553.0 at z= 0.23431362211704254 with n_source =  8401 theta_max is 45.089209135393354  arcmin = 9.999141202994123 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 32634.0 at z= 0.28674596548080444 with n_source =  5806 theta_max is 38.87511557016056  arcmin = 9.99920804366955 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 25937.0 at z= 0.344936341047287 with n_source =  4852 theta_max is 34.23641244979176  arcmin = 9.997569125090758 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 26346.0 at z= 0.24044087529182434 with n_source =  6358 theta_max is 44.19339532919497  arcmin = 9.99314759989732 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.15338004389374  -30.84990249476992 ... 18.844106674194336  4434.0\n",
+      "60.189275063181576 -30.261930359760694 ... 11.216619491577148  7121.0\n",
+      "60.162780713167805 -30.170345889294776 ... 10.896265029907227  8547.0\n",
+      " 60.12763083127777 -30.337487070312562 ... 14.877813339233398  7698.0\n",
+      "60.125099176098004  -30.74426694177359 ... 10.484380722045898 10146.0\n",
+      " 60.73839715022432  -30.24290857660279 ... 11.109834671020508 14476.0\n",
+      " 60.48457755028719 -30.793019321280497 ...  8.928373336791992 16657.0\n",
+      "60.054429554416735  -30.35999306037265 ...  8.974138259887695 13039.0\n",
+      " 60.74426729449158 -30.770085575179923 ...  8.791078567504883 15382.0\n",
+      " 60.98354871793877 -30.996867303772337 ...  7.753740310668945 17011.0\n",
+      "               ...                 ... ...                ...     ...\n",
+      " 60.77015429510177  -30.34177092134464 ...  6.640127182006836 37007.0\n",
+      " 60.77792158728647 -30.402391010530195 ...  5.953653335571289 44685.0\n",
+      " 60.03038636114792  -30.54048705270219 ...   8.50123405456543 25976.0\n",
+      "60.259817099939376  -30.82307688704441 ...  7.555425643920898 31596.0\n",
+      "60.881889055158645 -30.264969356070143 ...  5.312944412231445 26938.0\n",
+      " 60.19229962876435  -30.93397045748159 ...  5.785848617553711 62882.0\n",
+      " 60.71050096971124 -30.749563544549957 ...  5.175649642944336 16567.0\n",
+      "60.165975063232715 -30.665596607330865 ...  5.968908309936523 41321.0\n",
+      " 60.88425228697305 -30.334392706687627 ...  5.496004104614258 52451.0\n",
+      " 60.52944165719066 -30.104073019519454 ...  5.404474258422852 78132.0\n",
+      "Length = 28 rows\n",
+      "Ncluster 28\n",
+      "delta_sigma\n",
+      "For cluster 4434.0 at z= 0.5422797799110413 with n_source =  2149 theta_max is 26.1872535800939  arcmin = 9.994276984385321 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 7121.0 at z= 0.46572214365005493 with n_source =  3903 theta_max is 28.447758830156975  arcmin = 9.99733165689564 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 8547.0 at z= 0.4626827538013458 with n_source =  3294 theta_max is 28.55885796955221  arcmin = 9.998997052364723 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 7698.0 at z= 0.48154547810554504 with n_source =  3589 theta_max is 27.921476871660843  arcmin = 9.998709945548446 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 10146.0 at z= 0.548952579498291 with n_source =  2477 theta_max is 26.02386171468891  arcmin = 9.994331559874839 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 14476.0 at z= 0.5732821822166443 with n_source =  3236 theta_max is 25.47724399543762  arcmin = 9.999632475186491 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 16657.0 at z= 0.48545151948928833 with n_source =  4237 theta_max is 27.798966549407083  arcmin = 9.999640982054911 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 13039.0 at z= 0.48073717951774597 with n_source =  3203 theta_max is 27.94645753228624  arcmin = 9.998286473503612 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 15382.0 at z= 0.5205168724060059 with n_source =  3217 theta_max is 26.765613619605304  arcmin = 9.998894232587556 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 17011.0 at z= 0.4551392197608948 with n_source =  1554 theta_max is 28.827830627441873  arcmin = 9.99842353409287 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 13025.0 at z= 0.5495550036430359 with n_source =  1814 theta_max is 26.016429127595355  arcmin = 9.99706449320895 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 20888.0 at z= 0.5699324607849121 with n_source =  3100 theta_max is 25.547586618756437  arcmin = 9.998219197266605 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 11651.0 at z= 0.5927656888961792 with n_source =  2161 theta_max is 25.055846076716676  arcmin = 9.995622971448723 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 33429.0 at z= 0.5001161694526672 with n_source =  3646 theta_max is 27.346608524448584  arcmin = 9.999024627062909 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 15656.0 at z= 0.5939760208129883 with n_source =  2372 theta_max is 25.036518600320825  arcmin = 9.99769940289408 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 22630.0 at z= 0.5922470092773438 with n_source =  2313 theta_max is 25.075576371321915  arcmin = 9.999285256639787 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 33569.0 at z= 0.5305519104003906 with n_source =  2529 theta_max is 26.499531713839232  arcmin = 9.999442219558453 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 39996.0 at z= 0.43162813782691956 with n_source =  2263 theta_max is 29.738423940141566  arcmin = 9.998952770008419 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 37007.0 at z= 0.5187186598777771 with n_source =  4094 theta_max is 26.80268821810453  arcmin = 9.994389466739195 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 44685.0 at z= 0.4375304579734802 with n_source =  5253 theta_max is 29.495156722410957  arcmin = 9.997197969941768 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 25976.0 at z= 0.5719814300537109 with n_source =  2305 theta_max is 25.504033876695342  arcmin = 9.998922551853072 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 31596.0 at z= 0.48342546820640564 with n_source =  3178 theta_max is 27.86200006246653  arcmin = 9.999071823971107 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 26938.0 at z= 0.45206454396247864 with n_source =  3770 theta_max is 28.930166380733546  arcmin = 9.994704681462368 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 62882.0 at z= 0.41000840067863464 with n_source =  2946 theta_max is 30.656882887419567  arcmin = 9.993610923864567 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 16567.0 at z= 0.49412333965301514 with n_source =  3765 theta_max is 27.52216199611479  arcmin = 9.997184684466795 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 41321.0 at z= 0.5961660146713257 with n_source =  2588 theta_max is 24.980703463623314  arcmin = 9.993012947024907 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 52451.0 at z= 0.5932099223136902 with n_source =  2760 theta_max is 25.057414366942734  arcmin = 9.999846792667018 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 78132.0 at z= 0.5301636457443237 with n_source =  3542 theta_max is 26.5061278533721  arcmin = 9.99810456066382 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.24684072721323 -30.234968978217807 ...  50.26935577392578   827.0\n",
+      " 60.33037603819723 -30.155166403646447 ... 31.856599807739258  1985.0\n",
+      " 60.19798610694177 -30.288558532393417 ... 13.748945236206055  1632.0\n",
+      " 60.46305004484695   -30.4771453673221 ... 26.303789138793945  2678.0\n",
+      " 60.31124209468067 -30.644137931021007 ...   21.7120418548584  3939.0\n",
+      " 60.40601451137022 -30.889199256471997 ...  21.16286277770996  4709.0\n",
+      " 60.07871737759909  -30.87961443163394 ... 13.886240005493164  9429.0\n",
+      "60.326126517158364  -30.91291736913871 ...  7.997819900512695 11300.0\n",
+      "  60.3906485346257  -30.32504676069136 ...  8.272409439086914 21385.0\n",
+      " 60.56281963421085  -30.52347796810624 ...  7.341856002807617 21815.0\n",
+      " 60.42104325395154  -30.74695605874455 ...   8.36393928527832 27728.0\n",
+      " 60.29059774704672 -30.590332518294673 ...  7.936800003051758 30757.0\n",
+      " 60.18626761923588 -30.167117729820564 ... 10.881010055541992 17777.0\n",
+      " 60.13800305274047 -30.072928539498086 ...  8.074094772338867 24386.0\n",
+      " 60.29901520853626  -30.73384245958468 ...  6.045183181762695 48725.0\n",
+      " 60.32954746480673  -30.24791761027524 ...  6.502832412719727 27260.0\n",
+      " 60.47484833897218 -30.719415767017235 ...  6.197732925415039 36608.0\n",
+      "60.898622679672854 -30.329981860471364 ...  7.997819900512695 74483.0\n",
+      " 60.87221651027329  -30.00554561545366 ...  6.075693130493164 51057.0\n",
+      "60.702276976193374  -30.33353933696586 ...  7.067266464233398 14210.0\n",
+      " 60.63490889446615  -30.21708448572835 ...   6.65538215637207 33410.0\n",
+      "Ncluster 21\n",
+      "delta_sigma\n",
+      "For cluster 827.0 at z= 0.6460187435150146 with n_source =  2456 theta_max is 24.083637522549868  arcmin = 9.998637368826127 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 1985.0 at z= 0.641009509563446 with n_source =  2413 theta_max is 24.166811437147476  arcmin = 9.998254303130016 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 1632.0 at z= 0.6587467193603516 with n_source =  2346 theta_max is 23.877451698934042  arcmin = 9.998940401799496 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 2678.0 at z= 0.6254668831825256 with n_source =  3782 theta_max is 24.438905116690172  arcmin = 9.99868811505508 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 3939.0 at z= 0.7766682505607605 with n_source =  1830 theta_max is 22.34393749348601  arcmin = 9.999597122513025 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 4709.0 at z= 0.7267194390296936 with n_source =  1511 theta_max is 22.910794444281233  arcmin = 9.995890587192537 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 9429.0 at z= 0.6608030200004578 with n_source =  1219 theta_max is 23.837032632611585  arcmin = 9.995637515833982 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 11300.0 at z= 0.7256391644477844 with n_source =  1321 theta_max is 22.919482517107213  arcmin = 9.993773840158706 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 21385.0 at z= 0.7168400287628174 with n_source =  2637 theta_max is 23.045229477686867  arcmin = 9.999666115556877 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 21815.0 at z= 0.6154448986053467 with n_source =  3863 theta_max is 24.624993736016656  arcmin = 9.999830831727236 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 27728.0 at z= 0.608765184879303 with n_source =  3115 theta_max is 24.74517607120174  arcmin = 9.997453996283218 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 30757.0 at z= 0.6046680808067322 with n_source =  3282 theta_max is 24.80902403286646  arcmin = 9.991391139652416 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 17777.0 at z= 0.7892001271247864 with n_source =  1247 theta_max is 22.198956488006292  arcmin = 9.992822768343544 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 24386.0 at z= 0.6216957569122314 with n_source =  1586 theta_max is 24.504429051067234  arcmin = 9.997613796221609 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 48725.0 at z= 0.6061629056930542 with n_source =  2779 theta_max is 24.775944353521716  arcmin = 9.989710937194452 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 27260.0 at z= 0.7166086435317993 with n_source =  2228 theta_max is 23.030190224008617  arcmin = 9.991840782514595 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 36608.0 at z= 0.6384199261665344 with n_source =  3056 theta_max is 24.21108286930276  arcmin = 9.998331913323113 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 74483.0 at z= 0.7653682231903076 with n_source =  1528 theta_max is 22.452432935925334  arcmin = 9.99364403510921 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 51057.0 at z= 0.6163065433502197 with n_source =  1560 theta_max is 24.571046848723043  arcmin = 9.984423966067174 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 14210.0 at z= 0.7193936705589294 with n_source =  2515 theta_max is 23.013038083654518  arcmin = 9.999983513558279 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 33410.0 at z= 0.6530510187149048 with n_source =  2800 theta_max is 23.96410763697689  arcmin = 9.9969556650315 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      "60.486600234305534 -30.987929801464272 ... 25.281705856323242  2453.0\n",
+      " 60.86101302191349 -30.208113720652296 ... 19.820425033569336  4643.0\n",
+      " 60.93930110680046 -30.233165835592462 ... 11.323404312133789  8995.0\n",
+      " 60.92817627490327 -30.000803300701442 ... 12.421762466430664  9029.0\n",
+      " 60.23010184176201 -30.827430134550788 ...  6.579107284545898 29784.0\n",
+      " 60.55483814763497 -30.280744807372972 ...  6.716402053833008 35316.0\n",
+      "   60.598383979704  -30.83078557952782 ...   5.08411979675293 59428.0\n",
+      " 60.68142437164906 -30.915916676909713 ...  5.221414566040039 25770.0\n",
+      " 60.96024036136846  -30.14627416305006 ...  5.938398361206055 59302.0\n",
+      "60.719160516887946  -30.12308648557638 ...  5.557024002075195 53335.0\n",
+      " 60.01938474788308  -30.82153353230635 ...  5.602788925170898 52141.0\n",
+      "Ncluster 11\n",
+      "delta_sigma\n",
+      "For cluster 2453.0 at z= 0.8156213760375977 with n_source =  891 theta_max is 21.932662763401424  arcmin = 9.98853154548119 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 4643.0 at z= 0.8727886080741882 with n_source =  1069 theta_max is 21.453607753366903  arcmin = 9.991678261250547 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 8995.0 at z= 0.9868596792221069 with n_source =  626 theta_max is 20.728695264261653  arcmin = 9.999708646378417 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 9029.0 at z= 0.8443910479545593 with n_source =  645 theta_max is 21.64701404063508  arcmin = 9.974700496018222 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 29784.0 at z= 0.8731144070625305 with n_source =  963 theta_max is 21.458435834852434  arcmin = 9.995101965186793 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 35316.0 at z= 0.8984475135803223 with n_source =  1532 theta_max is 21.272049270663832  arcmin = 9.99604914606074 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 59428.0 at z= 0.8610619306564331 with n_source =  1270 theta_max is 21.557310864614582  arcmin = 9.996848270780752 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 25770.0 at z= 0.9812018275260925 with n_source =  626 theta_max is 20.729156918041713  arcmin = 9.98507100711446 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 59302.0 at z= 0.997758150100708 with n_source =  514 theta_max is 20.665568664621528  arcmin = 9.997175532563869 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 53335.0 at z= 0.8671668767929077 with n_source =  1152 theta_max is 21.493491388302335  arcmin = 9.989793703716519 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 52141.0 at z= 0.9101417064666748 with n_source =  558 theta_max is 21.164682764073333  arcmin = 9.98408272181414 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/my_cluster_shear_catalog_pdf_ds.hdf5\n",
+      "[0.2        0.27594593 0.38073079 0.52530556 0.72477966 1.\n",
+      " 1.37972966 1.90365394 2.6265278  3.62389832 5.        ]\n",
+      "celestial\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.78565382938074  -30.88821992888556 ... 19.576345443725586  6139.0\n",
+      " 60.82553784920792  -30.51639368681256 ... 12.238702774047852 10999.0\n",
+      " 60.48861752387501 -30.685130453859824 ... 11.491209030151367  8523.0\n",
+      " 60.90715205643431  -30.24513872352905 ...  9.660612106323242 17462.0\n",
+      "60.745430693132285 -30.722831995395694 ...  6.640127182006836 30553.0\n",
+      " 60.13513592278265  -30.18630075837688 ...  6.533342361450195 32634.0\n",
+      " 60.65087062021594 -30.035446420971837 ...  6.716402053833008 25937.0\n",
+      " 60.32661050735002 -30.939506455543473 ...  6.350282669067383 26346.0\n",
+      "Ncluster 8\n",
+      "delta_sigma\n",
+      "For cluster 6139.0 at z= 0.33334264159202576 with n_source =  4123 theta_max is 35.03023710694247  arcmin = 9.998842434359426 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 10999.0 at z= 0.3490130603313446 with n_source =  5985 theta_max is 33.979436474340474  arcmin = 9.999808317244387 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 8523.0 at z= 0.3723301589488983 with n_source =  6019 theta_max is 32.58250761415833  arcmin = 9.999955820642535 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 17462.0 at z= 0.2546227276325226 with n_source =  6292 theta_max is 42.37073978575504  arcmin = 9.998979828146442 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 30553.0 at z= 0.23431362211704254 with n_source =  7786 theta_max is 45.089209135393354  arcmin = 9.999141202994123 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 32634.0 at z= 0.28674596548080444 with n_source =  5370 theta_max is 38.87511557016056  arcmin = 9.99920804366955 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 25937.0 at z= 0.344936341047287 with n_source =  4503 theta_max is 34.23641244979176  arcmin = 9.997569125090758 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 26346.0 at z= 0.24044087529182434 with n_source =  5886 theta_max is 44.19339532919497  arcmin = 9.99314759989732 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.15338004389374  -30.84990249476992 ... 18.844106674194336  4434.0\n",
+      "60.189275063181576 -30.261930359760694 ... 11.216619491577148  7121.0\n",
+      "60.162780713167805 -30.170345889294776 ... 10.896265029907227  8547.0\n",
+      " 60.12763083127777 -30.337487070312562 ... 14.877813339233398  7698.0\n",
+      "60.125099176098004  -30.74426694177359 ... 10.484380722045898 10146.0\n",
+      " 60.73839715022432  -30.24290857660279 ... 11.109834671020508 14476.0\n",
+      " 60.48457755028719 -30.793019321280497 ...  8.928373336791992 16657.0\n",
+      "60.054429554416735  -30.35999306037265 ...  8.974138259887695 13039.0\n",
+      " 60.74426729449158 -30.770085575179923 ...  8.791078567504883 15382.0\n",
+      " 60.98354871793877 -30.996867303772337 ...  7.753740310668945 17011.0\n",
+      "               ...                 ... ...                ...     ...\n",
+      " 60.77015429510177  -30.34177092134464 ...  6.640127182006836 37007.0\n",
+      " 60.77792158728647 -30.402391010530195 ...  5.953653335571289 44685.0\n",
+      " 60.03038636114792  -30.54048705270219 ...   8.50123405456543 25976.0\n",
+      "60.259817099939376  -30.82307688704441 ...  7.555425643920898 31596.0\n",
+      "60.881889055158645 -30.264969356070143 ...  5.312944412231445 26938.0\n",
+      " 60.19229962876435  -30.93397045748159 ...  5.785848617553711 62882.0\n",
+      " 60.71050096971124 -30.749563544549957 ...  5.175649642944336 16567.0\n",
+      "60.165975063232715 -30.665596607330865 ...  5.968908309936523 41321.0\n",
+      " 60.88425228697305 -30.334392706687627 ...  5.496004104614258 52451.0\n",
+      " 60.52944165719066 -30.104073019519454 ...  5.404474258422852 78132.0\n",
+      "Length = 28 rows\n",
+      "Ncluster 28\n",
+      "delta_sigma\n",
+      "For cluster 4434.0 at z= 0.5422797799110413 with n_source =  1803 theta_max is 26.1872535800939  arcmin = 9.994276984385321 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 7121.0 at z= 0.46572214365005493 with n_source =  3401 theta_max is 28.447758830156975  arcmin = 9.99733165689564 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 8547.0 at z= 0.4626827538013458 with n_source =  2874 theta_max is 28.551186172067684  arcmin = 9.996311010768947 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 7698.0 at z= 0.48154547810554504 with n_source =  3085 theta_max is 27.921476871660843  arcmin = 9.998709945548446 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 10146.0 at z= 0.548952579498291 with n_source =  2072 theta_max is 26.02386171468891  arcmin = 9.994331559874839 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 14476.0 at z= 0.5732821822166443 with n_source =  2695 theta_max is 25.47724399543762  arcmin = 9.999632475186491 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 16657.0 at z= 0.48545151948928833 with n_source =  3612 theta_max is 27.798966549407083  arcmin = 9.999640982054911 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 13039.0 at z= 0.48073717951774597 with n_source =  2732 theta_max is 27.94645753228624  arcmin = 9.998286473503612 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 15382.0 at z= 0.5205168724060059 with n_source =  2714 theta_max is 26.765613619605304  arcmin = 9.998894232587556 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 17011.0 at z= 0.4551392197608948 with n_source =  1343 theta_max is 28.827830627441873  arcmin = 9.99842353409287 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 13025.0 at z= 0.5495550036430359 with n_source =  1520 theta_max is 26.016429127595355  arcmin = 9.99706449320895 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 20888.0 at z= 0.5699324607849121 with n_source =  2591 theta_max is 25.547586618756437  arcmin = 9.998219197266605 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 11651.0 at z= 0.5927656888961792 with n_source =  1777 theta_max is 25.055846076716676  arcmin = 9.995622971448723 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 33429.0 at z= 0.5001161694526672 with n_source =  3080 theta_max is 27.346608524448584  arcmin = 9.999024627062909 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 15656.0 at z= 0.5939760208129883 with n_source =  1942 theta_max is 25.036518600320825  arcmin = 9.99769940289408 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 22630.0 at z= 0.5922470092773438 with n_source =  1912 theta_max is 25.075576371321915  arcmin = 9.999285256639787 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 33569.0 at z= 0.5305519104003906 with n_source =  2126 theta_max is 26.497840345030124  arcmin = 9.998803991499885 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 39996.0 at z= 0.43162813782691956 with n_source =  1969 theta_max is 29.738423940141566  arcmin = 9.998952770008419 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 37007.0 at z= 0.5187186598777771 with n_source =  3464 theta_max is 26.79864383796969  arcmin = 9.992881367630247 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 44685.0 at z= 0.4375304579734802 with n_source =  4662 theta_max is 29.495156722410957  arcmin = 9.997197969941768 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 25976.0 at z= 0.5719814300537109 with n_source =  1944 theta_max is 25.504033876695342  arcmin = 9.998922551853072 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 31596.0 at z= 0.48342546820640564 with n_source =  2708 theta_max is 27.86200006246653  arcmin = 9.999071823971107 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 26938.0 at z= 0.45206454396247864 with n_source =  3301 theta_max is 28.930166380733546  arcmin = 9.994704681462368 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 62882.0 at z= 0.41000840067863464 with n_source =  2586 theta_max is 30.656882887419567  arcmin = 9.993610923864567 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 16567.0 at z= 0.49412333965301514 with n_source =  3197 theta_max is 27.510105905700122  arcmin = 9.992805415045115 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 41321.0 at z= 0.5961660146713257 with n_source =  2110 theta_max is 24.980703463623314  arcmin = 9.993012947024907 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 52451.0 at z= 0.5932099223136902 with n_source =  2288 theta_max is 25.057414366942734  arcmin = 9.999846792667018 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 78132.0 at z= 0.5301636457443237 with n_source =  2978 theta_max is 26.5061278533721  arcmin = 9.99810456066382 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.24684072721323 -30.234968978217807 ...  50.26935577392578   827.0\n",
+      " 60.33037603819723 -30.155166403646447 ... 31.856599807739258  1985.0\n",
+      " 60.19798610694177 -30.288558532393417 ... 13.748945236206055  1632.0\n",
+      " 60.46305004484695   -30.4771453673221 ... 26.303789138793945  2678.0\n",
+      " 60.31124209468067 -30.644137931021007 ...   21.7120418548584  3939.0\n",
+      " 60.40601451137022 -30.889199256471997 ...  21.16286277770996  4709.0\n",
+      " 60.07871737759909  -30.87961443163394 ... 13.886240005493164  9429.0\n",
+      "60.326126517158364  -30.91291736913871 ...  7.997819900512695 11300.0\n",
+      "  60.3906485346257  -30.32504676069136 ...  8.272409439086914 21385.0\n",
+      " 60.56281963421085  -30.52347796810624 ...  7.341856002807617 21815.0\n",
+      " 60.42104325395154  -30.74695605874455 ...   8.36393928527832 27728.0\n",
+      " 60.29059774704672 -30.590332518294673 ...  7.936800003051758 30757.0\n",
+      " 60.18626761923588 -30.167117729820564 ... 10.881010055541992 17777.0\n",
+      " 60.13800305274047 -30.072928539498086 ...  8.074094772338867 24386.0\n",
+      " 60.29901520853626  -30.73384245958468 ...  6.045183181762695 48725.0\n",
+      " 60.32954746480673  -30.24791761027524 ...  6.502832412719727 27260.0\n",
+      " 60.47484833897218 -30.719415767017235 ...  6.197732925415039 36608.0\n",
+      "60.898622679672854 -30.329981860471364 ...  7.997819900512695 74483.0\n",
+      " 60.87221651027329  -30.00554561545366 ...  6.075693130493164 51057.0\n",
+      "60.702276976193374  -30.33353933696586 ...  7.067266464233398 14210.0\n",
+      " 60.63490889446615  -30.21708448572835 ...   6.65538215637207 33410.0\n",
+      "Ncluster 21\n",
+      "delta_sigma\n",
+      "For cluster 827.0 at z= 0.6460187435150146 with n_source =  1982 theta_max is 24.083637522549868  arcmin = 9.998637368826127 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 1985.0 at z= 0.641009509563446 with n_source =  1929 theta_max is 24.166811437147476  arcmin = 9.998254303130016 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 1632.0 at z= 0.6587467193603516 with n_source =  1887 theta_max is 23.877451698934042  arcmin = 9.998940401799496 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 2678.0 at z= 0.6254668831825256 with n_source =  3068 theta_max is 24.436233670122622  arcmin = 9.997595146245002 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 3939.0 at z= 0.7766682505607605 with n_source =  1408 theta_max is 22.34393749348601  arcmin = 9.999597122513025 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 4709.0 at z= 0.7267194390296936 with n_source =  1196 theta_max is 22.910794444281233  arcmin = 9.995890587192537 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 9429.0 at z= 0.6608030200004578 with n_source =  996 theta_max is 23.82751596758849  arcmin = 9.991646870882631 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 11300.0 at z= 0.7256391644477844 with n_source =  1038 theta_max is 22.919482517107213  arcmin = 9.993773840158706 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 21385.0 at z= 0.7168400287628174 with n_source =  2090 theta_max is 23.045229477686867  arcmin = 9.999666115556877 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 21815.0 at z= 0.6154448986053467 with n_source =  3139 theta_max is 24.624993736016656  arcmin = 9.999830831727236 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 27728.0 at z= 0.608765184879303 with n_source =  2526 theta_max is 24.74517607120174  arcmin = 9.997453996283218 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 30757.0 at z= 0.6046680808067322 with n_source =  2665 theta_max is 24.805975072899518  arcmin = 9.990163225504784 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 17777.0 at z= 0.7892001271247864 with n_source =  942 theta_max is 22.198956488006292  arcmin = 9.992822768343544 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 24386.0 at z= 0.6216957569122314 with n_source =  1258 theta_max is 24.504429051067234  arcmin = 9.997613796221609 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 48725.0 at z= 0.6061629056930542 with n_source =  2258 theta_max is 24.775944353521716  arcmin = 9.989710937194452 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 27260.0 at z= 0.7166086435317993 with n_source =  1761 theta_max is 23.030190224008617  arcmin = 9.991840782514595 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 36608.0 at z= 0.6384199261665344 with n_source =  2469 theta_max is 24.21108286930276  arcmin = 9.998331913323113 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 74483.0 at z= 0.7653682231903076 with n_source =  1179 theta_max is 22.452432935925334  arcmin = 9.99364403510921 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 51057.0 at z= 0.6163065433502197 with n_source =  1273 theta_max is 24.571046848723043  arcmin = 9.984423966067174 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 14210.0 at z= 0.7193936705589294 with n_source =  1986 theta_max is 23.0036974959942  arcmin = 9.995924696018823 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 33410.0 at z= 0.6530510187149048 with n_source =  2256 theta_max is 23.93345263699588  arcmin = 9.984167512000138 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      "60.486600234305534 -30.987929801464272 ... 25.281705856323242  2453.0\n",
+      " 60.86101302191349 -30.208113720652296 ... 19.820425033569336  4643.0\n",
+      " 60.93930110680046 -30.233165835592462 ... 11.323404312133789  8995.0\n",
+      " 60.92817627490327 -30.000803300701442 ... 12.421762466430664  9029.0\n",
+      " 60.23010184176201 -30.827430134550788 ...  6.579107284545898 29784.0\n",
+      " 60.55483814763497 -30.280744807372972 ...  6.716402053833008 35316.0\n",
+      "   60.598383979704  -30.83078557952782 ...   5.08411979675293 59428.0\n",
+      " 60.68142437164906 -30.915916676909713 ...  5.221414566040039 25770.0\n",
+      " 60.96024036136846  -30.14627416305006 ...  5.938398361206055 59302.0\n",
+      "60.719160516887946  -30.12308648557638 ...  5.557024002075195 53335.0\n",
+      " 60.01938474788308  -30.82153353230635 ...  5.602788925170898 52141.0\n",
+      "Ncluster 11\n",
+      "delta_sigma\n",
+      "For cluster 2453.0 at z= 0.8156213760375977 with n_source =  639 theta_max is 21.932662763401424  arcmin = 9.98853154548119 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 4643.0 at z= 0.8727886080741882 with n_source =  722 theta_max is 21.453607753366903  arcmin = 9.991678261250547 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 8995.0 at z= 0.9868596792221069 with n_source =  354 theta_max is 20.715030166028928  arcmin = 9.993116480339536 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 9029.0 at z= 0.8443910479545593 with n_source =  444 theta_max is 21.64701404063508  arcmin = 9.974700496018222 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 29784.0 at z= 0.8731144070625305 with n_source =  662 theta_max is 21.454908171018626  arcmin = 9.9934588184081 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 35316.0 at z= 0.8984475135803223 with n_source =  1011 theta_max is 21.24514779133426  arcmin = 9.983407744846472 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 59428.0 at z= 0.8610619306564331 with n_source =  872 theta_max is 21.557310864614582  arcmin = 9.996848270780752 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 25770.0 at z= 0.9812018275260925 with n_source =  366 theta_max is 20.65703530108976  arcmin = 9.950330594406871 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 59302.0 at z= 0.997758150100708 with n_source =  280 theta_max is 20.665568664621528  arcmin = 9.997175532563869 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 53335.0 at z= 0.8671668767929077 with n_source =  776 theta_max is 21.493491388302335  arcmin = 9.989793703716519 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 52141.0 at z= 0.9101417064666748 with n_source =  368 theta_max is 21.164682764073333  arcmin = 9.98408272181414 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/my_cluster_shear_catalog_pdf_ds_shape.hdf5\n",
+      "[0.2        0.27594593 0.38073079 0.52530556 0.72477966 1.\n",
+      " 1.37972966 1.90365394 2.6265278  3.62389832 5.        ]\n",
+      "celestial\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.78565382938074  -30.88821992888556 ... 19.576345443725586  6139.0\n",
+      " 60.82553784920792  -30.51639368681256 ... 12.238702774047852 10999.0\n",
+      " 60.48861752387501 -30.685130453859824 ... 11.491209030151367  8523.0\n",
+      " 60.90715205643431  -30.24513872352905 ...  9.660612106323242 17462.0\n",
+      "60.745430693132285 -30.722831995395694 ...  6.640127182006836 30553.0\n",
+      " 60.13513592278265  -30.18630075837688 ...  6.533342361450195 32634.0\n",
+      " 60.65087062021594 -30.035446420971837 ...  6.716402053833008 25937.0\n",
+      " 60.32661050735002 -30.939506455543473 ...  6.350282669067383 26346.0\n",
+      "Ncluster 8\n",
+      "delta_sigma\n",
+      "For cluster 6139.0 at z= 0.33334264159202576 with n_source =  4123 theta_max is 35.03023710694247  arcmin = 9.998842434359426 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 10999.0 at z= 0.3490130603313446 with n_source =  5985 theta_max is 33.979436474340474  arcmin = 9.999808317244387 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 8523.0 at z= 0.3723301589488983 with n_source =  6019 theta_max is 32.58250761415833  arcmin = 9.999955820642535 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 17462.0 at z= 0.2546227276325226 with n_source =  6292 theta_max is 42.37073978575504  arcmin = 9.998979828146442 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 30553.0 at z= 0.23431362211704254 with n_source =  7786 theta_max is 45.089209135393354  arcmin = 9.999141202994123 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 32634.0 at z= 0.28674596548080444 with n_source =  5370 theta_max is 38.87511557016056  arcmin = 9.99920804366955 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 25937.0 at z= 0.344936341047287 with n_source =  4503 theta_max is 34.23641244979176  arcmin = 9.997569125090758 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 26346.0 at z= 0.24044087529182434 with n_source =  5886 theta_max is 44.19339532919497  arcmin = 9.99314759989732 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.15338004389374  -30.84990249476992 ... 18.844106674194336  4434.0\n",
+      "60.189275063181576 -30.261930359760694 ... 11.216619491577148  7121.0\n",
+      "60.162780713167805 -30.170345889294776 ... 10.896265029907227  8547.0\n",
+      " 60.12763083127777 -30.337487070312562 ... 14.877813339233398  7698.0\n",
+      "60.125099176098004  -30.74426694177359 ... 10.484380722045898 10146.0\n",
+      " 60.73839715022432  -30.24290857660279 ... 11.109834671020508 14476.0\n",
+      " 60.48457755028719 -30.793019321280497 ...  8.928373336791992 16657.0\n",
+      "60.054429554416735  -30.35999306037265 ...  8.974138259887695 13039.0\n",
+      " 60.74426729449158 -30.770085575179923 ...  8.791078567504883 15382.0\n",
+      " 60.98354871793877 -30.996867303772337 ...  7.753740310668945 17011.0\n",
+      "               ...                 ... ...                ...     ...\n",
+      " 60.77015429510177  -30.34177092134464 ...  6.640127182006836 37007.0\n",
+      " 60.77792158728647 -30.402391010530195 ...  5.953653335571289 44685.0\n",
+      " 60.03038636114792  -30.54048705270219 ...   8.50123405456543 25976.0\n",
+      "60.259817099939376  -30.82307688704441 ...  7.555425643920898 31596.0\n",
+      "60.881889055158645 -30.264969356070143 ...  5.312944412231445 26938.0\n",
+      " 60.19229962876435  -30.93397045748159 ...  5.785848617553711 62882.0\n",
+      " 60.71050096971124 -30.749563544549957 ...  5.175649642944336 16567.0\n",
+      "60.165975063232715 -30.665596607330865 ...  5.968908309936523 41321.0\n",
+      " 60.88425228697305 -30.334392706687627 ...  5.496004104614258 52451.0\n",
+      " 60.52944165719066 -30.104073019519454 ...  5.404474258422852 78132.0\n",
+      "Length = 28 rows\n",
+      "Ncluster 28\n",
+      "delta_sigma\n",
+      "For cluster 4434.0 at z= 0.5422797799110413 with n_source =  1803 theta_max is 26.1872535800939  arcmin = 9.994276984385321 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 7121.0 at z= 0.46572214365005493 with n_source =  3401 theta_max is 28.447758830156975  arcmin = 9.99733165689564 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 8547.0 at z= 0.4626827538013458 with n_source =  2874 theta_max is 28.551186172067684  arcmin = 9.996311010768947 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 7698.0 at z= 0.48154547810554504 with n_source =  3085 theta_max is 27.921476871660843  arcmin = 9.998709945548446 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 10146.0 at z= 0.548952579498291 with n_source =  2072 theta_max is 26.02386171468891  arcmin = 9.994331559874839 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 14476.0 at z= 0.5732821822166443 with n_source =  2695 theta_max is 25.47724399543762  arcmin = 9.999632475186491 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 16657.0 at z= 0.48545151948928833 with n_source =  3612 theta_max is 27.798966549407083  arcmin = 9.999640982054911 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 13039.0 at z= 0.48073717951774597 with n_source =  2732 theta_max is 27.94645753228624  arcmin = 9.998286473503612 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 15382.0 at z= 0.5205168724060059 with n_source =  2714 theta_max is 26.765613619605304  arcmin = 9.998894232587556 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 17011.0 at z= 0.4551392197608948 with n_source =  1343 theta_max is 28.827830627441873  arcmin = 9.99842353409287 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 13025.0 at z= 0.5495550036430359 with n_source =  1520 theta_max is 26.016429127595355  arcmin = 9.99706449320895 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 20888.0 at z= 0.5699324607849121 with n_source =  2591 theta_max is 25.547586618756437  arcmin = 9.998219197266605 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 11651.0 at z= 0.5927656888961792 with n_source =  1777 theta_max is 25.055846076716676  arcmin = 9.995622971448723 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 33429.0 at z= 0.5001161694526672 with n_source =  3080 theta_max is 27.346608524448584  arcmin = 9.999024627062909 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 15656.0 at z= 0.5939760208129883 with n_source =  1942 theta_max is 25.036518600320825  arcmin = 9.99769940289408 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 22630.0 at z= 0.5922470092773438 with n_source =  1912 theta_max is 25.075576371321915  arcmin = 9.999285256639787 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 33569.0 at z= 0.5305519104003906 with n_source =  2126 theta_max is 26.497840345030124  arcmin = 9.998803991499885 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 39996.0 at z= 0.43162813782691956 with n_source =  1969 theta_max is 29.738423940141566  arcmin = 9.998952770008419 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 37007.0 at z= 0.5187186598777771 with n_source =  3464 theta_max is 26.79864383796969  arcmin = 9.992881367630247 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 44685.0 at z= 0.4375304579734802 with n_source =  4662 theta_max is 29.495156722410957  arcmin = 9.997197969941768 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 25976.0 at z= 0.5719814300537109 with n_source =  1944 theta_max is 25.504033876695342  arcmin = 9.998922551853072 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 31596.0 at z= 0.48342546820640564 with n_source =  2708 theta_max is 27.86200006246653  arcmin = 9.999071823971107 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 26938.0 at z= 0.45206454396247864 with n_source =  3301 theta_max is 28.930166380733546  arcmin = 9.994704681462368 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 62882.0 at z= 0.41000840067863464 with n_source =  2586 theta_max is 30.656882887419567  arcmin = 9.993610923864567 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 16567.0 at z= 0.49412333965301514 with n_source =  3197 theta_max is 27.510105905700122  arcmin = 9.992805415045115 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 41321.0 at z= 0.5961660146713257 with n_source =  2110 theta_max is 24.980703463623314  arcmin = 9.993012947024907 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 52451.0 at z= 0.5932099223136902 with n_source =  2288 theta_max is 25.057414366942734  arcmin = 9.999846792667018 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 78132.0 at z= 0.5301636457443237 with n_source =  2978 theta_max is 26.5061278533721  arcmin = 9.99810456066382 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.24684072721323 -30.234968978217807 ...  50.26935577392578   827.0\n",
+      " 60.33037603819723 -30.155166403646447 ... 31.856599807739258  1985.0\n",
+      " 60.19798610694177 -30.288558532393417 ... 13.748945236206055  1632.0\n",
+      " 60.46305004484695   -30.4771453673221 ... 26.303789138793945  2678.0\n",
+      " 60.31124209468067 -30.644137931021007 ...   21.7120418548584  3939.0\n",
+      " 60.40601451137022 -30.889199256471997 ...  21.16286277770996  4709.0\n",
+      " 60.07871737759909  -30.87961443163394 ... 13.886240005493164  9429.0\n",
+      "60.326126517158364  -30.91291736913871 ...  7.997819900512695 11300.0\n",
+      "  60.3906485346257  -30.32504676069136 ...  8.272409439086914 21385.0\n",
+      " 60.56281963421085  -30.52347796810624 ...  7.341856002807617 21815.0\n",
+      " 60.42104325395154  -30.74695605874455 ...   8.36393928527832 27728.0\n",
+      " 60.29059774704672 -30.590332518294673 ...  7.936800003051758 30757.0\n",
+      " 60.18626761923588 -30.167117729820564 ... 10.881010055541992 17777.0\n",
+      " 60.13800305274047 -30.072928539498086 ...  8.074094772338867 24386.0\n",
+      " 60.29901520853626  -30.73384245958468 ...  6.045183181762695 48725.0\n",
+      " 60.32954746480673  -30.24791761027524 ...  6.502832412719727 27260.0\n",
+      " 60.47484833897218 -30.719415767017235 ...  6.197732925415039 36608.0\n",
+      "60.898622679672854 -30.329981860471364 ...  7.997819900512695 74483.0\n",
+      " 60.87221651027329  -30.00554561545366 ...  6.075693130493164 51057.0\n",
+      "60.702276976193374  -30.33353933696586 ...  7.067266464233398 14210.0\n",
+      " 60.63490889446615  -30.21708448572835 ...   6.65538215637207 33410.0\n",
+      "Ncluster 21\n",
+      "delta_sigma\n",
+      "For cluster 827.0 at z= 0.6460187435150146 with n_source =  1982 theta_max is 24.083637522549868  arcmin = 9.998637368826127 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 1985.0 at z= 0.641009509563446 with n_source =  1929 theta_max is 24.166811437147476  arcmin = 9.998254303130016 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 1632.0 at z= 0.6587467193603516 with n_source =  1887 theta_max is 23.877451698934042  arcmin = 9.998940401799496 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 2678.0 at z= 0.6254668831825256 with n_source =  3068 theta_max is 24.436233670122622  arcmin = 9.997595146245002 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 3939.0 at z= 0.7766682505607605 with n_source =  1408 theta_max is 22.34393749348601  arcmin = 9.999597122513025 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 4709.0 at z= 0.7267194390296936 with n_source =  1196 theta_max is 22.910794444281233  arcmin = 9.995890587192537 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 9429.0 at z= 0.6608030200004578 with n_source =  996 theta_max is 23.82751596758849  arcmin = 9.991646870882631 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 11300.0 at z= 0.7256391644477844 with n_source =  1038 theta_max is 22.919482517107213  arcmin = 9.993773840158706 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 21385.0 at z= 0.7168400287628174 with n_source =  2090 theta_max is 23.045229477686867  arcmin = 9.999666115556877 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 21815.0 at z= 0.6154448986053467 with n_source =  3139 theta_max is 24.624993736016656  arcmin = 9.999830831727236 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 27728.0 at z= 0.608765184879303 with n_source =  2526 theta_max is 24.74517607120174  arcmin = 9.997453996283218 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 30757.0 at z= 0.6046680808067322 with n_source =  2665 theta_max is 24.805975072899518  arcmin = 9.990163225504784 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 17777.0 at z= 0.7892001271247864 with n_source =  942 theta_max is 22.198956488006292  arcmin = 9.992822768343544 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 24386.0 at z= 0.6216957569122314 with n_source =  1258 theta_max is 24.504429051067234  arcmin = 9.997613796221609 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 48725.0 at z= 0.6061629056930542 with n_source =  2258 theta_max is 24.775944353521716  arcmin = 9.989710937194452 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 27260.0 at z= 0.7166086435317993 with n_source =  1761 theta_max is 23.030190224008617  arcmin = 9.991840782514595 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 36608.0 at z= 0.6384199261665344 with n_source =  2469 theta_max is 24.21108286930276  arcmin = 9.998331913323113 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 74483.0 at z= 0.7653682231903076 with n_source =  1179 theta_max is 22.452432935925334  arcmin = 9.99364403510921 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 51057.0 at z= 0.6163065433502197 with n_source =  1273 theta_max is 24.571046848723043  arcmin = 9.984423966067174 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 14210.0 at z= 0.7193936705589294 with n_source =  1986 theta_max is 23.0036974959942  arcmin = 9.995924696018823 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 33410.0 at z= 0.6530510187149048 with n_source =  2256 theta_max is 23.93345263699588  arcmin = 9.984167512000138 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      "60.486600234305534 -30.987929801464272 ... 25.281705856323242  2453.0\n",
+      " 60.86101302191349 -30.208113720652296 ... 19.820425033569336  4643.0\n",
+      " 60.93930110680046 -30.233165835592462 ... 11.323404312133789  8995.0\n",
+      " 60.92817627490327 -30.000803300701442 ... 12.421762466430664  9029.0\n",
+      " 60.23010184176201 -30.827430134550788 ...  6.579107284545898 29784.0\n",
+      " 60.55483814763497 -30.280744807372972 ...  6.716402053833008 35316.0\n",
+      "   60.598383979704  -30.83078557952782 ...   5.08411979675293 59428.0\n",
+      " 60.68142437164906 -30.915916676909713 ...  5.221414566040039 25770.0\n",
+      " 60.96024036136846  -30.14627416305006 ...  5.938398361206055 59302.0\n",
+      "60.719160516887946  -30.12308648557638 ...  5.557024002075195 53335.0\n",
+      " 60.01938474788308  -30.82153353230635 ...  5.602788925170898 52141.0\n",
+      "Ncluster 11\n",
+      "delta_sigma\n",
+      "For cluster 2453.0 at z= 0.8156213760375977 with n_source =  639 theta_max is 21.932662763401424  arcmin = 9.98853154548119 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 4643.0 at z= 0.8727886080741882 with n_source =  722 theta_max is 21.453607753366903  arcmin = 9.991678261250547 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 8995.0 at z= 0.9868596792221069 with n_source =  354 theta_max is 20.715030166028928  arcmin = 9.993116480339536 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 9029.0 at z= 0.8443910479545593 with n_source =  444 theta_max is 21.64701404063508  arcmin = 9.974700496018222 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 29784.0 at z= 0.8731144070625305 with n_source =  662 theta_max is 21.454908171018626  arcmin = 9.9934588184081 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 35316.0 at z= 0.8984475135803223 with n_source =  1011 theta_max is 21.24514779133426  arcmin = 9.983407744846472 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 59428.0 at z= 0.8610619306564331 with n_source =  872 theta_max is 21.557310864614582  arcmin = 9.996848270780752 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 25770.0 at z= 0.9812018275260925 with n_source =  366 theta_max is 20.65703530108976  arcmin = 9.950330594406871 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 59302.0 at z= 0.997758150100708 with n_source =  280 theta_max is 20.665568664621528  arcmin = 9.997175532563869 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 53335.0 at z= 0.8671668767929077 with n_source =  776 theta_max is 21.493491388302335  arcmin = 9.989793703716519 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "delta_sigma\n",
+      "For cluster 52141.0 at z= 0.9101417064666748 with n_source =  368 theta_max is 21.164682764073333  arcmin = 9.98408272181414 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/my_cluster_shear_catalog_mean_gamma.hdf5\n",
+      "[ 5.          5.98115599  7.15484541  8.5588493  10.23836256 12.24744871\n",
+      " 14.65078026 17.52572043 20.96481356 25.07876406 30.        ]\n",
+      "celestial\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.78565382938074  -30.88821992888556 ... 19.576345443725586  6139.0\n",
+      " 60.82553784920792  -30.51639368681256 ... 12.238702774047852 10999.0\n",
+      " 60.48861752387501 -30.685130453859824 ... 11.491209030151367  8523.0\n",
+      " 60.90715205643431  -30.24513872352905 ...  9.660612106323242 17462.0\n",
+      "60.745430693132285 -30.722831995395694 ...  6.640127182006836 30553.0\n",
+      " 60.13513592278265  -30.18630075837688 ...  6.533342361450195 32634.0\n",
+      " 60.65087062021594 -30.035446420971837 ...  6.716402053833008 25937.0\n",
+      " 60.32661050735002 -30.939506455543473 ...  6.350282669067383 26346.0\n",
+      "Ncluster 8\n",
+      "reduced_shear\n",
+      "For cluster 6139.0 at z= 0.33334264159202576 with n_source =  5685 theta_max is 39.99849857377121  arcmin = 11.416956260647936 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 10999.0 at z= 0.3490130603313446 with n_source =  7812 theta_max is 39.997606637253185  arcmin = 11.77089560690947 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 8523.0 at z= 0.3723301589488983 with n_source =  8175 theta_max is 39.99123353163466  arcmin = 12.273781173170622 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 17462.0 at z= 0.2546227276325226 with n_source =  6259 theta_max is 39.99917205951719  arcmin = 9.439318656884389 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 30553.0 at z= 0.23431362211704254 with n_source =  7592 theta_max is 39.99966337276127  arcmin = 8.87046567917104 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 32634.0 at z= 0.28674596548080444 with n_source =  6120 theta_max is 39.996295903355936  arcmin = 10.287590862387045 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 25937.0 at z= 0.344936341047287 with n_source =  5873 theta_max is 39.98636209469682  arcmin = 11.676644557571683 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 26346.0 at z= 0.24044087529182434 with n_source =  5704 theta_max is 39.991609089349794  arcmin = 9.043026665191684 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.15338004389374  -30.84990249476992 ... 18.844106674194336  4434.0\n",
+      "60.189275063181576 -30.261930359760694 ... 11.216619491577148  7121.0\n",
+      "60.162780713167805 -30.170345889294776 ... 10.896265029907227  8547.0\n",
+      " 60.12763083127777 -30.337487070312562 ... 14.877813339233398  7698.0\n",
+      "60.125099176098004  -30.74426694177359 ... 10.484380722045898 10146.0\n",
+      " 60.73839715022432  -30.24290857660279 ... 11.109834671020508 14476.0\n",
+      " 60.48457755028719 -30.793019321280497 ...  8.928373336791992 16657.0\n",
+      "60.054429554416735  -30.35999306037265 ...  8.974138259887695 13039.0\n",
+      " 60.74426729449158 -30.770085575179923 ...  8.791078567504883 15382.0\n",
+      " 60.98354871793877 -30.996867303772337 ...  7.753740310668945 17011.0\n",
+      "               ...                 ... ...                ...     ...\n",
+      " 60.77015429510177  -30.34177092134464 ...  6.640127182006836 37007.0\n",
+      " 60.77792158728647 -30.402391010530195 ...  5.953653335571289 44685.0\n",
+      " 60.03038636114792  -30.54048705270219 ...   8.50123405456543 25976.0\n",
+      "60.259817099939376  -30.82307688704441 ...  7.555425643920898 31596.0\n",
+      "60.881889055158645 -30.264969356070143 ...  5.312944412231445 26938.0\n",
+      " 60.19229962876435  -30.93397045748159 ...  5.785848617553711 62882.0\n",
+      " 60.71050096971124 -30.749563544549957 ...  5.175649642944336 16567.0\n",
+      "60.165975063232715 -30.665596607330865 ...  5.968908309936523 41321.0\n",
+      " 60.88425228697305 -30.334392706687627 ...  5.496004104614258 52451.0\n",
+      " 60.52944165719066 -30.104073019519454 ...  5.404474258422852 78132.0\n",
+      "Length = 28 rows\n",
+      "Ncluster 28\n",
+      "reduced_shear\n",
+      "For cluster 4434.0 at z= 0.5422797799110413 with n_source =  4727 theta_max is 39.99767658284489  arcmin = 15.264978332996348 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 7121.0 at z= 0.46572214365005493 with n_source =  6586 theta_max is 39.99869143477182  arcmin = 14.056649822668588 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 8547.0 at z= 0.4626827538013458 with n_source =  5807 theta_max is 39.99572559381259  arcmin = 14.003261010860053 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 7698.0 at z= 0.48154547810554504 with n_source =  6403 theta_max is 39.99764661962563  arcmin = 14.323198908582459 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 10146.0 at z= 0.548952579498291 with n_source =  5197 theta_max is 39.99999205749859  arcmin = 15.361793241829018 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 14476.0 at z= 0.5732821822166443 with n_source =  6185 theta_max is 39.999618675912096  arcmin = 15.69955863272943 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 16657.0 at z= 0.48545151948928833 with n_source =  6562 theta_max is 39.99801520337147  arcmin = 14.387793564830185 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 13039.0 at z= 0.48073717951774597 with n_source =  5860 theta_max is 39.98663278635262  arcmin = 14.305849292235463 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 15382.0 at z= 0.5205168724060059 with n_source =  6154 theta_max is 39.990695921821704  arcmin = 14.93941982772156 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 17011.0 at z= 0.4551392197608948 with n_source =  3308 theta_max is 39.999691777163676  arcmin = 13.873186116216047 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 13025.0 at z= 0.5495550036430359 with n_source =  4083 theta_max is 39.9861498341926  arcmin = 15.365064773763875 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 20888.0 at z= 0.5699324607849121 with n_source =  6305 theta_max is 39.999864370108135  arcmin = 15.654214928452708 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 11651.0 at z= 0.5927656888961792 with n_source =  4675 theta_max is 39.96896968648442  arcmin = 15.944971497674283 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 33429.0 at z= 0.5001161694526672 with n_source =  6214 theta_max is 39.992847462486466  arcmin = 14.623000374114433 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 15656.0 at z= 0.5939760208129883 with n_source =  5253 theta_max is 39.997925180064314  arcmin = 15.972158073311629 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 22630.0 at z= 0.5922470092773438 with n_source =  4922 theta_max is 39.993632363846004  arcmin = 15.948097564474654 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 33569.0 at z= 0.5305519104003906 with n_source =  5175 theta_max is 39.993941520860965  arcmin = 15.091478283044315 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 39996.0 at z= 0.43162813782691956 with n_source =  4048 theta_max is 39.997976481341055  arcmin = 13.448522979490921 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 37007.0 at z= 0.5187186598777771 with n_source =  7069 theta_max is 39.99185887221718  arcmin = 14.91246735459261 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 44685.0 at z= 0.4375304579734802 with n_source =  7755 theta_max is 39.985919489061494  arcmin = 13.552976066696582 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 25976.0 at z= 0.5719814300537109 with n_source =  5129 theta_max is 39.99230749302912  arcmin = 15.679087756313216 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 31596.0 at z= 0.48342546820640564 with n_source =  5900 theta_max is 39.9942283648024  arcmin = 14.353067298376835 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 26938.0 at z= 0.45206454396247864 with n_source =  6207 theta_max is 39.99745782758009  arcmin = 13.818198406979644 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 62882.0 at z= 0.41000840067863464 with n_source =  4920 theta_max is 39.99511232518443  arcmin = 13.037711397533295 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 16567.0 at z= 0.49412333965301514 with n_source =  6625 theta_max is 39.99875341331086  arcmin = 14.52919741834832 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 41321.0 at z= 0.5961660146713257 with n_source =  5672 theta_max is 39.995482364926886  arcmin = 15.999364216353017 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 52451.0 at z= 0.5932099223136902 with n_source =  5690 theta_max is 39.99564931933704  arcmin = 15.961358171665395 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 78132.0 at z= 0.5301636457443237 with n_source =  5820 theta_max is 39.986083384779235  arcmin = 15.082740295526968 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.24684072721323 -30.234968978217807 ...  50.26935577392578   827.0\n",
+      " 60.33037603819723 -30.155166403646447 ... 31.856599807739258  1985.0\n",
+      " 60.19798610694177 -30.288558532393417 ... 13.748945236206055  1632.0\n",
+      " 60.46305004484695   -30.4771453673221 ... 26.303789138793945  2678.0\n",
+      " 60.31124209468067 -30.644137931021007 ...   21.7120418548584  3939.0\n",
+      " 60.40601451137022 -30.889199256471997 ...  21.16286277770996  4709.0\n",
+      " 60.07871737759909  -30.87961443163394 ... 13.886240005493164  9429.0\n",
+      "60.326126517158364  -30.91291736913871 ...  7.997819900512695 11300.0\n",
+      "  60.3906485346257  -30.32504676069136 ...  8.272409439086914 21385.0\n",
+      " 60.56281963421085  -30.52347796810624 ...  7.341856002807617 21815.0\n",
+      " 60.42104325395154  -30.74695605874455 ...   8.36393928527832 27728.0\n",
+      " 60.29059774704672 -30.590332518294673 ...  7.936800003051758 30757.0\n",
+      " 60.18626761923588 -30.167117729820564 ... 10.881010055541992 17777.0\n",
+      " 60.13800305274047 -30.072928539498086 ...  8.074094772338867 24386.0\n",
+      " 60.29901520853626  -30.73384245958468 ...  6.045183181762695 48725.0\n",
+      " 60.32954746480673  -30.24791761027524 ...  6.502832412719727 27260.0\n",
+      " 60.47484833897218 -30.719415767017235 ...  6.197732925415039 36608.0\n",
+      "60.898622679672854 -30.329981860471364 ...  7.997819900512695 74483.0\n",
+      " 60.87221651027329  -30.00554561545366 ...  6.075693130493164 51057.0\n",
+      "60.702276976193374  -30.33353933696586 ...  7.067266464233398 14210.0\n",
+      " 60.63490889446615  -30.21708448572835 ...   6.65538215637207 33410.0\n",
+      "Ncluster 21\n",
+      "reduced_shear\n",
+      "For cluster 827.0 at z= 0.6460187435150146 with n_source =  5490 theta_max is 39.99911257779195  arcmin = 16.606155169281514 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 1985.0 at z= 0.641009509563446 with n_source =  5255 theta_max is 39.98707719989212  arcmin = 16.54338917333829 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 1632.0 at z= 0.6587467193603516 with n_source =  5355 theta_max is 39.99636152481426  arcmin = 16.748907723402404 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 2678.0 at z= 0.6254668831825256 with n_source =  6907 theta_max is 39.88549551914432  arcmin = 16.318350928822703 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 3939.0 at z= 0.7766682505607605 with n_source =  4933 theta_max is 39.99867304531232  arcmin = 17.900632599104295 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 4709.0 at z= 0.7267194390296936 with n_source =  4120 theta_max is 39.98764983433197  arcmin = 17.44645623507492 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 9429.0 at z= 0.6608030200004578 with n_source =  3434 theta_max is 39.999281986461504  arcmin = 16.77299057279903 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 11300.0 at z= 0.7256391644477844 with n_source =  3841 theta_max is 39.99402359662001  arcmin = 17.438928932372693 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 21385.0 at z= 0.7168400287628174 with n_source =  5637 theta_max is 39.999289359061244  arcmin = 17.35628360036217 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 21815.0 at z= 0.6154448986053467 with n_source =  6975 theta_max is 39.987904547179646  arcmin = 16.23847238597252 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 27728.0 at z= 0.608765184879303 with n_source =  5996 theta_max is 39.99704751510971  arcmin = 16.15945836751704 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 30757.0 at z= 0.6046680808067322 with n_source =  6669 theta_max is 39.994860561441826  arcmin = 16.107215459819642 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 17777.0 at z= 0.7892001271247864 with n_source =  3705 theta_max is 39.99248700091049  arcmin = 18.00255047489727 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 24386.0 at z= 0.6216957569122314 with n_source =  4093 theta_max is 39.99966858841431  arcmin = 16.319549322713407 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 48725.0 at z= 0.6061629056930542 with n_source =  5878 theta_max is 39.996992605843516  arcmin = 16.126868416730453 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 27260.0 at z= 0.7166086435317993 with n_source =  5174 theta_max is 39.99200735058658  arcmin = 17.350867106761637 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 36608.0 at z= 0.6384199261665344 with n_source =  6009 theta_max is 39.97902107517  arcmin = 16.50993986667565 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 74483.0 at z= 0.7653682231903076 with n_source =  4202 theta_max is 39.99900145876022  arcmin = 17.803673369359487 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 51057.0 at z= 0.6163065433502197 with n_source =  3696 theta_max is 39.9990065992476  arcmin = 16.25356227462331 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 14210.0 at z= 0.7193936705589294 with n_source =  5595 theta_max is 39.97465064308036  arcmin = 17.370407415915377 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 33410.0 at z= 0.6530510187149048 with n_source =  5616 theta_max is 39.942673702621356  arcmin = 16.66263330964984 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      "60.486600234305534 -30.987929801464272 ... 25.281705856323242  2453.0\n",
+      " 60.86101302191349 -30.208113720652296 ... 19.820425033569336  4643.0\n",
+      " 60.93930110680046 -30.233165835592462 ... 11.323404312133789  8995.0\n",
+      " 60.92817627490327 -30.000803300701442 ... 12.421762466430664  9029.0\n",
+      " 60.23010184176201 -30.827430134550788 ...  6.579107284545898 29784.0\n",
+      " 60.55483814763497 -30.280744807372972 ...  6.716402053833008 35316.0\n",
+      "   60.598383979704  -30.83078557952782 ...   5.08411979675293 59428.0\n",
+      " 60.68142437164906 -30.915916676909713 ...  5.221414566040039 25770.0\n",
+      " 60.96024036136846  -30.14627416305006 ...  5.938398361206055 59302.0\n",
+      "60.719160516887946  -30.12308648557638 ...  5.557024002075195 53335.0\n",
+      " 60.01938474788308  -30.82153353230635 ...  5.602788925170898 52141.0\n",
+      "Ncluster 11\n",
+      "reduced_shear\n",
+      "For cluster 2453.0 at z= 0.8156213760375977 with n_source =  3090 theta_max is 39.98403491586502  arcmin = 18.209453105674843 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 4643.0 at z= 0.8727886080741882 with n_source =  3421 theta_max is 39.985549850013356  arcmin = 18.62263698458027 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 8995.0 at z= 0.9868596792221069 with n_source =  2588 theta_max is 39.99383024413935  arcmin = 19.293382675349786 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 9029.0 at z= 0.8443910479545593 with n_source =  2356 theta_max is 39.99299642462363  arcmin = 18.428322748121836 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 29784.0 at z= 0.8731144070625305 with n_source =  3371 theta_max is 39.980316966201784  arcmin = 18.622389243704273 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 35316.0 at z= 0.8984475135803223 with n_source =  4190 theta_max is 39.99486782560604  arcmin = 18.7941772458336 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 59428.0 at z= 0.8610619306564331 with n_source =  3708 theta_max is 39.99334007048798  arcmin = 18.546253521011813 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 25770.0 at z= 0.9812018275260925 with n_source =  2616 theta_max is 39.97043030385212  arcmin = 19.253440279643886 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 59302.0 at z= 0.997758150100708 with n_source =  2228 theta_max is 39.99873071610677  arcmin = 19.349786039676676 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 53335.0 at z= 0.8671668767929077 with n_source =  3532 theta_max is 39.99859537124035  arcmin = 18.59063792746992 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 52141.0 at z= 0.9101417064666748 with n_source =  2354 theta_max is 39.99419929573963  arcmin = 18.86659009315268 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/my_cluster_shear_catalog_mode_gamma.hdf5\n",
+      "[ 5.          5.98115599  7.15484541  8.5588493  10.23836256 12.24744871\n",
+      " 14.65078026 17.52572043 20.96481356 25.07876406 30.        ]\n",
+      "celestial\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.78565382938074  -30.88821992888556 ... 19.576345443725586  6139.0\n",
+      " 60.82553784920792  -30.51639368681256 ... 12.238702774047852 10999.0\n",
+      " 60.48861752387501 -30.685130453859824 ... 11.491209030151367  8523.0\n",
+      " 60.90715205643431  -30.24513872352905 ...  9.660612106323242 17462.0\n",
+      "60.745430693132285 -30.722831995395694 ...  6.640127182006836 30553.0\n",
+      " 60.13513592278265  -30.18630075837688 ...  6.533342361450195 32634.0\n",
+      " 60.65087062021594 -30.035446420971837 ...  6.716402053833008 25937.0\n",
+      " 60.32661050735002 -30.939506455543473 ...  6.350282669067383 26346.0\n",
+      "Ncluster 8\n",
+      "reduced_shear\n",
+      "For cluster 6139.0 at z= 0.33334264159202576 with n_source =  5571 theta_max is 39.99849857377121  arcmin = 11.416956260647936 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 10999.0 at z= 0.3490130603313446 with n_source =  7661 theta_max is 39.997606637253185  arcmin = 11.77089560690947 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 8523.0 at z= 0.3723301589488983 with n_source =  7947 theta_max is 39.99123353163466  arcmin = 12.273781173170622 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 17462.0 at z= 0.2546227276325226 with n_source =  6189 theta_max is 39.99917205951719  arcmin = 9.439318656884389 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 30553.0 at z= 0.23431362211704254 with n_source =  7496 theta_max is 39.99966337276127  arcmin = 8.87046567917104 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 32634.0 at z= 0.28674596548080444 with n_source =  6060 theta_max is 39.996295903355936  arcmin = 10.287590862387045 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 25937.0 at z= 0.344936341047287 with n_source =  5765 theta_max is 39.98636209469682  arcmin = 11.676644557571683 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 26346.0 at z= 0.24044087529182434 with n_source =  5627 theta_max is 39.991609089349794  arcmin = 9.043026665191684 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.15338004389374  -30.84990249476992 ... 18.844106674194336  4434.0\n",
+      "60.189275063181576 -30.261930359760694 ... 11.216619491577148  7121.0\n",
+      "60.162780713167805 -30.170345889294776 ... 10.896265029907227  8547.0\n",
+      " 60.12763083127777 -30.337487070312562 ... 14.877813339233398  7698.0\n",
+      "60.125099176098004  -30.74426694177359 ... 10.484380722045898 10146.0\n",
+      " 60.73839715022432  -30.24290857660279 ... 11.109834671020508 14476.0\n",
+      " 60.48457755028719 -30.793019321280497 ...  8.928373336791992 16657.0\n",
+      "60.054429554416735  -30.35999306037265 ...  8.974138259887695 13039.0\n",
+      " 60.74426729449158 -30.770085575179923 ...  8.791078567504883 15382.0\n",
+      " 60.98354871793877 -30.996867303772337 ...  7.753740310668945 17011.0\n",
+      "               ...                 ... ...                ...     ...\n",
+      " 60.77015429510177  -30.34177092134464 ...  6.640127182006836 37007.0\n",
+      " 60.77792158728647 -30.402391010530195 ...  5.953653335571289 44685.0\n",
+      " 60.03038636114792  -30.54048705270219 ...   8.50123405456543 25976.0\n",
+      "60.259817099939376  -30.82307688704441 ...  7.555425643920898 31596.0\n",
+      "60.881889055158645 -30.264969356070143 ...  5.312944412231445 26938.0\n",
+      " 60.19229962876435  -30.93397045748159 ...  5.785848617553711 62882.0\n",
+      " 60.71050096971124 -30.749563544549957 ...  5.175649642944336 16567.0\n",
+      "60.165975063232715 -30.665596607330865 ...  5.968908309936523 41321.0\n",
+      " 60.88425228697305 -30.334392706687627 ...  5.496004104614258 52451.0\n",
+      " 60.52944165719066 -30.104073019519454 ...  5.404474258422852 78132.0\n",
+      "Length = 28 rows\n",
+      "Ncluster 28\n",
+      "reduced_shear\n",
+      "For cluster 4434.0 at z= 0.5422797799110413 with n_source =  4282 theta_max is 39.99379152940089  arcmin = 15.263495615455932 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 7121.0 at z= 0.46572214365005493 with n_source =  6228 theta_max is 39.99869143477182  arcmin = 14.056649822668588 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 8547.0 at z= 0.4626827538013458 with n_source =  5483 theta_max is 39.99572559381259  arcmin = 14.003261010860053 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 7698.0 at z= 0.48154547810554504 with n_source =  5988 theta_max is 39.99764661962563  arcmin = 14.323198908582459 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 10146.0 at z= 0.548952579498291 with n_source =  4754 theta_max is 39.99999205749859  arcmin = 15.361793241829018 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 14476.0 at z= 0.5732821822166443 with n_source =  5603 theta_max is 39.999618675912096  arcmin = 15.69955863272943 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 16657.0 at z= 0.48545151948928833 with n_source =  6156 theta_max is 39.99801520337147  arcmin = 14.387793564830185 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 13039.0 at z= 0.48073717951774597 with n_source =  5466 theta_max is 39.98663278635262  arcmin = 14.305849292235463 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 15382.0 at z= 0.5205168724060059 with n_source =  5689 theta_max is 39.990695921821704  arcmin = 14.93941982772156 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 17011.0 at z= 0.4551392197608948 with n_source =  3126 theta_max is 39.98261225351531  arcmin = 13.867262385311568 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 13025.0 at z= 0.5495550036430359 with n_source =  3736 theta_max is 39.9834960706033  arcmin = 15.36404503944054 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 20888.0 at z= 0.5699324607849121 with n_source =  5739 theta_max is 39.999864370108135  arcmin = 15.654214928452708 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 11651.0 at z= 0.5927656888961792 with n_source =  4229 theta_max is 39.96896968648442  arcmin = 15.944971497674283 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 33429.0 at z= 0.5001161694526672 with n_source =  5813 theta_max is 39.992847462486466  arcmin = 14.623000374114433 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 15656.0 at z= 0.5939760208129883 with n_source =  4722 theta_max is 39.997925180064314  arcmin = 15.972158073311629 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 22630.0 at z= 0.5922470092773438 with n_source =  4443 theta_max is 39.993632363846004  arcmin = 15.948097564474654 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 33569.0 at z= 0.5305519104003906 with n_source =  4778 theta_max is 39.993941520860965  arcmin = 15.091478283044315 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 39996.0 at z= 0.43162813782691956 with n_source =  3844 theta_max is 39.997976481341055  arcmin = 13.448522979490921 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 37007.0 at z= 0.5187186598777771 with n_source =  6640 theta_max is 39.99185887221718  arcmin = 14.91246735459261 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 44685.0 at z= 0.4375304579734802 with n_source =  7471 theta_max is 39.985919489061494  arcmin = 13.552976066696582 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 25976.0 at z= 0.5719814300537109 with n_source =  4613 theta_max is 39.99230749302912  arcmin = 15.679087756313216 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 31596.0 at z= 0.48342546820640564 with n_source =  5515 theta_max is 39.9942283648024  arcmin = 14.353067298376835 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 26938.0 at z= 0.45206454396247864 with n_source =  5896 theta_max is 39.99745782758009  arcmin = 13.818198406979644 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 62882.0 at z= 0.41000840067863464 with n_source =  4686 theta_max is 39.99511232518443  arcmin = 13.037711397533295 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 16567.0 at z= 0.49412333965301514 with n_source =  6208 theta_max is 39.99875341331086  arcmin = 14.52919741834832 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 41321.0 at z= 0.5961660146713257 with n_source =  5126 theta_max is 39.995482364926886  arcmin = 15.999364216353017 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 52451.0 at z= 0.5932099223136902 with n_source =  5129 theta_max is 39.99564931933704  arcmin = 15.961358171665395 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 78132.0 at z= 0.5301636457443237 with n_source =  5359 theta_max is 39.986083384779235  arcmin = 15.082740295526968 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.24684072721323 -30.234968978217807 ...  50.26935577392578   827.0\n",
+      " 60.33037603819723 -30.155166403646447 ... 31.856599807739258  1985.0\n",
+      " 60.19798610694177 -30.288558532393417 ... 13.748945236206055  1632.0\n",
+      " 60.46305004484695   -30.4771453673221 ... 26.303789138793945  2678.0\n",
+      " 60.31124209468067 -30.644137931021007 ...   21.7120418548584  3939.0\n",
+      " 60.40601451137022 -30.889199256471997 ...  21.16286277770996  4709.0\n",
+      " 60.07871737759909  -30.87961443163394 ... 13.886240005493164  9429.0\n",
+      "60.326126517158364  -30.91291736913871 ...  7.997819900512695 11300.0\n",
+      "  60.3906485346257  -30.32504676069136 ...  8.272409439086914 21385.0\n",
+      " 60.56281963421085  -30.52347796810624 ...  7.341856002807617 21815.0\n",
+      " 60.42104325395154  -30.74695605874455 ...   8.36393928527832 27728.0\n",
+      " 60.29059774704672 -30.590332518294673 ...  7.936800003051758 30757.0\n",
+      " 60.18626761923588 -30.167117729820564 ... 10.881010055541992 17777.0\n",
+      " 60.13800305274047 -30.072928539498086 ...  8.074094772338867 24386.0\n",
+      " 60.29901520853626  -30.73384245958468 ...  6.045183181762695 48725.0\n",
+      " 60.32954746480673  -30.24791761027524 ...  6.502832412719727 27260.0\n",
+      " 60.47484833897218 -30.719415767017235 ...  6.197732925415039 36608.0\n",
+      "60.898622679672854 -30.329981860471364 ...  7.997819900512695 74483.0\n",
+      " 60.87221651027329  -30.00554561545366 ...  6.075693130493164 51057.0\n",
+      "60.702276976193374  -30.33353933696586 ...  7.067266464233398 14210.0\n",
+      " 60.63490889446615  -30.21708448572835 ...   6.65538215637207 33410.0\n",
+      "Ncluster 21\n",
+      "reduced_shear\n",
+      "For cluster 827.0 at z= 0.6460187435150146 with n_source =  4822 theta_max is 39.99911257779195  arcmin = 16.606155169281514 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 1985.0 at z= 0.641009509563446 with n_source =  4598 theta_max is 39.98707719989212  arcmin = 16.54338917333829 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 1632.0 at z= 0.6587467193603516 with n_source =  4675 theta_max is 39.99636152481426  arcmin = 16.748907723402404 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 2678.0 at z= 0.6254668831825256 with n_source =  6193 theta_max is 39.88549551914432  arcmin = 16.318350928822703 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 3939.0 at z= 0.7766682505607605 with n_source =  3902 theta_max is 39.99867304531232  arcmin = 17.900632599104295 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 4709.0 at z= 0.7267194390296936 with n_source =  3392 theta_max is 39.98764983433197  arcmin = 17.44645623507492 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 9429.0 at z= 0.6608030200004578 with n_source =  2912 theta_max is 39.999281986461504  arcmin = 16.77299057279903 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 11300.0 at z= 0.7256391644477844 with n_source =  3143 theta_max is 39.99402359662001  arcmin = 17.438928932372693 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 21385.0 at z= 0.7168400287628174 with n_source =  4721 theta_max is 39.999289359061244  arcmin = 17.35628360036217 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 21815.0 at z= 0.6154448986053467 with n_source =  6280 theta_max is 39.987904547179646  arcmin = 16.23847238597252 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 27728.0 at z= 0.608765184879303 with n_source =  5420 theta_max is 39.99704751510971  arcmin = 16.15945836751704 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 30757.0 at z= 0.6046680808067322 with n_source =  6011 theta_max is 39.994860561441826  arcmin = 16.107215459819642 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 17777.0 at z= 0.7892001271247864 with n_source =  2969 theta_max is 39.99248700091049  arcmin = 18.00255047489727 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 24386.0 at z= 0.6216957569122314 with n_source =  3691 theta_max is 39.99966858841431  arcmin = 16.319549322713407 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 48725.0 at z= 0.6061629056930542 with n_source =  5295 theta_max is 39.996992605843516  arcmin = 16.126868416730453 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 27260.0 at z= 0.7166086435317993 with n_source =  4325 theta_max is 39.99608858196555  arcmin = 17.35263778315374 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 36608.0 at z= 0.6384199261665344 with n_source =  5352 theta_max is 39.97902107517  arcmin = 16.50993986667565 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 74483.0 at z= 0.7653682231903076 with n_source =  3355 theta_max is 39.99900145876022  arcmin = 17.803673369359487 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 51057.0 at z= 0.6163065433502197 with n_source =  3352 theta_max is 39.9990065992476  arcmin = 16.25356227462331 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 14210.0 at z= 0.7193936705589294 with n_source =  4721 theta_max is 39.97465064308036  arcmin = 17.370407415915377 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 33410.0 at z= 0.6530510187149048 with n_source =  4882 theta_max is 39.942673702621356  arcmin = 16.66263330964984 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      "60.486600234305534 -30.987929801464272 ... 25.281705856323242  2453.0\n",
+      " 60.86101302191349 -30.208113720652296 ... 19.820425033569336  4643.0\n",
+      " 60.93930110680046 -30.233165835592462 ... 11.323404312133789  8995.0\n",
+      " 60.92817627490327 -30.000803300701442 ... 12.421762466430664  9029.0\n",
+      " 60.23010184176201 -30.827430134550788 ...  6.579107284545898 29784.0\n",
+      " 60.55483814763497 -30.280744807372972 ...  6.716402053833008 35316.0\n",
+      "   60.598383979704  -30.83078557952782 ...   5.08411979675293 59428.0\n",
+      " 60.68142437164906 -30.915916676909713 ...  5.221414566040039 25770.0\n",
+      " 60.96024036136846  -30.14627416305006 ...  5.938398361206055 59302.0\n",
+      "60.719160516887946  -30.12308648557638 ...  5.557024002075195 53335.0\n",
+      " 60.01938474788308  -30.82153353230635 ...  5.602788925170898 52141.0\n",
+      "Ncluster 11\n",
+      "reduced_shear\n",
+      "For cluster 2453.0 at z= 0.8156213760375977 with n_source =  2446 theta_max is 39.98403491586502  arcmin = 18.209453105674843 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 4643.0 at z= 0.8727886080741882 with n_source =  2525 theta_max is 39.985549850013356  arcmin = 18.62263698458027 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 8995.0 at z= 0.9868596792221069 with n_source =  1693 theta_max is 39.98396933007983  arcmin = 19.288625681900605 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 9029.0 at z= 0.8443910479545593 with n_source =  1848 theta_max is 39.99299642462363  arcmin = 18.428322748121836 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 29784.0 at z= 0.8731144070625305 with n_source =  2484 theta_max is 39.980316966201784  arcmin = 18.622389243704273 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 35316.0 at z= 0.8984475135803223 with n_source =  3037 theta_max is 39.97561626662734  arcmin = 18.785130654823924 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 59428.0 at z= 0.8610619306564331 with n_source =  2793 theta_max is 39.99334007048798  arcmin = 18.546253521011813 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 25770.0 at z= 0.9812018275260925 with n_source =  1660 theta_max is 39.97043030385212  arcmin = 19.253440279643886 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 59302.0 at z= 0.997758150100708 with n_source =  1469 theta_max is 39.99873071610677  arcmin = 19.349786039676676 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 53335.0 at z= 0.8671668767929077 with n_source =  2676 theta_max is 39.99859537124035  arcmin = 18.59063792746992 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 52141.0 at z= 0.9101417064666748 with n_source =  1636 theta_max is 39.99419929573963  arcmin = 18.86659009315268 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/my_cluster_shear_catalog_pdf_gamma.hdf5\n",
+      "[0.2        0.27594593 0.38073079 0.52530556 0.72477966 1.\n",
+      " 1.37972966 1.90365394 2.6265278  3.62389832 5.        ]\n",
+      "celestial\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.78565382938074  -30.88821992888556 ... 19.576345443725586  6139.0\n",
+      " 60.82553784920792  -30.51639368681256 ... 12.238702774047852 10999.0\n",
+      " 60.48861752387501 -30.685130453859824 ... 11.491209030151367  8523.0\n",
+      " 60.90715205643431  -30.24513872352905 ...  9.660612106323242 17462.0\n",
+      "60.745430693132285 -30.722831995395694 ...  6.640127182006836 30553.0\n",
+      " 60.13513592278265  -30.18630075837688 ...  6.533342361450195 32634.0\n",
+      " 60.65087062021594 -30.035446420971837 ...  6.716402053833008 25937.0\n",
+      " 60.32661050735002 -30.939506455543473 ...  6.350282669067383 26346.0\n",
+      "Ncluster 8\n",
+      "reduced_shear\n",
+      "For cluster 6139.0 at z= 0.33334264159202576 with n_source =  4123 theta_max is 35.03023710694247  arcmin = 9.998842434359426 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 10999.0 at z= 0.3490130603313446 with n_source =  5985 theta_max is 33.979436474340474  arcmin = 9.999808317244387 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 8523.0 at z= 0.3723301589488983 with n_source =  6019 theta_max is 32.58250761415833  arcmin = 9.999955820642535 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 17462.0 at z= 0.2546227276325226 with n_source =  6292 theta_max is 42.37073978575504  arcmin = 9.998979828146442 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 30553.0 at z= 0.23431362211704254 with n_source =  7786 theta_max is 45.089209135393354  arcmin = 9.999141202994123 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 32634.0 at z= 0.28674596548080444 with n_source =  5370 theta_max is 38.87511557016056  arcmin = 9.99920804366955 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 25937.0 at z= 0.344936341047287 with n_source =  4503 theta_max is 34.23641244979176  arcmin = 9.997569125090758 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 26346.0 at z= 0.24044087529182434 with n_source =  5886 theta_max is 44.19339532919497  arcmin = 9.99314759989732 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.15338004389374  -30.84990249476992 ... 18.844106674194336  4434.0\n",
+      "60.189275063181576 -30.261930359760694 ... 11.216619491577148  7121.0\n",
+      "60.162780713167805 -30.170345889294776 ... 10.896265029907227  8547.0\n",
+      " 60.12763083127777 -30.337487070312562 ... 14.877813339233398  7698.0\n",
+      "60.125099176098004  -30.74426694177359 ... 10.484380722045898 10146.0\n",
+      " 60.73839715022432  -30.24290857660279 ... 11.109834671020508 14476.0\n",
+      " 60.48457755028719 -30.793019321280497 ...  8.928373336791992 16657.0\n",
+      "60.054429554416735  -30.35999306037265 ...  8.974138259887695 13039.0\n",
+      " 60.74426729449158 -30.770085575179923 ...  8.791078567504883 15382.0\n",
+      " 60.98354871793877 -30.996867303772337 ...  7.753740310668945 17011.0\n",
+      "               ...                 ... ...                ...     ...\n",
+      " 60.77015429510177  -30.34177092134464 ...  6.640127182006836 37007.0\n",
+      " 60.77792158728647 -30.402391010530195 ...  5.953653335571289 44685.0\n",
+      " 60.03038636114792  -30.54048705270219 ...   8.50123405456543 25976.0\n",
+      "60.259817099939376  -30.82307688704441 ...  7.555425643920898 31596.0\n",
+      "60.881889055158645 -30.264969356070143 ...  5.312944412231445 26938.0\n",
+      " 60.19229962876435  -30.93397045748159 ...  5.785848617553711 62882.0\n",
+      " 60.71050096971124 -30.749563544549957 ...  5.175649642944336 16567.0\n",
+      "60.165975063232715 -30.665596607330865 ...  5.968908309936523 41321.0\n",
+      " 60.88425228697305 -30.334392706687627 ...  5.496004104614258 52451.0\n",
+      " 60.52944165719066 -30.104073019519454 ...  5.404474258422852 78132.0\n",
+      "Length = 28 rows\n",
+      "Ncluster 28\n",
+      "reduced_shear\n",
+      "For cluster 4434.0 at z= 0.5422797799110413 with n_source =  1803 theta_max is 26.1872535800939  arcmin = 9.994276984385321 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 7121.0 at z= 0.46572214365005493 with n_source =  3401 theta_max is 28.447758830156975  arcmin = 9.99733165689564 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 8547.0 at z= 0.4626827538013458 with n_source =  2874 theta_max is 28.551186172067684  arcmin = 9.996311010768947 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 7698.0 at z= 0.48154547810554504 with n_source =  3085 theta_max is 27.921476871660843  arcmin = 9.998709945548446 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 10146.0 at z= 0.548952579498291 with n_source =  2072 theta_max is 26.02386171468891  arcmin = 9.994331559874839 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 14476.0 at z= 0.5732821822166443 with n_source =  2695 theta_max is 25.47724399543762  arcmin = 9.999632475186491 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 16657.0 at z= 0.48545151948928833 with n_source =  3612 theta_max is 27.798966549407083  arcmin = 9.999640982054911 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 13039.0 at z= 0.48073717951774597 with n_source =  2732 theta_max is 27.94645753228624  arcmin = 9.998286473503612 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 15382.0 at z= 0.5205168724060059 with n_source =  2714 theta_max is 26.765613619605304  arcmin = 9.998894232587556 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 17011.0 at z= 0.4551392197608948 with n_source =  1343 theta_max is 28.827830627441873  arcmin = 9.99842353409287 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 13025.0 at z= 0.5495550036430359 with n_source =  1520 theta_max is 26.016429127595355  arcmin = 9.99706449320895 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 20888.0 at z= 0.5699324607849121 with n_source =  2591 theta_max is 25.547586618756437  arcmin = 9.998219197266605 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 11651.0 at z= 0.5927656888961792 with n_source =  1777 theta_max is 25.055846076716676  arcmin = 9.995622971448723 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 33429.0 at z= 0.5001161694526672 with n_source =  3080 theta_max is 27.346608524448584  arcmin = 9.999024627062909 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 15656.0 at z= 0.5939760208129883 with n_source =  1942 theta_max is 25.036518600320825  arcmin = 9.99769940289408 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 22630.0 at z= 0.5922470092773438 with n_source =  1912 theta_max is 25.075576371321915  arcmin = 9.999285256639787 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 33569.0 at z= 0.5305519104003906 with n_source =  2126 theta_max is 26.497840345030124  arcmin = 9.998803991499885 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 39996.0 at z= 0.43162813782691956 with n_source =  1969 theta_max is 29.738423940141566  arcmin = 9.998952770008419 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 37007.0 at z= 0.5187186598777771 with n_source =  3464 theta_max is 26.79864383796969  arcmin = 9.992881367630247 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 44685.0 at z= 0.4375304579734802 with n_source =  4662 theta_max is 29.495156722410957  arcmin = 9.997197969941768 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 25976.0 at z= 0.5719814300537109 with n_source =  1944 theta_max is 25.504033876695342  arcmin = 9.998922551853072 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 31596.0 at z= 0.48342546820640564 with n_source =  2708 theta_max is 27.86200006246653  arcmin = 9.999071823971107 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 26938.0 at z= 0.45206454396247864 with n_source =  3301 theta_max is 28.930166380733546  arcmin = 9.994704681462368 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 62882.0 at z= 0.41000840067863464 with n_source =  2586 theta_max is 30.656882887419567  arcmin = 9.993610923864567 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 16567.0 at z= 0.49412333965301514 with n_source =  3197 theta_max is 27.510105905700122  arcmin = 9.992805415045115 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 41321.0 at z= 0.5961660146713257 with n_source =  2110 theta_max is 24.980703463623314  arcmin = 9.993012947024907 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 52451.0 at z= 0.5932099223136902 with n_source =  2288 theta_max is 25.057414366942734  arcmin = 9.999846792667018 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 78132.0 at z= 0.5301636457443237 with n_source =  2978 theta_max is 26.5061278533721  arcmin = 9.99810456066382 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      " 60.24684072721323 -30.234968978217807 ...  50.26935577392578   827.0\n",
+      " 60.33037603819723 -30.155166403646447 ... 31.856599807739258  1985.0\n",
+      " 60.19798610694177 -30.288558532393417 ... 13.748945236206055  1632.0\n",
+      " 60.46305004484695   -30.4771453673221 ... 26.303789138793945  2678.0\n",
+      " 60.31124209468067 -30.644137931021007 ...   21.7120418548584  3939.0\n",
+      " 60.40601451137022 -30.889199256471997 ...  21.16286277770996  4709.0\n",
+      " 60.07871737759909  -30.87961443163394 ... 13.886240005493164  9429.0\n",
+      "60.326126517158364  -30.91291736913871 ...  7.997819900512695 11300.0\n",
+      "  60.3906485346257  -30.32504676069136 ...  8.272409439086914 21385.0\n",
+      " 60.56281963421085  -30.52347796810624 ...  7.341856002807617 21815.0\n",
+      " 60.42104325395154  -30.74695605874455 ...   8.36393928527832 27728.0\n",
+      " 60.29059774704672 -30.590332518294673 ...  7.936800003051758 30757.0\n",
+      " 60.18626761923588 -30.167117729820564 ... 10.881010055541992 17777.0\n",
+      " 60.13800305274047 -30.072928539498086 ...  8.074094772338867 24386.0\n",
+      " 60.29901520853626  -30.73384245958468 ...  6.045183181762695 48725.0\n",
+      " 60.32954746480673  -30.24791761027524 ...  6.502832412719727 27260.0\n",
+      " 60.47484833897218 -30.719415767017235 ...  6.197732925415039 36608.0\n",
+      "60.898622679672854 -30.329981860471364 ...  7.997819900512695 74483.0\n",
+      " 60.87221651027329  -30.00554561545366 ...  6.075693130493164 51057.0\n",
+      "60.702276976193374  -30.33353933696586 ...  7.067266464233398 14210.0\n",
+      " 60.63490889446615  -30.21708448572835 ...   6.65538215637207 33410.0\n",
+      "Ncluster 21\n",
+      "reduced_shear\n",
+      "For cluster 827.0 at z= 0.6460187435150146 with n_source =  1982 theta_max is 24.083637522549868  arcmin = 9.998637368826127 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 1985.0 at z= 0.641009509563446 with n_source =  1929 theta_max is 24.166811437147476  arcmin = 9.998254303130016 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 1632.0 at z= 0.6587467193603516 with n_source =  1887 theta_max is 23.877451698934042  arcmin = 9.998940401799496 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 2678.0 at z= 0.6254668831825256 with n_source =  3068 theta_max is 24.436233670122622  arcmin = 9.997595146245002 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 3939.0 at z= 0.7766682505607605 with n_source =  1408 theta_max is 22.34393749348601  arcmin = 9.999597122513025 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 4709.0 at z= 0.7267194390296936 with n_source =  1196 theta_max is 22.910794444281233  arcmin = 9.995890587192537 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 9429.0 at z= 0.6608030200004578 with n_source =  996 theta_max is 23.82751596758849  arcmin = 9.991646870882631 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 11300.0 at z= 0.7256391644477844 with n_source =  1038 theta_max is 22.919482517107213  arcmin = 9.993773840158706 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 21385.0 at z= 0.7168400287628174 with n_source =  2090 theta_max is 23.045229477686867  arcmin = 9.999666115556877 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 21815.0 at z= 0.6154448986053467 with n_source =  3139 theta_max is 24.624993736016656  arcmin = 9.999830831727236 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 27728.0 at z= 0.608765184879303 with n_source =  2526 theta_max is 24.74517607120174  arcmin = 9.997453996283218 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 30757.0 at z= 0.6046680808067322 with n_source =  2665 theta_max is 24.805975072899518  arcmin = 9.990163225504784 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 17777.0 at z= 0.7892001271247864 with n_source =  942 theta_max is 22.198956488006292  arcmin = 9.992822768343544 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 24386.0 at z= 0.6216957569122314 with n_source =  1258 theta_max is 24.504429051067234  arcmin = 9.997613796221609 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 48725.0 at z= 0.6061629056930542 with n_source =  2258 theta_max is 24.775944353521716  arcmin = 9.989710937194452 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 27260.0 at z= 0.7166086435317993 with n_source =  1761 theta_max is 23.030190224008617  arcmin = 9.991840782514595 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 36608.0 at z= 0.6384199261665344 with n_source =  2469 theta_max is 24.21108286930276  arcmin = 9.998331913323113 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 74483.0 at z= 0.7653682231903076 with n_source =  1179 theta_max is 22.452432935925334  arcmin = 9.99364403510921 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 51057.0 at z= 0.6163065433502197 with n_source =  1273 theta_max is 24.571046848723043  arcmin = 9.984423966067174 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 14210.0 at z= 0.7193936705589294 with n_source =  1986 theta_max is 23.0036974959942  arcmin = 9.995924696018823 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 33410.0 at z= 0.6530510187149048 with n_source =  2256 theta_max is 23.93345263699588  arcmin = 9.984167512000138 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n",
+      "        ra                 dec         ...      richness         id  \n",
+      "------------------ ------------------- ... ------------------ -------\n",
+      "60.486600234305534 -30.987929801464272 ... 25.281705856323242  2453.0\n",
+      " 60.86101302191349 -30.208113720652296 ... 19.820425033569336  4643.0\n",
+      " 60.93930110680046 -30.233165835592462 ... 11.323404312133789  8995.0\n",
+      " 60.92817627490327 -30.000803300701442 ... 12.421762466430664  9029.0\n",
+      " 60.23010184176201 -30.827430134550788 ...  6.579107284545898 29784.0\n",
+      " 60.55483814763497 -30.280744807372972 ...  6.716402053833008 35316.0\n",
+      "   60.598383979704  -30.83078557952782 ...   5.08411979675293 59428.0\n",
+      " 60.68142437164906 -30.915916676909713 ...  5.221414566040039 25770.0\n",
+      " 60.96024036136846  -30.14627416305006 ...  5.938398361206055 59302.0\n",
+      "60.719160516887946  -30.12308648557638 ...  5.557024002075195 53335.0\n",
+      " 60.01938474788308  -30.82153353230635 ...  5.602788925170898 52141.0\n",
+      "Ncluster 11\n",
+      "reduced_shear\n",
+      "For cluster 2453.0 at z= 0.8156213760375977 with n_source =  639 theta_max is 21.932662763401424  arcmin = 9.98853154548119 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 4643.0 at z= 0.8727886080741882 with n_source =  722 theta_max is 21.453607753366903  arcmin = 9.991678261250547 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 8995.0 at z= 0.9868596792221069 with n_source =  354 theta_max is 20.715030166028928  arcmin = 9.993116480339536 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 9029.0 at z= 0.8443910479545593 with n_source =  444 theta_max is 21.64701404063508  arcmin = 9.974700496018222 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 29784.0 at z= 0.8731144070625305 with n_source =  662 theta_max is 21.454908171018626  arcmin = 9.9934588184081 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 35316.0 at z= 0.8984475135803223 with n_source =  1011 theta_max is 21.24514779133426  arcmin = 9.983407744846472 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 59428.0 at z= 0.8610619306564331 with n_source =  872 theta_max is 21.557310864614582  arcmin = 9.996848270780752 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 25770.0 at z= 0.9812018275260925 with n_source =  366 theta_max is 20.65703530108976  arcmin = 9.950330594406871 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 59302.0 at z= 0.997758150100708 with n_source =  280 theta_max is 20.665568664621528  arcmin = 9.997175532563869 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 53335.0 at z= 0.8671668767929077 with n_source =  776 theta_max is 21.493491388302335  arcmin = 9.989793703716519 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "reduced_shear\n",
+      "For cluster 52141.0 at z= 0.9101417064666748 with n_source =  368 theta_max is 21.164682764073333  arcmin = 9.98408272181414 Mpc\n",
+      "GCMetaData({'coordinate_system': 'celestial', 'cosmo': None})\n",
+      "cluster ensemble computed\n",
+      "covariance computed\n",
+      "cl_ensemble_created\n"
+     ]
+    }
+   ],
+   "source": [
+    "units = [\"mpc\", \"mpc\", \"mpc\", \"mpc\", \"arcmin\", \"arcmin\", \"mpc\"]\n",
+    "\n",
+    "test_ensemble = [\n",
+    "    run_different_options_ensemble(None, run[0], i, unit)\n",
+    "    for (i, run), unit in zip(enumerate(all_runs_names), units)\n",
+    "]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ab458cc",
+   "metadata": {},
+   "source": [
+    "## SACC File\n",
+    "Lastly we are running the SACC stage to show the profiles. THis stage has to be addapted for the reduced shear as we can measure it in angles instead of physical radii. Also, we need to decide on the type of data that we will assign to the different options."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "e5f9f0d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Options for this pipeline and their defaults:\n",
+      "{'d_min': 0.5, 'd_max': 5.0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Options for this pipeline and their defaults:\")\n",
+    "print(txpipe.extensions.CLClusterSACC.config_options)\n",
+    "def run_different_options_sacc(delta_sigma, name, name2):\n",
+    "    print(name)\n",
+    "    step5 = txpipe.extensions.CLClusterSACC.make_stage(\n",
+    "        cluster_profiles = name,\n",
+    "        survey_name = 'cosmodc2-1deg',\n",
+    "        area = 1.0,\n",
+    "        cluster_sacc_catalog = f\"{my_txpipe_dir}/data/example/outputs_shear_catalogs/cluster_sacc_catalog_{name2}.sacc\"\n",
+    "    )\n",
+    "    step5.run()\n",
+    "    step5.finalize()\n",
+    "    final_name = f\"{my_txpipe_dir}/data/example/outputs_shear_catalogs/cluster_sacc_catalog_{name2}.sacc\"\n",
+    "    return final_name\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "b40be33d-da22-4563-a27e-4fe30d5873f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.1.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sacc\n",
+    "print(sacc.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "88984fd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_profiles_0.pkl\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_profiles_0.pkl\n",
+      "{'bin_zbin_0_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.4), 'z_min': np.float64(0.2)}, 'n_cl': 8, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a79afdd0>, 'profile_type': 'delta_sigma'}, 'bin_zbin_1_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.6), 'z_min': np.float64(0.4)}, 'n_cl': 28, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a7847f50>, 'profile_type': 'delta_sigma'}, 'bin_zbin_2_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.8), 'z_min': np.float64(0.6)}, 'n_cl': 21, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a7778fe0>, 'profile_type': 'delta_sigma'}, 'bin_zbin_3_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(1.0), 'z_min': np.float64(0.8)}, 'n_cl': 11, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a77d67e0>, 'profile_type': 'delta_sigma'}}\n",
+      "delta_sigma\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_profiles_1.pkl\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_profiles_1.pkl\n",
+      "{'bin_zbin_0_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.4), 'z_min': np.float64(0.2)}, 'n_cl': 8, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a77112e0>, 'profile_type': 'delta_sigma'}, 'bin_zbin_1_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.6), 'z_min': np.float64(0.4)}, 'n_cl': 28, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a79acce0>, 'profile_type': 'delta_sigma'}, 'bin_zbin_2_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.8), 'z_min': np.float64(0.6)}, 'n_cl': 21, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a777ae10>, 'profile_type': 'delta_sigma'}, 'bin_zbin_3_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(1.0), 'z_min': np.float64(0.8)}, 'n_cl': 11, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a7778500>, 'profile_type': 'delta_sigma'}}\n",
+      "delta_sigma\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_profiles_2.pkl\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_profiles_2.pkl\n",
+      "{'bin_zbin_0_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.4), 'z_min': np.float64(0.2)}, 'n_cl': 8, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a7711ac0>, 'profile_type': 'delta_sigma'}, 'bin_zbin_1_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.6), 'z_min': np.float64(0.4)}, 'n_cl': 28, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a777ab70>, 'profile_type': 'delta_sigma'}, 'bin_zbin_2_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.8), 'z_min': np.float64(0.6)}, 'n_cl': 21, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a79ad820>, 'profile_type': 'delta_sigma'}, 'bin_zbin_3_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(1.0), 'z_min': np.float64(0.8)}, 'n_cl': 11, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a79ad880>, 'profile_type': 'delta_sigma'}}\n",
+      "delta_sigma\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_profiles_3.pkl\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_profiles_3.pkl\n",
+      "{'bin_zbin_0_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.4), 'z_min': np.float64(0.2)}, 'n_cl': 8, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a774d190>, 'profile_type': 'delta_sigma'}, 'bin_zbin_1_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.6), 'z_min': np.float64(0.4)}, 'n_cl': 28, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a79ad610>, 'profile_type': 'delta_sigma'}, 'bin_zbin_2_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.8), 'z_min': np.float64(0.6)}, 'n_cl': 21, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a79ad880>, 'profile_type': 'delta_sigma'}, 'bin_zbin_3_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(1.0), 'z_min': np.float64(0.8)}, 'n_cl': 11, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a7711ac0>, 'profile_type': 'delta_sigma'}}\n",
+      "delta_sigma\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_profiles_4.pkl\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_profiles_4.pkl\n",
+      "{'bin_zbin_0_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.4), 'z_min': np.float64(0.2)}, 'n_cl': 8, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a7b0cb00>, 'profile_type': 'reduced_shear'}, 'bin_zbin_1_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.6), 'z_min': np.float64(0.4)}, 'n_cl': 28, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a79adc70>, 'profile_type': 'reduced_shear'}, 'bin_zbin_2_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.8), 'z_min': np.float64(0.6)}, 'n_cl': 21, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a79af350>, 'profile_type': 'reduced_shear'}, 'bin_zbin_3_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(1.0), 'z_min': np.float64(0.8)}, 'n_cl': 11, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a7467b00>, 'profile_type': 'reduced_shear'}}\n",
+      "reduced_shear\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "NOTE/WARNING: Expected output file /sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_sacc_catalog_0.sacc was not generated.\n",
+      "NOTE/WARNING: Expected output file /sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_sacc_catalog_1.sacc was not generated.\n",
+      "NOTE/WARNING: Expected output file /sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_sacc_catalog_2.sacc was not generated.\n",
+      "NOTE/WARNING: Expected output file /sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_sacc_catalog_3.sacc was not generated.\n",
+      "NOTE/WARNING: Expected output file /sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_sacc_catalog_4.sacc was not generated.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_profiles_5.pkl\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_profiles_5.pkl\n",
+      "{'bin_zbin_0_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.4), 'z_min': np.float64(0.2)}, 'n_cl': 8, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a7772870>, 'profile_type': 'reduced_shear'}, 'bin_zbin_1_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.6), 'z_min': np.float64(0.4)}, 'n_cl': 28, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a7762480>, 'profile_type': 'reduced_shear'}, 'bin_zbin_2_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.8), 'z_min': np.float64(0.6)}, 'n_cl': 21, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a79b0050>, 'profile_type': 'reduced_shear'}, 'bin_zbin_3_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(1.0), 'z_min': np.float64(0.8)}, 'n_cl': 11, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a79b00b0>, 'profile_type': 'reduced_shear'}}\n",
+      "reduced_shear\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_profiles_6.pkl\n",
+      "/sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_profiles_6.pkl\n",
+      "{'bin_zbin_0_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.4), 'z_min': np.float64(0.2)}, 'n_cl': 8, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a774d4c0>, 'profile_type': 'reduced_shear'}, 'bin_zbin_1_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.6), 'z_min': np.float64(0.4)}, 'n_cl': 28, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a79b01d0>, 'profile_type': 'reduced_shear'}, 'bin_zbin_2_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(0.8), 'z_min': np.float64(0.6)}, 'n_cl': 21, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a7760800>, 'profile_type': 'reduced_shear'}, 'bin_zbin_3_richbin_0': {'cluster_bin_edges': {'rich_max': np.float64(80.0), 'rich_min': np.float64(1.0), 'z_max': np.float64(1.0), 'z_min': np.float64(0.8)}, 'n_cl': 11, 'clmm_cluster_ensemble': <clmm.clusterensemble.ClusterEnsemble object at 0x7f19a74108c0>, 'profile_type': 'reduced_shear'}}\n",
+      "reduced_shear\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "NOTE/WARNING: Expected output file /sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_sacc_catalog_5.sacc was not generated.\n",
+      "NOTE/WARNING: Expected output file /sps/lsst/users/ebarroso/TXPipe/data/example/outputs_shear_catalogs/cluster_sacc_catalog_6.sacc was not generated.\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_sacc = [run_different_options_sacc(None, run, i) for i,run in enumerate(test_ensemble)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcf7fda9-e1a8-4443-9984-1e4d01acbe12",
+   "metadata": {},
+   "source": [
+    "### Here we plot the profile of one bin for each file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "4ae71371",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{np.str_('bin_z_0'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a778de50>, np.str_('bin_rich_0'): <sacc.tracers.clusters.BinRichnessTracer object at 0x7f19a778f350>, np.str_('bin_z_1'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a778cfe0>, np.str_('bin_z_2'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a778c830>, np.str_('bin_z_3'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a778dfd0>, np.str_('radius_0'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a778eff0>, np.str_('radius_1'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a778f890>, np.str_('radius_2'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a778edb0>, np.str_('radius_3'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a778e120>, np.str_('radius_4'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a778f770>, np.str_('radius_5'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a778fc80>, np.str_('radius_6'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a778d1f0>, np.str_('radius_7'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a778d2b0>, np.str_('radius_8'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7438950>, np.str_('radius_9'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a743a9c0>, np.str_('cosmodc2-1deg'): <sacc.tracers.survey.SurveyTracer object at 0x7f19a743ba70>}\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_0'), value=43232369445235.26, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_1'), value=70320279975054.055, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_2'), value=47187478848111.43, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_3'), value=92711500339253.34, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_4'), value=93816258519126.61, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_5'), value=46782233532412.22, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_6'), value=28291662280124.676, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_7'), value=-19001457521196.992, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_8'), value=1098408063598.5385, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_9'), value=-5848376249004.868, )]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{np.str_('bin_z_0'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a78ad850>, np.str_('bin_rich_0'): <sacc.tracers.clusters.BinRichnessTracer object at 0x7f19a78af140>, np.str_('bin_z_1'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a78ad490>, np.str_('bin_z_2'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a78ac3e0>, np.str_('bin_z_3'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a78ac920>, np.str_('radius_0'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a78ac3b0>, np.str_('radius_1'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a78adfd0>, np.str_('radius_2'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a78ad040>, np.str_('radius_3'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a78acb60>, np.str_('radius_4'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a78af0e0>, np.str_('radius_5'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a78addf0>, np.str_('radius_6'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a78ac440>, np.str_('radius_7'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a78aec60>, np.str_('radius_8'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a78afaa0>, np.str_('radius_9'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a78acb30>, np.str_('cosmodc2-1deg'): <sacc.tracers.survey.SurveyTracer object at 0x7f19a78ac560>}\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_0'), value=51766060387544.89, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_1'), value=83487010276747.25, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_2'), value=48037946006744.55, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_3'), value=99084078021339.64, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_4'), value=104706250285470.61, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_5'), value=39188382363519.41, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_6'), value=25197366066269.98, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_7'), value=-17427160763626.484, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_8'), value=-358393284051.49866, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_9'), value=-5837570132826.892, )]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{np.str_('bin_z_0'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7773fb0>, np.str_('bin_rich_0'): <sacc.tracers.clusters.BinRichnessTracer object at 0x7f19a7771490>, np.str_('bin_z_1'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7828440>, np.str_('bin_z_2'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a782b710>, np.str_('bin_z_3'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a782b8c0>, np.str_('radius_0'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7828860>, np.str_('radius_1'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7829b50>, np.str_('radius_2'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7828590>, np.str_('radius_3'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a782b800>, np.str_('radius_4'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a782aa80>, np.str_('radius_5'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a782a300>, np.str_('radius_6'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a782b230>, np.str_('radius_7'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a782a630>, np.str_('radius_8'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a78295e0>, np.str_('radius_9'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a782a750>, np.str_('cosmodc2-1deg'): <sacc.tracers.survey.SurveyTracer object at 0x7f19a7829f40>}\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_0'), value=23235729610337.863, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_1'), value=95773674017431.6, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_2'), value=59050911050898.31, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_3'), value=116021345120290.33, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_4'), value=104409939235821.19, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_5'), value=36972839401928.3, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_6'), value=37500256647812.78, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_7'), value=-24746950237722.137, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_8'), value=744538690491.2012, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_9'), value=-5070906704620.338, )]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{np.str_('bin_z_0'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a74a0fe0>, np.str_('bin_rich_0'): <sacc.tracers.clusters.BinRichnessTracer object at 0x7f19a74a3d40>, np.str_('bin_z_1'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a74a2780>, np.str_('bin_z_2'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a74a0440>, np.str_('bin_z_3'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a74a0ad0>, np.str_('radius_0'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74a1550>, np.str_('radius_1'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74a0410>, np.str_('radius_2'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74a3b30>, np.str_('radius_3'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74a3fb0>, np.str_('radius_4'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74a0bf0>, np.str_('radius_5'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74a3cb0>, np.str_('radius_6'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74a0da0>, np.str_('radius_7'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74a29f0>, np.str_('radius_8'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74a0350>, np.str_('radius_9'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74a3f80>, np.str_('cosmodc2-1deg'): <sacc.tracers.survey.SurveyTracer object at 0x7f19a74a0c80>}\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_0'), value=24090600651254.53, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_1'), value=97050146487012.75, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_2'), value=59153786177162.07, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_3'), value=115629926825711.31, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_4'), value=104389290788519.53, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_5'), value=36562025536356.53, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_6'), value=37635288158347.41, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_7'), value=-24417800927831.51, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_8'), value=703552621184.9382, )]\n",
+      "[DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_9'), value=-5186277848951.807, )]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{np.str_('bin_z_0'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a77e6210>, np.str_('bin_rich_0'): <sacc.tracers.clusters.BinRichnessTracer object at 0x7f19a77e7830>, np.str_('bin_z_1'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a77e76b0>, np.str_('bin_z_2'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a77e5eb0>, np.str_('bin_z_3'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a77e7a10>, np.str_('radius_0'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a77e6720>, np.str_('radius_1'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a77e5610>, np.str_('radius_2'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a77e69c0>, np.str_('radius_3'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a77e7170>, np.str_('radius_4'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a77e7b00>, np.str_('radius_5'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a77e6d20>, np.str_('radius_6'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a77e4710>, np.str_('radius_7'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a77e4380>, np.str_('radius_8'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a77e4bf0>, np.str_('radius_9'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a77e6c00>, np.str_('cosmodc2-1deg'): <sacc.tracers.survey.SurveyTracer object at 0x7f19a77e5ca0>}\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_0'), value=-0.0057683758038830765, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_1'), value=0.008283846961274615, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_2'), value=0.004137058586064512, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_3'), value=-0.00920712646245946, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_4'), value=-0.00331654211614225, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_5'), value=0.00025137470727514097, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_6'), value=-0.0019937168031028568, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_7'), value=0.006646531468363067, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_8'), value=-0.0011068038872593955, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_9'), value=-0.004330218166654884, )]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{np.str_('bin_z_0'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a74ef140>, np.str_('bin_rich_0'): <sacc.tracers.clusters.BinRichnessTracer object at 0x7f19a74ef560>, np.str_('bin_z_1'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a74ef170>, np.str_('bin_z_2'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a74efad0>, np.str_('bin_z_3'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a74ef080>, np.str_('radius_0'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74ef230>, np.str_('radius_1'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74efbf0>, np.str_('radius_2'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74ef110>, np.str_('radius_3'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74ef800>, np.str_('radius_4'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74ef2c0>, np.str_('radius_5'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74ef1a0>, np.str_('radius_6'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74ef290>, np.str_('radius_7'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74ef3e0>, np.str_('radius_8'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74ef5c0>, np.str_('radius_9'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74ef5f0>, np.str_('cosmodc2-1deg'): <sacc.tracers.survey.SurveyTracer object at 0x7f19a74ef620>}\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_0'), value=-0.0042255111387835, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_1'), value=0.0071924286695982075, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_2'), value=0.003911751349786751, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_3'), value=-0.008854282600393053, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_4'), value=-0.0036009030807943924, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_5'), value=0.00038534929387622457, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_6'), value=-0.002454178840692148, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_7'), value=0.006014190394443744, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_8'), value=-0.00046170535596548476, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_9'), value=-0.0052782035950950735, )]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{np.str_('bin_z_0'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7483410>, np.str_('bin_rich_0'): <sacc.tracers.clusters.BinRichnessTracer object at 0x7f19a7480b00>, np.str_('bin_z_1'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7480740>, np.str_('bin_z_2'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a74825d0>, np.str_('bin_z_3'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7482210>, np.str_('radius_0'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7483590>, np.str_('radius_1'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7482270>, np.str_('radius_2'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74813d0>, np.str_('radius_3'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74825a0>, np.str_('radius_4'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7482ae0>, np.str_('radius_5'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7482d50>, np.str_('radius_6'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7482420>, np.str_('radius_7'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7483cb0>, np.str_('radius_8'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7482fc0>, np.str_('radius_9'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7483a40>, np.str_('cosmodc2-1deg'): <sacc.tracers.survey.SurveyTracer object at 0x7f19a74800e0>}\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_0'), value=0.0020864909471535156, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_1'), value=0.03020217112155864, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_2'), value=0.020086433269439832, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_3'), value=0.03571278246525472, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_4'), value=0.03858477276041311, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_5'), value=0.008899764731309526, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_6'), value=0.011089507336866951, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_7'), value=-0.00923431583127562, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_8'), value=0.0013931819276978062, )]\n",
+      "[DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_9'), value=-0.0013585282574536007, )]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{np.str_('bin_z_0'): <sacc.tracers.clusters.BinZTracer object at 0x7f19ac103dd0>, np.str_('bin_rich_0'): <sacc.tracers.clusters.BinRichnessTracer object at 0x7f19ac102fc0>, np.str_('bin_z_1'): <sacc.tracers.clusters.BinZTracer object at 0x7f19ac102b40>, np.str_('bin_z_2'): <sacc.tracers.clusters.BinZTracer object at 0x7f19ac103fb0>, np.str_('bin_z_3'): <sacc.tracers.clusters.BinZTracer object at 0x7f19ac103110>, np.str_('radius_0'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac103350>, np.str_('radius_1'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac103b30>, np.str_('radius_2'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac1034a0>, np.str_('radius_3'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac103620>, np.str_('radius_4'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac1034d0>, np.str_('radius_5'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac103140>, np.str_('radius_6'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac1033e0>, np.str_('radius_7'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac103500>, np.str_('radius_8'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac103050>, np.str_('radius_9'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac1035f0>, np.str_('cosmodc2-1deg'): <sacc.tracers.survey.SurveyTracer object at 0x7f19ac103320>}\n",
+      "[DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0'), value=8, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1'), value=28, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2'), value=21, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3'), value=11, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_0'), value=43232369445235.26, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_1'), value=70320279975054.055, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_2'), value=47187478848111.43, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_3'), value=92711500339253.34, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_4'), value=93816258519126.61, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_5'), value=46782233532412.22, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_6'), value=28291662280124.676, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_7'), value=-19001457521196.992, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_8'), value=1098408063598.5385, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_9'), value=-5848376249004.868, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_0'), value=219496690918895.7, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_1'), value=-89713089760284.06, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_2'), value=43743424268978.29, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_3'), value=65634116738675.055, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_4'), value=18371715556591.324, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_5'), value=33810446874692.145, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_6'), value=13199372800436.535, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_7'), value=16234118580966.072, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_8'), value=-1621605677803.1208, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_9'), value=209823608031.42047, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_0'), value=139770075710962.6, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_1'), value=-177950256324255.6, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_2'), value=-65724458298685.44, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_3'), value=-45520209344295.71, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_4'), value=-41001472814410.46, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_5'), value=70490745673923.19, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_6'), value=6378856801010.439, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_7'), value=35471480692388.56, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_8'), value=-10567391150088.664, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_9'), value=1439261371323.4778, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_0'), value=-342943662225435.94, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_1'), value=-533560373577665.0, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_2'), value=426755982212075.2, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_3'), value=-268841667022133.3, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_4'), value=-88637760260347.36, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_5'), value=-232320526256773.06, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_6'), value=33374113932790.777, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_7'), value=20419179815584.34, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_8'), value=-20392776877602.113, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_9'), value=-16086166112524.775, )]\n",
+      "{np.str_('bin_z_0'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7752f90>, np.str_('bin_rich_0'): <sacc.tracers.clusters.BinRichnessTracer object at 0x7f19a7751a60>, np.str_('bin_z_1'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7751c40>, np.str_('bin_z_2'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a782aed0>, np.str_('bin_z_3'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7413a40>, np.str_('radius_0'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a77521b0>, np.str_('radius_1'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7412f00>, np.str_('radius_2'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7412c60>, np.str_('radius_3'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74118b0>, np.str_('radius_4'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7412e70>, np.str_('radius_5'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7410f50>, np.str_('radius_6'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7412030>, np.str_('radius_7'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7411190>, np.str_('radius_8'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7413350>, np.str_('radius_9'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74129f0>, np.str_('cosmodc2-1deg'): <sacc.tracers.survey.SurveyTracer object at 0x7f19a79b0cb0>}\n",
+      "[DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0'), value=8, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1'), value=28, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2'), value=21, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3'), value=11, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_0'), value=51766060387544.89, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_1'), value=83487010276747.25, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_2'), value=48037946006744.55, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_3'), value=99084078021339.64, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_4'), value=104706250285470.61, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_5'), value=39188382363519.41, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_6'), value=25197366066269.98, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_7'), value=-17427160763626.484, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_8'), value=-358393284051.49866, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_9'), value=-5837570132826.892, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_0'), value=214102769908694.47, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_1'), value=-54793401767766.695, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_2'), value=35479592899299.836, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_3'), value=52448716714103.41, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_4'), value=6800912501516.929, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_5'), value=27609872409765.07, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_6'), value=11786607920357.125, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_7'), value=24275924418567.227, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_8'), value=2157156191738.7349, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_9'), value=-2576106578287.7974, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_0'), value=73657019695333.67, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_1'), value=-147283175683968.75, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_2'), value=-72331368276130.1, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_3'), value=-35274888925663.0, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_4'), value=-14092661278211.178, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_5'), value=62111859891476.09, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_6'), value=17082626728195.572, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_7'), value=27404663944197.465, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_8'), value=-13855959567153.73, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_9'), value=-5449485581849.679, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_0'), value=-205995131919311.3, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_1'), value=-554309544849439.0, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_2'), value=536655164598505.9, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_3'), value=2580616610131.4917, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_4'), value=-9154060999960.154, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_5'), value=-250028512581106.22, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_6'), value=53689256947329.48, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_7'), value=41378951993243.31, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_8'), value=-27641374201863.39, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_9'), value=-30411022908185.082, )]\n",
+      "{np.str_('bin_z_0'): <sacc.tracers.clusters.BinZTracer object at 0x7f19ac167830>, np.str_('bin_rich_0'): <sacc.tracers.clusters.BinRichnessTracer object at 0x7f19a79b0da0>, np.str_('bin_z_1'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7ce4ad0>, np.str_('bin_z_2'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7828410>, np.str_('bin_z_3'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7760dd0>, np.str_('radius_0'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac07b320>, np.str_('radius_1'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7763c50>, np.str_('radius_2'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7760500>, np.str_('radius_3'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7763d10>, np.str_('radius_4'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a77603b0>, np.str_('radius_5'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7761af0>, np.str_('radius_6'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7760560>, np.str_('radius_7'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7763800>, np.str_('radius_8'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7762030>, np.str_('radius_9'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7762b70>, np.str_('cosmodc2-1deg'): <sacc.tracers.survey.SurveyTracer object at 0x7f19a7762e70>}\n",
+      "[DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0'), value=8, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1'), value=28, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2'), value=21, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3'), value=11, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_0'), value=23235729610337.863, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_1'), value=95773674017431.6, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_2'), value=59050911050898.31, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_3'), value=116021345120290.33, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_4'), value=104409939235821.19, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_5'), value=36972839401928.3, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_6'), value=37500256647812.78, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_7'), value=-24746950237722.137, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_8'), value=744538690491.2012, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_9'), value=-5070906704620.338, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_0'), value=33223310765183.465, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_1'), value=-64530235003186.69, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_2'), value=37940915537357.63, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_3'), value=22573515832249.547, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_4'), value=8000354624024.683, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_5'), value=15272010227072.725, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_6'), value=15244593355849.025, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_7'), value=20058327600469.65, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_8'), value=2558394944490.7095, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_9'), value=-1234990145715.6582, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_0'), value=84499021446838.36, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_1'), value=-143614722968834.88, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_2'), value=-42093007197133.44, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_3'), value=-70788865545484.33, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_4'), value=-9778030552265.781, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_5'), value=74447236048132.62, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_6'), value=21158374870260.805, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_7'), value=25278218181334.23, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_8'), value=-4049934952274.171, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_9'), value=-4016492543779.4907, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_0'), value=-231732973745595.28, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_1'), value=-548042935915827.8, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_2'), value=474780379544927.0, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_3'), value=243784458205188.94, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_4'), value=-404574616088196.44, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_5'), value=-107720269364780.3, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_6'), value=-93064536918605.28, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_7'), value=34659712279829.023, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_8'), value=-58000678072166.55, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_9'), value=-19689789134751.742, )]\n",
+      "{np.str_('bin_z_0'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7411940>, np.str_('bin_rich_0'): <sacc.tracers.clusters.BinRichnessTracer object at 0x7f19a7413770>, np.str_('bin_z_1'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a74131d0>, np.str_('bin_z_2'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7412000>, np.str_('bin_z_3'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7411460>, np.str_('radius_0'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7410f50>, np.str_('radius_1'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7410c80>, np.str_('radius_2'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7410da0>, np.str_('radius_3'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7413da0>, np.str_('radius_4'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7412cf0>, np.str_('radius_5'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74137d0>, np.str_('radius_6'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a74137a0>, np.str_('radius_7'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7410a40>, np.str_('radius_8'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7412990>, np.str_('radius_9'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7411eb0>, np.str_('cosmodc2-1deg'): <sacc.tracers.survey.SurveyTracer object at 0x7f19a7411e50>}\n",
+      "[DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0'), value=8, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1'), value=28, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2'), value=21, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3'), value=11, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_0'), value=24090600651254.53, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_1'), value=97050146487012.75, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_2'), value=59153786177162.07, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_3'), value=115629926825711.31, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_4'), value=104389290788519.53, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_5'), value=36562025536356.53, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_6'), value=37635288158347.41, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_7'), value=-24417800927831.51, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_8'), value=703552621184.9382, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_9'), value=-5186277848951.807, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_0'), value=36472566763994.28, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_1'), value=-61609669856604.23, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_2'), value=38826313368912.81, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_3'), value=23368480195864.094, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_4'), value=7138646371295.009, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_5'), value=15771713737800.309, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_6'), value=15538572355902.691, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_7'), value=20140651595478.39, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_8'), value=2185995260600.0457, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_9'), value=-1235716606724.8362, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_0'), value=80467599707714.69, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_1'), value=-144290073150142.4, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_2'), value=-46836761573515.13, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_3'), value=-70763069185245.05, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_4'), value=-10364619677119.854, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_5'), value=74902148333725.05, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_6'), value=20689260083406.543, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_7'), value=25222775432996.832, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_8'), value=-3568667834031.3525, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_9'), value=-3710011153156.468, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_0'), value=-184858927314496.12, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_1'), value=-514266071746075.2, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_2'), value=467467336643461.1, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_3'), value=251234219347449.62, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_4'), value=-396559361275605.44, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_5'), value=-100167908418117.12, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_6'), value=-89867078432146.0, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_7'), value=32914244958628.387, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_8'), value=-58918627491784.664, ), DataPoint(data_type='cluster_delta_sigma', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_9'), value=-21658612387435.21, )]\n",
+      "{np.str_('bin_z_0'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7b0b650>, np.str_('bin_rich_0'): <sacc.tracers.clusters.BinRichnessTracer object at 0x7f19a7b0a8d0>, np.str_('bin_z_1'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7b0bcb0>, np.str_('bin_z_2'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7b0b890>, np.str_('bin_z_3'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a7b086b0>, np.str_('radius_0'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7b0b920>, np.str_('radius_1'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7b0aff0>, np.str_('radius_2'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7b08530>, np.str_('radius_3'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7b08650>, np.str_('radius_4'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7b092b0>, np.str_('radius_5'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7b0a720>, np.str_('radius_6'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7b09520>, np.str_('radius_7'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7b0be30>, np.str_('radius_8'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7b08710>, np.str_('radius_9'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a7b0bc80>, np.str_('cosmodc2-1deg'): <sacc.tracers.survey.SurveyTracer object at 0x7f19a7b0b1d0>}\n",
+      "[DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0'), value=8, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1'), value=28, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2'), value=21, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3'), value=11, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_0'), value=-0.0057683758038830765, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_1'), value=0.008283846961274615, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_2'), value=0.004137058586064512, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_3'), value=-0.00920712646245946, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_4'), value=-0.00331654211614225, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_5'), value=0.00025137470727514097, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_6'), value=-0.0019937168031028568, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_7'), value=0.006646531468363067, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_8'), value=-0.0011068038872593955, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_9'), value=-0.004330218166654884, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_0'), value=0.006771081661358412, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_1'), value=0.0054915081517508645, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_2'), value=-5.8877004577892406e-05, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_3'), value=0.0012871115249653944, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_4'), value=0.0003103730318653583, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_5'), value=-0.0021036277053298473, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_6'), value=-0.0016175714535282563, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_7'), value=0.005003208888972494, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_8'), value=-0.004003656472527919, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_9'), value=0.001379405778294387, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_0'), value=0.007343835036093456, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_1'), value=-0.005635522767596872, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_2'), value=0.0050871036219183, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_3'), value=-0.0007884972533394026, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_4'), value=0.0039023751950495286, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_5'), value=0.002246486421363454, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_6'), value=0.002809951461091495, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_7'), value=0.0019891965200479586, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_8'), value=0.006516828281586569, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_9'), value=-0.000536013644756326, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_0'), value=0.012019966779839898, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_1'), value=-0.022510671931513883, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_2'), value=-0.026809827722014633, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_3'), value=0.0007326389498961291, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_4'), value=0.0072968403660315815, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_5'), value=0.005424956841941073, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_6'), value=0.0045705409625082655, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_7'), value=0.0007556915303554206, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_8'), value=0.0018612239339584336, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_9'), value=8.856387352999347e-05, )]\n",
+      "{np.str_('bin_z_0'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a79a5d00>, np.str_('bin_rich_0'): <sacc.tracers.clusters.BinRichnessTracer object at 0x7f19a79a6ae0>, np.str_('bin_z_1'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a79a60f0>, np.str_('bin_z_2'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a79a4800>, np.str_('bin_z_3'): <sacc.tracers.clusters.BinZTracer object at 0x7f19a79a6930>, np.str_('radius_0'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a79a4980>, np.str_('radius_1'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a79a5970>, np.str_('radius_2'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a79a7770>, np.str_('radius_3'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a79a7980>, np.str_('radius_4'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a79a72c0>, np.str_('radius_5'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a79a6810>, np.str_('radius_6'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a79a72f0>, np.str_('radius_7'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a79a4e60>, np.str_('radius_8'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a79a6a20>, np.str_('radius_9'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19a79a4320>, np.str_('cosmodc2-1deg'): <sacc.tracers.survey.SurveyTracer object at 0x7f19a79a7a40>}\n",
+      "[DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0'), value=8, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1'), value=28, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2'), value=21, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3'), value=11, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_0'), value=-0.0042255111387835, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_1'), value=0.0071924286695982075, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_2'), value=0.003911751349786751, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_3'), value=-0.008854282600393053, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_4'), value=-0.0036009030807943924, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_5'), value=0.00038534929387622457, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_6'), value=-0.002454178840692148, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_7'), value=0.006014190394443744, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_8'), value=-0.00046170535596548476, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_9'), value=-0.0052782035950950735, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_0'), value=0.006958648224897677, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_1'), value=0.004043275728861616, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_2'), value=-0.0003029669166881909, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_3'), value=0.0018757935870906861, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_4'), value=2.2992135418432474e-06, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_5'), value=-0.0036401690339365636, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_6'), value=-0.0021044074976070562, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_7'), value=0.005846139020754599, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_8'), value=-0.003921876173506852, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_9'), value=0.001754685489436478, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_0'), value=0.012211631514308072, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_1'), value=-0.0061255733404953485, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_2'), value=0.002519251460474656, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_3'), value=0.0004948769006267108, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_4'), value=0.0006379882552031647, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_5'), value=0.0038393369675195884, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_6'), value=0.0010787884856649145, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_7'), value=0.0027647784908776145, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_8'), value=0.0050183370909845535, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_9'), value=-0.0012036889305720175, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_0'), value=0.010709523581723763, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_1'), value=-0.02509490878424697, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_2'), value=-0.011751072128718084, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_3'), value=-0.0026608136715321118, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_4'), value=0.015033610243235488, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_5'), value=0.0018351123827488787, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_6'), value=0.01490214074632418, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_7'), value=0.005113948259636509, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_8'), value=0.0032806516602288224, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_9'), value=0.004170243860181031, )]\n",
+      "{np.str_('bin_z_0'): <sacc.tracers.clusters.BinZTracer object at 0x7f19ac087da0>, np.str_('bin_rich_0'): <sacc.tracers.clusters.BinRichnessTracer object at 0x7f19ac084200>, np.str_('bin_z_1'): <sacc.tracers.clusters.BinZTracer object at 0x7f19ac087c20>, np.str_('bin_z_2'): <sacc.tracers.clusters.BinZTracer object at 0x7f19ac084080>, np.str_('bin_z_3'): <sacc.tracers.clusters.BinZTracer object at 0x7f19ac084230>, np.str_('radius_0'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac0841a0>, np.str_('radius_1'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac084590>, np.str_('radius_2'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac087ef0>, np.str_('radius_3'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac087b90>, np.str_('radius_4'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac084050>, np.str_('radius_5'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac0843b0>, np.str_('radius_6'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac0842f0>, np.str_('radius_7'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac0873b0>, np.str_('radius_8'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac087b60>, np.str_('radius_9'): <sacc.tracers.clusters.BinRadiusTracer object at 0x7f19ac087ad0>, np.str_('cosmodc2-1deg'): <sacc.tracers.survey.SurveyTracer object at 0x7f19ac084140>}\n",
+      "[DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0'), value=8, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1'), value=28, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2'), value=21, ), DataPoint(data_type='cluster_counts', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3'), value=11, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_0'), value=0.0020864909471535156, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_1'), value=0.03020217112155864, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_2'), value=0.020086433269439832, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_3'), value=0.03571278246525472, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_4'), value=0.03858477276041311, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_5'), value=0.008899764731309526, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_6'), value=0.011089507336866951, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_7'), value=-0.00923431583127562, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_8'), value=0.0013931819276978062, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', 'radius_9'), value=-0.0013585282574536007, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_0'), value=0.0030512007064141173, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_1'), value=-0.03319212610951841, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_2'), value=0.007674767965221173, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_3'), value=0.005397182529185962, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_4'), value=-0.006426719300782847, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_5'), value=0.009021874943466061, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_6'), value=0.005518854203795951, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_7'), value=0.004786667913317031, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_8'), value=0.0006652366662837896, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_1', 'radius_9'), value=0.00012146436689626265, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_0'), value=0.0273952122383648, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_1'), value=-0.045680426678401845, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_2'), value=-0.029476217424801842, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_3'), value=-0.036440626942344595, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_4'), value=-0.006162295723782367, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_5'), value=0.022004388068007807, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_6'), value=0.00934906790714723, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_7'), value=0.011428695430877473, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_8'), value=-0.0003186069798181489, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_2', 'radius_9'), value=0.002261500100275522, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_0'), value=-0.07130719528686981, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_1'), value=-0.10916170988158881, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_2'), value=0.11440781650087622, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_3'), value=0.06864723708168308, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_4'), value=-0.08529522651520056, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_5'), value=-0.03794510239575124, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_6'), value=-0.02613161593663707, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_7'), value=0.018297400234814895, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_8'), value=-0.01885207927804766, ), DataPoint(data_type='cluster_shear', tracers=('cosmodc2-1deg', 'bin_rich_0', 'bin_z_3', 'radius_9'), value=-0.001756422897200986, )]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sacc\n",
+    "\n",
+    "for j, name in enumerate(test_sacc):\n",
+    "    t = sacc.Sacc.load_fits(name)\n",
+    "    print(t.tracers)\n",
+    "    data2 = []\n",
+    "    radius2 = []\n",
+    "    if j<=3:\n",
+    "        data_type = sacc.data_types.standard_types.cluster_delta_sigma\n",
+    "    else:\n",
+    "        data_type = sacc.data_types.standard_types.cluster_shear\n",
+    "    for i in range(0,radius_bins):\n",
+    "        trac = ('cosmodc2-1deg', 'bin_rich_0', 'bin_z_0', f'radius_{i}')\n",
+    "        print(t.get_data_points(data_type, trac))\n",
+    "        data2.append(t.get_data_points(data_type, trac)[0].value)\n",
+    "        radius = t.tracers[f'radius_{i}'].center\n",
+    "        radius2.append(radius)\n",
+    "        plt.plot(radius2, data2, label='Calibrated Shear')\n",
+    "        plt.ylabel(\"shear profile\")\n",
+    "        plt.xlabel(\"Radius bin\")\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "import sacc\n",
+    "\n",
+    "for j, name in enumerate(test_sacc):\n",
+    "    t = sacc.Sacc.load_fits(name)\n",
+    "    print(t.tracers)\n",
+    "    data2 = []\n",
+    "    data_type = sacc.data_types.standard_types.cluster_counts\n",
+    "    print(t.data)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (txpipe_clp_group)",
+   "language": "python",
+   "name": "test"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/txpipe/extensions/cluster_counts/convert_to_sacc.py b/txpipe/extensions/cluster_counts/convert_to_sacc.py
index 3984c82aa..b3f6d85f3 100644
--- a/txpipe/extensions/cluster_counts/convert_to_sacc.py
+++ b/txpipe/extensions/cluster_counts/convert_to_sacc.py
@@ -14,9 +14,10 @@ class CLClusterSACC(PipelineStage):
     ]
 
     config_options = {
-        #radial bin definition
-        "r_min" : 0.2, #in Mpc
-        "r_max" : 5.0, #in Mpc
+        #distance bin definition
+        "d_min" : 0.5, #in Mpc or acrmin
+        "d_max" : 5.0, #in Mpc or arcmin
+        "cov_component": "tan_jk",
     }
 
     def run(self):
@@ -28,11 +29,12 @@ def run(self):
         survey_name = my_configs['survey_name']
         area = my_configs['area']
         output_filename = self.get_output("cluster_sacc_catalog", final_name=True)
-
+        first_bin = next(iter(data.values()))
+        profile_type = first_bin["profile_type"]        
         sacc_obj = sacc.Sacc()
         self.add_tracers(sacc_obj, data, survey_name, area)
         self.add_counts_data(sacc_obj, data, survey_name)
-        self.add_deltasigma_data(sacc_obj, data, survey_name)
+        self.add_shear_data(sacc_obj, data, survey_name, profile_type)
         self.add_covariance_data(sacc_obj, data)
 
         sacc_obj.to_canonical_order()
@@ -66,12 +68,12 @@ def get_bins(self, data: dict):
             bin_z_dict[bin_z] = z_edges
             bin_rich_dict[bin_rich] = rich_edges
 
-        radius_centers = np.array(data['bin_zbin_0_richbin_0']['clmm_cluster_ensemble'].stacked_data['radius'])
-        rmin = self.config_options['r_min']
-        rmax = self.config_options['r_max'] 
-        radius_edges = np.logspace(np.log10(rmin), np.log10(rmax), len(radius_centers) + 1)
-        for i in range(len(radius_edges) - 1):
-            bin_radius_dict[f'radius_{i}'] = (radius_edges[i], radius_edges[i+1], radius_centers[i])
+        distance_centers = np.array(data['bin_zbin_0_richbin_0']['clmm_cluster_ensemble'].stacked_data['radius'])
+        dmin = self.config_options['d_min']
+        dmax = self.config_options['d_max'] 
+        distance_edges = np.logspace(np.log10(dmin), np.log10(dmax), len(distance_centers) + 1)
+        for i in range(len(distance_edges) - 1):
+            bin_radius_dict[f'radius_{i}'] = (distance_edges[i], distance_edges[i+1], distance_centers[i])
 
         return bin_z_dict, bin_rich_dict, bin_radius_dict
 
@@ -103,12 +105,16 @@ def add_counts_data(self, sacc_obj, data: dict, survey_name: str):
             bin_z, bin_rich = self.transform_bin_string(bin_comb)
             sacc_obj.add_data_point(cluster_count, (survey_name, bin_rich, bin_z), int(bin_data['n_cl']))
 
-    def add_deltasigma_data(self, sacc_obj, data: dict, survey_name: str):
+    def add_shear_data(self, sacc_obj, data: dict, survey_name: str, profile_type: str):
         """
         Adds cluster shear (delta sigma) data to the SACC object.
         """
         import sacc
-        cluster_shear = sacc.standard_types.cluster_shear
+        print(profile_type)
+        if profile_type == 'reduced_shear':
+            cluster_shear = sacc.standard_types.cluster_shear
+        elif profile_type == 'delta_sigma':
+            cluster_shear = sacc.standard_types.cluster_delta_sigma    
         _, _, radius_bins = self.get_bins(data)
 
         for bin_comb, bin_data in data.items():
@@ -119,17 +125,20 @@ def add_deltasigma_data(self, sacc_obj, data: dict, survey_name: str):
 
     def add_covariance_data(self, sacc_obj, data: dict):
         """
-        Adds covariance data to the SACC object.
+        Adds full block-diagonal covariance data to the SACC object.
         """
         import sacc
+        from scipy.linalg import block_diag
+        cov_str = self.config["cov_component"]
         cluster_count = sacc.standard_types.cluster_counts
-        counts_points = np.array(sacc_obj.get_data_points(cluster_count))
-        counts_cov = np.array([point.value for point in counts_points])
+        counts_points = sacc_obj.get_data_points(cluster_count)
+    
+        counts_values = np.array([point.value for point in counts_points])
+        counts_cov_block = np.diag(counts_values) 
 
-        deltasigma_cov = [
-            bin_data['clmm_cluster_ensemble'].cov['tan_sc'].diagonal()
+        ds_blocks = [
+            bin_data['clmm_cluster_ensemble'].cov[cov_str]
             for bin_data in data.values()
         ]
-
-        diag_cov_vector = np.concatenate([counts_cov.flatten(), np.array(deltasigma_cov).flatten()])
-        sacc_obj.add_covariance(np.diag(diag_cov_vector))
+        full_cov = block_diag(counts_cov_block, *ds_blocks)
+        sacc_obj.add_covariance(full_cov)
diff --git a/txpipe/extensions/cluster_counts/make_ensemble_profile.py b/txpipe/extensions/cluster_counts/make_ensemble_profile.py
index dcc3a1b56..d2990a357 100644
--- a/txpipe/extensions/cluster_counts/make_ensemble_profile.py
+++ b/txpipe/extensions/cluster_counts/make_ensemble_profile.py
@@ -28,10 +28,16 @@ class CLClusterEnsembleProfiles(CLClusterShearCatalogs):
         "r_min" : 0.2, #in Mpc
         "r_max" : 3.0, #in Mpc
         "nbins" : 5, # number of bins
+        #Angular bin definition
+        "angle_arcmin_min" : 25.0, #in arcmin
+        "angle_arcmin_max" : 45.0, #in arcmin
+        "nbins" : 5, # number of bins
         #type of profile
-        "delta_sigma_profile" : True,
-        "shear_profile" : False,
         "magnification_profile" : False,
+        "units": "mpc", # options are mpc or arcmin
+        "cov_type" : "jackknife_covariance",
+        "jackknife_nside": 32,
+        "bootstrap_nboot": 100,
         #coordinate_system for shear
         #"coordinate_system" : 'euclidean' #Must be either 'celestial' or 'euclidean'
     }
@@ -41,14 +47,20 @@ def run(self):
         import astropy
         import h5py
         import clmm
-        import clmm.cosmology.ccl
-
-        self.radial_bins = clmm.dataops.make_bins(self.config["r_min"], self.config["r_max"], nbins=self.config["nbins"], method="evenlog10width")
-        print (self.radial_bins) 
+        import clmm.cosmology.ccl 
+        if self.config["units"].lower() == "arcmin":
+            bin_min = self.config["angle_arcmin_min"]
+            bin_max = self.config["angle_arcmin_max"]
+        else:
+            bin_min = self.config["r_min"]
+            bin_max = self.config["r_max"]
+        self.distance_bins = clmm.dataops.make_bins(bin_min, bin_max, nbins=self.config["nbins"], method="evenlog10width")
+        print (self.distance_bins) 
+        
+        self.cluster_shears_cat, self.coordinate_system, self.profile_type= self.load_cluster_shear_catalog() 
 
-        # Read cluster_shear_catalog groups data only
-        self.cluster_shears_cat, self.coordinate_system = self.load_cluster_shear_catalog() 
         print (self.coordinate_system) 
+
         
         with self.open_input("fiducial_cosmology", wrapper=True) as f:
             ccl_cosmo = f.to_ccl()
@@ -58,14 +70,9 @@ def run(self):
         # load cluster catalog as an astropy table
         #clusters = self.load_cluster_catalog()
         
-        if self.config["delta_sigma_profile"]==True:
-            
-            cluster_stack_dict = self.load_cluster_catalog_tomography_group() 
-            pickle.dump(cluster_stack_dict, open(self.get_output("cluster_profiles"), 'wb'))
-        
-        #cluster_stack.save(self.get_output("cluster_profiles"))
-        else :
-            print("Config option not supported, only delta_sigma_profiles supported")
+
+        cluster_stack_dict = self.load_cluster_catalog_tomography_group() 
+        pickle.dump(cluster_stack_dict, open(self.get_output("cluster_profiles"), 'wb'))
 
                     
 
@@ -76,24 +83,27 @@ def load_cluster_shear_catalog(self, indices = None) :
         if indices == None:
             with self.open_input("cluster_shear_catalogs") as f:
                 meta_coord_sys = f['provenance'].attrs['config/coordinate_system']
+                g_idx = f["index/"]
+                profile_type = g_idx.attrs.get("profile_type")
                 g = f["catalog/"]
                 cluster_id = g['cluster_id'][:]
                 cluster_sample_start = g['cluster_sample_start'][:]
                 cluster_sample_count = g['cluster_sample_count'][:]
                 cluster_theta_max_arcmin = g['cluster_theta_max_arcmin'][:]
-
             tab = Table({"cluster_id": cluster_id,
                          "cluster_sample_count": cluster_sample_count, "cluster_sample_start": cluster_sample_start,
                          "cluster_theta_max_arcmin": cluster_theta_max_arcmin})
 
-            return tab, meta_coord_sys
+            return tab, meta_coord_sys, profile_type
 
         # Read index group data corresponding to a specific cluster_id
         #     indices are defined base on parameter stored in catalog/cluster_sample_start and _count for the given cluster id
         with self.open_input("cluster_shear_catalogs") as f:
             ifirst,icount = indices
             ilast = ifirst+icount
+            meta_coord_sys = f['provenance'].attrs['config/coordinate_system']
             g = f["index/"]
+            profile_type = g.attrs["profile_type"]
             cluster_index = g['cluster_index'][ifirst:ilast]
             cluster_id = g['cluster_id'][ifirst:ilast]
             tangential_comp = g['tangential_comp'][ifirst:ilast]
@@ -106,15 +116,18 @@ def load_cluster_shear_catalog(self, indices = None) :
         tab = Table({"cluster_index": cluster_index, "cluster_id" : cluster_id, "tangential_comp_clmm": tangential_comp,
                       "cross_comp_clmm": cross_comp, "source_index": source_index,
                       "weight_clmm": weight, "distance_arcmin": distance_arcmin})
-    
-        return tab, None
+                  
+        return tab, meta_coord_sys, profile_type
+
 
 
 
     def create_cluster_ensemble(self, cluster_list, cluster_ensemble_id=0):
         import clmm
-
-
+        shear_units = self.config["units"].lower()
+        # load cluster shear catalog using similar astropy table set up as cluster catalog
+        #cluster_shears_cat, coordinate_system = self.load_cluster_shear_catalog() 
+        
         # Create empty cluster ensemble 
         cluster_ensemble = clmm.ClusterEnsemble(cluster_ensemble_id) 
         
@@ -141,8 +154,8 @@ def create_cluster_ensemble(self, cluster_list, cluster_ensemble_id=0):
                                     int(self.cluster_shears_cat["cluster_sample_count"][cluster_cat_index]))
             #print("-> cluster id ",id_cl,"  ",cluster_cat_index," ",cluster_cat_indices,"  ",cluster_cat_indices[0]+cluster_cat_indices[1])
             
-            bg_cat, _ = self.load_cluster_shear_catalog(cluster_cat_indices)
-               
+            bg_cat, _, self.profile_type = self.load_cluster_shear_catalog(cluster_cat_indices)
+            print(self.profile_type) 
             print('For cluster', id_cl, 'at z=',z_cl,'with n_source = ',len(bg_cat["source_index"]) , 'theta_max is', np.max(bg_cat["distance_arcmin"]), ' arcmin =', clmm.utils.convert_units(np.max(bg_cat["distance_arcmin"]), 'arcmin', 'Mpc', z_cl, self.clmm_cosmo), 'Mpc')
             
             # To use CLMM, need to have galaxy table in clmm.GCData type
@@ -155,17 +168,16 @@ def create_cluster_ensemble(self, cluster_list, cluster_ensemble_id=0):
             # Instantiating a CLMM galaxy cluster object
             gc_object = clmm.GalaxyCluster(np.int(id_cl), ra_cl, dec_cl, z_cl, galcat)
             gc_object.richness = rich_cl
-
-
-            if (clmm.utils.convert_units(np.max(bg_cat["distance_arcmin"]), 'arcmin', 'Mpc', z_cl, self.clmm_cosmo)< self.radial_bins[-1]):
-                print ("!!! maximum radial distance of source smaller than radial_bins")
+            cat_max_distance = (clmm.utils.convert_units(np.max(bg_cat["distance_arcmin"]), "arcmin", "Mpc", z_cl, self.clmm_cosmo) if shear_units == "mpc" else np.max(bg_cat["distance_arcmin"]))
+            if cat_max_distance < self.distance_bins[-1]:
+                print ("!!! maximum distance of source smaller than distance_bins")
 
             del bg_cat
 
             # Compute radial profile for the current cluster
             gc_object.make_radial_profile(
-                    "Mpc", 
-                    bins=self.radial_bins,
+                    shear_units, 
+                    bins=self.distance_bins,
                     cosmo=self.clmm_cosmo, 
                     tan_component_in = "tangential_comp_clmm", # name given in the CLClusterShearCatalogs stage
                     cross_component_in = "cross_comp_clmm", # name given in the CLClusterShearCatalogs stage
@@ -173,13 +185,11 @@ def create_cluster_ensemble(self, cluster_list, cluster_ensemble_id=0):
                     cross_component_out = "cross_comp",
                     weights_in = "weight_clmm", # name given in the CLClusterShearCatalogs stage
                     weights_out = "W_l",
-                    include_empty_bins = True 
+                    include_empty_bins = True ,
+                    use_weights=True
                     )
-
-
                 # Quick check - Print out the profile information for the first 2 cluster of the list
-            #if cluster_index == 0 or cluster_index == 1:
-            #if cluster_index <2:  
+            # if cluster_index == 0 or cluster_index == 1:
             #    print(gc_object.profile)
 
                 # Add the profile to the ensemble
@@ -196,7 +206,12 @@ def create_cluster_ensemble(self, cluster_list, cluster_ensemble_id=0):
         print("cluster ensemble computed")
             
         #compute sample covariance
-        cluster_ensemble.compute_sample_covariance(tan_component="tangential_comp", cross_component="cross_comp")
+        if self.config["cov_type"] == "sample_covariance":
+            cluster_ensemble.compute_sample_covariance(tan_component="tangential_comp", cross_component="cross_comp")
+        elif self.config["cov_type"] == "jackknife_covariance":
+            cluster_ensemble.compute_jackknife_covariance(tan_component="tangential_comp", cross_component="cross_comp", n_side = self.config["jackknife_nside"])
+        elif self.config["cov_type"] == "bootstrap_covariance":
+            cluster_ensemble.compute_bootstrap_covariance(tan_component="tangential_comp", cross_component="cross_comp", n_bootstrap = self.config["bootstrap_nboot"])
         print("covariance computed")
     
         return cluster_ensemble
@@ -212,8 +227,7 @@ def load_cluster_catalog_tomography_group(self): # NEED TO CHNAGE FUNCTION NAME
             for key in k :
                 group = f["cluster_bin"][key]
                 clusters = self.load_cluster_list(group=group) #elf.get_cluster_indice( DOES THIS FUNCTION COMES FROM ?
-                print(key, group, dict(group.attrs), len(clusters), clusters)
-                
+                print(clusters)
                 if  len(clusters)>1:
                     cluster_stack = self.create_cluster_ensemble(clusters, cluster_ensemble_id=key)
                 else :
@@ -222,7 +236,7 @@ def load_cluster_catalog_tomography_group(self): # NEED TO CHNAGE FUNCTION NAME
                 
                 #dict(dset_out[i].attrs), dset_out[i]['redshift'][:].size) 
                 
-                binned_cluster_stack[key]={'cluster_bin_edges':dict(group.attrs), 'n_cl':len(clusters), 'clmm_cluster_ensemble':cluster_stack}
+                binned_cluster_stack[key]={'cluster_bin_edges':dict(group.attrs), 'n_cl':len(clusters), 'clmm_cluster_ensemble':cluster_stack, 'profile_type': self.profile_type}
                 
             
         return binned_cluster_stack
diff --git a/txpipe/extensions/cluster_counts/sources_select_compute.py b/txpipe/extensions/cluster_counts/sources_select_compute.py
index 9ae4ed354..0450e42b8 100644
--- a/txpipe/extensions/cluster_counts/sources_select_compute.py
+++ b/txpipe/extensions/cluster_counts/sources_select_compute.py
@@ -36,6 +36,11 @@ class CLClusterShearCatalogs(PipelineStage):
         "subtract_mean_shear": False, # Not clear if this is useful for clusters
         "coordinate_system": "celestial",
         "use_true_shear": False,
+        "delta_sigma": False, # If True then we compute delta-sigma, otherwise we compute gamma
+        "use_shape_noise": False,
+        "use_radius": True,
+        "max_angle": 30, # arcmin
+        
     }
 
     def run(self):
@@ -48,15 +53,19 @@ def run(self):
         # load cluster catalog as an astropy table
         clusters = self.load_cluster_catalog()
         ncluster = len(clusters)
-
+        if self.config["use_radius"]:
         # turn the physical scale max_radius to an angular scale at the redshift of each cluster
-        cluster_theta_max = self.compute_theta_max(clusters["redshift"])
+            cluster_theta_max = self.compute_theta_max(clusters["redshift"])
 
         # For the neighbour search, sklearn doesn't let us have
         # a different distance per cluster. So we use the maximum
         # distance over all the clusters and then filter down later.
-        max_theta_max = cluster_theta_max.max()
-        max_theta_max_arcmin = np.degrees(max_theta_max) * 60
+            max_theta_max = cluster_theta_max.max()
+            max_theta_max_arcmin = np.degrees(max_theta_max) * 60
+        else:
+            max_theta_max_arcmin = self.config["max_angle"]
+            max_theta_max = np.deg2rad(max_theta_max_arcmin / 60)
+            cluster_theta_max = np.repeat(max_theta_max, ncluster)
         if self.rank == 0:
             print(f"Max theta_max = {max_theta_max} radians = {max_theta_max_arcmin} arcmin")
 
@@ -97,11 +106,11 @@ def run(self):
 
                 if gal_index.size == 0:
                     continue
-
-                # Cut down to galaxies close enough to this cluster
-                dist_good = distance < cluster_theta_max[cluster_index]
-                gal_index = gal_index[dist_good]
-                distance = distance[dist_good]
+                if self.config["use_radius"]:
+                    # Cut down to galaxies close enough to this cluster
+                    dist_good = distance < cluster_theta_max[cluster_index]
+                    gal_index = gal_index[dist_good]
+                    distance = distance[dist_good]
 
                 if gal_index.size == 0:
                     continue
@@ -109,25 +118,21 @@ def run(self):
                 cluster_z = clusters[cluster_index]["redshift"]
                 cluster_ra = clusters[cluster_index]["ra"]
                 cluster_dec = clusters[cluster_index]["dec"]
-
+                z_info = self.get_z_gal_good(redshift_cut_criterion, data, gal_index)
                 # # Cut down to clusters that are in front of this galaxy
                 if redshift_cut_criterion == "pdf":
                     # If we cut based on the PDF then we need the probability
                     # that the galaxy z is behind the cluster to be greater
                     # than a cut criterion.
-                    pdf_z = data["pdf_z"]
-                    pdf = data["pdf_pz"][gal_index]
+                    pdf_z = z_info[1]
+                    pdf = z_info[0]
                     pdf_frac = pdf[:, pdf_z > cluster_z + delta_z].sum(axis=1) / pdf.sum(axis=1)
                     z_good = pdf_frac > redshift_cut_criterion_pdf_fraction
-                elif redshift_cut_criterion == "ztrue":
-                    zgal = data["redshift_true"][gal_index]
-                    z_good = zgal > cluster_z + delta_z
-                elif redshift_cut_criterion == "zmode":
-                    # otherwise if we are not using the PDF we do a simple cut
-                    zgal = data["redshift"][gal_index]
+                elif redshift_cut_criterion in ["ztrue", "zmode", "zmean"]:
+                    zgal = z_info[0]
                     z_good = zgal > cluster_z + delta_z
                 else:
-                    raise NotImplementedError("Not implemented other z cuts than zmode")
+                    raise NotImplementedError("Not implemented other z cuts")
                 
                 gal_index = gal_index[z_good]
                 distance = distance[z_good]
@@ -182,6 +187,10 @@ def run(self):
             index_group.create_dataset("g1", shape=(total_count,), dtype=np.float64)
             index_group.create_dataset("g2", shape=(total_count,), dtype=np.float64)
             index_group.create_dataset("distance_arcmin", shape=(total_count,), dtype=np.float64)
+            profile_group_name = "reduced_shear"
+            if self.config['delta_sigma'] == True:
+                profile_group_name = "delta_sigma"
+            index_group.attrs["profile_type"] = profile_group_name
 
 
         # Now we loop through each cluster and collect all the galaxies
@@ -210,7 +219,11 @@ def run(self):
                 # Each process flattens the list of all the galaxies for this cluster
                 indices = np.concatenate([d[0] for d in per_cluster_data[i]])
                 distances = np.concatenate([d[1] for d in per_cluster_data[i]])
-                weights = np.concatenate([d[2] for d in per_cluster_data[i]])
+                if self.config['delta_sigma']:
+                    weights = np.concatenate([d[2] for d in per_cluster_data[i]])
+                else:
+                    #If we are using gamma, the weights do not deppend on each galaxy, so the function returns a float
+                    weights = np.full(len(indices), per_cluster_data[i][0][2])
                 tangential_comps = np.concatenate([d[3] for d in per_cluster_data[i]])
                 cross_comps = np.concatenate([d[4] for d in per_cluster_data[i]])
                 g1 = np.concatenate([d[5] for d in per_cluster_data[i]])
@@ -258,10 +271,8 @@ def run(self):
             index_group["g1"][start:start + n] = g1
             index_group["g2"][start:start + n] = g2
             index_group["distance_arcmin"][start:start + n] = np.degrees(distances) * 60
-           
 
             start += n
-
         if self.rank == 0:
             outfile.close()
 
@@ -367,49 +378,50 @@ def compute_theta_max(self, z):
             print("Min search angle = ", theta_max_arcmin.min(), "arcmin")
             print("Mean search angle = ", theta_max_arcmin.mean(), "arcmin")
             print("Max search angle = ", theta_max_arcmin.max(), "arcmin")
-
         return theta_max
 
 
     def compute_sources_quantities(self, clmm_cosmo, data, index, z_cluster, ra_cluster, dec_cluster):
         import clmm
-
+        z_cluster = float(z_cluster)
+        is_deltasigma = self.config["delta_sigma"]
+        criterion = self.config["redshift_weight_criterion"]
+        coordinate_system = self.config["coordinate_system"]
+        use_shape_noise = self.config["use_shape_noise"]
+        sigma_c = None
         # Depending on whether we are using the PDF or not, choose
         # some keywords to give to compute_galaxy_weights
-        if self.config["redshift_weight_criterion"] == "pdf":
+        z_info = self.get_z_gal_good(criterion, data, index)
+        if criterion == "pdf":
             # We need the z and PDF(z) arrays in this case
-            pdf_z = data["pdf_z"]
-            pdf_pz = data["pdf_pz"][index]
-            
+            pdf_pz = z_info[0]
+            pdf_z = z_info[1]
             # suppress user warnings containing string "nSome source redshifts are lower than the cluster redshift"
-            with warnings.catch_warnings():
-                warnings.filterwarnings("ignore")
-                sigma_c = clmm.theory.compute_critical_surface_density_eff(
-                    cosmo=clmm_cosmo,
-                    z_cluster=z_cluster,
-                    pzbins=pdf_z,
-                    pzpdf=pdf_pz,
-                )            
-        elif self.config["redshift_weight_criterion"] == "zmode":
-            # point-estimated redshift
-            z_source = data["redshift"][index]
-            sigma_c = clmm_cosmo.eval_sigma_crit(z_cluster, z_source)
-        elif self.config["redshift_weight_criterion"] == "ztrue":
-            z_source = data["redshift_true"][index] 
-            sigma_c = clmm_cosmo.eval_sigma_crit(z_cluster, z_source)
+            if is_deltasigma:
+                with warnings.catch_warnings():
+                    warnings.filterwarnings("ignore")
+                    sigma_c = clmm.theory.compute_critical_surface_density_eff(
+                        cosmo=clmm_cosmo,
+                        z_cluster=z_cluster,
+                        pzbins=pdf_z,
+                        pzpdf=pdf_pz,
+                        )
+        elif criterion in ["zmode", "ztrue", "zmean"]:
+            z_source = z_info[0]
+            if is_deltasigma:
+                sigma_c = clmm_cosmo.eval_sigma_crit(z_cluster, z_source)
         else:
-            raise NotImplementedError("Not implemented zmean weighting")
+            raise NotImplementedError(f"Not implemented {criterion} weighting")
             
-
+        g1 = data["g1"][index]
+        g2 = data["g2"][index]
         weight = clmm.dataops.compute_galaxy_weights(
             sigma_c = sigma_c,
-            is_deltasigma=True,
-            use_shape_noise=False,
+            is_deltasigma=is_deltasigma,
+            use_shape_noise=use_shape_noise,
+            shape_component1=np.array(g1),
+            shape_component2=np.array(g2),
         )
-
-        coordinate_system = self.config["coordinate_system"]
-        g1 = data["g1"][index]
-        g2 = data["g2"][index]
         _, tangential_comp, cross_comp = clmm.compute_tangential_and_cross_components(
             ra_cluster,
             dec_cluster,
@@ -419,7 +431,7 @@ def compute_sources_quantities(self, clmm_cosmo, data, index, z_cluster, ra_clus
             g2,
             coordinate_system=coordinate_system,
             geometry="curve",
-            is_deltasigma=True,
+            is_deltasigma=is_deltasigma,
             sigma_c=sigma_c,
             validate_input=True,
         )
@@ -546,18 +558,15 @@ def iterate_source_catalog(self):
             # index.  This comes in useful later because we will cut
             # down here to just objects in the WL sample.
             data["original_index"] = np.arange(s, e, dtype=int)
-
             # cut down to objects in the WL sample
 #            wl_sample = data["source_bin"] >= 0
             wl_sample = data["bin"] >= 0
             data = {name: col[wl_sample] for name, col in data.items()}
-
             # give the shear columns a unified name, whether
             # they are metacal, metadetect, etc., also rename
             # zmean or zmode to "redshift"
             for old, new in rename.items():
                 data[new] = data.pop(old)
-
             # Apply the shear calibration to this sample.
             # Optionally subtract the mean (of the whole WL sample,
             # not the local mean)
@@ -575,6 +584,23 @@ def iterate_source_catalog(self):
             # Give this chunk of data to the main run function
             yield s, e, data
 
+
+
+    def get_z_gal_good(self, criterion, data, gal_index):
+        zgal = None
+        pdf_z = None
+        if criterion == "pdf":
+            pdf_z = data["pdf_z"]
+            zgal = data["pdf_pz"][gal_index]
+        elif criterion == "ztrue":
+            zgal = data["redshift_true"][gal_index]
+        elif criterion == "zmode":
+            zgal = data["redshift"][gal_index]
+        elif criterion == "zmean":
+            zgal = data["zmean"][gal_index]
+        else:
+            raise NotImplementedError("Not implemented other z cuts")
+        return (zgal, pdf_z)
 #def get_cluster_shear_catalogs_index_from_cluster_id(cluster_file, cluster_id):
 #    outfile = HDFFile(cluster_file, "r").file
 #    cond = outfile['catalog/cluster_id'][:] == cluster_id
diff --git a/txpipe/ingest/mocks.py b/txpipe/ingest/mocks.py
index 39981b780..b5f5048b2 100755
--- a/txpipe/ingest/mocks.py
+++ b/txpipe/ingest/mocks.py
@@ -66,6 +66,7 @@ def data_iterator(self, gc):
             "size_true",
             "galaxy_id",
             "redshift_true",
+            "convergence",
         ]
         # Add any extra requestd columns
         cols += self.config["extra_cols"].split()
@@ -508,8 +509,8 @@ def make_mock_metadetect(self, data, photo):
 
         eps = np.random.normal(0, shape_noise, nobj) + 1.0j * np.random.normal(0, shape_noise, nobj)
         # True shears without shape noise
-        g1 = data["shear_1"]
-        g2 = data["shear_2"]
+        g1 = data["shear_1"]/ (1.0 - data["convergence"]) 
+        g2 = data["shear_2"]/ (1.0 - data["convergence"])
 
         if self.config["flip_g2"]:
             g2 *= -1
@@ -519,7 +520,8 @@ def make_mock_metadetect(self, data, photo):
         e = (eps + g) / (1 + g.conj() * eps)
         e1 = e.real
         e2 = e.imag
-
+        g1 = e1
+        g2 = e2
         zero = np.zeros(nobj)
         # Now collect together everything to go into the metacal
         # file