add planck cluster cosmology case

docs/index.rst

@@ -91,6 +91,13 @@ Cosmic Linear Anisotropy Solving System with Machine Learning Accelerated CMB, L
    :caption: TSZ X Galaxies
 
    notebooks/class_sz_ngal_tSZ
+
+
+.. toctree::
+   :maxdepth: 2
+   :caption: SZ cluster cosmology
+
+   notebooks/class_szfast_cluster_counts
 
 
 

docs/notebooks/class_szfast_cluster_counts.ipynb

@@ -0,0 +1,204 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b62b5ce8-fbf5-41f3-b109-c05ac94a0b35",
+   "metadata": {},
+   "source": [
+    "# Planck binned cluster counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2a2825a6-6e82-42e0-97b9-65d34f8af150",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using PATH_TO_CLASS_SZ_DATA: /Users/boris/class_sz_data_directory\n",
+      "Found class_sz_data_directory directory with all repositories at: /Users/boris/class_sz_data_directory\n",
+      "PATH_TO_CLASS_SZ_DATA is already correctly set.\n",
+      "Required directories already exist. Skipping download.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from classy_sz import Class as Class_sz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2a569f13-432f-4d4f-b246-13feabd4fc7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/boris/Work/CLASS-SZ/SO-SZ/mcfit/mcfit/mcfit.py:130: UserWarning: use backend='jax' if desired\n",
+      "  warnings.warn(\"use backend='jax' if desired\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ntot 5.5943177031e+02\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "class_sz = Class_sz()\n",
+    "\n",
+    "\n",
+    "class_sz.set({\n",
+    "\n",
+    "'output' : 'sz_cluster_counts',\n",
+    "'mass function' :  'T08M500c',\n",
+    "'has_selection_function' : 1,\n",
+    "'experiment' : 0,\n",
+    "'y_m_relation' : 0,\n",
+    "'use_skyaveraged_noise': 0,\n",
+    "'use_planck_binned_proba' : 0,\n",
+    "\n",
+    "\n",
+    "'m_pivot_ym_[Msun]': 3e14,    \n",
+    "\n",
+    "'M_min' : 1e13*0.7, \n",
+    "'M_max' : 1e16*0.7,\n",
+    "\n",
+    "'z_min' : 0.01,\n",
+    "'z_max' : 1.02,\n",
+    "\n",
+    "'omega_b': 0.0224178568132,\n",
+    "'omega_cdm':  0.11933148326520002,\n",
+    "'H0': 70.,\n",
+    "'tau_reio': 0.0561,\n",
+    "'ln10^{10}A_s': 2.9799585,\n",
+    "'n_s': 0.96,\n",
+    "\n",
+    "\n",
+    "\n",
+    "'bin_z_min_cluster_counts' : 0.01,\n",
+    "'bin_z_max_cluster_counts' : 1.01,\n",
+    "'bin_dz_cluster_counts' : 0.1,\n",
+    "\n",
+    "'bin_dlog10_snr': 0.25, # fiducial 0.25\n",
+    "'log10_snr_min' : 0.7, # fiducial 0.7\n",
+    "\n",
+    "#the paramater dlny is crucial, it controls the speed of the calculation\n",
+    "#important to check that lnymin and lnymax are broad enough\n",
+    "'dlny' : 0.01, # fiducial 0.05\n",
+    "'lnymin' : -15, # fiducial -11\n",
+    "'lnymax' : 1., # fiducial 1.\n",
+    "'sigmaM_ym' :0.173, # fiducial 0.173\n",
+    "\n",
+    "\n",
+    "'dlnM_cluster_count_completeness_grid' : 0.01, # 0.01 fiducial\n",
+    "\n",
+    "\n",
+    "'cluster_count_completeness_grid_z_cutoff_low' : 0.5,\n",
+    "'cluster_count_completeness_grid_z_cutoff_mid' : 1.5,\n",
+    "'dz_cluster_count_completeness_grid_low_z' : 5e-3,\n",
+    "'dz_cluster_count_completeness_grid_mid_z' : 1e-2,\n",
+    "'dz_cluster_count_completeness_grid_high_z' : 1e-1,\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "'signal-to-noise_cut-off_for_survey_cluster_completeness' : 6.,\n",
+    "\n",
+    "\n",
+    "# X ray mass bias (if applicable)\n",
+    "'B' : 1.25,\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# tabulation of mass function:\n",
+    "'n_z_dndlnM' : 200,\n",
+    "'n_m_dndlnM' : 200,\n",
+    "'ndim_redshifts' : 200,\n",
+    "\n",
+    "'szcc_dof': 0.,\n",
+    "'cosmo_model': 1,\n",
+    "\n",
+    "})\n",
+    "class_sz.compute_class_szfast()\n",
+    "\n",
+    "dNdzdy_theoretical = class_sz.dndzdy_theoretical()['dndzdy']\n",
+    "z_center = class_sz.dndzdy_theoretical()['z_center']\n",
+    "z_edges = class_sz.dndzdy_theoretical()['z_edges']\n",
+    "log10y_center = class_sz.dndzdy_theoretical()['log10y_center']\n",
+    "log10y_edges = class_sz.dndzdy_theoretical()['log10y_edges']\n",
+    "\n",
+    "N_z,N_y = np.shape(dNdzdy_theoretical)\n",
+    "N_clusters_z_theory = []\n",
+    "\n",
+    "for iz in range(N_z):\n",
+    "    N_clusters_z_theory.append(np.sum(dNdzdy_theoretical[iz][0:]))\n",
+    "N_clusters_y_theory = []\n",
+    "\n",
+    "for iy in range(N_y):\n",
+    "    N_clusters_y_theory.append(np.sum(np.asarray(dNdzdy_theoretical)[:,iy]))\n",
+    "print('Ntot %.10e'%(np.sum(N_clusters_z_theory)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "58039759-2291-44ca-890b-fb509af0a4ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.787079475530407"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class_sz.sigma8()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}