add Paper_v2.0/paper_formating.py

examples/Paper_v2.0/Figure_02_halo_miscentering_boost_theory.ipynb

@@ -0,0 +1,167 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Figure to show the new 2-halo term and miscentering functionality"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from paper_formating import add_grid, prep_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Imports specific to clmm "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import clmm\n",
+    "import clmm.utils as u\n",
+    "from cosmology_set import dc2_cosmo as cosmo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make sure we know which version we're using"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clmm.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the galaxy cluster model.  Here, we choose parameters that describe the galaxy cluster model, including the mass definition, concentration, and mass distribution.  For the mass distribution, we choose a distribution that follows an NFW profile."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moo = clmm.Modeling(massdef=\"mean\", delta_mdef=200, halo_profile_model=\"nfw\")\n",
+    "\n",
+    "mass_cl = 1.0e14\n",
+    "z_cl = 0.4\n",
+    "\n",
+    "conc_cl = 5.4  # Duffy08 value for this halo mass and redshift (see last commented cell)\n",
+    "halo_bias = 2.4  # Tkinker10 value for this halo mass and redshift (see last commented cell)\n",
+    "\n",
+    "moo.set_cosmo(cosmo)\n",
+    "moo.set_concentration(conc_cl)\n",
+    "moo.set_mass(mass_cl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r_proj = np.logspace(-2, 2, 100)\n",
+    "\n",
+    "DeltaSigma = moo.eval_excess_surface_density(r_proj, z_cl)\n",
+    "\n",
+    "# Miscentered DeltaSigma\n",
+    "DeltaSigma_mis = moo.eval_excess_surface_density(r_proj, z_cl, r_mis=0.2)\n",
+    "\n",
+    "# 2halo DeltaSigma\n",
+    "DeltaSigma_2h = moo.eval_excess_surface_density_2h(r_proj, z_cl, halobias=6)\n",
+    "\n",
+    "# boost model\n",
+    "r_scale = 0.3\n",
+    "nfw_boost = u.compute_nfw_boost(r_proj, r_scale, boost0=0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the predicted profiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = prep_plot(figsize=(9, 9))\n",
+    "ax = plt.axes()\n",
+    "\n",
+    "ax.loglog(r_proj, DeltaSigma, label=\"1-halo (reference)\", color=\"k\")\n",
+    "ax.loglog(r_proj, DeltaSigma_mis, ls=\"--\", label=r\"1-halo ($R_{\\rm mis} = 0.2$Mpc)\")\n",
+    "ax.loglog(r_proj, DeltaSigma / nfw_boost, ls=\":\", label=\"1-halo (boost correction)\")\n",
+    "ax.loglog(r_proj, DeltaSigma_2h, ls=\"dashdot\", label=\"2-halo\")\n",
+    "ax.legend(loc=1, fontsize=6.5)\n",
+    "\n",
+    "ax.set_xlabel(\"R [Mpc]\")\n",
+    "ax.set_ylabel(r\"$\\Delta\\Sigma$ [M$_\\odot$ Mpc$^{-2}$]\")\n",
+    "ax.set_ylim(8.0e10, 1e15)\n",
+    "ax.set_xlim(1e-2, 1e2)\n",
+    "\n",
+    "add_grid(ax)\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "fig.savefig(\"2h_miscentering_boost.png\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "clmm",
+   "language": "python",
+   "name": "clmm"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/Paper_v2.0/Figure_03_magnification_bias.ipynb

@@ -0,0 +1,168 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plot magnification bias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Imports specific to clmm "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import clmm\n",
+    "from cosmology_set import dc2_cosmo as cosmo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make sure we know which version we're using"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clmm.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the galaxy cluster model.  Here, we choose parameters that describe the galaxy cluster model, including the mass definition, concentration, and mass distribution.  For the mass distribution, we choose a distribution that follows an NFW profile."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moo = clmm.Modeling(massdef=\"mean\", delta_mdef=200, halo_profile_model=\"nfw\")\n",
+    "\n",
+    "moo.set_cosmo(cosmo)\n",
+    "moo.set_concentration(4)\n",
+    "moo.set_mass(1.0e15)\n",
+    "\n",
+    "z_cl = 1.0\n",
+    "\n",
+    "# source properties\n",
+    "z_src = 2.0  # all sources in the same plane\n",
+    "z_distrib_func = (\n",
+    "    clmm.utils.redshift_distributions.chang2013\n",
+    ")  # sources redshift following a distribution\n",
+    "alpha = [2, -0.5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute quantity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r3d = np.logspace(-2, 2, 100)\n",
+    "mu_bias = moo.eval_magnification_bias(r3d, z_cl, z_src, alpha)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the predicted profiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from paper_formating import prep_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = prep_plot(figsize=(9, 9))\n",
+    "ax = plt.axes()\n",
+    "\n",
+    "ax.plot(r3d, mu_bias[0] - 1, label=\"$\\\\alpha$ =\" + str(alpha[0]))\n",
+    "ax.plot(r3d, mu_bias[1] - 1, label=\"$\\\\alpha$ =\" + str(alpha[1]))\n",
+    "\n",
+    "ax.set_ylabel(\"$\\delta_{\\mu}$\")\n",
+    "ax.set_xlabel(\"R [Mpc]\")\n",
+    "ax.legend()  # fontsize=\"xx-large\")\n",
+    "ax.set_xscale(\"log\")\n",
+    "\n",
+    "ax.set_ylim(-1.1, 3)\n",
+    "ax.set_xlim(0.06, 1e1)\n",
+    "\n",
+    "ax.grid(lw=0.5)\n",
+    "ax.minorticks_on()\n",
+    "ax.grid(which=\"minor\", lw=0.1)\n",
+    "ax.set_axisbelow(True)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"delta_mu.png\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "clmm",
+   "language": "python",
+   "name": "clmm"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}