csiborgtools/scripts/mass_enclosed_8600.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright (C) 2024 Richard Stiskalek\n",
    "# This program is free software; you can redistribute it and/or modify it\n",
    "# under the terms of the GNU General Public License as published by the\n",
    "# Free Software Foundation; either version 3 of the License, or (at your\n",
    "# option) any later version.\n",
    "#\n",
    "# This program is distributed in the hope that it will be useful, but\n",
    "# WITHOUT ANY WARRANTY; without even the implied warranty of\n",
    "# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General\n",
    "# Public License for more details.\n",
    "#\n",
    "# You should have received a copy of the GNU General Public License along\n",
    "# with this program; if not, write to the Free Software Foundation, Inc.,\n",
    "# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.\n",
    "from gc import collect\n",
    "\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "from h5py import File\n",
    "from astropy.cosmology import FlatLambdaCDM\n",
    "import csiborgtools\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_particles_gadget(kind, fpath):\n",
    "    with File(fpath, \"r\") as f:\n",
    "        if kind == \"Masses\":\n",
    "            npart = f[\"Header\"].attrs[\"NumPart_Total\"][1]\n",
    "            x_high = numpy.ones(npart, dtype=numpy.float32)\n",
    "            x_high *= f[\"Header\"].attrs[\"MassTable\"][1]\n",
    "        else:\n",
    "            x_high = f[f\"PartType1/{kind}\"][...]\n",
    "\n",
    "        x_low = f[f\"PartType5/{kind}\"][...]\n",
    "\n",
    "    return x_high, x_low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "option = \"csiborg2\"\n",
    "chain_index = 16017\n",
    "distances = numpy.linspace(0, 155, 101)[1:]\n",
    "\n",
    "if option == \"csiborg1\":\n",
    "    if chain_index != 8600:\n",
    "        raise ValueError(f\"Invalid chain index: `{chain_index}`.\")\n",
    "\n",
    "    fpath_csiborg = \"/mnt/extraspace/rstiskalek/csiborg1/gadget4/chain_8600/output/snapshot_099.hdf5\"\n",
    "    fpath_borg = f\"/mnt/users/hdesmond/BORG_final/mcmc_{chain_index}.h5\"\n",
    "    Om0 = 0.307\n",
    "    boxsize = 677.7\n",
    "    H0 = 70.5\n",
    "elif option == \"csiborg2\":\n",
    "    fpath_csiborg = f\"/mnt/extraspace/rstiskalek/csiborg2_main/chain_{chain_index}/output/snapshot_099_full.hdf5\"\n",
    "    fpath_borg = f\"/mnt/extraspace/rstiskalek/BORG_STOPYRA_2023/mcmc_{chain_index}.h5\"\n",
    "    Om0 = 0.3111\n",
    "    H0 = 67.66\n",
    "    boxsize = 676.6\n",
    "else:\n",
    "    raise ValueError(f\"Invalid option: `{option}`.\")\n",
    "\n",
    "\n",
    "rho_matter_borg = Om0 * FlatLambdaCDM(H0=H0, Om0=Om0).critical_density(0).to(\"Msun/Mpc^3\").value\n",
    "rho_matter = Om0 * FlatLambdaCDM(H0=100, Om0=Om0).critical_density(0).to(\"Msun/Mpc^3\").value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting positions\n",
      "Getting masses\n",
      "Concatenating the high- and low-resolution particles.\n",
      "Calculating the radial distances.\n",
      "Sorting particles\n"
     ]
    }
   ],
   "source": [
    "print(\"Getting positions\")\n",
    "pos_high, pos_low = get_particles_gadget(\"Coordinates\", fpath_csiborg)\n",
    "print(\"Getting masses\")\n",
    "mass_high, mass_low = get_particles_gadget(\"Masses\", fpath_csiborg)\n",
    "\n",
    "print(\"Concatenating the high- and low-resolution particles.\")\n",
    "pos = numpy.concatenate([pos_high, pos_low])\n",
    "mass = numpy.concatenate([mass_high, mass_low]) * 1e10\n",
    "\n",
    "del pos_high, pos_low, mass_high, mass_low\n",
    "collect()\n",
    "\n",
    "print(\"Calculating the radial distances.\")\n",
    "pos -= boxsize / 2\n",
    "rdist = numpy.linalg.norm(pos, axis=1)\n",
    "\n",
    "del pos\n",
    "collect()\n",
    "\n",
    "print(\"Sorting particles\")\n",
    "indxs = numpy.argsort(rdist)\n",
    "rdist = rdist[indxs]\n",
    "mass = mass[indxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the enclosed mass in BORG.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2000/2304497414.py:20: RuntimeWarning: invalid value encountered in divide\n",
      "  borg_density = borg_mass / borg_volume\n"
     ]
    }
   ],
   "source": [
    "# print(\"Calculating the enclosed mass in CSiBORG.\")\n",
    "# csiborg_mass = csiborgtools.field.particles_enclosed_mass(rdist, mass, distances)\n",
    "# volume = 4 / 3 * numpy.pi * distances**3\n",
    "# csiborg_overdensity = csiborg_mass / volume / rho_matter - 1\n",
    "\n",
    "\n",
    "print(\"Calculating the enclosed mass in BORG.\")\n",
    "with File(fpath_borg) as f:\n",
    "    borg_density = f[\"scalars/BORG_final_density\"][...].T\n",
    "    borg_density += 1\n",
    "    borg_density *= rho_matter_borg\n",
    "\n",
    "h = H0 / 100\n",
    "\n",
    "# Now BORG_density is the density in each cell in units of borg H0.\n",
    "borg_mass, borg_volume = csiborgtools.field.field_enclosed_mass(\n",
    "    borg_density, distances, boxsize)\n",
    "\n",
    "# Now we need to conver the mass to units of h = 1\n",
    "borg_density = borg_mass / borg_volume\n",
    "borg_overdensity = borg_density / rho_matter_borg - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "plt.plot(distances, csiborg_overdensity, label=\"CSiBORG\")\n",
    "plt.plot(distances, borg_overdensity * 0.7, label=\"BORG\")\n",
    "\n",
    "# plt.plot(distances, borg_overdensity - csiborg_overdensity)\n",
    "plt.axhline(0, color=\"black\", linestyle=\"--\")\n",
    "\n",
    "# plt.ylim(0.7, 1.3)\n",
    "plt.ylim(-0.2, 0.2)\n",
    "plt.ylabel(r\"$\\delta_r$\")\n",
    "plt.xlabel(\"Distance [Mpc/h]\")\n",
    "\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "# plt.savefig(\"/mnt/users/rstiskalek/csiborgtools/plots/enclosed_overdensity_8600.png\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "csiborg_reader = csiborgtools.read.CSiBORG2Field(17417, \"main\")\n",
    "borg_reader = csiborgtools.read.BORG2Field(17417)\n",
    "Om0 = 0.3111\n",
    "boxsize = 676.6\n",
    "\n",
    "# csiborg_reader = csiborgtools.read.CSiBORG1Field(9844)\n",
    "# borg_reader = csiborgtools.read.BORG1Field(9844)\n",
    "# Om0 = 0.307\n",
    "# boxsize = 677.7\n",
    "\n",
    "cosmo = FlatLambdaCDM(H0=100, Om0=Om0)\n",
    "rho_matter = Om0 * cosmo.critical_density(0).to(\"Msun/kpc^3\").value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "csiborg_density = csiborg_reader.density_field(\"SPH\", 1024)\n",
    "\n",
    "\n",
    "csiborg_density /= rho_matter\n",
    "csiborg_density -= 1\n",
    "\n",
    "borg_overdensity = borg_reader.overdensity_field()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "csiborg_density_downsample = csiborg_density.reshape(256, 4, 256, 4, 256, 4).mean(axis=(1, 3, 5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/users/rstiskalek/csiborgtools/venv_csiborg/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/mnt/users/rstiskalek/csiborgtools/venv_csiborg/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    }
   ],
   "source": [
    "y_csiborg = []\n",
    "y_borg = []\n",
    "\n",
    "dks = range(0, 128 + 1)\n",
    "for dk in dks:\n",
    "    kmin = 128 - dk\n",
    "    kmax = 128 + dk\n",
    "\n",
    "    y_csiborg.append(numpy.mean(csiborg_density_downsample[kmin:kmax, kmin:kmax, kmin:kmax]))\n",
    "    y_borg.append(numpy.mean(borg_overdensity[kmin:kmax, kmin:kmax, kmin:kmax]))\n",
    "\n",
    "y_csiborg = numpy.array(y_csiborg)\n",
    "y_borg = numpy.array(y_borg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl0AAAHKCAYAAAA5Cf5NAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAACHxklEQVR4nO3dd3hUVf7H8feU9JCE0JLQe+81gIKCAgKKZVcQFSzYwIbdtTf82Rvq2sBdUVZdURYVRYogIj10kGooCQFCOmkz9/fHJUOG9GSSScLn9TzzJLlz7plzb+5Mvjnn3O+xGIZhICIiIiKVyurtBoiIiIicCxR0iYiIiFQBBV0iIiIiVUBBl4iIiEgVUNAlIiIiUgUUdImIiIhUAQVdIiIiIlVAQZeIiIhIFVDQJSIiIlIFFHSJiIiIVAEFXSIiIiJVQEGXlw0dOpR77rnnnG9DWVR1e6vL+SlNOzxVpjryVrsNw+CWW24hPDwci8VCTExMmeuoit9Ldfu9Vrf21HbePt/efv2zlac9VXEM9kqtvYabPHkyn376aYHtI0aMYOHChV5okXjDN998g4+Pj+vnoUOH0qNHD9544w2vtaMibTj7eKR4CxcuZPbs2SxbtoxWrVpRv379MtdRm8+5J98PM2bM4JtvvmHnzp0EBAQwcOBA/u///o/27du7lRs0aBCdO3fmgw8+qPBreoK3PhM8rbTHUROOtzzvuap4nyroKsHIkSOZNWuW2zY/Pz8vtebckZ2dja+vr7ebAUB4eLi3mwB4rh3V5Xhqir179xIZGcnAgQPLXYfOeen8+uuvTJ06lb59+5Kbm8ujjz7KxRdfzPbt2wkKCgLA6XSyadMmrrvuOi+31vOq0+deTZV3DsvznquK96mGF0vg5+dHRESE26Nu3bqAGe3fddddPPjgg4SHhxMREcFTTz3ltr/T6eSll16iTZs2+Pn50axZM55//vkiXy8rK4u77rqLhg0b4u/vz+DBg1m7dq3r+a+//pquXbsSEBBAvXr1GD58OOnp6a7XmjFjBi1btiQgIIDu3bvz9ddfu9Wfnp7O9ddfT3BwMJGRkbz66qslnoOS2vTBBx8QFRWF0+l02++yyy7jxhtvLFXbhg4dyrRp07jnnnuoX78+I0aMKHV7S1N3Sb+n4s5r/i7nyZMn8+uvv/Lmm29isViwWCwcOHCAf/3rX9SrV4+srCy3eseNG1foH4cFCxYQFhaGw+EAICYmBovFwsMPP+wqc/PNN3Pttde6Hcc999xTZBvyn4/ijvXsLvTSnJ+zLVy4kMGDBxMWFka9evUYM2YMe/fuLXaf4t4LLVq0KPBfc48ePQq0Izc3l2nTphEaGkr9+vV5/PHHMQzD7TVKeg+crbjre/Lkydx5553ExsZisVho0aJFkfWU9hoCz1zXJSnN/qX53aempjJx4kSCgoKIjIzk9ddf99i1eLaFCxcyefJkOnfuTPfu3Zk9ezaxsbGsX7/eVWbXrl2kp6fTq1evUp+H4j6DK/r5UdQ5KO35L+xzr6R9y/M5XtJ7tqTfZWnKlfT79vT7s7hzePZ7rrjrOH9dFf1sLJEhRZo0aZJx2WWXFfn8kCFDjJCQEOOpp54y/vzzT+PTTz81LBaL8fPPP7vKPPjgg0bdunWN2bNnG3v27DFWrFhhfPjhh2513H333a6f77rrLiMqKsr44YcfjG3bthmTJk0y6tata5w4ccI4cuSIYbfbjddee83Yv3+/sXnzZmPmzJlGamqqYRiG8dxzzxkdOnQwFi5caOzdu9eYNWuW4efnZyxbtsxV/+233240a9bM+OWXX4zNmzcbY8aMMerUqePWhrMV1ybDMIzExETD19fX+OWXX1z7nDhxwm1bSW0bMmSIERwcbDzwwAPGzp07jZ07d5a6vaWpu7jfU0nnNf/vKCkpyYiOjjamTJlixMXFGXFxcUZubq6RkZFhhIaGGl9++aWrXUePHjXsdruxZMmSAuc0KSnJsFqtxtq1aw3DMIw33njDqF+/vtG/f39XmTZt2hR6rRTVhtIc69nHU9p9zvb1118b//3vf43du3cbGzduNMaOHWt07drVcDgcRe5T3HuhefPmxuuvv+5Wvnv37saTTz7p1s7g4GDj7rvvNnbu3Gl89tlnRmBgoPHBBx+4ypTmPXC24q7vpKQk45lnnjGaNGlixMXFGQkJCYXWUZZryDA8c12f7ezXKM3+pfnd33zzzUbz5s2NX375xdiyZYtx+eWXu9pa0WuxJLt37zYAY8uWLa5tn332mWG3241Tp06Vqo6SPoMr+vlR1Dko7fkv7HOvpH3L8zle0nu2uN9lfkWVK83v29Pvz+LO4dnvh+Ku4/y/j4p+NpZEQVcxJk2aZNhsNiMoKMjt8fzzzxuGYf5CBg8e7LZP3759jYceesgwDMNISUkx/Pz83N7gZ8v/S05LSzN8fHyMOXPmuJ7Pzs42oqKijJdeeslYv369ARgHDhwoUE9mZqYRGBho/P77727bb7rpJmPChAmGYRhGamqq4evr6xYYnDhxwggICCjyzVpSm/Jcdtllxo033uj6+Z///KcRFRVlOByOUrVtyJAhRs+ePd2eL017S1t3cb+n4s5r3v7FvTHz3H777caoUaNcP7/66qtGq1atDKfTWWi9vXr1Ml5++WXDMAxj3LhxxvPPP2/4+voaqampxqFDhwzA+PPPPwt93aLaUNKxFnU8Je1TkmPHjhX445hfSe+F0gZdHTt2dDufDz30kNGxY0fDMEp3LZytNNf366+/bjRv3rzQ/fOU5Rry1HVd3GuUdv/SfIb5+PgYX331lev5pKQkIzAw0CPXYnEcDocxevRoY9CgQW7bp0+fbnTr1q1UdZR03Xni8yOvTP5zUJbzf/bnXkn7ludzvDCFvWeL+l2erbByJZ2nynp/FnYOz25jaa7jwo7LE5+NZ9OcrhJccMEFvPfee27b8o/7duvWze25yMhIEhISANixYwdZWVkMGzasVK+1d+9ecnJyGDRokGubj48P/fr1Y8eOHUyfPp1hw4bRtWtXRowYwcUXX8xVV11F3bp12bNnDxkZGVx00UVudWZnZ9OzZ09X/dnZ2fTv39/tWM6epFqWNuWZOHEiU6ZM4d1338XPz485c+Ywfvx4rFZrqdoG0Lt37wKvXVJ7S1t3cb+n7t27F3ley2LKlCn07duXw4cP07hxY2bPns3kyZOxWCyFlh8yZAjLli3jvvvuY8WKFcyYMYMvv/yS3377jcTERKKiomjbtm2Z2lDSsXpqn927d/PEE0+wevVqjh8/7hpajo2NpUuXLgXKl/W9UJQBAwa4nc/o6GheffVVHA5Hqa+F/Ep7fZekLNeQJ6/ropRl/+J+9/v27SMnJ4d+/fq5ng8NDS32M6O0dZdk6tSpbN26ld9++81t+4YNG0o9tFjSdeeJz4+K1AsFP/dK2rc8n+NQ9vdseRR3nirz/Xn2OTxbRa7jilzDhVHQVYKgoCDatGlT5PNn3+lgsVhcF3NAQIBH22Kz2Vi0aBG///47P//8M2+//Tb/+Mc/WL16NWlpaQB8//33NG7c2G2/qpj4P3bsWAzD4Pvvv6dv376sWLGC119/HaDUbcubKFsWpa27uN9Tcee1ZcuWpW5Lz5496d69O//617+4+OKL2bZtG99//32R5YcOHconn3zCpk2b8PHxoUOHDgwdOpRly5Zx8uRJhgwZUurXzq+4Y/XUPmPHjqV58+Z8+OGHrvl8Xbp0ITs7u9DyJb0XrFar29wsgJycnGL3OZs33wOeuobyVPRYyrJ/ea6X0ipv3dOmTWPBggUsX76cJk2auD0XExPDlVdeWarXL+m688TnR0XqhYKfeyXtm5iYWOTrFqes79nyKO48Veb7szx/O0rL0+8PTaSvRG3btiUgIIDFixeXqnzr1q3x9fVl5cqVrm05OTmsXbuWTp06AeYvfNCgQTz99NNs3LgRX19f5s2bR6dOnfDz8yM2NpY2bdq4PZo2beqq38fHh9WrV7vqP3nyJH/++WeF2gTg7+/PFVdcwZw5c/jiiy9o376967/R0rStqNcuqb3lrft
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dks_phys = numpy.array(dks) * (boxsize / 256)\n",
    "\n",
    "plt.figure()\n",
    "plt.title(\"Enclosed overdensity within a cube of side length $2 L$ centered at the origin.\", fontsize=10)\n",
    "plt.plot(dks_phys, y_csiborg, label=\"CSiBORG\")\n",
    "plt.plot(dks_phys, y_borg, label=\"BORG\")\n",
    "plt.xlabel(r\"$L ~ [\\mathrm{Mpc}/\\mathrm{h}]$\")\n",
    "plt.ylabel(r\"$\\delta_r$\")\n",
    "plt.axhline(0, color=\"black\", linestyle=\"--\")\n",
    "plt.legend()\n",
    "plt.ylim(-0.2, 0.1)\n",
    "plt.savefig(\"../plots/BORG_enclosed_gadget2.png\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.        ,   2.64296875,   5.2859375 ,   7.92890625,\n",
       "        10.571875  ,  13.21484375,  15.8578125 ,  18.50078125,\n",
       "        21.14375   ,  23.78671875,  26.4296875 ,  29.07265625,\n",
       "        31.715625  ,  34.35859375,  37.0015625 ,  39.64453125,\n",
       "        42.2875    ,  44.93046875,  47.5734375 ,  50.21640625,\n",
       "        52.859375  ,  55.50234375,  58.1453125 ,  60.78828125,\n",
       "        63.43125   ,  66.07421875,  68.7171875 ,  71.36015625,\n",
       "        74.003125  ,  76.64609375,  79.2890625 ,  81.93203125,\n",
       "        84.575     ,  87.21796875,  89.8609375 ,  92.50390625,\n",
       "        95.146875  ,  97.78984375, 100.4328125 , 103.07578125,\n",
       "       105.71875   , 108.36171875, 111.0046875 , 113.64765625,\n",
       "       116.290625  , 118.93359375, 121.5765625 , 124.21953125,\n",
       "       126.8625    , 129.50546875, 132.1484375 , 134.79140625,\n",
       "       137.434375  , 140.07734375, 142.7203125 , 145.36328125,\n",
       "       148.00625   , 150.64921875, 153.2921875 , 155.93515625,\n",
       "       158.578125  , 161.22109375, 163.8640625 , 166.50703125,\n",
       "       169.15      , 171.79296875, 174.4359375 , 177.07890625,\n",
       "       179.721875  , 182.36484375, 185.0078125 , 187.65078125,\n",
       "       190.29375   , 192.93671875, 195.5796875 , 198.22265625,\n",
       "       200.865625  , 203.50859375, 206.1515625 , 208.79453125,\n",
       "       211.4375    , 214.08046875, 216.7234375 , 219.36640625,\n",
       "       222.009375  , 224.65234375, 227.2953125 , 229.93828125,\n",
       "       232.58125   , 235.22421875, 237.8671875 , 240.51015625,\n",
       "       243.153125  , 245.79609375, 248.4390625 , 251.08203125,\n",
       "       253.725     , 256.36796875, 259.0109375 , 261.65390625,\n",
       "       264.296875  , 266.93984375, 269.5828125 , 272.22578125,\n",
       "       274.86875   , 277.51171875, 280.1546875 , 282.79765625,\n",
       "       285.440625  , 288.08359375, 290.7265625 , 293.36953125,\n",
       "       296.0125    , 298.65546875, 301.2984375 , 303.94140625,\n",
       "       306.584375  , 309.22734375, 311.8703125 , 314.51328125,\n",
       "       317.15625   , 319.79921875, 322.4421875 , 325.08515625,\n",
       "       327.728125  , 330.37109375, 333.0140625 , 335.65703125,\n",
       "       338.3       ])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "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": [
    "plt.figure()\n",
    "plt.imshow(numpy.log10(csiborg_density_downsample[:, :, 128]), origin=\"lower\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(numpy.log10(borg_overdensity[:, :, 128] + 1), origin=\"lower\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv_csiborg",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}