csiborgtools/notebooks/flow/reconstruction_slice.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 os.path import join\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.io import fits\n",
    "from tqdm import tqdm\n",
    "from scipy.ndimage import gaussian_filter\n",
    "import scienceplots\n",
    "from warnings import warn\n",
    "from astropy.cosmology import FlatLambdaCDM\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "import csiborgtools\n",
    "\n",
    "from reconstruction_comparison import simname_to_pretty\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)\n",
    "fdir = \"/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity\"\n",
    "\n",
    "CLUSTERS = {\n",
    "    \"Shapley\": (-116.37827331, 69.68752732, -14.90318191),\n",
    "    \"Virgo\": (-3.48, 14.86, -2.21),\n",
    "    \"Norma\": (-50.26, -7.06, 6.44),\n",
    "    \"Coma\": (0.48, 72.79, 10.59),\n",
    "    \"Perseus\": (49.94, -10.73, -12.98),\n",
    "    \"Centaurus\": (-34.25, 14.93, -7.56)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from astropy.coordinates import SkyCoord\n",
    "# from astropy.coordinates import Supergalactic\n",
    "# from astropy.units import deg, Mpc\n",
    "# from astropy.cosmology import FlatLambdaCDM\n",
    "\n",
    "# SPEED_OF_LIGHT = 299_792.458 \n",
    "\n",
    "\n",
    "# cosmo = FlatLambdaCDM(H0=100, Om0=0.3)\n",
    "\n",
    "# RA = 196.490821\n",
    "# dec = -33.067461\n",
    "# dist = cosmo.comoving_distance(0.048)\n",
    "\n",
    "# c = SkyCoord(ra= RA * deg, dec= dec * deg, distance=dist)\n",
    "# c = c.transform_to(Supergalactic)\n",
    "# c.cartesian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_field(simname, nsim, kind, MAS, grid):\n",
    "    # Open the field reader.\n",
    "    if simname == \"csiborg1\":\n",
    "        field_reader = csiborgtools.read.CSiBORG1Field(nsim)\n",
    "    elif \"csiborg2_\" in simname:\n",
    "        simkind = simname.split(\"_\")[-1]\n",
    "        field_reader = csiborgtools.read.CSiBORG2Field(nsim, simkind)\n",
    "    elif simname == \"csiborg2X\":\n",
    "        field_reader = csiborgtools.read.CSiBORG2XField(nsim)\n",
    "    elif simname == \"Carrick2015\":\n",
    "        folder = \"/mnt/extraspace/rstiskalek/catalogs\"\n",
    "        warn(f\"Using local paths from `{folder}`.\", RuntimeWarning)\n",
    "        if kind == \"density\":\n",
    "            fpath = join(folder, \"twompp_density_carrick2015.npy\")\n",
    "            return np.load(fpath).astype(np.float32)\n",
    "        elif kind == \"velocity\":\n",
    "            fpath = join(folder, \"twompp_velocity_carrick2015.npy\")\n",
    "            field = np.load(fpath).astype(np.float32)\n",
    "\n",
    "            # Because the Carrick+2015 data is in the following form:\n",
    "            # \"The velocities are predicted peculiar velocities in the CMB\n",
    "            # frame in Galactic Cartesian coordinates, generated from the\n",
    "            # \\(\\delta_g^*\\) field with \\(\\beta^* = 0.43\\) and an external\n",
    "            # dipole \\(V_\\mathrm{ext} = [89,-131,17]\\) (Carrick et al Table 3)\n",
    "            # has already been added.\"\"\n",
    "            field[0] -= 89\n",
    "            field[1] -= -131\n",
    "            field[2] -= 17\n",
    "            field /= 0.43\n",
    "\n",
    "            return field\n",
    "        else:\n",
    "            raise ValueError(f\"Unknown field kind: `{kind}`.\")\n",
    "    elif simname == \"CLONES\":\n",
    "        field_reader = csiborgtools.read.CLONESField(nsim)\n",
    "    elif simname == \"CF4\":\n",
    "        folder = \"/mnt/extraspace/rstiskalek/catalogs/CF4\"\n",
    "        warn(f\"Using local paths from `{folder}`.\", RuntimeWarning)\n",
    "\n",
    "        if kind == \"density\":\n",
    "            fpath = join(folder, f\"CF4_new_128-z008_realization{nsim}_delta.fits\")     # noqa\n",
    "        elif kind == \"velocity\":\n",
    "            fpath = join(folder, f\"CF4_new_128-z008_realization{nsim}_velocity.fits\")  # noqa\n",
    "        else:\n",
    "            raise ValueError(f\"Unknown field kind: `{kind}`.\")\n",
    "\n",
    "        field = fits.open(fpath)[0].data\n",
    "\n",
    "        # https://projets.ip2i.in2p3.fr//cosmicflows/ says to multiply by 52\n",
    "        if kind == \"velocity\":\n",
    "            field *= 52\n",
    "\n",
    "        return field.astype(np.float32)\n",
    "    elif simname == \"Lilow2024\":\n",
    "        folder = \"/mnt/extraspace/rstiskalek/catalogs\"\n",
    "        warn(f\"Using local paths from `{folder}`.\", RuntimeWarning)\n",
    "\n",
    "        if kind == \"density\":\n",
    "            fpath = join(folder, \"Lilow2024_density.npy\")\n",
    "            field = np.load(fpath)\n",
    "        elif kind == \"velocity\":\n",
    "            field = []\n",
    "            for p in [\"x\", \"y\", \"z\"]:\n",
    "                fpath = join(folder, f\"Lilow2024_{p}Velocity.npy\")\n",
    "                field.append(np.load(fpath).astype(np.float32))\n",
    "            field = np.stack(field)\n",
    "\n",
    "        return field.astype(np.float32)\n",
    "    else:\n",
    "        raise ValueError(f\"Unknown simulation name: `{simname}`.\")\n",
    "\n",
    "    # Read in the field.\n",
    "    if kind == \"density\":\n",
    "        field = field_reader.density_field(MAS=MAS, grid=grid)\n",
    "    elif kind == \"velocity\":\n",
    "        field = field_reader.velocity_field(MAS=MAS, grid=grid)\n",
    "    else:\n",
    "        raise ValueError(f\"Unknown field kind: `{kind}`.\")\n",
    "    \n",
    "    # NOTE added here \n",
    "    if kind == \"density\" and (\"csiborg\" in simname or simname == \"CLONES\"):\n",
    "        Om0 = csiborgtools.simname2Omega_m(simname)\n",
    "        cosmo = FlatLambdaCDM(H0=100, Om0=Om0)\n",
    "        mean_rho_matter = cosmo.critical_density0.to(\"Msun/kpc^3\").value\n",
    "        mean_rho_matter *= Om0\n",
    "        field /= mean_rho_matter\n",
    "\n",
    "    return field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_slice(simname, xmin, xmax, ngrid):\n",
    "    boxsize = csiborgtools.simname2boxsize(simname)\n",
    "\n",
    "    paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)\n",
    "    nsims = paths.get_ics(simname)\n",
    "\n",
    "    if simname in [\"Carrick2015\", \"Lilow2024\"]:\n",
    "        frame = \"galactic\"\n",
    "    elif simname in [\"CF4\", \"CLONES\"]:\n",
    "        frame = \"supergalactic\"\n",
    "    elif \"csiborg\" in simname:\n",
    "        frame = \"icrs\"\n",
    "    else:\n",
    "        raise ValueError(f\"Unknown frame for simulation: `{simname}`.\")\n",
    "\n",
    "    out = np.full((len(nsims), ngrid, ngrid), np.nan)\n",
    "\n",
    "    \n",
    "    for i, k in enumerate(tqdm(nsims, desc=simname)):\n",
    "        field = get_field(simname, k, \"density\", \"SPH\", 1024)\n",
    "        slice_values = csiborgtools.field.xy_supergalactic_slice(\n",
    "            field, boxsize, xmin, xmax, ngrid, frame)\n",
    "\n",
    "        if simname == \"Lilow2024\":\n",
    "            slice_values[np.isnan(slice_values)] = 1\n",
    "\n",
    "        out[i] = slice_values\n",
    "\n",
    "    return out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_from_disk = True\n",
    "fname = \"/mnt/extraspace/rstiskalek/dump/XY_slices.npz\"\n",
    "\n",
    "xmin = -155\n",
    "xmax = 155\n",
    "ngrid = 1000\n",
    "\n",
    "if not load_from_disk:\n",
    "    xy_carrick = make_slice(\"Carrick2015\", xmin, xmax, ngrid) + 1\n",
    "    xy_lilow = make_slice(\"Lilow2024\", xmin, xmax, ngrid)\n",
    "    xy_CF4 = make_slice(\"CF4\", xmin, xmax, ngrid)\n",
    "    xy_CB2 = make_slice(\"csiborg2_main\", xmin, xmax, ngrid)\n",
    "    xy_CB2X = make_slice(\"csiborg2X\", xmin, xmax, ngrid)\n",
    "    xy_CLONES = make_slice(\"CLONES\", xmin, xmax, ngrid)\n",
    "\n",
    "    np.savez(fname, carrick=xy_carrick, lilow=xy_lilow,\n",
    "             CF4=xy_CF4, CB2=xy_CB2, CB2X=xy_CB2X, CLONES=xy_CLONES)\n",
    "else:\n",
    "    data = np.load(fname)\n",
    "    xy_carrick = data[\"carrick\"]\n",
    "    xy_lilow = data[\"lilow\"]\n",
    "    xy_CF4 = data[\"CF4\"]\n",
    "    xy_CB2 = data[\"CB2\"]\n",
    "    xy_CB2X = data[\"CB2X\"]\n",
    "    xy_CLONES = data[\"CLONES\"]\n",
    "    data.close()\n",
    "\n",
    "\n",
    "\n",
    "rsmooth = None\n",
    "if rsmooth is not None:\n",
    "    sigma = rsmooth / ((xmax - xmin) / ngrid)\n",
    "    print(f\"Smoothing with sigma={sigma}\")\n",
    "\n",
    "    for field in [xy_carrick, xy_CF4, xy_CB2, xy_CB2X]:\n",
    "        for i in range(len(field)):\n",
    "            field[i] = gaussian_filter(field[i], sigma=sigma)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Individual plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
    "img = np.mean(xy_CLONES, axis=0)\n",
    "img = np.clip(img, None, np.percentile(img.ravel(), 99.75))\n",
    "img = np.log10(img)\n",
    "\n",
    "plt.imshow(img, origin=\"lower\", cmap=\"gray_r\", extent=[xmin, xmax, xmin, xmax])\n",
    "\n",
    "kwargs = {\"marker\": \"x\", \"s\": 15}\n",
    "for name, coords in CLUSTERS.items():\n",
    "    plt.scatter(*coords[:2], label=name, **kwargs)\n",
    "\n",
    "plt.scatter(0, 0, label=\"Local Group\", **kwargs)\n",
    "\n",
    "plt.xlabel(r\"$\\mathrm{SGX} ~ [\\mathrm{Mpc} / h]$\")\n",
    "plt.ylabel(r\"$\\mathrm{SGY} ~ [\\mathrm{Mpc} / h]$\")\n",
    "\n",
    "plt.legend(loc='lower center', bbox_to_anchor=(0.5, 1.025),\n",
    "           ncol=4, fontsize=\"small\", frameon=False,\n",
    "           handletextpad=0.1,  # Reduce space between marker and text\n",
    "           borderpad=0.1,      # Reduce padding between legend and border\n",
    "           columnspacing=0.1)  # Reduce space between columns\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"../../plots/slice_test.png\", dpi=450, bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CF4 data check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 830x622.5 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context(\"science\"):\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(8.3, 8.3 * 0.75), sharex=True, sharey=True)\n",
    "\n",
    "    kwargs = {\n",
    "        \"origin\": \"lower\", \"cmap\": \"viridis\", \"extent\": [xmin, xmax, xmin, xmax]}\n",
    "    im0 = axs[0].imshow(xy_CF4[0], **kwargs)\n",
    "    im1 = axs[1].imshow(xy_CF4[0] - xy_CF4[15], **kwargs)\n",
    "\n",
    "    axs[0].set_title(\"Random sample\")\n",
    "    axs[1].set_title(\"Difference of two random samples\")\n",
    "\n",
    "    # Adjust colorbars to be the same height as the plots using make_axes_locatable\n",
    "    labels = [r\"$\\delta$\", r\"$\\Delta \\delta$\"]\n",
    "    for i, im in enumerate([im0, im1]):\n",
    "        divider = make_axes_locatable(axs[i])\n",
    "        cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1)  # Create colorbar axis\n",
    "        fig.colorbar(im, label=labels[i], cax=cax, orientation=\"vertical\")\n",
    "\n",
    "    for i in range(2):\n",
    "        axs[i].set_ylabel(r\"$\\mathrm{SGY} ~ [\\mathrm{Mpc / h}]$\")\n",
    "        axs[i].set_xlabel(r\"$\\mathrm{SGX} ~ [\\mathrm{Mpc / h}]$\")\n",
    "\n",
    "    fig.tight_layout()\n",
    "    fig.savefig(\"../../plots/CF4_test.png\", dpi=450)\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paper plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Comparison of fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 830x556.1 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context(\"science\"):\n",
    "    plt.rcParams.update({'font.size': 9})\n",
    "\n",
    "    imshow_kwargs = {\"origin\": \"lower\", \"cmap\": \"coolwarm\",\n",
    "                     \"extent\": [xmin, xmax, xmin, xmax]}\n",
    "\n",
    "    figwidth = 8.3\n",
    "    fig, axs = plt.subplots(2, 3, figsize=(figwidth, 0.67 * figwidth),\n",
    "                            sharex=\"col\", sharey=True)\n",
    "    fig.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "    # Carrick 2015\n",
    "    img = np.mean(xy_carrick, axis=0)\n",
    "    img = np.clip(img, None, np.percentile(img.ravel(), 99.75))\n",
    "    axs[0, 0].imshow(img, **imshow_kwargs)\n",
    "    axs[0, 0].text(\n",
    "        0.05, 0.05, simname_to_pretty(\"Carrick2015\"), transform=axs[0, 0].transAxes,\n",
    "        bbox=dict(facecolor='white', alpha=0.75))\n",
    "\n",
    "    # Lilow 2024\n",
    "    img = np.mean(xy_lilow, axis=0)\n",
    "    img = np.clip(img, None, np.percentile(img.ravel(), 99.75))\n",
    "    axs[0, 1].imshow(img, **imshow_kwargs)\n",
    "    axs[0, 1].text(\n",
    "        0.05, 0.05, simname_to_pretty(\"Lilow2024\"), transform=axs[0, 1].transAxes,\n",
    "        bbox=dict(facecolor='white', alpha=0.75))\n",
    "\n",
    "    # CF4\n",
    "    img = np.mean(xy_CF4, axis=0)\n",
    "    img = np.clip(img, None, np.percentile(img.ravel(), 99.9))\n",
    "    axs[0, 2].imshow(img, **imshow_kwargs)\n",
    "    axs[0, 2].text(\n",
    "        0.05, 0.05, simname_to_pretty(\"CF4\"), transform=axs[0, 2].transAxes,\n",
    "        bbox=dict(facecolor='white', alpha=0.75))\n",
    "\n",
    "    # csiborg2_main \n",
    "    img = np.mean(xy_CB2, axis=0)\n",
    "    img = np.clip(img, None, np.percentile(img.ravel(), 99))\n",
    "    img = np.log10(img)\n",
    "    axs[1, 0].imshow(img, **imshow_kwargs)\n",
    "    axs[1, 0].text(\n",
    "        0.05, 0.05, simname_to_pretty(\"csiborg2_main\"), transform=axs[1, 0].transAxes,\n",
    "        bbox=dict(facecolor='white', alpha=0.75))\n",
    "\n",
    "    # Manticore\n",
    "    img = np.mean(xy_CB2X, axis=0)\n",
    "    img = np.clip(img, None, np.percentile(img.ravel(), 99))\n",
    "    img = np.log10(img)\n",
    "    axs[1, 1].imshow(img, **imshow_kwargs)\n",
    "    axs[1, 1].text(\n",
    "        0.05, 0.05, simname_to_pretty(\"csiborg2X\"), transform=axs[1, 1].transAxes,\n",
    "        bbox=dict(facecolor='white', alpha=0.75))\n",
    "\n",
    "    # Jenny's field\n",
    "    img = np.mean(xy_CLONES, axis=0)\n",
    "    img = np.clip(img, None, np.percentile(img.ravel(), 99.))\n",
    "    img = np.log10(img)\n",
    "    axs[1, 2].imshow(img, **imshow_kwargs)\n",
    "    axs[1, 2].text(\n",
    "        0.05, 0.05, simname_to_pretty(\"CLONES\"), transform=axs[1, 2].transAxes,\n",
    "        bbox=dict(facecolor='white', alpha=0.75))\n",
    "\n",
    "    for i in range(2):\n",
    "        axs[i, 0].set_ylabel(r\"$\\mathrm{SGY} ~ [\\mathrm{Mpc} / h]$\")\n",
    "\n",
    "    for i in range(3):\n",
    "        axs[-1, i].set_xlabel(r\"$\\mathrm{SGX} ~ [\\mathrm{Mpc} / h]$\")\n",
    "\n",
    "    for i in range(2):\n",
    "        for j in range(3):\n",
    "            axs[i, j].scatter(0, 0, marker=\"x\", s=15, color=\"firebrick\")\n",
    "\n",
    "    fig.tight_layout()\n",
    "    fig.savefig(\"../../plots/XY_slices.pdf\", dpi=450)\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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