{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c1c298df",
   "metadata": {},
   "source": [
    "## Simple toy model\n",
    "\n",
    "This run generates an artificial (i.e.) unphysical set of halos and evolves them following perfect exponential growth. The distribution of halos is used to paint a grid and compute the corresponding signal and their evolution over a few redshift snapshots. The remaining parameters are unphysical as well and chosen for illustration purposes.\n",
    "\n",
    "**NOTE:**\n",
    "- running this notebook will store output files in the current working directory:\n",
    "- cache files (intermediate computations) are stored under `./cache`\n",
    "- final files (the resulting grid data) are stored under `./output`\n",
    "\n",
    "Make sure to change these paths below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6b036b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "%matplotlib inline\n",
    "\n",
    "import logging\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "logger = logging.getLogger(__name__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaea3a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the output of the computatiojs\n",
    "FILE_ROOT = Path(\".\")\n",
    "CACHE_ROOT = FILE_ROOT / \"cache\"\n",
    "OUTPUT_ROOT = FILE_ROOT / \"output\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04b16464",
   "metadata": {},
   "outputs": [],
   "source": [
    "import beorn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "023b6df7",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = beorn.structs.Parameters()\n",
    "\n",
    "# Halo Mass bins\n",
    "parameters.simulation.halo_mass_bin_min = 1e7\n",
    "parameters.simulation.halo_mass_bin_max = 1e15\n",
    "parameters.simulation.halo_mass_bin_n = 200\n",
    "\n",
    "parameters.simulation.Lbox = 100\n",
    "parameters.simulation.Ncell = 128\n",
    "\n",
    "# Nbr of cores to use\n",
    "parameters.simulation.cores = 5\n",
    "\n",
    "\n",
    "# cosmo\n",
    "parameters.cosmology.Om = 0.31\n",
    "parameters.cosmology.Ob = 0.045\n",
    "parameters.cosmology.Ol = 0.69\n",
    "parameters.cosmology.h = 0.68\n",
    "\n",
    "# Source parameters\n",
    "# lyman-alpha\n",
    "parameters.simulation.halo_mass_accretion_alpha = np.array([0.785, 0.795])\n",
    "parameters.source.n_lyman_alpha_photons = 9690*10  # 1500\n",
    "parameters.source.lyman_alpha_power_law = 0.0\n",
    "# ion\n",
    "parameters.source.Nion = 5000 * 3\n",
    "# xray\n",
    "parameters.source.energy_cutoff_min_xray = 500\n",
    "parameters.source.energy_cutoff_max_xray = 2000\n",
    "parameters.source.energy_min_sed_xray = 500\n",
    "parameters.source.energy_max_sed_xray = 2000\n",
    "parameters.source.alS_xray = 1.5\n",
    "parameters.source.xray_normalisation = 3.4e40 * 3\n",
    "\n",
    "# fesc\n",
    "parameters.source.f0_esc = 0.2\n",
    "parameters.source.pl_esc = 0\n",
    "\n",
    "# fstar\n",
    "parameters.source.f_st = 1\n",
    "parameters.source.g1 = 0\n",
    "parameters.source.g2 = 0\n",
    "\n",
    "parameters.source.g3 = 4\n",
    "parameters.source.g4 = -1\n",
    "parameters.source.Mp = 1.6e11 * parameters.cosmology.h\n",
    "parameters.source.Mt = 1e7\n",
    "\n",
    "# Minimum star forming halo\n",
    "parameters.source.halo_mass_min = 1e5\n",
    "\n",
    "\n",
    "parameters.simulation.store_grids = ['Tk','bubbles','lyal', 'Grid_dTb']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3baf394",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17:44:43 - INFO - handler.py - Using persistence directory at .plotting and kwargs None\n"
     ]
    }
   ],
   "source": [
    "cache_handler = beorn.io.Handler(CACHE_ROOT)\n",
    "loader = beorn.load_input_data.ArtificialHaloLoader(\n",
    "    parameters,\n",
    "    halo_count = 100,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a3d8e4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17:44:43 - INFO - solver.py - param.solver.fXh is set to constant. We will assume f_X,h = 2e-4**0.225\n",
      "17:44:43 - INFO - solver.py - Computing profiles for 38 redshifts, 199 halo mass bins and 1 alpha bins.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17:44:49 - WARNING - base_struct.py - Field Tk not found in RadiationProfiles. It will not be written to the HDF5 file.\n",
      "17:44:49 - WARNING - base_struct.py - Field bubbles not found in RadiationProfiles. It will not be written to the HDF5 file.\n",
      "17:44:49 - WARNING - base_struct.py - Field lyal not found in RadiationProfiles. It will not be written to the HDF5 file.\n",
      "17:44:49 - WARNING - base_struct.py - Field Grid_dTb not found in RadiationProfiles. It will not be written to the HDF5 file.\n",
      "17:44:49 - INFO - base_struct.py - Data written to .plotting/RadiationProfiles_db7a2ba19daaf0b9cb026ba418579de5_.h5 (80.98 MB)\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    profiles = cache_handler.load_file(parameters, beorn.structs.RadiationProfiles)\n",
    "except FileNotFoundError:\n",
    "    s = beorn.radiation_profiles.ProfileSolver(parameters, cache_handler, loader.redshifts)\n",
    "    profiles = s.solve()\n",
    "\n",
    "    # since we want to skip recalculating the profiles, we save them\n",
    "    cache_handler.write_file(parameters, profiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "152abc66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17:44:49 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "halo_catalog = loader.load_halo_catalog(9)\n",
    "\n",
    "mesh = halo_catalog.to_mesh()\n",
    "stacked_mesh = np.sum(mesh, axis=0)\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(stacked_mesh, origin='lower', cmap='viridis')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70e86425",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17:44:49 - INFO - handler.py - Using persistence directory at .plotting/output and kwargs None\n",
      "17:44:49 - INFO - painter.py - Painting profiles onto grid for 38 redshift snapshots. Using 5 processes.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e74d908ec6f9467082df1e45ef9e3dfb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Painting redshift snapshots:   0%|          | 0/38 [00:00<?, ?snapshot/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17:44:49 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:44:49 - INFO - painter.py - Painting 100 halos at zgrid=24.50 (z_index=0).\n",
      "17:44:52 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:44:52 - INFO - painter.py - Profile painting took 0:00:02.621396.\n",
      "17:44:52 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0.\n",
      "17:44:52 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.149064.\n",
      "17:44:52 - INFO - painter.py - Postprocessing of the grids took 0:00:00.092145.\n",
      "17:44:52 - INFO - painter.py - Current snapshot took 0:00:02.873108.\n",
      "17:44:52 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:44:52 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:44:52 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:44:53 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=0.h5 (80.02 MB)\n",
      "17:44:53 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:44:53 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:44:53 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:44:53 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:44:53 - INFO - painter.py - Painting 100 halos at zgrid=24.00 (z_index=1).\n",
      "17:44:55 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:44:55 - INFO - painter.py - Profile painting took 0:00:02.619044.\n",
      "17:44:56 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0.\n",
      "17:44:56 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.167218.\n",
      "17:44:56 - INFO - painter.py - Postprocessing of the grids took 0:00:00.091559.\n",
      "17:44:56 - INFO - painter.py - Current snapshot took 0:00:02.885424.\n",
      "17:44:56 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:44:56 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:44:56 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:44:56 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=1.h5 (80.02 MB)\n",
      "17:44:56 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:44:56 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:44:56 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:44:57 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:44:57 - INFO - painter.py - Painting 100 halos at zgrid=23.50 (z_index=2).\n",
      "17:44:59 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:44:59 - INFO - painter.py - Profile painting took 0:00:02.546494.\n",
      "17:44:59 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0.\n",
      "17:44:59 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.149441.\n",
      "17:44:59 - INFO - painter.py - Postprocessing of the grids took 0:00:00.091451.\n",
      "17:44:59 - INFO - painter.py - Current snapshot took 0:00:02.794777.\n",
      "17:44:59 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:44:59 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:44:59 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:00 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=2.h5 (80.02 MB)\n",
      "17:45:00 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:00 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:00 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:00 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:45:00 - INFO - painter.py - Painting 100 halos at zgrid=23.00 (z_index=3).\n",
      "17:45:03 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:45:03 - INFO - painter.py - Profile painting took 0:00:02.556822.\n",
      "17:45:03 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0.\n",
      "17:45:03 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.151746.\n",
      "17:45:03 - INFO - painter.py - Postprocessing of the grids took 0:00:00.094143.\n",
      "17:45:03 - INFO - painter.py - Current snapshot took 0:00:02.810905.\n",
      "17:45:03 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:03 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:03 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:04 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=3.h5 (80.02 MB)\n",
      "17:45:04 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:04 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:04 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:04 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:45:04 - INFO - painter.py - Painting 100 halos at zgrid=22.50 (z_index=4).\n",
      "17:45:07 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:45:07 - INFO - painter.py - Profile painting took 0:00:02.709349.\n",
      "17:45:07 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0.\n",
      "17:45:07 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.211461.\n",
      "17:45:07 - INFO - painter.py - Postprocessing of the grids took 0:00:00.107734.\n",
      "17:45:07 - INFO - painter.py - Current snapshot took 0:00:03.037188.\n",
      "17:45:07 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:07 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:07 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:08 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=4.h5 (80.02 MB)\n",
      "17:45:08 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:08 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:08 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:08 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:45:08 - INFO - painter.py - Painting 100 halos at zgrid=22.00 (z_index=5).\n",
      "17:45:11 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:45:11 - INFO - painter.py - Profile painting took 0:00:02.989268.\n",
      "17:45:11 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0.\n",
      "17:45:11 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.198648.\n",
      "17:45:11 - INFO - painter.py - Postprocessing of the grids took 0:00:00.113694.\n",
      "17:45:12 - INFO - painter.py - Current snapshot took 0:00:03.310685.\n",
      "17:45:12 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:12 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:12 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:12 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=5.h5 (80.02 MB)\n",
      "17:45:12 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:12 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:12 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:13 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:45:13 - INFO - painter.py - Painting 100 halos at zgrid=21.50 (z_index=6).\n",
      "17:45:15 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:45:15 - INFO - painter.py - Profile painting took 0:00:02.725827.\n",
      "17:45:15 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0.\n",
      "17:45:15 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.150039.\n",
      "17:45:16 - INFO - painter.py - Postprocessing of the grids took 0:00:00.093026.\n",
      "17:45:16 - INFO - painter.py - Current snapshot took 0:00:02.977129.\n",
      "17:45:16 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:16 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:16 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:16 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=6.h5 (80.02 MB)\n",
      "17:45:16 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:16 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:16 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:16 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:45:16 - INFO - painter.py - Painting 100 halos at zgrid=21.00 (z_index=7).\n",
      "17:45:19 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:45:19 - INFO - painter.py - Profile painting took 0:00:02.947038.\n",
      "17:45:20 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0.\n",
      "17:45:20 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.185234.\n",
      "17:45:20 - INFO - painter.py - Postprocessing of the grids took 0:00:00.101561.\n",
      "17:45:20 - INFO - painter.py - Current snapshot took 0:00:03.242813.\n",
      "17:45:20 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:20 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:20 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:21 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=7.h5 (80.02 MB)\n",
      "17:45:21 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:21 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:21 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:21 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:45:21 - INFO - painter.py - Painting 100 halos at zgrid=20.50 (z_index=8).\n",
      "17:45:24 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:45:24 - INFO - painter.py - Profile painting took 0:00:03.114474.\n",
      "17:45:24 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0.\n",
      "17:45:24 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.155210.\n",
      "17:45:24 - INFO - painter.py - Postprocessing of the grids took 0:00:00.094838.\n",
      "17:45:24 - INFO - painter.py - Current snapshot took 0:00:03.373085.\n",
      "17:45:24 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:24 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:24 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:25 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=8.h5 (80.02 MB)\n",
      "17:45:25 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:25 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:25 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:25 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:45:25 - INFO - painter.py - Painting 100 halos at zgrid=20.00 (z_index=9).\n",
      "17:45:28 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:45:28 - INFO - painter.py - Profile painting took 0:00:02.792052.\n",
      "17:45:28 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0.\n",
      "17:45:28 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.153152.\n",
      "17:45:28 - INFO - painter.py - Postprocessing of the grids took 0:00:00.092770.\n",
      "17:45:28 - INFO - painter.py - Current snapshot took 0:00:03.047881.\n",
      "17:45:28 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:28 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:28 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:29 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=9.h5 (80.02 MB)\n",
      "17:45:29 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:29 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:29 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:29 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:45:29 - INFO - painter.py - Painting 100 halos at zgrid=19.50 (z_index=10).\n",
      "17:45:32 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:45:32 - INFO - painter.py - Profile painting took 0:00:02.722835.\n",
      "17:45:32 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0.\n",
      "17:45:32 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.171027.\n",
      "17:45:32 - INFO - painter.py - Postprocessing of the grids took 0:00:00.130702.\n",
      "17:45:32 - INFO - painter.py - Current snapshot took 0:00:03.035465.\n",
      "17:45:32 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:32 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:32 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:33 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=10.h5 (80.02 MB)\n",
      "17:45:33 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:33 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:33 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:33 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:45:33 - INFO - painter.py - Painting 100 halos at zgrid=19.00 (z_index=11).\n",
      "17:45:36 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:45:36 - INFO - painter.py - Profile painting took 0:00:02.929493.\n",
      "17:45:36 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0001.\n",
      "17:45:37 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:01.317198.\n",
      "17:45:37 - INFO - painter.py - Postprocessing of the grids took 0:00:00.094228.\n",
      "17:45:37 - INFO - painter.py - Current snapshot took 0:00:04.350272.\n",
      "17:45:37 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:37 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:37 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:38 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=11.h5 (80.02 MB)\n",
      "17:45:38 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:38 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:38 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:38 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:45:38 - INFO - painter.py - Painting 100 halos at zgrid=18.50 (z_index=12).\n",
      "17:45:41 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:45:41 - INFO - painter.py - Profile painting took 0:00:03.028838.\n",
      "17:45:41 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0001.\n",
      "17:45:43 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:01.296057.\n",
      "17:45:43 - INFO - painter.py - Postprocessing of the grids took 0:00:00.095458.\n",
      "17:45:43 - INFO - painter.py - Current snapshot took 0:00:04.429410.\n",
      "17:45:43 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:43 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:43 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:44 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=12.h5 (80.02 MB)\n",
      "17:45:44 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:44 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:44 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:44 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:45:44 - INFO - painter.py - Painting 100 halos at zgrid=18.00 (z_index=13).\n",
      "17:45:47 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:45:48 - INFO - painter.py - Profile painting took 0:00:03.789968.\n",
      "17:45:48 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0002.\n",
      "17:45:48 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.164567.\n",
      "17:45:48 - INFO - painter.py - Postprocessing of the grids took 0:00:00.150793.\n",
      "17:45:48 - INFO - painter.py - Current snapshot took 0:00:04.114117.\n",
      "17:45:48 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:48 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:48 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:49 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=13.h5 (80.02 MB)\n",
      "17:45:49 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:49 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:49 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:49 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:45:49 - INFO - painter.py - Painting 100 halos at zgrid=17.50 (z_index=14).\n",
      "17:45:52 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:45:53 - INFO - painter.py - Profile painting took 0:00:03.793716.\n",
      "17:45:53 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0003.\n",
      "17:45:53 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.393661.\n",
      "17:45:53 - INFO - painter.py - Postprocessing of the grids took 0:00:00.130712.\n",
      "17:45:53 - INFO - painter.py - Current snapshot took 0:00:04.328410.\n",
      "17:45:53 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:53 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:53 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:54 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=14.h5 (80.02 MB)\n",
      "17:45:54 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:54 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:54 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:54 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:45:54 - INFO - painter.py - Painting 100 halos at zgrid=17.00 (z_index=15).\n",
      "17:45:58 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:45:58 - INFO - painter.py - Profile painting took 0:00:03.720108.\n",
      "17:45:58 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0004.\n",
      "17:45:59 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:01.380507.\n",
      "17:45:59 - INFO - painter.py - Postprocessing of the grids took 0:00:00.139338.\n",
      "17:45:59 - INFO - painter.py - Current snapshot took 0:00:05.248452.\n",
      "17:45:59 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:59 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:45:59 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:00 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=15.h5 (80.02 MB)\n",
      "17:46:00 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:00 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:00 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:00 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:46:00 - INFO - painter.py - Painting 100 halos at zgrid=16.50 (z_index=16).\n",
      "17:46:04 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:46:04 - INFO - painter.py - Profile painting took 0:00:03.695125.\n",
      "17:46:04 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0006.\n",
      "17:46:05 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:01.384303.\n",
      "17:46:05 - INFO - painter.py - Postprocessing of the grids took 0:00:00.151858.\n",
      "17:46:05 - INFO - painter.py - Current snapshot took 0:00:05.240312.\n",
      "17:46:05 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:05 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:05 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:06 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=16.h5 (80.02 MB)\n",
      "17:46:06 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:06 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:06 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:06 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:46:06 - INFO - painter.py - Painting 100 halos at zgrid=16.00 (z_index=17).\n",
      "17:46:10 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:46:10 - INFO - painter.py - Profile painting took 0:00:03.427137.\n",
      "17:46:10 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.001.\n",
      "17:46:10 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.195154.\n",
      "17:46:10 - INFO - painter.py - Postprocessing of the grids took 0:00:00.128926.\n",
      "17:46:10 - INFO - painter.py - Current snapshot took 0:00:03.759521.\n",
      "17:46:10 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:10 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:10 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:11 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=17.h5 (80.02 MB)\n",
      "17:46:11 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:11 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:11 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:11 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:46:11 - INFO - painter.py - Painting 100 halos at zgrid=15.50 (z_index=18).\n",
      "17:46:15 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:46:15 - INFO - painter.py - Profile painting took 0:00:03.929310.\n",
      "17:46:15 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0014.\n",
      "17:46:15 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.199903.\n",
      "17:46:15 - INFO - painter.py - Postprocessing of the grids took 0:00:00.129144.\n",
      "17:46:15 - INFO - painter.py - Current snapshot took 0:00:04.266155.\n",
      "17:46:15 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:15 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:15 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:16 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=18.h5 (80.02 MB)\n",
      "17:46:16 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:16 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:16 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:16 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:46:16 - INFO - painter.py - Painting 100 halos at zgrid=15.00 (z_index=19).\n",
      "17:46:20 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:46:20 - INFO - painter.py - Profile painting took 0:00:03.741651.\n",
      "17:46:20 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0023.\n",
      "17:46:20 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.193064.\n",
      "17:46:20 - INFO - painter.py - Postprocessing of the grids took 0:00:00.128156.\n",
      "17:46:20 - INFO - painter.py - Current snapshot took 0:00:04.070485.\n",
      "17:46:20 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:20 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:20 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:21 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=19.h5 (80.02 MB)\n",
      "17:46:21 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:21 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:21 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:21 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:46:21 - INFO - painter.py - Painting 100 halos at zgrid=14.50 (z_index=20).\n",
      "17:46:25 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:46:25 - INFO - painter.py - Profile painting took 0:00:03.703592.\n",
      "17:46:25 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0032.\n",
      "17:46:27 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:02.092572.\n",
      "17:46:27 - INFO - painter.py - Postprocessing of the grids took 0:00:00.128018.\n",
      "17:46:27 - INFO - painter.py - Current snapshot took 0:00:05.932910.\n",
      "17:46:27 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:27 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:27 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:28 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=20.h5 (80.02 MB)\n",
      "17:46:28 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:28 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:28 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:28 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:46:28 - INFO - painter.py - Painting 100 halos at zgrid=14.00 (z_index=21).\n",
      "17:46:32 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:46:32 - INFO - painter.py - Profile painting took 0:00:03.645973.\n",
      "17:46:32 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0044.\n",
      "17:46:35 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:03.075817.\n",
      "17:46:35 - INFO - painter.py - Postprocessing of the grids took 0:00:00.128815.\n",
      "17:46:35 - INFO - painter.py - Current snapshot took 0:00:06.860382.\n",
      "17:46:35 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:35 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:35 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:36 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=21.h5 (80.02 MB)\n",
      "17:46:36 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:36 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:36 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:36 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:46:36 - INFO - painter.py - Painting 100 halos at zgrid=13.50 (z_index=22).\n",
      "17:46:40 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:46:40 - INFO - painter.py - Profile painting took 0:00:03.746450.\n",
      "17:46:40 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0073.\n",
      "17:46:42 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:02.194220.\n",
      "17:46:42 - INFO - painter.py - Postprocessing of the grids took 0:00:00.126613.\n",
      "17:46:42 - INFO - painter.py - Current snapshot took 0:00:06.075639.\n",
      "17:46:42 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:42 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:42 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:43 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=22.h5 (80.02 MB)\n",
      "17:46:43 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:43 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:43 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:43 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:46:43 - INFO - painter.py - Painting 100 halos at zgrid=13.00 (z_index=23).\n",
      "17:46:47 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:46:47 - INFO - painter.py - Profile painting took 0:00:03.680354.\n",
      "17:46:47 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0101.\n",
      "17:46:50 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:03.208861.\n",
      "17:46:50 - INFO - painter.py - Postprocessing of the grids took 0:00:00.132745.\n",
      "17:46:50 - INFO - painter.py - Current snapshot took 0:00:07.030831.\n",
      "17:46:50 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:50 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:50 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:51 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=23.h5 (80.02 MB)\n",
      "17:46:51 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:51 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:51 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:51 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:46:51 - INFO - painter.py - Painting 100 halos at zgrid=12.50 (z_index=24).\n",
      "17:46:55 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:46:55 - INFO - painter.py - Profile painting took 0:00:03.735740.\n",
      "17:46:55 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0166.\n",
      "17:46:57 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:02.240705.\n",
      "17:46:57 - INFO - painter.py - Postprocessing of the grids took 0:00:00.125489.\n",
      "17:46:57 - INFO - painter.py - Current snapshot took 0:00:06.115974.\n",
      "17:46:57 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:57 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:57 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:58 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=24.h5 (80.02 MB)\n",
      "17:46:58 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:58 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:58 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:46:58 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:46:58 - INFO - painter.py - Painting 100 halos at zgrid=12.00 (z_index=25).\n",
      "17:47:02 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:47:02 - INFO - painter.py - Profile painting took 0:00:03.588076.\n",
      "17:47:02 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0232.\n",
      "17:47:05 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:03.383949.\n",
      "17:47:05 - INFO - painter.py - Postprocessing of the grids took 0:00:00.128451.\n",
      "17:47:05 - INFO - painter.py - Current snapshot took 0:00:07.111845.\n",
      "17:47:05 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:05 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:05 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:06 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=25.h5 (80.02 MB)\n",
      "17:47:06 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:06 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:06 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:06 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:47:06 - INFO - painter.py - Painting 100 halos at zgrid=11.50 (z_index=26).\n",
      "17:47:10 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:47:10 - INFO - painter.py - Profile painting took 0:00:03.595604.\n",
      "17:47:10 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0381.\n",
      "17:47:14 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:03.761156.\n",
      "17:47:14 - INFO - painter.py - Postprocessing of the grids took 0:00:00.132746.\n",
      "17:47:14 - INFO - painter.py - Current snapshot took 0:00:07.497632.\n",
      "17:47:14 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:14 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:14 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:14 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=26.h5 (80.02 MB)\n",
      "17:47:14 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:14 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:14 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:15 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:47:15 - INFO - painter.py - Painting 100 halos at zgrid=11.00 (z_index=27).\n",
      "17:47:18 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:47:19 - INFO - painter.py - Profile painting took 0:00:03.934346.\n",
      "17:47:19 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0531.\n",
      "17:47:21 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:02.919672.\n",
      "17:47:22 - INFO - painter.py - Postprocessing of the grids took 0:00:00.128355.\n",
      "17:47:22 - INFO - painter.py - Current snapshot took 0:00:06.991058.\n",
      "17:47:22 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:22 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:22 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:22 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=27.h5 (80.02 MB)\n",
      "17:47:22 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:22 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:22 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:23 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:47:23 - INFO - painter.py - Painting 100 halos at zgrid=10.50 (z_index=28).\n",
      "17:47:26 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:47:26 - INFO - painter.py - Profile painting took 0:00:03.629965.\n",
      "17:47:26 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.0874.\n",
      "17:47:31 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:04.476950.\n",
      "17:47:31 - INFO - painter.py - Postprocessing of the grids took 0:00:00.130495.\n",
      "17:47:31 - INFO - painter.py - Current snapshot took 0:00:08.246552.\n",
      "17:47:31 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:31 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:31 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:32 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=28.h5 (80.02 MB)\n",
      "17:47:32 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:32 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:32 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:32 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:47:32 - INFO - painter.py - Painting 100 halos at zgrid=10.00 (z_index=29).\n",
      "17:47:35 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:47:35 - INFO - painter.py - Profile painting took 0:00:03.799711.\n",
      "17:47:36 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.1218.\n",
      "17:47:42 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:06.545997.\n",
      "17:47:42 - INFO - painter.py - Postprocessing of the grids took 0:00:00.143571.\n",
      "17:47:42 - INFO - painter.py - Current snapshot took 0:00:10.499135.\n",
      "17:47:42 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:42 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:42 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:43 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=29.h5 (80.02 MB)\n",
      "17:47:43 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:43 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:43 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:43 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:47:43 - INFO - painter.py - Painting 100 halos at zgrid=9.50 (z_index=30).\n",
      "17:47:47 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:47:47 - INFO - painter.py - Profile painting took 0:00:03.487577.\n",
      "17:47:47 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.1697.\n",
      "17:47:56 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:08.868893.\n",
      "17:47:56 - INFO - painter.py - Postprocessing of the grids took 0:00:00.136416.\n",
      "17:47:56 - INFO - painter.py - Current snapshot took 0:00:12.502402.\n",
      "17:47:56 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:56 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:56 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:56 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=30.h5 (80.02 MB)\n",
      "17:47:56 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:56 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:56 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:47:57 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:47:57 - INFO - painter.py - Painting 100 halos at zgrid=9.00 (z_index=31).\n",
      "17:48:00 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:48:00 - INFO - painter.py - Profile painting took 0:00:03.555975.\n",
      "17:48:01 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.2791.\n",
      "17:48:16 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:15.776641.\n",
      "17:48:16 - INFO - painter.py - Postprocessing of the grids took 0:00:00.134669.\n",
      "17:48:16 - INFO - painter.py - Current snapshot took 0:00:19.478287.\n",
      "17:48:16 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:16 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:16 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:17 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=31.h5 (80.02 MB)\n",
      "17:48:17 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:17 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:17 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:17 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:48:17 - INFO - painter.py - Painting 100 halos at zgrid=8.50 (z_index=32).\n",
      "17:48:21 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:48:21 - INFO - painter.py - Profile painting took 0:00:03.583298.\n",
      "17:48:21 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.3889.\n",
      "17:48:33 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:12.641136.\n",
      "17:48:33 - INFO - painter.py - Postprocessing of the grids took 0:00:00.149429.\n",
      "17:48:33 - INFO - painter.py - Current snapshot took 0:00:16.383140.\n",
      "17:48:33 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:33 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:33 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:34 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=32.h5 (80.02 MB)\n",
      "17:48:34 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:34 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:34 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:34 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:48:34 - INFO - painter.py - Painting 100 halos at zgrid=8.00 (z_index=33).\n",
      "17:48:38 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:48:38 - INFO - painter.py - Profile painting took 0:00:03.715269.\n",
      "17:48:39 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.6397.\n",
      "17:48:42 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:03.470978.\n",
      "17:48:42 - INFO - painter.py - Postprocessing of the grids took 0:00:00.139976.\n",
      "17:48:42 - INFO - painter.py - Current snapshot took 0:00:07.338906.\n",
      "17:48:42 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:42 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:42 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:43 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=33.h5 (80.02 MB)\n",
      "17:48:43 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:43 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:43 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:43 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:48:43 - INFO - painter.py - Painting 100 halos at zgrid=7.50 (z_index=34).\n",
      "17:48:46 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:48:46 - INFO - painter.py - Profile painting took 0:00:03.414091.\n",
      "17:48:46 - INFO - spread_old.py - Universe not fully ionized : xHII is 0.8913.\n",
      "17:48:51 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:04.825353.\n",
      "17:48:51 - INFO - painter.py - Postprocessing of the grids took 0:00:00.144148.\n",
      "17:48:51 - INFO - painter.py - Current snapshot took 0:00:08.392919.\n",
      "17:48:51 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:51 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:51 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:52 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=34.h5 (80.02 MB)\n",
      "17:48:52 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:52 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:52 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:52 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:48:52 - INFO - painter.py - Painting 100 halos at zgrid=7.00 (z_index=35).\n",
      "17:48:55 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:48:56 - INFO - painter.py - Profile painting took 0:00:03.514022.\n",
      "17:48:56 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.422761.\n",
      "17:48:56 - INFO - painter.py - Postprocessing of the grids took 0:00:00.062578.\n",
      "17:48:56 - INFO - painter.py - Current snapshot took 0:00:04.008426.\n",
      "17:48:56 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:56 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:56 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:57 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=35.h5 (64.02 MB)\n",
      "17:48:57 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:57 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:48:57 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:50:21 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:50:21 - INFO - painter.py - Painting 100 halos at zgrid=6.50 (z_index=36).\n",
      "17:50:24 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:50:24 - INFO - painter.py - Profile painting took 0:00:03.530383.\n",
      "17:50:25 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.518745.\n",
      "17:50:25 - INFO - painter.py - Postprocessing of the grids took 0:00:00.062307.\n",
      "17:50:25 - INFO - painter.py - Current snapshot took 0:00:04.120498.\n",
      "17:50:25 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:50:25 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:50:25 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:50:26 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=36.h5 (64.02 MB)\n",
      "17:50:26 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:50:26 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:50:26 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:51:49 - INFO - halo_catalog.py - No alpha values provided, using default value of 0.79 for all halos.\n",
      "17:51:49 - INFO - painter.py - Painting 100 halos at zgrid=6.00 (z_index=37).\n",
      "17:51:53 - INFO - painter.py - painted total_halos=100 with initially halo_catalog.size=100 halos.\n",
      "17:51:53 - INFO - painter.py - Profile painting took 0:00:03.567008.\n",
      "17:51:54 - INFO - painter.py - Redistributing excess photons from the overlapping regions took 0:00:00.526156.\n",
      "17:51:54 - INFO - painter.py - Postprocessing of the grids took 0:00:00.062740.\n",
      "17:51:54 - INFO - painter.py - Current snapshot took 0:00:04.164299.\n",
      "17:51:54 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:51:54 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:51:54 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:51:54 - INFO - base_struct.py - Data written to .plotting/GridData_db7a2ba19daaf0b9cb026ba418579de5_z_index=37.h5 (64.02 MB)\n",
      "17:51:54 - WARNING - base_struct.py - Field Tk not found in GridData. It will not be written to the HDF5 file.\n",
      "17:51:54 - WARNING - base_struct.py - Field bubbles not found in GridData. It will not be written to the HDF5 file.\n",
      "17:51:54 - WARNING - base_struct.py - Field lyal not found in GridData. It will not be written to the HDF5 file.\n",
      "17:53:18 - INFO - painter.py - Painting of 38 snapshots done.\n",
      "Did not find field file_root in group simulation.\n"
     ]
    }
   ],
   "source": [
    "output_handler = beorn.io.Handler(OUTPUT_ROOT)\n",
    "\n",
    "p = beorn.painting.Painter(\n",
    "    parameters,\n",
    "    loader = loader,\n",
    "    cache_handler = cache_handler,\n",
    "    output_handler = output_handler\n",
    ")\n",
    "\n",
    "multi_z_quantities = p.paint_full(profiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d57ba0e2",
   "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": [
    "z = 9\n",
    "z_index = np.argmin(np.abs(loader.redshifts - z))\n",
    "\n",
    "xHII_grid = multi_z_quantities.Grid_xHII[z_index, ...]\n",
    "plt.figure()\n",
    "plt.imshow(xHII_grid[:, 64, :], origin='lower', cmap='viridis')\n",
    "plt.show()\n",
    "\n",
    "dTb_grid = multi_z_quantities.Grid_dTb[z_index, ...]\n",
    "plt.figure()\n",
    "plt.imshow(dTb_grid[:, 64, :], origin='lower', cmap='viridis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a38618e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up the figure and axes\n",
    "fig = plt.figure(constrained_layout=True)\n",
    "fig.set_figwidth(18)\n",
    "fig.set_figheight(6)\n",
    "gs = gridspec.GridSpec(2, 3, figure=fig)\n",
    "ax1 = fig.add_subplot(gs[:,0])\n",
    "ax2 = fig.add_subplot(gs[0,1])\n",
    "ax3 = fig.add_subplot(gs[1,1], sharex=ax2)\n",
    "ax4 = fig.add_subplot(gs[:,2])\n",
    "\n",
    "# Plot for each grid data that is available\n",
    "\n",
    "beorn.plotting.draw_dTb_signal(ax1, multi_z_quantities)\n",
    "beorn.plotting.draw_Temp_signal(ax2, multi_z_quantities)\n",
    "beorn.plotting.draw_xHII_signal(ax3, multi_z_quantities)\n",
    "beorn.plotting.draw_dTb_power_spectrum_of_z(ax4, multi_z_quantities, parameters)\n",
    "\n",
    "ax4.legend(loc='best')\n",
    "ax2.axes.get_xaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f776e81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making lightcone between 0.039216 < z < 0.142703\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 547/547 [00:00<00:00, 766.91it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...done\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lightcone = beorn.structs.Lightcone.build(\n",
    "    parameters,\n",
    "    multi_z_quantities,\n",
    "    quantity = \"Grid_dTb\",\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_figwidth(18)\n",
    "fig.set_figheight(6)\n",
    "beorn.plotting.plot_lightcone(lightcone, ax, \"\", slice_number=80)\n",
    "fig.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}