{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Running and Plotting Coeval Cubes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The aim of this tutorial is to introduce you to how `21cmFAST` does the most basic operations: producing single coeval cubes, and visually verifying them. It is a great place to get started with `21cmFAST`." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2020-02-29T22:10:11.054977Z", "start_time": "2020-02-29T22:10:10.248058Z" }, "execution": { "iopub.execute_input": "2026-09-22T20:29:32.804459Z", "iopub.status.busy": "2026-09-22T20:29:32.804343Z", "iopub.status.idle": "2026-09-22T20:29:36.103458Z", "shell.execute_reply": "2026-09-22T20:29:36.102394Z" } }, "outputs": [], "source": [ "# We change the default level of the logger so that\n", "# we can see what's happening with caching.\n", "import logging\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "logger = logging.getLogger(\"py21cmfast\")\n", "logger.setLevel(logging.INFO)\n", "\n", "from tempfile import mkdtemp\n", "\n", "import py21cmfast as p21c\n", "\n", "# For plotting the cubes, we use the plotting submodule:\n", "from py21cmfast import plotting" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2020-02-29T22:10:11.059890Z", "start_time": "2020-02-29T22:10:11.056796Z" }, "execution": { "iopub.execute_input": "2026-09-22T20:29:36.107174Z", "iopub.status.busy": "2026-09-22T20:29:36.106305Z", "iopub.status.idle": "2026-09-22T20:29:36.111681Z", "shell.execute_reply": "2026-09-22T20:29:36.110876Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Using 21cmFAST version 4.3.dev77+g0d19d34ee\n" ] } ], "source": [ "print(f' Using 21cmFAST version {p21c.__version__}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run a simulation using `run_coeval` and `generate_coeval`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Specifying input parameters is a topic covered in a [different tutorial](defining_input_params.html).\n", "Here, we want something small and fast:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2026-09-22T20:29:36.152115Z", "iopub.status.busy": "2026-09-22T20:29:36.151909Z", "iopub.status.idle": "2026-09-22T20:29:36.168224Z", "shell.execute_reply": "2026-09-22T20:29:36.167693Z" } }, "outputs": [], "source": [ "inputs = p21c.InputParameters.from_template(\n", " [\"simple\", 'small'], random_seed=1234\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simplest (and typically most efficient) way to produce a coeval cube is simply to \n", "use the `run_coeval` method. This consistently performs all steps of the calculation, \n", "re-using any data that it can without re-computation or increased memory overhead." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we make a temporary cache to store our fields, in larger runs 21cmFAST can store its outputs in an `OutputCache` to avoid unnecessary regeneration" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2026-09-22T20:29:36.171509Z", "iopub.status.busy": "2026-09-22T20:29:36.171300Z", "iopub.status.idle": "2026-09-22T20:29:36.189479Z", "shell.execute_reply": "2026-09-22T20:29:36.188595Z" } }, "outputs": [], "source": [ "cache = p21c.OutputCache(mkdtemp())" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2026-09-22T20:29:36.192432Z", "iopub.status.busy": "2026-09-22T20:29:36.192092Z", "iopub.status.idle": "2026-09-22T20:29:43.608062Z", "shell.execute_reply": "2026-09-22T20:29:43.607592Z" } }, "outputs": [ { "data": { "text/html": [ "
/home/steven/Documents/work/21cmfast/21cmfast/.claude/worktrees/issue-565-discussion-46670d/.venv/lib/python3.13/si\n",
"te-packages/rich/live.py:260: UserWarning: install \"ipywidgets\" for Jupyter support\n",
" warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
"\n"
],
"text/plain": [
"/home/steven/Documents/work/21cmfast/21cmfast/.claude/worktrees/issue-565-discussion-46670d/.venv/lib/python3.13/si\n",
"te-packages/rich/live.py:260: UserWarning: install \"ipywidgets\" for Jupyter support\n",
" warnings.warn('install \"ipywidgets\" for Jupyter support')\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"coevals = p21c.run_coeval(\n",
" inputs=inputs,\n",
" out_redshifts=[8, 9, 10],\n",
" cache=cache,\n",
" progressbar=True\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `generate_coeval` method is a generator, which can be called as follows:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-22T20:29:43.609453Z",
"iopub.status.busy": "2026-09-22T20:29:43.609332Z",
"iopub.status.idle": "2026-09-22T20:29:43.744700Z",
"shell.execute_reply": "2026-09-22T20:29:43.744053Z"
}
},
"outputs": [],
"source": [
"coevals = []\n",
"for coeval,in_outputs in p21c.generate_coeval(\n",
" inputs=inputs,\n",
" out_redshifts=[8, 9, 10],\n",
" cache=cache,\n",
"):\n",
" if in_outputs:\n",
" coevals.append(coeval)\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that this run was much faster, since we cached the outputs from the previous run, and they were simply loaded instead of re-calculated"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are a number of possible inputs for `generate/run_coeval`, which you can check out either in the [API reference](../reference/py21cmfast.html) or by calling `help(p21c.run_coeval)`. \n",
"\n",
"Notably, in our case here, the `out_redshifts` must be given: these are the redshifts\n",
"at which we compute the coeval boxes. When the simulation requires evolution over \n",
"cosmic history, coeval boxes will be computed at every node _as well as_ any \n",
"`out_redshifts` specified. \n",
"\n",
"Since the function is a generator, it yields a `Coeval` box on each iteration. These\n",
"boxes can be saved or post-processed before completing the next iteration, allowing\n",
"arbitrarily flexible computations to be performed. The second returned values is a \n",
"boolean which tells you whether you are at a requested output point in `out_redshifts`.\n",
"\n",
"We've also given a `cache` parameter: this specifies a directory in which the various\n",
"component calculations of this simulation will be cached. By default, this will occur\n",
"in the current directory, but here we are using a temporary directory for convenience.\n",
"While by default all sub-components are written into the cache, this can be turned off\n",
"altogether, or toggled per subcomponent. The main reason to use the cache is if you \n",
"might want to re-use simulated component boxes in other simulations in the future.\n",
"A prime example of this is the `InitialConditions`, for which a single simulation can\n",
"be used for a multitude of astrophysical simulations with varying `AstroParams`. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The output on each iteration is, as we have mentioned, a `Coeval` object. This is a simple\n",
"object that contains a number of 3D arrays of data, one for each of the component fields\n",
"simulated. They can be accessed as attributes of the object:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-22T20:29:43.746497Z",
"iopub.status.busy": "2026-09-22T20:29:43.746343Z",
"iopub.status.idle": "2026-09-22T20:29:43.749617Z",
"shell.execute_reply": "2026-09-22T20:29:43.749105Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"10.0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coevals[0].redshift"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A list of all simulated fields can be accessed:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-22T20:29:43.750671Z",
"iopub.status.busy": "2026-09-22T20:29:43.750553Z",
"iopub.status.idle": "2026-09-22T20:29:43.778028Z",
"shell.execute_reply": "2026-09-22T20:29:43.777401Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['lowres_density',\n",
" 'lowres_vx',\n",
" 'lowres_vy',\n",
" 'lowres_vz',\n",
" 'hires_density',\n",
" 'hires_vx',\n",
" 'hires_vy',\n",
" 'hires_vz',\n",
" 'lowres_vx_2LPT',\n",
" 'lowres_vy_2LPT',\n",
" 'lowres_vz_2LPT',\n",
" 'hires_vx_2LPT',\n",
" 'hires_vy_2LPT',\n",
" 'hires_vz_2LPT',\n",
" 'lowres_vcb',\n",
" 'density',\n",
" 'velocity_z',\n",
" 'velocity_x',\n",
" 'velocity_y',\n",
" 'neutral_fraction',\n",
" 'ionisation_rate_G12',\n",
" 'z_reion',\n",
" 'mean_free_path',\n",
" 'cumulative_recombinations',\n",
" 'kinetic_temperature',\n",
" 'unnormalised_nion',\n",
" 'unnormalised_nion_mini',\n",
" 'brightness_temp',\n",
" 'tau_21',\n",
" 'spin_temperature',\n",
" 'xray_ionised_fraction',\n",
" 'kinetic_temp_neutral',\n",
" 'J_21_LW',\n",
" 'count',\n",
" 'halo_mass',\n",
" 'halo_stars',\n",
" 'halo_stars_mini',\n",
" 'halo_sfr',\n",
" 'halo_sfr_mini',\n",
" 'halo_xray',\n",
" 'n_ion',\n",
" 'whalo_sfr']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coevals[0].get_fields()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `Coeval` object also maintains a reference to the input parameters:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-22T20:29:43.779374Z",
"iopub.status.busy": "2026-09-22T20:29:43.779241Z",
"iopub.status.idle": "2026-09-22T20:29:43.808017Z",
"shell.execute_reply": "2026-09-22T20:29:43.807053Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"cosmo_params: CosmoParams(_SIGMA_8=None, hlittle=0.6766, OMm=0.30964144154550644, OMb=0.04897468161869667, POWER_INDEX=0.9665, _A_s=None, OMn=0.0, OMk=0.0, OMr=8.6e-05, OMtot=1.0, Y_He=0.24, wl=-1.0)\n",
"simulation_options: SimulationOptions(HII_DIM=64, _BOX_LEN=None, _DIM=None, _HIRES_TO_LOWRES_FACTOR=None, _LOWRES_CELL_SIZE_MPC=None, NON_CUBIC_FACTOR=1.0, N_THREADS=1, SAMPLER_MIN_MASS=100000000.0, SAMPLER_BUFFER_FACTOR=2.0, N_COND_INTERP=200, N_PROB_INTERP=400, MIN_LOGPROB=-12.0, HALOMASS_CORRECTION=0.89, PARKINSON_G0=1.0, PARKINSON_y1=0.0, PARKINSON_y2=0.0, Z_HEAT_MAX=35.0, ZPRIME_STEP_FACTOR=1.1, MIN_XE_FOR_FCOLL_IN_TAUX=0.001, INITIAL_REDSHIFT=300.0, DELTA_R_FACTOR=1.1, DENSITY_SMOOTH_RADIUS=0.2, DEXM_OPTIMIZE_MINMASS=100000000000.0, DEXM_R_OVERLAP=2.0, CORR_STAR=0.5, CORR_SFR=0.2, CORR_LX=0.2, K_MAX_FOR_CLASS=None)\n",
"matter_options: MatterOptions(HMF='ST', _USE_RELATIVE_VELOCITIES=None, V_CB_MODEL='NONE', POWER_SPECTRUM='EH', PERTURB_ON_HIGH_RES=False, USE_INTERPOLATION_TABLES='hmf-interpolation', MINIMIZE_MEMORY=False, KEEP_3D_VELOCITIES=False, SAMPLE_METHOD='MASS-LIMITED', FILTER='spherical-tophat', HALO_FILTER='spherical-tophat', SMOOTH_EVOLVED_DENSITY_FIELD=False, DEXM_OPTIMIZE=False, PERTURB_ALGORITHM='2LPT', USE_FFTW_WISDOM=False, SOURCE_MODEL='E-INTEGRAL')\n",
"astro_params: AstroParams(HII_EFF_FACTOR=30.0, F_STAR10=-1.3, ALPHA_STAR=0.5, F_STAR7_MINI=-2.8, ALPHA_STAR_MINI=0.5, F_ESC10=-1.0, ALPHA_ESC=-0.5, F_ESC7_MINI=-2.0, M_TURN=8.7, R_BUBBLE_MAX=32.0, R_BUBBLE_MIN=0.620350491, ION_Tvir_MIN=4.69897, L_X=40.5, L_X_MINI=40.5, NU_X_THRESH=500.0, X_RAY_SPEC_INDEX=1.0, X_RAY_Tvir_MIN=4.69897, F_H2_SHIELD=0.0, t_STAR=0.5, A_LW=2.0, BETA_LW=0.6, A_VCB=1.0, BETA_VCB=1.8, UPPER_STELLAR_TURNOVER_MASS=11.447, UPPER_STELLAR_TURNOVER_INDEX=-0.6, SIGMA_STAR=0.25, SIGMA_LX=0.5, SIGMA_SFR_LIM=0.19, SIGMA_SFR_INDEX=-0.12, T_RE=20000.0, _FIXED_VAVG=None, V_CB_AVG_DEBUG=27.0, POP2_ION=5000.0, POP3_ION=44021.0, PHOTONCONS_CALIBRATION_END=3.5, CLUMPING_FACTOR=2.0, ALPHA_UVB=5.0, R_MAX_TS=500.0, N_STEP_TS=40, MAX_DVDR=0.2, DELTA_R_HII_FACTOR=1.1, NU_X_BAND_MAX=2000.0, NU_X_MAX=10000.0)\n",
"astro_options: AstroOptions(USE_MINI_HALOS=False, USE_X_RAY_HEATING=True, USE_CMB_HEATING=True, USE_LYA_HEATING=True, _INHOMO_RECO=None, USE_TS_FLUCT=False, _FIX_VCB_AVG=None, USE_EXP_FILTER=False, CELL_RECOMB=False, LYA_MULTIPLE_SCATTERING=False, USE_ADIABATIC_FLUCTUATIONS=True, PHOTON_CONS_TYPE='no-photoncons', USE_UPPER_STELLAR_TURNOVER=False, M_MIN_in_Mass=True, HALO_SCALING_RELATIONS_MEDIAN=False, HII_FILTER='sharp-k', HEAT_FILTER='spherical-tophat', IONISE_ENTIRE_SPHERE=False, RECOMB_MODEL='none', INTEGRATION_METHOD_ATOMIC='GAUSS-LEGENDRE', INTEGRATION_METHOD_MINI='GAUSS-LEGENDRE')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coevals[0].inputs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also save/load a Coeval object to disk:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2020-02-29T22:10:27.644169Z",
"start_time": "2020-02-29T22:10:27.442997Z"
},
"execution": {
"iopub.execute_input": "2026-09-22T20:29:43.810465Z",
"iopub.status.busy": "2026-09-22T20:29:43.809899Z",
"iopub.status.idle": "2026-09-22T20:29:44.410053Z",
"shell.execute_reply": "2026-09-22T20:29:44.409180Z"
}
},
"outputs": [],
"source": [
"coevals[0].save(cache.direc / \"coeval.h5\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that, if you are also using the cache (as we are), then saving the `Coeval` object\n",
"creates redundant data on disk. The files created by `Coeval` are more convenient for \n",
"saving, sharing and using in further post-processing, but are not easily used for \n",
"caching purposes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The file that we wrote can be easily read:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-22T20:29:44.412985Z",
"iopub.status.busy": "2026-09-22T20:29:44.412790Z",
"iopub.status.idle": "2026-09-22T20:29:44.454358Z",
"shell.execute_reply": "2026-09-22T20:29:44.453336Z"
}
},
"outputs": [],
"source": [
"coeval10 = p21c.Coeval.from_file(cache.direc / \"coeval.h5\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some convenient plotting functions exist in the `plotting` module. These can work directly on `Coeval` objects, or any of the output structs (as we'll see further on in the tutorial). By default the `coeval_sliceplot` function will plot the `brightness_temp`, using the standard traditional colormap:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2020-02-29T22:10:29.863171Z",
"start_time": "2020-02-29T22:10:28.657079Z"
},
"execution": {
"iopub.execute_input": "2026-09-22T20:29:44.457971Z",
"iopub.status.busy": "2026-09-22T20:29:44.457720Z",
"iopub.status.idle": "2026-09-22T20:29:46.089984Z",
"shell.execute_reply": "2026-09-22T20:29:46.088768Z"
}
},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"