{ "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": 8, "metadata": { "ExecuteTime": { "end_time": "2020-02-29T22:10:11.054977Z", "start_time": "2020-02-29T22:10:10.248058Z" } }, "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": 9, "metadata": { "ExecuteTime": { "end_time": "2020-02-29T22:10:11.059890Z", "start_time": "2020-02-29T22:10:11.056796Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Using 21cmFAST version 4.0.0b1.dev312+gb6f5204f.d20250728\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": 10, "metadata": {}, "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": 11, "metadata": {}, "outputs": [], "source": [ "cache = p21c.OutputCache(mkdtemp())" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f5d4089be46e4e7abebbbb63b29731c3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a3211bda48244f749496b888b96b0b11", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "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": 13, "metadata": {}, "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": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10.0" ] }, "execution_count": 14, "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": 15, "metadata": {}, "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", " 'mean_free_path',\n", " 'z_reion',\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", " 'halo_mass',\n", " 'halo_stars',\n", " 'halo_stars_mini',\n", " 'count',\n", " 'halo_sfr',\n", " 'halo_sfr_mini',\n", " 'halo_xray',\n", " 'n_ion',\n", " 'whalo_sfr']" ] }, "execution_count": 15, "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": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "cosmo_params: CosmoParams(SIGMA_8=0.8102, hlittle=0.6766, OMm=0.30964144154550644, OMb=0.04897468161869667, POWER_INDEX=0.9665, 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, 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)\n", "matter_options: MatterOptions(HMF='ST', USE_RELATIVE_VELOCITIES=False, 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, USE_HALO_FIELD=False, FIXED_HALO_GRIDS=False, HALO_STOCHASTICITY=False)\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=25.86, 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_CMB_HEATING=True, USE_LYA_HEATING=True, USE_MASS_DEPENDENT_ZETA=True, INHOMO_RECO=False, USE_TS_FLUCT=False, FIX_VCB_AVG=False, USE_EXP_FILTER=False, CELL_RECOMB=False, 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, AVG_BELOW_SAMPLER=True, INTEGRATION_METHOD_ATOMIC='GAUSS-LEGENDRE', INTEGRATION_METHOD_MINI='GAUSS-LEGENDRE')" ] }, "execution_count": 16, "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": 17, "metadata": { "ExecuteTime": { "end_time": "2020-02-29T22:10:27.644169Z", "start_time": "2020-02-29T22:10:27.442997Z" } }, "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": 18, "metadata": {}, "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": 19, "metadata": { "ExecuteTime": { "end_time": "2020-02-29T22:10:29.863171Z", "start_time": "2020-02-29T22:10:28.657079Z" } }, "outputs": [ { "data": { "image/png": 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