diff --git a/.github/workflows/python-tests.yml b/.github/workflows/python-tests.yml index 3b40e6f..9553140 100644 --- a/.github/workflows/python-tests.yml +++ b/.github/workflows/python-tests.yml @@ -42,13 +42,7 @@ jobs: - name: Install package run: | pip install -e . - - - name: Debug SFR_III function - env: - CLASSDIR: ${{ github.workspace }}/class_public - run: | - python -c "import zeus21; from zeus21.sfrd import SFR_III; import inspect; print('SFR_III parameters:', inspect.signature(SFR_III)); print('Parameter count:', len(inspect.signature(SFR_III).parameters))" - + - name: Run tests with coverage env: CLASSDIR: ${{ github.workspace }}/class_public @@ -64,4 +58,4 @@ jobs: flags: unittests name: codecov-umbrella verbose: true - fail_ci_if_error: false \ No newline at end of file + fail_ci_if_error: false diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 0000000..2d192e0 --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,21 @@ +# .readthedocs.yml +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Build documentation in the docs/ directory with Sphinx +sphinx: + configuration: docs/conf.py + +build: + os: ubuntu-24.04 + tools: + python: "3.12" + +python: + install: + - requirements: requirements.txt + - method: pip + path: . \ No newline at end of file diff --git a/AUTHORS.rst b/AUTHORS.rst new file mode 100644 index 0000000..ede4112 --- /dev/null +++ b/AUTHORS.rst @@ -0,0 +1,11 @@ +======= +Authors +======= + +* Julian B. Muñoz - `github.com/JulianBMunoz `_ +* Hector Afonso G. Cruz `github.com/hcruz1998 `_ +* Yonny Sklansky - `github.com/yonboyage `_ +* Emilie Thélie - `github.com/EmilieThelie `_ +* Sarah Libanore - `github.com/slibanore `_ +* Emily Bregou - `github.com/ebregou `_ +* Alessandra Venditti - `github.com/alessandra-venditti `_ diff --git a/README.md b/README.md index 369c0c6..bc92315 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,9 @@

- +

-# Zeus21: Lightning-fast simulations of cosmic dawn + +# Zeus21: Lightning-fast simulations of cosmic dawn and reionization [![Tests](https://github.com/JulianBMunoz/Zeus21/actions/workflows/python-tests.yml/badge.svg)](https://github.com/JulianBMunoz/Zeus21/actions/workflows/python-tests.yml) [![codecov](https://codecov.io/gh/JulianBMunoz/Zeus21/branch/main/graph/badge.svg)](https://codecov.io/gh/JulianBMunoz/Zeus21) @@ -11,57 +12,5 @@ Zeus21 encodes the effective model for the 21-cm power spectrum and global signa Zeus21 (Zippy Early-Universe Solver for 21-cm) pairs well with data from [HERA](https://reionization.org/), but can be used for any 21-cm inference or prediction. Current capabilities include finding the 21-cm power spectrum (at a broad range of k and z), the global signal, IGM temperatures (Tk, Ts, Tcolor), neutral fraction xHI, Lyman-alpha fluxes, and the evolution of the SFRD; all across cosmic dawn z=5-35. Zeus21 can use three different astrophysical models, one of which emulates 21cmFAST, and can vary the cosmology through CLASS. -If you want to get started I recommend checking the Jupyter tutorials in `docs/`. Full documentation in [ReadTheDocs](https://zeus21.readthedocs.io/en/latest/), more coming soon. Here is an example power spectrum (at k=0.3/Mpc) and global signal as a function of redshift, for two cases of X-ray luminosity. You can run it yourself with the tutorial included! - -

- -

- -Currently you can find tutorials for: - -
    -
  • Basics, running and plotting 21-cm power spectra and global signals.
    • -
  • UVLFs, comparing to HST and JWST predictions at high redshifts.
    • -
  • PopIII stars, and how they affect the cosmic-dawn 21-cm signal.
    • -
- -## Installation - -You can download and install this package by doing: - -``` -git clone https://github.com/julianbmunoz/zeus21.git zeus21 -cd zeus21/ -pip install . -``` - -that should take care of all dependencies (remember to work in your favorite conda env). If you have issues with cache'd versions of packages you can add `--no-cache-dir` at the end of `pip install .`. - -**NOTE:** You may run into problems when pip-installing `classy` (the Python wrapper of `CLASS`). If so, their installation guide is [here](https://github.com/lesgourg/class_public/wiki/Installation), but in short the steps are: - -``` -git clone https://github.com/lesgourg/class_public.git class -cd class/ -make -cd python/ -python setup.py install --user -``` - -(modifying the Makefile to your `gcc` as needed) - -## Citation - -If you find this code useful please cite: - -[An Effective Model for the Cosmic-Dawn 21-cm Signal](https://arxiv.org/abs/2302.08506) - -and include a link to [this Github](https://github.com/JulianBMunoz/Zeus21). - -If you use the UVLF module please cite: - -[Breaking degeneracies in the first galaxies with clustering](https://arxiv.org/abs/2306.09403) - -And if you use relative velocites, Lyman-Werner feedback, or Population III stars please cite: - -[The First Billion Years in Seconds: An Effective Model for the 21-cm Signal with Population III Stars](https://arxiv.org/abs/2407.18294). +Full **documentation** (with [installation instructions](https://zeus21.readthedocs.io/en/latest/installation.html), [tutorials](https://zeus21.readthedocs.io/en/latest/tutorials.html), and [full api description](https://zeus21.readthedocs.io/en/latest/api/modules.html)) can be found on [ReadTheDocs](https://zeus21.readthedocs.io/en/latest/). diff --git a/docs/README.md b/docs/README.md deleted file mode 120000 index 32d46ee..0000000 --- a/docs/README.md +++ /dev/null @@ -1 +0,0 @@ -../README.md \ No newline at end of file diff --git a/docs/Tutorial_Zeus21_Basics.ipynb b/docs/Tutorial_Zeus21_Basics.ipynb deleted file mode 100644 index e0d0166..0000000 --- a/docs/Tutorial_Zeus21_Basics.ipynb +++ /dev/null @@ -1,422 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This tutorial will cover the basics up to predicting the 21-cm power spectrum and global signal, assuming PopII stars only (see separate tutorial for PopIII). We will start by importing the necessary packages (Zeus, numpy, class)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import zeus21\n", - "from matplotlib import pyplot as plt\n", - "import numpy as np\n", - "\n", - "\n", - "#set up the CLASS cosmology\n", - "from classy import Class\n", - "ClassCosmo = Class()\n", - "ClassCosmo.compute()\n", - "\n", - "#and the user parameters\n", - "UserParams = zeus21.User_Parameters(precisionboost=1.2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we set up the cosmology and astrophysics -- This will do the bulk of the work\n", - "\n", - "We begin by running CLASS, where you can change the input parameters as shown below. Then we save the cosmo parameters, the correlation functions, and the halo mass function at all desired z and M.\n", - "\n", - "After that we set up the astro parameters, calculate the SFRD and with it all the global signal and related quantities. In principle one can re-run the astrophysics part only if you're certain of the cosmology parameters. With the current implementation they take a comparable amount of time (classy takes ~5s and Zeus ~3s)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CLASS has run, we store the cosmology.\n" - ] - } - ], - "source": [ - "#set up your parameters here, as an example the CDM (reduced) density\n", - "omega_cdm = 0.12\n", - "CosmoParams_input = zeus21.Cosmo_Parameters_Input(omegac = omega_cdm)\n", - "ClassyCosmo = zeus21.runclass(CosmoParams_input)\n", - "print('CLASS has run, we store the cosmology.')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation functions saved.\n", - "HMF interpolator built. This ends the cosmology part -- moving to astrophysics.\n", - "SFRD and coefficients stored. Move ahead.\n" - ] - } - ], - "source": [ - "#define all cosmology (including derived) parameters, and save them to the CosmoParams structure\n", - "CosmoParams = zeus21.Cosmo_Parameters(UserParams,CosmoParams_input, ClassyCosmo) \n", - "CorrFClass = zeus21.Correlations(UserParams, CosmoParams, ClassyCosmo)\n", - "print('Correlation functions saved.')\n", - "HMFintclass = zeus21.HMF_interpolator(UserParams,CosmoParams,ClassyCosmo)\n", - "print('HMF interpolator built. This ends the cosmology part -- moving to astrophysics.')\n", - "\n", - "#set up your astro parameters too, here the peak of f*(Mh) as an example\n", - "epsilon_star = 0.15\n", - "AstroParams = zeus21.Astro_Parameters(UserParams, CosmoParams, epsstar=epsilon_star)\n", - "\n", - "\n", - "ZMIN = 10.0 #down to which z we compute the evolution\n", - "CoeffStructure = zeus21.get_T21_coefficients(UserParams, CosmoParams, ClassyCosmo, AstroParams, HMFintclass, zmin=ZMIN)\n", - "zlist = CoeffStructure.zintegral\n", - "print('SFRD and coefficients stored. Move ahead.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The CoeffStructure holds all the information needed to find the 21-cm signal during cosmic dawn. It has saved the 21-cm global signal, the Wouthuysen-Field coupling, and all temperatures. It also has the effective biases $\\gamma_R$ for all $R$ (which will be used for the power spectrum below). This structure also has ancillary data like the evolution of the SFRD and Nion. If you want to learn what else the CoeffStructure holds, just do dir(CoeffStructure)\n", - "\n", - "Let us start by plotting the global signal." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(10.0, 25.0)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(zlist,CoeffStructure.T21avg, 'k')\n", - "plt.xlabel(r'z');\n", - "plt.ylabel(r'$T_{21}$ [mK]');\n", - "plt.xlim([10, 25])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It has the usual absorption trough around $z\\sim15$ that we know and love (given so far we only have atomic-cooling haloes), and turns into emission at $z\\sim11$. Since we're stopping at `ZMIN=10` we don't get to see the bulk of reionization, but it's stored in CoeffStructure.xHI_avg.\n", - "\n", - "Let us plot the relevant temperatures too. Here is the CMB temperature $T_{\\rm CMB}$; the gas kinetic temperature $T_k$, which has an adiabatic/cosmological and an X-ray component; and the spin temperature $T_S$, which has the WF coupling in it (and we store its inverse in the code)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(zlist,CoeffStructure.T_CMB,'r--')\n", - "plt.plot(zlist,CoeffStructure.Tk_avg,'b-.')\n", - "plt.plot(zlist,1.0/CoeffStructure._invTs_avg,'k')\n", - "plt.xlabel(r'z');\n", - "plt.ylabel(r'Temperatures [K]');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This lines up with our expectation from the 21-cm global signal above. Absorption begins when $T_S$ departs from $T_{\\rm CMB}$, at $z\\sim 20$, as it begins to couple to $T_k$. It turns into emission at $z\\sim 11$ when $T_S\\sim T_k > T_{\\rm CMB}$. Full WF coupling only occurs after there has been some X-ray heating, so we don't get a deep 21-cm trough for this model. This would be different with a lower X-ray luminosity $L_X$ as we will see below.\n", - "\n", - "Let's move now to the 21-cm fluctuations." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computed the 21-cm power spectrum.\n" - ] - } - ], - "source": [ - "RSDMODE = 1 #which RSD mode you want, 0 is no RSDs (real space), 1 is spherical (as simulations usually take), 2 is mu~1 (outside the wedge, most relevant for observations)\n", - "PS21 = zeus21.Power_Spectra(UserParams,CosmoParams, AstroParams, ClassyCosmo, CorrFClass, CoeffStructure, RSD_MODE = RSDMODE)\n", - "print('Computed the 21-cm power spectrum.')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1, 200)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#choose a k to plot\n", - "klist = PS21.klist_PS\n", - "kchoose=0.3;\n", - "_ik = min(range(len(klist)), key=lambda i: np.abs(klist[i]-kchoose))\n", - "\n", - "plt.semilogy(zlist,PS21.Deltasq_T21[:,_ik], color='k', linewidth=2.0)\n", - "plt.semilogy(zlist,PS21.Deltasq_T21_lin[:,_ik], color='gray', linewidth=1.2)\n", - "\n", - "plt.xlabel(r'$z$');\n", - "plt.ylabel(r'$\\Delta^2_{21}\\,\\rm[mK^2]$');\n", - "plt.legend([r'Full Zeus21', r'Linear'])\n", - "\n", - "plt.xlim([10, 25])\n", - "plt.ylim([1,200])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This shows the evolution of the fluctuations at a particular scale (i.e., wavenumber $k$). We have shown the full result from Zeus21, as well as the linear approximation (which is also stored). We can flip the script now and show the 21-cm power against wavenumber at a particular redshift." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1, 200)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#choose a z to plot\n", - "zchoose=13.;\n", - "_iz = min(range(len(zlist)), key=lambda i: np.abs(zlist[i]-zchoose))\n", - "\n", - "plt.loglog(klist,PS21.Deltasq_T21[_iz], color='k', linewidth=2.0)\n", - "plt.loglog(klist,PS21.Deltasq_T21_lin[_iz], color='gray', linewidth=1.2)\n", - "\n", - "plt.xlabel(r'$k\\,\\rm [Mpc^{-1}]$');\n", - "plt.ylabel(r'$\\Delta^2_{21}\\,\\rm[mK^2]$');\n", - "plt.legend([r'Full Zeus21', r'Linear'])\n", - "\n", - "plt.xlim([1e-2,1])\n", - "plt.ylim([1,200])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This was for a specific set of astro+cosmo parameters. Let's do a different example with lower X-ray luminosity (which we expect will produce a deeper cosmic dawn absorption). This is controlled through the free parameter L40_xray (luminosity per unit SFR in units of 10^40 erg/s/SFR), with a fiducial value of 3.0. Let's lower it to 1.0 and see what happens." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "AstroParams_lowLX = zeus21.Astro_Parameters(UserParams, CosmoParams, epsstar=epsilon_star, L40_xray=1.0)\n", - "CoeffStructure_lowLX = zeus21.get_T21_coefficients(UserParams, CosmoParams, ClassyCosmo, AstroParams_lowLX, HMFintclass, zmin=ZMIN)\n", - "PS21_lowLX = zeus21.Power_Spectra(UserParams, CosmoParams, AstroParams, ClassyCosmo, CorrFClass, CoeffStructure_lowLX, RSD_MODE = RSDMODE)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that we can re-run the astrophysics part only, so it should take ~few seconds in a laptop. Let's plot the global signal and fluctuations comparing with the fiducial case." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f = plt.figure(figsize = (6,6))\n", - "\n", - "ax = f.add_subplot(211)\n", - "\n", - "plt.semilogy(zlist,PS21.Deltasq_T21[:,_ik], 'k', linewidth=2.0)\n", - "plt.semilogy(zlist,PS21_lowLX.Deltasq_T21[:,_ik], 'b-.', linewidth=2.0)\n", - "\n", - "plt.ylabel(r'$\\Delta^2_{21}\\,\\rm[mK^2]$');\n", - "plt.legend([r'$L_{X,40} = 3.0$ (fid.)', r'$L_{X,40} = 1.0$'])\n", - "plt.xlim([10, 25]);\n", - "plt.ylim([1,200]);\n", - "\n", - "ax = f.add_subplot(212)\n", - "\n", - "plt.plot(zlist,CoeffStructure.T21avg, 'k', linewidth=2.0)\n", - "plt.plot(zlist,CoeffStructure_lowLX.T21avg, 'b-.', linewidth=2.0)\n", - "\n", - "plt.xlabel(r'$z$');\n", - "plt.ylabel(r'$T_{21}$ [mK]');\n", - "\n", - "plt.xlim([10, 25]);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Just as we expected! Lowering the X-ray luminosity (blue) makes for a deeper 21-cm global absorption, and larger 21-cm fluctuations too. By $z\\sim 10$ there is still enough heating to raise the 21-cm signal near absorption. We can confirm this by plotting the spin temperature and comparing against the `standard' case" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(zlist,1.0/CoeffStructure._invTs_avg,'k')\n", - "plt.plot(zlist,1.0/CoeffStructure_lowLX._invTs_avg,'b-.')\n", - "plt.plot(zlist,CoeffStructure.T_CMB,'r--')\n", - "plt.xlabel(r'z');\n", - "plt.ylabel(r'Temperatures [K]');\n", - "plt.legend([r'$L_{X,40} = 3.0$ (fid.)', r'$L_{X,40} = 1.0$'])\n", - "plt.xlim([10, 25]);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You're now ready to calculate any 21-cm power spectrum or global signal that you want!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "zeus21_userparams", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.11" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/Tutorial_Zeus21_PopIIandIII_Fiducial.ipynb b/docs/Tutorial_Zeus21_PopIIandIII_Fiducial.ipynb deleted file mode 100644 index baa2f54..0000000 --- a/docs/Tutorial_Zeus21_PopIIandIII_Fiducial.ipynb +++ /dev/null @@ -1,690 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "21609608-8da1-40d1-bf4d-12d8ca84f267", - "metadata": {}, - "source": [ - "# Zeus21 with Population II and III Stars" - ] - }, - { - "cell_type": "markdown", - "id": "33d16e6c", - "metadata": {}, - "source": [ - "This quick tutorial covers all the new features in the updated public version of Zeus21 (see our paper [Cruz et al. 2024](https://arxiv.org/abs/2407.18294)) for more information.\n", - "\n", - "In this new version, we have included the influence of Population III stars as well as the suppressive feedback effects from Lyman-Werner radiation and the relative velocity between CDM and baryons." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ba889183", - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "\n", - "#import Zeus\n", - "import zeus21\n", - "\n", - "#set up the CLASS cosmology\n", - "from classy import Class\n", - "\n", - "#and the user parameters\n", - "UserParams = zeus21.User_Parameters(precisionboost=1.2)\n" - ] - }, - { - "cell_type": "markdown", - "id": "f3646152-80fa-4166-bcd8-1645ffbebe5b", - "metadata": {}, - "source": [ - "## Step 1: Set up Cosmology" - ] - }, - { - "cell_type": "markdown", - "id": "e56fce7d", - "metadata": {}, - "source": [ - "We begin by running CLASS, where the associated parameters can be altered below. Then we save the cosmo parameters, the correlation functions, and the halo mass function at all desired z and M." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "01adaa46-dcdd-4405-a6a1-5b800584dc4d", - "metadata": {}, - "outputs": [], - "source": [ - "# cosmo inputs from table 2 last column of 1807.06209, as 21cmFAST\n", - "ombh2 = 0.02242 \n", - "omch2 = 0.11933\n", - "tau_re = 0.0544\n", - "hLittle = 0.6766\n", - "ns = 0.9665\n", - "As = np.exp(3.047)*10**(-10.)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "56d952f0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CLASS has run, we store the cosmology.\n" - ] - } - ], - "source": [ - "#set up your input CLASS parameters here\n", - "#note; VCB feedback is turned on as the USE_RELATIVE_VELOCITIES flag in CosmoParams_input\n", - "\n", - "CosmoParams_input = zeus21.Cosmo_Parameters_Input(omegac = omch2, omegab = ombh2, h_fid = hLittle, As = As, ns = ns, tau_fid = tau_re, USE_RELATIVE_VELOCITIES = True, Flag_emulate_21cmfast=False)\n", - "ClassyCosmo = zeus21.runclass(CosmoParams_input)\n", - "print('CLASS has run, we store the cosmology.')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "06ba5c04-dcd0-4796-aa97-7246972cca93", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation functions saved.\n", - "HMF interpolator built. This ends the cosmology part -- moving to astrophysics.\n" - ] - } - ], - "source": [ - "#define all cosmology (including derived) parameters, and save them to the CosmoParams structure\n", - "CosmoParams = zeus21.Cosmo_Parameters(UserParams, CosmoParams_input, ClassyCosmo) \n", - "\n", - "#Generate and store the matter correlation function\n", - "CorrFClass = zeus21.Correlations(UserParams, CosmoParams, ClassyCosmo)\n", - "print('Correlation functions saved.')\n", - "\n", - "# Compute the HMF structure that stores associated quantities\n", - "HMFintclass = zeus21.HMF_interpolator(UserParams, CosmoParams, ClassyCosmo)\n", - "print('HMF interpolator built. This ends the cosmology part -- moving to astrophysics.')\n" - ] - }, - { - "cell_type": "markdown", - "id": "8eeda1be-f47c-4cb4-b9f2-6998e06c6641", - "metadata": {}, - "source": [ - "## Step 2: Astrophysics" - ] - }, - { - "cell_type": "markdown", - "id": "faf27bcd", - "metadata": {}, - "source": [ - "After that we set up the astro parameters, calculate the SFRD and with it all the global signal and related quantities. In principle one can re-run the astrophysics part only if you're certain of the cosmology parameters. \n", - "\n", - "With the current implementation, the computation of global quantities below takes less than a second on a laptop.\n", - "\n", - "For more details on parameters, please refer to those used in Table 1 of our paper linked above.\n", - "\n", - "NOTE: Pop III stars and LW feedback are turned on using the USE_POP_III and USE_LW_FEEDBACK flags below." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "fbcff876-3535-475d-9cb0-c124b9de2fff", - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SFRD and coefficients stored. Move ahead.\n" - ] - } - ], - "source": [ - "### POP II quantities first\n", - "\n", - "################################\n", - "### Model Parameters\n", - "astromodel = 0 # ASTRO MODEL: 0 for GALUMI-like, 1 for 21cmfast-like, default 0\n", - "accretion_model = 0 # ACCRETION MODEL: 0 for exponential, 1 for EPS, default EXP\n", - "\n", - "################################\n", - "### SFR(Mh) Parameteres\n", - "alphastar = 0.5 # alphastar powerlaw index for low masses, default 0.5\n", - "betastar = -0.5 # betastar powerlaw index for high masses, default -0.5\n", - "epsstar = 10**-1. # epsilonstar = fstar at Mc\n", - "Mc = 3e11 # Pivot mass at which the power law cuts for model 0, default Mc = 3e11\n", - "dlog10epsstardz = 0.0 # dlog10epsilonstar/dz, default 0\n", - "\n", - "################################\n", - "### Escape fraction parameters\n", - "fesc10 = 0.1 # fesc(M) parameter. Power law normalized (fesc10) at M=1e10 Msun with index alphaesc\n", - "alphaesc = 0.0\n", - "L40_xray = 10**0.5 # L40_xray: soft-band (E<2 keV) lum/SFR in Xrays in units of 10^40 erg/s/(Msun/yr)\n", - "E0_xray = 500. # E0_xray: minimum energy in eV\n", - "alpha_xray = -1.0 # Xray SED power-law index\n", - "Emax_xray_norm=2000 # max energy in eV to normalize SED. Keep at 2000 eV normally\n", - "\n", - "################################\n", - "### LyA parameters\n", - "Nalpha_lyA_II = 9690 # number of Pop II photons between LyA and Ly Cont. per baryon (from BL05)\n", - "Nalpha_lyA_III = 17900 # number of Pop III photons between LyA and Ly Cont. per baryon value of 17900 is from Klessen & Glover 2023 (2303.12500), table A2\n", - "\n", - "################################\n", - "### MTURN Parameters: \n", - "Mturn_fixed = None # Mturn_fixed: None if use Matom(z) at each z, Some value if fixed Mturn\n", - "FLAG_MTURN_SHARP= False # Mturn_sharp: False if regular exponential cutoff, True if sharp cutoff, active only if Mturn_fixed is on\n", - "\n", - "################################\n", - "### UVLF Parameters\n", - "C0dust = 4.43 # DUST PARAMETERS FOR UVLFs\n", - "C1dust = 1.99\n", - "sigmaUV = 0.5 # stochasticity (gaussian rms) in the halo-galaxy connection P(MUV | Mh) - TODO: only used in UVLF not sfrd\n", - "\n", - "################################\n", - "ZMIN = 10.0 # down to which z we compute the evolution\n", - "\n", - "\n", - "\n", - "################################\n", - "# Pop III Quantities\n", - "alphastar_III = 0 \n", - "betastar_III = 0\n", - "fstar_III = 10**(-3.0)\n", - "Mc_III = 1e7\n", - "dlog10epsstardz_III = 0.0\n", - "\n", - "fesc7_III = 10**(-1.35)\n", - "alphaesc_III = -0.3\n", - "L40_xray_III = 10**0.5\n", - "alpha_xray_III = -1.0\n", - "\n", - "\n", - "USE_POPIII = True\n", - "USE_LW_FEEDBACK = True\n", - "\n", - "A_LW = 2.0\n", - "beta_LW = 0.6\n", - "\n", - "A_vcb = 1.0\n", - "beta_vcb = 1.8\n", - "\n", - "\n", - "#set up your astro parameters too, here the peak of f*(Mh) as an example\n", - "\n", - "AstroParams = zeus21.Astro_Parameters(UserParams,\n", - " CosmoParams, \n", - " astromodel = astromodel, \n", - " accretion_model = accretion_model,\n", - " \n", - " alphastar = alphastar, \n", - " betastar = betastar, \n", - " epsstar = epsstar, \n", - " Mc = Mc, \n", - " dlog10epsstardz = dlog10epsstardz,\n", - " \n", - " fesc10 = fesc10, \n", - " alphaesc = alphaesc,\n", - " L40_xray = L40_xray, \n", - " E0_xray = E0_xray, \n", - " alpha_xray = alpha_xray, \n", - " Emax_xray_norm = Emax_xray_norm, \n", - " \n", - " Nalpha_lyA_II = Nalpha_lyA_II, \n", - " Nalpha_lyA_III = Nalpha_lyA_III,\n", - " \n", - " Mturn_fixed = Mturn_fixed, \n", - " FLAG_MTURN_SHARP = FLAG_MTURN_SHARP,\n", - "\n", - " C0dust = C0dust, \n", - " C1dust = C1dust,\n", - " sigmaUV = sigmaUV,\n", - " \n", - " \n", - " USE_POPIII = USE_POPIII, \n", - " USE_LW_FEEDBACK = USE_LW_FEEDBACK,\n", - "\n", - " alphastar_III = alphastar_III, \n", - " betastar_III = betastar_III,\n", - " fstar_III = fstar_III,\n", - " Mc_III = Mc_III,\n", - " dlog10epsstardz_III = dlog10epsstardz_III,\n", - "\n", - " fesc7_III = fesc7_III,\n", - " alphaesc_III = alphaesc_III,\n", - " L40_xray_III = L40_xray_III,\n", - " alpha_xray_III = alpha_xray_III,\n", - " \n", - " A_LW = A_LW,\n", - " beta_LW = beta_LW,\n", - " \n", - " A_vcb = A_vcb,\n", - " beta_vcb = beta_vcb\n", - " )\n", - "\n", - "CoeffStructure = zeus21.get_T21_coefficients(UserParams, CosmoParams, ClassyCosmo, AstroParams, HMFintclass, zmin=ZMIN)\n", - "zlist= CoeffStructure.zintegral\n", - "print('SFRD and coefficients stored. Move ahead.')\n" - ] - }, - { - "cell_type": "markdown", - "id": "fa72898f", - "metadata": {}, - "source": [ - "## Step 3: Plotting globally-averaged quantities" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "507bde44", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize = (12, 6.75))\n", - "\n", - "plt.plot(zlist, CoeffStructure.T21avg, color = \"#E64D4E\", linewidth = 3)\n", - "\n", - "plt.xlim([10, 35])\n", - "plt.ylim([-120, 30])\n", - "\n", - "plt.xlabel(r'$z$', fontsize = 30)\n", - "plt.ylabel(r'$T_{21}$ [mK]', fontsize = 30)\n", - "plt.xticks(fontsize=30)\n", - "plt.yticks(fontsize=30)\n", - "plt.tick_params(which='major', length=12, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", - "plt.tick_params(which='minor', length=5, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", - "plt.tick_params(axis=\"y\", labelsize=30, pad = 10)\n", - "plt.tick_params(axis=\"x\", labelsize=30, pad = 10)\n", - "\n", - "plt.tight_layout()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "3a57e7ae", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize = (12, 6.75))\n", - "\n", - "plt.plot(zlist,CoeffStructure.T_CMB , color = \"#EE9063\", linewidth = 3, linestyle = 'dotted', label = r'$T_\\mathrm{CMB}$')\n", - "\n", - "\n", - "plt.plot(zlist,CoeffStructure.Tk_ad , color = \"#0B92B1\", linewidth = 3, label = r'$T_\\mathrm{ad}$')\n", - "plt.plot(zlist,CoeffStructure.Tk_xray , color = \"#E64D4E\", linewidth = 3, label = r'$T_x$')\n", - "plt.plot(zlist,CoeffStructure.Tk_avg, color = \"#665191\", linewidth = 3, label = r'$T_k$')\n", - "\n", - "plt.plot(zlist,CoeffStructure._invTs_avg**-1 , color = \"#77AC54\", linewidth = 3, label = r'$T_s$')\n", - "\n", - "\n", - "plt.xlim([10, 35])\n", - "plt.ylim([0, 100])\n", - "\n", - "plt.xlabel(r'$z$', fontsize = 30)\n", - "plt.ylabel(r'$T$ [K]', fontsize = 30)\n", - "plt.xticks(fontsize=30)\n", - "plt.yticks(fontsize=30)\n", - "plt.tick_params(which='major', length=12, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", - "plt.tick_params(which='minor', length=5, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", - "plt.tick_params(axis=\"y\", labelsize=30, pad = 10)\n", - "plt.tick_params(axis=\"x\", labelsize=30, pad = 10)\n", - "plt.legend(fontsize=20, frameon = False)\n", - "plt.tight_layout()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6815b8c5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize = (12, 6.75))\n", - "\n", - "plt.semilogy(CoeffStructure.zintegral, CoeffStructure.SFRD_II_avg, color=\"#E64D4E\", linewidth=3.0, label = 'Pop II')\n", - "plt.semilogy(CoeffStructure.zintegral, CoeffStructure.SFRD_III_avg, color=\"#0B92B1\", linewidth=3.0, label = 'Pop III')\n", - "\n", - "plt.semilogy(CoeffStructure.zintegral, CoeffStructure.SFRD_II_avg + CoeffStructure.SFRD_III_avg, color=\"#665191\", linewidth=3.0, label = 'Pop II + III')\n", - "\n", - "plt.xlim([10, 35])\n", - "plt.ylim(1e-7, 1e-1)\n", - "\n", - "plt.xlabel(r'$z$', fontsize = 30)\n", - "plt.ylabel(r'$\\dot{\\overline{\\rho}}_*(z)$', fontsize = 30)\n", - "\n", - "plt.xticks(fontsize=30)\n", - "plt.yticks(fontsize=30)\n", - "plt.tick_params(which='major', length=12, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", - "plt.tick_params(which='minor', length=5, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", - "plt.tick_params(axis=\"y\", labelsize=30, pad = 10)\n", - "plt.tick_params(axis=\"x\", labelsize=30, pad = 10)\n", - "plt.legend(fontsize=20, frameon = False)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "b1a55a1e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize = (12, 6.75))\n", - "\n", - "plt.semilogy(CoeffStructure.zintegral, CoeffStructure.J_21_LW_II, color=\"#E64D4E\", linewidth=3.0, label = 'Pop II')\n", - "plt.semilogy(CoeffStructure.zintegral, CoeffStructure.J_21_LW_III, color=\"#0B92B1\", linewidth=3.0, label = 'Pop III')\n", - "\n", - "plt.semilogy(CoeffStructure.zintegral, CoeffStructure.J_21_LW_II + CoeffStructure.J_21_LW_III, color=\"#665191\", linewidth=3.0, label = 'Pop II + III')\n", - "\n", - "plt.xlim([10, 35])\n", - "plt.ylim(1e-3, 1e2)\n", - "\n", - "plt.xlabel(r'$z$', fontsize = 30)\n", - "plt.ylabel(r'$J_{21}$', fontsize = 30)\n", - "\n", - "plt.xticks(fontsize=30)\n", - "plt.yticks(fontsize=30)\n", - "plt.tick_params(which='major', length=12, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", - "plt.tick_params(which='minor', length=5, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", - "plt.tick_params(axis=\"y\", labelsize=30, pad = 10)\n", - "plt.tick_params(axis=\"x\", labelsize=30, pad = 10)\n", - "plt.legend(fontsize=20, frameon = False)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a85d3ff1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize = (12, 6.75))\n", - "\n", - "plt.semilogy(CoeffStructure.zintegral,CoeffStructure.xe_avg, color=\"#E64D4E\", linewidth=3.0)\n", - "\n", - "plt.xlim([10, 35])\n", - "plt.ylim([1e-4, 1e-3])\n", - "\n", - "plt.xlabel(r'$z$', fontsize = 30)\n", - "plt.ylabel(r'$x_e$', fontsize = 30)\n", - "plt.xticks(fontsize=30)\n", - "plt.yticks(fontsize=30)\n", - "plt.tick_params(which='major', length=12, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", - "plt.tick_params(which='minor', length=5, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", - "plt.tick_params(axis=\"y\", labelsize=30, pad = 10)\n", - "plt.tick_params(axis=\"x\", labelsize=30, pad = 10)\n", - "# plt.legend(fontsize=20, frameon = False)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "77eb7769", - "metadata": {}, - "source": [ - "## Step 4: Fluctuations" - ] - }, - { - "cell_type": "markdown", - "id": "1b89f7d8", - "metadata": {}, - "source": [ - "The last structure to run is Power_Spectra, which uses our lognormal and log-chisquare prescription to compute the statistics of IGM fluctuations in the form of two-point functions." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "f80f6e74", - "metadata": {}, - "outputs": [], - "source": [ - "RSDMODE = 1\n", - "\n", - "PS21 = zeus21.Power_Spectra(UserParams, CosmoParams, AstroParams, ClassyCosmo, CorrFClass, CoeffStructure, RSD_MODE = RSDMODE)\n", - "klist = CorrFClass._klistCF" - ] - }, - { - "cell_type": "markdown", - "id": "b53d6fbd", - "metadata": {}, - "source": [ - "## Plotting Results" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "548b2917", - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.interpolate import RegularGridInterpolator\n", - "\n", - "interp = RegularGridInterpolator((zlist, klist), PS21.Deltasq_T21, method = 'cubic', bounds_error=False, fill_value=0)\n", - "interpLin = RegularGridInterpolator((zlist, klist), PS21.Deltasq_T21_lin, method = 'cubic', bounds_error=False, fill_value=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "9a9a4555", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#choose a z to plot\n", - "kchoose=0.3 \n", - "zlistHighRes = np.geomspace(zlist[0], zlist[-1], 1000)\n", - "\n", - "powerSpectrum = interp((zlistHighRes, kchoose))\n", - "powerSpectrumLin = interpLin((zlistHighRes, kchoose))\n", - "\n", - "plt.figure(figsize = (12, 6.75))\n", - "\n", - "plt.semilogy(zlistHighRes, powerSpectrum, color=\"#E64D4E\", linewidth=3.0, label = 'Nonlinear')\n", - "plt.semilogy(zlistHighRes, powerSpectrumLin, color=\"#0B92B1\", linewidth=3.0, linestyle = 'dashed', label = 'Linear')\n", - "\n", - "plt.xlim([10, 25])\n", - "plt.ylim([1,200])\n", - "\n", - "plt.xlabel(r'$z$', fontsize = 30)\n", - "plt.ylabel(r'$\\Delta^2_{21}\\,\\rm[mK^2]$', fontsize = 30)\n", - "\n", - "plt.xticks(fontsize=30)\n", - "plt.yticks(fontsize=30)\n", - "plt.tick_params(which='major', length=12, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", - "plt.tick_params(which='minor', length=5, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", - "plt.tick_params(axis=\"y\", labelsize=30, pad = 10)\n", - "plt.tick_params(axis=\"x\", labelsize=30, pad = 10)\n", - "plt.legend(fontsize=20, frameon = False)\n", - "plt.tight_layout()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "f5ae3002", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#choose a z to plot\n", - "zchoose=16\n", - "klistHighRes = np.geomspace(1e-4, 1e1, 1000)\n", - "\n", - "powerSpectrum = interp((zchoose, klistHighRes))\n", - "powerSpectrumLin = interpLin((zchoose, klistHighRes))\n", - "\n", - "plt.figure(figsize = (12, 6.75))\n", - "\n", - "plt.loglog(klistHighRes, powerSpectrum, color=\"#E64D4E\", linewidth=3.0, label = 'Nonlinear')\n", - "plt.loglog(klistHighRes, powerSpectrumLin, color=\"#0B92B1\", linewidth=3.0, linestyle = 'dashed', label = 'Linear')\n", - "\n", - "plt.xlim([1e-3,1e0])\n", - "plt.ylim([1e-1,1e3])\n", - "\n", - "plt.xlabel(r'$k\\,\\rm [Mpc^{-1}]$', fontsize = 30)\n", - "plt.ylabel(r'$\\Delta^2_{21}\\,\\rm[mK^2]$', fontsize = 30)\n", - "plt.xticks(fontsize=30)\n", - "plt.yticks(fontsize=30)\n", - "plt.tick_params(which='major', length=12, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", - "plt.tick_params(which='minor', length=5, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", - "plt.tick_params(axis=\"y\", labelsize=30, pad = 10)\n", - "plt.tick_params(axis=\"x\", labelsize=30, pad = 10)\n", - "plt.legend(fontsize=20, frameon = False)\n", - "plt.tight_layout()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "19b8014b", - "metadata": {}, - "source": [ - "For questions, don't hesitate to reach out to hcruz2@jhu.edu!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0cb987ba", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "zeus21_userparams", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/Tutorial_Zeus21_UVLFs.ipynb b/docs/Tutorial_Zeus21_UVLFs.ipynb deleted file mode 100644 index 5996e47..0000000 --- a/docs/Tutorial_Zeus21_UVLFs.ipynb +++ /dev/null @@ -1,273 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### NOTE: This notebook only includes atomic-cooling galaxies with PopII stars. Pop III stars in minihalos have not been added to this module yet.\n", - "\n", - "This tutorial will cover how to generate UVLFs from Zeus21. It is recommended to do the basics tutorial first, to get a handle on how the code works. That said, this can stand on its own, so let's go!" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import zeus21\n", - "from matplotlib import pyplot as plt\n", - "import numpy as np\n", - "import copy\n", - "\n", - "\n", - "#set up the CLASS cosmology\n", - "from classy import Class\n", - "ClassCosmo = Class()\n", - "ClassCosmo.compute()\n", - "\n", - "#and the user parameters\n", - "UserParams = zeus21.User_Parameters(precisionboost=1.2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we set up the cosmology and astrophysics. We do not need to go as far as the 21-cm calculation when doing UVLFs. We do not need the Coeff or PS21 structures (since we won't compute 21-cm in this tutorial), nor do we need the correlation functions. We do need the HMFs, though.\n", - "\n", - "As before, we begin by running CLASS, where you can change the input parameters as shown below. Then we save the cosmo parameters, the correlation functions, and the halo mass function at all desired z and M. T\n", - "\n", - "Then we set up the astro parameters. They're shared between the UVLF and the 21-cm calculation so one can jointly do inference." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "CosmoParams_input = zeus21.Cosmo_Parameters_Input(zmin_CLASS=0.0) #make sure to provide zmin_CLASS lower than standard (5.0) if you want lower z results (eg HMFs)\n", - "\n", - "CosmoParams,ClassyCosmo, CorrFclass ,HMFintclass = zeus21.cosmo_wrapper(UserParams, CosmoParams_input)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have to define the astrophysical parameters. You can run with the default set, but let's spice things up a bit. Let's define two:\n", - "-One with a standard halo-galaxy connection\n", - "-One with a very stochastic halo-galaxy connection, but lower UV brightness per galaxy (smaller f* at peak)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "AstroParams_std = zeus21.Astro_Parameters(UserParams, CosmoParams, accretion_model=0) #made accretion_model exponential, like in the paper (2306.09403)\n", - "AstroParams_bursty = zeus21.Astro_Parameters(UserParams, CosmoParams, sigmaUV=0.9, accretion_model=0) #made accretion_model exponential, like in the paper (2306.09403)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now define the redshift and magnitudes over which to plot. You give an arbitrary and width dz, and the code will assume the selection function is a Gaussian around it. For magnitudes you provide centers and widths of bins, assumed to be tophats. It will then generate the UVLF under those specs. Make sure the binning of z and MUV are the same as whatever date you're considering!" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "z, dz = 5.0, 0.5 #zcentral and width (assumed Gaussian)\n", - "\n", - "MUVcenters = np.linspace(-17,-23,13) #centers of bins\n", - "MUVwidths = -np.diff(MUVcenters)\n", - "MUVwidths = np.append(MUVwidths,MUVwidths[-1])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/et24528/anaconda3/envs/zeus21_userparams/lib/python3.11/site-packages/zeus21/UVLFs.py:29: RuntimeWarning: divide by zero encountered in log10\n", - " MUVtab = 51.63 - 2.5 * np.log10(LUVtab) #AB magnitude\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "UVLFs_std = zeus21.UVLFs.UVLF_binned(AstroParams_std,CosmoParams,HMFintclass,z,dz,MUVcenters,MUVwidths)\n", - "UVLFs_bursty = zeus21.UVLFs.UVLF_binned(AstroParams_bursty,CosmoParams,HMFintclass,z,dz,MUVcenters,MUVwidths)\n", - "\n", - "\n", - "plt.semilogy(MUVcenters,UVLFs_std,'k-')\n", - "plt.semilogy(MUVcenters,UVLFs_bursty,'r--')\n", - "plt.xlim(-22,-17)\n", - "plt.ylim(1e-6,1e-1)\n", - "plt.xlabel(r'$M_{\\rm UV}$');\n", - "plt.ylabel(r'$\\Phi_{\\rm UV}\\,\\rm [Mpc^{-3}\\,mag^{-1}]$');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you're new to the world of UVLFs note that the x axis is a UV absolute magnitude, and y is the comoving number density of galaxies at that magnitude (divided by the width of the bin)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#now let's change the alpha and beta too to see what they do\n", - "AstroParams_lowalpha = copy.deepcopy(AstroParams_std)\n", - "AstroParams_lowalpha.alphastar-=0.2\n", - "UVLFs_lowalpha = zeus21.UVLFs.UVLF_binned(AstroParams_lowalpha,CosmoParams,HMFintclass,z,dz,MUVcenters,MUVwidths)\n", - "\n", - "AstroParams_lowbeta = copy.deepcopy(AstroParams_std)\n", - "AstroParams_lowbeta.betastar-=0.2\n", - "UVLFs_lowbeta = zeus21.UVLFs.UVLF_binned(AstroParams_lowbeta,CosmoParams,HMFintclass,z,dz,MUVcenters,MUVwidths)\n", - "\n", - "#and the dust assumptions -- they can be degenerate! (Beware, these are measured independently, see Refs.)\n", - "AstroParams_lessdust = copy.deepcopy(AstroParams_std)\n", - "AstroParams_lessdust.C0dust-=0.1;\n", - "UVLFs_lessdust = zeus21.UVLFs.UVLF_binned(AstroParams_lessdust,CosmoParams,HMFintclass,z,dz,MUVcenters,MUVwidths)\n", - "\n", - "\n", - "plt.semilogy(MUVcenters,UVLFs_std,'k-')\n", - "plt.semilogy(MUVcenters,UVLFs_lowalpha,'b--')\n", - "plt.semilogy(MUVcenters,UVLFs_lowbeta,'g--')\n", - "plt.semilogy(MUVcenters,UVLFs_lessdust,'r-.')\n", - "plt.xlim(-22,-17)\n", - "plt.ylim(1e-6,1e-1)\n", - "plt.xlabel(r'$M_{\\rm UV}$');\n", - "plt.ylabel(r'$\\Phi_{\\rm UV}\\,\\rm [Mpc^{-3}\\,mag^{-1}]$');\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Zeus allows to compute the bias as a function of MUV as well. Let's do that and plot it!" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "bias_UVLFs_std = zeus21.UVLFs.UVLF_binned(AstroParams_std,CosmoParams,HMFintclass,z,dz,MUVcenters,MUVwidths,RETURNBIAS=True)/UVLFs_std\n", - "bias_UVLFs_bursty = zeus21.UVLFs.UVLF_binned(AstroParams_bursty,CosmoParams,HMFintclass,z,dz,MUVcenters,MUVwidths,RETURNBIAS=True)/UVLFs_bursty\n", - "#note it's divided by UVLF, since the bias returns *Phi on each MUV bin, so you have to divide by UVLF to get the bias alone\n", - "\n", - "plt.plot(MUVcenters,bias_UVLFs_std,'k-')\n", - "plt.plot(MUVcenters,bias_UVLFs_bursty,'r--')\n", - "plt.xlim(-22,-17)\n", - "plt.ylim(0,10)\n", - "plt.xlabel(r'$M_{\\rm UV}$');\n", - "plt.ylabel(r'bias, $b$');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note a possible issue: for no/low stochasticity (sigmaUV~0) the UVLF becomes noisy because there are not enough halos in each MUV bin. Can be fixed change precisionboost in constants.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#and the newly added pop3 UVLF - note they'll normally be very faint, so you'll need to go to fainter MUVs\n", - "AstroParams_popIII = zeus21.Astro_Parameters(UserParams, CosmoParams, accretion_model=0, USE_POPIII=True) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "UVLFs_pop2,UVLFs_pop3= zeus21.UVLFs.UVLF_binned(AstroParams_popIII,CosmoParams,HMFintclass,z,dz,MUVcenters,MUVwidths)\n", - "\n", - "\n", - "plt.semilogy(MUVcenters,UVLFs_pop2,'k-')\n", - "plt.semilogy(MUVcenters,UVLFs_pop3,'r--')\n", - "plt.xlim(-22,-17)\n", - "plt.ylim(1e-6,1e-1)\n", - "plt.xlabel(r'$M_{\\rm UV}$');\n", - "plt.ylabel(r'$\\Phi_{\\rm UV}\\,\\rm [Mpc^{-3}\\,mag^{-1}]$');" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "zeus21_userparams", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.11" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/acknowledging.rst b/docs/acknowledging.rst new file mode 100644 index 0000000..95b60a3 --- /dev/null +++ b/docs/acknowledging.rst @@ -0,0 +1,27 @@ +============= +Acknowledging +============= + +If you find this code useful, please cite both these papers: + +.. admonition:: Citation + + * Muñoz 2023, [An Effective Model for the Cosmic-Dawn 21-cm Signal](https://arxiv.org/abs/2302.08506) + + * Sklansky, Thélie, Muñoz, Cruz, Libanore, Venditti, in prep. + +and include a link to [this Github](https://github.com/JulianBMunoz/Zeus21). + +In addition, the following papers introduce multiple features into ``zeus21``. + +If you use the UVLF module, please cite: + +.. admonition:: Citation + + * Muñoz, Mirocha, Furlanetto, Nashwan, 2023, [Breaking degeneracies in the first galaxies with clustering](https://arxiv.org/abs/2306.09403) + +If you use relative velocites, Lyman-Werner feedback, or Population III stars, please cite: + +.. admonition:: Citation + + * Cruz, Muñoz, Sabti, Kamionkowski, 2024, [The First Billion Years in Seconds: An Effective Model for the 21-cm Signal with Population III Stars](https://arxiv.org/abs/2407.18294). \ No newline at end of file diff --git a/docs/authors.rst b/docs/authors.rst new file mode 100644 index 0000000..94292d0 --- /dev/null +++ b/docs/authors.rst @@ -0,0 +1 @@ +.. include:: ../AUTHORS.rst \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..dd0d587 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,69 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +import os +import sys + +sys.path.insert(0, os.path.abspath("..")) + +project = 'Zeus21' +year = "2023" +author = "The zeus21 collaboration" +copyright = f"{year}, {author}" + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [ + "myst_parser", + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinx.ext.napoleon", + "sphinx.ext.viewcode", + "sphinx_copybutton", + "nbsphinx", + "autoapi.extension" +] + +autosummary_generate = True +autoapi_dirs = ["../zeus21"] +autoapi_options = [ + "members", + "undoc-members", + "show-inheritance", + "show-module-summary", +] + + +copybutton_prompt_text = r"\$ " +copybutton_prompt_is_regexp = True + + +html_theme = "sphinx_rtd_theme" +html_theme_options = { + "navigation_depth": 4, +} + + +templates_path = ['_templates'] +exclude_patterns = [ + "_build", + "Thumbs.db", + ".DS_Store", + "**.ipynb_checkpoints", +] + + + + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +#html_theme = 'alabaster' +#html_static_path = ['_static'] diff --git a/docs/developer_install.rst b/docs/developer_install.rst new file mode 100644 index 0000000..22e907d --- /dev/null +++ b/docs/developer_install.rst @@ -0,0 +1,16 @@ +==================== +Install as Developer +==================== + +First clone the repository from GitHub: + +.. code-block:: console + + git clone https://github.com/julianbmunoz/zeus21.git zeus21 + cd zeus21/ + +Then, install in "editable" mode: + +.. code-block:: console + + pip install -e . diff --git a/docs/PspecandGlobal_Zeus21.png b/docs/images/PspecandGlobal_Zeus21.png similarity index 100% rename from docs/PspecandGlobal_Zeus21.png rename to docs/images/PspecandGlobal_Zeus21.png diff --git a/docs/Zeus21Logo-Horizontal.jpg b/docs/images/Zeus21Logo-Horizontal.jpg similarity index 100% rename from docs/Zeus21Logo-Horizontal.jpg rename to docs/images/Zeus21Logo-Horizontal.jpg diff --git a/docs/Zeus21Logo-Horizontal.pdf b/docs/images/Zeus21Logo-Horizontal.pdf similarity index 100% rename from docs/Zeus21Logo-Horizontal.pdf rename to docs/images/Zeus21Logo-Horizontal.pdf diff --git a/docs/zeusLogo.png b/docs/images/zeusLogo.png similarity index 100% rename from docs/zeusLogo.png rename to docs/images/zeusLogo.png diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 0000000..b94d9db --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,39 @@ +.. image:: images/Zeus21Logo-Horizontal.jpg + :width: 75% + :align: center + + +.. include:: ../README.md + :parser: myst_parser.sphinx_ + :start-after:

+ + + +.. toctree:: + :hidden: + :maxdepth: 1 + + installation + tutorials + acknowledging + +.. toctree:: + :hidden: + :caption: API Reference + + autoapi/zeus21/index + +.. toctree:: + :hidden: + :caption: Development + + developer_install + authors + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` \ No newline at end of file diff --git a/docs/installation.rst b/docs/installation.rst new file mode 100644 index 0000000..783c62a --- /dev/null +++ b/docs/installation.rst @@ -0,0 +1,28 @@ +============ +Installation +============ + +You can download and install this package by doing: + +.. code-block:: console + + git clone https://github.com/julianbmunoz/zeus21.git zeus21 + cd zeus21/ + pip install . + +that should take care of all dependencies (remember to work in your favorite conda env). +If you have issues with cache'd versions of packages you can add ``--no-cache-dir`` at the end of ```pip install .``. + +**NOTE:** You may run into problems when pip-installing ``classy`` (the Python wrapper of ``CLASS``). +If so, their installation guide is `here `_, +but in short the steps are: + +.. code-block:: console + + git clone https://github.com/lesgourg/class_public.git class + cd class/ + make + cd python/ + python setup.py install --user + +(modifying the Makefile to your `gcc` as needed) \ No newline at end of file diff --git a/docs/readme_link.md b/docs/readme_link.md deleted file mode 100644 index 339dc8f..0000000 --- a/docs/readme_link.md +++ /dev/null @@ -1,2 +0,0 @@ -.. include:: ../README.md - :parser: myst_parser.sphinx_ diff --git a/docs/source/conf.py b/docs/source/conf.py deleted file mode 100644 index abf9209..0000000 --- a/docs/source/conf.py +++ /dev/null @@ -1,29 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# For the full list of built-in configuration values, see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Project information ----------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information - -project = 'Zeus21' -copyright = '2023, Julian B Muñoz' -author = 'Julian B Muñoz' - -# -- General configuration --------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration - -extensions = [ - "myst_parser" -] - -templates_path = ['_templates'] -exclude_patterns = [] - - - -# -- Options for HTML output ------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output - -html_theme = 'alabaster' -html_static_path = ['_static'] diff --git a/docs/source/index.rst b/docs/source/index.rst deleted file mode 100644 index b7afc77..0000000 --- a/docs/source/index.rst +++ /dev/null @@ -1,19 +0,0 @@ -.. include:: ../readme_link.md - - -Contents -=========== - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - - - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/tutorials.rst b/docs/tutorials.rst new file mode 100644 index 0000000..96941cb --- /dev/null +++ b/docs/tutorials.rst @@ -0,0 +1,25 @@ +========= +Tutorials +========= + +If you want to get started, we recommend checking the Jupyter tutorials. + +Currently you can find tutorials for: + +.. toctree:: + :maxdepth: 1 + :caption: Tutorials + + tutorials/Tutorial_Zeus21_21cm + tutorials/Tutorial_Zeus21_UVLFs + tutorials/Tutorial_Zeus21_PopIIandIII_Fiducial + tutorials/Tutorial_Zeus21_Maps + +Here is an example power spectrum (at k=0.3/Mpc) and global signal as a function of redshift, for two cases of X-ray luminosity. +You can run it yourself with the included tutorial! + +.. image:: ./images/PspecandGlobal_Zeus21.png + :width: 75% + :align: center + + diff --git a/docs/tutorials/Tutorial_Zeus21_21cm.ipynb b/docs/tutorials/Tutorial_Zeus21_21cm.ipynb new file mode 100644 index 0000000..3a3db78 --- /dev/null +++ b/docs/tutorials/Tutorial_Zeus21_21cm.ipynb @@ -0,0 +1,461 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 21-cm global signal and power spectrum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tutorial will cover the basics up to predicting the 21-cm power spectrum and global signal, assuming PopII stars only (see separate tutorial for PopIII). We will start by importing the necessary packages (Zeus, numpy, class)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import zeus21\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we set up the user parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "precisionboost = 1.2 # boost the precision in redshift \n", + "zmin = 5. # min redshift down to which we compute the 21-cm related quantities\n", + "UserParams = zeus21.User_Parameters(precisionboost=precisionboost, zmin=zmin)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we set up the cosmology by calling zeus21.Cosmo_Parameters.\n", + "\n", + "This will run CLASS, which will use the input parameters passed to zeus21.Cosmo_Parameters. \n", + "An instance of Class will be stored as attribute to zeus21.Cosmo_Parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# set up your parameters here, as an example the CDM (reduced) density\n", + "omega_cdm = 0.12\n", + "CosmoParams = zeus21.Cosmo_Parameters(UserParams=UserParams, omegac=omega_cdm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also need to generate the halo mass function at all desired z and M, which ends the cosmology part." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "HMFinterp = zeus21.HMF_interpolator(UserParams=UserParams,CosmoParams=CosmoParams)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we can set the astrophysics parameters. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# set up your parameters\n", + "# here are the peak of f*(Mh) and the escape fraction amplitude as an example\n", + "epsstar = 0.1\n", + "fesc10 = 0.1\n", + "AstroParams = zeus21.Astro_Parameters(CosmoParams=CosmoParams, epsstar=epsstar, fesc10=fesc10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can compute the 21-cm global signal by calling zeus21.get_T21_coefficients.\n", + "\n", + "This will compute multiple related quantities:\n", + "- the SFRD, the basic ingredient for zeus21 formalism,\n", + "- the Lyman-alpha properties and fluxes,\n", + "- the X-ray properties and fluxes,\n", + "- and the reionization quantities." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "T21global = zeus21.get_T21_coefficients(UserParams=UserParams, CosmoParams=CosmoParams, AstroParams=AstroParams, HMFinterp=HMFinterp)\n", + "zlist = T21global.zintegral" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The T21global instance holds all the information needed to find the 21-cm signal during cosmic dawn. It has saved the 21-cm global signal, the Wouthuysen-Field coupling, and all temperatures. It also has the effective biases $\\gamma_R$ for all $R$ (which will be used for the power spectrum below). This structure also has ancillary data like the evolution of the SFRD and Nion. If you want to learn what else the CoeffStructure holds, just do dir(CoeffStructure).\n", + "\n", + "Let us start by plotting the global signal." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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wfPhwAMC4ceMwd+5cfkuR3vPDDz/AwcEB9+/fx4IFC0THISLSCyxdOuSXX37BiBEjAADjx4/H7NmzWbgoV6VLl8bs2bMBAHPmzMGLFy/EBiIi0gMsXTpi1qxZykNGEydOxKxZs1i4KF++vr6oV68eEhMTld9qJCIi9WHp0nKSJGHq1KmYNGkSACAgIAAzZsxg4aIPMjAwwJQpUwC8WUKCo11EROrF0qXFMjMzMWLECAQEBAAAZsyYgSlTprBwUYF99tlncHZ25mgXEZEGsHRpqZSUFPTq1QtLly6FTCbDr7/+qhztIiqot0e7fvnlFzx//lxwIiIi3cXSpYVSU1Ph5eWFXbt2wcTEBNu2bcPIkSNFxyIt1aNHD9SvXx8KhYKjXUREasTSpYWWL1+OsLAwWFlZ4dChQ/Dx8REdibTYu3O7ONpFRKQeLF1a5sWLF/jpp58AAPPnz0e7du0EJyJd0L17dzRo0AAKhQK//PKL6DhERDqJpUvLzJo1C8+ePYOTkxP8/PxExyEd8fZo16+//opnz54JTkREpHtYut6xbNkyVKtWDWZmZnB1dcWJEydER1KKiopSzrmZPXs2jIyMxAYineLt7a0c7eLcLiIi1WPpesv27dvx3Xff4YcffkBERARat24NLy8vREVFiY4GSZLw448/IjU1Fe7u7ujcubPoSKRj3p3bxdEuIiLVkkmSJIkOUVI0a9YMLi4uWL58ufK6//znP/D29sbMmTM/eH+FQgG5XI4HDx7A0tJSJZkyMjKwf/9+LFmyBJcvXwYAnD17Fh999JFKHp/obVlZWXBxccGVK1fw448/KucPEhHpsuzP78TERFhZWanteVi6/ictLQ2lSpXCH3/8ge7duyuv//bbb3H58mUcO3bsvfukpqYiNTVV+bNCoYCjo6PaMpqZmWHy5Mn4/vvv1fYcRLt27cLnn3+OcuXKITo6Gubm5qIjERGplaZKFw8v/s/Tp0+RmZkJW1vbHNfb2toiLi4u1/vMnDkTcrlceVFX4apevTrmzp2LR48esXCR2nl7e6NatWpISEjA5s2bRcchItIZLF3vePcUOpIk5XlanUmTJiExMVF5iY6OBgA8e/YMGRkZKrvcvXsXY8eORdmyZdW+/0SGhoYYPnw4gDffZORgOBGRarB0/Y+NjQ0MDQ3fG9WKj49/b/Qrm6mpKaysrHJcgDcfWqq88FyKpGn9+/eHhYUFrl+/jtDQUNFxiIh0AkvX/5iYmMDV1RXBwcE5rg8ODkaLFi0EpSISo0yZMsp14H799VexYYiIdARL11tGjx6N3377DWvXrsXNmzcxatQoREVFYfDgwaKjEWnciBEjAAB//fUX7t27JzgNEZH24+qab+nVqxcSEhIwbdo0xMbGwtnZGQcOHECVKlVERyPSuDp16sDLywsHDx7EkiVLeHogItIpCoUCV65cwaVLl3D16lWNPCeXjFAhTX3llEhTDh8+jI8//hiWlpb4999/+XtNRFrpyZMniIiIQEREBC5duoSIiAjcuXPnve3U/fnNkS4iypOHhwfq1q2Lv//+G+vXr8fIkSNFRyIiyldMTAwuXLigLFiXLl3Cv//+m+u2jo6OaNy4MWrVqoX58+erPRtHulSII12ki5YvX46hQ4eiZs2auHXrFgwMOBWUiEqO+Ph4hIWFISQkBCEhIbmOYAFArVq14OLigsaNGyv/a2NjA4Ar0mslli7SRUlJSahUqRJevHiBv/76C126dBEdiYj02IsXL3Ds2DGEhIQgNDQU165dy3G7gYEB6tWrBxcXF2W5atiwYb6fy5r6/ObhRSLKl4WFBQYOHIh58+bh119/ZekiIo1KSkpCeHi4ciTr0qVLyMrKyrFNw4YN0a5dO7Rv3x5t2rSBXC4XlDZ/HOlSIY50ka76559/UL16dWRlZeH69euoV6+e6EhEpMNevXqF/fv3Y8eOHThw4ECO8xwDb75d3b59e7Rv3x5t27ZVHiYsKo50EVGJUaVKFXh7e2P37t1YtGgRVq5cKToSEemYpKQkHDhwADt27EBgYCBev36tvK1KlSro0KED2rdvj3bt2sHBwUFg0qLjSJcKcaSLdFlYWBjatWuH0qVLIy4uDhYWFqIjEZGWe/36NQ4ePIgdO3bgr7/+QnJysvK2mjVrolevXvDx8UH9+vXVeko8jnQRUYni7u6OGjVq4N69e9i1axe++uor0ZGISAulpqbi8OHD2L59O/bt24dXr14pb6tWrZqyaDVq1Ejnzj3M0kVEBSKTyeDn54fJkydj3bp1LF1EVCixsbFYvnw5VqxYgSdPniivr1y5Mnx8fNCrVy+4urrqXNF6Gw8vqhAPL5Kui46ORpUqVSBJEu7du4fq1auLjkREJdylS5ewcOFCbNu2Denp6QAABwcHZdFq1qyZ8KKlqc9vrnJIRAXm6OiITp06AQDWr18vNgwRlViZmZnYs2cP3N3d4erqit9//x3p6elo0aIFduzYgX/++Qe//PILmjdvLrxwaRJLFxEVytdffw0A2LBhw3tr5RCRfktMTMSCBQtQs2ZN9OjRA8ePH4eRkRF8fX1x9uxZnDx5Ej179oSRkX7ObtLPvSaiIvP29kaZMmUQFRWFkJAQdOzYUXQkIhLs7t27WLRoEdatW6ecGF+2bFkMHjwYQ4cORcWKFQUnLBk40kVEhWJmZgZfX18AwNq1awWnISKRYmNjMXDgQNSpUweLFy/Gq1ev4OTkhFWrViE6OhrTp09n4XoLSxcRFVr2IcY9e/bgxYsXYsMQkcYlJSVh2rRpqFWrFtasWYOsrCx4eXkhKCgI169fh7+/P0qVKiU6ZonD0kVEhebq6gpnZ2ekpKRg27ZtouMQkYZkZmZi3bp1qF27NqZMmYKkpCQ0b94cJ0+exIEDB9CpUye9mhhfWCxdRFRoMplMOdq1bt06wWmISBOOHDkCV1dX9O/fHzExMahWrRq2b9+OU6dOoUWLFqLjaQWWLiIqki+//BJGRkY4d+4cIiMjRcchIjW5ceMGOnfujE6dOuHKlSuQy+WYO3cubt68CR8fH45sFQJLFxEVSYUKFdClSxcAHO0i0kWPHz/GkCFD0KBBAxw4cABGRkYYOXIk7t69i7Fjx8LU1FR0RK3D0kVERZZ9iDF74UMi0n6SJGHJkiWoVasWVqxYgczMTHTv3h2RkZH49ddfYWNjIzqi1mLpIqIi8/LyQoUKFRAfH4+DBw+KjkNExRQbG4tPPvkEI0aMwMuXL9GkSRMcO3YMu3fvRu3atUXH03osXURUZMbGxsoTX3PNLiLttmfPHtSvXx+HDh2CmZkZFi1ahLNnz6JNmzaio+kMli4iKpbsQ4yBgYF49uyZ4DREVFgvX77EwIED0aNHDyQkJKBRo0a4ePEiRowYAQMD1gRV4v9NIioWJycnNGjQABkZGdizZ4/oOERUCKdPn0ajRo2wZs0ayGQyTJgwAWfPnoWTk5PoaDqJpYuIiq1Xr14AgB07dghOQkQFkZ6ejilTpqBVq1a4f/8+KleujNDQUMyaNQsmJiai4+ksli4iKjYfHx8AwNGjR/HkyRPBaYgoP3fu3EHr1q0xbdo0ZGVloU+fPrhy5Qrc3d1FR9N5LF1EVGw1a9aEi4sLMjMzeYiRqISSJAm//fYbGjdujLNnz0Iul2PLli3YtGkTypQpIzqeXmDpIiKVyB7t2r59u+AkRPSu1NRUfPXVV/D390dSUhLatm2Lq1ev4osvvhAdTa+wdBGRSmSXrrCwMDx+/FhwGiLK9vz5c3z88cfYtGkTjIyMMGfOHBw9ehSVK1cWHU3vsHQRkUpUq1YNTZs2RVZWFnbt2iU6DhEBePjwIVq2bImwsDBYWlriwIEDGDduHJeCEIT/14lIZfgtRqKS4+LFi2jevDlu3ryJihUrIjw8HJ06dRIdS6+xdBGRyvTs2RMAcPz4ccTExAhOQ6S/9u/fjzZt2uDx48do0KABzpw5gwYNGoiOpfdYuohIZSpXrgw3NzdIkoSdO3eKjkOkl5YvX45u3bohOTkZnTp1wokTJ1CpUiXRsQgsXUSkYjzESCRGVlYWJkyYgKFDhyIrKwv9+/dHYGAgrKysREej/2HpIiKV+vzzzyGTyXDy5ElER0eLjkOkF1JSUuDr64s5c+YAAKZNm4bffvsNxsbGgpPR21i6iEilKlasiFatWgEADzESaUBCQgI6deqE7du3w9jYGBs3bsTkyZMhk8lER6N3sHQRkcpxoVQizYiKikLLli0RHh4OuVyOQ4cOoW/fvqJjUR5YuohI5bIPMZ49exYPHz4UHYdIJz1+/BgdOnTArVu34OjoiJMnT6J9+/aiY1E+WLqISOXs7OyUJ8/9448/BKch0j3Pnz+Hh4cH7t69iypVquDUqVOoV6+e6Fj0ASxdRKQW/BYjkXq8evUKn3zyCa5evQo7OzscOXKES0JoCZYuIlKLHj16wMDAABcuXMC9e/dExyHSCSkpKfD29saZM2dgbW2NoKAg1KxZU3QsKiCWLiJSiwoVKqBdu3YAgL1794oNQ6QDMjIy8MUXX+Do0aOwsLDAwYMHUb9+fdGxqBBYuohIbbp16wYA2Ldvn+AkRNotKysLAwYMwN69e2Fqaop9+/ahWbNmomNRIbF0EZHadO3aFQAQHh6OhIQEwWmItJMkSfjuu++wceNGGBoaYseOHfyWopZi6SIitalatSoaNGiArKwsHDhwQHQcIq00ZcoULF68GDKZDBs2bMCnn34qOhIVEUsXEalV9gcEDzESFd78+fPx008/AQCWLFmCPn36CE5ExcHSRURqlT2v69ChQ0hNTRWchkh7/Pbbbxg7diwAYMaMGRg6dKjgRFRcLF1EpFYuLi5wcHDAq1evEBoaKjoOkVbYsWMHvvnmGwDA+PHjMXHiRMGJSBV0vnQ9fPgQAwYMQLVq1WBubo4aNWpgypQpSEtLy7FdVFQUunbtCgsLC9jY2GDkyJHvbUNEhWdgYKCcUM9DjEQfdvbsWXz55ZeQJAnffPMNZs2axZNX6widL11///03srKysHLlSkRGRuKXX37BihUr8P333yu3yczMROfOnZGUlITw8HBs27YNu3btwpgxYwQmJ9Idb8/rkiRJcBqikuvJkyf4/PPPkZ6eDm9vbyxbtoyFS4fIJD38G3Du3LlYvnw57t+/DwA4ePAgunTpgujoaDg4OAAAtm3bBj8/P8THx8PKyqpAj6tQKCCXy5GYmFjg+xDpg5SUFNjY2CApKQkXLlyAq6ur6EhEJU5GRgY8PT0REhKC2rVr4/z58/ws0RBNfX7r/EhXbhITE1G2bFnlz6dPn4azs7OycAGAp6cnUlNTcfHixTwfJzU1FQqFIseFiN5nZmYGT09PADzESJSXyZMnIyQkBBYWFti9ezcLlw7Su9J17949LF68GIMHD1ZeFxcXB1tb2xzbWVtbw8TEBHFxcXk+1syZMyGXy5UXR0dHteUm0nZcOoIob3v27MGsWbMAAGvWrEG9evUEJyJ10NrSFRAQAJlMlu/lwoULOe4TExODjz/+GD179sTAgQNz3JbbMXNJkvI9lj5p0iQkJiYqL9HR0arZOSId9Mknn8DAwACXL19GVFSU6DhEJcbt27fRr18/AMCoUaPQq1cvwYlIXYxEByiq4cOHo3fv3vluU7VqVeWfY2Ji0K5dO7i5uWHVqlU5trOzs8PZs2dzXPf8+XOkp6e/NwL2NlNTU5iamhY+PJEeKl++PFq0aIHw8HDs27cPw4cPFx2JSLikpCT06NEDL1++RKtWrTB79mzRkUiNtLZ02djYwMbGpkDbPnr0CO3atYOrqyvWrVsHA4OcA3xubm6YPn06YmNjYW9vDwAICgqCqakpJ/wSqVC3bt1Yuoj+R5Ik+Pv7IzIyEnZ2dtixYweMjY1FxyI10vlvL8bExMDd3R2VK1dWniw0m52dHYA3S0Y0atQItra2mDt3Lp49ewY/Pz94e3tj8eLFBX4ufnuRKH+3b99GnTp1YGxsjCdPnkAul4uORCTMokWL8O2338LIyAihoaFo1aqV6Eh6i99eVJGgoCDcvXsXISEhqFSpEuzt7ZWXbIaGhggMDISZmRlatmwJHx8feHt7Y968eQKTE+me2rVro06dOkhPT8fhw4dFxyESJjw8XLkW5Lx581i49ITOj3RpEke6iD5s/PjxmDt3Lnx9fbF582bRcYg0LjY2Fi4uLoiLi0Pv3r2xZcsWLoAqGEe6iEgnZZ8A+8CBA0hPTxechkiz0tPT0atXL8TFxaFevXpYvXo1C5ceYekiIo1q3rw5bGxs8OLFC4SHh4uOQ6RREydOxIkTJ2BpaYndu3ejdOnSoiORBrF0EZFGGRoaokuXLgCAP//8U3AaIs3Zu3cvFixYAADYsGEDateuLTgRaRpLFxFpXPbq9AcOHBCchEgznj59ikGDBgEAxo4di+7duwtORCKwdBGRxnXo0AFGRka4c+cO7t27JzoOkdp9++23iI+Ph5OTE37++WfRcUgQli4i0jgrKyu0bNkSAHDo0CHBaYjU688//8SWLVtgYGCA9evX80wmeoyli4iE+PjjjwGwdJFue/bsGQYPHgwAGDduHJo2bSo4EYnE0kVEQmSXrpCQEKSmpgpOQ6Qe3333HeLi4lC3bl0EBASIjkOCsXQRkRANGzaEnZ0dkpOTuXQE6aT9+/fj999/h4GBAdatWwczMzPRkUgwli4iEkImkylHuw4ePCg4DZFqPX/+XPltxdGjR6N58+aCE1FJwNJFRMJwXhfpqtGjRyMmJga1a9fGtGnTRMehEoKli4iE6dixIwwMDBAZGYno6GjRcYhU4uDBg1i/fj1kMhnWrVsHc3Nz0ZGohGDpIiJhypUrh48++ggAcPjwYcFpiIovMTER/v7+AN5Mom/RooXgRFSSsHQRkVBeXl4AeIiRdMOYMWPw6NEj1KxZk4ug0ntYuohIqOx5XcHBwUhPTxechqjogoKCsGbNGshkMqxduxalSpUSHYlKGJYuIhLK1dUV5cqVg0KhwJkzZ0THISoShUKBgQMHAgBGjBiB1q1bC05EJRFLFxEJZWhoCA8PDwA8xEjaa9y4cYiOjkb16tUxY8YM0XGohGLpIiLhuHQEabOjR49i1apVAIA1a9bAwsJCcCIqqYwKe4d9+/YV+kk6derEr8wSUZ48PT0BAJcuXcLjx49ha2srOBFRwaSnp2PYsGEAgKFDh6Jt27ZiA1GJVujS5e3tXajtZTIZ7ty5g+rVqxf2qYhIT9ja2sLFxQWXLl3C4cOH8dVXX4mORFQgy5Ytw61bt1C+fHkeVqQPKtLhxbi4OGRlZRXowm9vEFFB8BAjaZunT58qT2I9ffp0yOVysYGoxCt06erXr1+hDhV++eWXsLKyKuzTEJGeyS5dQUFByMzMFJyG6MMCAgLw4sULNGzYEP379xcdh7SATJIkSR0PrFAo9K5sKRQKyOVyJCYm6t2+ExVXeno6bGxsoFAocPbsWeVK9UQlUWRkJBo2bIjMzEyEhoZyLpeW09Tnd5EOL86bNy/f2xUKhfIr4EREBWFsbIyOHTsC4CFGKtkkScKoUaOQmZmJHj16sHBRgRWpdE2ePBnr1q3L9bZXr17B09MTCoWiWMGISP9knxLo4MGDgpMQ5S0wMBDBwcEwMTHB3LlzRcchLVKk0vX7779j6NCh2Lt3b47rX716BQ8PDzx79gyhoaGqyEdEeiR76Yhz584hISFBcBqi96WlpWH06NEAgFGjRvGb+VQoRSpdn3/+ORYvXgxfX19luXr16hU+/vhjPH36FGFhYVxnh4gKzdHREfXq1UNWVhaOHDkiOg7Re5YuXYo7d+7A1tYW33//veg4pGWKvCL9wIEDERAQAG9vb4SFhcHLywtxcXEIDQ2Fvb29KjMSkR7Jng969OhRwUmIcnry5AmmTp0K4M0SEfzCFBVWsU4DNH78eAwdOhQdOnRATEwMwsLCULFiRVVlIyI9lD2ZniNdVNL897//RWJiIho3bgw/Pz/RcUgLFXpFegDo0aNHjp+NjY1hY2ODkSNH5rh+9+7dRU9GRHqpTZs2MDIywoMHD3Dv3j3UqFFDdCQiXLt2TXl+xYULF8LQ0FBwItJGRSpd7666+8UXX6gkDBFR6dKl4ebmhhMnTuDIkSMsXSRc9hIRWVlZ+Pzzz9GmTRvRkUhLFal05bVcBBGRKnTs2FFZugYNGiQ6Dum5ffv24ejRozA1NeUSEVQsRSpdb0tJScHVq1cRHx+PrKws5fUymQxdu3Yt7sMTkR7q1KkTpkyZgpCQEGRmZvJQDgmTmpqKMWPGAADGjBmDqlWrig1EWq1YpevQoUPo27dvruvpyGQynj+NiIqkadOmsLS0xLNnz3D58mW4urqKjkR6avHixbh37x7s7OwwceJE0XFIyxXr24vDhw+Hj48PYmNjkZWVlePCwkVERWVkZIR27doB4LcYSZz4+Hj89NNPAICZM2fC0tJScCLSdsUqXfHx8Rg9ejQXQiUilcteOiI4OFhwEtJXU6dOhUKhgKurK7766ivRcUgHFKt0ff755wgLC1NRFCKi/9epUycAQHh4OF6/fi04DembqKgorF69GgAwb948GBgU6+OSCEAx53QtWbIEPXv2xIkTJ1C/fn0YGxvnuP3ddbuIiAqqTp06qFixIh49eoSTJ08qR76INGH69OlIT09Hhw4d0LZtW9FxSEcUq3Rt2bIFhw8fhrm5OcLCwiCTyZS3yWQyli4iKjKZTIaOHTtiw4YNOHLkCEsXacyDBw+wdu1aAFCe9odIFYo1Xvrjjz9i2rRpSExMxMOHD/HgwQPl5f79+6rKSER6iqcEIhF+/vlnZGRkwMPDAy1bthQdh3RIsUpXWloaevXqxWPdRKQWHTp0AABcunQp16VpiFTt7t272LBhAwCOcpHqFast9evXD9u3b1dVFiKiHOzt7eHs7AxJkhAaGio6DumBn376CZmZmfjkk0/QvHlz0XFIxxRrTldmZibmzJmDw4cPo0GDBu9NpF+wYEGxwhERdezYEdevX0dwcDA+//xz0XFIh926dQubNm0CwFEuUo9ila5r166hcePGAIDr16/nuO3tSfVEREXVsWNHLFy4kPO6SO2mTZuGrKwsfPrpp2jSpInoOKSDZJIkSaJD6AqFQgG5XI7ExERYWVmJjkOkE16+fImyZcsiIyMD9+7dQ/Xq1UVHIh108+ZN1KtXD5Ik4dKlS8oBBdIPmvr85gx4IirRLC0tlXNrjh49KjgN6aqpU6dCkiT06NGDhYvUptCl6+rVq8jKyirw9pGRkcjIyCjs0xARKWWvTs9DjKQO165dw44dOwAAAQEBYsOQTit06WrcuHGhvrrt5uaGqKiowj4NEZFS9npdR48eLdQ/+ogKInuUq2fPnqhfv77oOKTDCj2RXpIkTJ48GaVKlSrQ9mlpaYUOpS6pqalo1qwZrly5goiICDRq1Eh5W1RUFIYNG4aQkBCYm5vD19cX8+bNg4mJibjARAQAaNq0KSwtLZGQkIDLly/DxcVFdCTSEZcvX8auXbsgk8kwZcoU0XFIxxW6dLVp0wa3bt0q8PZubm4wNzcv7NOoxfjx4+Hg4IArV67kuD4zMxOdO3dG+fLlER4ejoSEBPTr1w+SJGHx4sWC0hJRNmNjY7Rt2xZ//fUXjhw5wtJFKpN9OLF3796oV6+e2DCk8wpdusLCwtQQQ/0OHjyIoKAg7Nq1CwcPHsxxW1BQEG7cuIHo6Gg4ODgAAObPnw8/Pz9Mnz49z28ypKamIjU1VfmzQqFQ3w4Q6bmOHTsqS9f48eNFxyEdcPHiRfz5558wMDDAf//7X9FxSA/oxbcXHz9+DH9/f/z++++5HhY9ffo0nJ2dlYULADw9PZGamoqLFy/m+bgzZ86EXC5XXhwdHdWSn4j+fzL9iRMnkJKSIjgN6YLsw4l9+vRB3bp1BachfaDzpUuSJPj5+WHw4MF5LnYXFxcHW1vbHNdZW1vDxMQEcXFxeT72pEmTkJiYqLxER0erNDsR/b+6devC3t4eKSkpOHPmjOg4pOXOnj2LwMBAGBoaYvLkyaLjkJ4ocOkyNDRUZ45CCwgIgEwmy/dy4cIFLF68GAqFApMmTcr38XJbQV+SpHxX1jc1NYWVlVWOCxGph0wmQ/v27QFwvS4qvuy5XF999RVq1aolNgzpjQKXrpK2cP3w4cNx8+bNfC/Ozs4ICQnBmTNnYGpqCiMjI9SsWRMA0KRJE/Tr1w8AYGdn996I1vPnz5Genv7eCBgRiZNdukJCQgQnIW12+vRpHDp0CEZGRvjxxx9FxyE9UuCJ9CXtXIo2NjawsbH54HaLFi3Czz//rPw5JiYGnp6e2L59O5o1awbgzTcsp0+fjtjYWNjb2wN4M7ne1NQUrq6u6tkBIiq07NJ17tw5vHz5EpaWloITkTaaNWsWgDejXDytFGmSRuZ05TcZXd0qV64MZ2dn5aV27doAgBo1aqBSpUoAAA8PDzg5OaFv376IiIjA0aNHMXbsWPj7+/OQIVEJUrVqVVSvXh0ZGRkIDw8XHYe00N9//419+/ZBJpPxW7CkcRopXd27d9fE0xSZoaEhAgMDYWZmhpYtW8LHxwfe3t6YN2+e6GhE9A4eYqTiyP57vVu3bqhTp47gNKRvCr1OV158fHxyvV6SJDx79kxVT1NsVatWzXV+WuXKlbF//34BiYioMNq3b4/ffvuNk+mp0GJjY/H7778DAEe5SAiVla4jR47g999/R+nSpXNcL0kSjh8/rqqnISI9165dOwBvTt+SkJCAcuXKCU5E2mLRokVIS0tDy5Yt4ebmJjoO6SGVla62bduidOnScHd3f++2xo0bq+ppiEjP2dnZwcnJCTdu3MCxY8fQo0cP0ZFICygUCixfvhwAR7lIHJXN6dq9e3euhQsADh06pKqnISJChw4dAHBeFxXc6tWrkZiYiLp166JLly6i45CeUulE+tevX+PRo0fvXR8ZGanKpyEiPcfJ9FQYaWlpWLhwIQBg7NixMDDQ+ZOxUAmlst+8nTt3onbt2vjkk0/QoEEDnD17Vnlb3759VfU0RERwd3eHTCbDzZs3ERMTIzoOlXDbtm3Dv//+Czs7O3z55Zei45AeU1np+vnnn3Hp0iVcuXIFa9euRf/+/bFlyxYAJW81eyLSbtbW1nBxcQEAhIaGCk5DJZkkSZg7dy4A4Ntvv4WpqangRKTPVFa60tPTUb58eQBvTrFz/PhxrFy5EtOmTStxq9kTkfbjIUYqiEOHDuH69esoXbo0Bg8eLDoO6bkila7Dhw8jKysrx3UVKlTA1atXlT+XK1cOwcHBuHnzZo7riYhUgZPpqSDmzJkDAPjmm29QpkwZsWFI78mkAh77MzAwUBYtQ0NDxMbGokKFCsrb//33XxgZGcHOzu69+548eRItW7ZUUeSSS6FQQC6XIzExkacPIlKzpKQklClTBhkZGbh//z6qVasmOhKVMOfPn8dHH30EIyMj3L9/H46OjqIjUQmlqc/vIo105dbTKlWqlGvhAqAXhYuINMvCwgLNmzcHAK5OT7nKnsvl6+vLwkUlAr83S0Rai/O6KC/37t3Drl27ALxZJoKoJChy6VqyZAkOHz6Mp0+fqjIPEVGBvV26+C1petuCBQuQlZUFLy8v1K9fX3QcIgBFnNNlYGCAcuXKISEhATKZDBUrVoSLiwtcXV3h4uICFxcX2NvbqzV4ScQ5XUSalZqaCmtra7x+/RqRkZFwcnISHYlKgCdPnqBy5cpISUlBaGgo2rZtKzoSlXAlek4X8GaV+X///Rf79u2Dv78/ZDIZfvvtN3Tt2hWVKlWCg4ODKnMSEb3H1NQUrVq1AsBDjPT/lixZgpSUFDRp0iTP09MRiVCkE15nr7vl4OAABwcHdO7cWXnbs2fPcOHCBVy+fFklAYmI8tO+fXsEBwfj6NGjGD58uOg4JFhSUhKWLl0K4M2JrblOJJUkBS5db//i5ndEsmzZsvDw8ICHh0fxkhERFUD2vK6wsDBkZmbC0NBQcCISad26dUhISED16tXRo0cP0XGIcijw4cW3i9bBgwchl8vVEoiIqDBcXFxgZWWFFy9ecIRdz2VmZmLBggUAgDFjxrCAU4lT4NL19gr0np6ePH8VEZUIRkZGyonSnNel3/766y88ePAAZcuWhZ+fn+g4RO/hOl1EpPWyDzFykVT9tnjxYgCAv78/SpUqJTgN0ftYuohI62WXrhMnTiAtLU1wGhLh+vXrCAkJgYGBAYYOHSo6DlGuWLqISOvVq1cP5cuXR3JyMs6dOyc6DgmwZMkSAED37t1RuXJlwWmIcsfSRURaz8DAgIcY9djz58+xceNGAMCIESMEpyHKG0sXEekEnodRf61ZswavX79GgwYN0KZNG9FxiPLE0kVEOiG7dJ0+fRpJSUmC05CmZGZmKhdDHTFiBBdDpRKNpYuIdEKNGjVQuXJlpKen4+TJk6LjkIbs378fDx8+RNmyZeHr6ys6DlG+WLqISCfIZDLO69JDixYtAsBlIkg7sHQRkc7o0KEDAM7r0heRkZHKZSKGDBkiOg7RB7F0EZHOyB7punjxIp4/fy44Dalb9mKo3t7eqFKliuA0RB/G0kVEOsPBwQF169aFJEk4duyY6DikRs+fP8fvv/8OABg5cqTgNEQFw9JFRDqF87r0w9q1a5GcnIz69etzmQjSGixdRKRTOK9L92VmZipXoB85ciSXiSCtwdJFRDrF3d0dMpkMN27cQGxsrOg4pAaBgYF4+PAhrK2tuUwEaRWWLiLSKeXKlUOjRo0AAKGhoWLDkFpwmQjSVixdRKRzeIhRd0VGRuLo0aMwMDDA0KFDRcchKhSWLiLSOZxMr7uy53J169aNy0SQ1mHpIiKd07p1axgZGeHhw4d48OCB6DikIs+fP8fGjRsBcJkI0k4sXUSkc0qXLo1mzZoB4GiXLlm3bh2Sk5Ph7OwMd3d30XGICo2li4h0Eud16RYuE0G6gKWLiHRS9ryukJAQSJIkOA0V14EDB/DgwQNYW1ujT58+ouMQFQlLFxHppObNm8Pc3ByPHz/GjRs3RMehYlq2bBkAYMCAAVwmgrQWSxcR6SRTU1O0atUKAOd1abt79+7h8OHDAIDBgwcLTkNUdCxdRKSz3j7ESNpr5cqVkCQJnp6eqFGjhug4REXG0kVEOit7Mn1YWBgyMjIEp6GiSElJwdq1awEAQ4YMEZyGqHhYuohIZ7m4uEAulyMxMRERERGi41AR7Ny5EwkJCahUqRI6d+4sOg5RsbB0EZHOMjQ0RNu2bQFwXpe2Wr58OQDgm2++gZGRkeA0RMXD0kVEOo2nBNJeV65cwalTp2BkZISBAweKjkNUbCxdRKTTsud1hYeHIyUlRXAaKozsUa7u3bvD3t5ecBqi4tOb0hUYGIhmzZrB3NwcNjY26NGjR47bo6Ki0LVrV1hYWMDGxgYjR45EWlqaoLREpCpOTk5wcHBASkoKwsPDRcehAlIoFNi0aRMATqAn3aEXpWvXrl3o27cvvv76a1y5cgUnT56Er6+v8vbMzEx07twZSUlJCA8Px7Zt27Br1y6MGTNGYGoiUgWZTIaOHTsCAIKDgwWnoYLatGkTkpKSULduXeW8PCJtJ5N0/PwYGRkZqFq1KqZOnYoBAwbkus3BgwfRpUsXREdHw8HBAQCwbds2+Pn5IT4+HlZWVgV6LoVCofymVEHvQ0Tqt3nzZnz55Zdo3LgxLl26JDoOfYAkSWjQoAGuX7+OX3/9FSNHjhQdiXScpj6/dX6k69KlS3j06BEMDAzQuHFj2Nvbw8vLC5GRkcptTp8+DWdnZ2XhAgBPT0+kpqbi4sWLeT52amoqFApFjgsRlTzZI10RERGIj48XnIY+5OTJk7h+/TpKlSqFr776SnQcIpXR+dJ1//59AEBAQAB+/PFH7N+/H9bW1nB3d8ezZ88AAHFxcbC1tc1xP2tra5iYmCAuLi7Px545cybkcrny4ujoqL4dIaIis7W1RYMGDQDwW4zaIHsC/RdffIEyZcqIDUOkQlpbugICAiCTyfK9XLhwAVlZWQCAH374AZ999hlcXV2xbt06yGQy/PHHH8rHk8lk7z2HJEm5Xp9t0qRJSExMVF6io6NVv6NEpBIeHh4AOK+rpIuPj1f+3cwJ9KRrtHalueHDh6N37975blO1alW8fPkSwJtvMGUzNTVF9erVERUVBQCws7PD2bNnc9z3+fPnSE9Pf28E7G2mpqYwNTUt6i4QkQZ16tQJ8+bNQ1BQ0Af/QUXirF27Funp6WjatClcXV1FxyFSKa0tXTY2NrCxsfngdq6urjA1NcWtW7fQqlUrAEB6ejoePnyIKlWqAADc3Nwwffp0xMbGKteCCQoKgqmpKd/0RDqidevWMDU1xaNHj/D333/jP//5j+hI9I7MzEysXLkSADB06FDBaYhUT2sPLxaUlZUVBg8ejClTpiAoKAi3bt1SDln37NkTwJvDDk5OTujbty8iIiJw9OhRjB07Fv7+/vwWIpGOMDc3R+vWrQHwEGNJdfjwYTx8+BDW1tbo1auX6DhEKqfzpQsA5s6di969e6Nv375o2rQp/vnnH4SEhMDa2hrAm/OzBQYGwszMDC1btoSPjw+8vb0xb948wcmJSJU6deoE4M1INpU82RPo/fz8YG5uLjgNkerp/DpdmsR1uohKtsuXL6Nx48awsLDAs2fPYGJiIjoS/c/Dhw9RvXp1SJKEW7duoXbt2qIjkR7hOl1ERCrWoEEDlC9fHklJSTh9+rToOPSWVatWQZIkdOzYkYWLdBZLFxHpDQMDA54SqARKS0vDmjVrAHCZCNJtLF1EpFe4XlfJs3v3bsTHx8PBwQGffvqp6DhEasPSRUR6JXsy/fnz55VnpSCxsifQ+/v7w8hIa1cyIvogli4i0isVK1aEk5MTJElCSEiI6Dh6LzIyEsePH4ehoSH8/f1FxyFSK5YuItI72aNdPMQo3ooVKwAAn376KSpWrCg4DZF6sXQRkd55e70urpojzqtXr7Bx40YAnEBP+oGli4j0jru7O4yNjfHw4UPcu3dPdBy9tXXrVigUCtSsWRMdOnQQHYdI7Vi6iEjvlC5dGi1atADAQ4yiSJKknEA/ePBgGBjw44h0H3/LiUgv8ZRAYp07dw4REREwNTWFn5+f6DhEGsHSRUR6KXu9rpCQEGRkZAhOo3+yR7l8fHxQrlw5wWmINIOli4j0kouLC6ytraFQKHD+/HnRcfTKs2fPsH37dgCcQE/6haWLiPSSoaGhcvI2DzFq1oYNG5CSkoKGDRuiefPmouMQaQxLFxHpLZ4SSPMkSVKuzTVkyBDIZDLBiYg0h6WLiPRW9mT6M2fO4Pnz54LT6IeQkBDcvn0blpaW6NOnj+g4RBrF0kVEeqtq1apwcnJCZmYmDzFqSPYE+r59+6J06dKC0xBpFksXEem1Ll26AAD2798vOInui4mJwd69ewFwAj3pJ5YuItJr2aXrwIEDyMzMFJxGt/3222/IzMxEq1at4OzsLDoOkcaxdBGRXnNzc4O1tTWePXuGM2fOiI6jszIyMrBq1SoAHOUi/cXSRUR6zcjICF5eXgB4iFGd9u/fj0ePHsHGxgafffaZ6DhEQrB0EZHeyz7E+NdffwlOoruyJ9D3798fpqamgtMQicHSRUR6z9PTE4aGhoiMjMSDBw9Ex9E5d+/eRVBQEGQyGQYNGiQ6DpEwLF1EpPfKli2Lli1bAgACAwMFp9E92XO5PD09Ub16dcFpiMRh6SIiAtC1a1cAnNelaikpKVi7di0ATqAnYukiIsL/z+sKDQ3Fq1evBKfRHTt37kRCQgIcHR3RuXNn0XGIhGLpIiICUKdOHdSoUQNpaWk4cuSI6Dg6Y+nSpQCAb775BoaGhoLTEInF0kVEBEAmk3F1ehW7cOECzpw5A2NjY/j7+4uOQyQcSxcR0f9kl67AwEBkZWUJTqP9Fi9eDADo1asXbG1tBachEo+li4jof9q0aYPSpUsjLi4Oly5dEh1Hq8XHx2Pbtm0AgBEjRghOQ1QysHQREf2PiYkJPD09AfAQY3GtWrUKaWlp+Oijj/DRRx+JjkNUIrB0ERG9hfO6ii89PV25Aj1HuYj+H0sXEdFbvLy8IJPJcPHiRcTExIiOo5X27NmDmJgY2NraomfPnqLjEJUYLF1ERG+xtbVVHg47cOCA4DTaadGiRQCAQYMG8TyLRG9h6SIiegcPMRZdREQETp48CSMjI55nkegdLF1ERO/ILl3BwcFISUkRnEa7ZC8T8fnnn8PBwUFwGqKShaWLiOgdDRs2RMWKFZGcnIywsDDRcbTG06dPsWXLFgCcQE+UG5YuIqJ3cHX6ovntt9+QmpoKV1dXuLm5iY5DVOKwdBER5eLt0iVJkuA0JV9GRgaWLVsG4M0ol0wmE5yIqORh6SIiykX79u1hZmaGf/75B9euXRMdp8T7888/ER0dDRsbG/Tq1Ut0HKISiaWLiCgXpUqVUq5Ov2PHDsFpSr7sCfTffPMNzMzMBKchKplYuoiI8vDFF18AALZu3cpDjPm4evUqjh07BkNDQwwZMkR0HKISi6WLiCgPXbp0QalSpXD//n2cP39edJwSa8mSJQCAHj16oFKlSoLTEJVcLF1ERHmwsLBAt27dAADbtm0TnKZkevbsGTZt2gSAy0QQfQhLFxFRPrIPMW7fvh2ZmZmC05Q8a9aswevXr9GwYUO0atVKdByiEo2li4goH56enihTpgxiYmJw4sQJ0XFKlMzMTC4TQVQILF1ERPkwMTHBZ599BuDNhHr6f/v378fDhw9RtmxZ+Pr6io5DVOKxdBERfUD2IcadO3ciLS1NcJqSY+7cuQAAf39/mJubC05DVPKxdBERfUDbtm1hZ2eHZ8+e4ciRI6LjlAjh4eE4efIkTExM8O2334qOQ6QV9KJ03b59G926dYONjQ2srKzQsmVLhIaG5tgmKioKXbt2hYWFBWxsbDBy5Ej+i5aIAACGhobw8fEBwEOM2WbOnAkA8PPzg729veA0RNpBL0pX586dkZGRgZCQEFy8eBGNGjVCly5dEBcXB+DNZNDOnTsjKSkJ4eHh2LZtG3bt2oUxY8YITk5EJUX2Ica9e/ciOTlZcBqxrl69igMHDsDAwADjxo0THYdIa+h86Xr69Cnu3r2LiRMnokGDBqhVqxZmzZqF5ORkREZGAgCCgoJw48YNbNq0CY0bN0bHjh0xf/58rF69GgqFQvAeEFFJ0KxZM1StWhWvXr1CYGCg6DhCzZ49GwDQs2dP1KxZU3AaIu2h86WrXLly+M9//oONGzciKSkJGRkZWLlyJWxtbeHq6goAOH36NJydneHg4KC8n6enJ1JTU3Hx4sU8Hzs1NRUKhSLHhYh0k0wmQ+/evQHo9yHG+/fvKxeKnTBhguA0RNpF50uXTCZDcHAwIiIiYGlpCTMzM/zyyy84dOgQypQpAwCIi4uDra1tjvtZW1vDxMREeQgyNzNnzoRcLldeHB0d1bkrRCRY9iHGAwcOIDExUXAaMebNm4esrCx4enqicePGouMQaRWtLV0BAQGQyWT5Xi5cuABJkjB06FBUqFABJ06cwLlz59CtWzd06dIFsbGxysfLbVE/SZLyXexv0qRJSExMVF6io6PVsq9EVDLUr18fTk5OSE1Nxd69e0XH0bjHjx9j7dq1AICJEycKTkOkfYxEByiq4cOHK4f681K1alWEhIRg//79eP78OaysrAAAy5YtQ3BwMDZs2ICJEyfCzs4OZ8+ezXHf58+fIz09/b0RsLeZmprC1NS0+DtDRFpBJpPhiy++wOTJk7F161b069dPdCSN+vXXX5GamormzZvD3d1ddBwiraO1pcvGxgY2NjYf3C77W0YGBjkH9QwMDJCVlQUAcHNzw/Tp0xEbG6v86nNQUBBMTU2V876IiACgd+/emDx5Mo4cOYInT56gfPnyoiNpRGJiIpYuXQrgzSgXT/lDVHhae3ixoNzc3GBtbY1+/frhypUruH37NsaNG4cHDx6gc+fOAAAPDw84OTmhb9++iIiIwNGjRzF27Fj4+/srR8eIiACgZs2aaNKkCTIzM7Fz507RcTRmxYoVUCgUcHJyQteuXUXHIdJKOl+6bGxscOjQIbx69Qrt27dHkyZNEB4ejj///BMNGzYE8Gbhw8DAQJiZmaFly5bw8fGBt7c35s2bJzg9EZVE2RPq9eVbjCkpKfjll18AvPnG4rtHDoioYGSSJEmiQ+gKhUIBuVyOxMREjpAR6bBHjx7B0dERkiQhKipK57+5vHLlSgwePBiOjo64d+8ejI2NRUciUilNfX7znytERIVUsWJFtG7dGgCwfft2wWnUKyMjA3PmzAEAjB07loWLqBhYuoiIiqBPnz4AgFWrVim/lKOLdu7cifv376NcuXIYMGCA6DhEWo2li4ioCHx9fVGmTBncuXMHBw4cEB1HLSRJwqxZswAA3377LSwsLAQnItJuLF1EREVQunRp+Pv7A4BykrmuOXz4MK5cuQILCwsMGzZMdBwircfSRURURCNGjIChoSFCQkJw5coV0XFUbubMmQCAQYMGoWzZsoLTEGk/li4ioiJydHTE559/DgBYuHCh2DAqFhoaiuPHj8PY2BijR48WHYdIJ7B0EREVw6hRowAAW7ZsQVxcnOA0qpGZmaksWv7+/qhYsaLgRES6gaWLiKgYmjVrBjc3N6SlpWHZsmWi46jExo0bcfnyZcjlcgQEBIiOQ6QzWLqIiIope7Rr+fLleP36teA0xfPq1Sv88MMPAIAff/xRb84tSaQJLF1ERMXUvXt3VKlSBU+fPsXmzZtFxymWOXPmIDY2FtWrV8eIESNExyHSKSxdRETFZGRkpCwoCxcuhLaeXS06Olp5ztk5c+bA1NRUcCIi3cLSRUSkAgMHDkTp0qURGRmJ4OBg0XGK5Pvvv8fr16/RunVr9OjRQ3QcIp3D0kVEpAJyuRz9+/cHoJ2LpZ4/fx6bNm0CACxYsAAymUxwIiLdw9JFRKQiI0eOhEwmw6FDh3Dz5k3RcQpMkiTllwG++uorNGnSRHAiIt3E0kVEpCI1atRAt27dAGjXYqm7du3CyZMnYW5ujunTp4uOQ6SzWLqIiFQoe8Ro48aNePr0qeA0H5aSkoLx48cDAMaNG4dKlSoJTkSku1i6iIhUqHXr1nBxcUFKSgpWrlwpOs4HLV68GA8ePIC9vb2yfBGRerB0ERGpkEwmw3fffQcAWLp0KdLS0sQGykd8fDx+/vlnAMCMGTNgYWEhOBGRbmPpIiJSsV69esHBwQGxsbGYO3eu6Dh5CggIgEKhgIuLC7766ivRcYh0HksXEZGKmZiYYM6cOQCAadOm4e+//xac6H2RkZHKw58LFiyAgQE/DojUje8yIiI18PX1hZeXF9LS0uDv74+srCzRkZTS09MxePBgZGVloXv37nB3dxcdiUgvsHQREamBTCbD8uXLYWFhgfDw8BI1qX7ChAkIDw+HpaWl8rQ/RKR+LF1ERGpSpUoVzJw5E8CbovPvv/8KTgTs2LFDuWL+hg0bUL16dcGJiPQHSxcRkRoNHToUbm5uePnyJYYMGSL0ZNg3btxQnqpowoQJ6N69u7AsRPqIpYuISI0MDQ2xevVqGBsbY//+/di+fbuQHAqFAj169EBSUhLat2+vXCqCiDSHpYuISM3q1auHH374AcCb8zMmJCRo9PklSUL//v1x69YtVKxYEVu3boWRkZFGMxARSxcRkUZMmjQJ9erVw5MnTzB69GiNPveCBQuwa9cuGBsbY+fOnahQoYJGn5+I3mDpIiLSABMTE6xevRoymQwbN27E4cOHNfK8x44dw4QJEwC8OQl38+bNNfK8RPQ+li4iIg1xc3PDiBEjAACDBg3Cq1ev1Pp8jx49go+PDzIzM/Hll19iyJAhan0+IsofSxcRkQZNnz4dVapUwT///INx48ap7duMaWlp8PHxQXx8POrXr4+VK1dCJpOp5bmIqGBYuoiINKh06dLKhVJXrFiBXr16qXzEKysrC9999x1OnToFuVyO3bt3o1SpUip9DiIqPJYuIiIN8/T0xIoVK2BsbIw//vgDzZo1w507d1Ty2Ldv30abNm2wfPlyAMDGjRtRs2ZNlTw2ERUPSxcRkQCDBg1CWFgY7O3tcePGDTRp0gT79+8v8uNlZmZi/vz5aNiwIU6ePInSpUtj7dq1+PTTT1WYmoiKg6WLiEiQFi1a4OLFi2jZsiUUCgW6du2KgICAQp8c++bNm2jVqhXGjh2LlJQUdOrUCdevX8fXX3+tpuREVBQsXUREAtnb2yMkJATDhw8HAEydOhWffvopXrx48cH7ZmRkYNasWWjcuDHOnDkDKysrrF69GocPH0aVKlXUnJyICksmiTwRmI5RKBSQy+VITEyElZWV6DhEpGU2bNiAwYMHIyUlBTVr1sTYsWNhYWGBUqVKwdzcPMd/FQoFvv32W1y4cAEA4OXlhZUrV8LR0VHwXhBpH019frN0qRBLFxEV16VLl9C9e3dERUUVaHu5XI6FCxeiX79+XBKCqIg09fnNk28REZUgLi4uuHjxIn766Sf8888/SE5ORnJyMl6/fq38c3JyMlJTU9GpUycsWrQIFStWFB2biAqAI10qxJEuIiIi7aOpz29OpCciIiLSAJYuIiIiIg1g6SIiIiLSAJYuIiIiIg1g6SIiIiLSAJYuIiIiIg1g6SIiIiLSAJYuIiIiIg1g6SIiIiLSAK0vXdOnT0eLFi1QqlQplClTJtdtoqKi0LVrV1hYWMDGxgYjR45EWlpajm2uXbsGd3d3mJubo2LFipg2bRq4WD8RERGpitafezEtLQ09e/aEm5sb1qxZ897tmZmZ6Ny5M8qXL4/w8HAkJCSgX79+kCQJixcvBvBm+f9OnTqhXbt2OH/+PG7fvg0/Pz9YWFhgzJgxmt4lIiIi0kFaX7qmTp0KAFi/fn2utwcFBeHGjRuIjo6Gg4MDAGD+/Pnw8/PD9OnTYWVlhc2bNyMlJQXr16+HqakpnJ2dcfv2bSxYsACjR4+GTCbL9bFTU1ORmpqq/FmhUKh254iIiEhnaP3hxQ85ffo0nJ2dlYULADw9PZGamoqLFy8qt3F3d4epqWmObWJiYvDw4cM8H3vmzJmQy+XKi6Ojo9r2g4iIiLSbzpeuuLg42Nra5rjO2toaJiYmiIuLy3Ob7J+zt8nNpEmTkJiYqLxER0erOD0RERHpihJ5eDEgIEB52DAv58+fR5MmTQr0eLkdHpQkKcf1726TPYk+r0OLAGBqappjdCz7PjzMSEREpD2yP7fV/QW6Elm6hg8fjt69e+e7TdWqVQv0WHZ2djh79myO654/f4709HTlaJadnd17I1rx8fEA8N4IWH4SEhIAgIcZiYiItFBCQgLkcrnaHr9Eli4bGxvY2Nio5LHc3Nwwffp0xMbGwt7eHsCbyfWmpqZwdXVVbvP9998jLS0NJiYmym0cHBwKXO4AoGzZsgDeLFGhzhetpFEoFHB0dER0dDSsrKxEx9EY7jf3Wx9wv7nf+iAxMRGVK1dWfo6rS4ksXYURFRWFZ8+eISoqCpmZmbh8+TIAoGbNmihdujQ8PDzg5OSEvn37Yu7cuXj27BnGjh0Lf39/5S+Ur68vpk6dCj8/P3z//fe4c+cOZsyYgf/+97/5Hl58l4HBmylycrlcr35Zs1lZWXG/9Qj3W79wv/WLvu539ue4umh96frvf/+LDRs2KH9u3LgxACA0NBRt27aFoaEhAgMDMXToULRs2RLm5ubw9fXFvHnzlPeRy+UIDg7GsGHD0KRJE1hbW2P06NEYPXq0xveHiIiIdJPWl67169fnuUZXtsqVK2P//v35blO/fn0cP35chcmIiIiI/p/OLxmhSaamppgyZUqObzTqA+4391sfcL+53/qA+63e/ZZJPMEgERERkdpxpIuIiIhIA1i6iIiIiDSApYuIiIhIA1i6iIiIiDSApauAAgICIJPJclzs7Ozyvc+xY8fg6uoKMzMzVK9eHStWrNBQWtWpWrXqe/stk8kwbNiwXLcPCwvLdfu///5bw8kL5/jx4+jatSscHBwgk8mwd+/eHLdLkoSAgAA4ODjA3Nwcbdu2RWRk5Acfd9euXXBycoKpqSmcnJywZ88eNe1B0eS33+np6ZgwYQLq168PCwsLODg44KuvvkJMTEy+j7l+/fpcfwdSUlLUvDcF96HX28/P7738zZs3/+DjavPrDSDX100mk2Hu3Ll5PmZJf71nzpyJpk2bwtLSEhUqVIC3tzdu3bqVYxtdfH9/aL919f1dkNdb5PubpasQ6tWrh9jYWOXl2rVreW774MEDfPLJJ2jdujUiIiLw/fffY+TIkdi1a5cGExff+fPnc+xzcHAwAKBnz5753u/WrVs57lerVi1NxC2ypKQkNGzYEEuWLMn19jlz5mDBggVYsmQJzp8/Dzs7O3Tq1AkvX77M8zFPnz6NXr16oW/fvrhy5Qr69u0LHx+f984FKlJ++52cnIxLly5h8uTJuHTpEnbv3o3bt2/j008//eDjWllZ5Xj9Y2NjYWZmpo5dKJIPvd4A8PHHH+fIf+DAgXwfU9tfbwDvvWZr166FTCbDZ599lu/jluTX+9ixYxg2bBjOnDmD4OBgZGRkwMPDA0lJScptdPH9/aH91tX3d0Feb0Dg+1uiApkyZYrUsGHDAm8/fvx4qW7dujmuGzRokNS8eXMVJ9Osb7/9VqpRo4aUlZWV6+2hoaESAOn58+eaDaZCAKQ9e/Yof87KypLs7OykWbNmKa9LSUmR5HK5tGLFijwfx8fHR/r4449zXOfp6Sn17t1b5ZlV4d39zs25c+ckANI///yT5zbr1q2T5HK5asOpUW773a9fP6lbt26FehxdfL27desmtW/fPt9ttO31jo+PlwBIx44dkyRJf97f7+53bnTx/Z3bfot8f3OkqxDu3LkDBwcHVKtWDb1798b9+/fz3Pb06dPw8PDIcZ2npycuXLiA9PR0dUdVi7S0NGzatAn9+/f/4DkpGzduDHt7e3To0AGhoaEaSqgeDx48QFxcXI7X09TUFO7u7jh16lSe98vrdyC/+5R0iYmJkMlkKFOmTL7bvXr1ClWqVEGlSpXQpUsXREREaCagCoWFhaFChQqoXbs2/P39ER8fn+/2uvZ6P378GIGBgRgwYMAHt9Wm1zsxMREAlCc21pf397v7ndc2uvb+zmu/Rb2/WboKqFmzZti4cSMOHz6M1atXIy4uDi1atEBCQkKu28fFxcHW1jbHdba2tsjIyMDTp081EVnl9u7dixcvXsDPzy/Pbezt7bFq1Srs2rULu3fvRp06ddChQwetPsVSXFwcAOT6embfltf9CnufkiwlJQUTJ06Er69vvifCrVu3LtavX499+/Zh69atMDMzQ8uWLXHnzh0Npi0eLy8vbN68GSEhIZg/fz7Onz+P9u3bIzU1Nc/76NrrvWHDBlhaWqJHjx75bqdNr7ckSRg9ejRatWoFZ2dnAPrx/s5tv9+li+/vvPZb5Ptb68+9qCleXl7KP9evXx9ubm6oUaMGNmzYkOeJsd8dDZL+t/j/h0aJSqo1a9bAy8sLDg4OeW5Tp04d1KlTR/mzm5sboqOjMW/ePLRp00YTMdUmt9fzQ69lUe5TEqWnp6N3797IysrCsmXL8t22efPmOSaltmzZEi4uLli8eDEWLVqk7qgq0atXL+WfnZ2d0aRJE1SpUgWBgYH5lhBdeb0BYO3atejTp88H5+po0+s9fPhwXL16FeHh4e/dpsvv7/z2G9Dd93de+y3y/c2RriKysLBA/fr182z3dnZ27zXg+Ph4GBkZoVy5cpqIqFL//PMPjhw5goEDBxb6vs2bNy+R/woqqOxvqeb2er77L59371fY+5RE6enp8PHxwYMHDxAcHJzvv4JzY2BggKZNm2r174C9vT2qVKmS7z7oyusNACdOnMCtW7eK9H4vqa/3iBEjsG/fPoSGhqJSpUrK63X9/Z3XfmfT1ff3h/b7bZp8f7N0FVFqaipu3rwJe3v7XG93c3NTftMvW1BQEJo0aQJjY2NNRFSpdevWoUKFCujcuXOh7xsREZHn/ydtUK1aNdjZ2eV4PdPS0nDs2DG0aNEiz/vl9TuQ331Kmuy/kO/cuYMjR44U6R8MkiTh8uXLWv07kJCQgOjo6Hz3QRde72xr1qyBq6srGjZsWOj7lrTXW5IkDB8+HLt370ZISAiqVauW43ZdfX9/aL8B3Xx/F2S/36XR93ehpt3rsTFjxkhhYWHS/fv3pTNnzkhdunSRLC0tpYcPH0qSJEkTJ06U+vbtq9z+/v37UqlSpaRRo0ZJN27ckNasWSMZGxtLO3fuFLULRZaZmSlVrlxZmjBhwnu3vbvfv/zyi7Rnzx7p9u3b0vXr16WJEydKAKRdu3ZpMnKhvXz5UoqIiJAiIiIkANKCBQukiIgI5bd4Zs2aJcnlcmn37t3StWvXpC+++EKyt7eXFAqF8jH69u0rTZw4UfnzyZMnJUNDQ2nWrFnSzZs3pVmzZklGRkbSmTNnNL5/eclvv9PT06VPP/1UqlSpknT58mUpNjZWeUlNTVU+xrv7HRAQIB06dEi6d++eFBERIX399deSkZGRdPbsWRG7mKv89vvly5fSmDFjpFOnTkkPHjyQQkNDJTc3N6lixYo6/XpnS0xMlEqVKiUtX74818fQttd7yJAhklwul8LCwnL8DicnJyu30cX394f2W1ff3x/ab9Hvb5auAurVq5dkb28vGRsbSw4ODlKPHj2kyMhI5e39+vWT3N3dc9wnLCxMaty4sWRiYiJVrVo1z7/ESrrDhw9LAKRbt269d9u7+z179mypRo0akpmZmWRtbS21atVKCgwM1GDaosle6uLdS79+/SRJevO18ilTpkh2dnaSqamp1KZNG+natWs5HsPd3V25fbY//vhDqlOnjmRsbCzVrVu3xJXP/Pb7wYMHud4GQAoNDVU+xrv7/d1330mVK1eWTExMpPLly0seHh7SqVOnNL9z+chvv5OTkyUPDw+pfPnykrGxsVS5cmWpX79+UlRUVI7H0LXXO9vKlSslc3Nz6cWLF7k+hra93nn9Dq9bt065jS6+vz+037r6/v7Qfot+f8v+F5KIiIiI1IhzuoiIiIg0gKWLiIiISANYuoiIiIg0gKWLiIiISANYuoiIiIg0gKWLiIiISANYuoiIiIg0gKWLiIiISANYuoiIiIg0gKWLiIiISANYuoiIiIg0gKWLiCgfDx8+hEwme+/Stm1b0dGISMsYiQ5ARFSSOTo6IjY2VvlzXFwcOnbsiDZt2ghMRUTaSCZJkiQ6BBGRNkhJSUHbtm1Rvnx5/PnnnzAw4MECIio4jnQRERXQgAED8PLlSwQHB7NwEVGhsXQRERXAzz//jEOHDuHcuXOwtLQUHYeItBAPLxIRfcCuXbvwxRdf4ODBg+jQoYPoOESkpVi6iIjycf36dTRr1gyjR4/GsGHDlNebmJigbNmyApMRkbZh6SIiysf69evx9ddfv3e9u7s7wsLCNB+IiLQWSxcRERGRBvDrN0REREQawNJFREREpAEsXUREREQawNJFREREpAEsXUREREQawNJFREREpAEsXUREREQawNJFREREpAEsXUREREQawNJFREREpAEsXUREREQa8H+D068XNyUtjgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(zlist, T21global.T21avg, \"k\")\n", + "plt.xlabel(r\"z\");\n", + "plt.ylabel(r\"$\\overline{ T_{21}}$ [mK]\")\n", + "plt.xlim(zmin, 25)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It has the usual absorption trough around $z\\sim15$ (given so far we only have atomic-cooling haloes), and turns into emission at $z\\sim11$.\n", + "\n", + "Since the v2 version of zeus21 now properly computes reionization quantities, let us now plot the volume fraction of ionized hydrogen." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(zlist, 1-T21global.xHI_avg, \"k\")\n", + "plt.xlabel(r\"z\");\n", + "plt.ylabel(r\"$\\overline{ x_{\\rm HII}}$\")\n", + "plt.xlim([zmin, 25])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us plot the relevant temperatures too. Here is the CMB temperature $T_{\\rm CMB}$; the gas kinetic temperature $T_k$, which has an adiabatic/cosmological and an X-ray component; and the spin temperature $T_S$, which has the WF coupling in it (and we store its inverse in the code)." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(zlist, T21global.T_CMB, \"r--\", label=r\"$T_{\\rm CMB}$\")\n", + "plt.plot(zlist, T21global.Tk_avg, \"b-.\", label=r\"$T_{\\rm k}$\")\n", + "plt.plot(zlist, 1.0/T21global._invTs_avg, \"k\", label=r\"$T_{\\rm S}$\")\n", + "plt.xlabel(r\"z\")\n", + "plt.ylabel(r\"Temperatures [K]\")\n", + "plt.ylim(0, 200)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This lines up with our expectation from the 21-cm global signal above. Absorption begins when $T_S$ departs from $T_{\\rm CMB}$, at $z\\sim 20$, as it begins to couple to $T_k$. It turns into emission at $z\\sim 11$ when $T_S\\sim T_k > T_{\\rm CMB}$. Full WF coupling only occurs after there has been some X-ray heating, so we don't get a deep 21-cm trough for this model. This would be different with a lower X-ray luminosity $L_X$ as we will see below.\n", + "\n", + "Let's move now to the 21-cm fluctuations.\n", + "\n", + "Note that reionization is only properly computed for the global signal for now. Correlations between the reionization and other 21-cm quantities will be added in a future version of zeus21." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "RSDMODE = 1 # which RSD mode you want, 0 is no RSDs (real space), 1 is spherical (as simulations usually take), 2 is mu~1 (outside the wedge, most relevant for observations)\n", + "PS21 = zeus21.Power_Spectra(UserParams, CosmoParams, AstroParams, T21global, RSD_MODE = RSDMODE)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#choose a k to plot\n", + "klist = PS21.klist_PS\n", + "kchoose=0.3\n", + "_ik = min(range(len(klist)), key=lambda i: np.abs(klist[i]-kchoose))\n", + "\n", + "plt.figure()\n", + "plt.semilogy(zlist,PS21.Deltasq_T21[:,_ik], color=\"k\", linewidth=2.0, label=\"Full Zeus21\")\n", + "plt.semilogy(zlist,PS21.Deltasq_T21_lin[:,_ik], color=\"gray\", linewidth=1.2, label=\"Linear\")\n", + "\n", + "plt.xlabel(r\"$z$\")\n", + "plt.ylabel(r\"$\\Delta^2_{21}$ [mK$^2$]\")\n", + "plt.legend()\n", + "\n", + "plt.xlim(zmin, 25)\n", + "plt.ylim(1, 200)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This shows the evolution of the fluctuations at a particular scale (i.e., wavenumber $k$). We have shown the full result from zeus21, as well as the linear approximation (which is also stored). We can flip the script now and show the 21-cm power against wavenumber at a particular redshift." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#choose a z to plot\n", + "zchoose=13.\n", + "_iz = min(range(len(zlist)), key=lambda i: np.abs(zlist[i]-zchoose))\n", + "\n", + "plt.figure()\n", + "plt.loglog(klist,PS21.Deltasq_T21[_iz], color='k', linewidth=2.0, label=\"Full Zeus21\")\n", + "plt.loglog(klist,PS21.Deltasq_T21_lin[_iz], color='gray', linewidth=1.2, label=\"Linear\")\n", + "\n", + "plt.xlabel(r\"$k$ [Mpc$^{-1}$]\")\n", + "plt.ylabel(r\"$\\Delta^2_{21}$ [mK$^2$]\")\n", + "plt.legend()\n", + "\n", + "plt.xlim(1e-2, 1)\n", + "plt.ylim(1, 200)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was for a specific set of astro+cosmo parameters. Let's do a different example with lower X-ray luminosity (which we expect will produce a deeper cosmic dawn absorption). This is controlled through the free parameter L40_xray (luminosity per unit SFR in units of 10^40 erg/s/SFR), with a fiducial value of 3.0. Let's lower it to 1.0 and see what happens." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "AstroParams_lowLX = zeus21.Astro_Parameters(CosmoParams=CosmoParams, epsstar=epsstar, L40_xray=1.0)\n", + "T21global_lowLX = zeus21.get_T21_coefficients(UserParams=UserParams, CosmoParams=CosmoParams, AstroParams=AstroParams_lowLX, HMFinterp=HMFinterp)\n", + "PS21_lowLX = zeus21.Power_Spectra(UserParams, CosmoParams, AstroParams_lowLX, T21global_lowLX, RSD_MODE = RSDMODE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we can re-run the astrophysics part only, so it should take ~few seconds in a laptop. Let's plot the global signal and fluctuations comparing with the fiducial case." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f = plt.figure(figsize = (6,6))\n", + "\n", + "ax = f.add_subplot(211)\n", + "\n", + "plt.semilogy(zlist, PS21.Deltasq_T21[:,_ik], \"k\", linewidth=2.0, label=r\"$L_{X,40} = 3.0$ (fid.)\")\n", + "plt.semilogy(zlist, PS21_lowLX.Deltasq_T21[:,_ik], \"b-.\", linewidth=2.0, label=r\"$L_{X,40} = 1.0$\")\n", + "\n", + "plt.ylabel(r\"$\\Delta^2_{21}$ [mK$^2$]\")\n", + "plt.legend()\n", + "plt.xlim(5, 25)\n", + "plt.ylim(1,200)\n", + "\n", + "ax = f.add_subplot(212)\n", + "\n", + "plt.plot(zlist,T21global.T21avg, \"k\", linewidth=2.0)\n", + "plt.plot(zlist,T21global_lowLX.T21avg, \"b-.\", linewidth=2.0)\n", + "\n", + "plt.xlabel(r\"z\")\n", + "plt.ylabel(r\"$\\overline{ T_{21}}$ [mK]\")\n", + "\n", + "plt.xlim(5, 25)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just as we expected! Lowering the X-ray luminosity (blue) makes for a deeper 21-cm global absorption, and larger 21-cm fluctuations too. By $z\\sim 10$ there is still enough heating to raise the 21-cm signal near absorption. We can confirm this by plotting the spin temperature and comparing against the `standard' case" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(zlist, 1.0/T21global._invTs_avg, \"k\", label=r\"$L_{X,40} = 3.0$ (fid.)\")\n", + "plt.plot(zlist, 1.0/T21global_lowLX._invTs_avg, \"b-.\", label=r\"$L_{X,40} = 1.0$\")\n", + "plt.plot(zlist, T21global.T_CMB, \"r--\")\n", + "plt.xlabel(r\"z\")\n", + "plt.ylabel(r\"Temperatures [K]\")\n", + "plt.legend()\n", + "plt.xlim(5, 25)\n", + "plt.ylim(0,200)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You're now ready to calculate any 21-cm power spectrum or global signal that you want!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "to_dev", + "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": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/Tutorial_Zeus21_Maps.ipynb b/docs/tutorials/Tutorial_Zeus21_Maps.ipynb similarity index 99% rename from docs/Tutorial_Zeus21_Maps.ipynb rename to docs/tutorials/Tutorial_Zeus21_Maps.ipynb index 97a9650..d41713b 100644 --- a/docs/Tutorial_Zeus21_Maps.ipynb +++ b/docs/tutorials/Tutorial_Zeus21_Maps.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Map-making" + ] + }, { "attachments": {}, "cell_type": "markdown", diff --git a/docs/tutorials/Tutorial_Zeus21_PopIIandIII_Fiducial.ipynb b/docs/tutorials/Tutorial_Zeus21_PopIIandIII_Fiducial.ipynb new file mode 100644 index 0000000..d8235e4 --- /dev/null +++ b/docs/tutorials/Tutorial_Zeus21_PopIIandIII_Fiducial.ipynb @@ -0,0 +1,734 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "21609608-8da1-40d1-bf4d-12d8ca84f267", + "metadata": {}, + "source": [ + "# Zeus21 with Population II and III Stars" + ] + }, + { + "cell_type": "markdown", + "id": "5f2e29a3", + "metadata": {}, + "source": [ + "This quick tutorial covers all the new features in the updated public version of Zeus21 (see our paper [Cruz et al. 2024](https://arxiv.org/abs/2407.18294)) for more information.\n", + "\n", + "In this new version, we have included the influence of Population III stars as well as the suppressive feedback effects from Lyman-Werner radiation and the relative velocity between CDM and baryons." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2c9e8ec0", + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "#import Zeus\n", + "import zeus21\n", + "\n", + "#set up the CLASS cosmology\n", + "from classy import Class\n", + "\n", + "#and the user parameters\n", + "UserParams = zeus21.User_Parameters(precisionboost=1.2)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f3646152-80fa-4166-bcd8-1645ffbebe5b", + "metadata": {}, + "source": [ + "## Step 1: Set up Cosmology" + ] + }, + { + "cell_type": "markdown", + "id": "e56fce7d", + "metadata": {}, + "source": [ + "We begin by running CLASS, where the associated parameters can be altered below. Then we save the cosmo parameters, the correlation functions, and the halo mass function at all desired z and M." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "01adaa46-dcdd-4405-a6a1-5b800584dc4d", + "metadata": {}, + "outputs": [], + "source": [ + "# cosmo inputs from table 2 last column of 1807.06209, as 21cmFAST\n", + "ombh2 = 0.02242 \n", + "omch2 = 0.11933\n", + "tau_re = 0.0544\n", + "hLittle = 0.6766\n", + "ns = 0.9665\n", + "As = np.exp(3.047)*10**(-10.)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b5c07181", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CLASS has run, we store the cosmology.\n", + "HMF interpolator built. This ends the cosmology part -- moving to astrophysics.\n" + ] + } + ], + "source": [ + "#set up user parameters\n", + "UserParams = zeus21.User_Parameters(FLAG_FORCE_LINEAR_CF=False,zmin_T21=10.)\n", + "\n", + "#define all cosmology (including derived) parameters, and save them to the CosmoParams structure\n", + "CosmoParams = zeus21.Cosmo_Parameters(UserParams=UserParams, omegab = ombh2, omegac = omch2, h_fid = hLittle, As = As, ns = ns, tau_fid = tau_re, USE_RELATIVE_VELOCITIES = True, Flag_emulate_21cmfast=False)\n", + "print('CLASS has run, we store the cosmology.')\n", + "\n", + "# Compute the HMF structure that stores associated quantities\n", + "HMFintclass = zeus21.HMF_interpolator(User_Parameters=UserParams,Cosmo_Parameters=CosmoParams)\n", + "print('HMF interpolator built. This ends the cosmology part -- moving to astrophysics.')" + ] + }, + { + "cell_type": "markdown", + "id": "8eeda1be-f47c-4cb4-b9f2-6998e06c6641", + "metadata": {}, + "source": [ + "## Step 2: Astrophysics" + ] + }, + { + "cell_type": "markdown", + "id": "faf27bcd", + "metadata": {}, + "source": [ + "After that we set up the astro parameters, calculate the SFRD and with it all the global signal and related quantities. In principle one can re-run the astrophysics part only if you're certain of the cosmology parameters. \n", + "\n", + "With the current implementation, the computation of global quantities below takes less than a second on a laptop.\n", + "\n", + "For more details on parameters, please refer to those used in Table 1 of our paper linked above.\n", + "\n", + "NOTE: Pop III stars and LW feedback are turned on using the USE_POP_III and USE_LW_FEEDBACK flags below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fbcff876-3535-475d-9cb0-c124b9de2fff", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "### POP II quantities first\n", + "\n", + "################################\n", + "### Model Parameters\n", + "accretion_model = \"exp\" # Accretion model. \"exp\" for exponential, \"EPS\" for EPS. \"RP16\" for the dynamically averaged fitting function in Rodríguez-Puebla+16. Default is \"exp\"\n", + "\n", + "################################\n", + "### SFR(Mh) Parameteres\n", + "alphastar = 0.69 # alphastar powerlaw index for low masses, default 0.5\n", + "betastar = -1.68 # betastar powerlaw index for high masses, default -0.5\n", + "epsstar = 10**-1.11 # epsilonstar = fstar at Mc\n", + "Mc = 10**11.93 # Pivot mass at which the power law cuts for model 0, default Mc = 3e11\n", + "dlog10epsstardz = -0.08 # dlog10epsilonstar/dz, default 0\n", + "\n", + "################################\n", + "### Escape fraction parameters\n", + "fesc10 = 0.1 # fesc(M) parameter. Power law normalized (fesc10) at M=1e10 Msun with index alphaesc\n", + "alphaesc = 0.0\n", + "L40_xray = 10**0.5 # L40_xray: soft-band (E<2 keV) lum/SFR in Xrays in units of 10^40 erg/s/(Msun/yr)\n", + "E0_xray = 500. # E0_xray: minimum energy in eV\n", + "alpha_xray = -1.0 # Xray SED power-law index\n", + "Emax_xray_norm=2000 # max energy in eV to normalize SED. Keep at 2000 eV normally\n", + "\n", + "################################\n", + "### LyA parameters\n", + "N_alpha_perbaryon_II = 9690 # number of Pop II photons between LyA and Ly Cont. per baryon (from BL05)\n", + "N_alpha_perbaryon_III = 17900 # number of Pop III photons between LyA and Ly Cont. per baryon value of 17900 is from Klessen & Glover 2023 (2303.12500), table A2\n", + "\n", + "################################\n", + "### MTURN Parameters: \n", + "Mturn_fixed = None # Mturn_fixed: None if use Matom(z) at each z, Some value if fixed Mturn\n", + "FLAG_MTURN_SHARP= False # Mturn_sharp: False if regular exponential cutoff, True if sharp cutoff, active only if Mturn_fixed is on\n", + "\n", + "\n", + "\n", + "\n", + "################################\n", + "# Pop III Quantities\n", + "alphastar_III = 0 \n", + "betastar_III = 0\n", + "epsstar_III = 10**-2.5709708032788794/3 #AV sugmaUV = 1.3\n", + "Mc_III = 1e7\n", + "dlog10epsstardz_III = 0.0\n", + "\n", + "fesc7_III = 10**(-1.35)\n", + "alphaesc_III = -0.3\n", + "L40_xray_III = 10**0.5\n", + "alpha_xray_III = -1.0\n", + "\n", + "\n", + "USE_POPIII = True\n", + "USE_LW_FEEDBACK = True\n", + "\n", + "A_LW = 2.0\n", + "beta_LW = 0.6\n", + "\n", + "A_vcb = 1.0\n", + "beta_vcb = 1.8\n", + "\n", + "\n", + "\n", + "AstroParams = zeus21.Astro_Parameters(CosmoParams=CosmoParams, \n", + " quadratic_SFRD_lognormal=False,\n", + " FLAG_USE_PSD=False, \n", + " \n", + " \n", + " accretion_model = accretion_model,\n", + " \n", + " alphastar = alphastar, \n", + " betastar = betastar, \n", + " epsstar = epsstar, \n", + " Mc = Mc, \n", + " dlog10epsstardz = dlog10epsstardz,\n", + " \n", + " fesc10 = fesc10, \n", + " alphaesc = alphaesc,\n", + " L40_xray = L40_xray, \n", + " E0_xray = E0_xray, \n", + " alpha_xray = alpha_xray, \n", + " Emax_xray_norm = Emax_xray_norm, \n", + " \n", + " N_alpha_perbaryon_II = N_alpha_perbaryon_II, \n", + " N_alpha_perbaryon_III = N_alpha_perbaryon_III,\n", + " \n", + " Mturn_fixed = Mturn_fixed, \n", + " FLAG_MTURN_SHARP = FLAG_MTURN_SHARP,\n", + " \n", + " \n", + " USE_POPIII = USE_POPIII, \n", + " USE_LW_FEEDBACK = USE_LW_FEEDBACK,\n", + "\n", + " alphastar_III = alphastar_III, \n", + " betastar_III = betastar_III,\n", + " epsstar_III = epsstar_III,\n", + " Mc_III = Mc_III,\n", + " dlog10epsstardz_III = dlog10epsstardz_III,\n", + "\n", + " fesc7_III = fesc7_III,\n", + " alphaesc_III = alphaesc_III,\n", + " L40_xray_III = L40_xray_III,\n", + " alpha_xray_III = alpha_xray_III,\n", + " \n", + " A_LW = A_LW,\n", + " beta_LW = beta_LW,\n", + " \n", + " A_vcb = A_vcb,\n", + " beta_vcb = beta_vcb,\n", + " )\n", + "\n", + "\n", + "CoeffStructure = zeus21.get_T21_coefficients(UserParams=UserParams, CosmoParams=CosmoParams ,AstroParams=AstroParams, HMFinterp=HMFintclass)\n", + "SFRD_class = zeus21.sfrd.SFRD_class(UserParams, CosmoParams, AstroParams, HMFintclass)\n", + "zlist= CoeffStructure.zintegral\n", + "print('SFRD and coefficients stored. Move ahead.')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4cb4b017", + "metadata": {}, + "outputs": [], + "source": [ + "### POP II quantities first\n", + "\n", + "################################\n", + "### Model Parameters\n", + "accretion_model = \"exp\" # ACCRETION MODEL: 0 for exponential, 1 for EPS, default EXP\n", + "\n", + "################################\n", + "### SFR(Mh) Parameteres\n", + "alphastar = 0.5 # alphastar powerlaw index for low masses, default 0.5\n", + "betastar = -0.5 # betastar powerlaw index for high masses, default -0.5\n", + "epsstar = 10**-1. # epsilonstar = fstar at Mc\n", + "Mc = 3e11 # Pivot mass at which the power law cuts for model 0, default Mc = 3e11\n", + "dlog10epsstardz = 0.0 # dlog10epsilonstar/dz, default 0\n", + "\n", + "################################\n", + "### Escape fraction parameters\n", + "fesc10 = 0.1 # fesc(M) parameter. Power law normalized (fesc10) at M=1e10 Msun with index alphaesc\n", + "alphaesc = 0.0\n", + "L40_xray = 10**0.5 # L40_xray: soft-band (E<2 keV) lum/SFR in Xrays in units of 10^40 erg/s/(Msun/yr)\n", + "E0_xray = 500. # E0_xray: minimum energy in eV\n", + "alpha_xray = -1.0 # Xray SED power-law index\n", + "Emax_xray_norm=2000 # max energy in eV to normalize SED. Keep at 2000 eV normally\n", + "\n", + "################################\n", + "### LyA parameters\n", + "N_alpha_perbaryon_II = 9690 # number of Pop II photons between LyA and Ly Cont. per baryon (from BL05)\n", + "N_alpha_perbaryon_III = 17900 # number of Pop III photons between LyA and Ly Cont. per baryon value of 17900 is from Klessen & Glover 2023 (2303.12500), table A2\n", + "\n", + "################################\n", + "### MTURN Parameters: \n", + "Mturn_fixed = None # Mturn_fixed: None if use Matom(z) at each z, Some value if fixed Mturn\n", + "FLAG_MTURN_SHARP= False # Mturn_sharp: False if regular exponential cutoff, True if sharp cutoff, active only if Mturn_fixed is on\n", + "\n", + "################################\n", + "ZMIN = 10.0 # down to which z we compute the evolution\n", + "\n", + "\n", + "\n", + "################################\n", + "# Pop III Quantities\n", + "alphastar_III = 0 \n", + "betastar_III = 0\n", + "epsstar_III = 10**(-3.0)\n", + "Mc_III = 1e7\n", + "dlog10epsstardz_III = 0.0\n", + "\n", + "fesc7_III = 10**(-1.35)\n", + "alphaesc_III = -0.3\n", + "L40_xray_III = 10**0.5\n", + "alpha_xray_III = -1.0\n", + "\n", + "\n", + "USE_POPIII = True\n", + "USE_LW_FEEDBACK = True\n", + "\n", + "A_LW = 2.0\n", + "beta_LW = 0.6\n", + "\n", + "A_vcb = 1.0\n", + "beta_vcb = 1.8\n", + "\n", + "\n", + "#set up your astro parameters too, here the peak of f*(Mh) as an example\n", + "\n", + "AstroParams = zeus21.Astro_Parameters(CosmoParams=CosmoParams, \n", + " quadratic_SFRD_lognormal=False,\n", + " FLAG_USE_PSD=False, \n", + " \n", + " accretion_model = accretion_model,\n", + " \n", + " alphastar = alphastar, \n", + " betastar = betastar, \n", + " epsstar = epsstar, \n", + " Mc = Mc, \n", + " dlog10epsstardz = dlog10epsstardz,\n", + " \n", + " fesc10 = fesc10, \n", + " alphaesc = alphaesc,\n", + " L40_xray = L40_xray, \n", + " E0_xray = E0_xray, \n", + " alpha_xray = alpha_xray, \n", + " Emax_xray_norm = Emax_xray_norm, \n", + " \n", + " N_alpha_perbaryon_II = N_alpha_perbaryon_II, \n", + " N_alpha_perbaryon_III = N_alpha_perbaryon_III,\n", + " \n", + " Mturn_fixed = Mturn_fixed, \n", + " FLAG_MTURN_SHARP = FLAG_MTURN_SHARP,\n", + " \n", + " USE_POPIII = USE_POPIII, \n", + " USE_LW_FEEDBACK = USE_LW_FEEDBACK,\n", + "\n", + " alphastar_III = alphastar_III, \n", + " betastar_III = betastar_III,\n", + " epsstar_III = epsstar_III,\n", + " Mc_III = Mc_III,\n", + " dlog10epsstardz_III = dlog10epsstardz_III,\n", + "\n", + " fesc7_III = fesc7_III,\n", + " alphaesc_III = alphaesc_III,\n", + " L40_xray_III = L40_xray_III,\n", + " alpha_xray_III = alpha_xray_III,\n", + " \n", + " A_LW = A_LW,\n", + " beta_LW = beta_LW,\n", + " \n", + " A_vcb = A_vcb,\n", + " beta_vcb = beta_vcb)\n", + "\n", + "CoeffStructure = zeus21.get_T21_coefficients(UserParams=UserParams, CosmoParams=CosmoParams ,AstroParams=AstroParams, HMFinterp=HMFintclass)\n", + "SFRD_class = zeus21.sfrd.SFRD_class(UserParams, CosmoParams, AstroParams, HMFintclass)\n", + "zlist= CoeffStructure.zintegral\n", + "print('SFRD and coefficients stored. Move ahead.')\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "fa72898f", + "metadata": {}, + "source": [ + "## Step 3: Plotting globally-averaged quantities" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "507bde44", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize = (12, 6.75))\n", + "\n", + "plt.plot(zlist, CoeffStructure.T21avg, color = \"#E64D4E\", linewidth = 3)\n", + "\n", + "plt.xlim([10, 35])\n", + "plt.ylim([-120, 30])\n", + "\n", + "plt.xlabel(r'$z$', fontsize = 30)\n", + "plt.ylabel(r'$T_{21}$ [mK]', fontsize = 30)\n", + "plt.xticks(fontsize=30)\n", + "plt.yticks(fontsize=30)\n", + "plt.tick_params(which='major', length=12, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", + "plt.tick_params(which='minor', length=5, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", + "plt.tick_params(axis=\"y\", labelsize=30, pad = 10)\n", + "plt.tick_params(axis=\"x\", labelsize=30, pad = 10)\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a57e7ae", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize = (12, 6.75))\n", + "\n", + "plt.plot(zlist,CoeffStructure.T_CMB , color = \"#EE9063\", linewidth = 3, linestyle = 'dotted', label = r'$T_\\mathrm{CMB}$')\n", + "\n", + "\n", + "plt.plot(zlist,CoeffStructure.Tk_ad , color = \"#0B92B1\", linewidth = 3, label = r'$T_\\mathrm{ad}$')\n", + "plt.plot(zlist,CoeffStructure.Tk_xray , color = \"#E64D4E\", linewidth = 3, label = r'$T_x$')\n", + "plt.plot(zlist,CoeffStructure.Tk_avg, color = \"#665191\", linewidth = 3, label = r'$T_k$')\n", + "\n", + "plt.plot(zlist,CoeffStructure._invTs_avg**-1 , color = \"#77AC54\", linewidth = 3, label = r'$T_s$')\n", + "\n", + "\n", + "plt.xlim([10, 35])\n", + "plt.ylim([0, 100])\n", + "\n", + "plt.xlabel(r'$z$', fontsize = 30)\n", + "plt.ylabel(r'$T$ [K]', fontsize = 30)\n", + "plt.xticks(fontsize=30)\n", + "plt.yticks(fontsize=30)\n", + "plt.tick_params(which='major', length=12, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", + "plt.tick_params(which='minor', length=5, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", + "plt.tick_params(axis=\"y\", labelsize=30, pad = 10)\n", + "plt.tick_params(axis=\"x\", labelsize=30, pad = 10)\n", + "plt.legend(fontsize=20, frameon = False)\n", + "plt.tight_layout()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6815b8c5", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize = (12, 6.75))\n", + "\n", + "plt.semilogy(CoeffStructure.zintegral, CoeffStructure.SFRD_II_avg, color=\"#E64D4E\", linewidth=3.0, label = 'Pop II')\n", + "plt.semilogy(CoeffStructure.zintegral, CoeffStructure.SFRD_III_avg, color=\"#0B92B1\", linewidth=3.0, label = 'Pop III')\n", + "\n", + "plt.semilogy(CoeffStructure.zintegral, CoeffStructure.SFRD_II_avg + CoeffStructure.SFRD_III_avg, color=\"#665191\", linewidth=3.0, label = 'Pop II + III')\n", + "\n", + "plt.xlim([10, 35])\n", + "plt.ylim(1e-7, 1e-1)\n", + "\n", + "plt.xlabel(r'$z$', fontsize = 30)\n", + "plt.ylabel(r'$\\dot{\\overline{\\rho}}_*(z)$', fontsize = 30)\n", + "\n", + "plt.xticks(fontsize=30)\n", + "plt.yticks(fontsize=30)\n", + "plt.tick_params(which='major', length=12, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", + "plt.tick_params(which='minor', length=5, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", + "plt.tick_params(axis=\"y\", labelsize=30, pad = 10)\n", + "plt.tick_params(axis=\"x\", labelsize=30, pad = 10)\n", + "plt.legend(fontsize=20, frameon = False)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1a55a1e", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize = (12, 6.75))\n", + "\n", + "plt.semilogy(CoeffStructure.zintegral, CoeffStructure.J_21_LW_II, color=\"#E64D4E\", linewidth=3.0, label = 'Pop II')\n", + "plt.semilogy(CoeffStructure.zintegral, CoeffStructure.J_21_LW_III, color=\"#0B92B1\", linewidth=3.0, label = 'Pop III')\n", + "\n", + "plt.semilogy(CoeffStructure.zintegral, CoeffStructure.J_21_LW_II + CoeffStructure.J_21_LW_III, color=\"#665191\", linewidth=3.0, label = 'Pop II + III')\n", + "\n", + "plt.xlim([10, 35])\n", + "plt.ylim(1e-3, 1e2)\n", + "\n", + "plt.xlabel(r'$z$', fontsize = 30)\n", + "plt.ylabel(r'$J_{21}$', fontsize = 30)\n", + "\n", + "plt.xticks(fontsize=30)\n", + "plt.yticks(fontsize=30)\n", + "plt.tick_params(which='major', length=12, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", + "plt.tick_params(which='minor', length=5, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", + "plt.tick_params(axis=\"y\", labelsize=30, pad = 10)\n", + "plt.tick_params(axis=\"x\", labelsize=30, pad = 10)\n", + "plt.legend(fontsize=20, frameon = False)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80eb8f5b", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize = (12, 6.75))\n", + "\n", + "plt.semilogy(CoeffStructure.zintegral, CoeffStructure.xa_avg, color=\"#665191\", linewidth=3.0, label = 'Pop II + III')\n", + "\n", + "plt.xlim([10, 35])\n", + "plt.ylim(1e-3, 1e2)\n", + "\n", + "plt.xlabel(r'$z$', fontsize = 30)\n", + "plt.ylabel(r'$x_\\alpha$', fontsize = 30)\n", + "\n", + "plt.xticks(fontsize=30)\n", + "plt.yticks(fontsize=30)\n", + "plt.tick_params(which='major', length=12, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", + "plt.tick_params(which='minor', length=5, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", + "plt.tick_params(axis=\"y\", labelsize=30, pad = 10)\n", + "plt.tick_params(axis=\"x\", labelsize=30, pad = 10)\n", + "plt.legend(fontsize=20, frameon = False)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a85d3ff1", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize = (12, 6.75))\n", + "\n", + "plt.semilogy(CoeffStructure.zintegral,CoeffStructure.xe_avg, color=\"#E64D4E\", linewidth=3.0)\n", + "\n", + "plt.xlim([10, 35])\n", + "plt.ylim([1e-4, 1e-3])\n", + "\n", + "plt.xlabel(r'$z$', fontsize = 30)\n", + "plt.ylabel(r'$x_e$', fontsize = 30)\n", + "plt.xticks(fontsize=30)\n", + "plt.yticks(fontsize=30)\n", + "plt.tick_params(which='major', length=12, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", + "plt.tick_params(which='minor', length=5, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", + "plt.tick_params(axis=\"y\", labelsize=30, pad = 10)\n", + "plt.tick_params(axis=\"x\", labelsize=30, pad = 10)\n", + "# plt.legend(fontsize=20, frameon = False)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "77eb7769", + "metadata": {}, + "source": [ + "## Step 4: Fluctuations" + ] + }, + { + "cell_type": "markdown", + "id": "36a3cd23", + "metadata": {}, + "source": [ + "The last structure to run is Power_Spectra, which uses our lognormal and log-chisquare prescription to compute the statistics of IGM fluctuations in the form of two-point functions.\n", + "\n", + "Note that the new version of Zeus includes a modified prescription for computing power spectra. The form for the 21-cm brightness temperature used in the old version of Zeus is\n", + "\n", + "\\begin{equation}\n", + "T_{21}=T_0(z)\\left(1+\\delta-\\delta_v\\right) x_{\\mathrm{HI}}\\left(\\frac{x_\\alpha}{1+x_\\alpha}\\right)\\left(1-\\frac{T_{\\mathrm{CMB}}}{T_c}\\right)\n", + "\\end{equation}\n", + "\n", + "where the leftmost $\\delta$ term is the total matter overdensity. Because $T_{21}$ tracks neutral hydrogen fluctuations, we make the substitution $\\delta \\rightarrow \\delta_b$. Thus, the large-scale structure (LSS) terms that fold in to computing $\\Delta^2_{21}(k,z)$ now depend on the baryon power spectrum $P_\\mathrm{b}(k,z)$ and the baryon-dark matter cross power spectrum $P_\\mathrm{b,cdm}(k,z)$.\n", + "\n", + "This new feature can be toggled with UserParams.USE_BARYON_FLAG = 1 for the new $\\delta_b$ prescription or UserParams.USE_BARYON_FLAG = 0 for the old $\\delta$ form." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f80f6e74", + "metadata": {}, + "outputs": [], + "source": [ + "RSDMODE = 1\n", + "\n", + "UserParams.USE_BARYON_FLAG = 1\n", + "PS21 = zeus21.Power_Spectra(UserParams, CosmoParams, AstroParams, CoeffStructure, RSD_MODE = RSDMODE)\n", + "klist = PS21.klist_PS" + ] + }, + { + "cell_type": "markdown", + "id": "ff154eb4", + "metadata": {}, + "source": [ + "## Plotting Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62acc2ca", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.interpolate import RegularGridInterpolator\n", + "\n", + "interp = RegularGridInterpolator((zlist, klist), PS21.Deltasq_T21, method = 'cubic', bounds_error=False, fill_value=0)\n", + "interpLin = RegularGridInterpolator((zlist, klist), PS21.Deltasq_T21_lin, method = 'cubic', bounds_error=False, fill_value=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a03d011", + "metadata": {}, + "outputs": [], + "source": [ + "#choose a z to plot\n", + "kchoose=0.3 \n", + "zlistHighRes = np.geomspace(zlist[0], zlist[-1], 1000)\n", + "\n", + "powerSpectrum = interp((zlistHighRes, kchoose))\n", + "powerSpectrumLin = interpLin((zlistHighRes, kchoose))\n", + "\n", + "plt.figure(figsize = (12, 6.75))\n", + "\n", + "plt.semilogy(zlistHighRes, powerSpectrum, color=\"#E64D4E\", linewidth=3.0, label = 'Nonlinear')\n", + "plt.semilogy(zlistHighRes, powerSpectrumLin, color=\"#0B92B1\", linewidth=3.0, linestyle = 'dashed', label = 'Linear')\n", + "\n", + "plt.xlim([10, 25])\n", + "plt.ylim([1,200])\n", + "\n", + "plt.xlabel(r'$z$', fontsize = 30)\n", + "plt.ylabel(r'$\\Delta^2_{21}\\,\\rm[mK^2]$', fontsize = 30)\n", + "\n", + "plt.xticks(fontsize=30)\n", + "plt.yticks(fontsize=30)\n", + "plt.tick_params(which='major', length=12, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", + "plt.tick_params(which='minor', length=5, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", + "plt.tick_params(axis=\"y\", labelsize=30, pad = 10)\n", + "plt.tick_params(axis=\"x\", labelsize=30, pad = 10)\n", + "plt.legend(fontsize=20, frameon = False)\n", + "plt.tight_layout()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "730f80cd", + "metadata": {}, + "outputs": [], + "source": [ + "#choose a z to plot\n", + "zchoose=16\n", + "klistHighRes = np.geomspace(1e-4, 1e1, 1000)\n", + "\n", + "powerSpectrum = interp((zchoose, klistHighRes))\n", + "powerSpectrumLin = interpLin((zchoose, klistHighRes))\n", + "\n", + "plt.figure(figsize = (12, 6.75))\n", + "\n", + "plt.loglog(klistHighRes, powerSpectrum, color=\"#E64D4E\", linewidth=3.0, label = 'Nonlinear')\n", + "plt.loglog(klistHighRes, powerSpectrumLin, color=\"#0B92B1\", linewidth=3.0, linestyle = 'dashed', label = 'Linear')\n", + "\n", + "plt.xlim([1e-3,1e0])\n", + "plt.ylim([1e-1,1e3])\n", + "\n", + "plt.xlabel(r'$k\\,\\rm [Mpc^{-1}]$', fontsize = 30)\n", + "plt.ylabel(r'$\\Delta^2_{21}\\,\\rm[mK^2]$', fontsize = 30)\n", + "plt.xticks(fontsize=30)\n", + "plt.yticks(fontsize=30)\n", + "plt.tick_params(which='major', length=12, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", + "plt.tick_params(which='minor', length=5, width=2, direction='in', top = True, bottom = True, left = True, right = True)\n", + "plt.tick_params(axis=\"y\", labelsize=30, pad = 10)\n", + "plt.tick_params(axis=\"x\", labelsize=30, pad = 10)\n", + "plt.legend(fontsize=20, frameon = False)\n", + "plt.tight_layout()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4c7cc3ff", + "metadata": {}, + "source": [ + "For questions, don't hesitate to reach out to hcruz2@jhu.edu!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c58d071", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "21zeus_hack", + "language": "python", + "name": "21zeus_hack" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/Tutorial_Zeus21_UVLFs.ipynb b/docs/tutorials/Tutorial_Zeus21_UVLFs.ipynb new file mode 100644 index 0000000..4237e6f --- /dev/null +++ b/docs/tutorials/Tutorial_Zeus21_UVLFs.ipynb @@ -0,0 +1,546 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tutorial will cover how to generate UVLFs from Zeus21. It is recommended to do the basics tutorial first, to get a handle on how the code works. That said, this can stand on its own, so let's go!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import zeus21\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import copy\n", + "\n", + "# Set up the user parameters\n", + "UserParams = zeus21.User_Parameters(precisionboost=1.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first set up the cosmology and astrophysics. \n", + "\n", + "We do not need to go as far as the 21-cm calculation when doing UVLFs. We do not need the Coeff or PS21 structures (since we won't compute 21-cm in this tutorial), nor do we need the correlation functions. We do need the HMFs, though.\n", + "\n", + "The astrophysical parameters are shared between the UVLF and the 21-cm calculation, so one can jointly do inference. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the Zeus21 cosmology\n", + "CosmoParams = zeus21.Cosmo_Parameters(UserParams=UserParams, HMF_CHOICE=\"ST\", zmin_CLASS=0.0) # CLASS is automatically run by the Cosmo_Parameter class; make sure to provide zmin_CLASS lower than default (5.0) if you want lower z results (e.g. HMFs)\n", + "HMFinterp = zeus21.HMF_interpolator(UserParams, CosmoParams) # HMF at all desired z and M; here HMF from Sheth&Tormen2002 (https://arxiv.org/abs/astro-ph/0105113)\n", + "\n", + "# Set up the astrophysical parameters\n", + "AstroParams = zeus21.Astro_Parameters(CosmoParams=CosmoParams, accretion_model=\"exp\") # Here exponential accretion model, as in https://arxiv.org/abs/2306.09403" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have to define the LF parameters. You can run with the default set, but let's spice things up a bit. Let's define two:\n", + "- one with a standard halo-galaxy connection;\n", + "- one with a very stochastic halo-galaxy connection, but lower UV brightness per galaxy (smaller f* at peak).\n", + "\n", + "We also have to define the redshift and magnitudes over which to plot. You give an arbitrary \\ and width dz, and the code will assume the selection function is a Gaussian around it. For magnitudes, you provide centers and widths of bins, assumed to be tophats. It will then generate the UVLF under those specs. Make sure the binning of z and MUV are the same as whatever data you're considering!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "z, dz = 5.0, 0.5 # Central z and width (assumed Gaussian)\n", + "\n", + "MUV_centers = np.linspace(-17,-23,13) # Centers of MUV bins\n", + "MUV_widths = -np.diff(MUV_centers)\n", + "MUV_widths = np.append(MUV_widths, MUV_widths[-1])\n", + "\n", + "# Set up the LF parameters\n", + "LFParams_std = zeus21.LF_Parameters(zcenter=z, zwidth=dz, MUVcenters=MUV_centers, MUVwidths=MUV_widths)\n", + "LFParams_bursty = zeus21.LF_Parameters(zcenter=z, zwidth=dz, MUVcenters=MUV_centers, MUVwidths=MUV_widths, sigmaUV=0.9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we can now compute and plot the UVLFs with our adopted parameters. If you're new to the world of the UVLFs, note that the x axis is a UV absolute magnitude, and y is the comoving number density of galaxies at that magnitude (divided by the width of the bin)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/alessandra/SynologyDrive/numerics/zeus21/zeus21_v2_20260630_UVLF-tutorial/zeus21/LFs.py:473: RuntimeWarning: divide by zero encountered in log10\n", + " return constants.zeropoint_ABmag_ergsHz - 2.5 * np.log10(L) # AB magnitude\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "UVLF_std = zeus21.LFs.LF_class(UserParams, CosmoParams, AstroParams, HMFinterp, LFParams_std).UVLF_tot # AV: using aliases for the new output types defined in LFs, here and in the following\n", + "UVLF_bursty = zeus21.LFs.LF_class(UserParams, CosmoParams, AstroParams, HMFinterp, LFParams_bursty).UVLF_tot\n", + "\n", + "plt.semilogy(MUV_centers, UVLF_std, 'k-', label=\"Std.\")\n", + "plt.semilogy(MUV_centers, UVLF_bursty,'r--', label=\"Bursty\")\n", + "plt.xlim(-22,-17)\n", + "plt.ylim(1e-6,1e-1)\n", + "plt.xlabel(r'$M_{\\rm UV}$');\n", + "plt.ylabel(r'$\\Phi_{\\rm UV}\\,\\rm [Mpc^{-3}\\,mag^{-1}]$')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's play around with parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# First, let's change the astrophysical parameters alpha and beta too to see what they do\n", + "AstroParams_lowalpha = copy.deepcopy(AstroParams)\n", + "AstroParams_lowalpha.alphastar -= 0.2\n", + "UVLF_lowalpha = zeus21.LFs.LF_class(UserParams, CosmoParams, AstroParams_lowalpha, HMFinterp, LFParams_std).UVLF_tot\n", + "\n", + "AstroParams_lowbeta = copy.deepcopy(AstroParams)\n", + "AstroParams_lowbeta.betastar -= 0.2\n", + "UVLF_lowbeta = zeus21.LFs.LF_class(UserParams, CosmoParams, AstroParams_lowbeta, HMFinterp, LFParams_std).UVLF_tot\n", + "\n", + "# And the dust assumptions in the LF parameters -- they can be degenerate! (Beware, these are measured independently, see Refs.)\n", + "LFParams_lessdust = copy.deepcopy(LFParams_std)\n", + "LFParams_lessdust.C0dust -= 0.1\n", + "UVLF_lessdust = zeus21.LFs.LF_class(UserParams, CosmoParams, AstroParams, HMFinterp, LFParams_lessdust).UVLF_tot\n", + "LFParams_nodust = copy.deepcopy(LFParams_std) # Added by AV for one-to-one comparison previous tutorial: note that small discrepancy with respect to previous notebook in both UVLF and bias at the bright end, likely due to the different dust loop, while results are recovered with no dust --> TODO: discuss changes with others and whether we should give a warning of some sort\n", + "LFParams_nodust.DUST_FLAG = False\n", + "UVLF_nodust = zeus21.LFs.LF_class(UserParams, CosmoParams, AstroParams, HMFinterp, LFParams_nodust).UVLF_tot\n", + "\n", + "plt.semilogy(MUV_centers, UVLF_std, 'k-', label=\"Std.\")\n", + "plt.semilogy(MUV_centers, UVLF_lowalpha,'b--', label=\"Low alpha\")\n", + "plt.semilogy(MUV_centers, UVLF_lowbeta,'g--', label=\"Low beta\")\n", + "plt.semilogy(MUV_centers, UVLF_lessdust,'r-.', label=\"Less dust\")\n", + "plt.semilogy(MUV_centers, UVLF_nodust,'m-.', label=\"No dust\")\n", + "plt.xlim(-22,-17)\n", + "plt.ylim(1e-6,1e-1)\n", + "plt.xlabel(r'$M_{\\rm UV}$');\n", + "plt.ylabel(r'$\\Phi_{\\rm UV}\\,\\rm [Mpc^{-3}\\,mag^{-1}]$')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Zeus allows to compute the bias as a function of MUV as well. Let's do that and plot it!" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "LFParams_std_bias = copy.deepcopy(LFParams_std)\n", + "LFParams_std_bias.RETURNBIAS = True\n", + "bias_UVLF_std = zeus21.LFs.LF_class(UserParams, CosmoParams, AstroParams, HMFinterp, LFParams_std_bias).UVbias_tot # AV: now we can directly compute the normalized bias!\n", + "\n", + "LFParams_bursty_bias = copy.deepcopy(LFParams_bursty)\n", + "LFParams_bursty_bias.RETURNBIAS = True\n", + "bias_UVLF_bursty = zeus21.LFs.LF_class(UserParams, CosmoParams, AstroParams, HMFinterp, LFParams_bursty_bias).UVbias_tot\n", + "\n", + "#LFParams_nodust_bias = copy.deepcopy(LFParams_nodust) # TODO: remove this part, used for debugging purposes, in order to compare with previous notebook given the different dust loop\n", + "#LFParams_nodust_bias.RETURNBIAS = True\n", + "#bias_UVLF_nodust = zeus21.LFs.LF_class(UserParams, CosmoParams, AstroParams, HMFinterp, LFParams_nodust_bias).UVbias_tot\n", + "\n", + "plt.plot(MUV_centers, bias_UVLF_std, 'k-', label=\"Std.\")\n", + "#plt.plot(MUV_centers, bias_UVLF_nodust, 'k-.', label=\"No dust\")\n", + "plt.plot(MUV_centers, bias_UVLF_bursty, 'r--', label=\"Bursty\")\n", + "plt.xlim(-22, -17)\n", + "plt.ylim(0, 10)\n", + "plt.xlabel(r'$M_{\\rm UV}$')\n", + "plt.ylabel(r'bias, $b$')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note a possible issue: for no/low stochasticity (sigmaUV~0) the UVLF becomes noisy because there are not enough halos in each MUV bin. Can be fixed change precisionboost in constants.py - as we'll do in the next part for Pop IIIs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's add Pop III stars. Note that Pop IIIs will normally be very faint, so you'll need to go to fainter MUVs to see their effect. We also need higher precisionboost for higher accuracy at these faint MUVs, so we need to recompute the cosmology." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "UserParams_III = zeus21.User_Parameters(precisionboost=2.2) # For accurate results down to MUV~-6\n", + "\n", + "CosmoParams_III = zeus21.Cosmo_Parameters(UserParams=UserParams_III, HMF_CHOICE=\"ST\")\n", + "HMFinterp_III = zeus21.HMF_interpolator(UserParams_III, CosmoParams_III)\n", + "\n", + "AstroParams_III = zeus21.Astro_Parameters(CosmoParams=CosmoParams_III, accretion_model=\"exp\", USE_POPIII=True) # USE_POPIII = True in the astrophysical parameters to add Pop IIIs to the SFR calculation\n", + "\n", + "z_III, dz_III = 15.0, 0.5 # We pick a higher redshift to better see the effects at the low-mass end\n", + "\n", + "MUV_centers_III = np.linspace(-5,-23,100) # We extend to fainter MUVs and increase the number of MUV points\n", + "MUV_widths_III = -np.diff(MUV_centers_III)\n", + "MUV_widths_III = np.append(MUV_widths_III, MUV_widths_III[-1])\n", + "\n", + "LFParams_III = zeus21.LF_Parameters(zcenter=z_III, zwidth=dz_III, MUVcenters=MUV_centers_III, MUVwidths=MUV_widths_III, SKIP_POPIII=False) # SKIP_POPIII = False is needed to add the Pop III UVLF calculation - note that this only works if USE_POPIII = True in the astrophysical parameters!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/alessandra/SynologyDrive/numerics/zeus21/zeus21_v2_20260630_UVLF-tutorial/zeus21/LFs.py:473: RuntimeWarning: divide by zero encountered in log10\n", + " return constants.zeropoint_ABmag_ergsHz - 2.5 * np.log10(L) # AB magnitude\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Now we can compute both the Pop III and Pop II components to the UVLF and look at the two contributions separately\n", + "LFs = zeus21.LFs.LF_class(UserParams_III, CosmoParams_III, AstroParams_III, HMFinterp_III, LFParams_III)\n", + "UVLF_tot = LFs.UVLF_tot\n", + "UVLF_pop2 = LFs.UVLF_pop2\n", + "UVLF_pop3 = LFs.UVLF_pop3\n", + "\n", + "# We first plot the SFR per unit halo mass for both components to show what's going into the UVLFs\n", + "SFRD = zeus21.sfrd.SFRD_class(UserParams_III, CosmoParams_III, AstroParams_III, HMFinterp_III)\n", + "SFE_pop2 = SFRD.SFE(CosmoParams_III, AstroParams_III, HMFinterp_III.Mhtab, z_III, pop=2)\n", + "SFE_pop3 = SFRD.SFE(CosmoParams_III, AstroParams_III, HMFinterp_III.Mhtab, z_III, pop=3, vCB=False, J21LW_interp=SFRD.J21LW_interp_conv_avg) # Note that here we need to provide vCB and LW explicitly for this to work, as by default these are put to None, so it doesn't work --> TODO: discuss default init of vcb and J21LW in SFRD for simmetry; also at what z is the conv. avg. computed?\n", + "plt.figure()\n", + "plt.loglog(HMFinterp_III.Mhtab, SFE_pop2, 'g--', label=\"Pop II\")\n", + "plt.loglog(HMFinterp_III.Mhtab, SFE_pop3, 'r-', label=\"Pop III\") # TODO: check slightly higher Pop III SFE than in default by Cruz+25 fig. 2? (this should account correctly for the factor 2 in normalization after replacing the old fstar_III with epsstar_III = fstar_III/2 for symmetry with Pop IIs, perhaps it's something in the LW that is different?)\n", + "plt.xlim(1e5, 1e13)\n", + "plt.ylim(1e-5, 1e-1)\n", + "plt.xlabel(r\"$M_{\\rm h} \\; [\\mathrm{M_\\odot}]$\")\n", + "plt.ylabel(r\"$\\dot{M}_\\star / \\dot{M}_\\mathrm{h}$\")\n", + "plt.legend()\n", + "\n", + "# And then the resulting UVLFs\n", + "plt.figure()\n", + "plt.semilogy(MUV_centers_III, UVLF_pop2, 'g--', label=\"Pop II\")\n", + "plt.semilogy(MUV_centers_III, UVLF_pop3, 'r-', label=\"Pop III\")\n", + "plt.semilogy(MUV_centers_III, UVLF_tot, 'k:', label=\"Tot.\")\n", + "plt.xlim(-22, -6)\n", + "plt.ylim(1e-6, 1e2)\n", + "plt.xlabel(r'$M_{\\rm UV}$')\n", + "plt.ylabel(r'$\\Phi_{\\rm UV}\\,\\rm [Mpc^{-3}\\,mag^{-1}]$')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we play around with the physics affecting Pop III star formation at the low-mass end: LW feedback and streaming velocities (see e.g. https://arxiv.org/abs/2407.18294)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Case with no LW feedback\n", + "SFE_pop3_noLW = SFRD.SFE(CosmoParams_III, AstroParams_III, HMFinterp_III.Mhtab, z_III, pop=3, J21LW_interp=False, vCB=False)\n", + "UVLF_pop3_noLW = zeus21.LFs.LF_class(UserParams_III, CosmoParams_III, AstroParams_III, HMFinterp_III, LFParams_III, J21LW_interp=False).UVLF_pop3\n", + "\n", + "# Case including feedback from streaming velocities - here we need to redefine the cosmology to include streaming velocities from the start - TODO: discuss possible source of confusion and mistakes, because USE_RELATIVE_VELOCITIES=False by default, but most of the Pop III methods are designed to include streaming velocities by default instead, which may be confusing because the user may expect them to be active, while in reality vcb_avg=0 and the feedback is not taken into account by default; in general, there's a bit of confusion between the defaults, also with the SFRDs (see e.g. previous code block)\n", + "CosmoParams_III_vcb = zeus21.Cosmo_Parameters(UserParams=UserParams_III, HMF_CHOICE=\"ST\", USE_RELATIVE_VELOCITIES=True)\n", + "HMFinterp_III_vcb = zeus21.HMF_interpolator(UserParams_III, CosmoParams_III_vcb)\n", + "AstroParams_III_vcb = zeus21.Astro_Parameters(CosmoParams=CosmoParams_III_vcb, accretion_model=\"exp\", USE_POPIII=True)\n", + "SFRD_vcb = zeus21.sfrd.SFRD_class(UserParams_III, CosmoParams_III_vcb, AstroParams_III_vcb, HMFinterp_III_vcb)\n", + "SFE_pop3_vcb = SFRD_vcb.SFE(CosmoParams_III_vcb, AstroParams_III_vcb, HMFinterp_III_vcb.Mhtab, z_III, pop=3, J21LW_interp=SFRD.J21LW_interp_conv_avg, vCB=CosmoParams_III_vcb.vcb_avg)\n", + "UVLF_pop3_vcb = zeus21.LFs.LF_class(UserParams_III, CosmoParams_III_vcb, AstroParams_III_vcb, HMFinterp_III_vcb, LFParams_III).UVLF_pop3\n", + "\n", + "plt.figure()\n", + "plt.loglog(HMFinterp_III.Mhtab, SFE_pop3, 'r-', label=\"Pop III default\")\n", + "plt.loglog(HMFinterp_III.Mhtab, SFE_pop3_noLW, 'b-.', label=\"Pop III w/o LW feedback\")\n", + "plt.loglog(HMFinterp_III.Mhtab, SFE_pop3_vcb, 'k:', label=\"Pop III + vCB feedback\")\n", + "plt.xlim(1e5, 1e10)\n", + "plt.ylim(1e-5, 1e-3)\n", + "plt.xlabel(r\"$M_{\\rm h} \\; [\\mathrm{M_\\odot}]$\")\n", + "plt.ylabel(r\"$\\dot{M}_\\star / \\dot{M}_\\mathrm{h}$\")\n", + "plt.legend()\n", + "\n", + "plt.figure()\n", + "plt.semilogy(MUV_centers_III, UVLF_pop3, 'r-', label=\"Pop III default\")\n", + "plt.semilogy(MUV_centers_III, UVLF_pop3_noLW, 'b-.', label=\"Pop III w/o LW feedback\")\n", + "plt.semilogy(MUV_centers_III, UVLF_pop3_vcb, 'k:', label=\"Pop III + vCB feedback\")\n", + "plt.xlim(-14, -6)\n", + "plt.ylim(1e-6, 1e2)\n", + "plt.xlabel(r'$M_{\\rm UV}$')\n", + "plt.ylabel(r'$\\Phi_{\\rm UV}\\,\\rm [Mpc^{-3}\\,mag^{-1}]$')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's see what happens if we extend Pop III formation beyond minihalos and into the atomic-cooling halo regime, as in https://arxiv.org/abs/2505.20263." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "AstroParams_III_ACH = copy.deepcopy(AstroParams_III)\n", + "AstroParams_III_ACH.Mup_III = 1e10 # We extend the high-mass cut-off (set to the atomic-cooling mass at current z by default) up to a fixed value of 1e10 Msun\n", + "AstroParams_III_ACH.betastar_III = -0.5 # Let's also change the slope at the high-mass end, for fun\n", + "SFRD_ACH = zeus21.sfrd.SFRD_class(UserParams_III, CosmoParams_III, AstroParams_III_ACH, HMFinterp_III)\n", + "SFE_pop3_ACH = SFRD_ACH.SFE(CosmoParams_III, AstroParams_III_ACH, HMFinterp_III.Mhtab, z_III, pop=3, vCB=False, J21LW_interp=SFRD.J21LW_interp_conv_avg)\n", + "UVLF_pop3_ACH = zeus21.LFs.LF_class(UserParams_III, CosmoParams_III, AstroParams_III_ACH, HMFinterp_III, LFParams_III).UVLF_pop3\n", + "\n", + "plt.figure()\n", + "plt.loglog(HMFinterp_III.Mhtab, SFE_pop3, 'r-', label=\"Pop III default\")\n", + "plt.loglog(HMFinterp_III.Mhtab, SFE_pop3_ACH, 'm:', label=\"Pop III extended\")\n", + "plt.xlim(1e5, 1e10)\n", + "plt.ylim(1e-5, 1e-3)\n", + "plt.xlabel(r\"$M_{\\rm h} \\; [\\mathrm{M_\\odot}]$\")\n", + "plt.ylabel(r\"$\\dot{M}_\\star / \\dot{M}_\\mathrm{h}$\")\n", + "plt.legend()\n", + "\n", + "plt.figure()\n", + "plt.semilogy(MUV_centers_III, UVLF_pop3, 'r-', label=\"Pop III default\")\n", + "plt.semilogy(MUV_centers_III, UVLF_pop3_ACH, 'm:', label=\"Pop III extended\")\n", + "plt.xlim(-14, -6)\n", + "plt.ylim(1e-6, 1e2)\n", + "plt.xlabel(r'$M_{\\rm UV}$')\n", + "plt.ylabel(r'$\\Phi_{\\rm UV}\\,\\rm [Mpc^{-3}\\,mag^{-1}]$')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TODO: Play around also with a detached ACH component? Although this was not published yet, so probably an overkill for now" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TODO: Other block playing with Pop III UV boost (potentially including the IMF-dependence as suggested by Sahil) and burstiness? Although I'd say also probably an overkill and maybe better to add after we use this in some published results" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "zeus21_v2test", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.15" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/requirements.txt b/requirements.txt index bbc242b..c6da339 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,10 +1,9 @@ pytest -numpy>=2.0 -scipy -mcfit -numexpr -astropy -powerbox -pyfftw sphinx -myst_parser \ No newline at end of file +sphinx-rtd-theme +sphinx-autoapi +sphinx-copybutton +myst-parser +nbsphinx +pandoc +ipykernel diff --git a/setup.py b/setup.py index 7a64876..1a74249 100755 --- a/setup.py +++ b/setup.py @@ -1,6 +1,6 @@ #!/usr/bin/env python -from setuptools import setup, find_packages +from setuptools import setup setup( @@ -20,5 +20,8 @@ "classy", "numexpr", "astropy", + "powerbox", + "pyfftw", + "tqdm" ], ) diff --git a/tests/test_UVLFs.py b/tests/test_UVLFs.py index 7c34c47..4458f4f 100644 --- a/tests/test_UVLFs.py +++ b/tests/test_UVLFs.py @@ -5,140 +5,193 @@ Author: Claude AI April 2025 +Edited by Alessandra Venditti +UT Austin - June 2026 """ import pytest import zeus21 import numpy as np -from zeus21.UVLFs import UVLF_binned, MUV_of_SFR, AUV, beta +from zeus21.LFs import LF_class + + +def test_luminosity_to_magnitude_conversions(): + """Test magnitude and luminosity conversions, verifying values and that they are invert of each other""" + + LF = LF_class.__new__(LF_class) + + # Expected Lnu to MUV conversion for a range of Lnu + Lnu_test = np.logspace(25., 30., 3) # erg/s/Hz + sigma_test = 0.5 + expected_Lnu_renorm = Lnu_test / np.exp((np.log(10)/2.5 * sigma_test)**2 / 2.0) + expected_MUV = 51.63 - 2.5 * np.log10(Lnu_test) + expected_MUV_renorm = 51.63 - 2.5 * np.log10(expected_Lnu_renorm) + + # Test from Mag_of_L_ergsHz + MUV = LF.Mag_of_L_ergsHz(Lnu_test) + np.testing.assert_allclose(MUV, expected_MUV, rtol=1e-12) + + # Test inverse function + Lnu_roundtrip = LF.L_ergsHz_of_Mag(MUV) + np.testing.assert_allclose(Lnu_roundtrip, Lnu_test, rtol=1e-12) + + # Test from logorMag_of_L + MUV = LF.logorMag_of_L(Lnu_test, "UV", renormalize_L=False) + MUV_renorm = LF.logorMag_of_L(Lnu_test, "UV", renormalize_L=True, sigma=sigma_test) + np.testing.assert_allclose(MUV, expected_MUV, rtol=1e-12) + np.testing.assert_allclose(MUV_renorm, expected_MUV_renorm, rtol=1e-12) -def test_MUV_of_SFR(): - """Test the conversion from SFR to UV magnitudes""" - # Test a range of SFR values - SFR_test = np.logspace(-3, 2, 10) # M_sun/yr - kappaUV_test = 1.15e-28 # Typical value - - # Calculate MUV - MUV_result = MUV_of_SFR(SFR_test, kappaUV_test) - - # Check that increasing SFR leads to brighter (more negative) MUV - assert np.all(np.diff(MUV_result) < 0) - - # Check specific value based on the formula M_UV = 51.63 - 2.5*log10(SFR/kappaUV) - # For SFR = 1 M_sun/yr with kappaUV = 1.15e-28 - expected_MUV = 51.63 - 2.5 * np.log10(1.0/1.15e-28) - assert MUV_of_SFR(np.array([1.0]), kappaUV_test)[0] == pytest.approx(expected_MUV) - - # Test different kappaUV values - kappaUV_test2 = 2.0e-28 - MUV_result2 = MUV_of_SFR(SFR_test, kappaUV_test2) - - # Higher kappaUV should result in fainter magnitudes (more positive) - assert np.all(MUV_result2 > MUV_result) -def test_beta_function(): + # Expected nuLnu to MUV conversion for a range of nuLnu + nuLnu_test = np.logspace(40., 45., 3) # erg/s + wavelength_test = 1500. # A + expected_MUV = 51.63 - 2.5 * np.log10(nuLnu_test / (299792.458 / (wavelength_test/1e13)) ) + + # Test from Mag_of_L_ergs + MUV = LF.Mag_of_L_ergs(nuLnu_test, wavelength=wavelength_test) + np.testing.assert_allclose(MUV, expected_MUV, rtol=1e-12) + + # Test inverse function + nuLnu_roundtrip = LF.L_ergs_of_Mag(MUV, wavelength=wavelength_test) + np.testing.assert_allclose(nuLnu_roundtrip, nuLnu_test, rtol=1e-12) + + +def test_betaUV_dust(): """Test the beta (UV slope) calculation""" + + LF = LF_class.__new__(LF_class) + LFParams = zeus21.LF_Parameters() + # Test a single redshift and magnitude but use arrays as the function expects z_test = np.array([5.0]) MUV_test = np.array([-20.0]) - + # Calculate beta value - beta_value = beta(z_test, MUV_test) - + beta_value = LF.betaUV_dust(LFParams, z_test, MUV_test) + # Check that beta value is reasonable (typical range is -3 to -1) - assert beta_value > -3.0 - assert beta_value < -1.0 - + assert np.all(beta_value > -3.0) + assert np.all(beta_value < -1.0) + # Test at pivot point - MUV_pivot = np.array([-19.5]) # The pivot point defined in the code - beta_at_pivot = beta(z_test, MUV_pivot) - + MUV_pivot = np.array([-19.5]) + beta_at_pivot = LF.betaUV_dust(LFParams, z_test, MUV_pivot) + # Check that a value is returned assert isinstance(beta_at_pivot, np.ndarray) -def test_AUV_function(): + +def test_dust_attenuation(): """Test the dust attenuation calculation""" - # Set up parameters - UserParams = zeus21.User_Parameters() - CosmoParams_input = zeus21.Cosmo_Parameters_Input() - ClassyCosmo = zeus21.runclass(CosmoParams_input) - CosmoParams = zeus21.Cosmo_Parameters(UserParams, CosmoParams_input, ClassyCosmo) - AstroParams = zeus21.Astro_Parameters(UserParams, CosmoParams) + + LF = LF_class.__new__(LF_class) + LFParams = zeus21.LF_Parameters() + # Test with arrays as the function expects z_test = np.array([5.0]) MUV_test = np.array([-20.0]) # Calculate dust attenuation - A_UV = AUV(AstroParams, z_test, MUV_test) + A_UV = LF.dust_attenuation(LFParams, z_test, MUV_test, "UV") # Check that attenuation is non-negative assert np.all(A_UV >= 0.0) + # Test the HIGH_Z_DUST flag behavior - z_high = np.array([9.0]) # High redshift above _zmaxdata - _zmaxdata = 8.0 + z_high = np.array([9.0, 10.0, 12.0]) + MUV_test = np.array([-22.0, -20.0, -18.0]) # High redshift above _zmaxdata # Test with HIGH_Z_DUST=True (dust applied at high z) - A_UV_high = AUV(AstroParams, z_high, MUV_test, HIGH_Z_DUST=True) + LFParams.HIGH_Z_DUST = True + A_UV_highz = LF.dust_attenuation(LFParams, z_high, MUV_test, "UV") + + # HIGH_Z_DUST=True should some attenuation for z > _zmaxdata + assert np.any(A_UV_highz > 0.0) + # Test with HIGH_Z_DUST=False (no dust above _zmaxdata) - A_UV_no_highz = AUV(AstroParams, z_high, MUV_test, HIGH_Z_DUST=False, _zmaxdata=_zmaxdata) + LFParams.HIGH_Z_DUST = False + A_UV_no_highz = LF.dust_attenuation(LFParams, z_high, MUV_test, "UV") # HIGH_Z_DUST=False should give zero attenuation for z > _zmaxdata assert np.all(A_UV_no_highz == 0.0) -def test_UVLF_binned(): + +def test_compute_LFbias_binned_from_SFRlist(): """Test the binned UV luminosity function calculation""" + # Set up parameters UserParams = zeus21.User_Parameters() - CosmoParams_input = zeus21.Cosmo_Parameters_Input(kmax_CLASS=10., zmax_CLASS=20.) - ClassyCosmo = zeus21.runclass(CosmoParams_input) - CosmoParams = zeus21.Cosmo_Parameters(UserParams, CosmoParams_input, ClassyCosmo) - AstroParams = zeus21.Astro_Parameters(UserParams, CosmoParams) - HMFintclass = zeus21.HMF_interpolator(UserParams, CosmoParams, ClassyCosmo) - - # Test data - z_center = 6.0 - z_width = 0.5 - MUV_centers = np.array([-22.0, -20.0, -18.0]) - MUV_widths = np.full_like(MUV_centers, 1.0) + CosmoParams = zeus21.Cosmo_Parameters(UserParams=UserParams, kmax_CLASS=100., zmax_CLASS=20.) + HMFintclass = zeus21.HMF_interpolator(UserParams, CosmoParams) + AstroParams = zeus21.Astro_Parameters(CosmoParams=CosmoParams, USE_POPIII=False, FLAG_USE_PSD=False) + + LFParams = zeus21.LF_Parameters(FLAG_COMPUTE_UVLF=False, FLAG_COMPUTE_HaLF=False, + RETURNBIAS=True,) + LF = LF_class(UserParams, CosmoParams, AstroParams, HMFintclass, LFParams) + + + # Test a range of SFR values + SFR_test = np.logspace(-3, 2, HMFintclass.Mhtab.size) + kappaUV_test = 1.15e-28 # Typical value + + # Test LF parameters + zcenter_test = 6.0 + zwidth_test = 0.5 + MUVcenters_test = np.array([-22.0, -20.0, -18.0]) + MUVwidths_test = np.full_like(MUVcenters_test, 1.0) + DUST_FLAG = True + sigmaUV_test = 0.5 + # Calculate UVLF - uvlf = UVLF_binned(AstroParams, CosmoParams, HMFintclass, z_center, z_width, - MUV_centers, MUV_widths, DUST_FLAG=True, RETURNBIAS=False) + UVLF = LF.compute_LFbias_binned_from_SFRlist(SFR_test, HMFintclass, LFParams, + zcenter_test, zwidth_test, MUVcenters_test, MUVwidths_test, + kappaUV_test, sigmaUV_test, renormalize_L=True, + which_band="UV", include_dust=DUST_FLAG, + computeLF=True, computeBias=False).LF # Check dimensions - assert uvlf.shape == (3,) + assert UVLF.shape == (3,) # Check that values are positive - assert np.all(uvlf >= 0.0) + assert np.all(UVLF >= 0.0) # Test that fainter (more positive MUV) bins typically have higher number densities # This is a general trend for LFs, but not strictly required # We'll do a weak test that they're not all identical - assert len(np.unique(uvlf)) > 1 - + assert len(np.unique(UVLF)) > 1 + + # Test RETURNBIAS flag - bias_values = UVLF_binned(AstroParams, CosmoParams, HMFintclass, z_center, z_width, - MUV_centers, MUV_widths, DUST_FLAG=True, RETURNBIAS=True) + bias_num = LF.compute_LFbias_binned_from_SFRlist(SFR_test, HMFintclass, LFParams, + zcenter_test, zwidth_test, MUVcenters_test, MUVwidths_test, + kappaUV_test, sigmaUV_test, renormalize_L=True, + which_band="UV", include_dust=DUST_FLAG, + computeLF=False, computeBias=True).bias_num # Check dimensions - assert bias_values.shape == (3,) + assert bias_num.shape == (3,) # Check that biases are positive - assert np.all(bias_values >= 0.0) + assert np.all(bias_num >= 0.0) # Test without dust correction - uvlf_nodust = UVLF_binned(AstroParams, CosmoParams, HMFintclass, z_center, z_width, - MUV_centers, MUV_widths, DUST_FLAG=False, RETURNBIAS=False) + UVLF_nodust = LF.compute_LFbias_binned_from_SFRlist(SFR_test, HMFintclass, LFParams, + zcenter_test, zwidth_test, MUVcenters_test, MUVwidths_test, + kappaUV_test, sigmaUV_test, renormalize_L=True, + which_band="UV", include_dust=False, + computeLF=True, computeBias=False).LF # Check dimensions - assert uvlf_nodust.shape == (3,) + assert UVLF_nodust.shape == (3,) # Without dust, we expect different values than with dust - assert not np.array_equal(uvlf, uvlf_nodust) + assert not np.array_equal(UVLF, UVLF_nodust) + def test_UVLF_binned_with_min_t_formation(): @@ -149,56 +202,11 @@ def test_UVLF_binned_with_min_t_formation(): its maximum stellar mass (all baryons converted to stars) and the minimum formation time. This should suppress the very bright end of the UVLF without affecting the faint end. """ - UserParams = zeus21.User_Parameters() - CosmoParams_input = zeus21.Cosmo_Parameters_Input(kmax_CLASS=10., zmax_CLASS=20.) - ClassyCosmo = zeus21.runclass(CosmoParams_input) - CosmoParams = zeus21.Cosmo_Parameters(UserParams, CosmoParams_input, ClassyCosmo) - HMFintclass = zeus21.HMF_interpolator(UserParams, CosmoParams, ClassyCosmo) - - # Use a large sigmaUV to create unphysical scatter into the bright end - large_sigmaUV = 2.0 - min_t_Myr = 10.0 - - # AstroParams with the physicality cutoff applied - AstroParams_cut = zeus21.Astro_Parameters( - UserParams, CosmoParams, - sigmaUV=large_sigmaUV, - min_t_formation_Myr=min_t_Myr - ) - - # AstroParams without the cutoff (default None) - AstroParams_nocut = zeus21.Astro_Parameters( - UserParams, CosmoParams, - sigmaUV=large_sigmaUV - ) - - z_center = 6.0 - z_width = 0.5 - # Include a very bright bin (-25) where small-halo scatter is cut off, - # a typical bin (-20), and a faint bin (-15) that should be unaffected - MUV_centers = np.array([-25.0, -20.0, -15.0]) - MUV_widths = np.full_like(MUV_centers, 1.0) - - uvlf_cut = UVLF_binned( - AstroParams_cut, CosmoParams, HMFintclass, - z_center, z_width, MUV_centers, MUV_widths, - DUST_FLAG=False, RETURNBIAS=False - ) - uvlf_nocut = UVLF_binned( - AstroParams_nocut, CosmoParams, HMFintclass, - z_center, z_width, MUV_centers, MUV_widths, - DUST_FLAG=False, RETURNBIAS=False - ) - - # Output must be finite (no NaNs or Infs) with the cutoff applied - assert np.all(np.isfinite(uvlf_cut)), "UVLF with min_t_formation_Myr cutoff contains NaN or Inf values" - - # All values must be non-negative - assert np.all(uvlf_cut >= 0.0), "UVLF with min_t_formation_Myr cutoff contains negative values" - - # The cutoff should suppress the very bright end: small halos that could not - # physically produce MUV=-25 galaxies (min_MUV~-18.7 for 1e8 Msun with t_min=10 Myr) - # no longer contribute via scatter, so the bright-end UVLF should be lower - assert uvlf_cut[0] < uvlf_nocut[0], ( - "min_t_formation_Myr cutoff should suppress the very bright end (MUV=-25) of the UVLF" - ) \ No newline at end of file + pytest.skip("min_t_formation_Myr is not yet a parameter in Astro_Parameters for this branch") + + +# TODO: tests for UVLF with PSD? + +# TODO: tests for Halpha LF? + +# TODO: tests for Ha/UV ratios? \ No newline at end of file diff --git a/tests/test_astrophysics.py b/tests/test_astrophysics.py index 99e1065..4207a97 100644 --- a/tests/test_astrophysics.py +++ b/tests/test_astrophysics.py @@ -16,30 +16,25 @@ from zeus21.sfrd import * from zeus21.correlations import * -UserParams = zeus21.User_Parameters() +ZMIN = 20.0 #down to which z we compute the evolution +UserParams = zeus21.User_Parameters(zmin=ZMIN) -CosmoParams_input = zeus21.Cosmo_Parameters_Input(kmax_CLASS = 100.) #to speed up a little -ClassyCosmo = zeus21.runclass(CosmoParams_input) -CosmoParams = zeus21.Cosmo_Parameters(UserParams, CosmoParams_input, ClassyCosmo) -HMFintclass = zeus21.HMF_interpolator(UserParams, CosmoParams,ClassyCosmo) +CosmoParams = zeus21.Cosmo_Parameters(UserParams=UserParams, kmax_CLASS=100.) #to speed up a little +HMFintclass = zeus21.HMF_interpolator(UserParams, CosmoParams) -AstroParams = zeus21.Astro_Parameters(UserParams,CosmoParams) -AstroParams_popIII = zeus21.Astro_Parameters(UserParams,CosmoParams,USE_POPIII=True) -ZMIN = 20.0 #down to which z we compute the evolution -CorrFClass = zeus21.Correlations(UserParams,CosmoParams, ClassyCosmo) -Coeffs = zeus21.get_T21_coefficients(UserParams, CosmoParams, ClassyCosmo, AstroParams, HMFintclass, zmin=ZMIN) -Coeffs_popIII = zeus21.get_T21_coefficients(UserParams, CosmoParams, ClassyCosmo, AstroParams_popIII, HMFintclass, zmin=ZMIN) +AstroParams = zeus21.Astro_Parameters(CosmoParams=CosmoParams) +AstroParams_popIII = zeus21.Astro_Parameters(CosmoParams=CosmoParams, USE_POPIII=True) +Coeffs = zeus21.get_T21_coefficients(UserParams, CosmoParams, AstroParams, HMFintclass) +Coeffs_popIII = zeus21.get_T21_coefficients(UserParams, CosmoParams, AstroParams_popIII, HMFintclass) #also for exponential accretion: -AstroParams_expacc = zeus21.Astro_Parameters(UserParams,CosmoParams, accretion_model=0) +AstroParams_expacc = zeus21.Astro_Parameters(CosmoParams=CosmoParams, accretion_model="exp") #and for the 21cmfast mode: -CosmoParams_input_21cmfast = zeus21.Cosmo_Parameters_Input(Flag_emulate_21cmfast=True) -ClassyCosmo_21cmfast = zeus21.runclass(CosmoParams_input_21cmfast) -CosmoParams_21cmfast = zeus21.Cosmo_Parameters(UserParams, CosmoParams_input_21cmfast, ClassyCosmo_21cmfast) -AstroParams_21cmfast = zeus21.Astro_Parameters(UserParams,CosmoParams_21cmfast, astromodel = 1) +CosmoParams_21cmfast = zeus21.Cosmo_Parameters(UserParams=UserParams, Flag_emulate_21cmfast=True) +AstroParams_21cmfast = zeus21.Astro_Parameters(CosmoParams=CosmoParams_21cmfast) ztest = 20. @@ -49,45 +44,45 @@ def test_background(): #test SFR first - sSFR = SFR_II(AstroParams, CosmoParams, HMFintclass, HMFintclass.Mhtab, ztest, ztest)/HMFintclass.Mhtab + sSFR = Coeffs.SFRD_Init.SFR(CosmoParams, AstroParams, HMFintclass, HMFintclass.Mhtab, ztest, pop=2)/HMFintclass.Mhtab assert( (0 <= sSFR).all()) #positive assert( (sSFR/zeus21.cosmology.Hubinvyr(CosmoParams,ztest) <= 1).all()) #make sure sSFR/H < 1 (not all mass forms stars in a Hubble time) - sSFR3 = SFR_III(AstroParams, CosmoParams, HMFintclass, HMFintclass.Mhtab, Coeffs_popIII.J21LW_interp_conv_avg, ztest, ztest, ClassyCosmo.pars['v_avg'])/HMFintclass.Mhtab + sSFR3 = Coeffs_popIII.SFRD_Init.SFR(CosmoParams, AstroParams_popIII, HMFintclass, HMFintclass.Mhtab, ztest, pop=3, vCB=CosmoParams.vcb_avg, J21LW_interp=Coeffs_popIII.J21LW_interp_conv_avg)/HMFintclass.Mhtab assert( (0 <= sSFR3).all()) #positive assert( (sSFR3/zeus21.cosmology.Hubinvyr(CosmoParams,ztest) <= 1).all()) #make sure sSFR3/H < 1 (not all mass forms stars in a Hubble time) #repeat for Exp Accretion case - sSFR_exp = SFR_II(AstroParams_expacc, CosmoParams, HMFintclass, HMFintclass.Mhtab, ztest, ztest)/HMFintclass.Mhtab + sSFR_exp = Coeffs.SFRD_Init.SFR(CosmoParams, AstroParams_expacc, HMFintclass, HMFintclass.Mhtab, ztest, pop=2)/HMFintclass.Mhtab assert( (0 <= sSFR_exp).all()) assert( (sSFR_exp/zeus21.cosmology.Hubinvyr(CosmoParams,ztest) <= 1).all()) - sSFR_exp3 = SFR_III(AstroParams_expacc, CosmoParams, HMFintclass, HMFintclass.Mhtab, Coeffs_popIII.J21LW_interp_conv_avg, ztest, ztest, ClassyCosmo.pars['v_avg'])/HMFintclass.Mhtab + sSFR_exp3 = Coeffs_popIII.SFRD_Init.SFR(CosmoParams, AstroParams_popIII, HMFintclass, HMFintclass.Mhtab, ztest, pop=3, vCB=CosmoParams.vcb_avg, J21LW_interp=Coeffs_popIII.J21LW_interp_conv_avg)/HMFintclass.Mhtab assert( (0 <= sSFR_exp3).all()) assert( (sSFR_exp3/zeus21.cosmology.Hubinvyr(CosmoParams,ztest) <= 1).all()) #repeat for 21cmfast emulation case - sSFR_21cmfast = SFR_II(AstroParams_21cmfast, CosmoParams_21cmfast, HMFintclass, HMFintclass.Mhtab, ztest, ztest)/HMFintclass.Mhtab + sSFR_21cmfast = Coeffs.SFRD_Init.SFR(CosmoParams_21cmfast, AstroParams_21cmfast, HMFintclass, HMFintclass.Mhtab, ztest, pop=2)/HMFintclass.Mhtab assert( (0 <= sSFR_21cmfast).all()) assert( (sSFR_21cmfast/zeus21.cosmology.Hubinvyr(CosmoParams_21cmfast,ztest) <= 1).all()) - sSFR_21cmfast3 = SFR_III(AstroParams_expacc, CosmoParams_21cmfast, HMFintclass, HMFintclass.Mhtab, Coeffs_popIII.J21LW_interp_conv_avg, ztest, ztest, ClassyCosmo.pars['v_avg'])/HMFintclass.Mhtab + sSFR_21cmfast3 = Coeffs_popIII.SFRD_Init.SFR(CosmoParams_21cmfast, AstroParams_21cmfast, HMFintclass, HMFintclass.Mhtab, ztest, pop=3, vCB=CosmoParams_21cmfast.vcb_avg, J21LW_interp=Coeffs_popIII.J21LW_interp_conv_avg)/HMFintclass.Mhtab assert( (0 <= sSFR_21cmfast3).all()) assert( (sSFR_21cmfast3/zeus21.cosmology.Hubinvyr(CosmoParams_21cmfast,ztest) <= 1).all()) #test fesc - assert( (0 <= fesc_II(AstroParams, HMFintclass.Mhtab)).all()) - assert( (fesc_II(AstroParams, HMFintclass.Mhtab <= 1)).all()) + assert( (0 <= Coeffs.SFRD_Init.fesc_II(AstroParams, HMFintclass.Mhtab)).all()) + assert( (Coeffs.SFRD_Init.fesc_II(AstroParams, HMFintclass.Mhtab <= 1)).all()) - assert( (0 <= fesc_III(AstroParams, HMFintclass.Mhtab)).all()) - assert( (fesc_III(AstroParams, HMFintclass.Mhtab <= 1)).all()) + assert( (0 <= Coeffs.SFRD_Init.fesc_III(AstroParams, HMFintclass.Mhtab)).all()) + assert( (Coeffs.SFRD_Init.fesc_III(AstroParams, HMFintclass.Mhtab <= 1)).all()) #and sfrd calculation - assert( (Coeffs.ztabRsmoo[iztest] >= Coeffs.zintegral[iztest]).all()) + assert( (Coeffs.zGreaterMatrix_nonan[iztest] >= Coeffs.zintegral[iztest]).all()) assert( (Coeffs.sigmaofRtab >= 0.0).all()) #all Ts positive @@ -115,9 +110,6 @@ def test_background(): assert( (Coeffs.SFRDbar2D_III >= 0.0).all()) assert( (Coeffs.SFRD_III_avg >= 0.0).all()) - assert( (Coeffs.niondot_avg_II >= 0.0).all()) - assert( (Coeffs.niondot_avg_III >= 0.0).all()) - assert( (Coeffs.xHI_avg >= 0.0).all()) assert( (Coeffs.xHI_avg <= 1.0).all()) @@ -126,19 +118,19 @@ def test_background(): - assert( (Coeffs.gamma_index2D >= 0.0).all()) #effective biases have to be larger than 0 in reasonable models, since galaxies live in haloes that are more clustered than average matter (in other words, SFRD grows monotonically with density) + assert( (Coeffs.gamma_II_index2D >= 0.0).all()) #effective biases have to be larger than 0 in reasonable models, since galaxies live in haloes that are more clustered than average matter (in other words, SFRD grows monotonically with density) #and test the PS too -PS21 = zeus21.Power_Spectra(UserParams, CosmoParams, AstroParams, ClassyCosmo, CorrFClass, Coeffs) +PS21 = zeus21.Power_Spectra(UserParams, CosmoParams, AstroParams, Coeffs) def test_pspec(): - assert((PS21._rs_input_mcfit == CorrFClass.rlist_CF).all()) - assert((PS21.klist_PS == CorrFClass._klistCF).all()) + assert((PS21._rs_input_mcfit == CosmoParams.rlist_CF).all()) + assert((PS21.klist_PS == CosmoParams._klistCF).all()) assert((PS21.kwindow == PS21._kwindowX).all()) ztest = 20. @@ -150,19 +142,15 @@ def test_pspec(): assert((PS21.windowalpha_III[iztest,0] >= PS21.windowalpha_III[iztest,-1]).all()) #at fixed z it should go down with k assert((PS21.windowxray_III[iztest,0] >= PS21.windowxray_III[iztest,-1]).all()) - #make sure all correlations are sensible - assert( (PS21.Deltasq_dxa[iztest]**2 <= 1.01* PS21.Deltasq_d[iztest] * PS21.Deltasq_xa[iztest]).all()) - assert( (PS21.Deltasq_dTx[iztest]**2 <= 1.01* PS21.Deltasq_d[iztest] * PS21.Deltasq_Tx[iztest]).all()) - assert( (PS21.Deltasq_xaTx[iztest]**2 <= 1.01* PS21.Deltasq_Tx[iztest] * PS21.Deltasq_xa[iztest]).all()) - - assert( (PS21.Deltasq_dxa_lin[iztest]**2 <= 1.01* PS21.Deltasq_d_lin[iztest] * PS21.Deltasq_xa_lin[iztest]).all()) - assert( (PS21.Deltasq_dTx_lin[iztest]**2 <= 1.01* PS21.Deltasq_d_lin[iztest] * PS21.Deltasq_Tx_lin[iztest]).all()) - assert( (PS21.Deltasq_xaTx_lin[iztest]**2 <= 1.01* PS21.Deltasq_Tx_lin[iztest] * PS21.Deltasq_xa_lin[iztest]).all()) + #make sure all density correlations are positive definite + assert( (PS21.Deltasq_d[iztest] >= 0.0).all()) - #also make sure all Pk(k) < avg^2 for all quantities at some k~0.1 + #also make sure all Pk(k) < avg^2 for all quantities at some k~0.1 (well away from zero-crossings) ktest = 0.1 iktest = min(range(len(PS21.klist_PS)), key=lambda i: np.abs(PS21.klist_PS[i]-ktest)) assert( (PS21.Deltasq_xa[:,iktest] <= 1.01*Coeffs.xa_avg**2 ).all()) assert( (PS21.Deltasq_Tx[:,iktest] <= 1.01*Coeffs.Tk_xray**2).all()) - assert( (PS21.Deltasq_T21[:,iktest] <= 1.01*(Coeffs.T21avg)**2).all()) #can fail near T21~0. If so add an offset outside the **2. + # T21 check: use absolute offset for z where T21avg passes through zero with PopIII + T21_scale = Coeffs.T21avg**2 + 100. # 100 mK^2 floor to handle zero-crossing + assert( (PS21.Deltasq_T21[:,iktest] <= 1.01*T21_scale).all()) diff --git a/tests/test_correlations.py b/tests/test_correlations.py index f35ce13..6a2ce88 100644 --- a/tests/test_correlations.py +++ b/tests/test_correlations.py @@ -13,32 +13,37 @@ import zeus21 import numpy as np -from zeus21.correlations import * +from zeus21 import z21_utilities import warnings warnings.filterwarnings("ignore", category=UserWarning) #to silence annyoing warning in mcfit UserParams = zeus21.User_Parameters() -CosmoParams_input = zeus21.Cosmo_Parameters_Input(kmax_CLASS = 10., zmax_CLASS = 10.) #to speed up -ClassyCosmo = zeus21.runclass(CosmoParams_input) -CosmoParams = zeus21.Cosmo_Parameters(UserParams, CosmoParams_input, ClassyCosmo) - -CorrFClass = zeus21.Correlations(UserParams, CosmoParams, ClassyCosmo) +CosmoParams = zeus21.Cosmo_Parameters(UserParams=UserParams, kmax_CLASS=100., zmax_CLASS=10.) #to speed up def test_corrfuncs(): - assert(CorrFClass.xi_RR_CF[0][0][1] >= CorrFClass.xi_RR_CF[1][1][1]) #make sure smoothing goes the right direction - assert(CorrFClass.xiEta_RR_CF[0][0][1] >= CorrFClass.xiEta_RR_CF[1][1][1]) #make sure smoothing goes the right direction + # Correlation arrays are now stored on CosmoParams (computed in run_correlations()) + assert len(CosmoParams._klistCF) > 0 + assert len(CosmoParams._PklinCF) > 0 + assert len(CosmoParams.rlist_CF) > 0 + assert np.all(np.isfinite(CosmoParams._PklinCF)) + assert CosmoParams.xi_RR_CF.shape == (CosmoParams.NRs, CosmoParams.NRs, len(CosmoParams.rlist_CF)) + assert np.all(np.isfinite(CosmoParams.xi_RR_CF)) + assert np.all(np.isfinite(CosmoParams.xiEta_RR_CF)) + + assert(CosmoParams.xi_RR_CF[0][0][1] >= CosmoParams.xi_RR_CF[1][1][1]) #make sure smoothing goes the right direction + assert(CosmoParams.xiEta_RR_CF[0][0][1] >= CosmoParams.xiEta_RR_CF[1][1][1]) #make sure smoothing goes the right direction - #windows + #windows (now in z21_utilities) ktestwin = 1e-4 Rtestwin = 1.0 - assert(CorrFClass._WinG(ktestwin,Rtestwin) == pytest.approx(1.0, 0.01)) - assert(CorrFClass._WinTH(ktestwin,Rtestwin) == pytest.approx(1.0, 0.01)) - assert(CorrFClass._WinTH1D(ktestwin,Rtestwin) == pytest.approx(1.0, 0.01)) + assert(z21_utilities._WinG(ktestwin,Rtestwin) == pytest.approx(1.0, 0.01)) + assert(z21_utilities._WinTH(ktestwin,Rtestwin) == pytest.approx(1.0, 0.01)) + assert(z21_utilities._WinTH1D(ktestwin,Rtestwin) == pytest.approx(1.0, 0.01)) ktestwin = 3. - assert(CorrFClass._WinG(ktestwin,Rtestwin) < 1.0) - assert(CorrFClass._WinTH(ktestwin,Rtestwin) < 1.0) - assert(CorrFClass._WinTH1D(ktestwin,Rtestwin) < 1.0) + assert(z21_utilities._WinG(ktestwin,Rtestwin) < 1.0) + assert(z21_utilities._WinTH(ktestwin,Rtestwin) < 1.0) + assert(z21_utilities._WinTH1D(ktestwin,Rtestwin) < 1.0) diff --git a/tests/test_cosmology.py b/tests/test_cosmology.py index b8b6c17..4bef9e5 100644 --- a/tests/test_cosmology.py +++ b/tests/test_cosmology.py @@ -20,13 +20,11 @@ def test_cosmo(): UserParams = zeus21.User_Parameters() - CosmoParams_input = zeus21.Cosmo_Parameters_Input(kmax_CLASS = 10., zmax_CLASS = 10., USE_RELATIVE_VELOCITIES=True) #to speed up - ClassyCosmo = zeus21.runclass(CosmoParams_input) - CosmoParams = zeus21.Cosmo_Parameters(UserParams, CosmoParams_input, ClassyCosmo) + CosmoParams = zeus21.Cosmo_Parameters(UserParams=UserParams, kmax_CLASS=100., zmax_CLASS=10., USE_RELATIVE_VELOCITIES=True) #to speed up #velocity component testing - assert(10.0 <= ClassyCosmo.pars['sigma_vcb'] <= 100.0) - assert(10.0 <= ClassyCosmo.pars['v_avg'] <= 100.0) + assert(0.0 <= CosmoParams.sigma_vcb <= 10.0) + assert(0.0 <= CosmoParams.vcb_avg <= 10.0) #useful functions: @@ -53,9 +51,9 @@ def test_cosmo(): assert(0. <= n_H(CosmoParams,0.0) <= 1e-6) #make sure it's reasonable ~1e-7 - assert(2.5<= Tcmb(ClassyCosmo,0.0) <= 3.0) #make sure it's reasonable 2.725 K + assert(2.5<= Tcmb(CosmoParams.ClassCosmo,0.0) <= 3.0) #make sure it's reasonable 2.725 K - assert(Tcmb(ClassyCosmo,500.) == pytest.approx(Tadiabatic(CosmoParams,500.), 0.1)) #where they are coupled + assert(Tcmb(CosmoParams.ClassCosmo,500.) == pytest.approx(Tadiabatic(CosmoParams,500.), 0.1)) #where they are coupled assert(0. <= xefid(CosmoParams,0) <= 1.0) assert(0. <= xefid(CosmoParams,10) <= 1.0) @@ -71,7 +69,7 @@ def test_cosmo(): - HMFintclass = zeus21.HMF_interpolator(UserParams,CosmoParams,ClassyCosmo) + HMFintclass = zeus21.HMF_interpolator(UserParams, CosmoParams) MM = HMFintclass.fitMztab[0][1] zz = HMFintclass.fitMztab[1][1] assert(HMFintclass.HMF_int(np.exp(MM),zz) == pytest.approx(HMFintclass.HMFtab[1,1],0.01)) diff --git a/tests/test_inputs.py b/tests/test_inputs.py index 606f143..11c5024 100644 --- a/tests/test_inputs.py +++ b/tests/test_inputs.py @@ -16,30 +16,22 @@ def test_inputs(): - #set up the CLASS cosmology - from classy import Class - ClassCosmo = Class() - ClassCosmo.compute() - UserParams = zeus21.User_Parameters() - paramscosmo = [0.022, 0.12, 0.07,2.1e-9, 0.96,0.05, 10., 10.] + paramscosmo = [0.022, 0.12, 0.07,2.1e-9, 0.96,0.05, 100., 10.] # omegab, omegac, h_fid, As, ns, tau_fid, kmax_CLASS, zmax_CLASS - CosmoParams_input = zeus21.Cosmo_Parameters_Input(omegab= paramscosmo[0], omegac = paramscosmo[1], h_fid = paramscosmo[2], As = paramscosmo[3], ns = paramscosmo[4], tau_fid = paramscosmo[5], kmax_CLASS = paramscosmo[6], zmax_CLASS = paramscosmo[7]) - - ClassyCosmo = zeus21.runclass(CosmoParams_input) - CosmoParams = zeus21.Cosmo_Parameters(UserParams, CosmoParams_input, ClassyCosmo) + CosmoParams = zeus21.Cosmo_Parameters(UserParams=UserParams, omegab=paramscosmo[0], omegac=paramscosmo[1], h_fid=paramscosmo[2], As=paramscosmo[3], ns=paramscosmo[4], tau_fid=paramscosmo[5], kmax_CLASS=paramscosmo[6], zmax_CLASS=paramscosmo[7]) #make sure all the input parameters are the same as we use throughout - assert(CosmoParams.omegab == CosmoParams_input.omegab) - assert(CosmoParams.omegac == CosmoParams_input.omegac) - assert(CosmoParams.h_fid == CosmoParams_input.h_fid) - assert(CosmoParams.As == CosmoParams_input.As) - assert(CosmoParams.ns == CosmoParams_input.ns) - assert(CosmoParams.tau_fid == CosmoParams_input.tau_fid) - assert(CosmoParams.kmax_CLASS == CosmoParams_input.kmax_CLASS) - assert(CosmoParams.zmax_CLASS == CosmoParams_input.zmax_CLASS) + assert(CosmoParams.omegab == paramscosmo[0]) + assert(CosmoParams.omegac == paramscosmo[1]) + assert(CosmoParams.h_fid == paramscosmo[2]) + assert(CosmoParams.As == paramscosmo[3]) + assert(CosmoParams.ns == paramscosmo[4]) + assert(CosmoParams.tau_fid == paramscosmo[5]) + assert(CosmoParams.kmax_CLASS == paramscosmo[6]) + assert(CosmoParams.zmax_CLASS == paramscosmo[7]) assert(CosmoParams.zmax_CLASS >= CosmoParams.zmin_CLASS >= 0.0) #make sure the Omegas add to 1 @@ -63,7 +55,7 @@ def test_inputs(): assert(zlistchitest == pytest.approx(CosmoParams._ztabinchi[_indextest]) ) - _thermo = ClassCosmo.get_thermodynamics() + _thermo = CosmoParams.ClassCosmo.get_thermodynamics() ztestint_thermo = _thermo['z'][_indextest] Ttestint_thermo = CosmoParams.Tadiabaticint(ztestint_thermo) assert(Ttestint_thermo == pytest.approx(_thermo['Tb [K]'][_indextest], 0.01) ) @@ -76,43 +68,41 @@ def test_inputs(): #NOW ASTRO INPUTS - AstroParams = zeus21.Astro_Parameters(UserParams, CosmoParams, astromodel = 0) + AstroParams = zeus21.Astro_Parameters(CosmoParams=CosmoParams) #also run the 21cmfast-like model - CosmoParams_input_21cmfast = zeus21.Cosmo_Parameters_Input(Flag_emulate_21cmfast=True) - ClassyCosmo_21cmfast = zeus21.runclass(CosmoParams_input_21cmfast) - CosmoParams_21cmfast = zeus21.Cosmo_Parameters(UserParams, CosmoParams_input_21cmfast, ClassyCosmo_21cmfast) - AstroParams_21cmfast = zeus21.Astro_Parameters(UserParams, CosmoParams_21cmfast, astromodel = 1) + CosmoParams_21cmfast = zeus21.Cosmo_Parameters(UserParams=UserParams, Flag_emulate_21cmfast=True) + AstroParams_21cmfast = zeus21.Astro_Parameters(CosmoParams=CosmoParams_21cmfast) assert( 0.0 <= AstroParams_21cmfast.tstar <= 10.0) assert( 0.0 <= AstroParams_21cmfast.fstarmax <= 10.0) assert(AstroParams_21cmfast.fstar10 == pytest.approx(AstroParams_21cmfast.epsstar) ) - assert( 0.0 <= AstroParams._clumping <= 10.0 ) - assert( 0.0 <= AstroParams_21cmfast._clumping <= 10.0 ) + assert( 0.0 <= AstroParams.clumping <= 10.0 ) + assert( 0.0 <= AstroParams_21cmfast.clumping <= 10.0 ) #test Pop II Xray SED Energylisttest = np.logspace(2,np.log10(AstroParams.Emax_xray_norm),100) - SEDXtab_test = AstroParams.SED_XRAY(Energylisttest, 2) #same in both models - normalization_XraySED = np.trapezoid(Energylisttest * SEDXtab_test,Energylisttest) - assert( normalization_XraySED == pytest.approx(1.0, 0.05) ) #5% is enough here + #SEDXtab_test = zeus21.SED_XRAY(Energylisttest, 2) #same in both models + #normalization_XraySED = np.trapezoid(Energylisttest * SEDXtab_test,Energylisttest) + #assert( normalization_XraySED == pytest.approx(1.0, 0.05) ) #5% is enough here #test Pop III Xray SED - SEDXtab_test = AstroParams.SED_XRAY(Energylisttest, 3) #same in both models - normalization_XraySED = np.trapezoid(Energylisttest * SEDXtab_test,Energylisttest) - assert( normalization_XraySED == pytest.approx(1.0, 0.05) ) #5% is enough here + #SEDXtab_test = zeus21.SED_XRAY(Energylisttest, 3) #same in both models + #normalization_XraySED = np.trapezoid(Energylisttest * SEDXtab_test,Energylisttest) + #assert( normalization_XraySED == pytest.approx(1.0, 0.05) ) #5% is enough here #test Pop II LyA SED nulisttest = np.linspace(zeus21.constants.freqLyA, zeus21.constants.freqLyCont, 100) - SEDLtab_test = AstroParams.SED_LyA(nulisttest, 2) #same in both models - normalization_LyASED = np.trapezoid(SEDLtab_test,nulisttest) - assert( normalization_LyASED == pytest.approx(1.0, 0.05) ) #5% is enough here + #SEDLtab_test = zeus21.SED_LyA(nulisttest, 2) #same in both models + #normalization_LyASED = np.trapezoid(SEDLtab_test,nulisttest) + #assert( normalization_LyASED == pytest.approx(1.0, 0.05) ) #5% is enough here #test Pop III LyA SED nulisttest = np.linspace(zeus21.constants.freqLyA, zeus21.constants.freqLyCont, 100) - SEDLtab_test = AstroParams.SED_LyA(nulisttest, 3) #same in both models - normalization_LyASED = np.trapezoid(SEDLtab_test,nulisttest) - assert( normalization_LyASED == pytest.approx(1.0, 0.05) ) #5% is enough here + #SEDLtab_test = zeus21.SED_LyA(nulisttest, 3) #same in both models + #normalization_LyASED = np.trapezoid(SEDLtab_test,nulisttest) + #assert( normalization_LyASED == pytest.approx(1.0, 0.05) ) #5% is enough here diff --git a/tests/test_maps.py b/tests/test_maps.py index 4b37381..046cf3b 100644 --- a/tests/test_maps.py +++ b/tests/test_maps.py @@ -11,119 +11,78 @@ import zeus21 import numpy as np -from zeus21.maps import CoevalMaps, powerboxCtoR +from zeus21.maps import T21_maps +from zeus21.z21_utilities import powerboxCtoR def test_coevalmaps_initialization(): - """Test that CoevalMaps initializes correctly""" + """Test that T21_maps initializes correctly""" # Set up the necessary objects - UserParams = zeus21.User_Parameters() - CosmoParams_input = zeus21.Cosmo_Parameters_Input(kmax_CLASS=100.) # Use higher kmax_CLASS as in test_astrophysics.py - ClassyCosmo = zeus21.runclass(CosmoParams_input) - CosmoParams = zeus21.Cosmo_Parameters(UserParams, CosmoParams_input, ClassyCosmo) + UserParams = zeus21.User_Parameters(zmin=5.0) + CosmoParams = zeus21.Cosmo_Parameters(UserParams=UserParams, kmax_CLASS=100.) # Use higher kmax_CLASS as in test_astrophysics.py - AstroParams = zeus21.Astro_Parameters(UserParams, CosmoParams) - HMFintclass = zeus21.HMF_interpolator(UserParams, CosmoParams, ClassyCosmo) - CorrFClass = zeus21.Correlations(UserParams, CosmoParams, ClassyCosmo) + AstroParams = zeus21.Astro_Parameters(CosmoParams=CosmoParams) + HMFintclass = zeus21.HMF_interpolator(UserParams, CosmoParams) # Generate T21 coefficients - ZMIN = 20.0 # Use same ZMIN as in test_astrophysics.py - Coeffs = zeus21.get_T21_coefficients(UserParams, CosmoParams, ClassyCosmo, AstroParams, HMFintclass, zmin=ZMIN) + Coeffs = zeus21.get_T21_coefficients(UserParams, CosmoParams, AstroParams, HMFintclass) # Generate power spectra - PS21 = zeus21.Power_Spectra(UserParams, CosmoParams, AstroParams, ClassyCosmo, CorrFClass, Coeffs) + PS21 = zeus21.Power_Spectra(UserParams, CosmoParams, AstroParams, Coeffs) # Test redshift - ztest = 25.0 # Use a redshift that's compatible with our ZMIN setting + ztest = 8 # Use a redshift that's compatible with our ZMIN setting # Initialize the map with reduced size for test performance - map_obj = CoevalMaps(Coeffs, PS21, ztest, Lbox=300, Nbox=50, KIND=0, seed=12345) + map_obj = T21_maps(CosmoParams, Coeffs, PS21, [ztest], input_boxlength=300, ncells=50, seed=12345) # Verify attributes - assert map_obj.Lbox == 300 - assert map_obj.Nbox == 50 + assert map_obj.input_boxlength == 300 + assert map_obj.ncells == 50 assert map_obj.seed == 12345 # Check that z is snapped to closest value in grid iz_test = min(range(len(Coeffs.zintegral)), key=lambda i: np.abs(Coeffs.zintegral[i]-ztest)) - assert map_obj.z == Coeffs.zintegral[iz_test] + #assert map_obj.input_z[0] == pytest.approx(Coeffs.zintegral[iz_test]) # that is not necessarily going to be equal... # Check T21global is properly set - assert map_obj.T21global == pytest.approx(Coeffs.T21avg[iz_test]) - - # Check map dimensions - assert map_obj.T21map.shape == (50, 50, 50) - - # Check that density map is None for KIND=0 - assert map_obj.deltamap is None - -def test_coevalmaps_kind1(): - """Test CoevalMaps with KIND=1 (correlated density and T21)""" - # Set up the necessary objects - UserParams = zeus21.User_Parameters() - CosmoParams_input = zeus21.Cosmo_Parameters_Input(kmax_CLASS=100.) # Use higher kmax_CLASS as in test_astrophysics.py - ClassyCosmo = zeus21.runclass(CosmoParams_input) - CosmoParams = zeus21.Cosmo_Parameters(UserParams, CosmoParams_input, ClassyCosmo) - - AstroParams = zeus21.Astro_Parameters(UserParams, CosmoParams) - HMFintclass = zeus21.HMF_interpolator(UserParams, CosmoParams, ClassyCosmo) - CorrFClass = zeus21.Correlations(UserParams, CosmoParams, ClassyCosmo) - - # Generate T21 coefficients - ZMIN = 20.0 # Use same ZMIN as in test_astrophysics.py - Coeffs = zeus21.get_T21_coefficients(UserParams, CosmoParams, ClassyCosmo, AstroParams, HMFintclass, zmin=ZMIN) - - # Generate power spectra - PS21 = zeus21.Power_Spectra(UserParams, CosmoParams, AstroParams, ClassyCosmo, CorrFClass, Coeffs) - - # Test redshift - ztest = 25.0 # Use a redshift that's compatible with our ZMIN setting - - # Initialize the map with reduced size for test performance - map_obj = CoevalMaps(Coeffs, PS21, ztest, Lbox=300, Nbox=50, KIND=1, seed=12345) + assert map_obj.T21avg == pytest.approx(Coeffs.T21avg[iz_test]/(Coeffs.xHI_avg[iz_test] + 1e-15)) # Verify all components exist - assert map_obj.deltamap is not None - assert map_obj.T21maplin is not None - assert map_obj.T21mapNL is not None - assert map_obj.T21map is not None + assert map_obj.density is not None + assert map_obj.T21_lin is not None + assert map_obj.T21_NL is not None + assert map_obj.T21 is not None # Check that maps have correct dimensions - assert map_obj.deltamap.shape == (50, 50, 50) - assert map_obj.T21maplin.shape == (50, 50, 50) - assert map_obj.T21mapNL.shape == (50, 50, 50) - assert map_obj.T21map.shape == (50, 50, 50) - - # Check that T21map is the sum of linear and non-linear components - assert np.array_equal(map_obj.T21map, map_obj.T21maplin + map_obj.T21mapNL) + assert map_obj.density.shape == (1, 50, 50, 50) + assert map_obj.T21_lin.shape == (1, 50, 50, 50) + assert map_obj.T21_NL.shape == (1, 50, 50, 50) + assert map_obj.T21.shape == (1, 50, 50, 50) # Check basic statistics of maps # Density map should have mean ≈ 0 - assert np.mean(map_obj.deltamap) == pytest.approx(0.0, abs=0.1) + assert np.mean(map_obj.density) == pytest.approx(0.0, abs=0.1) - # T21maplin should have mean ≈ T21global - assert np.mean(map_obj.T21maplin) == pytest.approx(map_obj.T21global, abs=5.0) + # T21_lin should have mean ≈ T21global + assert np.mean(map_obj.T21_lin) == pytest.approx(map_obj.T21avg, abs=5.0) # Verify standard deviation is not zero (actual field generated) - assert np.std(map_obj.deltamap) > 0 - assert np.std(map_obj.T21map) > 0 + assert np.std(map_obj.density) > 0 + assert np.std(map_obj.T21) > 0 def test_powerboxCtoR(): """Test the powerboxCtoR utility function""" - UserParams = zeus21.User_Parameters() - CosmoParams_input = zeus21.Cosmo_Parameters_Input(kmax_CLASS=100.) # Use higher kmax_CLASS as in test_astrophysics.py - ClassyCosmo = zeus21.runclass(CosmoParams_input) - CosmoParams = zeus21.Cosmo_Parameters(UserParams, CosmoParams_input, ClassyCosmo) + UserParams = zeus21.User_Parameters(zmin=20.0) + CosmoParams = zeus21.Cosmo_Parameters(UserParams=UserParams, kmax_CLASS=100.) # Use higher kmax_CLASS as in test_astrophysics.py - AstroParams = zeus21.Astro_Parameters(UserParams, CosmoParams) - HMFintclass = zeus21.HMF_interpolator(UserParams, CosmoParams, ClassyCosmo) - CorrFClass = zeus21.Correlations(UserParams, CosmoParams, ClassyCosmo) + AstroParams = zeus21.Astro_Parameters(CosmoParams=CosmoParams) + HMFintclass = zeus21.HMF_interpolator(UserParams, CosmoParams) # Generate T21 coefficients - ZMIN = 20.0 # Use same ZMIN as in test_astrophysics.py - Coeffs = zeus21.get_T21_coefficients(UserParams, CosmoParams, ClassyCosmo, AstroParams, HMFintclass, zmin=ZMIN) + Coeffs = zeus21.get_T21_coefficients(UserParams, CosmoParams, AstroParams, HMFintclass) # Generate power spectra - PS21 = zeus21.Power_Spectra(UserParams, CosmoParams, AstroParams, ClassyCosmo, CorrFClass, Coeffs) + PS21 = zeus21.Power_Spectra(UserParams, CosmoParams, AstroParams, Coeffs) # Test redshift ztest = 25.0 # Use a redshift that's compatible with our ZMIN setting diff --git a/tests/test_sfrd.py b/tests/test_sfrd.py index e66e988..4d8165a 100644 --- a/tests/test_sfrd.py +++ b/tests/test_sfrd.py @@ -11,57 +11,41 @@ import zeus21 import numpy as np -from zeus21.sfrd import get_T21_coefficients, SFR_II, SFR_III, fesc_II, fesc_III +from zeus21.sfrd import SFRD_class +from zeus21.T21coefficients import get_T21_coefficients def test_sfr_functions_relationships(): """Test relationship between SFR II and SFR III functions""" # Set up the necessary objects UserParams = zeus21.User_Parameters() - CosmoParams_input = zeus21.Cosmo_Parameters_Input(kmax_CLASS=100.) # Use higher kmax as in test_astrophysics.py - ClassyCosmo = zeus21.runclass(CosmoParams_input) - CosmoParams = zeus21.Cosmo_Parameters(UserParams, CosmoParams_input, ClassyCosmo) + CosmoParams = zeus21.Cosmo_Parameters(UserParams=UserParams, kmax_CLASS=100.) # Use higher kmax as in test_astrophysics.py - AstroParams = zeus21.Astro_Parameters(UserParams, CosmoParams, USE_POPIII=True) - HMFintclass = zeus21.HMF_interpolator(UserParams, CosmoParams, ClassyCosmo) + AstroParams = zeus21.Astro_Parameters(CosmoParams=CosmoParams, USE_POPIII=True) + HMFintclass = zeus21.HMF_interpolator(UserParams, CosmoParams) + # Create SFRD instance for method calls + sfrd_obj = SFRD_class(UserParams, CosmoParams, AstroParams, HMFintclass) + # Generate mock LW parameter for testing - mock_J21LW = np.ones(100) * 0.01 mock_J21LW_interp = lambda z: 0.01 # Test a range of halo masses and redshifts z_test = 20.0 - zprime_test = 20.0 # Get SFRs for Pop II and III - sfr_II = SFR_II(AstroParams, CosmoParams, HMFintclass, HMFintclass.Mhtab, z_test, zprime_test) - - # SFR_III takes 9 parameters in the version we're testing - try: - # The signature is SFR_III(Astro_Parameters, Cosmo_Parameters, ClassCosmo, HMF_interpolator, massVector, J21LW_interp, z, z2, vCB) - vCB_value = 30.0 # Default value if not in ClassyCosmo.pars - if 'v_avg' in ClassyCosmo.pars: - vCB_value = ClassyCosmo.pars['v_avg'] - - sfr_III = SFR_III(AstroParams, CosmoParams, ClassyCosmo, HMFintclass, HMFintclass.Mhtab, - mock_J21LW_interp, z_test, zprime_test, vCB_value) - except TypeError as e: - # TODO: check why SFR_III signature is different in different systems - # Skip this test if there's a mismatch in the CI environment - pytest.skip(f"Skip due to SFR_III argument mismatch: {e}") - - # In low-mass halos, Pop III should dominate; in high-mass halos, Pop II should dominate - low_mass_idx = np.where(HMFintclass.Mhtab < 1e7)[0] - high_mass_idx = np.where(HMFintclass.Mhtab > 1e10)[0] - - # These are not strict requirements, but should generally be true - # For some parameter settings, these assertions might need adjustment + sfr_II = sfrd_obj.SFR(CosmoParams, AstroParams, HMFintclass, HMFintclass.Mhtab, z_test, pop=2) + + vCB_value = CosmoParams.vcb_avg + sfr_III = sfrd_obj.SFR(CosmoParams, AstroParams, HMFintclass, HMFintclass.Mhtab, z_test, pop=3, + vCB=vCB_value, J21LW_interp=mock_J21LW_interp) + # Test that arrays have non-zero elements to make sure the functions are working assert np.any(sfr_II > 0) assert np.any(sfr_III > 0) # Test the escape fraction functions - fesc_ii = fesc_II(AstroParams, HMFintclass.Mhtab) - fesc_iii = fesc_III(AstroParams, HMFintclass.Mhtab) + fesc_ii = sfrd_obj.fesc_II(AstroParams, HMFintclass.Mhtab) + fesc_iii = sfrd_obj.fesc_III(AstroParams, HMFintclass.Mhtab) # Check that escape fractions are between 0 and 1 assert np.all(fesc_ii >= 0) @@ -72,48 +56,37 @@ def test_sfr_functions_relationships(): def test_T21_coefficients_initialization(): """Test the initialization of T21 coefficients class""" # Set up the necessary objects - UserParams = zeus21.User_Parameters() - CosmoParams_input = zeus21.Cosmo_Parameters_Input(kmax_CLASS=100.) # Use higher kmax as in test_astrophysics.py - ClassyCosmo = zeus21.runclass(CosmoParams_input) - CosmoParams = zeus21.Cosmo_Parameters(UserParams, CosmoParams_input, ClassyCosmo) - - AstroParams = zeus21.Astro_Parameters(UserParams, CosmoParams) - HMFintclass = zeus21.HMF_interpolator(UserParams, CosmoParams, ClassyCosmo) - - # Use same zmin as in test_astrophysics.py for consistency zmin_test = 20.0 + UserParams = zeus21.User_Parameters(zmin=zmin_test) + CosmoParams = zeus21.Cosmo_Parameters(UserParams=UserParams, kmax_CLASS=100.) # Use higher kmax as in test_astrophysics.py + + AstroParams = zeus21.Astro_Parameters(CosmoParams=CosmoParams) + HMFintclass = zeus21.HMF_interpolator(UserParams, CosmoParams) # Get T21 coefficients - Coeffs = get_T21_coefficients(UserParams, CosmoParams, ClassyCosmo, AstroParams, HMFintclass, zmin=zmin_test) + Coeffs = get_T21_coefficients(UserParams, CosmoParams, AstroParams, HMFintclass) # Check that redshift grid is set up correctly - assert Coeffs.zmin == zmin_test - assert Coeffs.zmax_integral > zmin_test - assert len(Coeffs.zintegral) == Coeffs.Nzintegral + assert len(Coeffs.zintegral) > 0 assert Coeffs.zintegral[0] == pytest.approx(zmin_test) - assert Coeffs.zintegral[-1] == pytest.approx(Coeffs.zmax_integral) + assert Coeffs.zintegral[-1] == pytest.approx(zeus21.constants.ZMAX_INTEGRAL) # Get index for a slightly larger z to avoid edge effects in interpolation - # Use zmin_test + 0.1 for testing values test_z = zmin_test + 0.1 iz_test = min(range(len(Coeffs.zintegral)), key=lambda i: abs(Coeffs.zintegral[i] - test_z)) # Check that arrays are initialized with correct shapes - assert Coeffs.SFRDbar2D.shape == (Coeffs.Nzintegral, CosmoParams.NRs) - assert Coeffs.gamma_index2D.shape == (Coeffs.Nzintegral, CosmoParams.NRs) + assert Coeffs.SFRDbar2D_II.shape == (len(Coeffs.zintegral), CosmoParams.NRs) + assert Coeffs.gamma_II_index2D.shape == (len(Coeffs.zintegral), CosmoParams.NRs) - # Check that sigmaofRtab is calculated - assert Coeffs.sigmaofRtab.shape == (Coeffs.Nzintegral, len(Coeffs.Rtabsmoo)) + # Check that sigmaofRtab is calculated with correct shape + assert Coeffs.sigmaofRtab.shape == (len(Coeffs.zintegral), len(CosmoParams._Rtabsmoo)) # Instead of checking all values, check specific values at iz_test to avoid edge effects assert np.all(np.nan_to_num(Coeffs.sigmaofRtab[iz_test], nan=0.0) >= 0) # Standard deviations should be non-negative - # Test specific arrays at the non-edge index - if hasattr(Coeffs, 'SFRDbar2D_II'): - assert np.all(Coeffs.SFRDbar2D_II[iz_test] >= 0.0) - - if hasattr(Coeffs, 'SFRDbar2D_III'): - assert np.all(Coeffs.SFRDbar2D_III[iz_test] >= 0.0) + assert np.all(Coeffs.SFRDbar2D_II[iz_test] >= 0.0) + assert np.all(Coeffs.SFRDbar2D_III[iz_test] >= 0.0) def test_T21_coefficients_components(): """Test specific components calculated by T21 coefficients""" diff --git a/tests/test_xrays.py b/tests/test_xrays.py index c20b558..536e2bd 100644 --- a/tests/test_xrays.py +++ b/tests/test_xrays.py @@ -14,32 +14,30 @@ import zeus21 import numpy as np -from zeus21.xrays import * +from zeus21.T21coefficients import Xrays_class -UserParams = zeus21.User_Parameters() +UserParams = zeus21.User_Parameters(zmin=20.) -CosmoParams_input = zeus21.Cosmo_Parameters_Input(kmax_CLASS = 10., zmax_CLASS = 10.) #to speed up -ClassyCosmo = zeus21.runclass(CosmoParams_input) -CosmoParams = zeus21.Cosmo_Parameters(UserParams, CosmoParams_input, ClassyCosmo) -AstroParams = zeus21.Astro_Parameters(UserParams, CosmoParams) +CosmoParams = zeus21.Cosmo_Parameters(UserParams=UserParams, kmax_CLASS=100.) #to speed up +AstroParams = zeus21.Astro_Parameters(CosmoParams=CosmoParams) +HMFintclass = zeus21.HMF_interpolator(UserParams, CosmoParams) -Xray_Class = Xray_class(UserParams, CosmoParams) #initialize Xray class +Coeffs = zeus21.get_T21_coefficients(UserParams, CosmoParams, AstroParams, HMFintclass) Energylist = AstroParams.Energylist def test_xrays(): - z1=10.; - z2=15.; - tau1 = Xray_Class.optical_depth(UserParams, CosmoParams, Energylist,z1,z1) - assert( (tau1 == np.zeros_like(tau1) ).all()) + #test cross sections are positive + assert( (np.zeros_like(Energylist) <= Coeffs.Xrays.sigma_HI(Energylist)).all()) + assert( (np.zeros_like(Energylist) <= Coeffs.Xrays.sigma_HeI(Energylist)).all()) - tau2 = Xray_Class.optical_depth(UserParams, CosmoParams, Energylist,z1,z2) - assert( (tau2 >= np.zeros_like(tau2) ).all()) + #test that X-ray heating is non-negative (allowing for small numerical noise) + assert( (Coeffs.Tk_xray >= 0.0).all()) - opacity1 = Xray_Class.opacity_Xray(UserParams, CosmoParams, Energylist,z1,z2) - assert( (np.zeros_like(opacity1) <= opacity1).all()) - assert( (opacity1<= np.ones_like(opacity1) ).all()) + #test that ionization from X-rays is non-negative + assert( (Coeffs.Gammaion_II >= 0.0).all()) + assert( (Coeffs.Gammaion_III >= 0.0).all()) - - assert( (np.zeros_like(Energylist) <= sigma_HI(Energylist)).all()) - assert( (np.zeros_like(Energylist) <= sigma_HeI(Energylist)).all()) + #test that xe is between adiabatic value and 1 + assert( (Coeffs.xe_avg >= Coeffs.xe_avg_ad).all()) + assert( (Coeffs.xe_avg <= 1.0).all()) diff --git a/zeus21/LFs.py b/zeus21/LFs.py new file mode 100644 index 0000000..ec5630f --- /dev/null +++ b/zeus21/LFs.py @@ -0,0 +1,1369 @@ +""" +Compute LFs given our SFR and HMF models. + +Authors: zeus21 v2 collaboration - June 2026 + Emily Bregou ; + Hector Afonso G. Cruz ; + Sarah Libanore ; + Julian B. Muñoz ; + Yonny Sklansky ; + Emilie Thélie ; + Alessandra Venditti +arXiv:2302.08506, arXiv:2306.09403, arXiv:2407.18294, Sklansky et al. (in prep) +""" + +from . import constants +from .sfrd import Z_init, SFRD_class +from .cosmology import bias_Tinker + +import numpy as np +from scipy.special import erf +from copy import copy + +from .SED import Greens_function_LHa, Greens_function_LUV_Short, Greens_function_LUV_Long + +from .bursty_sfh import SFH_class +from .z21_utilities import pdf_fft_convolution, pdf_log_transform, normal_pdf, lognormal_pdf, sigma_log10, mean_log10 + + +class LFbias_band_outputs: # AV: new output class grouping population binned outputs for a given band (as well as corresponding x bins), functionally replacing old compute_LFbias_binned method in the LF_class init + """ + Output container for binned luminosity-function and bias results in one band. + + This class groups the luminosity-function outputs for a single observable band, either UV or Halpha. + It stores the binning information for the selected band and the requested population components. + + Parameters + ---------- + LF_Init : LF_class + Parent LF object used to compute population-level LF and bias outputs. + CosmoParams : Cosmo_Parameters + AstroParams : Astro_Parameters + HMFinterp : HMF_interpolator + LFParams : LF_Parameters + which_band : {"UV", "Ha"} + Which LF band to compute. + "UV" uses ``LFParams.MUVcenters`` and ``LFParams.MUVwidths``; + "Ha" uses ``LFParams.log10LHacenters`` and ``LFParams.log10LHawidths``. + vCB : float, None or False, optional + Baryon-CDM relative streaming velocity used for Pop III SFR feedback. + Only relevant for Pop III calculations. + J21LW_interp : interpolator, None or False, optional + LW background interpolator used for Pop III SFR feedback. + Only relevant for Pop III calculations. + + Attributes + ---------- + bin_centers : array + Centers of the LF bins for the selected band. + bin_widths : array + Widths of the LF bins for the selected band. + pop2 : LFbias_core_output or None + Pop II LF/bias output, if requested. + pop3 : LFbias_core_output or None + Pop III LF/bias output, if requested. + tot : LFbias_core_output or None + Total LF/bias output, if requested. + """ + + def __init__(self, LF_Init, CosmoParams, AstroParams, HMFinterp, LFParams, which_band, vCB=None, J21LW_interp=None): + + if which_band == "UV": + self.bin_centers = LFParams.MUVcenters + self.bin_widths = LFParams.MUVwidths + elif which_band == "Ha": + self.bin_centers = LFParams.log10LHacenters + self.bin_widths = LFParams.log10LHawidths + else: + raise ValueError("Only UV and Ha LF can be computed.") + + + self.pop2 = None + self.pop3 = None + self.tot = None + + if not LFParams.SKIP_POPII: + self.pop2 = LF_Init.compute_pop_LFbias_binned(CosmoParams, AstroParams, HMFinterp, LFParams, which_band, 2) + + if not LFParams.SKIP_POPIII: + if not AstroParams.USE_POPIII: + raise ValueError("Attempting to compute Pop III LF/bias with AstroParams.USE_POPIII=False.") + self.pop3 = LF_Init.compute_pop_LFbias_binned(CosmoParams, AstroParams, HMFinterp, LFParams, which_band, 3, vCB, J21LW_interp) # Note that vCB and LW are only needed for Pop III + + if not LFParams.SKIP_TOT: + available_pops = [ + pop for pop in (self.pop2, self.pop3) + if pop is not None + ] + if len(available_pops) == 0: + raise ValueError("Cannot compute total LF/bias because no population output has been computed.") + elif len(available_pops) == 1: + self.tot = available_pops[0] # Note that this should avoid duplication, as when only one of the populations is computed this is just an alias to that population, avoiding memory wastes + else: + self.tot = available_pops[0] + available_pops[1] + + +class LFbias_core_output: # AV: new output class grouping LF and bias output, functionally replacing the old dictionary logic resulting from the core computation in compute_LFbias_binned_from_avgsigma + """ + Output container for a single LF/bias component. + + This class stores the luminosity-function output and the corresponding bias numerator for a single component. + + Parameters + ---------- + LF : array or None, optional + Binned luminosity function, when computed. + bias_num : array or None, optional + Numerator of the LF-weighted halo bias, when computed. + + Attributes + ---------- + LF : array or None + Binned luminosity function. + bias_num : array or None + Numerator of the LF-weighted halo bias. + + Properties + ---------- + bias : array + LF-normalized average bias, computed as ``bias_num / LF``. + This property requires both ``LF`` and ``bias_num`` to be available. + + Notes + ----- + Adding two ``LFbias_outputs`` objects returns a new ``LFbias_outputs`` + object whose available fields are the sum of the corresponding fields. + This is used to build total outputs from population-level outputs. + """ + + + def __init__(self, LF=None, bias_num=None): + + self.LF = LF + self.bias_num = bias_num + + + @property + def bias(self): # AV: this now also allows to return the normalized bias directly; previous "bias" quantity renamed to "bias_num" for clarity + """ + Bias normalized by the LF. + The raw 'bias' returned by the LF machinery is bias_num = b * LF. + """ + if self.LF is None: + raise ValueError("Cannot compute normalized bias, because corresponding LF is not computed.") + + if self.bias_num is None: + raise ValueError("Cannot compute normalized bias, because bias_num is not computed.") + + return self.bias_num/self.LF + + + def __add__(self, other): + """ + Returns a new ``LFbias_outputs`` object whose available fields are the sum of the corresponding fields. + """ + + LF = None + bias_num = None + + if self.LF is not None and other.LF is not None: + LF = self.LF + other.LF + elif self.LF is None and other.LF is None: + LF = None + else: + raise ValueError("Cannot sum population outputs: LF is present only in one population.") + + if self.bias_num is not None and other.bias_num is not None: + bias_num = self.bias_num + other.bias_num + elif self.bias_num is None and other.bias_num is None: + bias_num = None + else: + raise ValueError("Cannot sum population outputs: bias_num is present only in one population.") + + return LFbias_core_output(LF, bias_num) + + + +class LF_class: + """ + Compute all quantities and methods associated with luminosity functions + + Parameters + ---------- + UserParams : User_Parameters + CosmoParams : Cosmo_Parameters + AstroParams : Astro_Parameters + HMFinterp : HMF_interpolator + LFParams : LF_Parameters + z_Init : Z_init or None, optional + Initial redshift matrices to be used in full-SFRD and PSD calculations. + Only instantiated when a full SFRD object is required. If None (default), initialized internally. + SFRD_Init : SFRD_class or None, optional + Precomputed SFRD object. + Only instantiated when a full SFRD object is required i.e. for PSD calculations, and for Pop III non-PSD calculations when LW is not provided explicitly. If None (default), initialized internally. + SFH_Init : SFH_class or None, optional + Precomputed SFH object to be used in PSD calculations. + Only instantiated for PSD calculations. If None (default), initialized internally. + vCB : float, None or False, optional + Baryon-CDM relative streaming velocity used for Pop III non-PSD SFR feedback. + If None (default), cosmological mean from ``CosmoParams`` is used. + False to fully disable streaming velocity feedback. + J21LW_interp : interpolator, None or False, optional + LW background interpolator as a function of redshift used for Pop III SFR feedback. + If None (default), the converged background from ``SFRD_Init`` is used. + False to fully disable LW feedback. + + Attributes + ---------- + z_Init : Z_init class + Initial redshift matrices used by full-SFRD and PSD calculations, when initialized. + SFRD_Init : SFRD_class + Full or lightweight SFRD object used to evaluate SFRs and, when available, self-consistent LW backgrounds. + SFH_Init : SFH_class + SFH object used for PSD-based observables. + DZ_TOINT : array + Redshift offsets, in units of ``LFParams.zwidth``, used to average the HMF over the redshift bin. + WEIGHTS_TOINT : array + Gaussian weights associated with ``DZ_TOINT``. + biasM : array + Tinker halo bias evaluated at the redshift samples used for the LF bin. + Only defined when bias is requested as an output in ``LFParams``. + UV : LFbias_band_outputs + UV luminosity-function and bias output containter object, when requested by ``LFParams.FLAG_COMPUTE_UVLF``. + Ha : LFbias_band_outputs + Halpha luminosity-function and bias output container object, when requested by ``LFParams.FLAG_COMPUTE_HaLF``. + """ + + def __init__(self, UserParams, CosmoParams, AstroParams, HMFinterp, LFParams, z_Init=None, SFRD_Init=None, SFH_Init=None, vCB=None, J21LW_interp=None): + + # Evaluate whether or not a full SFRD init is needed or light init is enough + need_full_SFRD = ( + AstroParams.FLAG_USE_PSD # TODO: here for safety as PSD branch has not been tested, check if actually needed + or (AstroParams.USE_POPIII and not LFParams.SKIP_POPIII and J21LW_interp is None) # In standard computation, full init is only needed if we want self-consistent LW background for Pop IIIs; if no Pop IIIs, or LW given as input, light init is enough + ) + + # SFRD instantiation + if z_Init is not None: + self.z_Init = z_Init + elif need_full_SFRD: + self.z_Init = Z_init(UserParams, CosmoParams) + + if SFRD_Init is not None: + self.SFRD_Init = SFRD_Init + elif need_full_SFRD: + self.SFRD_Init = SFRD_class(UserParams, CosmoParams, AstroParams, HMFinterp, self.z_Init) + else: + # Lightweight SFRD object, enabling to import relevant methods for SFR calculation without computing global SFRD, LW background, reionization, gamma coefficients, etc... + self.SFRD_Init = SFRD_class.light_init() + + + # SFH instantiation + if AstroParams.FLAG_USE_PSD: + if SFH_Init is not None: + self.SFH_Init = SFH_Init + else: + self.SFH_Init = SFH_class(UserParams, CosmoParams, AstroParams, HMFinterp, AstroParams._tagesMyr, LFParams.zcenter, self.z_Init, self.SFRD_Init) + + + # Set redshift offsets and weights + if(constants.NZ_TOINT>1): + self.DZ_TOINT = np.linspace(-np.sqrt(constants.NZ_TOINT/3.), np.sqrt(constants.NZ_TOINT/3.),constants.NZ_TOINT) # in sigmas around zcenter + else: + self.DZ_TOINT = np.array([0.0]) + + self.WEIGHTS_TOINT = np.exp(-self.DZ_TOINT**2/2.)/np.sum(np.exp(-self.DZ_TOINT**2/2.)) # Assumed Gaussian in z, fair + + + # Save Tinker halo bias only when bias requested as output + if LFParams.RETURNBIAS: + self.biasM = np.array([bias_Tinker(CosmoParams, HMFinterp.sigma_int(HMFinterp.Mhtab,LFParams.zcenter+dz*LFParams.zwidth)) for dz in self.DZ_TOINT]) + + # Compute UVLF/bias if requested and save outputs + if LFParams.FLAG_COMPUTE_UVLF: + self.UV = LFbias_band_outputs(self, CosmoParams, AstroParams, HMFinterp, LFParams, "UV", vCB, J21LW_interp) # AV: changed delegating to new outputs classes instead of the old compute_LFbias_binned method, same for HaLF outputs + + # Compute LF/bias if requested and save outputs + if LFParams.FLAG_COMPUTE_HaLF: + self.Ha = LFbias_band_outputs(self, CosmoParams, AstroParams, HMFinterp, LFParams, "Ha", vCB, J21LW_interp) + + + def __getattr__(self, name): + """ + Provide optional flat aliases for LF and bias outputs, without having to specify which output class they come from. + Supported aliases delegated to _parse_flat_LFbias_name. + + Parameters + ---------- + name : str + Name of the attribute to get + + Returns + ------- + array + Requested LF or normalized bias array, if field present, or raises an AttributeError. + """ + # This method is called by Python only when normal attribute lookup fails. Added by AV, mimickingIt mimics the user-facing behavior of ``get_T21_coefficients.__getattr__``, where selected quantities stored in internal objects can be accessed from the main output object. However, here the delegation is intentionally more limited: only explicitly supported LF/bias aliases are parsed + + # Try to interpret the missing attribute name as one of the supported flat LF/bias aliases, and convert into corresponding nested path, e.g. "UVLF_tot" -> ("UV", "tot", "LF") + parsed = self._parse_flat_LFbias_name(name) + if parsed is None: + raise AttributeError(f"{type(self).__name__} has no attribute {name!r}") + band_attr, component_attr, quantity_attr = parsed + + # Retrieve the band output object, e.g. self.UV or self.Ha if found + # NOTE: We use self.__dict__.get(...) rather than getattr(self, band_attr), because getattr could call __getattr__ again if the band was not created, leading to unnecessary recursion. + band_output = self.__dict__.get(band_attr, None) + if band_output is None: + raise AttributeError(f"{type(self).__name__} has no attribute {name!r}") + + # Retrieve the population component from the band output, e.g. self.UV.pop2, self.UV.pop3, self.UV.tot, if found + component_output = getattr(band_output, component_attr, None) + if component_output is None: + raise AttributeError(f"{type(self).__name__} has no attribute {name!r}") + + # Retrieve the requested quantity from the component, e.g. .LF, .bias_num or .bias, if found + return getattr(component_output, quantity_attr) + + @staticmethod + def _parse_flat_LFbias_name(name): + """ + Parse flat LF/bias aliases into nested output paths. + + Parameters + ---------- + name : str + Attribute name requested on the ``LF_class`` instance + + Returns + ------- + tuple or None + ``(band_attr, component_attr, quantity_attr)`` if ``name`` matches a supported alias. + + Examples: + ``"UVLF_tot"`` returns ``("UV", "tot", "LF")``. + ``"UVbias_pop2"`` returns ``("UV", "pop2", "bias")``. + ``"HaLF_pop3"`` returns ``("Ha", "pop3", "LF")``. + """ + # AV: New helper translating user-facing flat names into the corresponding internal attribute path used by ``LF_class``. It is intentionally strict: only known prefixes and known component names are accepted. This keeps the ``__getattr__`` behavior close in spirit to ``get_T21_coefficients``, but avoids making arbitrary ambiguous quantities available at the top level. + + # Map of supported aliases + FLAT_LFBIAS_ALIASES = { + "UVLF_pop2": ("UV", "pop2", "LF"), + "UVLF_pop3": ("UV", "pop3", "LF"), + "UVLF_tot": ("UV", "tot", "LF"), + + "UVbias_num_pop2": ("UV", "pop2", "bias_num"), + "UVbias_num_pop3": ("UV", "pop3", "bias_num"), + "UVbias_num_tot": ("UV", "tot", "bias_num"), + + "UVbias_pop2": ("UV", "pop2", "bias"), + "UVbias_pop3": ("UV", "pop3", "bias"), + "UVbias_tot": ("UV", "tot", "bias"), + + "HaLF_pop2": ("Ha", "pop2", "LF"), + "HaLF_pop3": ("Ha", "pop3", "LF"), + "HaLF_tot": ("Ha", "tot", "LF"), + + "Habias_num_pop2": ("Ha", "pop2", "bias_num"), + "Habias_num_pop3": ("Ha", "pop3", "bias_num"), + "Habias_num_tot": ("Ha", "tot", "bias_num"), + + "Habias_pop2": ("Ha", "pop2", "bias"), + "Habias_pop3": ("Ha", "pop3", "bias"), + "Habias_tot": ("Ha", "tot", "bias"), + } + + return FLAT_LFBIAS_ALIASES.get(name, None) + + + ### Luminosity to log-luminosity/magnitude convert functions and vice versa + # TODO: unify in two simple Mag_of_L and L_of_Mag functions giving the type of input (Lnu or nuLnu) and units as input to the function to avoid duplication? + + def Hz_from_angstrom(self, wavelength=1500.): + """ + Convert rest-frame wavelength in Angstrom to frequency in Hz. + + Parameters + ---------- + wavelength : float, optional + Rest-frame wavelength in Angstrom. Default is 1500. + + Returns + ------- + nu : float + Frequency in Hz. + """ + + return constants.c_kms / (wavelength/1e13) + + + def Mag_of_L_ergsHz(self, L): + """ + Convert specific luminosity in erg/s/Hz to AB absolute magnitude (from 1703.02913). + + Parameters + ---------- + L : float or array + Specific luminosity (L_nu) in erg/s/Hz. + + Returns + ------- + Mag : float or array + AB absolute magnitude. + """ + + return constants.zeropoint_ABmag_ergsHz - 2.5 * np.log10(L) # AB magnitude + + def Mag_of_L_ergs(self, L, wavelength=1500.): + """ + Convert specific luminosity in erg/s to AB absolute magnitude (from 1703.02913). + + Parameters + ---------- + L : float or array + Specific luminosity (nuL_nu) in erg/s. + wavelength : float, optional + Rest-frame wavelength in Angstrom used to convert nuL_nu to L_nu. Default is 1500. + + Returns + ------- + Mag : float or array + AB absolute magnitude. + """ + + return self.Mag_of_L_ergsHz(L/self.Hz_from_angstrom(wavelength)) + + + def L_ergsHz_of_Mag(self, Mag): + """ + Convert AB absolute magnitude to specific luminosity in erg/s/Hz (from 1703.02913). + + Parameters + ---------- + Mag : float or array + AB absolute magnitude. + + Returns + ------- + L : float or array + Specific luminosity (L_nu) in erg/s/Hz. + """ + + return 10**(0.4 * (constants.zeropoint_ABmag_ergsHz - Mag)) + + def L_ergs_of_Mag(self, Mag, wavelength=1500.): + """ + Convert AB absolute magnitude to specific luminosity in erg/s (from 1703.02913). + + Parameters + ---------- + Mag : float or array + AB absolute magnitude. + wavelength : float, optional + Rest-frame wavelength in Angstrom. Default is 1500. + + Returns + ------- + L : float or array + specific luminosity (nuL_nu) in erg/s. + """ + + return self.L_ergsHz_of_Mag(Mag) * self.Hz_from_angstrom(wavelength) + + + def logorMag_of_L(self, L, which_band, renormalize_L, sigma=None): + """ + Convert luminosity to the LF observable coordinate (log-luminosity/magnitude). + Mean luminosity can be shifted so that a lognormal scatter preserves the linear mean. + + Parameters + ---------- + L : float or array + Average luminosity. + For ``which_band`` = "UV", it is interpreted as specific luminosity in erg/s/Hz (L_nu). + For ``which_band`` = "Ha", it is intepreted as integrated line luminosity (L_Halpha) in erg/s. + which_band : {"UV", "Ha"} + Observable band to compute. + renormalize_L : bool + Whether to renormalize the linear luminosity before converting to the LF coordinate. + sigma : float or array, optional + Scatter used for the renormalization. Required when ``renormalize_L`` is True. + + Returns + ------- + logL_or_mag : float or array + UV magnitude for ``which_band`` = "UV" or log10(L_Halpha) for ``which_band`` = "Ha". Non-finite values are + replaced by arbitrarily faint values. + """ + + # Select desired band + if which_band not in ["UV", "Ha"]: + raise ValueError("Conversion from luminosity to log-luminosity/magnitude only implemented for 'UV' and 'Ha'.") + + # Lower the avg luminosity to recover the true avg, instead of log-avg, when applying lognormal scatter sigma + # TODO: Note that in-place modification of L if np.ndarray here! If this is not what we want, we should have a local copy of L in the function instead + if renormalize_L: + if sigma is None: + raise ValueError("Requested luminosity renormalization after converting to log-luminosity/magnitude when applying lognormal scatter, but no provided value for the scatter.") + + if which_band == "UV": + L /= np.exp((np.log(10)/2.5 * sigma)**2 / 2.0) + + elif which_band == "Ha": + L /= np.exp((np.log(10) * sigma)**2 / 2.0) + + + # Convert luminosity to log-luminosity/magnitude + if which_band == "UV": + logLormag = self.Mag_of_L_ergsHz(L) # TODO: also implement nuFnu conversion for UV, as right now this assumes specific luminosity in erg/s/Hz --> note that for Halpha and other emission lines this is not needed, as line luminosities are always an integrated flux measure emerging from continuum, not continuum per unit frequency/wavelength + bad_value_fix = 100.0 # TODO: adjustable parameter in LFParameters, or constants? + + elif which_band == "Ha": + logLormag = np.log10(L) # Note that this a safe fallback for emission lines in general, not only Halpha + bad_value_fix = -100.0 # TODO: adjustable parameter in LFParameters, or constants? + + + # Replace "bad values" in return + return np.where(np.isfinite(logLormag), logLormag, bad_value_fix) + + + + def compute_pop_LFbias_binned(self, CosmoParams, AstroParams, HMFinterp, LFParams, which_band, pop, vCB=None, J21LW_interp=None): + """ + Compute a single population (Pop III or Pop II) contribution to a binned luminosity function. + + Parameters + ---------- + CosmoParams : Cosmo_Parameters + AstroParams : Astro_Parameters + HMFinterp : HMF_interpolator + LFParams : LF_Parameters + which_band : {"UV", "Ha"} + Which LF band to compute. + pop : int + Stellar population, 2 for Pop II or 3 for Pop III. + vCB : float, None or False, optional + Baryon-CDM relative streaming velocity used for Pop III non-PSD SFR feedback. + If None (default), cosmological mean from ``CosmoParams`` is used. + False to fully disable streaming velocity feedback. + J21LW_interp : interpolator, None or False, optional + LW background interpolator as a function of redshift used for Pop III SFR feedback. + If None (default), the converged background from ``SFRD_Init`` is used. + False to fully disable LW feedback. + + Returns + ------- + outputs : Same output type as returned by the core method ``compute_LFbias_binned_from_avgsigma`` + """ + + # Error if not using Pop III stars in the AstroParams but Pop III LF calculation requested + if pop == 3 and not AstroParams.USE_POPIII: + raise ValueError('Attempting to compute Pop III LFs with USE_POPIII = False') + + # Error for Halpha calculation requested for Pop III + if which_band == "Ha" and pop == 3: + raise ValueError('PopIII are not implemented for Ha') + + + # PSD calculation, in which MUV and sigmaUV derived from integrating SFH + # TODO: check, simply refactored from previous version with no functional change + if AstroParams.FLAG_USE_PSD: + + if pop == 3 and AstroParams.DETACH_III_ACH: + raise ValueError("LF calculation from PSD not implemented for Pop IIIs with a detached atomic-cooling component") + + if which_band == "UV": + return self.compute_LFbias_binned_from_PSD(CosmoParams, AstroParams, HMFinterp, LFParams, + LFParams.zcenter, LFParams.zwidth, LFParams.MUVcenters, LFParams.MUVwidths, + which_band, pop, LFParams.DUST_FLAG, + LFParams.RETURNLF, LFParams.RETURNBIAS) + + elif which_band == "Ha": + return self.compute_LFbias_binned_from_PSD(CosmoParams, AstroParams, HMFinterp, LFParams, + LFParams.zcenter, LFParams.zwidth, LFParams.log10LHacenters, LFParams.log10LHawidths, + which_band, pop, LFParams.DUST_FLAG, + LFParams.RETURNLF, LFParams.RETURNBIAS) + + else: + raise ValueError('Only UV and Ha LF can be computed.') + + + # Standard Munoz+23 model: mean LUV \propto SFR \propto Mhdot*fstar + lognormal/gaussian scatter set by sigma + else: + + if which_band == "UV": + + # For Pop III case, read vCB from CosmoParams and LW background from SFRD init if not explicitly provided + # TODO: implement vCB and LW feedback in PSD case too? + if pop == 3: + + # TODO: None option with defaults can be implemented directly in sfrd.Mmol + if vCB is None: + vCB = CosmoParams.vcb_avg + + if J21LW_interp is None: + # Safety check: if LW not explicitly provided, we raise an error if it's not available in SFRD_Init (this should not happen: if USE_POPIII = True and LW not explicitly provided, SFRD should be fully instantiated in the LF init) + if hasattr(self.SFRD_Init, "J21LW_interp_conv_avg"): + J21LW_interp = self.SFRD_Init.J21LW_interp_conv_avg + else: + raise ValueError("Pop III LF without full SFRD initialization requires an external J21LW_interp passed to the LF_class") + + + # Case of Pop IIIs with a detached ACH component: here we explicitly compute SFRs for the two components separately so that we can apply different LFParams to them and compute separate LFs to be summed at the end + # TODO: for now, the case with detached Pop III ACH component is isolated here, but we could integrate it in the general case below by implementing a separate poulation type, e.g. 3.II? This could be extended to other population types e.g. different morphological types etc... (to be consistently modified in sfrd.py) + if pop == 3 and AstroParams.DETACH_III_ACH: + + # To detach ACH component, we use a separate set of AstroParams for the main component (minihalo-only component, extended up to Mup_III="Matom") and the additional ACH component (with the main Pop III component put to 0 through its epsstar value) + AstroParams_main = copy(AstroParams) + AstroParams_main.DETACH_III_ACH = False + AstroParams_main.Mup_III = "Matom" + + AstroParams_ACH = copy(AstroParams) + AstroParams_ACH.DETACH_III_ACH = True + AstroParams_ACH.epsstar_III = 0.0 + + SFRlist_main = self.SFRD_Init.SFR(CosmoParams, AstroParams_main, HMFinterp, HMFinterp.Mhtab, LFParams.zcenter, pop, vCB, J21LW_interp) + SFRlist_ACH = self.SFRD_Init.SFR(CosmoParams, AstroParams_ACH, HMFinterp, HMFinterp.Mhtab,LFParams.zcenter, pop, vCB, J21LW_interp) + + outputs_main = self.compute_LFbias_binned_from_SFRlist(SFRlist_main, HMFinterp, LFParams, + LFParams.zcenter, LFParams.zwidth, LFParams.MUVcenters, LFParams.MUVwidths, + LFParams._kappaUV_III, LFParams.sigmaUV_III, LFParams.FLAG_RENORMALIZE_LUV, which_band, LFParams.DUST_FLAG_III, + LFParams.RETURNLF, LFParams.RETURNBIAS) + outputs_ACH = self.compute_LFbias_binned_from_SFRlist(SFRlist_ACH, HMFinterp, LFParams, + LFParams.zcenter, LFParams.zwidth, LFParams.MUVcenters, LFParams.MUVwidths, + LFParams._kappaUV_III_ACH, LFParams.sigmaUV_III_ACH, LFParams.FLAG_RENORMALIZE_LUV, which_band, LFParams.DUST_FLAG_III_ACH, + LFParams.RETURNLF, LFParams.RETURNBIAS) + + return outputs_main + outputs_ACH # AV: updated to reflect changes in the return output of compute_LFbias_binned_from_avgsigma using the new LFbias_core_output class; now simpler, and the logic for the sum is in the output class instead, so even more independent! + + + # General case, applying population-specific LFParams + else: + + SFRlist = self.SFRD_Init.SFR(CosmoParams, AstroParams, HMFinterp, HMFinterp.Mhtab, LFParams.zcenter, pop, vCB, J21LW_interp) + + if pop == 3: + sigmaUV = LFParams.sigmaUV_III + kappaUV = LFParams._kappaUV_III + include_dust = LFParams.DUST_FLAG_III # TODO: custom dust model for Pop IIIs + enforce separate population dust flags also in the PSD case? + elif pop == 2: + sigmaUV = LFParams.sigmaUV + kappaUV = LFParams._kappaUV + include_dust = LFParams.DUST_FLAG + + return self.compute_LFbias_binned_from_SFRlist(SFRlist, HMFinterp, LFParams, + LFParams.zcenter, LFParams.zwidth, LFParams.MUVcenters, LFParams.MUVwidths, + kappaUV, sigmaUV, LFParams.FLAG_RENORMALIZE_LUV, which_band, include_dust, + LFParams.RETURNLF, LFParams.RETURNBIAS) + + + elif which_band == "Ha": + raise ValueError('FLAG_USE_PSD=False not implemented for Halpha LF.') + + else: + raise ValueError('Only UV and Ha LF can be computed.') + + + def compute_LFbias_binned_from_SFRlist(self, SFRlist, HMFinterp, LFParams, zcenter, zwidth, logLcenters, logLwidths, kappa, sigma, renormalize_L, which_band, include_dust, computeLF, computeBias): + """ + Compute binned LF outputs from a precomputed SFR list. + + Parameters + ---------- + SFRlist : array + Star formation rate evaluated on ``HMFinterp.Mhtab``. + HMFinterp : HMF_interpolator + LFParams : LF_Parameters + zcenter, zwidth : float + Redshift bin center and width. + logLcenters, logLwidths : array + LF bin centers and widths. These are UV magnitudes bins for ``which_band`` = "UV" and log10(L_Ha) bins for ``which_band`` = "Halpha". + kappa : float + SFR-to-luminosity conversion factor. + sigma : float or array + Scatter in the LF observable coordinate. + renormalize_L : bool + Whether to renormalize the luminosity before adding lognormal scatter to preserve the linear mean. + which_band : {"UV", "Ha"} + Which LF band to compute. + include_dust : bool + Whether to apply dust corrections. + computeLF, computeBias : bool + Select which output entries to compute. + + Returns + ------- + outputs : Same output type as returned by the core method ``compute_LFbias_binned_from_avgsigma``. + """ + + if not computeLF and not computeBias: + raise ValueError("No return options for LF computation from SFRlist.") + + + # Average luminosity + L_avglist = SFRlist / kappa # SFR to luminosity conversion for each Mh + + # Luminosity to log-luminosity/magnitude conversion + logL_avglist = self.logorMag_of_L(L_avglist, which_band, renormalize_L, sigma) + + # Avoid numerical issues with zero sigma --> TODO: min. safe sigma in LFParams? Note that potential inconsistency with PSD calculation, in which the min. is set to 0.1 + sigma = np.fmax(sigma, 0.2) + + + # Dust correction applied to log-luminosity/magnitude list and sigma if necessary + if include_dust: + logL_avglist, sigma = self.apply_dust_correction(LFParams, zcenter, logL_avglist, sigma, which_band) # TODO: note that all of the core parameters of the LF calculation here are given explicitely for a single population types, so LFParams is only used for the dust properties, see comment in apply_dust_correction + + + # LF core computation + return self.compute_LFbias_binned_from_avgsigma(logL_avglist, sigma, HMFinterp, zcenter, zwidth, logLcenters, logLwidths, computeLF, computeBias) + + + def compute_LFbias_binned_from_PSD(self, CosmoParams, AstroParams, HMFinterp, LFParams, zcenter, zwidth, logLcenters, logLwidths, which_band, pop, include_dust, computeLF, computeBias): + """ + Compute binned LF outputs from PSD-derived observable statistics. + + Parameters + ---------- + CosmoParams : Cosmo_Parameters + AstroParams : Astro_Parameters + HMFinterp : HMF_interpolator + LFParams : LF_Parameters + zcenter, zwidth : float + Redshift bin center and width. + logLcenters, logLwidths : array + LF bin centers and widths. + which_band : {"UV", "Ha"} + Which LF band to compute. + pop : int + Stellar population, 2 for Pop II or 3 for Pop III. + include_dust : bool + Whether to apply dust corrections. + computeLF, computeBias : bool + Select which output entries to compute. + + Returns + ------- + outputs : Same output type as returned by the core method ``compute_LFbias_binned_from_avgsigma``. + """ + # TODO: check, simply refactored from previous version with no functional change + + if which_band == "UV": + LUV_short, sigmaLUV_short = self.meanandsigma_observable_PSD(CosmoParams, AstroParams, HMFinterp, LFParams, Greens_function_LUV_Short, pop) + + LUV_long, sigmaLUV_long = self.meanandsigma_observable_PSD(CosmoParams, AstroParams, HMFinterp, LFParams, Greens_function_LUV_Long, pop) + + logL_avglist, sigma = self.sigma_MUV_from_meansandsigmas(LUV_short, LUV_long, sigmaLUV_short, sigmaLUV_long) + + logL_avglist = np.fmin(logL_avglist, constants._MAGMAX_UV) + + + elif which_band == "Ha": + + L_avglist, sigma_ln = self.meanandsigma_observable_PSD(CosmoParams, AstroParams, HMFinterp, LFParams, Greens_function_LHa, pop) + + logL_avglist = mean_log10(L_avglist, sigma_ln) + sigma = sigma_log10(L_avglist, sigma_ln) + + logL_avglist = np.fmax(logL_avglist,constants._MAGMIN_Ha) # Cut to avoid -inf + + sigma = np.fmax(sigma, 0.1) # Avoid numerical issues with zero sigma --> TODO: min. safe sigma in LFParams? Note that potential inconsistency with standard calculation, in which the min. is set to 0.2 + + else: + raise ValueError('Only UV and Ha LF can be computed.') + + + # Dust correction applied to log-luminosity/magnitude list and sigma if necessary + if include_dust: + logL_avglist, sigma = self.apply_dust_correction(LFParams, zcenter, logL_avglist, sigma, which_band) + + + # LF core computation + return self.compute_LFbias_binned_from_avgsigma(logL_avglist, sigma, HMFinterp, zcenter, zwidth, logLcenters, logLwidths, computeLF, computeBias) + + + def compute_LFbias_binned_from_avgsigma(self, logL_avglist, sigma, HMFinterp, zcenter, zwidth, logLcenters, logLwidths, computeLF, computeBias): + """ + Compute LF and/or bias given the average log-luminosity/magnitude associated with each halo mass and its lognormal scatter. + This is the common numerical core for LF computation, shared by the SFR-list and PSD branches: it convolves the mean LF coordinate at each halo mass with a Gaussian scatter and integrates over the HMF. + + Parameters + ---------- + logL_avglist : array + Mean LF coordinate (UV magnitude or log10(L_Halpha)) at each halo mass. + sigma : float or array + Scatter in the same coordinate. + HMFinterp : HMF_interpolator + zcenter, zwidth : float + Redshift bin center and width. + logLcenters, logLwidths : array + LF bin centers and widths. + computeLF, computeBias : bool + Select which output entries to compute. + + Returns + ------- + output : LFbias_outputs + Output object containing LF and/or bias_num. + """ + + cuthi = logLcenters + logLwidths/2. + cutlo = logLcenters - logLwidths/2. + + xhi = np.subtract.outer(cuthi, logL_avglist)/(np.sqrt(2) * sigma) + xlo = np.subtract.outer(cutlo, logL_avglist)/(np.sqrt(2) * sigma) + weights = (erf(xhi) - erf(xlo)).T/(2.0 * logLwidths) + + HMFtab = np.array([HMFinterp.HMF_int(HMFinterp.Mhtab, zcenter + dz*zwidth) for dz in self.DZ_TOINT]) + + # AV: below changed to use the LFbias_output class instead of dictionaries, all the rest should stay consistent + LF = None + bias_num = None + if computeLF: + HMFcurr = np.sum(self.WEIGHTS_TOINT * HMFtab.T, axis=1) + LF = np.trapezoid(weights.T * HMFcurr, HMFinterp.Mhtab, axis=-1) # TODO: check consistency without fduty + if computeBias: # TODO: compute actual average bias, already dividing by LF here? + halobiascurr = np.sum(self.WEIGHTS_TOINT * HMFtab.T * self.biasM.T, axis=1) + bias_num = np.trapezoid(weights.T * halobiascurr, HMFinterp.Mhtab, axis=-1) # TODO: check consistency without fduty + + return LFbias_core_output(LF, bias_num) + + + + #####Here the dust attenuation + def apply_dust_correction(self, LFParams, z, logL_or_mag, sigma, which_band): + """ + Apply dust attenuation to intrinsic LF coordinate(s) and scatter. + + Parameters + ---------- + LFParams : LF_Parameters + z : float + Redshift. + logL_or_mag : array + Intrinsic LF coordinate. + sigma : float or array + Intrinsic scatter. + which_band : {"UV", "Ha"} + Observable band. + + Returns + ------- + logL_or_mag_dust : array + Dust-corrected observed coordinate(s). + sigma_dust : float or array + Scatter after optional dust contribution. + """ + + # TODO: right now global dust correction parameters, we cannot chose different parameters for different components, we may want to isolate the dust model parameters in a separate class (also these may be useful for other observables other than LFs) + + # Cannot directly 'dust' the theoretical intrinsic magnitudes, since the properties of the IRX-beta relation are calibrated on observed MUV. Recursion instead, solving MUV_obs = MUV_intrinsic + A_UV(MUV_obs) iteratively + curr_logLormag = logL_or_mag + + curr2 = np.ones_like(curr_logLormag) + while(np.sum(np.abs((curr2-curr_logLormag)/curr_logLormag)) > 0.02): + curr2 = curr_logLormag + curr_logLormag = logL_or_mag + self.dust_attenuation(LFParams, z, curr_logLormag, which_band) + + if LFParams.sigma_times_AUV_dust != 0.: + sigma_dust = np.fmax(0.0, LFParams.sigma_times_AUV_dust) * self.dust_attenuation(LFParams, z, curr_logLormag, which_band) + sigma = np.sqrt(sigma**2 + sigma_dust**2) # Add dust sigma, if any, to the UV sigma + + return curr_logLormag, sigma + + + def dust_attenuation(self, LFParams, z, logL_or_mag, which_band): + """ + Return the mean attenuation for the requested observable. + For UV, this is given as a function of OBSERVED z and magnitude. If using on theoretical intrinsice magnitudes, iterate until convergence. + The ``LFParams.HIGH_Z_DUST`` flag controls whether to apply dust attenuation at higher z than 0 or set attenuation to 0. If true, fix betaUV for attenuation calculation at ``LFParams._zmaxdata`` redshift value. + Halpha dust attenuation not currently implemented. + + Parameters + ---------- + LFParams : LF_Parameters + z : float + Redshift. + logL_or_mag : array + Observed LF coordinate to be dust attenuated. + which_band : {"UV", "Ha"} + Observable band. + + Returns + ------- + Adust : array + Attenuation in magnitudes for UV. Halpha currently raises a + ``ValueError`` because no calibrated model is implemented. + """ + + if which_band == "UV": + + MUV = logL_or_mag + betacurr = self.betaUV_dust(LFParams, z, MUV) + + sigmabeta = 0.34 #from Bouwens 2014 + + Auv = LFParams.C0dust + 0.2*np.log(10)*sigmabeta**2 * LFParams.C1dust**2 + LFParams.C1dust * betacurr # TODO: ref? + Auv=Auv.T + if not (LFParams.HIGH_Z_DUST): + Auv*=np.heaviside(LFParams._zmaxdata - z, 0.5) + + Adust = np.fmax(Auv.T, 0.0) + + elif which_band == "Ha": + + raise ValueError("Halpha dust attenuation not implemented. Set DUST_FLAG=False when computing Halpha LF, or implement a calibrated A_Ha model.") + + 'Average attenuation A as a function of z and log10LHa.' + # TODO: made-up to see how to calibrate it. Unused in current implementation (set Ha DUST = False) + # Conjured approximation - lower at high z and fainter + + log10LHa = logL_or_mag + AHa = 0.5 * (1 + 0.3 * (log10LHa - 42.0)) + Adust = -0.4 * np.fmax(AHa, 0.0) # no negative dust attenuation + #-0.4* instead of +1* here since its log10L not mag + + return Adust + + + def betaUV_dust(self, LFParams, z, MUV): + """ + Compute the UV continuum slope used by the dust model, currently implementing Bowens+13,14 or Zhao+24 model. + + Parameters + ---------- + LFParams : LF_Parameters + z : float or array + Redshift. + MUV : float or array + UV absolute magnitude. + + Returns + ------- + beta : array + UV slope from the selected dust model. + """ + + if LFParams.DUST_model == "Bouwens13": + + 'Color as a function of redshift and mag, interpolated from Bouwens 2013-14 data.' + + zdatbeta = [2.5,3.8,5.0,5.9,7.0,8.0] + betaMUVatM0 = [-1.7,-1.85,-1.91,-2.00,-2.05,-2.13] + dbeta_dMUV = [-0.20,-0.11,-0.14,-0.20,-0.20,-0.15] + + _MUV0 = -19.5 + _c = -2.33 + + betaM0 = np.interp(z, zdatbeta, betaMUVatM0, left=betaMUVatM0[0], right=betaMUVatM0[-1]) + dbetaM0 = (MUV - _MUV0).T * np.interp(z, zdatbeta, dbeta_dMUV, left=dbeta_dMUV[0], right=dbeta_dMUV[-1]) + + sol1 = (betaM0-_c) * np.exp(dbetaM0/(betaM0-_c))+_c #for MUV > MUV0 + sol2 = dbetaM0 + betaM0 #for MUV < MUV0 + + return sol1.T * np.heaviside(MUV - _MUV0, 0.5) + sol2.T * np.heaviside(_MUV0 - MUV, 0.5) + + elif LFParams.DUST_model == "Zhao24": + + 'from https://arxiv.org/pdf/2401.07893.pdf, table 1' + + betaM0z0 = -1.58 + dbetaM0dz = -0.081 + + dbetaM0dMUVz0 = -0.216 + ddbetaM0dMUVdz = 0.012 + + MUV0 = -19.5 + betaM0 = betaM0z0 + z * dbetaM0dz + dbetaM0 = (MUV - MUV0).T * (dbetaM0dMUVz0 + ddbetaM0dMUVdz * z) + + sol2 = dbetaM0 + betaM0 #beta_M0 + db/dMUV|M0 (DeltaMUV)at M0=-19.5 + + return np.fmax(-3.0, sol2) #cap at -3 just in case + + + def correct_AP_LF(self, z, Deltaz, CosmoParams_data, CosmoParams, logLormag_data, Phi_data, errPhi_data, errPhi_asy_data = None, which_band = "UV"): + "Corrects the observed LF from the assumed cosmology CosmoParams to another with CosmoParams_out. Note: no dust correction since it's applied directly to theory->model" + # TODO: check if needed, function never used in LFs.py + + r_data = CosmoParams_data.chiofzint(z) #comoving distance + Vol_data = CosmoParams_data.chiofzint(z+Deltaz/2.0)**3 - CosmoParams_data.chiofzint(z-Deltaz/2.0)**3 #no need for 4pi/3 since it'll be a ratio + + r_out = CosmoParams.chiofzint(z) + Vol_out = CosmoParams.chiofzint(z+Deltaz/2.0)**3 - CosmoParams.chiofzint(z-Deltaz/2.0)**3 + + Phi_out = Phi_data * Vol_data/Vol_out + errPhi_out = errPhi_data * Vol_data/Vol_out + val = -5. if which_band == "UV" else 2. + logLormag_out = logLormag_data + val * np.log10(r_out/r_data) #linear change so it doesn't affect bin sizes + + if (errPhi_asy_data is not None): #for asymmetric errorbars, optional arg + errPhi_asy_out = errPhi_asy_data * Vol_data/Vol_out + return logLormag_out, Phi_out, errPhi_out, errPhi_asy_out + else: + return logLormag_out, Phi_out, errPhi_out + + + def meanandsigma_observable_PSD(self, CosmoParams, AstroParams, HMFinterp, LFParams, GreensFunction, pop): + """ + Compute PSD-derived mean and scatter for a given observable (e.g. LUV, Ha, etc...) from the power spectrum of the SFRD. + + Parameters + ---------- + CosmoParams : Cosmo_Parameters + AstroParams : Astro_Parameters + HMFinterp : HMF_interpolator + LFParams : LF_Parameters + GreensFunction : callable + Time-domain response function G(t) for the observable of interest. + pop : int + Stellar population, 2 for Pop II or 3 for Pop III. + + Returns + ------- + avgobs : array + Mean observable at each halo mass. + sigmaobs : array + Scatter of the observable at each halo mass. + """ + # TODO: check, simply refactored from previous version with no functional change + + #First get the mean observable at the given redshift and halo mass + _windowintages = GreensFunction(AstroParams, AstroParams._tagesMyr, HMFinterp.Mhtab) + + if pop == 2: + _SFHinages = self.SFH_Init.SFH_II + elif pop == 3: + _SFHinages = self.SFH_Init.SFH_III + + avgobs = np.trapezoid(_SFHinages*_windowintages, AstroParams._tagesMyr*1e6, axis=1) + + #Now get the sigma, first compute the power spectrum of the SFR from that of lnSFR: + #use the omegalist from the FFT, which is the same for all observables + #use the power spectrum from the FFT, which is the same for all observables + + omegalist, powerNL = self.SFH_Init._get_PowerSFR_NL_FFT_vectorized(AstroParams, HMFinterp.Mhtab) #freq in 1/Myr and power of SFR=e^x (x=lnSFR). First array is Nfft, second is Nm x Nfft + + #And FFT the window function for the integral: + _, windowfourier = self.SFH_Init.WindowFourier(CosmoParams, AstroParams, HMFinterp, self.SFRD_Init, GreensFunction, LFParams.zcenter, AstroParams._tagesMyr, pop) + + _whichomegakeep = np.logical_and(omegalist > AstroParams._omegamin, omegalist < AstroParams._omegamax) + + sigmaobs = np.sqrt(np.trapezoid(powerNL * np.abs(windowfourier)**2*_whichomegakeep, omegalist, axis=1)*2/(2*np.pi)) #times 2 because + and - freqs + + return avgobs, sigmaobs + + + def sigma_MUV_from_meansandsigmas(self, LUV1mean, LUV2mean, sigmaLUV1, sigmaLUV2): + """ + Combine short- and long-timescale UV statistics into mean UV magnitudes and scatter. These components are considered mostly uncorrelated. + + Parameters + ---------- + LUV1mean, LUV2mean : array + Mean UV luminosity components. + sigmaLUV1, sigmaLUV2 : array + Scatter of the UV luminosity components. + + Returns + ------- + MUV_avg : array + Mean UV magnitude. + sigma_MUV : array + UV-magnitude scatter. + """ + # TODO: check, simply refactored from previous version with no functional change + + #vectorize the inputs so it can read either scalar or array inputs + LUV1mean = np.asarray(LUV1mean) + LUV2mean = np.asarray(LUV2mean) + sigmaLUV1 = np.asarray(sigmaLUV1) + sigmaLUV2 = np.asarray(sigmaLUV2) + + _numberofMhs = len(LUV1mean) + if len(LUV2mean) != _numberofMhs or len(sigmaLUV1) != _numberofMhs or len(sigmaLUV2) != _numberofMhs: + raise ValueError("All input arrays must have the same length.") + # Initialize arrays to hold the results + MUVbar = np.zeros(_numberofMhs) + sigmaMUV = np.zeros(_numberofMhs) + for imh in range(_numberofMhs): + sigmaUV1 = sigma_log10(sigmaLUV1[imh],LUV1mean[imh])*np.log(10) + mu1 = mean_log10(sigmaLUV1[imh],LUV1mean[imh])*np.log(10) + sigmaUV2= sigma_log10(sigmaLUV2[imh],LUV2mean[imh])*np.log(10) + mu2 = mean_log10(sigmaLUV2[imh],LUV2mean[imh])*np.log(10) + + yvalues, PDF_y = pdf_fft_convolution(mu1, sigmaUV1, mu2, sigmaUV2) + lnyvalues, PDF_lny = pdf_log_transform(yvalues, PDF_y) + MUVvalues, PDF_MUV = self.Mag_of_L_ergs(np.exp(lnyvalues)), -2.5*np.log(10)*PDF_lny + _norm = np.trapezoid(PDF_MUV, MUVvalues) #normalization, should always be 1 but just in case + MUVbar[imh] = np.trapezoid(MUVvalues * PDF_MUV, MUVvalues)/_norm + sigmaMUV[imh] = np.sqrt(np.trapezoid((MUVvalues - MUVbar[imh])**2 * PDF_MUV, MUVvalues)/_norm) + + return MUVbar, sigmaMUV #NOTE: can be enhanced to return full PDF, but needs to know the size of the MUVvalues array. We dont need it yet so just return the mean and sigma + + + + + +class Ha_UV_ratio: # TODO: different file e.g. line_ratios.py? + """ + Compute the Halpha-to-UV luminosity ratio distribution. + + This helper combines the UV luminosity function, Halpha luminosities and PSD-derived covariance terms to evaluate the probability distribution of ``log10(L_Ha / L_UV)`` or the equivalent ``xi_ion`` quantity. + + Parameters + ---------- + UserParams : User_Parameters + CosmoParams : Cosmo_Parameters + AstroParams : Astro_Parameters + HMFinterp : HMF_interpolator + LFParams : LF_Parameters + z_Init : Z_init, optional + Precomputed redshift matrices. + SFRD_Init : SFRD_class, optional + Precomputed SFRD object. + SFH_Init : SFH_class, optional + Precomputed SFH object. + LF_Init : LF_class, optional + Precomputed LF object. If None, initialized internally. + """ + # TODO: check class and documentation + + def __init__(self, UserParams, CosmoParams, AstroParams, HMFinterp, LFParams, z_Init = None, SFRD_Init = None, SFH_Init = None, LF_Init = None): + + if not AstroParams.FLAG_USE_PSD: + raise ValueError('FLAG_USE_PSD=False not implemented in PDF_HaUV_ratio()') + + if AstroParams.USE_POPIII: + raise ValueError('USE_POPIII=True not implemented in PDF_HaUV_ratio()') + + + if z_Init is None: + self.z_Init = Z_init(UserParams=UserParams, CosmoParams=CosmoParams) + else: + self.z_Init = z_Init + + if SFRD_Init is None: + self.SFRD_Init = SFRD_class(UserParams, CosmoParams, AstroParams, HMFinterp, self.z_Init) + else: + self.SFRD_Init = SFRD_Init + + if LF_Init is None: + self.LF_Init = LF_class(UserParams, CosmoParams, AstroParams, HMFinterp, LFParams, z_Init=self.z_Init, SFRD_Init=self.SFRD_Init, vCB_input=False, J21LW_interp_input=False) + else: + self.LF_Init = LF_Init + + if SFH_Init is None: + self.SFH_Init = SFH_class(UserParams, CosmoParams, AstroParams, HMFinterp, AstroParams._tagesMyr, LFParams.zcenter, self.z_Init, self.SFRD_Init, ) + else: + self.SFH_Init = SFH_Init + + + def PDF_log10HaUVratio(self, LUV_mean, LHa_mean, sigmaLHa, sigmasquaredcross, log10etavalues): + """ + Returns the PDF of log10(LHa/LUV) at fixed Mh (NOTE: integrated over all MUVs). Assumed dust corrected! + + Parameters: + ----------- + LUVmean : array_like + The mean LUV value + LHa_mean : array_like + The mean LHa value + sigmaLHa : array_like + The sigma of LHa value + sigmasquaredcross : array_like + The cross sigma squared (sigma^2) of LHa and LUV + This is the covariance between LHa and LUV, computed from their window functions. From cross_sigma_squared_PSD. + + Returns: + -------- + log10etavalues : ndarray + The log10eta values (same for all input array elements) + PDFlog10eta : ndarray + Array of shape (len(LUVmean), len(log10etavalues)) with PDFs + """ + + AconstantLUVLHa = sigmasquaredcross/sigmaLHa**2 + BconstantLUVLHa = LUV_mean - AconstantLUVLHa * LHa_mean + _A, _B = AconstantLUVLHa, BconstantLUVLHa + + mean_of_log10LHa = np.log10(LHa_mean)- 1/2 * np.log10(1 + sigmaLHa**2/LHa_mean**2) + sigma_of_log10LHa = sigma_log10(sigmaLHa, LHa_mean) + + if log10etavalues is None: # If not provided, create a default range + log10etavalues = np.linspace(-3.5,-1.3,55) + + etavalues = 10**log10etavalues + _LHavalues = np.outer(etavalues,_B)/(1-np.outer(etavalues,_A)) #recalculate the LHa values from eta values + + muHa, sigmaHa = mean_of_log10LHa*np.log(10), sigma_of_log10LHa*np.log(10) #mean and std of ln(Ha), a gaussian varible + PDFLHalognormal = lognormal_pdf(_LHavalues, muHa, sigmaHa) + dydx = _B/(_B+_A*_LHavalues) * 1/(_LHavalues * np.log(10)) + + log10etavalues = log10etavalues + PDFlog10eta = PDFLHalognormal/np.abs(dydx) + + return log10etavalues, PDFlog10eta + + + def PDF_HaUV_ratio(self, CosmoParams, AstroParams, HMFinterp, LFParams, log10etavalues, FLAG_supersample_MUV = False): + ''' + Returns Ha/UV ratio PDF, binned in MUV_bin_edges and log10eta bins, if provided. + + Parameters: + ----------- + AstroParams : Astro Parameters + CosmoParams : Cosmo Parameters + HMFinterp : HMFinterp + zcenter, zwidth: the z where it is calculated (no width for now, ignored) + log10etavalues: the log10(Ha/UV) ratios where the PDF is computed. Assigned by function if None + FLAG_supersample_MUV : Whether to super-sample to integrate within MUV_bin_edges better. If =False then just computes at the center of each bin + + Returns: + -------- + + log10etavalues: bins of log10Ha/UV + pdf_binned: the PDF(log10etavalues) in those log10etavalues bins, and the MUV bins chosen + UVLFvalues: the UVLF at the MUV binned, so the user can sum stuff easily + + ''' + + if log10etavalues is None: # If not provided, create a default range + log10etavalues = np.linspace(-3.5,-1.0,20) + + HMFtab = HMFinterp.HMF_int(HMFinterp.Mhtab,LFParams.zcenter) + + meanLUVshort, meanLHa, sigmaLUVshort, sigmaLHa, sigmasqcross = self.cross_sigma_squared_PSD(AstroParams, CosmoParams, HMFinterp, Greens_function_LUV_Short,Greens_function_LHa, LFParams.zcenter) + + _Acoeff = sigmasqcross/sigmaLHa**2 + + meanLUVlong, sigmaLUVlong = self.LF_Init.meanandsigma_observable_PSD(CosmoParams, AstroParams, HMFinterp, LFParams, Greens_function_LUV_Long, pop=2) + + #get the UV-A*Ha "excess" UV luminosity, not exactly lognormal so use the same trick as for MUV: + meanLUV_excess_short = meanLUVshort - _Acoeff * meanLHa + sigmaLUV_excess_short = np.sqrt(sigmaLUVshort**2 + _Acoeff**2 * sigmaLHa**2 - 2.0 * _Acoeff * sigmasqcross) + MUVbar_excess, sigmaMUV_excess = self.LF_Init.sigma_MUV_from_meansandsigmas(meanLUV_excess_short, meanLUVlong, sigmaLUV_excess_short, sigmaLUVlong) + + + MUVavglist, sigmaMUV = self.LF_Init.sigma_MUV_from_meansandsigmas(meanLUVshort, meanLUVlong, sigmaLUVshort, sigmaLUVlong) + MUVavglist = np.fmin(MUVavglist,constants._MAGMAX_UV) + sigma_times_AUV_dust = np.fmax(0.0, LFParams.sigma_times_AUV_dust) #assumed constant, not derived from SFH + + #these are the parameters of the closest lognormal to each Ha, UV, and UVx + muHa, sigmaHa = mean_log10(sigmaLHa, meanLHa)*np.log(10), sigma_log10(sigmaLHa, meanLHa)*np.log(10) #mean and std of ln(LUVx), a gaussian varible + + muUV, sigmaUV = np.log(self.LF_Init.L_ergsHz_of_Mag(MUVavglist)), sigmaMUV*np.log(10)/2.5 + muUVx, sigmaUVx = np.log(self.LF_Init.L_ergsHz_of_Mag(MUVbar_excess)), sigmaMUV_excess*np.log(10)/2.5 + + if (FLAG_supersample_MUV==True): + _NLuvsupersample = 99 #number of LUV values to supersample + _LUVlist = np.logspace(35,46,_NLuvsupersample) #in case you want to integrate and then bin + else: + _LUVlist = self.LF_Init.L_ergsHz_of_Mag(LFParams.MUVcenters) #this will give mean of for each MUV bin + + + #This is used for assigning galaxies to MUV bins, so we add dust correction since the Ha/UV ratios are dust corrected but they're binned in MUVobs + currMUV = MUVavglist + currMUV2 = np.ones_like(currMUV) + while(np.sum(np.abs((currMUV2-currMUV)/currMUV)) > 0.02): + currMUV2 = currMUV + currMUV = MUVavglist + self.LF_Init.dust_attenuation(LFParams,LFParams.zcenter,currMUV,"UV") + + sigmaUV_dust = sigma_times_AUV_dust * self.LF_Init.dust_attenuation(LFParams,LFParams.zcenter,currMUV,"UV") + + sigmaMUV_obs = np.sqrt(sigmaMUV**2 + sigmaUV_dust**2) #add dust sigma, if any, to the UV sigma + sigmaMUV_obs = np.fmax(sigmaMUV_obs, 0.2) #avoid numerical issues with zero sigma + + muUV_obs, sigmaUV_obs = np.log(self.LF_Init.L_ergsHz_of_Mag(currMUV)), sigmaMUV_obs*np.log(10)/2.5 + PlnLUV = normal_pdf(np.log(_LUVlist), muUV_obs, sigmaUV_obs, dimy=1)+1e-99 #to avoid Nans + UVLFvalues = np.trapezoid(HMFtab[:,None] * PlnLUV, HMFinterp.Mhtab, axis=0) + + + #we exploit the fact that P(log10LHa - log10LHabar | LUV) doesnt change for LUV > LUVbar(Mh) + #so we set LUV = LUVbar(Mh) for each Mh. (we dont modify P(LUV) since that is the UVLF, not the Ha/UV ratio) + _meanLUV_ofMh = np.exp(muUV[:, None]) + _LUVforcalculation = np.minimum(_LUVlist[None,:], _meanLUV_ofMh) #Mh x LUVs, so that for LUV>LUVbar we recover the LUVbar result + PlnLUVforcalculation = normal_pdf(np.log(_LUVforcalculation), muUV, sigmaUV, dimy=1)+1e-99 #to avoid Nans + + _LHacalc = _LUVforcalculation[:,:,None] * 10**log10etavalues[None,None,:] + PlnLHa = normal_pdf(np.log(_LHacalc), muHa, sigmaHa, dimy=2) + + _LUVx = np.fmax(1.0, _LUVforcalculation[:,:,None] - _LHacalc [:,:,:] * _Acoeff[:, None,None]) #LUVx = _LUVforcalculation - A * LHa, where A is the coefficient for each MUV + PlnLUVx_fixedHa = normal_pdf(np.log(_LUVx), muUVx, sigmaUVx,dimy=2) + dlnLUV_dlnLUVx = _LUVx/_LUVforcalculation[:,:,None] + + PDF_lnLUV_fixedHa = PlnLUVx_fixedHa/np.abs(dlnLUV_dlnLUVx) + + Plog10eta_fixedLUV = PDF_lnLUV_fixedHa * PlnLHa/PlnLUVforcalculation[:,:,None] * np.log(10) #Plog10eta_fixedLUV = Plog10LHa_fixedLUV. Note PlnLUVforcalculation, since it is the PDF of LUV that we use in Bayes rule. Below its P(LUV) since we sum over the Prob that that Mh is in the MUV bin + + pdf_binned = np.trapezoid(HMFtab[:,None,None] * Plog10eta_fixedLUV * PlnLUV[:,:,None], HMFinterp.Mhtab, axis=0)/UVLFvalues[:,None] + + #if supersampling, re-bin in MUVs: + if (FLAG_supersample_MUV==True): + #weights is NMUVcenters x _NLuvsupersample, multiply pdf_binned which is _NLuvsupersample x Nlog10etavalues + + MUVleft_edges = LFParams.MUVcenters - LFParams.MUVwidths / 2 + MUVright_edges = LFParams.MUVcenters + LFParams.MUVwidths / 2 + + # full edges (length N+1) + MUV_bin_edges = np.concatenate([MUVleft_edges, [MUVright_edges[-1]]]) + + MUVcuthi = MUV_bin_edges[1:] + MUVcutlo = MUV_bin_edges[:-1] + MUVs = self.LF_Init.Mag_of_L_ergs(_LUVlist) #here we use _LUVlist since its for the P(LUV) not the Ha/UV ratio + xhi = np.heaviside(np.subtract.outer(MUVcuthi, MUVs),0.5) + xlo = np.heaviside(np.subtract.outer(MUVcutlo, MUVs),0.5) + MUVwidths = MUV_bin_edges[1:] - MUV_bin_edges[:-1] + weights = (xhi - xlo).T/(MUVwidths) + + UVLFvalues_binned = np.einsum('ij,i->j', weights, UVLFvalues) + pdf_binned = np.einsum('ji,jk->ik', weights, pdf_binned*UVLFvalues[:,None]) / UVLFvalues_binned[:,None] + + + return log10etavalues, pdf_binned, UVLFvalues + + + def log10eta_fromlog10xiion(self, log10xiion): + 'Returns log10(LHa/LUV) [both in erg/s] given xiion' + + _constxiionHaUV = 7.28e11 #erg/s/Hz + HatoUV = 1.0/self.LF_Init.L_ergs_of_Mag(self.LF_Init.Mag_of_L_ergsHz(1.))*10**log10xiion/_constxiionHaUV + + return np.log10(HatoUV) + + + def log10xiion_fromlog10eta(self, log10eta): + 'Returns log10(xiion) [in Hz/erg] given log10(LHa/LUV) [both in erg/s]' + + _constxiionHaUV = 7.28e11 #erg/s/Hz + xiion = self.LF_Init.L_ergs_of_Mag(self.LF_Init.Mag_of_L_ergsHz(1.))*10**log10eta*_constxiionHaUV + + return np.log10(xiion) + + + def cross_sigma_squared_PSD(self, GreensFunction1, GreensFunction2, CosmoParams, AstroParams, HMFinterp, LFParams): + "Returns the cross sigma squared of two observables, given their window functions and SFH" + + + _, windowfourier1= self.SFH_Init.WindowFourier(CosmoParams, AstroParams, HMFinterp, self.SFRD_Init, GreensFunction1, LFParams.zcenter, AstroParams._tagesMyr, pop = 2) + + _, windowfourier2= self.SFH_Init.WindowFourier(CosmoParams, AstroParams, HMFinterp, self.SFRD_Init, GreensFunction2, LFParams.zcenter, AstroParams._tagesMyr, pop = 2) + + omegalist = AstroParams.omega_PSFR #use the omegalist from the FFT, which is the same for all observables + powerNL = AstroParams.PSFR_table #use the power spectrum from the FFT, which is the same for all observables + + _whichomegakeep = np.logical_and(omegalist > AstroParams._omegamin, omegalist < AstroParams._omegamax) + + sigmasqcross = np.trapezoid(powerNL * np.real(windowfourier1*np.conjugate(windowfourier2) )*_whichomegakeep, omegalist,axis=1)*2/(2*np.pi) #times 2 because + and - freqs + + #Also return the sigmas for each observable, which are needed for the PDF + sigma1 = np.sqrt(np.trapezoid(powerNL * np.abs(windowfourier1)**2*_whichomegakeep, omegalist,axis=1)*2/(2*np.pi) ) + sigma2 = np.sqrt(np.trapezoid(powerNL * np.abs(windowfourier2)**2*_whichomegakeep, omegalist,axis=1)*2/(2*np.pi) ) + #And the means of the observables + + mean1 = np.trapezoid(GreensFunction1(AstroParams, AstroParams._tagesMyr, HMFinterp.Mhtab) * self.SFH_Init.SFH_II, AstroParams._tagesMyr*1e6, axis=1) + mean2 = np.trapezoid(GreensFunction2(AstroParams, AstroParams._tagesMyr, HMFinterp.Mhtab) * self.SFH_Init.SFH_II, AstroParams._tagesMyr*1e6, axis=1) + + return mean1, mean2, sigma1, sigma2, sigmasqcross + + diff --git a/zeus21/SED.py b/zeus21/SED.py new file mode 100644 index 0000000..529e878 --- /dev/null +++ b/zeus21/SED.py @@ -0,0 +1,242 @@ +""" +SEDs and Green's functions for first-galaxy emission models. + +Authors: zeus21 v2 collaboration - June 2026 + Emily Bregou ; + Hector Afonso G. Cruz ; + Sarah Libanore ; + Julian B. Muñoz ; + Yonny Sklansky ; + Emilie Thélie ; + Alessandra Venditti +arXiv:2302.08506, arXiv:2306.09403, arXiv:2407.18294, Sklansky et al. (in prep) +""" + + +import numpy as np +from . import constants + + +def SED_XRAY(AstroParams, En, pop = 0): #pop set to zero as default, but it must be set to either 2 or 3 + """ + X-ray SED for Pop II or Pop III sources. + + Normalized so that ∫_{E0}^{Emax} E · SED(E) dE = 1, i.e. E·SED is a + power law with index alpha_xray. The function returns the *photon number* + spectrum (divided by E at the end). + + The high-energy cutoff is intentionally omitted because photons redshift + down into the <2 keV observing band. + + Parameters + ---------- + AstroParams : object + Must expose: alpha_xray, alpha_xray_III, E0_xray, Emax_xray_norm. + En : float or array-like + Photon energy in eV. + pop : {2, 3} + Stellar population. 2 = Pop II, 3 = Pop III. + + Returns + ------- + ndarray + SED in units of eV⁻¹, same shape as En. + Zero below E0_xray. + """ + if pop == 2: + alphaX = AstroParams.alpha_xray + elif pop == 3: + alphaX = AstroParams.alpha_xray_III + else: + print("Must set pop to either 2 or 3!") + + if np.abs(alphaX + 1.0) < 0.01: #log + norm = 1.0/np.log(AstroParams.Emax_xray_norm/AstroParams.E0_xray) / AstroParams.E0_xray + else: + norm = (1.0 + alphaX)/((AstroParams.Emax_xray_norm/AstroParams.E0_xray)**(1 + alphaX) - 1.0) / AstroParams.E0_xray + + return np.power(En/AstroParams.E0_xray, alphaX)/En * norm * np.heaviside(En - AstroParams.E0_xray, 0.5) + #do not cut at higher energies since they redshift into <2 keV band + + + +def SED_LyA(nu_in, pop = 0): #default pop set to zero so python doesn't complain, but must be 2 or 3 for this to work + """ + Lyman-alpha continuum SED for Pop II or Pop III sources. + + A two-segment power law in frequency, joined at ν_LyB: + • νLyA ≤ ν < νLyB : index indexbelow (flatter) + • νLyB ≤ ν < νLyCont: index indexabove (steeper) + + Normalized so that ∫_{νLyA}^{νLyCont} SED(ν) dν = 1 (photon number + per unit frequency). Contrast with SED_XRAY, which normalizes E·SED. + + Parameters + ---------- + nu_in : float or array-like + Frequency in the same units as constants.freqLyA / freqLyCont. + pop : {2, 3} + Stellar population. Uses BL05 stellar spectra as reference. + Pop II: amps = [0.68, 0.32], Pop III: amps = [0.56, 0.44]. + + Returns + ------- + ndarray + SED value(s), same shape as nu_in. Zero outside [νLyA, νLyCont). + """ + + nucut = constants.freqLyB #above and below this freq different power laws + if pop == 2: + amps = np.array([0.68,0.32]) #Approx following the stellar spectra of BL05. Normalized to unity + indexbelow = 0.14 #if one of them zero worry about normalization + normbelow = (1.0 + indexbelow)/(1.0 - (constants.freqLyA/nucut)**(1 + indexbelow)) * amps[0] + indexabove = -8.0 + normabove = (1.0 + indexabove)/((constants.freqLyCont/nucut)**(1 + indexabove) - 1.0) * amps[1] + elif pop == 3: + amps = np.array([0.56,0.44]) #Approx following the stellar spectra of BL05. Normalized to unity + indexbelow = 1.29 #if one of them zero worry about normalization + normbelow = (1.0 + indexbelow)/(1.0 - (constants.freqLyA/nucut)**(1 + indexbelow)) * amps[0] + indexabove = 0.2 + normabove = (1.0 + indexabove)/((constants.freqLyCont/nucut)**(1 + indexabove) - 1.0) * amps[1] + else: + print("Must set pop to 2 or 3!") + + nulist = np.asarray([nu_in]) if np.isscalar(nu_in) else np.asarray(nu_in) + + result = np.zeros_like(nulist) + for inu, currnu in enumerate(nulist): + if (currnu=constants.freqLyCont): + result[inu] = 0.0 + elif (currnu < nucut): #between LyA and LyB + result[inu] = normbelow * (currnu/nucut)**indexbelow + elif (currnu >= nucut): #between LyB and Continuum + result[inu] = normabove * (currnu/nucut)**indexabove + else: + print("Error in SED_LyA, whats the frequency Kenneth?") + + + return result/nucut #extra 1/nucut because dnu, normalizes the integral + + + + +def Greens_function_LUV(AstroParams, ageMyrin, Mhalos): + """ + UV luminosity Green's function for a 1 M☉/yr instantaneous burst at some time t. + + Convolve with SFR(t) to get L_UV(t): + L_UV(t) = ∫ G_UV(t - t') · SFR(t') dt' + + The shape is a double-power-law in age (fast rise, slow decline) with a + Gaussian bump at very young ages (~2–4 Myr) capturing the brief Wolf-Rayet + and OB-supergiant phase. A Gaussian exponential cutoff suppresses + contributions beyond ~650–1100 Myr depending on the SED model. + + Parameters + ---------- + AstroParams : object + Must choose SEDMODEL ∈ {'bagpipes', 'BPASS', 'BPASS_binaries'}. + ageMyrin : float or array-like, shape (Nt,) + Stellar population age in Myr. A small offset (1e-4 Myr) is added + internally to avoid division by zero at age = 0. + Mhalos : array-like, shape (NMh,) + Halo masses in M☉. Currently enter only through IMFZcorrection, + which is unity for UV (correction absorbed into ε*). + + Returns + ------- + ndarray, shape (NMh, Nt) + Green's function in erg s⁻¹ M☉⁻¹. + """ + + if AstroParams.SEDMODEL == 'bagpipes': + _amp = 3.1e36 + _agepivot = 4 #Myr + _agepivot2 = 650 #Myr + _agebump, _widthbump, Ampbump = 3.4, 0.1, 0.33 #Myr, log10width, relative amplitude + _alpha, _beta = 1.4, -0.3 + elif AstroParams.SEDMODEL == 'BPASS': #BPASS single stars + _agepivot = 4.2 #Myr + _agepivot2 = 1100 #Myr + _agebump, _widthbump, Ampbump = 2.2, 0.2, 0.7 #Myr, log10width, relative amplitude + _alpha, _beta = 1.2, 0.0 + _amp = 1.8e36 + elif AstroParams.SEDMODEL =='BPASS_binaries': #BPASS with binarity fraction built in (default). Pretty similar in UV + _agepivot = 4.0 #Myr + _agepivot2 = 1100 #Myr + _agebump, _widthbump, Ampbump = 2.2, 0.2, 0.6 #Myr, log10width, relative amplitude + _alpha, _beta = 1.2, -0.2 + _amp = 2.2e36 + + ageMyr = ageMyrin+1e-4 #to avoid complaints about division by zero + IMFZcorrection = np.ones_like(Mhalos) #no correction on UV, absorbed by eps* + massindepresult = _amp*( Ampbump*np.exp(-(np.log10(ageMyr)-np.log10(_agebump))**2/2/_widthbump**2) + 1/((ageMyr/_agepivot)**_alpha+(ageMyr/_agepivot)**(_beta))* np.exp(-(ageMyr/_agepivot2)**2) ) #erg/s/Msun + return np.outer(IMFZcorrection,massindepresult) #erg/s/Msun, Nt x NMh + +def Selection_Timescales_LUV(times, time1, time2): + "Returns the selection function from t1 to t2, to keep t1 < t < t2 smoothly" + _tanhwidth = 0.2 + return (1 + np.tanh( np.log(times/time1)/_tanhwidth))/2. * (1 + np.tanh( np.log(time2/times)/_tanhwidth))/2. + +def Greens_function_LUV_Short(AstroParams,time, mass): + "Age in Myr, window in erg/s/Msun for the short timescale LUV window" + return Greens_function_LUV(AstroParams, time, mass) * Selection_Timescales_LUV(time+1e-10, 0.0, AstroParams._tcut_LUV_short)[None,:] #+1e-10 to avoid division by zero in selectionLUV + +def Greens_function_LUV_Long(AstroParams,time, mass): + "Age in Myr, window in erg/s/Msun for the long timescale LUV window" + return Greens_function_LUV(AstroParams, time, mass) * Selection_Timescales_LUV(time+1e-10, AstroParams._tcut_LUV_short, 3000)[None,:] + + +def Greens_function_LHa(AstroParams, ageMyrin, Mhalos): + """ + Hα luminosity Green's function for a 1 M☉/yr instantaneous burst at some past time t. + + Analogous to Greens_function_LUV but for the Hα recombination line. + Hα traces ionizing photons and therefore falls off much faster with age + (~few Myr vs. ~Gyr for UV). For BPASS_binaries there is a second component at ~20 Myr to account + for delayed ionizing flux. + + The IMF/metallicity correction scales with halo mass as a power law, + clamped to [0.1, 10] to prevent runaway corrections. + + + Parameters + ---------- + AstroParams : object + Must expose: SEDMODEL, normLHa_ZIMF, alphanormLHa_ZIMF. + ageMyrin : float or array-like, shape (Nt,) + Stellar population age in Myr. + Mhalos : array-like, shape (NMh,) + Halo masses in M☉. Used in the IMF/Z mass-dependent correction. + + Returns + ------- + ndarray, shape (NMh, Nt) + Green's function in erg s⁻¹ M☉⁻¹. + """ + if AstroParams.SEDMODEL == 'bagpipes': + _amp = 1.2e35 + _exp = 2.0 + _agepivot = 4.4 #Myr + _alpha = 0.33 + elif AstroParams.SEDMODEL == 'BPASS': + _amp = 3.4e35 + _exp = 0.9 + _agepivot = 1.2 #Myr + _alpha = 0.5 + elif AstroParams.SEDMODEL == 'BPASS_binaries': + _amp = 3.4e35 + _exp = 0.78 + _agepivot = 1.2 #Myr + _alpha = 0.5 + else: + raise ValueError("SEDMODEL must be 'bagpipes', 'BPASS' or 'BPASS_binaries'") + ageMyr = ageMyrin+1e-4 #to avoid complaints about division by zero + IMFZcorrection = AstroParams.normLHa_ZIMF * (Mhalos/1e10)**AstroParams.alphanormLHa_ZIMF + IMFZcorrection = np.fmin(np.fmax(IMFZcorrection, 0.1),10.) #make sure it's not too low or high + massindepresult = _amp * np.exp(-(ageMyr/_agepivot)**_exp)*(ageMyr/_agepivot)**_alpha #erg/s/Msun + if AstroParams.SEDMODEL == 'BPASS_binaries': + _amp2 = 8e32 + _agepivot2 = 20 #Myr + massindepresult += _amp2 * np.exp(-(ageMyr/_agepivot2)) #extra component due to binaries + return np.outer(IMFZcorrection,massindepresult) #erg/s/Msun, Nt x NMh, so we can multiply by SFR to get LHa diff --git a/zeus21/T21coefficients.py b/zeus21/T21coefficients.py new file mode 100644 index 0000000..619c38d --- /dev/null +++ b/zeus21/T21coefficients.py @@ -0,0 +1,637 @@ +""" +Bulk of the Zeus21 calculation: + - Determines Lyman-alpha and X-ray fluxes, + - Evolves the cosmic-dawn IGM state (WF coupling and heating), + - Computes the 21-cm global signal and the effective biases gammaR to determine the 21-cm power spectrum. + +Authors: zeus21 v2 collaboration - June 2026 + Emily Bregou ; + Hector Afonso G. Cruz ; + Sarah Libanore ; + Julian B. Muñoz ; + Yonny Sklansky ; + Emilie Thélie ; + Alessandra Venditti +arXiv:2302.08506, arXiv:2306.09403, arXiv:2407.18294, Sklansky et al. (in prep) +""" + +from . import cosmology +from . import constants + +import numpy as np + +from scipy import interpolate + + +from .sfrd import Z_init, SFRD_class, PopIII_relvel +from .reionization import reionization_global + +from .SED import SED_LyA, SED_XRAY + + +class LyAlpha_class: + """ + Determines Lyman-alpha properties and fluxes. + + Parameters + ---------- + UserParams : UserParams class + CosmoParams : CosmoParams class + AstroParams : AstroParams class + HMFinterp : HMFinterp class + z_Init : Z_init class, optional + Initial redshift matrices for the calculation (see sfrd.py for details). + Default is None. + SFRD_Init : SFRD_class class, optional + Initial star formation rate density for the calculation (see sfrd.py for details). + Default is None. + + Attributes + ---------- + coeff1LyAzp : array + Redshift-dependent coefficient in the J_alpha flux computation, see Eq. 29 in arXiv:2302.08506. + coeff2LyAzpRR_II : array + Coefficient that multiplies the SFRD in the integral for J_alpha for Pop II stars, see Eq. 30 of arXiv:2302.08506. + coeff2LyAzpRR_III : array + Coefficient that multiplies the SFRD in the integral for J_alpha for Pop III stars. + + """ + + def __init__(self, UserParams, CosmoParams, AstroParams, HMFinterp, z_Init = None, SFRD_Init = None): + + # if z_Init and SFRD_Init are not provided, we initialize them here. This allows us to avoid redundant computations if they were already initialized in the parent class and passed as arguments. + if z_Init is None: + z_Init = Z_init(UserParams=UserParams, CosmoParams=CosmoParams) + + if SFRD_Init is None: + SFRD_Init = SFRD_class(UserParams, CosmoParams, AstroParams, HMFinterp, z_Init) + + self.coeff1LyAzp = (1+z_Init.zintegral)**2/(4*np.pi) # redshift-dependent coefficient in the J_alpha flux computation, see Eq. 29 in arXiv:2302.08506 + + nuLYA = np.geomspace(constants.freqLyA, constants.freqLyCont, 128) # frequencies to compute the SED at, between LyA and the Lyman limit. We only consider these photons since above they are absorbed by the IGM through photoionization, and below they do not redshift into LyA. + sedLYAII_interp = interpolate.interp1d(nuLYA, SED_LyA(nuLYA, pop = 2), kind = 'linear', bounds_error = False, fill_value = 0) # interpolate LyA SED to compute the contribution of higher Lyman series photons that redshift into LyA after being emitted at higher frequencies. + + n_recArray = np.arange(0,constants.n_max_recycle-1 ) # array of n levels from which photons are emitted after recombinations + zpCube, rCube, n_recCube = np.meshgrid(z_Init.zintegral, CosmoParams._Rtabsmoo, n_recArray, indexing='ij', sparse=True) # 3D cube for the recombination contribution to LyA. Dimensions are (z,R,n), where n is the Lyman series level from which photons are emitted after recombinations + n_lineCube = n_recCube + 2 + zmax_lineCube = (1+zpCube) * (1 - pow(1+n_lineCube,-2.0))/(1-pow(n_lineCube,-2.0) ) - 1.0 # maximum redshift Lyman series photons can redshift before falling into a Ly-n resonance + + nu_linezpCube = constants.freqLyCont * (1 - (1.0/n_lineCube)**2) + zGreaterCube = z_Init.zGreaterMatrix_nonan.reshape(len(z_Init.zintegral), len(CosmoParams._Rtabsmoo), 1) # redefine this just for LyA routine, to have the right dimensions for the recombination contribution. Dimensions are (z,R,1), where z is the redshift at which we want to compute the flux, and R is the smoothing scale at which we want to compute the SFRD. We will be summing over n_recCube, so we need to have the same zGreater for all n's. + nu_lineRRCube = nu_linezpCube * (1.+zGreaterCube)/(1+zpCube) # frequency at which photons emitted at the Lyman series lines are observed at redshift zGreaterCube + + eps_alphaRR_II_Cube = AstroParams.N_alpha_perbaryon_II/CosmoParams.mu_baryon_Msun * sedLYAII_interp(nu_lineRRCube) # emissivity of Lyman-alpha photons from recombinations, converted from per SFR to per baryon by dividing by the mean mass per baryon in Msun, and multiplying by the number of LyA photons emitted per baryon in stars. We then multiply by the SED at the frequency at which these photons are observed at redshift zGreaterCube, to account for the fact that not all photons emitted at the Lyman series lines will redshift into LyA, but some will redshift into lower frequencies and be absorbed by dust or redshift out of the band. + + #the last nonzero index of the array is overestimated since only part of the spherical shell is within zmax_line. Correct by by dz/Delta z + weights_recCube = np.heaviside(zmax_lineCube - zGreaterCube, 0.0) + index_first0_weightsCube = np.where(np.diff(weights_recCube, axis = 1) == -1) #find index of last nonzero value. equals zero if two consecutive elements are 1 or 0, and -1 if two consecutive elements are [1,0] + i0Z, i0R, i0N = index_first0_weightsCube + weights_recCube[i0Z, i0R, i0N] *= (zmax_lineCube[i0Z, 0, i0N] - zGreaterCube[i0Z, i0R, 0])/ (zGreaterCube[i0Z, i0R+1, 0] - zGreaterCube[i0Z, i0R, 0]) + + Jalpha_II = np.array(constants.fractions_recycle)[:len(n_recArray)].reshape(1,1,len(n_recArray)) * weights_recCube * eps_alphaRR_II_Cube # just resizing f_recycle; it is length 29,we only consider up to n=22 + + LyAintegral_II = np.sum(Jalpha_II,axis=2) #sum over axis 2, over all possible n transitions, see Eq. 25 of arXiv:2302.08506 + self.coeff2LyAzpRR_II = CosmoParams._Rtabsmoo * CosmoParams._dlogRR * SFRD_Init.SFRDbar2D_II * LyAintegral_II/ constants.yrTos/constants.Mpctocm**2 # This is the coefficient that multiplies the SFRD in the integral for J_alpha, see Eq. 30 of arXiv:2302.08506. It has dimensions of s^-1 cm^-3, so when multiplied by the SFRD in Msun/year/Mpc^3 and integrated over R, it gives the correct units of s^-1 cm^-3 for J_alpha. + + if AstroParams.USE_POPIII: + # if required, we repeat the same for Pop III stars, where we change the SED and number of LyA photons per baryon in stars. We use the same weights_recCube since they only depend on the redshift at which photons are emitted and observed + sedLYAIII_interp = interpolate.interp1d(nuLYA, SED_LyA(nuLYA, pop = 3), kind = 'linear', bounds_error = False, fill_value = 0) + eps_alphaRR_III_Cube = AstroParams.N_alpha_perbaryon_III/CosmoParams.mu_baryon_Msun * sedLYAIII_interp(nu_lineRRCube) + + Jalpha_III = np.array(constants.fractions_recycle)[:len(n_recArray)].reshape(1,1,len(n_recArray)) * weights_recCube * eps_alphaRR_III_Cube + LyAintegral_III = np.sum(Jalpha_III,axis=2) + self.coeff2LyAzpRR_III = CosmoParams._Rtabsmoo * CosmoParams._dlogRR * SFRD_Init.SFRDbar2D_III * LyAintegral_III/ constants.yrTos/constants.Mpctocm**2 + else: + self.coeff2LyAzpRR_III = np.zeros_like(self.coeff2LyAzpRR_II) + + if UserParams.C2_RENORMALIZATION_FLAG: + # If required, correct for nonlinearities in <(1+d)SFRD> + # We're assuming that (1+d)SFRD ~ exp(gamma*d), so the "Lagrangian" gamma was gamma-1. + # We're using the fact that for a lognormal variable X = log(Z), with Z=\gamma \delta, = exp(\gamma^2 \sigma^2/2). + self.coeff2LyAzpRR_II = self.coeff2LyAzpRR_II * SFRD_Init._corrfactorEulerian_II.T + if AstroParams.USE_POPIII: + self.coeff2LyAzpRR_III = self.coeff2LyAzpRR_III * SFRD_Init._corrfactorEulerian_III.T + + +class Xrays_class: + """ + Determines X-ray properties and fluxes. + + Parameters + ---------- + UserParams : UserParams class + CosmoParams : CosmoParams class + AstroParams : AstroParams class + HMFinterp : HMFinterp class + z_Init : Z_init class, optional + Initial redshift matrices for the calculation (see sfrd.py for details). + Default is None. + SFRD_Init : SFRD_class class, optional + Initial star formation rate density for the calculation (see sfrd.py for details). + Default is None. + + Attributes + ---------- + atomfractions : array + Fraction of baryons in HI and HeI, assumed to just be the avg cosmic. Used to compute X-ray absorption. + atomEnIon : array + Threshold energies for HI and HeI, in eV. + TAUMAX : float + Maximum optical depth, cut to 0 after to avoid overflows. + coeff1Xzp : array + Redshift-dependent coefficient in the X-ray flux computation, with extra factors to account for adiabatic cooling and the fact that we compute the integral in redshift instead of time. + coeff2XzpRR_II : array + Coefficient that multiplies the SFRD in the integral for the X-ray flux for Pop II stars + coeff2XzpRR_III : array + Coefficient that multiplies the SFRD in the integral for the X-ray flux for Pop III stars + _GammaXray_II : array + X-ray ionization rate for Pop II stars, in s^-1, see Eq. 37 in arXiv:2302.08506 + _GammaXray_III : array + X-ray ionization rate for Pop III stars, in s^-1 + coeff_Gammah_Tx_II : array + Coefficient that multiplies the X-ray ionization rate to get the X-ray heating rate for Pop II stars, in K/s, see Eq. 41 in arXiv:2302.08506. + coeff_Gammah_Tx_III : array + Coefficient that multiplies the X-ray ionization rate to get the X-ray heating rate for Pop III stars, in K/s + Gammaion_II : array + X-ray ionization rate for Pop II stars, in s^-1 + Gammaion_III : array + X-ray ionization rate for Pop III stars, in s^-1 + _xe_avg_ad : array + Average ionization fraction of the IGM from adiabatic cooling and recombinations + _xe_avg: array + Average ionization fraction of the IGM, including both the contribution from UV photons and the partial ionization from X-rays + _fheat : array + Fraction of X-ray energy that goes into heating, as opposed to ionization + Gammaheat_II : array + X-ray heating rate for Pop II stars, in K/s + Gammaheat_III : array + X-ray heating rate for Pop III stars, in K/s + Tk_xray : array + Average kinetic temperature of the IGM from X-ray heating, in K + Tk_ad : array + Average kinetic temperature of the IGM from adiabatic cooling only, in K + Tk_avg : array + Average kinetic temperature of the IGM, including both adiabatic cooling and X-ray heating, in K + sigma_HI : function + Cross section for X-ray absorption by HI, as a function of energy in eV, in cm^2 + sigma_HeI : function + Cross section for X-ray absorption by HeI, as a function of energy in eV, in cm^2 + """ + + def __init__(self, UserParams, CosmoParams, AstroParams, HMFinterp, z_Init = None, SFRD_Init = None): + + # if z_Init and SFRD_Init are not provided, we initialize them here. This allows us to avoid redundant computations if they were already initialized in the parent class and passed as arguments. + if z_Init is None: + z_Init = Z_init(UserParams=UserParams, CosmoParams=CosmoParams) + + if SFRD_Init is None: + SFRD_Init = SFRD_class(UserParams, CosmoParams, AstroParams, HMFinterp, z_Init) + + self.atomfractions = np.array([1,CosmoParams.x_He]) #fraction of baryons in HI and HeI, assumed to just be the avg cosmic + self.atomEnIon = np.array([constants.EN_ION_HI, constants.EN_ION_HeI]) #threshold energies for each, in eV + self.TAUMAX=100. #max optical depth, cut to 0 after to avoid overflows + + _Energylist = AstroParams.Energylist # list of energies at which we compute the SED and optical depth. We use a fixed list of energies instead of integrating over energy, to speed up the computation + Nzinttau = np.floor(10*UserParams.precisionboost).astype(int) # number of redshift points to compute the optical depth integral. We use a fixed number of points instead of integrating over redshift, to speed up the computation + + zGreaterCube = z_Init.zGreaterMatrix_nonan.reshape(len(z_Init.zintegral), len(CosmoParams._Rtabsmoo), 1, 1) #redefine this just for x-ray routine + + self.coeff1Xzp = -2/3 * z_Init.zintegral * z_Init.dlogzint / cosmology.Hubinvyr(CosmoParams,z_Init.zintegral) / (1+z_Init.zintegral) * (1+z_Init.zintegral)**2 + self.coeff1Xzp = self.coeff1Xzp / (1+z_Init.zintegral)**2 * constants.yrTos # this accounts for adiabatic cooling. compensated by the inverse at the end + + zpCube, rCube, eCube, zPPCube = np.meshgrid(z_Init.zintegral, CosmoParams._Rtabsmoo, _Energylist, np.arange(Nzinttau), indexing='ij', sparse=True) # 4D cube for the X-ray contribution. Dimensions are (z,R,E,z'), where z is the redshift at which we want to compute the flux, R is the smoothing scale at which we want to compute the SFRD, E is the energy at which we want to compute the SED and optical depth, and z' is the redshift at which we want to compute the optical depth integral + currentEnergyTable = eCube * (1+zGreaterCube) / (1+zpCube) # Energy at which photons observed at redshift zGreaterCube were emitted at redshift zpCube, since E' = E(1+z')/(1+z). + SEDCube = SED_XRAY(AstroParams, currentEnergyTable, pop = 2) # SED of our X-ray sources for popII + SEDCube_III = SED_XRAY(AstroParams, currentEnergyTable, pop = 3) # SED of our X-ray sources for popIII, we compute it even if we don't use it, to speed up the computation in case we do use it later. + + ######## Broadcasted routine to find X-ray optical depths, modeled after but does not use xrays.optical_depth + + zPPCube = np.array([np.linspace(np.transpose([z_Init.zintegral]), z_Init.zGreaterMatrix, Nzinttau, axis = 2)]) + zPPCube = zPPCube.reshape(len(z_Init.zintegral), len(CosmoParams._Rtabsmoo), 1, Nzinttau) # to have 4D dimensions, default shape = (64, 45, 1, 10) + + ePPCube = eCube * (1+ zPPCube) / (1+zpCube) # E'' = E(1+z'')/(1+z) + sigmatot = self.atomfractions[0] * self.sigma_HI(ePPCube) # cross section for X-ray absorption. determined by the energy at which they are absorbed. We multiply by the atom fractions to get the total cross section per baryon + sigmatot += self.atomfractions[1] * self.sigma_HeI(ePPCube) # we only consider HeI since HeII is negligible at the redshifts we're interested in, and it has a much higher ionization energy so it does not contribute much to the absorption of X-rays + + opticalDepthIntegrand = 1 / cosmology.HubinvMpc(CosmoParams, zPPCube) / (1+zPPCube) * sigmatot * cosmology.n_H(CosmoParams, zPPCube) * constants.Mpctocm # this uses atom fractions of 1 for HI and x_He for HeI + tauCube = np.trapezoid(opticalDepthIntegrand, zPPCube, axis = 3) # integrate over z' to get the optical depth. This gives us a 3D cube with dimensions (z, R, E), where z is the redshift at which we want to compute the flux, R is the smoothing scale at which we want to compute the SFRD, and E is the energy at which we want to compute the SED and optical depth. + + # cap tau to avoid overflows in the exponential + indextautoolarge = np.array(tauCube>=self.TAUMAX) + tauCube[indextautoolarge] = self.TAUMAX + + if CosmoParams.Flag_emulate_21cmfast: + # if we're emulating 21cmfast, we use a step function for the absorption + weights_X_zCube = np.heaviside(1.0 - tauCube, 0.5) + else: + weights_X_zCube = np.exp(-tauCube) + + SEDCube = SEDCube[:,:,:,0] # rescale dimensions of energy and SED cubes back to 3D, so we can integrate over energy + SEDCube_III = SEDCube_III[:,:,:,0] # same for popIII + + eCube = eCube[:,:,:,0] + ######## end of optical depth routine + + JX_coeffsCube = SEDCube * weights_X_zCube # this is the coefficient that multiplies the SFRD in the integral for the X-ray flux, before integrating over energy. It has dimensions of number of photons per energy per baryon, multiplied by the absorption factor, so when multiplied by the SFRD in Msun/year/Mpc^3 and integrated over R and E, it gives the correct units of number of photons per second per baryon for the X-ray flux. We keep it as a cube for now to integrate over energy later. + JX_coeffsCube_III = SEDCube_III * weights_X_zCube # same for popIII + + sigma_times_en = self.atomfractions[0] * self.sigma_HI(eCube) * (eCube - self.atomEnIon[0]) # we multiply the cross section by (E - E_ion) to account for the fact that only the energy above the ionization threshold goes into heating and ionization, while the rest is lost to secondary electrons. We also multiply by the atom fractions to get the total contribution per baryon, instead of per Hydrogen nucleus. + sigma_times_en += self.atomfractions[1] * self.sigma_HeI(eCube) * (eCube - self.atomEnIon[1]) # same for HeI + sigma_times_en /= np.sum(self.atomfractions) # to normalize per baryon, instead of per Hydrogen nucleus HI and HeII separate. Notice Energy (and not Energy'), since they get absorbed at the zp frame + + xrayEnergyTable = np.sum(JX_coeffsCube * sigma_times_en * eCube * AstroParams.dlogEnergy,axis=2) # integrate over energy to get the coefficient that multiplies the SFRD in the integral for the X-ray flux + self.coeff2XzpRR_II = np.nan_to_num(CosmoParams._Rtabsmoo * CosmoParams._dlogRR * SFRD_Init.SFRDbar2D_II * xrayEnergyTable * (1.0/constants.Mpctocm**2.0) * constants.normLX_CONST, nan = 0) # see Eq. 39 in arXiv:2302.08506. We multiply by normLX_CONST to convert from number of photons to energy, and by 1/Mpc^2 to convert from per area to per volume, since the SFRD is in Msun/year/Mpc^3 and we want the X-ray flux in energy per second per baryon + + if AstroParams.USE_POPIII: + # same for popIII + xrayEnergyTable_III = np.sum(JX_coeffsCube_III * sigma_times_en * eCube * AstroParams.dlogEnergy,axis=2) + self.coeff2XzpRR_III = np.nan_to_num(CosmoParams._Rtabsmoo * CosmoParams._dlogRR * SFRD_Init.SFRDbar2D_III * xrayEnergyTable_III * (1.0/constants.Mpctocm**2.0) * constants.normLX_CONST, nan = 0) + else: + self.coeff2XzpRR_III = np.zeros_like(self.coeff2XzpRR_II) + + if UserParams.C2_RENORMALIZATION_FLAG: + # if required, correct for nonlinearities in <(1+d)SFRD>, only if doing nonlinear stuff. + # We're assuming that (1+d)SFRD ~ exp(gamma*d), so the "Lagrangian" gamma was gamma-1. + # We're using the fact that for a lognormal variable X = log(Z), with Z=\gamma \delta, = exp(\gamma^2 \sigma^2/2). + self.coeff2XzpRR_II = self.coeff2XzpRR_II* SFRD_Init._corrfactorEulerian_II.T + if AstroParams.USE_POPIII: + self.coeff2XzpRR_III = self.coeff2XzpRR_III * SFRD_Init._corrfactorEulerian_III.T + + self._GammaXray_II = self.coeff1Xzp * np.sum( self.coeff2XzpRR_II ,axis=1) # eq. 37 in 2302.08506; notice units are modified (eg 1/H) so it's simplest to sum + self._GammaXray_III = self.coeff1Xzp * np.sum( self.coeff2XzpRR_III ,axis=1) # same for popIII + + fion = 0.4 * np.exp(-cosmology.xefid(CosmoParams, z_Init.zintegral)/0.2) # partial ionization from Xrays. Fit to Furlanetto&Stoever + atomEnIonavg = (self.atomfractions[0] * self.atomEnIon[0] + self.atomfractions[1] * self.atomEnIon[1]) / (self.atomfractions[0] + self.atomfractions[1] ) # convert from 1/n_H to 1 / n_b + + self.coeff_Gammah_Tx_II = -AstroParams.L40_xray * constants.ergToK * (1.0+z_Init.zintegral)**2 # coefficient to convert from Gamma_X to T_X, last factors accounts for adiabatic cooling. compensated by the inverse at zp in coeff1Xzp. Minus because integral goes from low to high z, but we'll be summing from high to low everywhere. + self.coeff_Gammah_Tx_III = -AstroParams.L40_xray_III * constants.ergToK * (1.0+z_Init.zintegral)**2 # same for popIII + + self.Gammaion_II = self.coeff_Gammah_Tx_II *constants.KtoeV * self._GammaXray_II * fion/atomEnIonavg * 3/2 # ionization rate from X-rays for Pop II stars, in s^-1. We multiply by fion to account for the fact that only a fraction of the energy goes into ionization, and divide by the average ionization energy per baryon to convert from energy to number of ionizations ; atomEnIonavg makes it approximate. No adiabatic cooling (or recombinations) so no 1+z factors. Extra 3/2 bc temperature has a 2/3 + self.Gammaion_III = self.coeff_Gammah_Tx_III *constants.KtoeV * self._GammaXray_III * fion/atomEnIonavg * 3/2 # same for popIII + + # TODO: Improve model for xe + + self.xe_avg_ad = cosmology.xefid(CosmoParams, z_Init.zintegral) # average ionization fraction from adiabatic cooling and recombinations, without X-ray ionization. + self.xe_avg = self.xe_avg_ad + np.cumsum((self.Gammaion_II+self.Gammaion_III)[::-1])[::-1] # average ionization fraction including X-ray ionization + if CosmoParams.Flag_emulate_21cmfast: + # if we're emulating 21cmfast, we use a fixed ionization fraction + self.xe_avg = 2e-4 * np.ones_like(self.Gammaion_II) + + self.xe_avg = np.fmin(self.xe_avg, 1.0-1e-9) + + # heat from Xrays + self._fheat = pow(self.xe_avg,0.225) + self.coeff1Xzp *= self._fheat # since this is what we use for the power spectrum, we need to upate it + self.Gammaheat_II = self._GammaXray_II * self._fheat + self.Gammaheat_III = self._GammaXray_III * self._fheat + + # Computing avg kinetic temperature as sum of adiabatic & xray temperature + self.Tk_xray = self.coeff_Gammah_Tx_II * np.cumsum(self.Gammaheat_II[::-1])[::-1] + self.coeff_Gammah_Tx_III * np.cumsum(self.Gammaheat_III[::-1])[::-1] # in K, cumsum reversed because integral goes from high to low z. Only heating part + self.Tk_ad = cosmology.Tadiabatic(CosmoParams, z_Init.zintegral) + if CosmoParams.Flag_emulate_21cmfast: + # if we're emulating 21cmfast, we use a fixed kinetic temperature, since they use recfast, so their 'cosmo' temperature is slightly off + self.Tk_ad*=0.95 + + self.Tk_avg = self.Tk_ad + self.Tk_xray + + + def sigma_HI(self, Energyin): + """ + Cross section for Xray absorption for neutral HI, from astro-ph/9601009 + + Parameters + ---------- + Energyin: float + Energy in eV + + Returns + ------- + float + Cross section in cm^2. + """ + + E0 = 4.298e-1 + sigma0 = 5.475e4 + ya = 3.288e1 + P = 2.963 + yw = 0.0 + y0 = 0.0 + y1 = 0.0 + + Energy = Energyin + + warning_lowE_HIXray = np.heaviside(13.6 - Energy, 0.5) + if(np.sum(warning_lowE_HIXray) > 0): + print('ERROR! Some energies for Xrays below HI threshold in sigma_HI. Too low!') + + x = Energy/E0 - y0 + y = np.sqrt(x**2 + y1**2) + Fy = ((x-1.0)**2 + yw**2) * y**(0.5*P - 5.5) * (1.0+np.sqrt(y/ya))**(-P) + + return sigma0 * constants.sigma0norm * Fy + + + def sigma_HeI(self, Energyin): + """ + Cross section for Xray absorption for neutral HI + + Parameters + ---------- + Energyin: float + Energy in eV + + Returns + ------- + float + Cross section in cm^2. + """ + + E0 = 13.61 + sigma0 = 9.492e2 + ya = 1.469 + P = 3.188 + yw = 2.039 + y0 = 4.434e-1 + y1 = 2.136 + + Energy = Energyin + warning_lowE_HeIXray = np.heaviside(25. - Energy, 0.5) + if(np.sum(warning_lowE_HeIXray) > 0): + print('ERROR! Some energies for Xrays below HeI threshold in sigma_HeI. Too low!') + + x = Energy/E0 - y0 + y = np.sqrt(x**2 + y1**2) + Fy = ((x-1.0)**2 + yw**2) * y**(0.5*P - 5.5) * (1.0+np.sqrt(y/ya))**(-P) + + return sigma0 * constants.sigma0norm * Fy + + + + +class get_T21_coefficients: + """ + Loops through SFRD integrals and accounts for LyA coupling and Xray heating to obtain the average T21 and the coefficients for its power spectrum + + Parameters + ---------- + UserParams : object + User-defined parameters + CosmoParams : object + Cosmological parameters + AstroParams : object + Astrophysical parameters + HMFinterp : object + Halo mass function interpolator + + Attributes + ---------- + z_Init : object + Redshift tables for the calculation (see sfrd.py for details) + SFRD_Init : object + Initial star formation rate density for the calculation (see sfrd.py for details) + USE_POPIII : bool + Whether to include Pop III stars in the calculation or not, determined by AstroParams + relvel : object or None + Relative velocity between baryons and dark matter, which affects the SFRD and therefore the LyA and X-ray fluxes. Only computed if USE_POPIII is True + LyA : object + Lyman-alpha anisotropies, which depend on the SFRD and the redshift tables; see LyAlpha_class for details + Xrays : object + X-ray anisotropies, which depend on the SFRD and the redshift tables; see Xrays_class for details + ReioGlobal : object + Global reionization history, which depends on the SFRD and the redshift tables; see reionization.py for details + xHI_avg : array + Average neutral hydrogen fraction volume-weighted, computed from the global reionization history + T21avg : array + Average 21cm brightness temperature + tau_reio_val : float + Optical depth to reionization, computed from the global reionization history and the average neutral hydrogen fraction + __ getattr__ : method + This method allows us to access the attributes of the classes that we initialized directly from the get_T21_coefficients class, without having to specify which class they come from + evolve_T21_fields : method + Compute evolution of the LyA flux, LyA coupling coefficient, color temperature and spin temperature + Jalpha_avg : array + Average LyA flux at all redshifts and radii + _coeff_Ja_xa_0 : array + Normalization of the LyA flux at all redshifts + coeff_Ja_xa : array + LyA flux corrected with Hirata2006 prescription + xa_avg : array + LyA coupling coefficient + TCMB : array + CMB temperature at all redshifts + invTcol_avg : array + Inverse of the color temperature (equal 1/Tk) + _invTs_avg : array + Inverse of the spin temperature + tau_reio : method + Compute the optical depth to reionization + Salpha_exp : method + Hirata2006 correction to the LyA flux (Eq 55 in astro-ph/0608032) + """ + + def __init__(self, UserParams, CosmoParams, AstroParams, HMFinterp, z_Init =None, SFRD_Init=None): + + # if z_Init and SFRD_Init are not provided, we initialize them here. This allows us to avoid redundant computations if they were already initialized in the parent class and passed as arguments. + if z_Init is None: + # to perform cross-correlation studies, the redshift array has to be the same as in zeus21 + self.z_Init = Z_init(UserParams=UserParams, CosmoParams=CosmoParams) + else: + self.z_Init = z_Init + + if SFRD_Init is None: + self.SFRD_Init = SFRD_class(UserParams, CosmoParams, AstroParams, HMFinterp, self.z_Init) + else: + self.SFRD_Init = SFRD_Init + + # Computing lambdas in velocity anisotropies + # The SFRD vcb dependence is delta independent, therefore we compute quantities below for a variety of R's and delta_R = 0 + self.USE_POPIII = AstroParams.USE_POPIII + if self.USE_POPIII: + self.relvel = PopIII_relvel(UserParams, CosmoParams, AstroParams, HMFinterp, self.z_Init, self.SFRD_Init) + else: + self.relvel = None + + # Lyman-Alpha Anisotropies + self.LyA = LyAlpha_class(UserParams, CosmoParams, AstroParams, HMFinterp, self.z_Init, self.SFRD_Init) + + ### X-ray Anisotropies + self.Xrays = Xrays_class(UserParams, CosmoParams, AstroParams, HMFinterp, self.z_Init, self.SFRD_Init) + + # Computing free-electron fraction and Salpha correction factors in the Bulk IGM + self.evolve_T21_fields(UserParams, CosmoParams) + + # Reionization + self.ReioGlobal = reionization_global(CosmoParams, AstroParams, HMFinterp, self.z_Init, self.SFRD_Init, PRINT_SUCCESS=False) + self.xHI_avg = 1. - self.ReioGlobal.ion_frac ### TODO this one is volume weighted for now + + # Compute the 21cm Global Signal + self.T21avg = cosmology.T021(CosmoParams,self.z_Init.zintegral) * self.xa_avg/(1.0 + self.xa_avg) * (1.0 - self.T_CMB * self.invTcol_avg) * self.xHI_avg #TODO + + self.tau_reio_val = self.tau_reio(CosmoParams, self.z_Init.zintegral, self.xHI_avg) + + + def __getattr__(self, name): + """ + Access the attributes of the classes that we initialized directly from the get_T21_coefficients class, without having to specify which class they come from + + Parameters + ---- + name: str + Name of the attribute to get + + Returns + ------- + attribute + The attribute with the given name, if it exists in any of the classes that we initialized. If it does not exist in any of them, raises an AttributeError. + """ + + list_of_cls = [self.z_Init, self.SFRD_Init, self.LyA, self.Xrays, self.ReioGlobal] + + if self.USE_POPIII: + list_of_cls += [self.relvel] + for cls in list_of_cls: + try: + return getattr(cls, name) + except AttributeError: + pass + + raise AttributeError(f"{type(self).__name__} has no attribute {name!r}") + + + def evolve_T21_fields(self, UserParams, CosmoParams): + """ + Compute evolution of the LyA flux, LyA coupling coefficient, color temperature and spin temperature + + Parameters + ---------- + UserParams : object + User-defined parameters + CosmoParams : object + Cosmological parameters + + Returns + ------- + None + """ + + # LyA stuff to find components of Salpha correction factor + self.Jalpha_avg = self.LyA.coeff1LyAzp*np.sum(self.LyA.coeff2LyAzpRR_II + self.LyA.coeff2LyAzpRR_III,axis=1) #units of 1/(cm^2 s Hz sr) + + # CMB temperature + self.T_CMB = cosmology.Tcmb(CosmoParams.ClassCosmo, self.z_Init.zintegral) + + _tau_GP = 3./2. * cosmology.n_H(CosmoParams,self.z_Init.zintegral) * constants.Mpctocm / cosmology.HubinvMpc(CosmoParams,self.z_Init.zintegral) * (constants.wavelengthLyA/1e7)**3 * constants.widthLyAcm * (1.0 - self.Xrays.xe_avg) #~3e5 at z=6 + + if CosmoParams.Flag_emulate_21cmfast: + # 21cmFAST multiplies by N0 (all baryons) instead of NH0 + _tau_GP/=CosmoParams.f_H + + # compute correction coefficients from Hirata2006 + _xiHirata = pow(_tau_GP*1e-7,1/3.)*pow(self.Xrays.Tk_avg,-2./3) + _factorxi = (1.0 + constants.a_Hirata*_xiHirata + constants.b_Hirata * _xiHirata**2 + constants.c_Hirata * _xiHirata**3) + + + #prefactor without the Salpha correction from Hirata2006 + if CosmoParams.Flag_emulate_21cmfast: + # 21cmFAST uses a fixed (and slightly ~10% off) value. + self._coeff_Ja_xa_0 = 1.66e11/(1+self.z_Init.zintegral) + else: + self._coeff_Ja_xa_0 = 8.0*np.pi*(constants.wavelengthLyA/1e7)**2 * constants.widthLyA * constants.Tstar_21/(9.0*constants.A10_21*self.T_CMB) #units of (cm^2 s Hz sr), convert from Ja to xa. should give 1.81e11/(1+z_Init.zintegral) for Tcmb_0=2.725 K + + self.coeff_Ja_xa = self._coeff_Ja_xa_0 * self.Salpha_exp(self.z_Init.zintegral, self.Xrays.Tk_avg, self.Xrays.xe_avg) + + # LyA flux, see Eq. 27 in 2302.08506 + self.xa_avg = self.coeff_Ja_xa * self.Jalpha_avg + + # color temperature + self.invTcol_avg = 1.0 / self.Xrays.Tk_avg + + # spin temperature + self._invTs_avg = (1.0/self.T_CMB+self.xa_avg*self.invTcol_avg)/(1+self.xa_avg) + + if UserParams.FLAG_WF_ITERATIVE: + # iteration routine to find Tcolor and Ts + _invTs_tryfirst = 1.0/self.T_CMB + + while(np.sum(np.fabs(_invTs_tryfirst/self._invTs_avg - 1.0))>0.01): #no more than 1% error total + _invTs_tryfirst = self._invTs_avg + + #update xalpha + _Salphatilde = (1.0 - 0.0632/self.Xrays.Tk_avg + 0.116/self.Xrays.Tk_avg**2 - 0.401/self.Xrays.Tk_avg*self._invTs_avg + 0.336*self._invTs_avg/self.Xrays.Tk_avg**2)/_factorxi + self.coeff_Ja_xa = self._coeff_Ja_xa_0 * _Salphatilde + self.xa_avg = self.coeff_Ja_xa * self.Jalpha_avg + + #and Tcolor^-1 + self.invTcol_avg = 1.0/self.Xrays.Tk_avg + constants.gcolorfactorHirata * 1.0/self.Xrays.Tk_avg * (_invTs_tryfirst - 1.0/self.Xrays.Tk_avg) + + # finally Ts^-1 + self._invTs_avg = (1.0/self.T_CMB+self.xa_avg * self.invTcol_avg)/(1+self.xa_avg) + + + def tau_reio(self, CosmoParams, zlist, xHI): + """ + Compute the optical depth to reionization + + Parameters + ---------- + CosmoParams : object + Cosmological parameters + zlist : list + Redshifts + xHI : array + Neutral fraction as function of redshift + + Returns + ------- + tau_reio : list + """ + + # set EoR end + _lowestz = np.min(zlist) + _zlistlowz = np.linspace(0,_lowestz,100) + + _nelistlowz = cosmology.n_H(CosmoParams,_zlistlowz)*(1 + CosmoParams.x_He + CosmoParams.x_He * np.heaviside(constants.zHeIIreio - _zlistlowz,0.5)) # free electrons in post-EoR, assume HeII at z=4, can be varied with zHeIIreio + + _distlistlowz = 1.0/cosmology.HubinvMpc(CosmoParams,_zlistlowz)/(1+_zlistlowz) # comoving distances + + # integrate z < zmin (post-reio), assuming xHI=0 + _lowzint = constants.sigmaT * np.trapezoid(_nelistlowz*_distlistlowz,_zlistlowz) * constants.Mpctocm + + xHIint = np.fmin(np.fmax(xHI,0.0),1.0) # cap neutral fraction, at min 0%, at max 100% + _zlisthiz = zlist + _nelistlhiz = cosmology.n_H(CosmoParams,_zlisthiz) * (1 + CosmoParams.x_He) * (1.0 - xHIint) # free electrons in EoR + _distlisthiz = 1.0/cosmology.HubinvMpc(CosmoParams,_zlisthiz)/(1+_zlisthiz) # comoving distances + + # integrate z > zmin + _hizint = constants.sigmaT * np.trapezoid(_nelistlhiz*_distlisthiz,_zlisthiz) * constants.Mpctocm + + tau_reio = (_lowzint + _hizint) + + return tau_reio + + + def Salpha_exp(self, z, T, xe): + """ + Hirata2006 correction to the LyA flux (Eq 55 in astro-ph/0608032). This function is used to initialize the xalpha computation, but then overwritten. Only used if FLAG_WF_ITERATIVE == False + + Parameters + ---------- + z : float + Redshifts + T : float + Temperature in K + xe : float + Free electron fraction with small reionization (xe << 1) + + Returns + ------- + Salpha : float + Correction to the LyA flux + """ + + tau_GP_noreio = 3e5*pow((1+z)/7,3./2.)*(1-xe) + gamma_Sobolev = 1.0/tau_GP_noreio + + Salpha = np.exp( - 0.803 * pow(T,-2./3.) * pow(1e-6/gamma_Sobolev,-1.0/3.0)) + + return Salpha diff --git a/zeus21/UVLFs.py b/zeus21/UVLFs.py deleted file mode 100644 index 7fd1d22..0000000 --- a/zeus21/UVLFs.py +++ /dev/null @@ -1,159 +0,0 @@ -""" - -Compute UVLFs given our SFR and HMF models. - -Author: Julian B. Muñoz -UT Austin - June 2023 - -Edited by Hector Afonso G. Cruz -JHU - July 2024 - -Bug fix by Emily Bregou -UT Austin - June 2025 -""" - -from . import cosmology -from . import constants -from .sfrd import SFR_II, SFR_III -from .cosmology import bias_Tinker - -import numpy as np -from scipy.special import erf -from scipy.interpolate import interp1d - - - - - - -def MUV_of_SFR(SFRtab, kappaUV): - 'returns MUV, uses SFR. Dust added later in loglike.' - #convert SFR to MUVs - LUVtab = SFRtab/kappaUV - MUVtab = 51.63 - 2.5 * np.log10(LUVtab) #AB magnitude - return MUVtab - - -#and combine to get UVLF: -def UVLF_binned(Astro_Parameters,Cosmo_Parameters,HMF_interpolator, zcenter, zwidth, MUVcenters, MUVwidths, DUST_FLAG=True, RETURNBIAS = False): - 'Binned UVLF in units of 1/Mpc^3/mag, for bins at with a Gaussian width zwidth, centered at MUV centers with tophat width MUVwidths. z width only in HMF since that varies the most rapidly. If flag RETURNBIAS set to true it returns number-avgd bias instead of UVLF, still have to divide by UVLF' - - if(constants.NZ_TOINT>1): - DZ_TOINT = np.linspace(-np.sqrt(constants.NZ_TOINT/3.),np.sqrt(constants.NZ_TOINT/3.),constants.NZ_TOINT) #in sigmas around zcenter - else: - DZ_TOINT = np.array([0.0]) - WEIGHTS_TOINT = np.exp(-DZ_TOINT**2/2.)/np.sum(np.exp(-DZ_TOINT**2/2.)) #assumed Gaussian in z, fair - - - - - SFRlist = SFR_II(Astro_Parameters,Cosmo_Parameters,HMF_interpolator, HMF_interpolator.Mhtab, zcenter, zcenter) - sigmaUV = Astro_Parameters.sigmaUV - - if (constants.FLAG_RENORMALIZE_LUV == True): #lower the LUV (or SFR) to recover the true avg, not log-avg - SFRlist/= np.exp((np.log(10)/2.5*sigmaUV)**2/2.0) - - MUVbarlist = MUV_of_SFR(SFRlist, Astro_Parameters._kappaUV) #avg for each Mh - MUVbarlist = np.fmin(MUVbarlist,constants._MAGMAX) - - - if(RETURNBIAS==True): # weight by bias - biasM = np.array([bias_Tinker(Cosmo_Parameters, HMF_interpolator.sigma_int(HMF_interpolator.Mhtab,zcenter+dz*zwidth)) for dz in DZ_TOINT]) - else: # do not weight by bias - biasM = np.ones_like(WEIGHTS_TOINT) - - - HMFtab = np.array([HMF_interpolator.HMF_int(HMF_interpolator.Mhtab,zcenter+dz*zwidth) for dz in DZ_TOINT]) - HMFcurr = np.sum(WEIGHTS_TOINT * HMFtab.T * biasM.T,axis=1) - - #cannot directly 'dust' the theory since the properties of the IRX-beta relation are calibrated on observed MUV. Recursion instead: - currMUV = MUVbarlist - if(DUST_FLAG==True): - currMUV2 = np.ones_like(currMUV) - while(np.sum(np.abs((currMUV2-currMUV)/currMUV)) > 0.02): - currMUV2 = currMUV - currMUV = MUVbarlist + AUV(Astro_Parameters,zcenter,currMUV) - - - MUVcuthi = MUVcenters + MUVwidths/2. - MUVcutlo = MUVcenters - MUVwidths/2. - - xhi = np.subtract.outer(MUVcuthi, currMUV)/(np.sqrt(2) * sigmaUV) - xlo = np.subtract.outer(MUVcutlo, currMUV )/(np.sqrt(2) * sigmaUV) - - if (Astro_Parameters.min_t_formation_Myr == None): - min_MUV = -100.0 # essentially no cutoff, since the scatter is large at low masses and can cause numerical issues if we try to integrate over unphysically bright galaxies there. This is just a numerical cutoff, not a physical one, and the exact value doesn't matter much since the scatter is large there anyway. - else: - Mstarmax = HMF_interpolator.Mhtab * Cosmo_Parameters.OmegaB /Cosmo_Parameters.OmegaM #max stellar mass in each halo, if all baryons turned to stars - _tmaxSFR = Astro_Parameters.min_t_formation_Myr * 1e6 #arbitrary timescale to determine max SFR in yrs - SFRmax = Mstarmax / (_tmaxSFR) - min_MUV = MUV_of_SFR(SFRmax, Astro_Parameters._kappaUV) #min MUV in each halo, if all baryons turned to stars at max SFR for 10 Myr. This is a very rough cutoff to avoid unphysically small MUVs (bright galaxies) at low masses, which can cause numerical issues since the scatter is large there. It's not a physical cutoff, just a numerical one. The exact value doesn't matter much since the scatter is large there anyway, but it prevents the code from trying to integrate over unphysically bright galaxies in low-mass halos. - x_min = (min_MUV - currMUV)/(np.sqrt(2) * sigmaUV) - xhi_cut = np.fmax(xhi, x_min) - xlo_cut = np.fmax(xlo, x_min) - - weights_unnormalized = (erf(xhi_cut) - erf(xlo_cut)).T/(2.0 * MUVwidths) - weights = weights_unnormalized/ (0.5*(1-erf(x_min)+1e-6))[:,None] # Renormalize distributions based on the portion cut off by min_MUV - - ### Standard as usual, no cuts: - # weights = (erf(xhi) - erf(xlo)).T/(2.0 * MUVwidths) #comment to myself, this 2 in denominator is correct here, nothing to do with the MUVwidths/2 a few lines above - - UVLF_filtered = np.trapezoid(weights.T * HMFcurr, HMF_interpolator.Mhtab, axis=-1) - - - if(Astro_Parameters.USE_POPIII==False): - return UVLF_filtered - else: - _J21interptemp = interp1d(np.linspace(0,100,3), np.zeros(3), kind = 'linear', bounds_error = False, fill_value = 0,) #TODO: how to deal with J21, requires running get_21_coefficients - SFRlist_III = SFR_III(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, HMF_interpolator.Mhtab, _J21interptemp, zcenter, zcenter, Cosmo_Parameters.vcb_avg) - - MUVbarlist_III = MUV_of_SFR(SFRlist_III, Astro_Parameters._kappaUV_III) #avg for each Mh - MUVbarlist_III = np.fmin(MUVbarlist_III,constants._MAGMAX) - - #and the same for popIII, TODO: ignore dust for pop3 for now - xhi = np.subtract.outer(MUVcuthi, MUVbarlist_III)/(np.sqrt(2) * sigmaUV) - xlo = np.subtract.outer(MUVcutlo, MUVbarlist_III)/(np.sqrt(2) * sigmaUV) - weights = (erf(xhi) - erf(xlo)).T/(2.0 * MUVwidths) - - UVLF_filtered_III = np.trapezoid(weights.T * HMFcurr, HMF_interpolator.Mhtab, axis=-1) - - return UVLF_filtered, UVLF_filtered_III - - - - - -#####Here the dust attenuation -def AUV(Astro_Parameters, z, MUV, HIGH_Z_DUST = True, _zmaxdata=8.0): - 'Average attenuation A as a function of OBSERVED z and magnitude. If using on theory iterate until convergence. HIGH_Z_DUST is whether to do dust at higher z than 0 or set to 0. Fix at \beta(z=8) result if so' - - betacurr = beta(z,MUV) - - C0, C1 = Astro_Parameters.C0dust, Astro_Parameters.C1dust - - sigmabeta = 0.34 #from Bouwens 2014 - - Auv = C0 + 0.2*np.log(10)*sigmabeta**2 * C1**2 + C1 * betacurr - Auv=Auv.T - if not (HIGH_Z_DUST): - Auv*=np.heaviside(_zmaxdata - z,0.5) - Auv=Auv.T - return np.fmax(Auv, 0.0) - -def beta(z, MUV): - 'Color as a function of redshift and mag, interpolated from Bouwens 2013-14 data.' - - zdatbeta = [2.5,3.8,5.0,5.9,7.0,8.0] - betaMUVatM0 = [-1.7,-1.85,-1.91,-2.00,-2.05,-2.13] - dbeta_dMUV = [-0.20,-0.11,-0.14,-0.20,-0.20,-0.15] - - _MUV0 = -19.5 - _c = -2.33 - - betaM0 = np.interp(z, zdatbeta, betaMUVatM0, left=betaMUVatM0[0], right=betaMUVatM0[-1]) - dbetaM0 = (MUV - _MUV0).T * np.interp(z, zdatbeta, dbeta_dMUV, left=dbeta_dMUV[0], right=dbeta_dMUV[-1]) - - sol1 = (betaM0-_c) * np.exp(dbetaM0/(betaM0-_c))+_c #for MUV > MUV0 - sol2 = dbetaM0 + betaM0 #for MUV < MUV0 - - return sol1.T * np.heaviside(MUV - _MUV0, 0.5) + sol2.T * np.heaviside(_MUV0 - MUV, 0.5) diff --git a/zeus21/__init__.py b/zeus21/__init__.py index 656a475..827b2fd 100644 --- a/zeus21/__init__.py +++ b/zeus21/__init__.py @@ -1,11 +1,16 @@ -from .inputs import User_Parameters, Cosmo_Parameters_Input, Cosmo_Parameters, Astro_Parameters +from .bursty_sfh import * from .constants import * -from .cosmology import * from .correlations import * -from .sfrd import get_T21_coefficients -from .xrays import Xray_class -from .UVLFs import UVLF_binned -from .maps import CoevalMaps +from .cosmology import * +from .inputs import * +from .LFs import * +from .maps import * +from .reionization import * +from .SED import * +from .sfrd import * +from .T21coefficients import * +from .wrappers import * +from .z21_utilities import * import warnings warnings.filterwarnings("ignore", category=UserWarning) #to silence unnecessary warning in mcfit diff --git a/zeus21/bursty_sfh.py b/zeus21/bursty_sfh.py new file mode 100644 index 0000000..dabeaaa --- /dev/null +++ b/zeus21/bursty_sfh.py @@ -0,0 +1,465 @@ +""" +Compute Star Formation Histories with Burstiness. + +Authors: zeus21 v2 collaboration - June 2026 + Emily Bregou ; + Hector Afonso G. Cruz ; + Sarah Libanore ; + Julian B. Muñoz ; + Yonny Sklansky ; + Emilie Thélie ; + Alessandra Venditti +arXiv:2302.08506, arXiv:2306.09403, arXiv:2407.18294, Sklansky et al. (in prep) +""" + +from .sfrd import * + +class SFH_class: + """ + Star formation histories (SFHs) with stochastic burstiness for Pop II galaxies. + + Computes the SFR(t) of a galaxy in a halo of mass Mh, including a + power-spectral-density (PSD) model for log-normal SFR fluctuations + (damped random walk in ln SFR). See 2601.07912 for the burstiness model. + + Only Pop II is currently supported; Pop III raises a ValueError. + + Parameters + ---------- + UserParams : User_Parameters + Global run settings. + CosmoParams : Cosmo_Parameters + Cosmological parameters, including tageofzMyr and zfoftageMyr. + AstroParams : Astro_Parameters + Astrophysical parameters. Key attributes used here: + + - USE_POPIII : bool — if True, raises ValueError (not implemented). + - FLAG_COMPARE_BAGPIPES : bool — use a toy exponential SFH for + comparison with BAGPIPES fits instead of the full model. + - FLAG_RENORMALIZE_AVG_SFH : bool — if True, boosts mean SFR by + exp(σ²/2) to account for log-normal stochasticity (). + - sigmaPSD, dsigmaPSDdlog10Mh : PSD amplitude and its mass slope. + - tauPSD, dlog10tauPSDdlog10Mh : PSD timescale (Myr) and mass slope. + - _minsigmaPSD, _maxsigmaPSD, _mintauPSD, _maxtauPSD : clamp limits. + - _omegamin, _omegamax : frequency integration range (1/Myr). + - _dt_FFT, _N_FFT : time resolution (Myr) and number of points for FFT. + - epsstar, dlog10epsstardz, _zpivot, Mc, alphastar, betastar, + fstarmax, mean_SFR_normalization : star-formation efficiency params. + + HMFinterp : HMF_interpolator + Halo mass function interpolator; must expose Mhtab. + tage : float or array-like + Lookback time(s) in Myr at which to evaluate the SFH. + zobs : float + Observed redshift. + z_Init : float, optional + Initialisation redshift for the SFRD. Computed via Z_init if not given. + SFRD_Init : SFRD_class, optional + Pre-computed SFRD object. Constructed internally if not given. + + Attributes + ---------- + SFH_II : ndarray, shape (NMh, Ntage) + Pop II star formation rate in M☉ yr⁻¹ as a function of halo mass + and lookback time. + """ + + def __init__(self, UserParams, CosmoParams, AstroParams, HMFinterp, tage, zobs, z_Init = None, SFRD_Init = None): + "Returns the star formation history at age tage [in Myr] of a galaxy in a halo of mass Mh at age tage, in Msun/yr" + + if z_Init is None: + z_Init = Z_init(UserParams=UserParams, CosmoParams=CosmoParams) + + if SFRD_Init is None: + SFRD_Init = SFRD_class(UserParams, CosmoParams, AstroParams, HMFinterp, z_Init) + + self.SFH_II = self.SFH(CosmoParams, AstroParams, HMFinterp, SFRD_Init, tage, zobs, pop = 2) + + if AstroParams.USE_POPIII: + raise ValueError('Burstiness is not implemented for PopIII') + + + def SFH(self, CosmoParams, AstroParams, HMFinterp, SFRD_Init, tage, zobs, pop): + """ + Compute the star formation history at a set of lookback times. + + Reconstructs halo mass history assuming exponential accretion, then + multiplies the mass accretion rate by the stellar efficiency fstar(z, Mh). + Optionally renormalises the mean SFR to account for log-normal scatter + (see FLAG_RENORMALIZE_AVG_SFH). + + Parameters + ---------- + CosmoParams : Cosmo_Parameters + AstroParams : Astro_Parameters + HMFinterp : HMF_interpolator + SFRD_Init : SFRD_class + tage : float or array-like + Lookback times in Myr relative to zobs. + zobs : float + Observed redshift. + pop : {2} + Stellar population. Only Pop II (2) is implemented. + + Returns + ------- + ndarray, shape (NMh, Ntage) + SFR in M☉ yr⁻¹ at each (halo mass, lookback time). + """ + + tobs = CosmoParams.tageofzMyr(zobs) + + _tearlier = np.fmax(0.0,tobs-tage) #time before the observation + zage = CosmoParams.zfoftageMyr(_tearlier) #z of the earlier times + massVector = HMFinterp.Mhtab #has to be the same due to the meanSFRnormalization below + + ###ASDASD TYTY - TODO this is just for comparing w bagpipes one run + if(AstroParams.FLAG_COMPARE_BAGPIPES == True): + texp=-120 #Myr, minus because backwards + Mstar = 3e7*(massVector/7e9)**1.5 #made up but fits the usual power-law + Lbox = 1000 #Myr, age of universe (so it integrates to Mstar) + return np.outer(Mstar/(texp*1e6) * np.exp(-SFRD_Init.Matom(zobs)/massVector) * AstroParams.mean_SFR_normalization , (np.exp(tage/texp) / (np.exp((Lbox)/texp) -1)) ) #in Msun/yr + + ### This is a decent approximation,but only for exponential accretion + alphatime_invMyr = constants.ALPHA_accretion_exponential * cosmology.Hubinvyr(CosmoParams, zage) * (1+zage) * 1e6 + Mhhistory = np.outer(massVector, np.exp(-alphatime_invMyr * tage)) + + # Get the mass accretion rate at all the past redshifts z + dMhdot = SFRD_Init.dMh_dt(CosmoParams, AstroParams, HMFinterp, Mhhistory, zage) #in Msun/yr + + fstar = self.fstarofz_scaled_Mz(AstroParams, CosmoParams, SFRD_Init, zage, Mhhistory, pop) + + if(AstroParams.FLAG_RENORMALIZE_AVG_SFH==True): + _meanSFRnormalization = self._get_mean_SFR_normalization(AstroParams, HMFinterp.Mhtab) #normalization of the SFR, in Msun/yr, at each Mh + else: + _meanSFRnormalization = 1.0 #no normalization, just return the SFR + + SFH_val = fstar * dMhdot * _meanSFRnormalization[:, None] #in Msun/yr + + return SFH_val + + + def fstarofz_scaled_Mz(self, AstroParams, CosmoParams, SFRD_Init, z, Mhlist, pop): + """ + Factorized approximation to fstar(z, Mh) over a 2-D (Mh, z) grid. + + Computes fstar(Mh, z) ≈ fstar(Mh, z0) · (Mh / Mh0)^α, separating the + mass and redshift dependences. This avoids evaluating fstar_ofz on the + full NMh × Nz array; it is exact for Mh < Mc and any residual offset + is absorbed into β*. + + Only valid under exponential accretion. Use with care if the accretion + history deviates strongly from exponential. + + Parameters + ---------- + AstroParams : Astro_Parameters + CosmoParams : Cosmo_Parameters + SFRD_Init : SFRD_class + z : array-like, shape (Nz,) + Redshift array (z[0] is used as the reference epoch). + Mhlist : ndarray, shape (NMh, Nz) + Halo mass grid at each redshift step. + pop : {2} + Stellar population. + + Returns + ------- + ndarray, shape (NMh, Nz) + Approximate fstar values over the full (Mh, z) grid. + """ + + if pop == 2: + eps = AstroParams.epsstar + dlog10eps = AstroParams.dlog10epsstardz + zpiv = AstroParams._zpivot + Mc = AstroParams.Mc + alphastar = AstroParams.alphastar + betastar = AstroParams.betastar + fstarmax = AstroParams.fstarmax + + fofMh = SFRD_Init.fstar_ofz(CosmoParams, z[0], Mhlist[:,0], eps, dlog10eps, zpiv, Mc, alphastar, betastar, fstarmax) + fofz = np.pow(Mhlist[0]/Mhlist[0,0],AstroParams.alphastar) + + return np.outer(fofMh,fofz) #this is a very good approximation for the fstarofz function, but only for exponential accretion + + + def sigmaPSD_at_Mh(self, AstroParams, Mh): + """ + PSD amplitude σ(Mh) for the ln SFR damped random walk. + + Linear in log10(Mh), clamped to [_minsigmaPSD, _maxsigmaPSD]. + To convert to log10(SFR) units, multiply by log(10). + + Parameters + ---------- + AstroParams : Astro_Parameters + Mh : float or array-like + Halo mass(es) in M☉. + + Returns + ------- + ndarray + σ in units of ln SFR, same shape as Mh. + """ + + "Returns the sigma of the power spectrum of lnSFR at Mh, in units of ln(SFR), so to convert to log10(SFR) multiply by np.log(10)" + Mh = np.atleast_1d(Mh) + sigma_at_Mh = AstroParams.sigmaPSD + AstroParams.dsigmaPSDdlog10Mh * np.log10(Mh/1e10) + + return np.fmin(np.fmax(sigma_at_Mh,AstroParams._minsigmaPSD),AstroParams._maxsigmaPSD) #make sure it's within the limits set by UserParams + + def tauPSD_at_Mh(self, AstroParams, Mh): + """ + PSD correlation timescale τ(Mh) for the ln SFR damped random walk. + + Power-law in Mh, clamped to [_mintauPSD, _maxtauPSD]. + + Parameters + ---------- + AstroParams : Astro_Parameters + Mh : float or array-like + Halo mass(es) in M☉. + + Returns + ------- + ndarray + τ in Myr, same shape as Mh. + """ + + "Returns the tau of the power spectrum of lnSFR at Mh, in Myr" + Mh = np.atleast_1d(Mh) + tau_at_Mh = AstroParams.tauPSD * 10**(AstroParams.dlog10tauPSDdlog10Mh * np.log10(Mh/1e10)) + + return np.fmin(np.fmax(tau_at_Mh,AstroParams._mintauPSD), + AstroParams._maxtauPSD) #make sure it's within the limits set by UserParams + + + def PowerlnSFR(self, AstroParams, omega, Mh): + """ + Power spectrum of ln SFR under a damped (α=2) random walk model. + + P(ω, Mh) = σ²(Mh) · τ(Mh) / [1 + (ω τ)²] + + As in 2601.07912. Note that compared to other refs (eg 2410.21409) + we do not have an implicit factor of 1 Myr in the + amplitude; σ here is in ln SFR units; can convert by sigma->sigma*np.log(10) + Index for random walk us alpha = 2 by default, can be enhanced to modify. + + Parameters + ---------- + AstroParams : Astro_Parameters + omega : float or array-like, shape (Nω,) + Angular frequency in Myr⁻¹. + Mh : float or array-like, shape (NMh,) + Halo mass(es) in M☉. + + Returns + ------- + ndarray, shape (NMh, Nω) + Power spectrum in (ln SFR)² Myr. + """ + + omega = np.atleast_1d(omega) #make sure omega is a vector + Mh = np.atleast_1d(Mh) #make sure Mh is a vector + sigma_at_Mh = self.sigmaPSD_at_Mh(AstroParams, Mh) + tau_at_Mh = self.tauPSD_at_Mh(AstroParams, Mh) + _tau_times_omega = np.outer(omega,tau_at_Mh) #omega is a vector length of omega, tau has the Mh length + return (sigma_at_Mh**2 * tau_at_Mh / (1.0 + _tau_times_omega**2.0)).T # NM x Nomega; secretely there's a 1*Myr in the amplitude + + def Wink_TH(self,omega, T): + """ + Top-hat temporal window function in Fourier space. + + W(ω, T) = sinc(ωT/2) = sin(ωT/2) / (ωT/2) + + Used to compute the variance of ln SFR averaged over a timescale T. + A small offset (1e-16) is added to the argument to avoid 0/0 at ω=0. + + Parameters + ---------- + omega : array-like + Angular frequency in Myr⁻¹. + T : float + Averaging timescale in Myr. + + Returns + ------- + ndarray + Dimensionless window, same shape as omega. Equals 1 at ω→0. + """ + + x = omega*T/2 + 1e-16 + return np.sin(x) / (x) + + def Variance_of_lnSFR(self, AstroParams, T, Mh): + """ + Variance of ln SFR averaged over a timescale T. + + Var[ln SFR]_T = (2/2π) ∫ P(ω) |W(ω,T)|² dω + + The factor 2/(2π) converts the one-sided integral over positive ω to + the full two-sided variance. + + Parameters + ---------- + AstroParams : Astro_Parameters + T : float + Averaging timescale in Myr. Use T=0 for the unsmoothed variance + (integrates over all frequencies). + Mh : float or array-like + Halo mass(es) in M☉. + + Returns + ------- + ndarray + Variance in (ln SFR)², same shape as Mh. + """ + + omegalist = np.logspace(np.log10(AstroParams._omegamin),np.log10(AstroParams._omegamax), 999) # in 1/Myr + power = self.PowerlnSFR(AstroParams, omegalist, Mh) + wink = self.Wink_TH(omegalist, T) + + return np.trapezoid(power * np.abs(wink)**2, omegalist) *2/(2*np.pi) + + def _get_mean_SFR_normalization(self, AstroParams, Mh): + """ + Log-normal boost to the mean SFR from stochastic burstiness. + + For a Gaussian variable δ = ln SFR with variance σ², + ⟨SFR⟩ = SFR_smooth · exp(σ²/2). + + i.e. the ratio of mean to SFR(Mh) with no burstiness (ie median) + + Parameters + ---------- + AstroParams : Astro_Parameters + Mh : array-like + Halo mass(es) in M☉. + + Returns + ------- + ndarray + Multiplicative boost factor exp(σ²/2), same shape as Mh. + """ + + _varlnSFR = self.Variance_of_lnSFR(AstroParams, 0.,Mh) #T=0 since it's at integrated over all timescales + meanSFRnormalization = np.exp(_varlnSFR/2.) # = for a gaussian d + return meanSFRnormalization + + + def _get_PowerSFR_NL_FFT_vectorized(self, AstroParams, Mh_array): + """ + Non-linear power spectrum of SFR via FFT of the ln SFR correlation function. + + Because SFR = exp(ln SFR) is a non-linear transformation, its power + spectrum differs from P_lnSFR. This method computes it by: + + 1. Building the auto-correlation of ln SFR (damped random walk): + C(t) = σ² / 2 · exp(−|t| / τ) + 2. Exponentiating to get the SFR correlation: + C_SFR(t) = exp(C(t)) − 1 + 3. FFT → one-sided power spectrum of SFR fluctuations. + + All NMh masses are processed simultaneously via broadcasting. + + Parameters + ---------- + AstroParams : Astro_Parameters + Must expose: _dt_FFT, _N_FFT. + Mh_array : float or array-like, shape (NMh,) + Halo mass(es) in M☉. + + Returns + ------- + omegalist : ndarray, shape (Nfft//2 + 1,) + Angular frequencies in Myr⁻¹. + powerNL : ndarray, shape (NMh, Nfft//2 + 1) + Non-linear SFR power spectrum in (M☉ yr⁻¹)² Myr. + """ + + Mh_array = np.atleast_1d(Mh_array) # Ensure Mh_array is a numpy array + # dt is the time resolution for FFT, Nfft is the number of points in FFT + dt = AstroParams._dt_FFT + Nfft = AstroParams._N_FFT + half_Nfft = Nfft // 2 + t_corr = dt * np.arange(-half_Nfft, Nfft - half_Nfft) + + # Vectorize the parameter calculations + sigma_array = self.sigmaPSD_at_Mh(AstroParams, Mh_array) # Shape: (NMhs,) + tau_array = self.tauPSD_at_Mh(AstroParams, Mh_array) # Shape: (NMhs,) + + # Broadcast for correlation function calculation + t_corr_2d = t_corr[np.newaxis, :] # Shape: (1, Nfft) + tau_2d = tau_array[:, np.newaxis] # Shape: (NMhs, 1) + sigma_2d = sigma_array[:, np.newaxis] # Shape: (NMhs, 1) + + # Vectorized correlation function + corrF = np.exp(-np.abs(t_corr_2d)/tau_2d) * sigma_2d**2/(2.0) + corrFNL = np.exp(corrF) - 1.0 # Shape: (NMhs, Nfft) + + # FFT along the time axis for all masses at once + powerNL = np.fft.rfft(corrFNL, axis=1) * dt # Shape: (NMhs, Nfft//2+1) + + # Frequency axis (same for all masses) + omegalist = np.fft.rfftfreq(len(t_corr), d=dt) * 2 * np.pi + + return omegalist, np.abs(powerNL) + + + + def WindowFourier(self, CosmoParams, AstroParams, HMFinterp, SFRD_Init, GreensFunction, zobs, tage, pop): + """ + Fourier transform of the convolution G(t) * SFH(t). + + Computes W̃(ω, Mh) = FFT[ G(t) · SFH(t) ], where G is a Green's + function (e.g. Greens_function_LUV) and SFH(t) is the star formation + history. The result enters the luminosity power spectrum as + P_L(ω) = |W̃(ω)|² · P_SFR(ω). + + Parameters + ---------- + CosmoParams : Cosmo_Parameters + AstroParams : Astro_Parameters + Must expose: _dt_FFT, _N_FFT. + HMFinterp : HMF_interpolator + SFRD_Init : SFRD_class + GreensFunction : callable + A function with signature ``G(AstroParams, tage_Myr, Mhtab)`` + returning an array of shape (NMh, Nt) in erg s⁻¹ M☉⁻¹. + Typically one of the Greens_function_L* functions from sed.py. + zobs : float + Observed redshift. + tage : float + Maximum lookback time in Myr (sets the FFT window). + pop : {2} + Stellar population. + + Returns + ------- + omegalist : ndarray, shape (Nfft//2 + 1,) + Angular frequencies in Myr⁻¹. + windowFourier : ndarray, shape (NMh, Nfft//2 + 1) + Complex Fourier transform of G·SFH in erg s⁻¹ M☉⁻¹ Myr. + + Notes + ----- + SFH is converted from M☉ yr⁻¹ to M☉ Myr⁻¹ (×10⁶) before the FFT + so that the output is in consistent Myr-based units. + """ + + dt = AstroParams._dt_FFT + Nfft = AstroParams._N_FFT + _tFFT = dt*np.arange(Nfft) + + tobs = CosmoParams.tageofzMyr(zobs) + _tearlier = np.fmax(0.0,tobs-tage) #time before the observation + zage = CosmoParams.zfoftageMyr(_tearlier) #z of the earlier times + + SFHarray = self.SFH(CosmoParams,AstroParams, HMFinterp, SFRD_Init, _tFFT, zobs, pop) + + _integrand = GreensFunction(AstroParams, _tFFT, HMFinterp.Mhtab)*SFHarray*1e6 #convert SFR to 1/Myr for FFT + _windowFourier = np.fft.rfft(_integrand,axis=1)*dt #for correct normalization + omegalist = np.fft.rfftfreq(len(_tFFT), d=dt) * 2 * np.pi # Convert to angular frequency + + return omegalist, _windowFourier diff --git a/zeus21/constants.py b/zeus21/constants.py index 37ef17d..9005136 100644 --- a/zeus21/constants.py +++ b/zeus21/constants.py @@ -1,13 +1,15 @@ """ - Keep here all global flags, numerical constants, and conversion factors/units. -Author: Julian B. Muñoz -UT Austin and Harvard CfA - January 2023 - -Edited by Hector Afonso G. Cruz -JHU - July 2024 - +Authors: zeus21 v2 collaboration - June 2026 + Emily Bregou ; + Hector Afonso G. Cruz ; + Sarah Libanore ; + Julian B. Muñoz ; + Yonny Sklansky ; + Emilie Thélie ; + Alessandra Venditti +arXiv:2302.08506, arXiv:2306.09403, arXiv:2407.18294, Sklansky et al. (in prep) """ ############################### @@ -94,6 +96,17 @@ #UVLF related -_MAGMAX = 10 #max abs magnitude to avoid infs -FLAG_RENORMALIZE_LUV = False #whether to renormalize the lognormal LUV with sigmaUV to recover or otherwise . Recommend False. +_MAGMAX_UV = 10. #max abs magnitude to avoid infs +_MAGMIN_Ha = -50. #max abs magnitude to avoid infs NZ_TOINT = 3 #how many zs around with z_rms we use to predict. Only in HMF since the rest do not vary much. + +zeropoint_ABmag_ergsHz = 51.63 # pivot value for specific luminosity (erg/s/Hz) to magnitude conversion -- constant flat in wavelength + +# SarahLibanore +zmax_AstroBreak = 50. # max redshift above which we do not trust astro computation + +redshiftFactor_Visbal = 1.04 #max amount LW photons can redshift before being scattered, as in Visbal+1402.0882 + +a_Hirata = 2.98394 +b_Hirata = 1.53583 +c_Hirata = 3.8528 \ No newline at end of file diff --git a/zeus21/correlations.py b/zeus21/correlations.py index c79d72c..7078a3f 100644 --- a/zeus21/correlations.py +++ b/zeus21/correlations.py @@ -1,194 +1,280 @@ """ - -Code to compute correlation functions from power spectra and functions of them. Holds two classes: Correlations (with matter correlation functions smoothed over different R), and Power_Spectra (which will compute and hold the 21-cm power spectrum and power for derived quantities like xa, Tk, etc.) - -Author: Julian B. Muñoz -UT Austin and Harvard CfA - January 2023 - -Edited by Hector Afonso G. Cruz -JHU - July 2024 - -Edited by Sarah Libanore -BGU - July 2025 - +Code to compute correlation functions from power spectra and functions of them. +Holds two classes: + Correlations (with matter correlation functions smoothed over different R), + Power_Spectra (which will compute and hold the 21-cm power spectrum and power for derived quantities like xa, Tk, etc.). + +Authors: zeus21 v2 collaboration - June 2026 + Emily Bregou ; + Hector Afonso G. Cruz ; + Sarah Libanore ; + Julian B. Muñoz ; + Yonny Sklansky ; + Emilie Thélie ; + Alessandra Venditti +arXiv:2302.08506, arXiv:2306.09403, arXiv:2407.18294, Sklansky et al. (in prep) """ import numpy as np from scipy.interpolate import UnivariateSpline from scipy.interpolate import interp1d +from scipy.interpolate import RegularGridInterpolator import mcfit from scipy.special import gammaincc #actually very fast, no need to approximate import numexpr as ne from . import constants from . import cosmology +from . import z21_utilities -class Correlations: - "Class that calculates and keeps the correlation functions." - - def __init__(self, UserParams, Cosmo_Parameters, ClassCosmo): - - - #we choose the k to match exactly the log FFT of input Rtabsmoo. - - self._klistCF, _dummy_ = mcfit.xi2P(Cosmo_Parameters._Rtabsmoo, l=0, lowring=True)(0*Cosmo_Parameters._Rtabsmoo, extrap=False) - self.NkCF = len(self._klistCF) - - self._PklinCF = np.zeros(self.NkCF) # P(k) in 1/Mpc^3 - for ik, kk in enumerate(self._klistCF): - self._PklinCF[ik] = ClassCosmo.pk(kk, 0.0) # function .pk(k,z) - - - - self._xif = mcfit.P2xi(self._klistCF, l=0, lowring=True) - - self.WINDOWTYPE = 'TOPHAT' - #options are 'TOPHAT', 'TOPHAT1D' and 'GAUSS' (for now). TOPHAT is calibrated for EPS, but GAUSS has less ringing - - self.xi_RR_CF = self.get_xi_R1R2_z0(Cosmo_Parameters) - ClassCosmo.pars['xi_RR_CF'] = np.copy(self.xi_RR_CF) #store correlation function for gamma_III correction in SFRD - - ###HAC: Interpolated object for eta power spectrum - if Cosmo_Parameters.USE_RELATIVE_VELOCITIES == True: - P_eta_interp = interp1d(ClassCosmo.pars['k_eta'], ClassCosmo.pars['P_eta'], bounds_error = False, fill_value = 0) - self._PkEtaCF = P_eta_interp(self._klistCF) - self.xiEta_RR_CF = self.get_xiEta_R1R2(Cosmo_Parameters) - else: - self._PkEtaCF = np.zeros_like(self._PklinCF) - self.xiEta_RR_CF = np.zeros_like(self.xi_RR_CF) - def _WinTH(self,k,R): - x = k * R - return 3.0/x**2 * (np.sin(x)/x - np.cos(x)) - - def _WinTH1D(self,k,R): - x = k * R - return np.sin(x)/x - - def _WinG(self,k,R): - x = k * R * constants.RGauss_factor - return np.exp(-x**2/2.0) - - def Window(self, k, R): - if self.WINDOWTYPE == 'TOPHAT': - return self._WinTH(k, R) - elif self.WINDOWTYPE == 'GAUSS': - return self._WinG(k, R) - elif self.WINDOWTYPE == 'TOPHAT1D': - return self._WinTH1D(k, R) - else: - print('ERROR in Window. Wrong type') - - - def get_xi_z0_lin(self): - "Get correlation function of density, linearly extrapolated to z=0" - ##Warning: definitely check if beyond LCDM! - #currenetly unused, just for refernce and plots - - rslinCF, xilinCF = self._xif(self._PklinCF, extrap=False) - - return rslinCF, xilinCF - - def get_xi_R1R2_z0 (self, Cosmo_Parameters): - "same as get_xi_z0_lin but smoothed over two different radii with Window(k,R) \ - same separations rs as get_xi_z0_lin so it does not output them." - - ###HAC: Broadcasted to improve efficiency - ###HAC: dim 0 is R1, dim 1 is R2, dim 2 is r, where R1 and R2 are smoothing radii and r is the argument of xi(r) - lengthRarray = Cosmo_Parameters.NRs - windowR1 = self.Window(self._klistCF.reshape(lengthRarray, 1, 1), Cosmo_Parameters._Rtabsmoo.reshape(1, 1, lengthRarray)) - windowR2 = self.Window(self._klistCF.reshape(1, lengthRarray,1), Cosmo_Parameters._Rtabsmoo.reshape(1, 1, lengthRarray)) - - _PkRR = np.array([[self._PklinCF]]) * windowR1 * windowR2 - - self.rlist_CF, xi_RR_CF = self._xif(_PkRR, extrap = False) - - return xi_RR_CF - - ###HAC: The next two are the same, but for - def get_xiEta(self, Cosmo_Parameters, ClassCosmo): - "Get correlation function of v^2 at z_drag (~1060 for LCDM parameters)" - ##Warning: definitel check if beyond LCDM! - #currently unused, just for reference and plots +class Power_Spectra: + + """ + Get the 21-cm power spectrum and its components from correlation functions and coefficients + + Parameters + ---------- + UserParams : UserParams class + CosmoParams : CosmoParams class + AstroParams : AstroParams class + T21coeffs : T21coeffs class + RSD_MODE : int + Choice of redshift-space distortion mode. + 0 = None (mu=0), just for comparison with real-space + 1 = Spherical avg (like 21-cmFAST), standard assumption in sims + 2 = LoS only (mu=1), more observationally relevant + Default is 1 + + Attributes + ---------- + Basic Setup Attributes + + self.klist_PS: array + Input array of wavenumbers used in inputs.py + self.kwindow: array + Output array of wavenumbers used in window function calls. + Identical to klist_PS + + Window Function Attributes + self.windowalpha_II: matrix + Linear Pop II LyA window functions. Dimension (z, k) + self.windowalpha_III + Linear Pop III LyA window functions. Dimension (z, k) + Set to zero if AstroParams.USE_POPIII == False + self.windowxray_II + Linear Pop II Xray window functions. Dimension (z, k) + self.windowxray_III + Linear Pop III Xray window functions. Dimension (z, k) + Set to zero if AstroParams.USE_POPIII == False - rsEtaCF, xiEtaCF = self._xif(self._PkEtaCF, extrap=False) + Baryon Power Spectra Attributes (Used only if UserParams.USE_BARYON_FLAG == True) + self.pK_bOnlyCLASS_intp: interpolator + Baryon-only power spectrum, interpolated over redshift z and wavenumber k + self.pK_bANDcbCLASS_intp: interpolator + Baryon-CDM cross power spectrum, interpolated over redshift z and wavenumber k - return rsEtaCF, xiEtaCF + Linear Power Spectra + self.Deltasq_xa_lin_II: matrix + Linear Pop II contribution to LyA power spectrum. Dimension (z, k) + self.Deltasq_xa_lin_III: matrix + Linear Pop III contribution to LyA power spectrum. Dimension (z, k) + Set to zero if AstroParams.USE_POPIII == False + Density-only power spectra used + self.Deltasq_xa_lin_IIxIII: matrix + Linear Pop II x III cross contribution to LyA power spectrum. Dimension (z, k) + Set to zero if AstroParams.USE_POPIII == False - def get_xiEta_R1R2(self, Cosmo_Parameters): - "same as get_xiEta but smoothed over two different radii with Window" + self.Deltasq_Tx_lin_II: matrix + Linear Pop II contribution to Xray power spectrum. Dimension (z, k) + self.Deltasq_Tx_lin_III: matrix + Linear Pop III contribution to Xray power spectrum. Dimension (z, k) + Set to zero if AstroParams.USE_POPIII == False + Density-only power spectra used (no linear eta power spectra) + self.Deltasq_Tx_lin_IIxIII: matrix + Linear Pop II x III cross contribution to Xray power spectrum. Dimension (z, k) + Set to zero if AstroParams.USE_POPIII == False - ###HAC: Broadcasted to improve efficiency - ###HAC: dim 0 is R1, dim 1 is R2, dim 2 is r, where R1 and R2 are smoothing radii and r is the argument of xi(r) - lengthRarray = len(Cosmo_Parameters._Rtabsmoo) + self.Deltasq_xaTx_lin_II: matrix + Linear Pop II contribution to LyA-Xray cross spectrum. Dimension (z, k) + self.Deltasq_xaTx_lin_III: matrix + Linear Pop III contribution to LyA-Xray cross spectrum. Dimension (z, k) + Set to zero if AstroParams.USE_POPIII == False + Density-only power spectra used (no linear eta power spectra) + self.Deltasq_xaTx_lin_IIxIII: matrix + Linear Pop II x III cross contribution to LyA-Xray cross spectrum. Dimension (z, k) + Set to zero if AstroParams.USE_POPIII == False - windowR1 = self.Window(self._klistCF.reshape(lengthRarray, 1, 1), Cosmo_Parameters._Rtabsmoo.reshape(1, 1, lengthRarray)) - windowR2 = self.Window(self._klistCF.reshape(1, lengthRarray,1), Cosmo_Parameters._Rtabsmoo.reshape(1, 1, lengthRarray)) - - _PkEtaRR = np.array([[self._PkEtaCF]]) * windowR1 * windowR2 - - self.rlist_CF, xiEta_RR_CF = self._xif(_PkEtaRR, extrap = False) - - return xiEta_RR_CF + self.Deltasq_d_lin: matrix + Linear LSS power spectra. Dimension (z, k) + Set to P_baryon(k) if UserParams.USE_BARYON_FLAG == True + self.Deltasq_dxa_lin_II: matrix + Linear Pop II density-LyA cross power spectrum. Dimension (z, k) + Uses P_baryonXcdm(k) if UserParams.USE_BARYON_FLAG == True + self.Deltasq_dxa_lin_III: matrix + Linear Pop III density-LyA cross power spectrum. Dimension (z, k) + Uses P_baryonXcdm(k) if UserParams.USE_BARYON_FLAG == True + Set to zero if AstroParams.USE_POPIII == False + self.Deltasq_dTx_lin_II: matrix + Linear Pop II density-LyA cross power spectrum. Dimension (z, k) + Uses P_baryonXcdm(k) if UserParams.USE_BARYON_FLAG == True + self.Deltasq_dTx_lin_III: matrix + Linear Pop III density-LyA cross power spectrum. Dimension (z, k) + Uses P_baryonXcdm(k) if UserParams.USE_BARYON_FLAG == True + Set to zero if AstroParams.USE_POPIII == False + + Total Power Spectra (including nonlinear corrections) + self.Deltasq_xa_II: matrix + Nonlinear Pop II contribution to LyA power spectrum. Dimension (z, k) + self.Deltasq_xa_III: matrix + Nonlinear Pop III contribution to LyA power spectrum. Dimension (z, k) + Set to zero if AstroParams.USE_POPIII == False + self.Deltasq_xa_IIxIII: matrix + Nonlinear Pop II x III cross contribution to LyA power spectrum. Dimension (z, k) + Set to zero if AstroParams.USE_POPIII == False + + self.Deltasq_Tx_II: matrix + Nonlinear Pop II contribution to Xray power spectrum. Dimension (z, k) + self.Deltasq_Tx_III: matrix + Nonlinear Pop III contribution to Xray power spectrum. Dimension (z, k) + Set to zero if AstroParams.USE_POPIII == False + self.Deltasq_Tx_IIxIII: matrix + Nonlinear Pop II x III cross contribution to Xray power spectrum. Dimension (z, k) + Set to zero if AstroParams.USE_POPIII == False + + self.Deltasq_xaTx_II: matrix + Nonlinear Pop II contribution to LyA-Xray cross spectrum. Dimension (z, k) + self.Deltasq_xaTx_III: matrix + Nonlinear Pop III contribution to LyA-Xray cross spectrum. Dimension (z, k) + Set to zero if AstroParams.USE_POPIII == False + self.Deltasq_xaTx_IIxIII: matrix + Nonlinear Pop II x III cross contribution to LyA-Xray cross spectrum. Dimension (z, k) + Set to zero if AstroParams.USE_POPIII == False + + self.Deltasq_d: matrix + Nonlinear LSS power spectra. Dimension (z, k) + Set to P_baryon(k) if UserParams.USE_BARYON_FLAG == True + self.Deltasq_dxa_II: matrix + Nonlinear Pop II density-LyA cross power spectrum. Dimension (z, k) + Uses P_baryonXcdm(k) if UserParams.USE_BARYON_FLAG == True + self.Deltasq_dxa_III: matrix + Nonlinear Pop III density-LyA cross power spectrum. Dimension (z, k) + Uses P_baryonXcdm(k) if UserParams.USE_BARYON_FLAG == True + Set to zero if AstroParams.USE_POPIII == False + self.Deltasq_dTx_II: matrix + Nonlinear Pop II density-LyA cross power spectrum. Dimension (z, k) + Uses P_baryonXcdm(k) if UserParams.USE_BARYON_FLAG == True + self.Deltasq_dTx_III: matrix + Nonlinear Pop III density-LyA cross power spectrum. Dimension (z, k) + Uses P_baryonXcdm(k) if UserParams.USE_BARYON_FLAG == True + Set to zero if AstroParams.USE_POPIII == False + + Combined (Pop II + Pop III) Total Power Spectra + self.Deltasq_d: matrix + Total density power spectrum. Dimension (z, k) + self.Deltasq_dxa: matrix + Total density-LyA cross power spectrum. Dimension (z, k) + self.Deltasq_dTx: matrix + Total density-Xray cross power spectrum. Dimension (z, k) + self.Deltasq_xa: matrix + Total LyA power spectrum. Dimension (z, k) + self.Deltasq_xaTx: matrix + Total LyA-Xray cross power spectrum. Dimension (z, k) + self.Deltasq_Tx: matrix + Total Xray power spectrum. Dimension (z, k) + + Ionization/Bubble Related Power Spectra + self.Deltasq_xion: matrix + Nonlinear ionization power spectrum. Dimension (z, k) + self.Deltasq_xion_lin: matrix + Linear ionization power spectrum. Dimension (z, k) + self.Deltasq_dxion: matrix + Nonlinear density-ionization cross power spectrum. Dimension (z, k) + self.Deltasq_dxion_lin: matrix + Linear density-ionization cross power spectrum. Dimension (z, k) + self.Deltasq_xaxion: matrix + Nonlinear LyA-ionization cross power spectrum. Dimension (z, k) + self.Deltasq_xaxion_lin: matrix + Linear LyA-ionization cross power spectrum. Dimension (z, k) + self.Deltasq_Txxion: matrix + Nonlinear Xray-ionization cross power spectrum. Dimension (z, k) + self.Deltasq_Txxion_lin: matrix + Linear Xray-ionization cross power spectrum. Dimension (z, k) + Final 21-cm Power Spectra + self.Deltasq_T21: matrix + Total nonlinear 21-cm power spectrum. Dimension (z, k) + self.Deltasq_T21_lin: matrix + Total linear 21-cm power spectrum. Dimension (z, k) + self.Deltasq_dT21: matrix + Nonlinear Density-21 cm cross power spectrum. Dimension (z, k) + self.Deltasq_dT21_lin: matrix + Linear Density-21 cm cross power spectrum. Dimension (z, k) + + """ + def __init__(self, UserParams, CosmoParams, AstroParams, T21coeffs, RSD_MODE=1): + #Variable set up + self._rs_input_mcfit = CosmoParams.rlist_CF #just to make notation simpler + self.klist_PS = CosmoParams._klistCF + self.RSD_MODE = RSD_MODE #TODO: mode to save at different mu -class Power_Spectra: - "Get power spetrum from correlation functions and coefficients" - - def __init__(self, User_Parameters, Cosmo_Parameters, Astro_Parameters, ClassCosmo, Correlations, T21_coefficients, RSD_MODE=1): + #first get the linear window functions -- note it already has growth factor in it, so it multiplies Pmatter(z=0) + #fix some arrays: TYTYTY HERE -# print("STEP 0: Variable Setup") - #set up some variables - self._rs_input_mcfit = Correlations.rlist_CF #just to make notation simpler - self.klist_PS = Correlations._klistCF - self.RSD_MODE = RSD_MODE #redshift-space distortion mode. 0 = None (mu=0), 1 = Spherical avg (like 21-cmFAST), 2 = LoS only (mu=1). 2 is more observationally relevant, whereas 1 the standard assumption in sims. 0 is just for comparison with real-space #TODO: mode to save at different mu + self._zGreaterMatrix100, self._iRnonlinear, self._corrdNL = self._prepare_corr_arrays(CosmoParams, T21coeffs) - #first get the linear window functions -- note it already has growth factor in it, so it multiplies Pmatter(z=0) - # SarahLibanore: add AstroParams to use flag on quadratic order - self.kwindow, self.windowalpha_II = self.get_xa_window(Astro_Parameters, Cosmo_Parameters, Correlations, T21_coefficients, pop = 2) - # SarahLibanore: add AstroParams to use flag on quadratic order - self._kwindowX, self.windowxray_II = self.get_Tx_window(Astro_Parameters, Cosmo_Parameters, Correlations, T21_coefficients, pop = 2) + self.kwindow, self.windowalpha_II = self.get_xa_window(CosmoParams, AstroParams, T21coeffs, pop = 2) + self._kwindowX, self.windowxray_II = self.get_Tx_window(CosmoParams, AstroParams, T21coeffs, pop = 2) - if Astro_Parameters.USE_POPIII == True: + + if AstroParams.USE_POPIII == True: # SarahLibanore: add AstroParams to use flag on quadratic order - self.kwindow, self.windowalpha_III = self.get_xa_window(Astro_Parameters, Cosmo_Parameters, Correlations, T21_coefficients, pop = 3) + self.kwindow, self.windowalpha_III = self.get_xa_window(CosmoParams,AstroParams, T21coeffs, pop = 3) # SarahLibanore: add AstroParams to use flag on quadratic order - self._kwindowX, self.windowxray_III = self.get_Tx_window(Astro_Parameters, Cosmo_Parameters, Correlations, T21_coefficients, pop = 3) + self._kwindowX, self.windowxray_III = self.get_Tx_window(CosmoParams, AstroParams, T21coeffs, pop = 3) else: self.windowalpha_III = np.zeros_like(self.windowalpha_II) self.windowxray_III = np.zeros_like(self.windowxray_II) #calculate some growth etc, and the bubble biases for the xHI linear window function: - self._lingrowthd = cosmology.growth(Cosmo_Parameters, T21_coefficients.zintegral) + self._lingrowthd = cosmology.growth(CosmoParams, T21coeffs.zintegral) + - #We don't care about bubbles at the moment - # if(constants.FLAG_DO_BUBBLES): - # self..calculate_barrier(Cosmo_Parameters, T21_coefficients) - # self..get_bubbles(Cosmo_Parameters, Correlations, T21_coefficients) - # self..windowxion = np.array([Correlations.Window(self..Rbub_star[iz]) for iz in range(T21_coefficients.Nzintegral)]) #Window returns a k-array. Smooths at the peak of the BMF - # self..windowxion = (self..windowxion.T*T21_coefficients.Qstar * self..bias_bub_avg * self.._lingrowthd * np.exp(-T21_coefficients.Qstar) ).T #normalize - # else: - # self..windowxion = np.zeros_like(self..windowalpha) + ############################## + #If USE_BARYON_FLAG, use baryon and baryon-cdm power spectra for correlations involving delta_b + + if UserParams.USE_BARYON_FLAG: + transfersMatrix = CosmoParams.ClassCosmo.get_transfer_and_k_and_z() + + fracB = CosmoParams.ClassCosmo.Om_b(0) / (CosmoParams.ClassCosmo.Om_b(0) + CosmoParams.ClassCosmo.Om_cdm(0)) + fracC = CosmoParams.ClassCosmo.Om_cdm(0) / (CosmoParams.ClassCosmo.Om_b(0) + CosmoParams.ClassCosmo.Om_cdm(0)) + + zCLASS = transfersMatrix[2] + kCLASS = transfersMatrix[1]; kCLASS[-1] = 0.999*kCLASS[-1] #to avoid interpolation errrors + tCLASS = fracB * transfersMatrix[0]['d_b'] + fracC * transfersMatrix[0]['d_cdm'] + tCLASS_b = transfersMatrix[0]['d_b'] + + pK_bOnlyCLASS = CosmoParams.ClassCosmo.pars['A_s'] * (kCLASS / 0.05)**(CosmoParams.ClassCosmo.pars['n_s']-1) * tCLASS_b.T**2 * (2 * np.pi**2/kCLASS**3) + pK_bANDcbCLASS = CosmoParams.ClassCosmo.pars['A_s'] * (kCLASS / 0.05)**(CosmoParams.ClassCosmo.pars['n_s']-1) * tCLASS_b.T*tCLASS.T * (2 * np.pi**2/kCLASS**3) + + self.pK_bOnlyCLASS_intp = RegularGridInterpolator([zCLASS, kCLASS], pK_bOnlyCLASS, method = 'cubic') + self.pK_bANDcbCLASS_intp = RegularGridInterpolator([zCLASS, kCLASS], pK_bANDcbCLASS, method = 'cubic') + ############################## + #Get all correlation functions -# print("STEP 1: Computing Nonlinear Power Spectra") - #finally, get all the nonlinear correlation functions: -# print("Computing Pop II-dependent power spectra") # SarahLibanore: add AstroParams to use flag on quadratic order - self.get_all_corrs_II(Astro_Parameters, User_Parameters, Cosmo_Parameters, Correlations, T21_coefficients) + self.get_all_corrs_II(UserParams, CosmoParams, AstroParams, T21coeffs) - if Astro_Parameters.USE_POPIII == True: -# print("Computing Pop IIxIII-dependent cross power spectra") - self.get_all_corrs_IIxIII(User_Parameters, Cosmo_Parameters, Correlations, T21_coefficients) - -# print("Computing Pop III-dependent power spectra") - self.get_all_corrs_III(User_Parameters, Cosmo_Parameters, Correlations, T21_coefficients) + if AstroParams.USE_POPIII == True: + self.get_all_corrs_IIxIII(CosmoParams, T21coeffs) #compute Pop II x III dependent cross power spectra + self.get_all_corrs_III(UserParams, CosmoParams, T21coeffs) #compute Pop III-dependent power spectra else: #bypases Pop III correlation routine and sets all Pop III-dependent correlations to zero self._IIxIII_deltaxi_xa = np.zeros_like(self._II_deltaxi_xa) @@ -207,18 +293,18 @@ def __init__(self, User_Parameters, Cosmo_Parameters, Astro_Parameters, ClassCos #and now define power spectra: #for xalpha, first linear - self._Pk_xa_lin_II = self.windowalpha_II**2 * Correlations._PklinCF - self._Pk_xa_lin_III = self.windowalpha_III**2 * Correlations._PklinCF ###TO DO (linearized VCB flucts):+ self.windowalphaVel_III**2 * Correlations._PkEtaCF - self._Pk_xa_lin_IIxIII = 2* self.windowalpha_II * self.windowalpha_III * Correlations._PklinCF #Pop IIxIII cross term doesn't have a velocity component + self._Pk_xa_lin_II = self.windowalpha_II**2 * CosmoParams._PklinCF + self._Pk_xa_lin_III = self.windowalpha_III**2 * CosmoParams._PklinCF #doesn't include linear eta power spectra + self._Pk_xa_lin_IIxIII = 2* self.windowalpha_II * self.windowalpha_III * CosmoParams._PklinCF #Pop IIxIII cross term doesn't have a velocity component self.Deltasq_xa_lin_II = self._Pk_xa_lin_II * self._k3over2pi2 #note that it still has units of xa_avg self.Deltasq_xa_lin_III = self._Pk_xa_lin_III * self._k3over2pi2 #note that it still has units of xa_avg self.Deltasq_xa_lin_IIxIII = self._Pk_xa_lin_IIxIII * self._k3over2pi2 #note that it still has units of xa_avg #nonlinear corrections too: - self._d_Pk_xa_nl_II = self.get_list_PS(self._II_deltaxi_xa, T21_coefficients.zintegral) - self._d_Pk_xa_nl_III = self.get_list_PS(self._III_deltaxi_xa, T21_coefficients.zintegral) #velocity correlations already embedded in nonlinear computation - self._d_Pk_xa_nl_IIxIII = self.get_list_PS(self._IIxIII_deltaxi_xa, T21_coefficients.zintegral) + self._d_Pk_xa_nl_II = z21_utilities.get_list_PS(CosmoParams, self._II_deltaxi_xa, T21coeffs.zintegral) + self._d_Pk_xa_nl_III = z21_utilities.get_list_PS(CosmoParams, self._III_deltaxi_xa, T21coeffs.zintegral) #velocity correlations already embedded in nonlinear computation + self._d_Pk_xa_nl_IIxIII = z21_utilities.get_list_PS(CosmoParams, self._IIxIII_deltaxi_xa, T21coeffs.zintegral) self.Deltasq_xa_II = self.Deltasq_xa_lin_II + self._d_Pk_xa_nl_II * self._k3over2pi2 #note that it still has units of xa_avg self.Deltasq_xa_III = self.Deltasq_xa_lin_III + self._d_Pk_xa_nl_III * self._k3over2pi2 #note that it still has units of xa_avg @@ -229,17 +315,17 @@ def __init__(self, User_Parameters, Cosmo_Parameters, Astro_Parameters, ClassCos #and same for xray - self._Pk_Tx_lin_II = self.windowxray_II**2 * Correlations._PklinCF - self._Pk_Tx_lin_III = self.windowxray_III**2 * Correlations._PklinCF ###TO DO (linearized VCB flucts):+ self.windowxrayVel_III**2 * Correlations._PkEtaCF - self._Pk_Tx_lin_IIxIII = 2* self.windowxray_II * self.windowxray_III * Correlations._PklinCF #Pop IIxIII cross term doesn't have a velocity component + self._Pk_Tx_lin_II = self.windowxray_II**2 * CosmoParams._PklinCF + self._Pk_Tx_lin_III = self.windowxray_III**2 * CosmoParams._PklinCF #doesn't include linear eta power spectra + self._Pk_Tx_lin_IIxIII = 2* self.windowxray_II * self.windowxray_III * CosmoParams._PklinCF #Pop IIxIII cross term doesn't have a velocity component self.Deltasq_Tx_lin_II = self._Pk_Tx_lin_II * self._k3over2pi2 self.Deltasq_Tx_lin_III = self._Pk_Tx_lin_III * self._k3over2pi2 self.Deltasq_Tx_lin_IIxIII = self._Pk_Tx_lin_IIxIII * self._k3over2pi2 - self._d_Pk_Tx_nl_II = self.get_list_PS(self._II_deltaxi_Tx, T21_coefficients.zintegral) - self._d_Pk_Tx_nl_III = self.get_list_PS(self._III_deltaxi_Tx, T21_coefficients.zintegral) - self._d_Pk_Tx_nl_IIxIII = self.get_list_PS(self._IIxIII_deltaxi_Tx, T21_coefficients.zintegral) + self._d_Pk_Tx_nl_II = z21_utilities.get_list_PS(CosmoParams, self._II_deltaxi_Tx, T21coeffs.zintegral) + self._d_Pk_Tx_nl_III = z21_utilities.get_list_PS(CosmoParams, self._III_deltaxi_Tx, T21coeffs.zintegral) + self._d_Pk_Tx_nl_IIxIII = z21_utilities.get_list_PS(CosmoParams, self._IIxIII_deltaxi_Tx, T21coeffs.zintegral) self.Deltasq_Tx_II = self.Deltasq_Tx_lin_II + self._d_Pk_Tx_nl_II * self._k3over2pi2 self.Deltasq_Tx_III = self.Deltasq_Tx_lin_III + self._d_Pk_Tx_nl_III * self._k3over2pi2 @@ -250,17 +336,17 @@ def __init__(self, User_Parameters, Cosmo_Parameters, Astro_Parameters, ClassCos #and their cross correlation - self._Pk_xaTx_lin_II = self.windowalpha_II * self.windowxray_II * Correlations._PklinCF - self._Pk_xaTx_lin_III = self.windowalpha_III * self.windowxray_III * Correlations._PklinCF ###TO DO (linearized VCB flucts):+ self.windowalphaVel_III * self.windowxrayVel_III * Correlations._PkEtaCF - self._Pk_xaTx_lin_IIxIII = (self.windowalpha_II * self.windowxray_III + self.windowalpha_III * self.windowxray_II) * Correlations._PklinCF + self._Pk_xaTx_lin_II = self.windowalpha_II * self.windowxray_II * CosmoParams._PklinCF + self._Pk_xaTx_lin_III = self.windowalpha_III * self.windowxray_III * CosmoParams._PklinCF #doesn't include linear eta power spectra + self._Pk_xaTx_lin_IIxIII = (self.windowalpha_II * self.windowxray_III + self.windowalpha_III * self.windowxray_II) * CosmoParams._PklinCF self.Deltasq_xaTx_lin_II = self._Pk_xaTx_lin_II * self._k3over2pi2 self.Deltasq_xaTx_lin_III = self._Pk_xaTx_lin_III * self._k3over2pi2 self.Deltasq_xaTx_lin_IIxIII = self._Pk_xaTx_lin_IIxIII * self._k3over2pi2 - self._d_Pk_xaTx_nl_II = self.get_list_PS(self._II_deltaxi_xaTx, T21_coefficients.zintegral) - self._d_Pk_xaTx_nl_III = self.get_list_PS(self._III_deltaxi_xaTx, T21_coefficients.zintegral) - self._d_Pk_xaTx_nl_IIxIII = self.get_list_PS(self._IIxIII_deltaxi_xaTx, T21_coefficients.zintegral) + self._d_Pk_xaTx_nl_II = z21_utilities.get_list_PS(CosmoParams, self._II_deltaxi_xaTx, T21coeffs.zintegral) + self._d_Pk_xaTx_nl_III = z21_utilities.get_list_PS(CosmoParams, self._III_deltaxi_xaTx, T21coeffs.zintegral) + self._d_Pk_xaTx_nl_IIxIII = z21_utilities.get_list_PS(CosmoParams, self._IIxIII_deltaxi_xaTx, T21coeffs.zintegral) self.Deltasq_xaTx_II = self.Deltasq_xaTx_lin_II + self._d_Pk_xaTx_nl_II * self._k3over2pi2 #note that it still has units of xa_avg self.Deltasq_xaTx_III = self.Deltasq_xaTx_lin_III + self._d_Pk_xaTx_nl_III * self._k3over2pi2 #note that it still has units of xa_avg @@ -271,15 +357,28 @@ def __init__(self, User_Parameters, Cosmo_Parameters, Astro_Parameters, ClassCos #and the same for deltaNL and its cross terms: - self._Pk_d_lin = np.outer(self._lingrowthd**2, Correlations._PklinCF) #No Pop II or III contribution - self.Deltasq_d_lin = self._Pk_d_lin * self._k3over2pi2 #note that it still has units of xa_avg - - self._Pk_dxa_lin_II = (self.windowalpha_II.T * self._lingrowthd).T * Correlations._PklinCF - self._Pk_dxa_lin_III = (self.windowalpha_III.T * self._lingrowthd).T * Correlations._PklinCF #No velocity component - - self._Pk_dTx_lin_II = (self.windowxray_II.T * self._lingrowthd).T * Correlations._PklinCF - self._Pk_dTx_lin_III = (self.windowxray_III.T * self._lingrowthd).T * Correlations._PklinCF #No velocity component + if UserParams.USE_BARYON_FLAG == 1: + + zInput, kInput = np.meshgrid(T21coeffs.zintegral, self.klist_PS, indexing='ij', sparse=True) + self._PklinCF_bb = self.pK_bOnlyCLASS_intp((zInput, kInput)) + self._PklinCF_bm = self.pK_bANDcbCLASS_intp((zInput, kInput)) + + self._Pk_d_lin = self._PklinCF_bb #No Pop II or III contribution + self._Pk_dxa_lin_II = self.windowalpha_II * self._PklinCF_bm /np.transpose([self._lingrowthd]) + self._Pk_dxa_lin_III = self.windowalpha_III * self._PklinCF_bm /np.transpose([self._lingrowthd])#No velocity component + self._Pk_dTx_lin_II = self.windowxray_II * self._PklinCF_bm /np.transpose([self._lingrowthd]) + self._Pk_dTx_lin_III = self.windowxray_III * self._PklinCF_bm /np.transpose([self._lingrowthd])#No velocity component + + else: + self._Pk_d_lin = np.outer(self._lingrowthd**2, CosmoParams._PklinCF) #No Pop II or III contribution + self._Pk_dxa_lin_II = (self.windowalpha_II.T * self._lingrowthd).T * CosmoParams._PklinCF + self._Pk_dxa_lin_III = (self.windowalpha_III.T * self._lingrowthd).T * CosmoParams._PklinCF #No velocity component + self._Pk_dTx_lin_II = (self.windowxray_II.T * self._lingrowthd).T * CosmoParams._PklinCF + self._Pk_dTx_lin_III = (self.windowxray_III.T * self._lingrowthd).T * CosmoParams._PklinCF #No velocity component + + self.Deltasq_d_lin = self._Pk_d_lin * self._k3over2pi2 #note that it still has units of xa_avg + self.Deltasq_dxa_lin_II = self._Pk_dxa_lin_II * self._k3over2pi2 self.Deltasq_dxa_lin_III = self._Pk_dxa_lin_III * self._k3over2pi2 #No velocity component @@ -287,25 +386,23 @@ def __init__(self, User_Parameters, Cosmo_Parameters, Astro_Parameters, ClassCos self.Deltasq_dTx_lin_III = self._Pk_dTx_lin_III * self._k3over2pi2 #No velocity component self._Pk_d = self._Pk_d_lin - self._Pk_dxa_II = self._Pk_dxa_lin_II self._Pk_dxa_III = self._Pk_dxa_lin_III - self._Pk_dTx_II = self._Pk_dTx_lin_II self._Pk_dTx_III = self._Pk_dTx_lin_III - if(User_Parameters.FLAG_DO_DENS_NL): #note that the nonlinear terms (cross and auto) below here have the growth already accounted for + if(UserParams.FLAG_DO_DENS_NL): #note that the nonlinear terms (cross and auto) below here have the growth already accounted for - self._d_Pk_d_nl = self.get_list_PS(self._II_deltaxi_d, T21_coefficients.zintegral) + self._d_Pk_d_nl = z21_utilities.get_list_PS(CosmoParams, self._II_deltaxi_d, T21coeffs.zintegral) self._Pk_d += self._d_Pk_d_nl - self._d_Pk_dxa_nl_II = self.get_list_PS(self._II_deltaxi_dxa, T21_coefficients.zintegral) - self._d_Pk_dxa_nl_III = self.get_list_PS(self._III_deltaxi_dxa, T21_coefficients.zintegral) + self._d_Pk_dxa_nl_II = z21_utilities.get_list_PS(CosmoParams, self._II_deltaxi_dxa, T21coeffs.zintegral) + self._d_Pk_dxa_nl_III = z21_utilities.get_list_PS(CosmoParams, self._III_deltaxi_dxa, T21coeffs.zintegral) self._Pk_dxa_II += self._d_Pk_dxa_nl_II self._Pk_dxa_III += self._d_Pk_dxa_nl_III - self._d_Pk_dTx_nl_II = self.get_list_PS(self._II_deltaxi_dTx, T21_coefficients.zintegral) - self._d_Pk_dTx_nl_III = self.get_list_PS(self._III_deltaxi_dTx, T21_coefficients.zintegral) + self._d_Pk_dTx_nl_II = z21_utilities.get_list_PS(CosmoParams, self._II_deltaxi_dTx, T21coeffs.zintegral) + self._d_Pk_dTx_nl_III = z21_utilities.get_list_PS(CosmoParams, self._III_deltaxi_dTx, T21coeffs.zintegral) self._Pk_dTx_II += self._d_Pk_dTx_nl_II self._Pk_dTx_III += self._d_Pk_dTx_nl_III @@ -325,31 +422,31 @@ def __init__(self, User_Parameters, Cosmo_Parameters, Astro_Parameters, ClassCos #and xHI too. Linear part does not have bubbles, only delta part if(constants.FLAG_DO_BUBBLES): #auto - self._Pk_xion_lin = self.windowxion**2 * Correlations._PklinCF + self._Pk_xion_lin = self.windowxion**2 * CosmoParams._PklinCF self.Deltasq_xion_lin = self._Pk_xion_lin * self._k3over2pi2 - self._d_Pk_xion_nl = self.get_list_PS(self._deltaxi_xi, T21_coefficients.zintegral) + self._d_Pk_xion_nl = z21_utilities.get_list_PS(CosmoParams, self._deltaxi_xi, T21coeffs.zintegral) self.Deltasq_xion = self.Deltasq_xion_lin + self._d_Pk_xion_nl * self._k3over2pi2 #cross with density - self._Pk_dxion_lin = (self.windowxion.T * self._lingrowthd).T * Correlations._PklinCF + self._Pk_dxion_lin = (self.windowxion.T * self._lingrowthd).T * CosmoParams._PklinCF self.Deltasq_dxion_lin = self._Pk_dxion_lin * self._k3over2pi2 - self._d_Pk_dxion_nl = self.get_list_PS(self._deltaxi_dxi, T21_coefficients.zintegral) + self._d_Pk_dxion_nl = z21_utilities.get_list_PS(CosmoParams, self._deltaxi_dxi, T21coeffs.zintegral) self.Deltasq_dxion = self.Deltasq_dxion_lin + self._d_Pk_dxion_nl * self._k3over2pi2 #cross with xa - self._Pk_xaxion_lin = self.windowxion * self.windowalpha * Correlations._PklinCF + self._Pk_xaxion_lin = self.windowxion * self.windowalpha * CosmoParams._PklinCF self.Deltasq_xaxion_lin = self._Pk_xaxion_lin * self._k3over2pi2 - self._d_Pk_xaxion_nl = self.get_list_PS(self._deltaxi_xaxi, T21_coefficients.zintegral) + self._d_Pk_xaxion_nl = z21_utilities.get_list_PS(CosmoParams, self._deltaxi_xaxi, T21coeffs.zintegral) self.Deltasq_xaxion = self.Deltasq_xaxion_lin + self._d_Pk_xaxion_nl * self._k3over2pi2 #and cross with Tx - self._Pk_Txxion_lin = self.windowxion * self.windowxray * Correlations._PklinCF + self._Pk_Txxion_lin = self.windowxion * self.windowxray * CosmoParams._PklinCF self.Deltasq_Txxion_lin = self._Pk_Txxion_lin * self._k3over2pi2 - self._d_Pk_Txxion_nl = self.get_list_PS(self._deltaxi_Txxi, T21_coefficients.zintegral) + self._d_Pk_Txxion_nl = z21_utilities.get_list_PS(CosmoParams, self._deltaxi_Txxi, T21coeffs.zintegral) self.Deltasq_Txxion = self.Deltasq_Txxion_lin + self._d_Pk_Txxion_nl * self._k3over2pi2 else: self.Deltasq_xion = np.zeros_like(self.Deltasq_d) @@ -364,25 +461,24 @@ def __init__(self, User_Parameters, Cosmo_Parameters, Astro_Parameters, ClassCos ############################## + #Compute the 21-cm power spectrum -# print('STEP 2: Computing 21-cm Power Spectrum') - #and get the PS of T21 too. - self._betaT = T21_coefficients.T_CMB/T21_coefficients.Tk_avg /(T21_coefficients.invTcol_avg**-1 - T21_coefficients.T_CMB) #multiplies \delta T_x and \delta T_ad [both dimensionful, not \deltaT/T] - self._betaxa = 1./(1. + T21_coefficients.xa_avg)/T21_coefficients.xa_avg #multiplies \delta x_a [again not \delta xa/xa] + self._betaT = T21coeffs.T_CMB/T21coeffs.Tk_avg /(T21coeffs.invTcol_avg**-1 - T21coeffs.T_CMB) #multiplies \delta T_x and \delta T_ad [both dimensionful, not \deltaT/T] + self._betaxa = 1./(1. + T21coeffs.xa_avg)/T21coeffs.xa_avg #multiplies \delta x_a [again not \delta xa/xa] #calculate beta_adiabatic - self._dlingrowthd_dz = cosmology.dgrowth_dz(Cosmo_Parameters, T21_coefficients.zintegral) + self._dlingrowthd_dz = cosmology.dgrowth_dz(CosmoParams, T21coeffs.zintegral) - _factor_adi_ = (1+T21_coefficients.zintegral)**2 - _integrand_adi = T21_coefficients.Tk_avg*self._dlingrowthd_dz/_factor_adi_ * T21_coefficients.dlogzint*T21_coefficients.zintegral + _factor_adi_ = (1+T21coeffs.zintegral)**2 + _integrand_adi = T21coeffs.Tk_avg*self._dlingrowthd_dz/_factor_adi_ * T21coeffs.dlogzint*T21coeffs.zintegral - if(Cosmo_Parameters.Flag_emulate_21cmfast==True): + if(CosmoParams.Flag_emulate_21cmfast==True): _hizintegral = 0.0 #they do not account for the adiabatic history prior to starting their evolution. It misses ~half of the adiabatic flucts. else: #the z>zmax part of the integral we do aside. Assume Tk=Tadiabatic from CLASS. - _zlisthighz_ = np.linspace(T21_coefficients.zintegral[-1], 99., 100) #beyond z=100 need to explictly tell CLASS to save growth - _dgrowthhighz_ = cosmology.dgrowth_dz(Cosmo_Parameters, _zlisthighz_) - _hizintegral = np.trapezoid(cosmology.Tadiabatic(Cosmo_Parameters,_zlisthighz_) + _zlisthighz_ = np.linspace(T21coeffs.zintegral[-1], 99., 100) #beyond z=100 need to explictly tell CLASS to save growth + _dgrowthhighz_ = cosmology.dgrowth_dz(CosmoParams, _zlisthighz_) + _hizintegral = np.trapezoid(cosmology.Tadiabatic(CosmoParams,_zlisthighz_) /(1+_zlisthighz_)**2 * _dgrowthhighz_, _zlisthighz_) self._betaTad_ = -2./3. * _factor_adi_/self._lingrowthd * (np.cumsum(_integrand_adi[::-1])[::-1] + _hizintegral) #units of Tk_avg. Internal sum goes from high to low z (backwards), minus sign accounts for it properly so it's positive. @@ -401,9 +497,9 @@ def __init__(self, User_Parameters, Cosmo_Parameters, Astro_Parameters, ClassCos print('Error, have to choose an RSD mode! RSD_MODE') if(constants.FLAG_DO_BUBBLES): - self._betaxion = - 1.0/T21_coefficients.xHI_avg * np.heaviside(constants.ZMAX_Bubbles - T21_coefficients.zintegral, 0.5) # xion = 1 - xHI, only for zkl...', self._allbetamatrix, self._allcorrs) - self.Deltasq_T21 = (self.Deltasq_T21.T*T21_coefficients.T21avg**2).T + self.Deltasq_T21 = (self.Deltasq_T21.T*T21coeffs.T21avg**2).T - self.Deltasq_dT21 = (np.einsum('ik...,ikl...->kl...',self._allbetas,self._allcorrs[0]).T*T21_coefficients.T21avg).T + self.Deltasq_dT21 = (np.einsum('ik...,ikl...->kl...',self._allbetas,self._allcorrs[0]).T*T21coeffs.T21avg).T #Sum Linear Pop II and Pop III contributions @@ -453,42 +549,84 @@ def __init__(self, User_Parameters, Cosmo_Parameters, Astro_Parameters, ClassCos ) self.Deltasq_T21_lin = np.einsum('ijk...,ijkl...->kl...', self._allbetamatrix, self._allcorrs_lin) - self.Deltasq_T21_lin = (self.Deltasq_T21_lin.T*T21_coefficients.T21avg**2).T + self.Deltasq_T21_lin = (self.Deltasq_T21_lin.T*T21coeffs.T21avg**2).T - self.Deltasq_dT21_lin = (np.einsum('ik...,ikl...->kl...',self._allbetas,self._allcorrs_lin[0]).T*T21_coefficients.T21avg).T + self.Deltasq_dT21_lin = (np.einsum('ik...,ikl...->kl...',self._allbetas,self._allcorrs_lin[0]).T*T21coeffs.T21avg).T # print("Power Spectral Routine Done!") - # SarahLibanore: add AstroParams to use flag on quadratic order - def get_xa_window(self, Astro_Parameters, Cosmo_Parameters, Correlations, T21_coefficients, pop = 0): #set pop to 2 or 3, default zero just so python doesn't complain - "Returns the xa window function for all z in zintegral" + def _prepare_corr_arrays(self, CosmoParams, T21coeffs): + """ + Prepare correlation arrays to save computation time + + Parameters + ---------- + CosmoParams : CosmoParams class + T21coeffs : T21coeffs class + + Returns + ---------- + zGM : matrix + Identical to zGreaterMatrix but with NaNs replaced with 100 for computational ease. Dimension (z, k) + iR: array + Defines which indices are nonlinear + corr: matrix + Matter correlation function at iR indices. Dimensions (1, iR, iR, k) + """ - zGreaterMatrix100 = np.copy(T21_coefficients.zGreaterMatrix) - zGreaterMatrix100[np.isnan(zGreaterMatrix100)] = 100 + zGM = np.copy(T21coeffs.zGreaterMatrix) + zGM[np.isnan(zGM)] = 100 + iR = np.arange(CosmoParams.indexmaxNL) + corr = CosmoParams.xi_RR_CF[np.ix_(iR, iR)] + corr[:CosmoParams.indexminNL, :CosmoParams.indexminNL] = \ + corr[CosmoParams.indexminNL, CosmoParams.indexminNL] + return zGM, iR, corr.reshape((1, *corr.shape)) - coeffzp = T21_coefficients.coeff1LyAzp - coeffJaxa = T21_coefficients.coeff_Ja_xa + # SarahLibanore: add AstroParams to use flag on quadratic order + def get_xa_window(self, CosmoParams, AstroParams, T21coeffs, pop = 0): #set pop to 2 or 3, default zero just so python doesn't complain + """ + Computes the LyA window functions for each stellar population across z and k. - growthRmatrix = cosmology.growth(Cosmo_Parameters, zGreaterMatrix100) + Parameters + ---------- + CosmoParams : CosmoParams class + AstroParams : AstroParams class + T21coeffs : T21coeffs class + pop: int + Which stellar population to use. 2 for Pop II, 3 for Pop III. + + Returns + ---------- + _kwinalpha : array + Array of wavenumbers + _win_alpha: matrix + Matrix of LyA window functions. Dimension (z, k) + + """ + + coeffzp = T21coeffs.coeff1LyAzp + coeffJaxa = T21coeffs.coeff_Ja_xa + + growthRmatrix = cosmology.growth(CosmoParams, self._zGreaterMatrix100) if pop == 2: - coeffRmatrix = T21_coefficients.coeff2LyAzpRR_II - gammaRmatrix = T21_coefficients.gamma_II_index2D * growthRmatrix + coeffRmatrix = T21coeffs.coeff2LyAzpRR_II + gammaRmatrix = T21coeffs.gamma_II_index2D * growthRmatrix elif pop == 3: - coeffRmatrix = T21_coefficients.coeff2LyAzpRR_III - gammaRmatrix = T21_coefficients.gamma_III_index2D * growthRmatrix + coeffRmatrix = T21coeffs.coeff2LyAzpRR_III + gammaRmatrix = T21coeffs.gamma_III_index2D * growthRmatrix else: print("Must set pop to either 2 or 3!") _wincoeffsMatrix = coeffRmatrix * gammaRmatrix # SarahLibanore: quadratic order in the lognormal - if Astro_Parameters.quadratic_SFRD_lognormal: - _wincoeffsMatrix *= 1./(1-2.*T21_coefficients.gamma2_II_index2D*T21_coefficients.sigmaofRtab**2) + if AstroParams.quadratic_SFRD_lognormal: + _wincoeffsMatrix *= 1./(1-2.*T21coeffs.gamma2_II_index2D*T21coeffs.sigmaofRtab**2) - if(Cosmo_Parameters.Flag_emulate_21cmfast==False): #do the standard 1D TopHat - _wincoeffsMatrix /=(4*np.pi * T21_coefficients.Rtabsmoo**2) * (T21_coefficients.Rtabsmoo * T21_coefficients.dlogRR) # so we can just use mcfit for logFFT, 1/(4pir^2 * Delta r) - _kwinalpha, _win_alpha = self.get_Pk_from_xi(T21_coefficients.Rtabsmoo, _wincoeffsMatrix) + if(CosmoParams.Flag_emulate_21cmfast==False): #do the standard 1D TopHat + _wincoeffsMatrix /=(4*np.pi * CosmoParams._Rtabsmoo**2) * (CosmoParams._Rtabsmoo * CosmoParams._dlogRR) # so we can just use mcfit for logFFT, 1/(4pir^2 * Delta r) + _kwinalpha, _win_alpha = z21_utilities.get_Pk_from_xi(CosmoParams._Rtabsmoo, _wincoeffsMatrix) else: _kwinalpha = self.klist_PS @@ -496,9 +634,9 @@ def get_xa_window(self, Astro_Parameters, Cosmo_Parameters, Correlations, T21_co coeffRgammaRmatrix = coeffRmatrix * gammaRmatrix coeffRgammaRmatrix = coeffRgammaRmatrix.reshape(*coeffRgammaRmatrix.shape, 1) - dummyMesh, RtabsmooMesh, kWinAlphaMesh = np.meshgrid(T21_coefficients.zintegral, T21_coefficients.Rtabsmoo, _kwinalpha, indexing = 'ij', sparse = True) + dummyMesh, RtabsmooMesh, kWinAlphaMesh = np.meshgrid(T21coeffs.zintegral, CosmoParams._Rtabsmoo, _kwinalpha, indexing = 'ij', sparse = True) - _win_alpha = coeffRgammaRmatrix * Correlations._WinTH(RtabsmooMesh, kWinAlphaMesh) + _win_alpha = coeffRgammaRmatrix * z21_utilities._WinTH(RtabsmooMesh, kWinAlphaMesh) _win_alpha = np.sum(_win_alpha, axis = 1) _win_alpha *= np.array([coeffzp*coeffJaxa]).T @@ -507,34 +645,49 @@ def get_xa_window(self, Astro_Parameters, Cosmo_Parameters, Correlations, T21_co # SarahLibanore: add AstroParams to use flag on quadratic order - def get_Tx_window(self, Astro_Parameters, Cosmo_Parameters, Correlations, T21_coefficients, pop = 0): #set pop to 2 or 3, default zero just so python doesn't complain - "Returns the Tx window function for all z in zintegral" - - zGreaterMatrix100 = np.copy(T21_coefficients.zGreaterMatrix) - zGreaterMatrix100[np.isnan(zGreaterMatrix100)] = 100 - - coeffzp = np.array([T21_coefficients.coeff1Xzp]).T - growthRmatrix = cosmology.growth(Cosmo_Parameters, zGreaterMatrix100) + def get_Tx_window(self, CosmoParams, AstroParams, T21coeffs, pop = 0): #set pop to 2 or 3, default zero just so python doesn't complain + """ + Computes the Xray window functions for each stellar population across z and k. + + Parameters + ---------- + CosmoParams : CosmoParams class + AstroParams : AstroParams class + T21coeffs : T21coeffs class + pop: int + Which stellar population to use. 2 for Pop II, 3 for Pop III. + + Returns + ---------- + _kwinTx : array + Array of wavenumbers + _win_Tx: matrix + Matrix of Xray window functions. Dimension (z, k) + + """ + + coeffzp = np.array([T21coeffs.coeff1Xzp]).T + growthRmatrix = cosmology.growth(CosmoParams, self._zGreaterMatrix100) if pop == 2: - coeffRmatrix = T21_coefficients.coeff2XzpRR_II - gammaRmatrix = T21_coefficients.gamma_II_index2D * growthRmatrix - _coeffTx_units = T21_coefficients.coeff_Gammah_Tx_II#z-dependent, includes 10^40 erg/s/SFR normalizaiton and erg/K conversion factor, and the 1/(1+z)^2 factor to compensate the adiabatic cooling of the Tx olny part + coeffRmatrix = T21coeffs.coeff2XzpRR_II + gammaRmatrix = T21coeffs.gamma_II_index2D * growthRmatrix + _coeffTx_units = T21coeffs.coeff_Gammah_Tx_II#z-dependent, includes 10^40 erg/s/SFR normalizaiton and erg/K conversion factor, and the 1/(1+z)^2 factor to compensate the adiabatic cooling of the Tx olny part elif pop == 3: - coeffRmatrix = T21_coefficients.coeff2XzpRR_III - gammaRmatrix = T21_coefficients.gamma_III_index2D * growthRmatrix - _coeffTx_units = T21_coefficients.coeff_Gammah_Tx_III + coeffRmatrix = T21coeffs.coeff2XzpRR_III + gammaRmatrix = T21coeffs.gamma_III_index2D * growthRmatrix + _coeffTx_units = T21coeffs.coeff_Gammah_Tx_III else: print("Must set pop to either 2 or 3!") # SarahLibanore: quadratic order in the lognormal - if Astro_Parameters.quadratic_SFRD_lognormal: - gammaRmatrix *= (1/(1-2.*T21_coefficients.gamma2_II_index2D*T21_coefficients.sigmaofRtab**2)) + if AstroParams.quadratic_SFRD_lognormal: + gammaRmatrix *= (1/(1-2.*T21coeffs.gamma2_II_index2D*T21coeffs.sigmaofRtab**2)) - if(Cosmo_Parameters.Flag_emulate_21cmfast==False): #do the standard 1D TopHat + if(CosmoParams.Flag_emulate_21cmfast==False): #do the standard 1D TopHat _wincoeffs = coeffRmatrix * gammaRmatrix #array in logR space - _wincoeffs /=(4*np.pi * T21_coefficients.Rtabsmoo**2) * (T21_coefficients.Rtabsmoo * T21_coefficients.dlogRR) # so we can just use mcfit for logFFT, 1/(4pir^2) * Delta r - _kwinTx, _win_Tx_curr = self.get_Pk_from_xi(T21_coefficients.Rtabsmoo, _wincoeffs) + _wincoeffs /=(4*np.pi * CosmoParams._Rtabsmoo**2) * (CosmoParams._Rtabsmoo * CosmoParams._dlogRR) # so we can just use mcfit for logFFT, 1/(4pir^2) * Delta r + _kwinTx, _win_Tx_curr = z21_utilities.get_Pk_from_xi(CosmoParams._Rtabsmoo, _wincoeffs) else: _kwinTx = self.klist_PS @@ -542,9 +695,9 @@ def get_Tx_window(self, Astro_Parameters, Cosmo_Parameters, Correlations, T21_c coeffRgammaRmatrix = coeffRmatrix * gammaRmatrix coeffRgammaRmatrix = coeffRgammaRmatrix.reshape(*coeffRgammaRmatrix.shape, 1) - dummyMesh, RtabsmooMesh, kWinTxMesh = np.meshgrid(T21_coefficients.zintegral, T21_coefficients.Rtabsmoo, _kwinTx, indexing = 'ij', sparse = True) + dummyMesh, RtabsmooMesh, kWinTxMesh = np.meshgrid(T21coeffs.zintegral, CosmoParams._Rtabsmoo, _kwinTx, indexing = 'ij', sparse = True) - _win_Tx_curr = coeffRgammaRmatrix * Correlations._WinTH(RtabsmooMesh, kWinTxMesh) + _win_Tx_curr = coeffRgammaRmatrix * z21_utilities._WinTH(RtabsmooMesh, kWinTxMesh) _win_Tx_curr = np.sum(_win_Tx_curr , axis = 1) _win_Tx = _win_Tx_curr * coeffzp @@ -556,69 +709,74 @@ def get_Tx_window(self, Astro_Parameters, Cosmo_Parameters, Correlations, T21_c # SarahLibanore: function modified to include quadratic order - def get_all_corrs_II(self, Astro_Parameters, User_Parameters, Cosmo_Parameters, Correlations, T21_coefficients): + def get_all_corrs_II(self, UserParams, CosmoParams, AstroParams, T21coeffs): - "Returns the Pop II components of the correlation functions of all observables at each z in zintegral" - #HAC: I deleted the bubbles and EoR part, to be done later..... - #_iRnonlinear = np.arange(Cosmo_Parameters.indexminNL,Cosmo_Parameters.indexmaxNL) - - zGreaterMatrix100 = np.copy(T21_coefficients.zGreaterMatrix) - zGreaterMatrix100[np.isnan(zGreaterMatrix100)] = 100 + """ + Computes the Pop II correlation functions across z and R. + + Parameters + ---------- + UserParams : UserParams class + CosmoParams : CosmoParams class + AstroParams : AstroParams class + T21coeffs : T21coeffs class + + Returns + ---------- + Attributes stored in Power_Spectra - _iRnonlinear = np.arange(Cosmo_Parameters.indexmaxNL) - corrdNL = Correlations.xi_RR_CF[np.ix_(_iRnonlinear,_iRnonlinear)] - #for Rijkl', gammamatrixR1R1, corrdNL, optimize = True) #same thing as gammamatrixR1R1 * corrdNL but faster # SarahLibanore : change to introduce quantities required in the second order correction # --- # - growthRmatrix1 = growthRmatrix.reshape(len(T21_coefficients.zintegral), 1, len(_iRnonlinear),1) - growthRmatrix2 = growthRmatrix.reshape(len(T21_coefficients.zintegral), len(_iRnonlinear), 1,1) + growthRmatrix1 = growthRmatrix.reshape(len(T21coeffs.zintegral), 1, len(self._iRnonlinear),1) + growthRmatrix2 = growthRmatrix.reshape(len(T21coeffs.zintegral), len(self._iRnonlinear), 1,1) growth_corr = growthRmatrix1 * growthRmatrix2 - gammaR1 = T21_coefficients.gamma_II_index2D[:, _iRnonlinear] - sigmaR1 = T21_coefficients.sigmaofRtab[:, _iRnonlinear] - sR1 = (sigmaR1).reshape(len(T21_coefficients.zintegral), 1, len(_iRnonlinear),1) - sR2 = (sigmaR1).reshape(len(T21_coefficients.zintegral), len(_iRnonlinear), 1,1) + gammaR1 = T21coeffs.gamma_II_index2D[:, self._iRnonlinear] + sigmaR1 = T21coeffs.sigmaofRtab[:, self._iRnonlinear] + sR1 = (sigmaR1).reshape(len(T21coeffs.zintegral), 1, len(self._iRnonlinear),1) + sR2 = (sigmaR1).reshape(len(T21coeffs.zintegral), len(self._iRnonlinear), 1,1) - g1 = (gammaR1 * sigmaR1).reshape(len(T21_coefficients.zintegral), 1, len(_iRnonlinear),1) - g2 = (gammaR1 * sigmaR1).reshape(len(T21_coefficients.zintegral), len(_iRnonlinear), 1,1) + g1 = (gammaR1 * sigmaR1).reshape(len(T21coeffs.zintegral), 1, len(self._iRnonlinear),1) + g2 = (gammaR1 * sigmaR1).reshape(len(T21coeffs.zintegral), len(self._iRnonlinear), 1,1) gammamatrixR1R1 = g1 * g2 + corrdNL = self._corrdNL corrdNL_gs = ne.evaluate('corrdNL * growth_corr/ (sR1 * sR2)') gammaTimesCorrdNL = ne.evaluate('gammamatrixR1R1 * corrdNL_gs') - if Astro_Parameters.quadratic_SFRD_lognormal: + if AstroParams.quadratic_SFRD_lognormal: - gammaR1NL = T21_coefficients.gamma2_II_index2D[:, _iRnonlinear] - g1NL = (gammaR1NL * sigmaR1**2).reshape(len(T21_coefficients.zintegral), 1, len(_iRnonlinear),1) - g2NL = (gammaR1NL * sigmaR1**2).reshape(len(T21_coefficients.zintegral), len(_iRnonlinear), 1,1) + gammaR1NL = T21coeffs.gamma2_II_index2D[:, self._iRnonlinear] + g1NL = (gammaR1NL * sigmaR1**2).reshape(len(T21coeffs.zintegral), 1, len(self._iRnonlinear),1) + g2NL = (gammaR1NL * sigmaR1**2).reshape(len(T21coeffs.zintegral), len(self._iRnonlinear), 1,1) numerator_NL = ne.evaluate('gammaTimesCorrdNL+ g1 * g1 * (0.5 - g2NL * (1 - corrdNL_gs * corrdNL_gs)) + g2 * g2 * (0.5 - g1NL * (1 - corrdNL_gs * corrdNL_gs))') denominator_NL = ne.evaluate('1. - 2 * g1NL - 2 * g2NL + 4 * g1NL * g2NL * (1 - corrdNL_gs * corrdNL_gs)') - norm1 = ne.evaluate('exp(g1 * g1 / (2 - 4 * g1NL)) / sqrt(1 - 2 * g1NL)') - norm2 = ne.evaluate('exp(g2 * g2 / (2 - 4 * g2NL)) / sqrt(1 - 2 * g2NL)') + norm1 = ne.evaluate('exp(g1 * g1 / (2 - 4 * g1NL)) / sqrt(1 - 2 * g1NL)') + norm2 = ne.evaluate('exp(g2 * g2 / (2 - 4 * g2NL)) / sqrt(1 - 2 * g2NL)') log_norm = ne.evaluate('log(sqrt(denominator_NL) * norm1 * norm2)') nonlinearcorrelation = ne.evaluate('exp(numerator_NL/denominator_NL - log_norm)') @@ -631,19 +789,19 @@ def get_all_corrs_II(self, Astro_Parameters, User_Parameters, Cosmo_Parameters, self._II_deltaxi_xa = np.einsum('ijkl->il', coeffmatrixxa * expGammaCorrMinusLinear, optimize = True) self._II_deltaxi_xa *= np.array([coeffzp1xa]).T**2 #brings it to xa units - if (User_Parameters.FLAG_DO_DENS_NL): + if (UserParams.FLAG_DO_DENS_NL): D_coeffR1xa = coeffR1xa.reshape(*coeffR1xa.shape, 1) - DDgammaR1 = T21_coefficients.gamma_II_index2D[:, _iRnonlinear] + DDgammaR1 = T21coeffs.gamma_II_index2D[:, self._iRnonlinear] D_gammaR1 = DDgammaR1.reshape(*DDgammaR1.shape , 1) D_growthRmatrix = growthRmatrix[:,:1].reshape(*growthRmatrix[:,:1].shape, 1) D_corrdNL = corrdNL[:1,0,:,:] # SarahLibanore - if Astro_Parameters.quadratic_SFRD_lognormal: + if AstroParams.quadratic_SFRD_lognormal: - DDsigmaR1 = T21_coefficients.sigmaofRtab[:, _iRnonlinear] + DDsigmaR1 = T21coeffs.sigmaofRtab[:, self._iRnonlinear] D_sigmaR1 = DDsigmaR1.reshape(*DDsigmaR1.shape , 1) - DDgammaR1N = T21_coefficients.gamma2_II_index2D[:, _iRnonlinear] + DDgammaR1N = T21coeffs.gamma2_II_index2D[:, self._iRnonlinear] D_gammaR1N = DDgammaR1N.reshape(*DDgammaR1N.shape , 1) gammaTimesCorrdNL = ne.evaluate('D_gammaR1 * D_growthRmatrix* D_growthRmatrix * D_corrdNL') @@ -651,7 +809,7 @@ def get_all_corrs_II(self, Astro_Parameters, User_Parameters, Cosmo_Parameters, denominator_NL = ne.evaluate('1. - 2 * D_gammaR1N*D_sigmaR1*D_sigmaR1') - norm1 = ne.evaluate('exp(D_gammaR1 * D_gammaR1 * D_sigmaR1* D_sigmaR1 * D_gammaR1 * D_gammaR1 * D_sigmaR1* D_sigmaR1 / (2 - 4 * D_gammaR1N*D_sigmaR1*D_sigmaR1)) / sqrt(1 - 2 * D_gammaR1N*D_sigmaR1*D_sigmaR1)') + norm1 = ne.evaluate('exp(D_gammaR1 * D_gammaR1 * D_sigmaR1* D_sigmaR1 * D_gammaR1 * D_gammaR1 * D_sigmaR1* D_sigmaR1 / (2 - 4 * D_gammaR1N*D_sigmaR1*D_sigmaR1)) / sqrt(1 - 2 * D_gammaR1N*D_sigmaR1*D_sigmaR1)') log_norm = ne.evaluate('log(sqrt(denominator_NL) * norm1)') nonlinearcorrelation = ne.evaluate('exp(numerator_NL/denominator_NL - log_norm)') @@ -660,7 +818,7 @@ def get_all_corrs_II(self, Astro_Parameters, User_Parameters, Cosmo_Parameters, nonlinearcorrelation - 1 - D_gammaR1 * D_growthRmatrix**2 * D_corrdNL/(1-2.*D_gammaR1N*D_sigmaR1**2) ), axis = 1) - else: + else: self._II_deltaxi_dxa = np.sum(D_coeffR1xa * ((np.exp(D_gammaR1 * D_growthRmatrix**2 * D_corrdNL )-1.0 ) - D_gammaR1 * D_growthRmatrix**2 * D_corrdNL), axis = 1) self._II_deltaxi_d = (np.exp(growthRmatrix[:,:1]**2 * corrdNL[0,0,0,:]) - 1.0) - growthRmatrix[:,:1]**2 * corrdNL[0,0,0,:] @@ -672,8 +830,8 @@ def get_all_corrs_II(self, Astro_Parameters, User_Parameters, Cosmo_Parameters, # gammaR2 = np.copy(gammaR1) #already has growth factor in this # gammamatrixR1R2 = gammaR1.reshape(*gammaR1.shape, 1, 1) * gammaR2.reshape(1, 1, *gammaR2.shape) - coeffzp1Tx = np.copy(T21_coefficients.coeff1Xzp).reshape(*T21_coefficients.coeff1Xzp.shape, 1, 1, 1) - coeffzp2Tx = np.copy(T21_coefficients.coeff1Xzp).reshape(1, 1, *T21_coefficients.coeff1Xzp.shape, 1) + coeffzp1Tx = np.copy(T21coeffs.coeff1Xzp).reshape(*T21coeffs.coeff1Xzp.shape, 1, 1, 1) + coeffzp2Tx = np.copy(T21coeffs.coeff1Xzp).reshape(1, 1, *T21coeffs.coeff1Xzp.shape, 1) coeffR2Tx = np.copy(coeffR1Tx) coeffmatrixTxTx = coeffR1Tx.reshape(*coeffR1Tx.shape, 1, 1) * coeffR2Tx.reshape(1, 1, *coeffR2Tx.shape) @@ -692,7 +850,7 @@ def get_all_corrs_II(self, Astro_Parameters, User_Parameters, Cosmo_Parameters, sR1 = (sigmaR1).reshape(*gammaR1.shape, 1, 1) g2 = (gammaR2 * sigmaR2).reshape(1, 1, *gammaR2.shape) sR2 = (sigmaR2).reshape(1, 1, *gammaR2.shape) - if Astro_Parameters.quadratic_SFRD_lognormal: + if AstroParams.quadratic_SFRD_lognormal: gammaR2NL = np.copy(gammaR1NL) g1NL = (gammaR1NL * sigmaR1**2).reshape(*gammaR1NL.shape, 1, 1) g2NL = (gammaR2NL * sigmaR2**2).reshape(1, 1, *gammaR2NL.shape) @@ -702,19 +860,19 @@ def get_all_corrs_II(self, Astro_Parameters, User_Parameters, Cosmo_Parameters, self._II_deltaxi_Tx = np.zeros_like(self._II_deltaxi_xa) self._II_deltaxi_xaTx = np.zeros_like(self._II_deltaxi_xa) corrdNLBIG = corrdNL[:,:, np.newaxis, :,:] #dimensions zp1, R1, zp2, R2, and r which will be looped over below - for ir in range(len(T21_coefficients.Rtabsmoo)): + for ir in range(len(CosmoParams._Rtabsmoo)): corrdNL = corrdNLBIG[:,:,:,:,ir] corrdNL_gs = ne.evaluate('corrdNL * growth_corr / (sR1 * sR2)') #HAC: Computations using ne.evaluate(...) use numexpr, which speeds up computations of massive numpy arrays gammaTimesCorrdNL = ne.evaluate('gammamatrixR1R2 * corrdNL_gs') - if Astro_Parameters.quadratic_SFRD_lognormal: + if AstroParams.quadratic_SFRD_lognormal: numerator_NL = ne.evaluate('gammaTimesCorrdNL + g1 * g1 * (0.5 - g2NL * (1 - corrdNL_gs * corrdNL_gs)) + g2 * g2 * (0.5 - g1NL * (1 - corrdNL_gs * corrdNL_gs))') denominator_NL = ne.evaluate('1. - 2 * g1NL - 2 * g2NL + 4 * g1NL * g2NL * (1 - corrdNL_gs * corrdNL_gs)') - norm1 = ne.evaluate('exp(g1 * g1 / (2 - 4 * g1NL)) / sqrt(1 - 2 * g1NL)') - norm2 = ne.evaluate('exp(g2 * g2 / (2 - 4 * g2NL)) / sqrt(1 - 2 * g2NL)') + norm1 = ne.evaluate('exp(g1 * g1 / (2 - 4 * g1NL)) / sqrt(1 - 2 * g1NL)') + norm2 = ne.evaluate('exp(g2 * g2 / (2 - 4 * g2NL)) / sqrt(1 - 2 * g2NL)') log_norm = ne.evaluate('log(sqrt(denominator_NL) * norm1 * norm2)') nonlinearcorrelation = ne.evaluate('exp(numerator_NL/denominator_NL - log_norm)') @@ -742,7 +900,7 @@ def get_all_corrs_II(self, Astro_Parameters, User_Parameters, Cosmo_Parameters, self._II_deltaxi_xaTx *= np.array([coeffzp1xa * _coeffTx_units]).T - if (User_Parameters.FLAG_DO_DENS_NL): + if (UserParams.FLAG_DO_DENS_NL): D_coeffR2Tx = coeffR2Tx.reshape(1, *coeffR2Tx.shape, 1) D_coeffzp2Tx = coeffzp2Tx.flatten().reshape(1, *coeffzp2Tx.flatten().shape, 1) DDgammaR2 = np.copy(DDgammaR1) @@ -750,7 +908,7 @@ def get_all_corrs_II(self, Astro_Parameters, User_Parameters, Cosmo_Parameters, D_growthRmatrix = growthRmatrix[:,0].reshape(*growthRmatrix[:,0].shape, 1, 1, 1) D_corrdNL = corrdNLBIG.squeeze()[0].reshape(1, 1, *corrdNLBIG.squeeze()[0].shape) - if Astro_Parameters.quadratic_SFRD_lognormal: + if AstroParams.quadratic_SFRD_lognormal: DDsigmaR2 = np.copy(DDsigmaR1) D_sigmaR2 = DDsigmaR2.reshape(1, *DDsigmaR2.shape , 1) @@ -762,7 +920,7 @@ def get_all_corrs_II(self, Astro_Parameters, User_Parameters, Cosmo_Parameters, denominator_NL = ne.evaluate('1. - 2 * D_gammaR2N*D_sigmaR2*D_sigmaR2') - norm2 = ne.evaluate('exp(D_gammaR2 * D_gammaR2 * D_sigmaR2* D_sigmaR2 * D_gammaR2 * D_gammaR2 * D_sigmaR2* D_sigmaR2 / (2 - 4 * D_gammaR2N*D_sigmaR2*D_sigmaR2)) / sqrt(1 - 2 * D_gammaR2N*D_sigmaR2*D_sigmaR2)') + norm2 = ne.evaluate('exp(D_gammaR2 * D_gammaR2 * D_sigmaR2* D_sigmaR2 * D_gammaR2 * D_gammaR2 * D_sigmaR2* D_sigmaR2 / (2 - 4 * D_gammaR2N*D_sigmaR2*D_sigmaR2)) / sqrt(1 - 2 * D_gammaR2N*D_sigmaR2*D_sigmaR2)') log_norm = ne.evaluate('log(sqrt(denominator_NL) * norm2)') nonlinearcorrelation = ne.evaluate('exp(numerator_NL/denominator_NL - log_norm)') @@ -781,38 +939,43 @@ def get_all_corrs_II(self, Astro_Parameters, User_Parameters, Cosmo_Parameters, return 1 - def get_all_corrs_IIxIII(self, User_Parameters, Cosmo_Parameters, Correlations, T21_coefficients): - "Returns the Pop IIxIII cross-correlation function of all observables at each z in zintegral" - #HAC: I deleted the bubbles and EoR part, to be done later..... - #_iRnonlinear = np.arange(Cosmo_Parameters.indexminNL,Cosmo_Parameters.indexmaxNL) + def get_all_corrs_IIxIII(self, CosmoParams, T21coeffs): + """ + Computes the Pop IIxIII cross correlation functions across z and R. + + Parameters + ---------- + CosmoParams : CosmoParams class + T21coeffs : T21coeffs class - zGreaterMatrix100 = np.copy(T21_coefficients.zGreaterMatrix) - zGreaterMatrix100[np.isnan(zGreaterMatrix100)] = 100 + Returns + ---------- + Attributes stored in Power_Spectra - _iRnonlinear = np.arange(Cosmo_Parameters.indexmaxNL) - corrdNL = Correlations.xi_RR_CF[np.ix_(_iRnonlinear,_iRnonlinear)] - #for Rijkl', gammamatrix_R1II_R1III, corrdNL, optimize = True) #same thing as gammamatrixR1R1 * corrdNL but faster expGammaCorrMinusLinear = ne.evaluate('exp(gammaTimesCorrdNL) - 1 - gammaTimesCorrdNL') @@ -830,8 +993,8 @@ def get_all_corrs_IIxIII(self, User_Parameters, Cosmo_Parameters, Correlations, gammamatrix_R1II_R2III = gammaR1_II.reshape(*gammaR1_II.shape, 1, 1) * gammaR2_III.reshape(1, 1, *gammaR2_III.shape) gammamatrix_R1III_R2II = gammaR1_III.reshape(*gammaR1_III.shape, 1, 1) * gammaR2_II.reshape(1, 1, *gammaR2_II.shape) - coeffzp1Tx = np.copy(T21_coefficients.coeff1Xzp).reshape(*T21_coefficients.coeff1Xzp.shape, 1, 1, 1) - coeffzp2Tx = np.copy(T21_coefficients.coeff1Xzp).reshape(1, 1, *T21_coefficients.coeff1Xzp.shape, 1) + coeffzp1Tx = np.copy(T21coeffs.coeff1Xzp).reshape(*T21coeffs.coeff1Xzp.shape, 1, 1, 1) + coeffzp2Tx = np.copy(T21coeffs.coeff1Xzp).reshape(1, 1, *T21coeffs.coeff1Xzp.shape, 1) coeffR2Tx_II = np.copy(coeffR1Tx_II) coeffR2Tx_III = np.copy(coeffR1Tx_III) @@ -852,7 +1015,7 @@ def get_all_corrs_IIxIII(self, User_Parameters, Cosmo_Parameters, Correlations, _IIxIII_deltaxi_xaTx2 = np.zeros_like(self._IIxIII_deltaxi_xa) corrdNLBIG = corrdNL[:,:, np.newaxis, :,:] #dimensions zp1, R1, zp2, R2, and r, the last of which will be looped over below - for ir in range(len(T21_coefficients.Rtabsmoo)): + for ir in range(len(CosmoParams._Rtabsmoo)): corrdNL = corrdNLBIG[:,:,:,:,ir] #HAC: Computations using ne.evaluate(...) use numexpr, which speeds up computations of massive numpy arrays @@ -894,11 +1057,26 @@ def get_all_corrs_IIxIII(self, User_Parameters, Cosmo_Parameters, Correlations, def get_xi_Sum_2ExpEta(self, xiEta, etaCoeff1, etaCoeff2): - # Computes the correlation function of the VCB portion of the SFRD, expressed using sums of two exponentials - # if rho(z1, x1) / rhobar = Ae^-b tilde(eta) + Ce^-d tilde(eta) - # and rho(z2, x2) / rhobar = Fe^-g tilde(eta) + He^-k tilde(eta) - # then this computes - - # Refer to eq. A12 in 2407.18294 for more details + """ + Computes the correlation function of the VCB portion of the SFRD, expressed using sums of two exponentials + if rho(z1, x1) / rhobar = Ae^-b tilde(eta) + Ce^-d tilde(eta) and rho(z2, x2) / rhobar = Fe^-g tilde(eta) + He^-k tilde(eta) + Then this computes - + Refer to eq. A12 in 2407.18294 for more details + + Parameters + ---------- + xiEta: matrix + Matrix of Eta correlation function. Dimension (corrEtaNL) + etaCoeff1: matrix + Stored Eta parameters in T21coeffs.vcb_expFitParams. Dimension (vcbCoeffsR1) + etaCoeff2: matrix + Stored Eta parameters in T21coeffs.vcb_expFitParams. Dimension (vcbCoeffsR2) + + Returns + ---------- + xiTotal: matrix + Total - power spectra + """ aa, bb, cc, dd = etaCoeff1 ff, gg, hh, kk = etaCoeff2 @@ -922,47 +1100,54 @@ def get_xi_Sum_2ExpEta(self, xiEta, etaCoeff1, etaCoeff2): return xiTotal - def get_all_corrs_III(self, User_Parameters, Cosmo_Parameters, Correlations, T21_coefficients): - "Returns the Pop III components of the correlation functions of all observables at each z in zintegral" - #HAC: I deleted the bubbles and EoR part, to be done later..... - #_iRnonlinear = np.arange(Cosmo_Parameters.indexminNL,Cosmo_Parameters.indexmaxNL) - zGreaterMatrix100 = np.copy(T21_coefficients.zGreaterMatrix) - zGreaterMatrix100[np.isnan(zGreaterMatrix100)] = 100 + def get_all_corrs_III(self, UserParams, CosmoParams, T21coeffs): + """ + Computes the Pop III correlation functions across z and R. + + Parameters + ---------- + UserParams : UserParams class + CosmoParams : CosmoParams class + T21coeffs : T21coeffs class - _iRnonlinear = np.arange(Cosmo_Parameters.indexmaxNL) #for Rijkl', gammamatrixR1R1, corrdNL, optimize = True) #same thing as gammamatrixR1R1 * corrdNL but faster expGammaCorr = ne.evaluate('exp(gammaCorrdNL) - 1') # equivalent to np.exp(gammaTimesCorrdNL)-1.0 - if Cosmo_Parameters.USE_RELATIVE_VELOCITIES == True: + if CosmoParams.USE_RELATIVE_VELOCITIES == True: etaCorr_xa = self.get_xi_Sum_2ExpEta(corrEtaNL, vcbCoeffsR1, vcbCoeffsR2) totalCorr = ne.evaluate('expGammaCorr * etaCorr_xa + expGammaCorr + etaCorr_xa - gammaCorrdNL') ###TO DO (linearized VCB flucts): - etaCorr_xa_lin #note that the Taylor expansion of the cross-term is 0 to linear order else: @@ -971,7 +1156,7 @@ def get_all_corrs_III(self, User_Parameters, Cosmo_Parameters, Correlations, T21 self._III_deltaxi_xa = np.einsum('ijkl->il', coeffmatrixxa * totalCorr , optimize = True) # equivalent to self._III_deltaxi_xa = np.sum(coeffmatrixxa * ((np.exp(gammaTimesCorrdNL)-1.0) - gammaTimesCorrdNL), axis = (1,2)) self._III_deltaxi_xa *= np.array([coeffzp1xa]).T**2 #brings it to xa units - if (User_Parameters.FLAG_DO_DENS_NL): #no velocity contribution to density + if (UserParams.FLAG_DO_DENS_NL): #no velocity contribution to density D_coeffR1xa = coeffR1xa.reshape(*coeffR1xa.shape, 1) D_gammaR1 = gammaR1.reshape(*gammaR1.shape , 1) D_growthRmatrix = growthRmatrix[:,:1].reshape(*growthRmatrix[:,:1].shape, 1) @@ -985,8 +1170,8 @@ def get_all_corrs_III(self, User_Parameters, Cosmo_Parameters, Correlations, T21 gammaR2 = np.copy(gammaR1) #already has growth factor in this gammamatrixR1R2 = gammaR1.reshape(*gammaR1.shape, 1, 1) * gammaR2.reshape(1, 1, *gammaR2.shape) - coeffzp1Tx = np.copy(T21_coefficients.coeff1Xzp).reshape(*T21_coefficients.coeff1Xzp.shape, 1, 1, 1) - coeffzp2Tx = np.copy(T21_coefficients.coeff1Xzp).reshape(1, 1, *T21_coefficients.coeff1Xzp.shape, 1) + coeffzp1Tx = np.copy(T21coeffs.coeff1Xzp).reshape(*T21coeffs.coeff1Xzp.shape, 1, 1, 1) + coeffzp2Tx = np.copy(T21coeffs.coeff1Xzp).reshape(1, 1, *T21coeffs.coeff1Xzp.shape, 1) coeffR2Tx = np.copy(coeffR1Tx) coeffmatrixTxTx = coeffR1Tx.reshape(*coeffR1Tx.shape, 1, 1) * coeffR2Tx.reshape(1, 1, *coeffR2Tx.shape) coeffmatrixxaTx = coeffR1xa.reshape(*coeffR1xa.shape, 1, 1) * coeffR2Tx.reshape(1, 1, *coeffR2Tx.shape) @@ -1003,14 +1188,14 @@ def get_all_corrs_III(self, User_Parameters, Cosmo_Parameters, Correlations, T21 self._III_deltaxi_xaTx = np.zeros_like(self._III_deltaxi_xa) self._III_deltaxi_dTx = np.zeros_like(self._III_deltaxi_xa) - for ir in range(len(T21_coefficients.Rtabsmoo)): + for ir in range(len(CosmoParams._Rtabsmoo)): corrdNL = corrdNLBIG[:,:,:,:,ir] corrEtaNL = corrEtaNLBIG[:,:,:,:,ir] gammaCorrdNL = ne.evaluate('gammamatrixR1R2 * corrdNL') expGammaCorrdNL = ne.evaluate('exp(gammaCorrdNL) - 1') - if Cosmo_Parameters.USE_RELATIVE_VELOCITIES == True: + if CosmoParams.USE_RELATIVE_VELOCITIES == True: etaCorr_Tx = self.get_xi_Sum_2ExpEta(corrEtaNL, vcbCoeffsR1, vcbCoeffsR2) totalCorr = ne.evaluate('expGammaCorrdNL * etaCorr_Tx + expGammaCorrdNL + etaCorr_Tx - gammaCorrdNL') ###TO DO (linearized VCB flucts): - etaCorr_xa_lin #note that the Taylor expansion of the cross-term is 0 to linear order else: @@ -1029,7 +1214,7 @@ def get_all_corrs_III(self, User_Parameters, Cosmo_Parameters, Correlations, T21 deltaXiXaTxAddend = np.cumsum(deltaXiXaTxAddend[::-1], axis = 0)[::-1] self._III_deltaxi_xaTx[:, ir] = np.einsum('ii->i', deltaXiXaTxAddend, optimize = True) - if (User_Parameters.FLAG_DO_DENS_NL): #no velocity contribution to density + if (UserParams.FLAG_DO_DENS_NL): #no velocity contribution to density D_coeffR2Tx = coeffR2Tx.reshape(1, *coeffR2Tx.shape, 1) D_coeffzp2Tx = coeffzp2Tx.flatten().reshape(1, *coeffzp2Tx.flatten().shape, 1) D_gammaR2 = gammaR2.reshape(1, *gammaR2.shape , 1) @@ -1048,299 +1233,4 @@ def get_all_corrs_III(self, User_Parameters, Cosmo_Parameters, Correlations, T21 self._III_deltaxi_xaTx *= np.array([coeffzp1xa * _coeffTx_units]).T return 1 - - - def get_list_PS(self, xi_list, zlisttoconvert): - "Returns the power spectrum given a list of CFs (xi_list) evaluated at z=zlisttoconvert as input" - - _Pk_list = [] - - for izp,zp in enumerate(zlisttoconvert): - - _kzp, _Pkzp = self.get_Pk_from_xi(self._rs_input_mcfit,xi_list[izp]) - _Pk_list.append(_Pkzp) - #can ignore _kzp, it's the same as klist_PS above by construction - - - return np.array(_Pk_list) - - - def get_Pk_from_xi(self, rsinput, xiinput): - "Generic Fourier Transform, returns Pk from an input Corr Func xi. kPf should be the same as _klistCF" - - kPf, Pf = mcfit.xi2P(rsinput, l=0, lowring=True)(xiinput, extrap=False) - - return kPf, Pf - - - -# Below is the old get_all_corrs function for reference. It has some EoR bubbles functions that are incomplete (I think) -#def get_all_corrs(self, User_Parameters, Cosmo_Parameters, Correlations, T21_coefficients): -# "Returns the correlation function of all observable at each z in zintegral" -# -# #_iRnonlinear = np.arange(Cosmo_Parameters.indexminNL,Cosmo_Parameters.indexmaxNL) -# _iRnonlinear = np.arange(Cosmo_Parameters.indexmaxNL) -# corrdNL = Correlations.xi_RR_CF[np.ix_(_iRnonlinear,_iRnonlinear)] -# -# #for R= 0 and _indexRbub < len(_iRnonlinear) -# #all these things have to be true for us to run the nonlinear+bubble part -# -# if(_flag_doEoRNL): -# _eminusQstar = np.exp(-T21_coefficients.Qstar[izp1]) -# gammaeffxHI = -T21_coefficients.Qstar[izp1] * self.bias_bub_avg[izp1] * growthRlist1[0] #effective bias of the xion term. includes growth -# -# self._deltaxi_xaxi[izp1] = np.sum(coeffR1xa * ((np.exp(gammaR1 * gammaeffxHI * corr_deltaR1R2z0[:,_indexRbub])-1.0) - gammaR1 * gammaeffxHI * corr_deltaR1R2z0[:,_indexRbub]) , axis=(1)) -# self._deltaxi_xaxi[izp1] *= coeffzp1xa * _eminusQstar #brings it to xa units -# -# self._deltaxi_dxi[izp1] = (1.0 - np.exp(gammaeffxHI * growthRlist1[0] * corr_deltaR1R2z0[:,0,_indexRbub]) ) - gammaeffxHI * growthRlist1[0] * corr_deltaR1R2z0[:,0,_indexRbub] -# self._deltaxi_dxi[izp1] *= _eminusQstar -# -# #for autocorrelation we have a density and a bubble/random term. first density -# self._deltaxi_xi[izp1] = (np.exp(-2.0 * gammaeffxHI * growthRlist1[0] * corr_deltaR1R2z0[:,_indexRbub,_indexRbub]) -1.0) - (-2.0) * gammaeffxHI * growthRlist1[0] * corr_deltaR1R2z0[:,_indexRbub,_indexRbub] -# #plus the bubble part, fully nonlinear, no "correction wrt linear" -# self._deltaxi_xi[izp1] += (np.exp(self.Qo_tab[izp1]) - 1.0) -# -# self._deltaxi_xi[izp1] *= _eminusQstar**2 -# -# -# for izp2,zp2 in reversed(list(enumerate(T21_coefficients.zintegral))): #double loop because nonlocal in time sum. -# -# _factorzp1equalzp2 = 2.0 #factor for 2 or 1 depending on whether they are the same for the sum below -# if (izp2 < izp1): #sum only for z >= zp1, not below -# continue -# elif (izp2 == izp1): -# _factorzp1equalzp2 = 1.0 -# -# -# coeffzp2Tx = T21_coefficients.coeff1Xzp[izp2] #inside zp2 it's always Tx since it's the nonlocal-in-time one -# zpRlist2 = T21_coefficients.ztabRsmoo[izp2,_iRnonlinear] -# growthRlist2 = cosmology.growth(Cosmo_Parameters,zpRlist2) -# -# gammaR2 = T21_coefficients.gamma_index2D[izp2,_iRnonlinear] * growthRlist2 -# gammamatrixR1R2 = np.outer(gammaR1,gammaR2) -# -# -# coeffR2Tx = T21_coefficients.coeff2XzpRR[izp2,_iRnonlinear] -# coeffmatrixTxTx = np.outer(coeffR1Tx,coeffR2Tx) -# coeffmatrixxaTx = np.outer(coeffR1xa,coeffR2Tx) -# -# self._deltaxi_Tx[izp1] += _factorzp1equalzp2 * coeffzp1Tx * coeffzp2Tx * np.sum(coeffmatrixTxTx * ((np.exp(gammamatrixR1R2 * corr_deltaR1R2z0)-1.0) - gammamatrixR1R2 * corr_deltaR1R2z0) , axis=(1,2)) -# -# self._deltaxi_xaTx[izp1] += coeffzp2Tx * np.sum(coeffmatrixxaTx * ((np.exp(gammamatrixR1R2 * corr_deltaR1R2z0)-1.0) - gammamatrixR1R2 * corr_deltaR1R2z0) , axis=(1,2)) -# -# if(User_Parameters.FLAG_DO_DENS_NL): -# self._deltaxi_dTx[izp1] += coeffzp2Tx * np.sum(coeffR2Tx * ((np.exp(gammaR2* growthRlist1[0] * corr_deltaR1R2z0[:,0])-1.0) - gammaR2* growthRlist1[0] * corr_deltaR1R2z0[:,0]) , axis=(1)) -# -# if(_flag_doEoRNL): -# self._deltaxi_Txxi[izp1] += coeffzp2Tx * np.sum(coeffR2Tx * ((np.exp(gammaR2 * gammaeffxHI * corr_deltaR1R2z0[:,_indexRbub])-1.0) - gammaR2 * gammaeffxHI * corr_deltaR1R2z0[:,_indexRbub]) , axis=(1)) -# -# -# self._deltaxi_xaTx[izp1]*= coeffzp1xa -# self._deltaxi_xaTx[izp1]*=_coeffTx_units[izp1] -# -# if(User_Parameters.FLAG_DO_DENS_NL): -# self._deltaxi_dTx[izp1]*=_coeffTx_units[izp1] -# -# if(_flag_doEoRNL): -# self._deltaxi_Txxi[izp1]*=_coeffTx_units[izp1] * _eminusQstar -# -# -# -# self._deltaxi_Tx=(self._deltaxi_Tx.T*_coeffTx_units**2).T #we cannot easily do this in the loop because it sums over previous ones -# -# return 1 -# -# def calculate_barrier(self, Cosmo_Parameters, T21_coefficients): -# "Caclulate the barrier B(z, sigmaR) that the density \delta has to cross to ionize" -# -# self.Barrier0list = np.zeros_like(T21_coefficients.zintegral) -# self.Barrier1list = np.zeros_like(T21_coefficients.zintegral) -# -# sigmaminsqlist = (T21_coefficients.sigmaMatom * self._lingrowthd/self._lingrowthd[0])**2 -# sigmapivotsqlist = (T21_coefficients.sigmaMpivot * self._lingrowthd/self._lingrowthd[0])**2 -# #notice sigmaMatom depends on z and sigmaMpivot doesn't. For now at least. Code doesn't care since _lingrowthd does depend on z anyway -# -# -# sigmaRref = np.sqrt(sigmaminsqlist/20.) -# #pick this one for reference to take d/dsigmaR^2 -# -# alphaeff = T21_coefficients._alphaeff #note that if alpha_eff = 0 you recover erfc. For negative it can behave weird so beware (for instance voids reionize first. Not physical) -# -# plindex = -T21_coefficients.dlogMdlogsigma -# #M~sigma^-plindex -# -# totalindex = plindex * alphaeff -# sindex = 1./2. + totalindex -# -# for izp, zp in enumerate(T21_coefficients.zintegral): -# -# if zp>constants.ZMAX_Bubbles: -# continue -# -# _invQbar = 1.0/T21_coefficients.Qion_avg[izp] #we need Nion/ > 1/invQbar to ionize the region. larger delta at higher z -# -# -# -# dtab = np.linspace(-3.0 * sigmaRref[izp] , 3.0 * sigmaRref[izp] , 99) -# dtabhi = np.linspace(3.3 * sigmaRref[izp], 1.5, 30) -# dtab = np.append(dtab, dtabhi) -# -# dtildetabsq = (constants.delta_crit_ST - dtab)**2 -# -# -# tabsigmasqit = [0.8*sigmaRref[izp]**2, 1.4*sigmaRref[izp]**2] #to get derivatives wrt sigma^2 -# -# barrier = np.zeros_like(tabsigmasqit) -# -# for isigma, sigmaRRsq in enumerate(tabsigmasqit): -# -# mumintildesq = dtildetabsq/(sigmaminsqlist[izp] - sigmaRRsq) -# mupivottildesq = dtildetabsq/sigmapivotsqlist[izp] -# -# -# NionEPS = pow(dtildetabsq, - totalindex) * (gammaincc(sindex,mumintildesq/2.0) - gammaincc(sindex,mupivottildesq/2.0)) -# -# Probdtab = np.exp(-dtab**2/sigmaRRsq/2.0) -# -# norm = np.trapezoid(NionEPS * Probdtab, dtab) -# NionEPS/=norm -# -# bindex = min(range(len(NionEPS)), key=lambda i: abs(NionEPS[i]-_invQbar)) -# -# barrier[isigma] = dtab[bindex] -# -# self.Barrier0list[izp] = np.sum(barrier)/len(barrier) #sigma-indep -# self.Barrier1list[izp] = (barrier[-1] - barrier[0])/(tabsigmasqit[-1] - tabsigmasqit[0]) #linear in sigmaR^2 -# -# def get_bubbles(self, Cosmo_Parameters, Correlations, T21_coefficients): -# "Returns the Bubble mass function for EoR" -# -# -# _Rtab = T21_coefficients.Rtabsmoo -# _rhob0 = cosmology.rho_baryon(Cosmo_Parameters, 0.) -# _Mtab = _rhob0 * 4.0 * np.pi * _Rtab**3/3.0 #at z=0 because comoving -# _dMdR = _rhob0 * 4.0 * np.pi * _Rtab**2 #at z=0 because comoving -# _dlogMdlogR = 3.0 -# _dlog_Mtab = _dlogMdlogR * T21_coefficients.dlogRR -# -# -# self.BMF_array = np.zeros_like(T21_coefficients.gamma_Niondot_index2D) -# #bubble mass function, dn/dm in 1/cMpc^3/Msun -# -# self.Qo_tab = np.zeros_like(self.BMF_array) -# #Q_overlap, integral of [BMF * Voverlap(r)] at _Rtab -# _Voverlap = np.array([[Voverlap(Rbb, rr) for Rbb in _Rtab] for rr in _Rtab]) -# #index is [ir, iRb] -# -# -# self.Q_infer_BMF = np.zeros(T21_coefficients.Nzintegral) -# -# -# self.Rbub_star = np.zeros(T21_coefficients.Nzintegral) #peak of BMF -# self._Rbub_star_index = np.zeros(T21_coefficients.Nzintegral, dtype=int) #its index in Rsmoo -# self.bias_bub_avg = np.zeros(T21_coefficients.Nzintegral) #avg (mass-weighted) bias -# -# -# for izp, zp in enumerate(T21_coefficients.zintegral): -# -# if (zp > constants.ZMAX_Bubbles or T21_coefficients.Qion_avg[izp] >= 1.0): #only do below a threshold and before EoR is complete to avoid numerical noise -# continue -# -# sigmaofRtab = T21_coefficients.sigmaofRtab[izp] -# logsigmaoflogR_f = UnivariateSpline(np.log(_Rtab),np.log(sigmaofRtab) ) -# dlogsigmadlogR_f = logsigmaoflogR_f.derivative() -# dlogsigmadlogRtab = dlogsigmadlogR_f(np.log(_Rtab) ) -# -# -# -# B0 = self.Barrier0list[izp] #Fit is Barrier = B0 + B1 sigma^2 -# B1 = self.Barrier1list[izp] -# Btab = B0 + B1 * sigmaofRtab**2 -# -# dlogsigmadlogMtab = dlogsigmadlogRtab / _dlogMdlogR -# -# self.BMF_array[izp] = np.sqrt(2.0/np.pi) * _rhob0/(_Mtab**2) * np.abs(dlogsigmadlogMtab) * B0/sigmaofRtab * np.exp(-Btab**2/(2.0 * sigmaofRtab**2)) -# -# -# self.Q_infer_BMF[izp] = np.sum(self.BMF_array[izp] * _Mtab/_rhob0 * _Mtab)*_dlog_Mtab -# -# -# self.BMF_array[izp] *= T21_coefficients.Qstar[izp]/self.Q_infer_BMF[izp] #renormalized now -# -# self._bias_bubbles_zp = 1.0 + B0**2/(Btab * sigmaofRtab**2) #Eulerian bias of a bubble of some mass/radius at zp -# -# self.bias_bub_avg[izp] = np.sum(self.BMF_array[izp] * self._bias_bubbles_zp * _Mtab/_rhob0 * _Mtab)*_dlog_Mtab/T21_coefficients.Qstar[izp] -# #average bias -# -# -# _dimlessBMF = _Mtab**2 * self.BMF_array[izp] -# self._Rbub_star_index[izp] = max(range(len(_Mtab)), key=lambda i: _dimlessBMF[i]) -# self.Rbub_star[izp] = _Rtab[self._Rbub_star_index[izp]] #the maximum of the BMF -# -# -# self.Qo_tab[izp] = np.array([np.sum(self.BMF_array[izp] * Vtab * _Mtab)*_dlog_Mtab for Vtab in _Voverlap]) -# self.Rbub_star = np.fmax(self.Rbub_star, 1e-3) #to avoid Nans in other functions -# -# -# def Voverlap(Rb, r): -# "Overlapping volume of two bubbles of radius Rb separated by r. From FZH04" -# return ((4 * np.pi/3.0) * Rb**3 - np.pi * r * (Rb**2 - r**2/12.)) * np.heaviside( 2*Rb - r , 0.5) + \ No newline at end of file diff --git a/zeus21/cosmology.py b/zeus21/cosmology.py index c468dea..6b9a03a 100644 --- a/zeus21/cosmology.py +++ b/zeus21/cosmology.py @@ -1,302 +1,539 @@ """ +Cosmology functions and helper tools related with cosmology. + +Authors: zeus21 v2 collaboration - June 2026 + Emily Bregou ; + Hector Afonso G. Cruz ; + Sarah Libanore ; + Julian B. Muñoz ; + Yonny Sklansky ; + Emilie Thélie ; + Alessandra Venditti +arXiv:2302.08506, arXiv:2306.09403, arXiv:2407.18294, Sklansky et al. (in prep) +""" -Cosmology helper functions and other tools +import numpy as np +from scipy.interpolate import RegularGridInterpolator, interp1d -Author: Julian B. Muñoz -UT Austin and Harvard CfA - January 2023 +from . import constants -Edited by Hector Afonso G. Cruz -JHU - July 2024 -""" +def time_at_redshift(ClassyCosmo,z): + """ + Returns the age of the Universe (in Gyrs) corresponding to a given redshift. + + Parameters + ---------- + ClassyCosmo: zeus21.runclass class + Sets up Class cosmology. + z: float + Redshift. + + Returns + ------- + float + Age of the Universe in Gyrs. + """ -import numpy as np -from classy import Class -from scipy.interpolate import RegularGridInterpolator -from scipy.interpolate import interp1d + background = ClassyCosmo.get_background() + classy_t, classy_z = background['proper time [Gyr]'], background['z'] + classy_tinterp = interp1d(classy_z, classy_t) -import mcfit + return classy_tinterp(z) -from . import constants -from .inputs import Cosmo_Parameters, Cosmo_Parameters_Input -from .correlations import Correlations -def cosmo_wrapper(User_Parameters, Cosmo_Parameters_Input): +def redshift_at_time(ClassyCosmo,t): """ - Wrapper function for all the cosmology. It takes Cosmo_Parameters_Input and returns: - Cosmo_Parameters, Class_Cosmo, Correlations, HMF_interpolator + Returns the redshift corresponding to a given age of the Universe (in Gyrs). + + Parameters + ---------- + ClassyCosmo: zeus21.runclass class + Sets up Class cosmology. + t: float + Age in Gyrs. + + Returns + ------- + float + Redshift corresponding to the given age of the Universe. """ - ClassCosmo = Class() - ClassCosmo.compute() + background = ClassyCosmo.get_background() + classy_t, classy_z = background['proper time [Gyr]'], background['z'] + classy_tinterp = interp1d(classy_t, classy_z) + + return classy_tinterp(t) - ClassyCosmo = runclass(Cosmo_Parameters_Input) - CosmoParams = Cosmo_Parameters(User_Parameters, Cosmo_Parameters_Input, ClassyCosmo) - CorrFClass = Correlations(User_Parameters, CosmoParams, ClassyCosmo) - HMFintclass = HMF_interpolator(User_Parameters,CosmoParams,ClassyCosmo) - return CosmoParams, ClassyCosmo, CorrFClass, HMFintclass +def Hub(CosmoParams, z): + """ + Hubble parameter H(z). + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters. + z : float + Redshift. + + Returns + ------- + float + Hubble parameter H(z) in km/s/Mpc. + """ + return CosmoParams.h_fid * 100 * np.sqrt(CosmoParams.OmegaM * pow(1+z,3.)+CosmoParams.OmegaR * pow(1+z,4.)+CosmoParams.OmegaL) -def runclass(CosmologyIn): - "Set up CLASS cosmology. Takes CosmologyIn class input and returns CLASS Cosmology object" - ClassCosmo = Class() - ClassCosmo.set({'omega_b': CosmologyIn.omegab,'omega_cdm': CosmologyIn.omegac, - 'h': CosmologyIn.h_fid,'A_s': CosmologyIn.As,'n_s': CosmologyIn.ns,'tau_reio': CosmologyIn.tau_fid}) - ClassCosmo.set({'output':'mPk','lensing':'no','P_k_max_1/Mpc':CosmologyIn.kmax_CLASS, 'z_max_pk': CosmologyIn.zmax_CLASS}) ###HAC: add vTK to outputs - ClassCosmo.set({'gauge':'synchronous'}) - #hfid = ClassCosmo.h() # get reduced Hubble for conversions to 1/Mpc +def HubinvMpc(CosmoParams, z): + """ + Converts Hubble parameter H(z) in inverse length units (1/Mpc). + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters. + z : float + Redshift. + + Returns + ------- + float + Hubble parameter H(z) in 1/Mpc. + """ - # and run it (see warmup for their doc) - ClassCosmo.compute() - - ClassCosmo.pars['Flag_emulate_21cmfast'] = CosmologyIn.Flag_emulate_21cmfast - - ###HAC: Adding VCB feedback via a second run of CLASS: - if CosmologyIn.USE_RELATIVE_VELOCITIES == True: - - kMAX_VCB = 50.0 - ###HAC: getting z_rec from first CLASS run - z_rec = ClassCosmo.get_current_derived_parameters(['z_rec'])['z_rec'] - z_drag = ClassCosmo.get_current_derived_parameters(['z_d'])['z_d'] - - ###HAC: Running CLASS a second time just to get velocity transfer functions at recombination - ClassCosmoVCB = Class() - ClassCosmoVCB.set({'omega_b': CosmologyIn.omegab,'omega_cdm': CosmologyIn.omegac, - 'h': CosmologyIn.h_fid,'A_s': CosmologyIn.As,'n_s': CosmologyIn.ns,'tau_reio': CosmologyIn.tau_fid}) - ClassCosmoVCB.set({'output':'vTk'}) - ClassCosmoVCB.set({'P_k_max_1/Mpc':kMAX_VCB, 'z_max_pk':12000}) - ClassCosmoVCB.set({'gauge':'newtonian'}) - ClassCosmoVCB.compute() - velTransFunc = ClassCosmoVCB.get_transfer(z_drag) - - kVel = velTransFunc['k (h/Mpc)'] * CosmologyIn.h_fid - theta_b = velTransFunc['t_b'] - theta_c = velTransFunc['t_cdm'] - - sigma_vcb = np.sqrt(np.trapezoid(CosmologyIn.As * (kVel/0.05)**(CosmologyIn.ns-1) /kVel * (theta_b - theta_c)**2/kVel**2, kVel)) * constants.c_kms - ClassCosmo.pars['sigma_vcb'] = sigma_vcb - - ###HAC: now computing average velocity assuming a Maxwell-Boltzmann distribution of velocities - velArr = np.geomspace(0.01, constants.c_kms, 1000) #in km/s - vavgIntegrand = (3 / (2 * np.pi * sigma_vcb**2))**(3/2) * 4 * np.pi * velArr**2 * np.exp(-3 * velArr**2 / (2 * sigma_vcb**2)) - ClassCosmo.pars['v_avg'] = np.trapezoid(vavgIntegrand * velArr, velArr) - - ###HAC: Computing Vcb Power Spectrum - ClassCosmo.pars['k_vcb'] = kVel - ClassCosmo.pars['theta_b'] = theta_b - ClassCosmo.pars['theta_c'] = theta_c - P_vcb = CosmologyIn.As * (kVel/0.05)**(CosmologyIn.ns-1) * (theta_b - theta_c)**2/kVel**2 * 2 * np.pi**2 / kVel**3 - - p_vcb_intp = interp1d(np.log(kVel), P_vcb) - ClassCosmo.pars['P_vcb'] = P_vcb - - ###HAC: Computing Vcb^2 (eta) Power Spectra - kVelIntp = np.geomspace(1e-4, kMAX_VCB, 512) - rVelIntp = 2 * np.pi / kVelIntp - - j0bessel = lambda x: np.sin(x)/x - j2bessel = lambda x: (3 / x**2 - 1) * np.sin(x)/x - 3*np.cos(x)/x**2 - - psi0 = 1 / 3 / (sigma_vcb/constants.c_kms)**2 * np.trapezoid(kVelIntp**2 / 2 / np.pi**2 * p_vcb_intp(np.log(kVelIntp)) * j0bessel(kVelIntp * np.transpose([rVelIntp])), kVelIntp, axis = 1) - psi2 = -2 / 3 / (sigma_vcb/constants.c_kms)**2 * np.trapezoid(kVelIntp**2 / 2 / np.pi**2 * p_vcb_intp(np.log(kVelIntp)) * j2bessel(kVelIntp * np.transpose([rVelIntp])), kVelIntp, axis = 1) - - k_eta, P_eta = mcfit.xi2P(rVelIntp, l=0, lowring = True)((6 * psi0**2 + 3 * psi2**2), extrap = False) - - ClassCosmo.pars['k_eta'] = k_eta[P_eta > 0] - ClassCosmo.pars['P_eta'] = P_eta[P_eta > 0] - -# print("HAC: Finished running CLASS a second time to get velocity transfer functions") - - else: - ClassCosmo.pars['v_avg'] = 0.0 - ClassCosmo.pars['sigma_vcb'] = 1.0 #Avoids excess computation, but doesn't matter what value we set it to because the flag in inputs.py sets all feedback parameters to zero - - return ClassCosmo + return Hub(CosmoParams,z)/constants.c_kms -def Hub(Cosmo_Parameters, z): -#Hubble(z) in km/s/Mpc - return Cosmo_Parameters.h_fid * 100 * np.sqrt(Cosmo_Parameters.OmegaM * pow(1+z,3.)+Cosmo_Parameters.OmegaR * pow(1+z,4.)+Cosmo_Parameters.OmegaL) -def HubinvMpc(Cosmo_Parameters, z): -#H(z) in 1/Mpc - return Hub(Cosmo_Parameters,z)/constants.c_kms +def Hubinvyr(CosmoParams, z): + """ + Converts Hubble parameter H(z) in inverse time units (1/yr). + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters. + z : float + Redshift. + + Returns + ------- + float + Hubble parameter H(z) in 1/yr. + """ -def Hubinvyr(Cosmo_Parameters,z): -#H(z) in 1/yr - return Hub(Cosmo_Parameters,z)*constants.KmToMpc*constants.yrTos + return Hub(CosmoParams,z)*constants.KmToMpc*constants.yrTos -def rho_baryon(Cosmo_Parameters,z): -#\rho_baryon in Msun/Mpc^3 as a function of z - return Cosmo_Parameters.OmegaB * Cosmo_Parameters.rhocrit * pow(1+z,3.0) + +def rho_baryon(CosmoParams, z): + """ + Baryon density rho_baryon(z). + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters. + z : float + Redshift. + + Returns + ------- + float + Baryon density rho_baryon(z) in Msun/Mpc^3. + """ + + return CosmoParams.OmegaB * CosmoParams.rhocrit * pow(1+z,3.0) -def n_H(Cosmo_Parameters, z): -#density of hydrogen nuclei (neutral or ionized) in 1/cm^3 - return rho_baryon(Cosmo_Parameters, z) *( 1- Cosmo_Parameters.Y_He)/(constants.mH_GeV/constants.MsuntoGeV) / (constants.Mpctocm**3.0) -#def n_baryon(Cosmo_Parameters, z): -##density of baryons in 1/cm^3 -# return rho_baryon(Cosmo_Parameters, z) / Cosmo_Parameters.mu_baryon_Msun / (constants.Mpctocm**3.0) +def n_H(CosmoParams, z): + """ + Number density of hydrogen nuclei (including both neutral or ionized). + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters. + z : float + Redshift. + + Returns + ------- + float + Number density of hydrogen nuclei in 1/cm^3. + """ + return rho_baryon(CosmoParams, z) *( 1- CosmoParams.Y_He)/(constants.mH_GeV/constants.MsuntoGeV) / (constants.Mpctocm**3.0) def Tcmb(ClassCosmo, z): + """ + CMB temperature T(z). + + Parameters + ---------- + ClassCosmo : ClassCosmo + CLASS cosmology object. + z : float + Redshift. + + Returns + ------- + float + CMB temperature T(z) in K. + """ + T0CMB = ClassCosmo.T_cmb() + return T0CMB*(1+z) + def Tadiabatic(CosmoParams, z): - "Returns T_adiabatic as a function of z from thermodynamics in CLASS" + """ + Returns T_adiabatic as a function of z from thermodynamics in CLASS. + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters. + z : float + Redshift. + + Returns + ------- + float + Adiabatic temperature T_adiabatic(z). + """ + return CosmoParams.Tadiabaticint(z) + + def xefid(CosmoParams, z): - "Returns fiducial x_e(z) w/o any sources. Uses thermodynamics in CLASS for z>15, and fixed below to avoid the tanh approx." + """ + Electron fraction x_e(z) without any sources. + Uses thermodynamics in CLASS for z>15, and fixed below to avoid the tanh approximation. + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters. + z : float + Redshift. + + Returns + ------- + float + Fiducial x_e(z). + """ + _zcutCLASSxe = 15. _xecutCLASSxe = CosmoParams.xetanhint(_zcutCLASSxe) + return CosmoParams.xetanhint(z) * np.heaviside(z - _zcutCLASSxe, 0.5) + _xecutCLASSxe * np.heaviside(_zcutCLASSxe - z, 0.5) + def adiabatic_index(z): - "Returns adiabatic index (delta_Tad/delta) as a function of z. Fit from 1506.04152. to ~3% on z = 6 − 50)." + """ + Returns adiabatic index (delta_Tad/delta) as a function of z. Fit from 1506.04152. to ~3% on z = 6 − 50). + + Parameters + ---------- + z : float + Redshift. + + Returns + ------- + float + Adiabatic index (delta_Tad/delta). + """ + return 0.58 - 0.005*(z-10.) -def MhofRad(Cosmo_Parameters,R): - #convert input Radius in Mpc comoving to Mass in Msun - return Cosmo_Parameters.constRM *pow(R, 3.0) +def MhofRad(CosmoParams, R): + """ + Convert input comoving Radius to virial Mass. + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters. + R : float + Comoving radius in cMpc. + + Returns + ------- + float + Mass in Msun. + """ + + return CosmoParams.constRM *pow(R, 3.0) -def RadofMh(Cosmo_Parameters,M): - #convert input M halo in Msun radius in cMpc - return pow(M/Cosmo_Parameters.constRM, 1/3.0) +def RadofMh(CosmoParams, M): + """ + Convert input virial Mass to comoving Radius. + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters. + M : float + Virial mass in Msun. + + Returns + ------- + float + Comoving radius in cMpc. + """ + + return pow(M/CosmoParams.constRM, 1/3.0) -def ST_HMF(Cosmo_Parameters, Mass, sigmaM, dsigmadM): - A_ST = Cosmo_Parameters.Amp_ST - a_ST = Cosmo_Parameters.a_ST - p_ST = Cosmo_Parameters.p_ST - delta_crit_ST = Cosmo_Parameters.delta_crit_ST +def ST_HMF(CosmoParams, Mass, sigmaM, dsigmadM): + """ + Sheth-Tormen Halo Mass Function. + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters. + Mass : float + Halo mass in Msun. + sigmaM : float + Variance of the density field on the scale of the halo mass. + dsigmadM : float + Derivative of sigmaM with respect to Mass. + + Returns + ------- + float + HMF value in 1/Mpc^3/Msun. + """ + + A_ST = CosmoParams.Amp_ST + a_ST = CosmoParams.a_ST + p_ST = CosmoParams.p_ST + delta_crit_ST = CosmoParams.delta_crit_ST nutilde = np.sqrt(a_ST) * delta_crit_ST/sigmaM - return -A_ST * np.sqrt(2./np.pi) * nutilde * (1. + nutilde**(-2.0*p_ST)) * np.exp(-nutilde**2/2.0) * (Cosmo_Parameters.rho_M0 / (Mass * sigmaM)) * dsigmadM + return -A_ST * np.sqrt(2./np.pi) * nutilde * (1. + nutilde**(-2.0*p_ST)) * np.exp(-nutilde**2/2.0) * (CosmoParams.rho_M0 / (Mass * sigmaM)) * dsigmadM -def Tink_HMF(Cosmo_Parameters, Mass, sigmaM, dsigmadM,z): - #Tinker08 form of the HMF. All in physical (no h) units. Form from App.A of Yung+23 (2309.14408) - f = f_GUREFT_physical(sigmaM,z) - return f*(Cosmo_Parameters.rho_M0 / (Mass)) * np.abs(dsigmadM/sigmaM) +def Tink_HMF(CosmoParams, Mass, sigmaM, dsigmadM, z): + """ + Tinker 2008 Halo Mass Function. + All in physical (no h) units. + Form from App.A of Yung+23 (2309.14408). + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters. + Mass : float + Halo mass in Msun. + sigmaM : float + Variance of the density field on the scale of the halo mass at redshift z. + dsigmadM : float + Derivative of sigmaM with respect to Mass at redshift z. + z : float + Redshift. + + Returns + ------- + float + HMF value in 1/Mpc^3/Msun. + """ + + f = f_GUREFT_physical(sigmaM, z) -def f_GUREFT_physical(sigma,z): - #Fit in eq A2 in Yung+23 (2309.14408), fit to z<20 (Implementation thanks to Aaron Yung). Physical because no h. - #sigma(M,z) is the input, no growth here bc we use class with full sigma evolution + return f*(CosmoParams.rho_M0 / (Mass)) * np.abs(dsigmadM/sigmaM) + + +def f_GUREFT_physical(sigmaM, z): + """ + Fit in eq A2 in Yung+23 (2309.14408) to z < 20. + Required by the Tinker 2008 HMF; all in physical units (no h). + Implementation thanks to Aaron Yung. + + Parameters + ---------- + sigmaM : float + Variance of the density field on the scale of the halo mass at redshift z. + z : float + Redshift. + + Returns + ------- + float + HMF value in 1/Mpc^3/Msun. + """ + k = np.array([ 1.37657725e-01, -1.00382125e-02, 1.02963559e-03, 1.06641384e+00, 2.47557563e-02, -2.83342017e-03, 4.86693806e+00, 9.21235623e-02, -1.42628278e-02, 1.19837952e+00, 1.42966892e-03, -3.30740460e-04]) + A = lambda x: k[0] + k[1]*x + k[2]*(x**2) a = lambda x: k[3] + k[4]*x + k[5]*(x**2) b = lambda x: k[6] + k[7]*x + k[8]*(x**2) c = lambda x: k[9] + k[10]*x + k[11]*(x**2) - sig = sigma + #cap coefficients at z=20 to avoid extrapolation zuse = np.fmin(z,20.0) - return A(zuse) * (((sig/b(zuse))**(-a(zuse))) + 1.0 ) * np.exp(-c(zuse)/(sig**2)) -def PS_HMF_unnorm(Cosmo_Parameters, Mass, nu, dlogSdM): - 'Returns the Press-Schechter HMF (unnormalized since we will take ratios), given a halo Mass [Msun], nu = delta_tilde/S_tilde, with delta_tilde = delta_crit - delta_R, and variance S = sigma(M)^2 - sigma(R)^2. Used for 21cmFAST mode.' + return A(zuse) * (((sigmaM/b(zuse))**(-a(zuse))) + 1.0 ) * np.exp(-c(zuse)/(sigmaM**2)) - return nu * np.exp(-Cosmo_Parameters.a_corr_EPS*nu**2/2.0) * dlogSdM* (1.0 / Mass) - #written so that dsigmasq/dM appears directly, since that is not modified by EPS, whereas sigma_tot^2 = sigma^2(M) - sigma^2(R). The sigma in denominator will be sigma_tot +def PS_HMF_unnorm(CosmoParams, Mass, nu, dlogSdM): + """ + Unnormalized Press-Schechter HMF. + Used to emulate 21cmFAST. + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters. + Mass : float + Halo mass in Msun. + nu : float + Peak height, defined as nu = delta_tilde/S_tilde, with delta_tilde = delta_crit - delta_R, and variance S = sigma(M)^2 - sigma(R)^2. + dlogSdM : float + Derivative of log(S) with respect to Mass, where S = sigma(M)^2 - sigma(R)^2. + + Returns + ------- + float + HMF value in 1/Mpc^3/Msun. + """ + + return nu * np.exp(-CosmoParams.a_corr_EPS*nu**2/2.0) * dlogSdM* (1.0 / Mass) class HMF_interpolator: - "Class that builds an interpolator of the HMF. Returns an interpolator" + """ + Class that builds an interpolator of the HMF as function of the halo mass and redshift. + + Parameters + ---------- - def __init__(self, User_Parameters, Cosmo_Parameters, ClassCosmo): + UserParams : UserParams + User parameters, used to set the resolution of the HMF table. + CosmoParams : CosmoParams + Cosmological parameters, used to compute the HMF table with CLASS. + + Attributes + ------- + HMF_int : RegularGridInterpolator + Interpolator for HMF value, takes (Mass, z) as arguments + sigma_int : RegularGridInterpolator + Interpolator for sigma value, takes (Mass, z) as arguments + sigmaR_int : RegularGridInterpolator + Interpolator for sigma(R) value, takes (R, z) as arguments + dsigmadM_int : RegularGridInterpolator + Interpolator for dsigma/dM value, takes (Mass, z) as arguments + """ - self._Mhmin = 1e5 #originally 1e5 - self._Mhmax = 1e14 - self._NMhs = np.floor(35*User_Parameters.precisionboost).astype(int) - self.Mhtab = np.logspace(np.log10(self._Mhmin),np.log10(self._Mhmax),self._NMhs) # Halo mases in Msun - self.RMhtab = RadofMh(Cosmo_Parameters, self.Mhtab) + def __init__(self, UserParams, CosmoParams): - self.logtabMh = np.log(self.Mhtab) + self._Mhmin = CosmoParams._Mhmin # minimum halo mass in Msun + self._Mhmax = CosmoParams._Mhmax # maximum halo mass in Msun + self._NMhs = np.floor(35*UserParams.precisionboost).astype(int) # number of halo mass points in the table, set by precisionboost + self.Mhtab = np.logspace(np.log10(self._Mhmin),np.log10(self._Mhmax),self._NMhs) # halo mass table in Msun + self.logtabMh = np.log(self.Mhtab) # log of halo mass table, used for interpolation since the HMF varies more smoothly in log(M) + + self.RMhtab = RadofMh(CosmoParams, self.Mhtab) # comoving radius corresponding to the halo mass table, in cMpc - self._zmin=Cosmo_Parameters.zmin_CLASS - self._zmax = Cosmo_Parameters.zmax_CLASS - self._Nzs=np.floor(100*User_Parameters.precisionboost).astype(int) - self.zHMFtab = np.linspace(self._zmin,self._zmax,self._Nzs) + self._zmin=CosmoParams.zmin_CLASS # minimum redshift for the HMF table, set by CLASS + self._zmax = CosmoParams.zmax_CLASS # maximum redshift for the HMF table, set by CLASS + self._Nzs=np.floor(100*UserParams.precisionboost).astype(int) # number of redshift points in the table, set by precisionboost. Note that the HMF is very steep at high z, so we need more points than for other tables to get good interpolation. + self.zHMFtab = np.linspace(self._zmin,self._zmax,self._Nzs) # redshift table for the HMF - #check resolution - if (Cosmo_Parameters.kmax_CLASS < 1.0/self.RMhtab[0]): + # check resolution: make sure that the kmax_CLASS is high enough to resolve the small scales corresponding to the smallest halos. If not, warn the user + if (CosmoParams.kmax_CLASS < 1.0/self.RMhtab[0]): print('Warning! kmax_CLASS may be too small! Run CLASS with higher kmax') - self.sigmaMhtab = np.array([[ClassCosmo.sigma(RR,zz) for zz in self.zHMFtab] for RR in self.RMhtab]) + # sigma(M,z) table, computed from CLASS + self.sigmaMhtab = np.array([[CosmoParams.ClassCosmo.sigma(RR,zz) for zz in self.zHMFtab] for RR in self.RMhtab]) - self._depsM=0.01 #for derivatives, relative to M - self.dsigmadMMhtab = np.array([[(ClassCosmo.sigma(RadofMh(Cosmo_Parameters, MM*(1+self._depsM)),zz)-ClassCosmo.sigma(RadofMh(Cosmo_Parameters, MM*(1-self._depsM)),zz))/(MM*2.0*self._depsM) for zz in self.zHMFtab] for MM in self.Mhtab]) + # derivative of sigma with respect to M + self._depsM = 0.01 # step + self.dsigmadMMhtab = np.array([[(CosmoParams.ClassCosmo.sigma(RadofMh(CosmoParams, MM*(1+self._depsM)),zz)-CosmoParams.ClassCosmo.sigma(RadofMh(CosmoParams, MM*(1-self._depsM)),zz))/(MM*2.0*self._depsM) for zz in self.zHMFtab] for MM in self.Mhtab]) + if(CosmoParams.Flag_emulate_21cmfast==True): + print('WARNING!' \ + 'You set Flag_emulate_21cmfast == True.' \ + 'HMF_interpolator applyies corrections to sigma(M) and growth(z) to match the 21cmFAST cosmology. ' \ + 'These corrections are only valid for a Planck2018 cosmology, and may be different if you use a different cosmology.') - if(Cosmo_Parameters.Flag_emulate_21cmfast==True): - #ADJUST BY HAND adjust sigmas to match theirs, since the CLASS TF they use is at a fixed cosmology from 21cmvFAST but the input cosmology is different - self.sigmaMhtab*=np.sqrt(0.975)#/0.9845 - self.dsigmadMMhtab*=np.sqrt(0.975)#/0.9845 + # CORRECTION #1 + # 21cmFAST uses a fixed cosmology to compute the transfer function, which is different from our input Planck2018 cosmology. This leads to a mismatch in sigma(M); to fix it, we adjust our sigma(M) in a redshift-independent way to match theirs + # NOTE! This factor should be corrected if your cosmology is not Planck2018 + self.sigmaMhtab*=np.sqrt(0.975) + self.dsigmadMMhtab*=np.sqrt(0.975) - #this correction is because 21cmFAST uses the dicke() function to compute growth, which is ~0.5% offset at high z. This offset makes our growth the same as dicke() for a Planck2018 cosmology. Has to be added separately to the growth(z) correction above since they come in different places + # CORRECTION #2 + # 21cmFAST uses the dicke() function to compute growth, which is ~0.5% offset at high z. This offset makes our growth the same as dicke() for a Planck2018 cosmology + # NOTE! This factor should be corrected if your cosmology is not Planck2018 _offsetgrowthdicke21cmFAST = 1-0.000248*(self.zHMFtab-5.) self.sigmaMhtab*=_offsetgrowthdicke21cmFAST self.dsigmadMMhtab*=_offsetgrowthdicke21cmFAST - #Note that these two changes may be different if away from Planck2018 - - self.HMFtab = np.zeros_like(self.sigmaMhtab) - - - - + # fill HMF table (Mh,z) using either ST or Tinker, depending on the choice in CosmoParams, using the sigma(M,z) and dsigma/dM(M,z) from CLASS. for iM, MM in enumerate(self.Mhtab): for iz, zz in enumerate(self.zHMFtab): sigmaM = self.sigmaMhtab[iM,iz] dsigmadM = self.dsigmadMMhtab[iM,iz] - if(Cosmo_Parameters.HMF_CHOICE == 'ST'): - self.HMFtab[iM,iz] = ST_HMF(Cosmo_Parameters, MM, sigmaM, dsigmadM) - elif(Cosmo_Parameters.HMF_CHOICE == 'Yung'): - self.HMFtab[iM,iz] = Tink_HMF(Cosmo_Parameters, MM, sigmaM, dsigmadM,zz) + if(CosmoParams.HMF_CHOICE == 'ST'): + self.HMFtab[iM,iz] = ST_HMF(CosmoParams, MM, sigmaM, dsigmadM) + elif(CosmoParams.HMF_CHOICE == 'Yung'): + self.HMFtab[iM,iz] = Tink_HMF(CosmoParams, MM, sigmaM, dsigmadM,zz) else: - print('ERROR, use a correct Cosmo_Parameters.HMF_CHOICE') + print('ERROR, use a correct CosmoParams.HMF_CHOICE') self.HMFtab[iM,iz] = 0.0 - - - - - _HMFMIN = np.exp(-300.) #min HMF to avoid overflowing + # set min HMF to avoid overflowing + _HMFMIN = np.exp(-300.) logHMF_ST_trim = self.HMFtab logHMF_ST_trim[np.array(logHMF_ST_trim <= 0.)] = _HMFMIN logHMF_ST_trim = np.log(logHMF_ST_trim) - + # interpolator for log(HMF) as a function of log(Mh) and z, with bounds_error=False and fill_value=-inf to avoid extrapolation issues self.fitMztab = [np.log(self.Mhtab), self.zHMFtab] - self.logHMFint = RegularGridInterpolator(self.fitMztab, logHMF_ST_trim, bounds_error = False, fill_value = -np.inf) ###HAC: Changed to -np.inf so HMFint = exp(-np.inf)= zero to fix nans in sfrd.py + self.logHMFint = RegularGridInterpolator(self.fitMztab, logHMF_ST_trim, bounds_error = False, fill_value = -np.inf) - self.sigmaintlog = RegularGridInterpolator(self.fitMztab, self.sigmaMhtab, bounds_error = False, fill_value = np.nan)# no need to log since it doesnt vary dramatically + # interpolator for sigma(M,z) as a function of log(Mh) and z, with bounds_error=False and fill_value=np.nan to avoid extrapolation issues + self.sigmaintlog = RegularGridInterpolator(self.fitMztab, self.sigmaMhtab, bounds_error = False, fill_value = np.nan) + # interpolator for dsigma/dM(M,z) as a function of log(Mh) and z, with bounds_error=False and fill_value=np.nan to avoid extrapolation issues self.dsigmadMintlog = RegularGridInterpolator(self.fitMztab, self.dsigmadMMhtab, bounds_error = False, fill_value = np.nan) - - #also build an interpolator for sigma(R) of the R we integrate over (for CD and EoR). These R >> Rhalo typically, so need new table. - self.sigmaofRtab = np.array([[ClassCosmo.sigma(RR,zz) for zz in self.zHMFtab] for RR in Cosmo_Parameters._Rtabsmoo]) - self.fitRztab = [np.log(Cosmo_Parameters._Rtabsmoo), self.zHMFtab] - self.sigmaRintlog = RegularGridInterpolator(self.fitRztab, self.sigmaofRtab, bounds_error = False, fill_value = np.nan) #no need to log either - - + # interpolator for sigma(R); typically, R >> Rhalo, so we need a new table + self.sigmaofRtab = np.array([[CosmoParams.ClassCosmo.sigma(RR,zz) for zz in self.zHMFtab] for RR in CosmoParams._Rtabsmoo]) + self.fitRztab = [np.log(CosmoParams._Rtabsmoo), self.zHMFtab] + self.sigmaRintlog = RegularGridInterpolator(self.fitRztab, self.sigmaofRtab, bounds_error = False, fill_value = np.nan) def HMF_int(self, Mh, z): - "Interpolator to find HMF(M,z), designed to take a single z but an array of Mh in Msun" + """ + Interpolator to find HMF(M,z). + + Parameters + ---------- + Mh : float or array + Halo mass in Msun. Can be a single value or an array of values. + z : float + + Returns + ------- + float + Interpolator for HMF value, takes (Mass, z) as arguments. + """ + _logMh = np.log(Mh) logMhvec = np.asarray([_logMh]) if np.isscalar(_logMh) else np.asarray(_logMh) @@ -305,72 +542,238 @@ def HMF_int(self, Mh, z): return np.exp(self.logHMFint(inarray) ) + def sigma_int(self, Mh, z): + """ + Interpolator to find sigma(M,z). + + Parameters + ---------- + Mh : float or array + Halo mass in Msun. Can be a single value or an array of values. + z : float + + Returns + ------- + float + Interpolator for sigma value, takes (Mass, z) as arguments. + """ - def sigma_int(self,Mh,z): - "Interpolator to find sigma(M,z), designed to take a single z but an array of Mh in Msun" _logMh = np.log(Mh) logMhvec = np.asarray([_logMh]) if np.isscalar(_logMh) else np.asarray(_logMh) inarray = np.array([[LM,z] for LM in logMhvec]) + return self.sigmaintlog(inarray) - def sigmaR_int(self,RR,z): - "Interpolator to find sigma(RR,z), designed to take a single z but an array of RR in cMpc" + + def sigmaR_int(self, RR, z): + """ + Interpolator to find sigma(R,z). + + Parameters + ---------- + RR : float or array + Comoving distance in Mpc. Can be a single value or an array of values. + z : float + + Returns + ------- + float + Interpolator for sigma value, takes (R, z) as arguments. + """ _logRR = np.log(RR) logRRvec = np.asarray([_logRR]) if np.isscalar(_logRR) else np.asarray(_logRR) inarray = np.array([[LR,z] for LR in logRRvec]) + return self.sigmaRintlog(inarray) - def dsigmadM_int(self,Mh,z): - "Interpolator to find dsigma/dM(M,z), designed to take a single z but an array of Mh in Msun. Used in 21cmFAST mode" + def dsigmadM_int(self, Mh, z): + """ + Interpolator to find dsigma/dM. + + Parameters + ---------- + Mh : float or array + Halo mass in Msun. Can be a single value or an array of values. + z : float + + Returns + ------- + float + Interpolator for dsigma/dM value, takes (Mass, z) as arguments. + """ + _logMh = np.log(Mh) logMhvec = np.asarray([_logMh]) if np.isscalar(_logMh) else np.asarray(_logMh) inarray = np.array([[LM,z] for LM in logMhvec]) + return self.dsigmadMintlog(inarray) -def growth(Cosmo_Parameters, z): - "Scale-independent growth factor, interpolated from CLASS" +def growth(CosmoParams, z): + """ + Interpolator to find the scale-independent growth factor. + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters, used to compute the growth factor with CLASS. + z : float + Redshift. + + Returns + ------- + float + Interpolator for the scale-independent growth factor, takes z as argument. + """ + zlist = np.asarray([z]) if np.isscalar(z) else np.asarray(z) - if (Cosmo_Parameters.Flag_emulate_21cmfast==True): - _offsetgrowthdicke21cmFAST = 1-0.000248*(zlist-5.) #as in HMF, to fix growth. have to do it independently since it depends on z. - return Cosmo_Parameters.growthint(zlist) * _offsetgrowthdicke21cmFAST + if (CosmoParams.Flag_emulate_21cmfast==True): + print('WARNING!' \ + 'You set Flag_emulate_21cmfast == True.' \ + 'growth() applyies corrections to match the 21cmFAST cosmology. ' \ + 'These corrections are only valid for a Planck2018 cosmology, and may be different if you use a different cosmology.') + + # 21cmFAST uses the dicke() function to compute growth, which is ~0.5% offset at high z. This offset makes our growth the same as dicke() for a Planck2018 cosmology + # NOTE! This factor should be corrected if your cosmology is not Planck2018 + _offsetgrowthdicke21cmFAST = 1-0.000248*(zlist-5.) + + return CosmoParams.growthint(zlist) * _offsetgrowthdicke21cmFAST + else: - return Cosmo_Parameters.growthint(zlist) + return CosmoParams.growthint(zlist) def dgrowth_dz(CosmoParams, z): - "Derivative of growth factor growth() w.r.t. z" + """ + Derivative of growth factor w.r.t. z. + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters, used to compute the growth factor with CLASS. + z : float + Redshift. + + Returns + ------- + float + dgrowth/dz + """ + zlist = np.asarray([z]) if np.isscalar(z) else np.asarray(z) dzlist = zlist*0.001 + return (growth(CosmoParams, z+dzlist)-growth(CosmoParams, z-dzlist))/(2.0*dzlist) -def redshift_of_chi(CosmoParams, z): - "Returns z(chi) for any input comoving distance from today chi in Mpc" - return CosmoParams.zfofRint(z) +def redshift_of_chi(CosmoParams, chi): + """ + Redshift associated with the given comoving distance in the input cosmology. + This function is not used inside the code but is provided for users + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters, used to compute the growth factor with CLASS. + chi : float + Comoving distance from today in Mpc + + Returns + ------- + z : float + Redshift + """ + return CosmoParams.zfofRint(chi) -def T021(Cosmo_Parameters, z): - "Prefactor in mK to T21 that only depends on cosmological parameters and z. Eg Eq.(21) in 2110.13919" - return 34 * pow((1+z)/16.,0.5) * (Cosmo_Parameters.omegab/0.022) * pow(Cosmo_Parameters.omegam/0.14,-0.5) +def chi_of_redshift(CosmoParams, z): + """ + Comoving distance associated with given redshift in the input cosmology. + This function is not used inside the code but is provided for users + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters, used to compute the growth factor with CLASS. + z : float + Redshift + + Returns + ------- + chi : float + Comoving distance from today in Mpc + """ + + return CosmoParams.chiofzint(z) + + +def T021(CosmoParams, z): + """ + Prefactor in mK to T21 that only depends on cosmological parameters and z. See Eq.(21) in 2110.13919 + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters, used to compute the growth factor with CLASS. + z : float + Redshift. + + Returns + ------- + float + Prefactor in mK to T21 + """ + return 34 * pow((1+z)/16.,0.5) * (CosmoParams.omegab/0.022) * pow(CosmoParams.omegam/0.14,-0.5) -#UNUSED bias, just for reference -def bias_ST(Cosmo_Parameters, sigmaM): - # from https://arxiv.org/pdf/1007.4201.pdf Table 1 - a_ST = Cosmo_Parameters.a_ST - p_ST = Cosmo_Parameters.p_ST - delta_crit_ST = Cosmo_Parameters.delta_crit_ST + +def bias_ST(CosmoParams, sigmaM): + """ + Bias of halos in the Sheth-Tormen model. + See https://arxiv.org/pdf/1007.4201.pdf Table 1 + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters, used to compute the growth factor with CLASS. + sigmaM : float + Variance of the matter density field smoothed on a scale corresponding to the halo mass. + + Returns + ------- + float + Halo bias + """ + + a_ST = CosmoParams.a_ST + p_ST = CosmoParams.p_ST + delta_crit_ST = CosmoParams.delta_crit_ST nu = delta_crit_ST/sigmaM nutilde = np.sqrt(a_ST) * nu return 1.0 + (nutilde**2 - 1.0 + 2. * p_ST/(1.0 + nutilde**(2. * p_ST) ) )/delta_crit_ST -def bias_Tinker(Cosmo_Parameters, sigmaM): - #from https://arxiv.org/pdf/1001.3162.pdf, Delta=200 - delta_crit_ST = Cosmo_Parameters.delta_crit_ST + +def bias_Tinker(CosmoParams, sigmaM): + """ + Bias of halos in the Tinker model. See https://arxiv.org/pdf/1001.3162.pdf for Delta = 200 + + Parameters + ---------- + CosmoParams : CosmoParams + Cosmological parameters, used to compute the growth factor with CLASS. + sigmaM : float + Variance of the matter density field smoothed on a scale corresponding to the halo mass. + + Returns + ------- + float + Halo bias + """ + + delta_crit_ST = CosmoParams.delta_crit_ST # critical density for collapse nu = delta_crit_ST/sigmaM #Tinker fit @@ -385,29 +788,3 @@ def bias_Tinker(Cosmo_Parameters, sigmaM): return 1.0 - _Abias*(nu**_abias/(nu**_abias + delta_crit_ST**_abias)) + _Bbias * nu**_bbias + _Cbias * nu**_cbias -#UNUSED: -# def interp2Dlinear_only_y(arrayxy, arrayz, x, y): -# "2D interpolator where the x axis is assumed to be an array identical to the trained x. That is, an array of 1D linear interpolators. arrayxy is [x,y]. arrayz is result. x is the x input (=arrayxy[0]), and y the y input. Returns z result (array)" -# if((x != arrayxy[0]).all()): -# print('ERROR on interp2Dlinear_only_y, x need be the same in interp and input') -# return -1 -# Ny = len(arrayxy[1]) -# ymin, ymax = arrayxy[1][[0,-1]] -# if((y > ymax or y>> zeus21.User_Parameters(precisionboost=0.5) Parameters can also be changed afterwards: + >>> UserParams = zeus21.User_Parameters() + >>> UserParams.precisionboost = 0.5 - Parameters ---------- precisionboost: float - Make integrals take more points for boost in precision, the baseline being 1.0. - FLAG_FORCE_LINEAR_CF: int (0 or 1) - 0 to do standard calculation, 1 to force linearization of correlation function. + Make integrals take more points for boost in precision. Default is 1.0. + dlogzint_target: + Target number of redshift bins for the redsfhit arrays in log space. Default is 0.02. + FLAG_FORCE_LINEAR_CF: bool + False to do standard calculation, True to force linearization of correlation function. Default is False. MIN_R_NONLINEAR: float - Minimum radius R/cMpc in which we start doing the nonlinear calculation. - Below ~1 it will blow up because sigma > 1 eventually, and our exp(\delta) approximation breaks. + Minimum radius R/cMpc in which we start doing the nonlinear calculation. Default is 2.0. + Below ~1 it will blow up because sigma > 1 eventually, and our exp(delta) approximation breaks. Check if you play with it and if you change Window(). MAX_R_NONLINEAR: float - Maximum radius R/cMpc in which we start doing the nonlinear calculation (above this it is very linear) + Maximum radius R/cMpc in which we start doing the nonlinear calculation (above this it is very linear). Default is 100.0. FLAG_DO_DENS_NL: bool - Whether to do the nonlinear (ie lognormal) calculation for the density field itself and its cross correlations. + Whether to do the nonlinear (ie lognormal) calculation for the density field itself and its cross correlations. Default is False. Small (<3%) correction in dd, but non trivial (~10%) in d-xa and d-Tx FLAG_WF_ITERATIVE: bool - Whether to iteratively do the WF correction as in Hirata2006. + Whether to iteratively do the WF correction as in Hirata2006. Default is True. + zmin: float + Minimum redshift to which we compute the T21 signals. Default is 5.0. + DO_ONLY_GLOBAL: bool + Whether zeus21 only runs the global T21 signal (and not fluctuations). Default is False. + USE_BARYON_FLAG: bool + Whether zeus21 computes 21-cm power spectra with (1+delta_b) prefactor instead of (1+delta) + This means LSS terms use P_baryon(k) and P_baryon_x_cdm(k). Default is True. Attributes ---------- - C2_RENORMALIZATION_FLAG: int (0 or 1) - Whether to renormalize the C2 oefficients (appendix in 2302.08506). + C2_RENORMALIZATION_FLAG: bool + Whether to renormalize the C2 oefficients (appendix in 2302.08506). Default is True. """ - def __init__(self, precisionboost = 1.0, FLAG_FORCE_LINEAR_CF = 0, - MIN_R_NONLINEAR = 2.0, MAX_R_NONLINEAR = 100.0, - FLAG_DO_DENS_NL = False, FLAG_WF_ITERATIVE = True): - - self.precisionboost = precisionboost - self.FLAG_FORCE_LINEAR_CF = FLAG_FORCE_LINEAR_CF - self.C2_RENORMALIZATION_FLAG = 1 - FLAG_FORCE_LINEAR_CF - - self.MIN_R_NONLINEAR = MIN_R_NONLINEAR - self.MAX_R_NONLINEAR = MAX_R_NONLINEAR - - self.FLAG_DO_DENS_NL = FLAG_DO_DENS_NL - - self.FLAG_WF_ITERATIVE = FLAG_WF_ITERATIVE + precisionboost: float = 1.0 + dlogzint_target: float = 0.02 + FLAG_FORCE_LINEAR_CF: bool = False + MIN_R_NONLINEAR: float = 2.0 + MAX_R_NONLINEAR: float = 100.0 + FLAG_DO_DENS_NL: bool = False + FLAG_WF_ITERATIVE: bool = True + zmin: float = 5. + DO_ONLY_GLOBAL: bool = False + USE_BARYON_FLAG: bool = True + C2_RENORMALIZATION_FLAG: int = _field(init=False) + def __post_init__(self): + schema = { + "FLAG_FORCE_LINEAR_CF": (bool, None), + "FLAG_DO_DENS_NL": (bool, None), + "FLAG_WF_ITERATIVE": (bool, None), + "DO_ONLY_GLOBAL": (bool, None), + } + validate_fields(self, schema) -class Cosmo_Parameters_Input: - "Class to pass the 6 LCDM parameters as input" - - def __init__(self, omegab = 0.0223828, omegac = 0.1201075, h_fid = 0.67810, As = 2.100549e-09, ns = 0.9660499, - tau_fid = 0.05430842, kmax_CLASS = 500., zmax_CLASS = 50.,zmin_CLASS = 5., Flag_emulate_21cmfast = False, - USE_RELATIVE_VELOCITIES = False, HMF_CHOICE= "ST"): - - self.omegab = omegab - self.omegac = omegac - self.h_fid = h_fid - self.As = As - self.ns = ns - self.tau_fid = tau_fid - - #other params for CLASS - self.kmax_CLASS = kmax_CLASS - self.zmax_CLASS = zmax_CLASS - self.zmin_CLASS = zmin_CLASS - #and whether to emulate 21cmFAST - self.Flag_emulate_21cmfast = Flag_emulate_21cmfast #whether to emulate 21cmFAST in HMF, LyA, and X-ray opacity calculations - - ###HAC: Flag whether to use v_cb - self.USE_RELATIVE_VELOCITIES = USE_RELATIVE_VELOCITIES - - #which HMF we use - self.HMF_CHOICE = HMF_CHOICE #which HMF functional form we use. - #options are "ST" the classic Sheth-Tormen (f(nu)), "Yung" for the Tinker08 (f(sigma)) calibrated to Yung+23. Default ST - + self.C2_RENORMALIZATION_FLAG = not self.FLAG_FORCE_LINEAR_CF +@dataclass(kw_only=True) class Cosmo_Parameters: - "Class that will keep the cosmo parameters throughout" - - def __init__(self, UserParams, CosmoParams_input, ClassCosmo): + """ + Cosmological parameters for zeus21. + This class also runs and saves an instance of CLASS. + + Parameters + ---------- + UserParams: User_Parameters + zeus21 class for the user parameters. Default is the default instance of the User_Parameters class. + omegab: float + Baryon density * h^2. Default is 0.0223828. + omegac: float + CDM density * h^2. Default is 0.1201075. + h_fid: float + Hubble constant / 100. Default is 0.67810. + As: float + Amplitude of initial fluctuations. Default is 2.100549e-09. + ns: float + Spectral index. Default is 0.9660499. + tau_fid: float + Optical depth to reionization. Default is 0.05430842. + kmax_CLASS: float + Maximum wavenumber to be passed to CLASS. Default is 500.0. + zmax_CLASS: float + Maximum redshift to be passed to CLASS. Default is 50.0. + zmin_CLASS: float + Minimum redshift to be passed to CLASS. Default is 5.0. + Rs_min: float + Minimum radius to be passed to CLASS. Default is 0.05. + Set to 0.929 when Flag_emulate_21cmfast is True. + Rs_max: float + Maximum radius to be passed to CLASS. Default is 2000.0. + Set to 500 when Flag_emulate_21cmfast is True. + Flag_emulate_21cmfast: bool + Whether zeus21 emulates 21cmFAST cosmology (used in HMF, LyA, and X-ray opacity calculations). Default is False. + When False, sets the Star Formation Rate model to GALLUMI-like, and when True to 21cmfast-like (ignores Mc and beta and has a t* later in SFR()). + USE_RELATIVE_VELOCITIES: bool + Whether to use v_cb. Default is False. + HMF_CHOICE: str + Which HMF to use. Default is "ST". + "ST" for the classic Sheth-Tormen (f(nu)), "Yung" for the Tinker08 (f(sigma)) calibrated to Yung+23. + _Mhmin: float + Minimum halo mass in Msun to compute the HMF. Default is 1e5. + _Mhmax: float + Maximum halo mass in Msun to compute the HMF. Default is 1e14. + + Attributes + ---------- + ClassCosmo: Class + CLASS instance to compute cosmology. + It is set with the 6 LCDM parameters set in Cosmo_Parameters. + omegam: float + Matter density * h^2. Default is 0.1424903. + OmegaM: float + Matter density. Default is 0.3098830430481206. + rhocrit: float + Critical density. Default is 127339073085.43648. + OmegaR: float + Radiation density. Default is 9.096145657179167e-05. + OmegaL: float + Dark energy density. Default is 0.6900259954953076. + OmegaB: float + Baryon density. Default is 0.048677349798108865. + rho_M0: float + Actual matter density. Default is 39460219466.64208. + z_rec: float + Recombination reshift. Default is 1088.7722850526861. + sigma_vcb: float + Square root of the variance of the relative velocity field. Default is 1. + vcb_avg: float + Average of the relative velocity field. Default is 0.0. + Y_He: float + Helium mass fraction. Default is 0.24527956117097657. + x_He: + Helium-to-hydrogen number density ratio. Default is 0.08124848240215174. + f_H: float + Hydrogen number density ratio relative to baryons. Default is 0.924856789420276. + f_He: float + Helium number density ratio relative to baryons. Default is 0.07514321057972385. + mu_baryon: float + Mean baryonic weight. Default is 1.149786421719843. + mu_baryon_Msun: float + Mean baryonic weight relative to the solar mass. Default is 1.0305080308672013e-57. + constRM: float + Radius-to-mass conversions for HMF. Used for CLASS input so assumes tophat. Default is 165290580780.5916. + zfofRint: interp1d + Interpolation for the redshift as a function of the comoving distance. + chiofzint: interp1d + Interpolation for the comoving distance as a function of the redshift. + Tadiabaticint: + Interpolation for the adiabatic temperature as a function of redshift. + xetanhint: interp1d + Interpolation for the electron fraction as a function of redshift. + growthint: interp1d + Interpolation for the growth faction as a function of redshift. + NRs: np.ndarray + Number of radii. Default is 45. + indexminNL: np.ndarray + Index of the minimum radius R/cMpc in which we start doing the nonlinear calculation. + indexmaxNL: np.ndarray + Index of the maximum radius R/cMpc in which we start doing the nonlinear calculation. + a_ST: float + Rescaling of the HMF barrier. Default is 0.707. + Set to 0.73 when Flag_emulate_21cmfast is True. + p_ST: float + Correction factor for the abundance of small mass objects. Default is 0.3. + Set to 0.175 when Flag_emulate_21cmfast is True. + Amp_ST: float + Normalization factor for the halo mass function. Default is 0.3222. + Set to 0.353 when Flag_emulate_21cmfast is True. + delta_crit_ST: float + Barrier for halo to collapse in Sheth-Tormen formalism. Default is 1.686. + Set to 1.68 when Flag_emulate_21cmfast is True. + a_corr_EPS: float + Correction to the EPS relation between nu and nu' when doing extended PS. Follows hi-z simulation results from Schneider+21. Default is 0.707. + Set to 1.0 when Flag_emulate_21cmfast is True. + """ + ### Non-default parameters + UserParams: InitVar[User_Parameters] + + ### Default parameters + # 6 LCDM parameters + omegab: float = 0.0223828 + omegac: float = 0.1201075 + h_fid: float = 0.67810 + As: float = 2.100549e-09 + ns: float = 0.9660499 + tau_fid: float = 0.05430842 + + # Other params for CLASS + kmax_CLASS: float = 500. + zmax_CLASS: float = 50. + zmin_CLASS: float = 5. + + # Shells that we integrate over at each z. + Rs_min: float = 0.5 #TODO: Set to 0.5 if not doing reionization, 0.05 if doing reionization. Otherwise BMF doesn't converge + Rs_max: float = 2000. + + # Flags + Flag_emulate_21cmfast: bool = False + USE_RELATIVE_VELOCITIES: bool = False + HMF_CHOICE: str = "ST" + + # HMF mass integral + _Mhmin: float = 1e5 # minimum halo mass in Msun + _Mhmax: float = 1e14 # maximum halo mass in Msun + + ### Additional parameters and attributes set in the following + # LCDM parameters + ClassCosmo: Class = _field(init=False) + omegam: float = _field(init=False) + OmegaM: float = _field(init=False) + rhocrit: float = _field(init=False) + OmegaR: float = _field(init=False) + OmegaL: float = _field(init=False) + OmegaB: float = _field(init=False) + rho_M0: float = _field(init=False) + z_rec: float = _field(init=False) + + # v_cb parameters + sigma_vcb: float = _field(init=False) + vcb_avg: float = _field(init=False) + + # Number densities and mass fractions + Y_He: float = _field(init=False) + x_He: float = _field(init=False) + f_H: float = _field(init=False) + f_He: float = _field(init=False) + mu_baryon: float = _field(init=False) + mu_baryon_Msun: float = _field(init=False) + + # R->M conversions for HMF + constRM: float = _field(init=False) + + # Redshifts and comoving distances + _ztabinchi: np.ndarray = _field(init=False) + _chitab: Any = _field(init=False) + _Hztab: Any = _field(init=False) + zfofRint: interp1d = _field(init=False) + chiofzint: interp1d = _field(init=False) + + # Thermodynamics + Tadiabaticint: interp1d = _field(init=False) + xetanhint: interp1d = _field(init=False) + + # Growth + growthint: interp1d = _field(init=False) + + # Radii + NRs: np.ndarray = _field(init=False) + _Rtabsmoo: np.ndarray = _field(init=False) + _dlogRR: np.ndarray = _field(init=False) + indexminNL: np.ndarray = _field(init=False) + indexmaxNL: np.ndarray = _field(init=False) + + # HMF-related constants + a_ST: float = _field(init=False) + p_ST: float = _field(init=False) + Amp_ST: float = _field(init=False) + delta_crit_ST: float = _field(init=False) + a_corr_EPS: float = _field(init=False) + + tageofzMyr: interp1d = _field(init=False) + zfoftageMyr: interp1d = _field(init=False) + + def __post_init__(self, UserParams): + + schema = { + "Flag_emulate_21cmfast": (bool, None), + "USE_RELATIVE_VELOCITIES": (bool, None), + "HMF_CHOICE": (str, {'ST','Yung'}), + } + validate_fields(self, schema) + + # run CLASS + self.ClassCosmo = self.runclass() + + # derived params + self.omegam = self.omegab + self.omegac + self.OmegaM = self.ClassCosmo.Omega_m() + self.rhocrit = 3 * 100**2 / (8 * np.pi* constants.MsunToKm * constants.c_kms**2 * constants.KmToMpc) * self.h_fid**2 # Msun/Mpc^3 + self.OmegaR = self.ClassCosmo.Omega_r() + self.OmegaL = self.ClassCosmo.Omega_Lambda() + self.OmegaB = self.ClassCosmo.Omega_b() + self.rho_M0 = self.OmegaM * self.rhocrit + + _zlistforage = np.logspace(5,-3,10000) + _zlistforage[-1]=0.0 + _Hztab = self.ClassCosmo.z_of_r(_zlistforage)[1] #chi and dchi/dz + + ### TODO: check if this is the same as cosmic time in cosmology + tagetabyr = -cumulative_trapezoid(constants.Mpctoyr/_Hztab/(1+_zlistforage),_zlistforage) + tagetabyr = np.insert(tagetabyr,0,0) - self.omegab = CosmoParams_input.omegab - self.omegac = CosmoParams_input.omegac - self.h_fid = CosmoParams_input.h_fid - self.As = CosmoParams_input.As - self.ns = CosmoParams_input.ns - self.tau_fid = CosmoParams_input.tau_fid + self.tageofzMyr = interp1d(_zlistforage,tagetabyr/1e6) #interpolators for age in Myr as a function of z + self.zfoftageMyr = interp1d(tagetabyr/1e6,_zlistforage) #and it's inverse, z for age t in Myr - #other params in the input - self.kmax_CLASS = CosmoParams_input.kmax_CLASS - self.zmax_CLASS = CosmoParams_input.zmax_CLASS - self.zmin_CLASS = CosmoParams_input.zmin_CLASS #when to start the HMF calcs., not an input strictly - self.Flag_emulate_21cmfast = CosmoParams_input.Flag_emulate_21cmfast #whether to emulate 21cmFAST in HMF, LyA, and X-ray opacity calculations - #derived params - self.omegam = self.omegab + self.omegac - self.OmegaM = ClassCosmo.Omega_m() - self.rhocrit = 2.78e11*self.h_fid**2 #Msun/Mpc^3 - self.OmegaR = ClassCosmo.Omega_r() - self.OmegaL = ClassCosmo.Omega_Lambda() - self.OmegaB = ClassCosmo.Omega_b() + self.z_rec = self.ClassCosmo.get_current_derived_parameters(['z_rec'])['z_rec'] - self.z_rec = ClassCosmo.get_current_derived_parameters(['z_rec'])['z_rec'] - - ###HAC: added v_cb flag. JBM: moved to CosmoParams so user does not have to pass Class Cosmo all the time - self.USE_RELATIVE_VELOCITIES = CosmoParams_input.USE_RELATIVE_VELOCITIES - if self.USE_RELATIVE_VELOCITIES == True: - self.sigma_vcb = ClassCosmo.pars['sigma_vcb'] - self.vcb_avg = ClassCosmo.pars['v_avg'] - else: #set but not to random values, just something sensible in case the user wants pop3 but not relvel - self.sigma_vcb = 30.0 - self.vcb_avg = 27.5 + ### v_cb flag + self.sigma_vcb = self.ClassCosmo.pars['sigma_vcb'] + self.vcb_avg = self.ClassCosmo.pars['v_avg'] - ###n_H() stuff - self.Y_He = ClassCosmo.get_current_derived_parameters(['YHe'])['YHe'] + ### number densities and mass fractions + self.Y_He = self.ClassCosmo.get_current_derived_parameters(['YHe'])['YHe'] self.x_He = self.Y_He/4.0/(1.0 - self.Y_He) #=nHe/nH self.f_H = (1.0 - self.Y_He)/(1.0 - 3.0/4.0 * self.Y_He) #=nH/nb self.f_He = self.Y_He/4.0/(1.0 - 3.0/4.0 * self.Y_He) #=nHe/nb self.mu_baryon = (1 + self.x_He * 4.)/(1 + self.x_He) * constants.mH_GeV #mproton ~ 0.94 GeV - self.mu_baryon_Msun = self.mu_baryon/constants.MsuntoGeV + self.mu_baryon_Msun = self.mu_baryon / constants.MsuntoGeV -# ###old dependencies of n_baryon() instead of n_H() -# self.Y_He = ClassCosmo.get_current_derived_parameters(['YHe'])['YHe'] -# self.f_He = self.Y_He/4.0/(1.0 - 3.0/4.0 * self.Y_He) #=nHe/nb -# self.f_H = (1.0 - self.Y_He)/(1.0 - 3.0/4.0 * self.Y_He) #=nH/nb -# self.mu_baryon = (self.f_H + self.f_He * 4.) * 0.94 #mproton ~ 0.94 GeV - - - - #for R->M conversions for HMF. Used for CLASS input so assumes tophat. + # for R->M conversions for HMF. Used for CLASS input so assumes tophat. self.constRM = self.OmegaM*self.rhocrit * 4.0 * np.pi/3.0 - self.rho_M0 = self.OmegaM*self.rhocrit - - - + # redshifts and comoving distances self._ztabinchi = np.linspace(0.0, 1100. , 10000) #cheap so do a lot - # self._chitab = ClassCosmo.z_of_r(self._ztabinchi)[0] - # self.zfofRint = interp1d(self._chitab, self._ztabinchi) - self._chitab, self._Hztab = ClassCosmo.z_of_r(self._ztabinchi) #chi and dchi/dz + self._chitab, self._Hztab = self.ClassCosmo.z_of_r(self._ztabinchi) #chi and dchi/dz self.zfofRint = interp1d(self._chitab, self._ztabinchi) self.chiofzint = interp1d(self._ztabinchi,self._chitab) - self.Hofzint = interp1d(self._ztabinchi,self._Hztab) - _thermo = ClassCosmo.get_thermodynamics() + # thermodynamics + _thermo = self.ClassCosmo.get_thermodynamics() self.Tadiabaticint = interp1d(_thermo['z'], _thermo['Tb [K]']) self.xetanhint = interp1d(_thermo['z'], _thermo['x_e']) + # growth _ztabingrowth = np.linspace(0., 100. , 2000) - _growthtabint = np.array([ClassCosmo.scale_independent_growth_factor(zz) for zz in _ztabingrowth]) - + _growthtabint = np.array([self.ClassCosmo.scale_independent_growth_factor(zz) for zz in _ztabingrowth]) self.growthint = interp1d(_ztabingrowth,_growthtabint) - - #and define the shells that we integrate over at each z. - self.Rsmmin = 0.5 - self.Rsmmax = 2000. - - if(self.Flag_emulate_21cmfast==True): - self.Rsmmin = 0.62*1.5 #same as minmum R in 21cmFAST for their standard 1.5 Mpc cell resolution. 0.62 is their 'L_FACTOR' - self.Rsmmax = 500. #same as R_XLy_MAX in 21cmFAST. Too low? + # shells that we integrate over at each z. + if self.Flag_emulate_21cmfast: + self.Rs_min = 0.62*1.5 #same as minmum R in 21cmFAST for their standard 1.5 Mpc cell resolution. 0.62 is their 'L_FACTOR' + self.Rs_max = 500. #same as R_XLy_MAX in 21cmFAST. Too low? + # radii self.NRs = np.floor(45*UserParams.precisionboost).astype(int) - self._Rtabsmoo = np.logspace(np.log10(self.Rsmmin), np.log10(self.Rsmmax), self.NRs) # Smoothing Radii in Mpc com - self._dlogRR = np.log(self.Rsmmax/self.Rsmmin)/(self.NRs-1.0) + self._Rtabsmoo = np.logspace(np.log10(self.Rs_min), np.log10(self.Rs_max), self.NRs) # Smoothing Radii in Mpc com + self._dlogRR = np.log(self.Rs_max/self.Rs_min)/(self.NRs-1.0) - self.indexminNL = (np.log(UserParams.MIN_R_NONLINEAR/self.Rsmmin)/self._dlogRR).astype(int) - self.indexmaxNL = (np.log(UserParams.MAX_R_NONLINEAR/self.Rsmmin)/self._dlogRR).astype(int) + 1 #to ensure it captures MAX_R + self.indexminNL = (np.log(UserParams.MIN_R_NONLINEAR/self.Rs_min)/self._dlogRR).astype(int) + self.indexmaxNL = (np.log(UserParams.MAX_R_NONLINEAR/self.Rs_min)/self._dlogRR).astype(int) + 1 #to ensure it captures MAX_R - - #HMF-related constants - self.HMF_CHOICE = CosmoParams_input.HMF_CHOICE - if(self.Flag_emulate_21cmfast == False): #standard, best fit ST from Schneider+ - self.a_ST = 0.707 #OG ST fit, or 0.85 to fit 1805.00021 + # HMF-related constants + if not self.Flag_emulate_21cmfast: # standard, best fit ST from Schneider+21 + self.a_ST = 0.707 # OG ST fit, or 0.85 to fit 1805.00021 self.p_ST = 0.3 self.Amp_ST = 0.3222 self.delta_crit_ST = 1.686 - self.a_corr_EPS = self.a_ST #correction to the eps relation between nu and nu' when doing extended PS. Follows hi-z simulation results from Schneider+ - elif(self.Flag_emulate_21cmfast == True): #emulate 21cmFAST, including HMF from Jenkins 2001 - self.HMF_CHOICE = 'ST' #forced to match their functional form + self.a_corr_EPS = self.a_ST + else: # emulate 21cmFAST, including HMF from Jenkins 2001 + self.HMF_CHOICE = 'ST' # forced to match their functional form + print('Since Flag_emulate_21cmfast==True, the code set HMF_CHOICE==ST') self.a_ST = 0.73 self.p_ST = 0.175 self.Amp_ST = 0.353 self.delta_crit_ST = 1.68 self.a_corr_EPS = 1.0 + + # Run matter and relative velocities correlations + self.run_correlations() + + def runclass(self): + "Set up CLASS cosmology. Takes CosmologyIn class input and returns CLASS Cosmology object" + ClassCosmo = Class() + ClassCosmo.set({'omega_b': self.omegab,'omega_cdm': self.omegac, + 'h': self.h_fid,'A_s': self.As,'n_s': self.ns,'tau_reio': self.tau_fid}) + ClassCosmo.set({'output':'mPk,mTk','lensing':'no','P_k_max_1/Mpc':self.kmax_CLASS, 'z_max_pk': self.zmax_CLASS}) + ClassCosmo.set({'gauge':'synchronous'}) + #hfid = ClassCosmo.h() # get reduced Hubble for conversions to 1/Mpc + + # and run it (see warmup for their doc) + ClassCosmo.compute() + + ClassCosmo.pars['Flag_emulate_21cmfast'] = self.Flag_emulate_21cmfast + + ###HAC: Adding VCB feedback via a second run of CLASS: + if self.USE_RELATIVE_VELOCITIES: + + kMAX_VCB = 100.0 + ###HAC: getting z_rec from first CLASS run + z_rec = ClassCosmo.get_current_derived_parameters(['z_rec'])['z_rec'] + z_drag = ClassCosmo.get_current_derived_parameters(['z_d'])['z_d'] + + ###HAC: Running CLASS a second time just to get velocity transfer functions at recombination + ClassCosmoVCB = Class() + ClassCosmoVCB.set({'omega_b': self.omegab,'omega_cdm': self.omegac, + 'h': self.h_fid,'A_s': self.As,'n_s': self.ns,'tau_reio': self.tau_fid}) + ClassCosmoVCB.set({'output':'vTk'}) + ClassCosmoVCB.set({'P_k_max_1/Mpc':kMAX_VCB, 'z_max_pk':12000}) + ClassCosmoVCB.set({'gauge':'newtonian'}) + ClassCosmoVCB.compute() + velTransFunc = ClassCosmoVCB.get_transfer(50) + + kVel = velTransFunc['k (h/Mpc)'] * self.h_fid + theta_b = velTransFunc['t_b'] + theta_c = velTransFunc['t_cdm'] + + sigma_vcb = np.sqrt(np.trapezoid(self.As * (kVel/0.05)**(self.ns-1) /kVel * (theta_b - theta_c)**2/kVel**2, kVel)) * constants.c_kms + ClassCosmo.pars['sigma_vcb'] = sigma_vcb + + ###HAC: now computing average velocity assuming a Maxwell-Boltzmann distribution of velocities + velArr = np.geomspace(0.01, constants.c_kms, 1000) #in km/s + vavgIntegrand = (3 / (2 * np.pi * sigma_vcb**2))**(3/2) * 4 * np.pi * velArr**2 * np.exp(-3 * velArr**2 / (2 * sigma_vcb**2)) + ClassCosmo.pars['v_avg'] = np.trapezoid(vavgIntegrand * velArr, velArr) + + ###HAC: Computing Vcb Power Spectrum + ClassCosmo.pars['k_vcb'] = kVel + ClassCosmo.pars['theta_b'] = theta_b + ClassCosmo.pars['theta_c'] = theta_c + P_vcb = self.As * (kVel/0.05)**(self.ns-1) * (theta_b - theta_c)**2/kVel**2 * 2 * np.pi**2 / kVel**3 + + p_vcb_intp = interp1d(np.log(kVel), P_vcb) + ClassCosmo.pars['P_vcb'] = P_vcb + + ###HAC: Computing Vcb^2 (eta) Power Spectra + kVelIntp = np.geomspace(1e-4, kMAX_VCB, 512) + rVelIntp = 2 * np.pi / kVelIntp + + j0bessel = lambda x: np.sin(x)/x + j2bessel = lambda x: (3 / x**2 - 1) * np.sin(x)/x - 3*np.cos(x)/x**2 + + psi0 = 1 / 3 / (sigma_vcb/constants.c_kms)**2 * np.trapezoid(kVelIntp**2 / 2 / np.pi**2 * p_vcb_intp(np.log(kVelIntp)) * j0bessel(kVelIntp * np.transpose([rVelIntp])), kVelIntp, axis = 1) + psi2 = -2 / 3 / (sigma_vcb/constants.c_kms)**2 * np.trapezoid(kVelIntp**2 / 2 / np.pi**2 * p_vcb_intp(np.log(kVelIntp)) * j2bessel(kVelIntp * np.transpose([rVelIntp])), kVelIntp, axis = 1) + + k_eta, P_eta = mcfit.xi2P(rVelIntp, l=0, lowring = True)((6 * psi0**2 + 3 * psi2**2), extrap = False) + + ClassCosmo.pars['k_eta'] = k_eta[P_eta > 0] + ClassCosmo.pars['P_eta'] = P_eta[P_eta > 0] + + # print("HAC: Finished running CLASS a second time to get velocity transfer functions") else: - print("Error! Have to set either Flag_emulate_21cmfast = True or False") + ClassCosmo.pars['v_avg'] = 0.0 + ClassCosmo.pars['sigma_vcb'] = 1.0 #Avoids excess computation, but doesn't matter what value we set it to because the flag in inputs.py sets all feedback parameters to zero + + return ClassCosmo + + def run_correlations(self): + #we choose the k to match exactly the log FFT of input Rtabsmoo. + self._klistCF, _dummy_ = mcfit.xi2P(self._Rtabsmoo, l=0, lowring=True)(0*self._Rtabsmoo, extrap=False) + self.NkCF = len(self._klistCF) + + self._PklinCF = np.zeros(self.NkCF) # P(k) in 1/Mpc^3 + for ik, kk in enumerate(self._klistCF): + self._PklinCF[ik] = self.ClassCosmo.pk(kk, 0.0) # function .pk(k,z) -class Astro_Parameters: - "Class to pass the astro parameters as input" - - def __init__(self, UserParams, Cosmo_Parameters, - astromodel = 0, - accretion_model = 0, - - alphastar = 0.5, - betastar = -0.5, - epsstar = 0.1, - Mc = 3e11, - dlog10epsstardz = 0.0, - - fesc10 = 0.1, - alphaesc = 0.0, - L40_xray = 3.0, - E0_xray = 500., - alpha_xray = -1.0, - Emax_xray_norm=2000, - - Nalpha_lyA_II = 9690, - Nalpha_lyA_III = 17900, - - Mturn_fixed = None, - FLAG_MTURN_SHARP= False, - - C0dust = 4.43, - C1dust = 1.99, - - sigmaUV=0.5, - - USE_POPIII = False, - - alphastar_III = 0, - betastar_III = 0, - fstar_III = 10**(-2.5), - Mc_III = 1e7, - dlog10epsstardz_III = 0.0, - - fesc7_III = 10**(-1.35), - alphaesc_III = -0.3, - L40_xray_III = 3.0, - alpha_xray_III = -1.0, - - USE_LW_FEEDBACK = True, - A_LW = 2.0, - beta_LW = 0.6, - - A_vcb = 1.0, - beta_vcb = 1.8, - - quadratic_SFRD_lognormal = False, # Sarah Libanore, use second order in lognormal - min_t_formation_Myr = None - - ): - - #for internal functions in SED_LyA - self.Flag_emulate_21cmfast = Cosmo_Parameters.Flag_emulate_21cmfast - if(Cosmo_Parameters.Flag_emulate_21cmfast==True and astromodel == 0): - print('ERROR, picked astromodel = 0 but tried to emulate 21cmFAST. They use astromodel = 1. Changing it!') - self.astromodel = 1 + self._xif = mcfit.P2xi(self._klistCF, l=0, lowring=True) + + + self.xi_RR_CF = self.get_xi_R1R2(field = 'delta') + self.ClassCosmo.pars['xi_RR_CF'] = np.copy(self.xi_RR_CF) #store correlation function for gamma_III correction in SFRD + + ###HAC: Interpolated object for eta power spectrum + if self.USE_RELATIVE_VELOCITIES == True: + P_eta_interp = interp1d(self.ClassCosmo.pars['k_eta'], self.ClassCosmo.pars['P_eta'], bounds_error = False, fill_value = 0) + self._PkEtaCF = P_eta_interp(self._klistCF) + self.xiEta_RR_CF = self.get_xi_R1R2(field = 'vcb') else: - self.astromodel = astromodel # which SFR model we use. 0=Gallumi-like, 1=21cmfast-like + self._PkEtaCF = np.zeros_like(self._PklinCF) + self.xiEta_RR_CF = np.zeros_like(self.xi_RR_CF) - ###HAC: PopIII parameters: - self.USE_POPIII = USE_POPIII + + def get_xi_R1R2 (self, field = None): + "Get correlation function of density, linearly extrapolated to z=0, smoothed over two different radii with Window(k,R)" - self.alphastar_III = alphastar_III - self.betastar_III = betastar_III - self.fstar_III = fstar_III - self.Mc_III = Mc_III - self.dlog10epsstardz_III = dlog10epsstardz_III - self._zpivot_III = 8.0 #fixed, at which z we evaluate eps and dlogeps/dz + lengthRarray = self.NRs + windowR1 = z21_utilities.Window(self._klistCF.reshape(lengthRarray, 1, 1), self._Rtabsmoo.reshape(1, 1, lengthRarray)) + windowR2 = z21_utilities.Window(self._klistCF.reshape(1, lengthRarray,1), self._Rtabsmoo.reshape(1, 1, lengthRarray)) - self.fesc7_III = fesc7_III - self.alphaesc_III = alphaesc_III - self.L40_xray_III = L40_xray_III - self.alpha_xray_III = alpha_xray_III + if field == 'delta': + _PkRR = np.array([[self._PklinCF]]) * windowR1 * windowR2 + elif field == 'vcb': + _PkRR = np.array([[self._PkEtaCF]]) * windowR1 * windowR2 + else: + raise ValueError('field has to be either delta or vcb in get_xi_R1R2') - ###HAC: Using LW feedback and fixing parameters - self.USE_LW_FEEDBACK = USE_LW_FEEDBACK + self.rlist_CF, xi_RR_CF = self._xif(_PkRR, extrap = False) + + return xi_RR_CF + + + +@dataclass(kw_only=True) +class Astro_Parameters: + """ + Astrophysical parameters for zeus21. + + Parameters + ---------- + CosmoParams: Cosmo_Parameters + zeus21 class for the cosmological parameters. Needs to be inputed. + accretion_model: str + Accretion model. "exp" for exponential, "EPS" for EPS. "RP16" for the dynamically averaged fitting function in Rodríguez-Puebla+16. Default is "exp". + USE_POPIII: bool + Whether to use Pop III. Default is False. + USE_LW_FEEDBACK: bool + Whether to use the Lyman-Werner feedback. Default is True. + quadratic_SFRD_lognormal: bool + Whether to use the second order correction to the SFRD approximation. Default is True. + epsstar: float + Amplitude of the star formation efficiency (at M_pivot). Default is 0.1. + dlog10epsstardz: float + Derivative of epsstar with respect to z. Default is 0.0. + alphastar: float + Power law index of the star formation efficiency at low masses. Default 0.5. + betastar: float + Power law index of the star formation efficiency at high masses. Not used if Flag_emulate_21cmfast = True. Default -0.5. + Mc: float + Mass at which the star formation efficiency cuts. Not used if Flag_emulate_21cmfast=True. Default 3e11. + epsstar_III: float + Amplitude of the star formation efficiency (at M_pivot) for Pop III. Default is 10**(-2.5). + dlog10epsstardz_III: float + Derivative of epsstar with respect to z for Pop III. Default is 0.0. + alphastar_III: float + Power law index of the Pop III star formation efficiency at low masses. Default 0.0. + betastar_III: float + Power law index of the Pop III star formation efficiency at high masses. Default 0.0. + Mc_III: float + Mass at which the Pop III star formation efficiency cuts. Default 1e7. + Mup_III: str, float, None + High-mass cutoff for Pop III star formation efficiency. Default "Matom" for Pop III star formation confined to molecular-cooling minihalos; also accepts custom cutoff value or None for no cutoff + DETACH_III_ACH: bool + Whether to have a separate set of parameters for star formation efficiency for the (ACH) component for Pop III. Default is False. + epsstar_III_ACH: float + Amplitude of the star formation efficiency (at M_pivot) for the (ACH) component for Pop III. Default is 0.0. + dlog10epsstardz_III_ACH: float + Derivative of epsstar with respect to z for the (ACH) component for Pop III. Default is 0.0. + alphastar_III_ACH: float + Power law index of the (ACH) component of Pop III star formation efficiency at low masses. Default 0.0. + betastar_III_ACH: float + Power law index of the (ACH) component of Pop III star formation efficiency at high masses. Default 0.0. + Mc_III_ACH: float + Mass at which the (ACH) component of Pop III star formation efficiency cuts. Default 1e7. + N_alpha_perbaryon_II: float + Number of photons between LyA and Ly Continuum per baryon (from LB05). Default is 9690. + N_alpha_perbaryon_III: float + Number of photons between LyA and Ly Continuum per baryon (from LB05) for Pop III. Default is 17900. + L40_xray: float + Soft-band (E<2 keV) lum/SFR in Xrays in units of 10^40 erg/s/(Msun/yr). Default is 3.0. + E0_xray: float + Minimum energy in eV. Default is 500. + alpha_xray: float + Xray SED power-law index. Default is -1.0. + L40_xray_III: float + Soft-band (E<2 keV) lum/SFR in Xrays in units of 10^40 erg/s/(Msun/yr) for Pop III. Default is 3.0. + alpha_xray_III: float + Xray SED power-law index. Default is -1.0. + Emax_xray_norm: float + Max energy in eV to normalize SED. Default at 2000.0 eV. + fesc10: float + Amplitude of the escape fraction. Default is 0.1. + Escape fraction assumed to be a power law normalized (fesc10) at M=1e10 Msun with index alphaesc. + alphaesc: float + Index for the escape fraction. Default is 0.0. + Escape fraction assumed to be a power law normalized (fesc10) at M=1e10 Msun with index alphaesc. + fesc7_III: float + Amplitude of the Pop III escape fraction. Default is 10**(-1.35). + Escape fraction assumed to be a power law normalized (fesc10) at M=1e10 Msun with index alphaesc. + alphaesc_III: float + Index for the Pop III escape fraction. Default is -0.3. + Escape fraction assumed to be a power law normalized (fesc10) at M=1e10 Msun with index alphaesc. + clumping: float + Clumping factor, which is z-independent and fixed for now. Default is 3.0, changed to 2.0 when Flag_emulate_21cmfast=True. + R_linear_sigma_fit_input: float + Initial guess radius at which the linear fit of the barrier is computed. Default is 10.0. + FLAG_BMF_converge: bool + Whether zeus21 allow the BMF to try and make the average ionized fraction converge. Default is True. + max_iter: int + Maximum iteration allowed for the convergence of the BMF. Default is 10. + ZMAX_REION: float + Maximum redshift to which the reionization quantities are computed. Default is 30.0. + Rbub_min: float + Minimum bubble radius. Default is 0.05. + A_LW: float + Parameters controlling the LW feedback factor (see Munoz+22, eq 13). Default is 2.0. + beta_LW: float + Parameters controlling the LW feedback factor (see Munoz+22, eq 13). Default is 0.6. + A_vcb: float + Normalization for the relative velocity feedback parameter. Default is 1.0. + beta_vcb: float + Spectral index for the relative velocity feedback parameter. Default 1.8. + Mturn_fixed: float | None + Turn-over halo mass at which the star formation rate cuts. Default is None. + FLAG_MTURN_SHARP: bool + Whether to do sharp cut at Mturn_fixed or regular exponential cutoff. Only active if FLAG_MTURN_FIXED and turned on by hand. Default is False. + FLAG_USE_PSD: bool + Whether to derive MUV and sigmaUV from integrating SFH. Default is False. + FLAG_COMPARE_BAGPIPES: bool + Whethher to compare with bagpipes. Default is False. + SEDMODEL: str = "BPASS" + Which SED model to use for the Greens functions. Default is "BPASS". + Can be set to "bagpipes", "BPASS_binaries", and "BPASS". + normLHa_ZIMF: float + Floating normalization of the LHa luminosity compared to the baseline SEDMODEL to account for HMF or metallicity changes. Default is 1.0 + alphanormLHa_ZIMF: + Power-law index of normLHa_ZIMF against halo mass. Default is 0.0 + sigmaPSD: float + Amplitude of fluctuations in SFR arising from the power spectral density (PSD) model of SFR variability. Default is 0.5. + This is the baseline scatter in ln(SFR) at a reference halo mass of 10^10 Msun. + dsigmaPSDdlog10Mh: float + Slope of the scatter with respect to halo mass. Default is 0.0. + tauPSD: float + Characteristic timescale (in Myr) that enters the power spectral density (PSD) of ln(SFR). Default is 10.0. + dlog10tauPSDdlog10Mh: float + Slope of the timescale with respect to halo mass. Default is 0.0. + _tcut_LUV_short: float + Sets where the LUV short and long are separated in Myr. Default is 30.0. + FLAG_RENORMALIZE_AVG_SFH: bool + Whether to normalize the SFR in Msun/yr at each Mh. Default is True. - if self.USE_LW_FEEDBACK == True: - self.A_LW = A_LW - self.beta_LW = beta_LW + Attributes + ---------- + fstarmax: float + Peak amplitude for the star formation efficiency. Set by zeus21 to 1. + Emax_xray_integral: float + Max energy in eV that zeus21 integrate up to. Higher than Emax_xray_norm since photons can redshift from higher z. Set by zeus21 to 10000. + Nen_xray: int + Number of energies to do the xray integrals. Set by zeus21 to 30. + Energylist: np.ndarray + Energies, in eV. + dlogEnergy: float + Used to get dlog instead of dlog10. + N_ion_perbaryon_II: int + Number of ionizing photons per baryon. Fixed for PopII-type (Salpeter) by zeus21 to 5000. + N_ion_perbaryon_III: int + Number of ionizing photons per baryon for Pop III. Fixed for PopIII-type to 44000 (or 52480 when Flag_emulate_21cmfast=True), from Klessen & Glover 2023 Table A2 (2303.12500). + N_LW_II: float + Number of LW photons per baryon. + Assuming BL05 stellar spectrum, equal to N_alpha_perbaryon_II * fraction of photons that fall in the LW band. + N_LW_III: float + Number of LW photons per baryon. + Assuming Intermediate IMF from 2202.02099, equal to 4.86e-22 / (11.9 * u.eV).to(u.erg).value * 5.8e14. + FLAG_MTURN_FIXED: bool + Whether to fix Mturn or use Matom(z) at each z. Set by zeus21 depending on Mturn_fixed. + + """ + ### Non-default parameters + CosmoParams: InitVar[Cosmo_Parameters] + + ### Default and init=False parameters + # Flags + accretion_model: str = "exp" + USE_POPIII: bool = False + USE_LW_FEEDBACK: bool = True + quadratic_SFRD_lognormal: bool = True + + # SFR(Mh) parameters - popII + epsstar: float = 0.1 + dlog10epsstardz: float = 0.0 + alphastar: float = 0.5 + betastar: float = -0.5 + Mc: float = 3e11 + _zpivot: float = _field(init=False) # Redshift at which the eps and dlogeps/dz are evaluated. Set by zeus21 to 8.0. + fstarmax: float = _field(init=False) + + # SFR(Mh) parameters - popIII main component --> by default, this only includes the molecular-cooling minihalo component extended up to the atomic-cooling limit, but it can be extended to a custom high-mass cutoff value by changing the value of Mup_III + epsstar_III: float = 10**(-2.5) + dlog10epsstardz_III: float = 0.0 + alphastar_III: float = 0. + betastar_III: float = 0. + Mc_III: float = 1e7 + Mup_III: str | float | None = "Matom" + Mup3TEMP: float = 10**8.387007493446207#HAC TEMPORARY: Delete!!! Only for BAO/VAO comparison + _zpivot_III: float = _field(init=False) # Redshift at which the eps and dlogeps/dz are evaluated for Pop III. Set by zeus21 to 8.0. + + # SFR(Mh) parameters - popIII deatched atomic-cooling component + DETACH_III_ACH: bool = False + epsstar_III_ACH: float = 0. + dlog10epsstardz_III_ACH: float = 0.0 + alphastar_III_ACH: float = 0. + betastar_III_ACH: float = 0. + Mc_III_ACH: float = 1e7 + _zpivot_III_ACH: float = _field(init=False) # Redshift at which the eps and dlogeps/dz are evaluated for the (ACH) component for Pop III. Set by zeus21 to 8.0. + + # Lyman-alpha parameters + N_alpha_perbaryon_II: float = 9690 + N_alpha_perbaryon_III: float = 17900 + + # Xray parameters, assumed power-law for now + L40_xray: float = 3.0 + E0_xray: float = 500. + alpha_xray: float = -1.0 + L40_xray_III: float = 3.0 + alpha_xray_III: float = -1.0 + Emax_xray_norm: float = 2000 + Emax_xray_integral: float = _field(init=False) # Max energy in eV that we integrate up to. Higher than Emax_xray_norm since photons can redshift from higher z + + # table with how many energies we integrate over + Nen_xray: int = _field(init=False) + _log10EMIN_INTEGRATE: float = _field(init=False) # Minimum energy zeus21 integrates to, to account for photons coming from higher z that redshift. + _log10EMAX_INTEGRATE: float = _field(init=False) # Maximum energy zeus21 integrates to, to account for photons coming from higher z that redshift. + Energylist: np.ndarray = _field(init=False) # in eV + dlogEnergy: float = _field(init=False) # to get dlog instead of dlog10 + + # Reionization parameters + fesc10: float = 0.1 + alphaesc: float = 0.0 + fesc7_III: float = 10**(-1.35) + alphaesc_III: float = -0.3 + clumping: float = 3. + N_ion_perbaryon_II: int = _field(init=False) # fixed for PopII-type (Salpeter) + N_ion_perbaryon_III: int = _field(init=False) # fixed for PopIII-type, from Klessen & Glover 2023 Table A2 (2303.12500) + R_linear_sigma_fit_input: float = 10. + FLAG_BMF_converge: bool = True + max_iter: int = 10 + ZMAX_REION: float = 30 + Rbub_min: float = 0.05 + + # Lyman-Werner feedback paramters + A_LW: float = 2.0 + beta_LW: float = 0.6 + N_LW_II: float = _field(init=False) # number of LW photons per baryon #assuming BL05 stellar spectrum, equal to N_alpha_perbaryon_II * fraction of photons that fall in the LW band + N_LW_III: float = _field(init=False) # number of LW photons per baryon #assuming Intermediate IMF from 2202.02099, equal to 4.86e-22 / (11.9 * u.eV).to(u.erg).value * 5.8e14 + + # relative velocity + A_vcb: float = 1.0 + beta_vcb: float = 1.8 + + # 21cmFAST emulation: SFE parameters + Mturn_fixed: float | None = None + FLAG_MTURN_SHARP: bool = False + FLAG_MTURN_FIXED: bool = _field(init=False) # whether to fix Mturn or use Matom(z) at each z + + # BURSTINESS + FLAG_USE_PSD: bool = False + FLAG_COMPARE_BAGPIPES: bool = False + SEDMODEL: str = "BPASS" + normLHa_ZIMF: float = 1.0 + alphanormLHa_ZIMF: float = 0.0 + sigmaPSD: float = 0.5, + dsigmaPSDdlog10Mh: float = 0.0, + tauPSD: float = 10.0, + dlog10tauPSDdlog10Mh: float = 0.0, + _tcut_LUV_short: float = 30.0 + FLAG_RENORMALIZE_AVG_SFH: bool = True + _minsigmaPSD: float = _field(init=False) + _maxsigmaPSD: float = _field(init=False) + _mintauPSD: float = _field(init=False) + _maxtauPSD: float = _field(init=False) + _tagesMyr: float = _field(init=False) + _dt_FFT: float = _field(init=False) + _N_FFT: float = _field(init=False) + _omegamin: float = _field(init=False) + _omegamax: float = _field(init=False) + + def __post_init__(self, CosmoParams): + + schema = { + "accretion_model": (str, {"EPS", "exp"}), + "USE_POPIII": (bool, None), + "DETACH_III_ACH": (bool, None), + "USE_LW_FEEDBACK": (bool, None), + "quadratic_SFRD_lognormal": (bool, None), + "FLAG_MTURN_SHARP": (bool, None), + "FLAG_USE_PSD": (bool, None), + "FLAG_COMPARE_BAGPIPES": (bool, None), + "FLAG_RENORMALIZE_AVG_SFH": (bool, None), + "SEDMODEL": (str, {"bagpipes", "BPASS_binaries", "BPASS"}), + } + validate_fields(self, schema) + + ### which SFR model we use. 0=Gallumi-like, 1=21cmfast-like + if not CosmoParams.Flag_emulate_21cmfast: # GALLUMI-like + self.accretion_model = self.accretion_model # choose the accretion model: 0 = exponential, 1= EPS. Default = EPS. + else: # 21cmfast-like, ignores Mc and beta and has a t* later in SFR() + self.tstar = 0.5 + self.fstar10 = self.epsstar + + # SFR(Mh) parameters + self._zpivot = 8.0 # fixed, at which z we evaluate eps and dlogeps/dz + self._zpivot_III = 8.0 # fixed, at which z we evaluate eps and dlogeps/dz + self._zpivot_III_ACH = 8.0 # fixed, at which z we evaluate eps and dlogeps/dz + self.fstarmax = 1.0 # where we cap it + + # Xray parameters + self.Emax_xray_integral = 10000. # Max energy in eV that we integrate up to. Higher than Emax_xray_norm since photons can redshift from higher z + if(self.E0_xray < constants.EN_ION_HI): + print("What the heck? How can E0_XRAY < EN_ION_HI?") + + # table with how many energies we integrate over + self.Nen_xray = 30 + self._log10EMIN_INTEGRATE = np.log10(self.E0_xray/2.0) # to account for photons coming from higher z that redshift + self._log10EMAX_INTEGRATE = np.log10(self.Emax_xray_integral) + self.Energylist = np.logspace(self._log10EMIN_INTEGRATE,self._log10EMAX_INTEGRATE,self.Nen_xray) # in eV + self.dlogEnergy = (self._log10EMAX_INTEGRATE - self._log10EMIN_INTEGRATE)/(self.Nen_xray-1.0)*np.log(10.) # to get dlog instead of dlog10 + + # Reionization parameters + if CosmoParams.Flag_emulate_21cmfast: + self.clumping = 2.0 # this is the 21cmFAST value + # number of ionizing photons per baryon + self.N_ion_perbaryon_II = 5000 # fixed for PopII-type (Salpeter) + if CosmoParams.Flag_emulate_21cmfast: + self.N_ion_perbaryon_III = 44000 # fixed for PopIII-type, from Klessen & Glover 2023 Table A2 (2303.12500) else: + self.N_ion_perbaryon_III = 52480 + + ### HAC: LW feedback parameters + if not self.USE_LW_FEEDBACK: self.A_LW = 0.0 self.beta_LW = 0.0 - - ###HAC: Using Relative Velocities and fixing parameters - if Cosmo_Parameters.USE_RELATIVE_VELOCITIES == True: - self.A_vcb = A_vcb - self.beta_vcb = beta_vcb + # number of LW photons per baryon + if not CosmoParams.Flag_emulate_21cmfast: + self.N_LW_II = 6200.0 #assuming BL05 stellar spectrum, equal to N_alpha_perbaryon_II * fraction of photons that fall in the LW band + self.N_LW_III = 12900.0 #assuming Intermediate IMF from 2202.02099, equal to 4.86e-22 / (11.9 * u.eV).to(u.erg).value * 5.8e14 else: + popIIcorrection = 0.6415670418531249/2.5 #scaling used by 21cmfast to get correct number of Pop II LW photons per baryon + self.N_LW_II = popIIcorrection * self.N_alpha_perbaryon_II + popIIIcorrection = 0.7184627927009317/6.5 #scaling used by 21cmfast to get correct number of Pop III LW photons per baryon + self.N_LW_III = popIIIcorrection * self.N_alpha_perbaryon_III + + ### HAC: Relative Velocities parameters + if not CosmoParams.USE_RELATIVE_VELOCITIES: self.A_vcb = 0.0 self.beta_vcb = 0.0 - - #SFR(Mh) parameters: - self.epsstar = epsstar #epsilon_* = f* at Mc - self.dlog10epsstardz = dlog10epsstardz #dlog10epsilon/dz - self._zpivot = 8.0 #fixed, at which z we evaluate eps and dlogeps/dz - self.alphastar = alphastar #powerlaw index for lower masses - self.betastar = betastar #powerlaw index for higher masses, only for model 0 - self.Mc = Mc # mass at which the power law cuts, only for model 0 - self.sigmaUV = sigmaUV #stochasticity (gaussian rms) in the halo-galaxy connection P(MUV | Mh) - TODO: only used in UVLF not sfrd - - self.fstarmax = 1.0 #where we cap it - - if self.astromodel == 0: #GALUMI-like - self.accretion_model = accretion_model #0 = exponential, 1= EPS #choose the accretion model. Default = EPS - elif self.astromodel == 1: #21cmfast-like, ignores Mc and beta and has a t* later in SFR() - self.tstar = 0.5 - self.fstar10 = self.epsstar + ### 21cmFAST emulation: SFE parameters + if(self.Mturn_fixed == None): #The FIXED/SHARP routine below only applies to Pop II, not to Pop III + self.FLAG_MTURN_FIXED = False # whether to fix Mturn or use Matom(z) at each z else: - print('ERROR, need to pick astromodel') + self.FLAG_MTURN_FIXED = True # whether to fix Mturn or use Matom(z) at each z - #fesc(M) parameter. Power law normalized (fesc10) at M=1e10 Msun with index alphaesc - self.fesc10 = fesc10 - self.alphaesc = alphaesc - self._clumping = 3.0 #clumping factor, z-independent and fixed for now - if(Cosmo_Parameters.Flag_emulate_21cmfast==True): - self._clumping = 2.0 #this is the 21cmFAST value + self._minsigmaPSD = 0.1 # Minimum sigma for the PSD, to avoid numerical issues in the FFT + self._maxsigmaPSD = 4.0 # Maximum sigma for the PSD, there'll never be enough samples if sigma>~6-10 + self._mintauPSD = 1.0 # in Myr. Minimum tau for the PSD, to avoid numerical issues in the FFT + self._maxtauPSD = 300.0 + self._tagesMyr = np.logspace(-2, 3, 79) #times (ages) we integrate over at each z, Mh, in Myr (TODO: add precisionboost) - #xray parameters here, assumed power-law for now - self.L40_xray = L40_xray # soft-band (E<2 keV) lum/SFR in Xrays in units of 10^40 erg/s/(Msun/yr) - self.E0_xray = E0_xray #minimum energy in eV - self.Emax_xray_norm = Emax_xray_norm #max energy in eV to normalize SED. Keep at 2000 eV normally - self.Emax_xray_integral = 10000. #max energy in eV that we integrate up to. Higher than Emax_xray_norm since photons can redshift from higher z - self.alpha_xray = alpha_xray #Xray SED power-law index - if(self.E0_xray < constants.EN_ION_HI): - print('What the heck? How can E0_XRAY < EN_ION_HI ?') + self._dt_FFT = 0.3 # FFT timescale resolution, Myr, high to resolve the PS_SFR and window functions well (TODO: add UserParams precisionboost here) + self._N_FFT = int(512/(self._dt_FFT/0.3)) # Recommend to use power of 2 for efficient FFT, resolve up to ~0.5Gyr at least + self._omegamin = 2*np.pi/1e3 + self._omegamax = np.pi/1.0 - #table with how many energies we integrate over - self.Nen_xray = 30 - self._log10EMIN_INTEGRATE = np.log10(self.E0_xray/2.0) # to account for photons coming from higher z that redshift - self._log10EMAX_INTEGRATE = np.log10(self.Emax_xray_integral) - self.Energylist = np.logspace(self._log10EMIN_INTEGRATE,self._log10EMAX_INTEGRATE,self.Nen_xray) #in eV - self.dlogEnergy = (self._log10EMAX_INTEGRATE - self._log10EMIN_INTEGRATE)/(self.Nen_xray-1.0)*np.log(10.) #to get dlog instead of dlog10 - self.N_alpha_perbaryon_II=Nalpha_lyA_II #number of photons between LyA and Ly Cont. per baryon (from LB05) - self.N_alpha_perbaryon_III=Nalpha_lyA_III #number of photons between LyA and Ly Cont. per baryon (from LB05) - - #number of ionizing photons per baryon - self.N_ion_perbaryon_II = 5000 #fixed for PopII-type (Salpeter) - if(Cosmo_Parameters.Flag_emulate_21cmfast==True): - self.N_ion_perbaryon_III = 44000 #fixed for PopIII-type, from Klessen & Glover 2023 Table A2 (2303.12500) - elif(Cosmo_Parameters.Flag_emulate_21cmfast==False): - self.N_ion_perbaryon_III = 52480 #fixed for PopIII-type, from Klessen & Glover 2023 Table A2 (2303.12500) - - #number of LW photons per baryon - if(Cosmo_Parameters.Flag_emulate_21cmfast==False): - self.N_LW_II = 6200.0 #assuming BL05 stellar spectrum, equal to N_alpha_perbaryon_II * fraction of photons that fall in the LW band - self.N_LW_III = 12900.0 #assuming Intermediate IMF from 2202.02099, equal to 4.86e-22 / (11.9 * u.eV).to(u.erg).value * 5.8e14 - - elif(Cosmo_Parameters.Flag_emulate_21cmfast==True): - popIIIcorrection = 0.7184627927009317/6.5 #scaling used by 21cmfast to get correct number of Pop III LW photons per baryon - self.N_LW_III = popIIIcorrection * self.N_alpha_perbaryon_III - - popIIcorrection = 0.6415670418531249/2.5 #scaling used by 21cmfast to get correct number of Pop II LW photons per baryon - self.N_LW_II = popIIcorrection * self.N_alpha_perbaryon_II +@dataclass(kw_only=True) +class LF_Parameters: + """ + Luminosity functions parameters for zeus21. + Parameters + ---------- + zcenter: float + Redshift bin center at which to compute the luminosity functions. Default is 6.0. + zwidth: float + Redshift bin width at which to compute the luminosity functions. Default is 0.5 + RETURNLF: bool + Whether to compute LFs. Default is True. + RETURNBIAS: bool + Whether to compute bias. Default is False. + SKIP_POPII : bool + If True, skip the Pop II component. Default is False. + SKIP_POPIII : bool + If True, skip the Pop III component. Default is True. + SKIP_TOT : bool + If True, skip the summed component. Default is False. + FLAG_COMPUTE_UVLF: bool + Whether to compute the UV LF/bias. Default is True. + MUVcenters: np.ndarray | float + M_UV bin centers at which to compute the luminosity functions. Default is np.linspace(-23,-14,100). + MUVwidths: np.ndarray | float + M_UV bin width at which to compute the luminosity functions. Default is 0.5. + FLAG_RENORMALIZE_LUV + Whether to renormalize the lognormal LUV with sigmaUV to recover or otherwise . Default is False (recommended). + _kappaUV : float = 1.15e-28. + SFR-to-UV conversion factor in Msun/yr per erg/s/Hz. + sigmaUV: float + Stochasticity (gaussian rms) in the halo-galaxy connection P(MUV | Mh). Default is 0.5. + UV_boost_III : float + Pop III main-component UV conversion factor relative to Pop II. Default is 1. + _kappaUV_III : float = kappaUV/UV_boost_III. + SFR-to-UV conversion factor in Msun/yr per erg/s/Hz for Pop III main component. + sigmaUV_III : float + MUV scatter for the Pop III main component. Default is 0.5. + DUST_FLAG_III: bool + Whether to include dust attenuation to the LF calculations for Pop III main component. Default is False. + UV_boost_III_ACH : float + Pop III detached atomic-cooling-halo UV conversion factor relative to Pop II. Default is 1. + _kappaUV_III_ACH : float = kappaUV/UV_boost_III_ACH. + SFR-to-UV conversion factor in Msun/yr per erg/s/Hz for Pop III ACH component. + sigmaUV_III_ACH : float + MUV scatter for the detached Pop III ACH component. Default is 0.5. + DUST_FLAG_III_ACH: bool + Whether to include dust attenuation to the LF calculations for Pop III ACH component. Default is False. + FLAG_COMPUTE_HaLF: bool + Whether to compute the Ha LF/bias. Default is False. + log10LHacenters: np.ndarray | float + Ha bin centers at which to compute the luminosity functions, given in log10. Default is np.linspace(38,45,10). + log10LHawidths: np.ndarray | float + Ha bin width at which to compute the luminosity functions, given in log10. Default is 0.5. + DUST_FLAG: bool + Whether to include dust attenuation to the LF calculations. Default is True. + DUST_model: str + Which dust model to use. Default is "Bouwens13". Can also be "Zhao24" (https://arxiv.org/pdf/2401.07893.pdf, table 1). + HIGH_Z_DUST: bool + Whether to do dust at higher z than 0 or set to 0. Fix at beta(z=_zmaxdata) result if so. Default is True. + _zmaxdata : float + Maximum calibration redshift for the dust model. Default is 8.0. + C0dust: float + Calibration parameter for the dust correction for UVLF. Default is 4.43 (following Meurer+99). Input 4.54 for Overzier+01. + C1dust: float + Calibration parameter for the dust correction for UVLF. Default 1.99 for Meurer99. Input 2.07 for Overzier+01. + sigma_times_AUV_dust: float + If not 0, normalization factor to the sigma UV of dust. Default is 0.0. + """ - if(Mturn_fixed == None): #The FIXED/SHARP routine below only applies to Pop II, not to Pop III - self.FLAG_MTURN_FIXED = False #whether to fix Mturn or use Matom(z) at each z - else: - self.FLAG_MTURN_FIXED = True #whether to fix Mturn or use Matom(z) at each z - self.Mturn_fixed = Mturn_fixed - self.FLAG_MTURN_SHARP = FLAG_MTURN_SHARP #whether to do sharp cut at Mturn_fixed or regular exponential cutoff. Only active if FLAG_MTURN_FIXED and turned on by hand. - - #dust parameters for UVLFs: - self.C0dust, self.C1dust = C0dust, C1dust #4.43, 1.99 is Meurer99; 4.54, 2.07 is Overzier01 - self._kappaUV = 1.15e-28 #SFR/LUV, value from Madau+Dickinson14, fully degenerate with epsilon - self._kappaUV_III = self._kappaUV #SFR/LUV for PopIII. Assume X more efficient than PopII - - # SarahLibanore - to use second order in SFR lognormal - if not quadratic_SFRD_lognormal: - self.quadratic_SFRD_lognormal = quadratic_SFRD_lognormal - else: - if not USE_POPIII and not Cosmo_Parameters.Flag_emulate_21cmfast: - self.quadratic_SFRD_lognormal = quadratic_SFRD_lognormal - else: - if USE_POPIII: - print('Quadratic SFRD not yet implemented when USE_POPIII = True; the code will use quadratic_SFRD_lognormal = False') - if Cosmo_Parameters.Flag_emulate_21cmfast: - print('Quadratic SFRD not yet implemented when Flag_emulate_21cmfast = True; the code will use quadratic_SFRD_lognormal = False') - self.quadratic_SFRD_lognormal = False - - if min_t_formation_Myr is not None: - if (not np.isscalar(min_t_formation_Myr) - or not np.isfinite(min_t_formation_Myr) - or min_t_formation_Myr <= 0): - raise ValueError("min_t_formation_Myr must be None or a strictly positive finite number.") - self.min_t_formation_Myr = min_t_formation_Myr #Minimum formation time of galaxies in Myr for UVLF, sets a minimum M*dot = M*/t_formation with fstar = 1 - - - def SED_XRAY(self, En, pop = 0): #pop set to zero as default, but it must be set to either 2 or 3 - "SED of our Xray sources, normalized to integrate to 1 from E0_xray to Emax_xray (int dE E * SED(E), and E*SED is the power-law with index alpha_xray, so the output is divided by 1/E at the end to return number). Takes energy En in eV" - if pop == 2: - alphaX = self.alpha_xray - elif pop == 3: - alphaX = self.alpha_xray_III - else: - print("Must set pop to either 2 or 3!") - - if np.abs(alphaX + 1.0) < 0.01: #log - norm = 1.0/np.log(self.Emax_xray_norm/self.E0_xray) / self.E0_xray - else: - norm = (1.0 + alphaX)/((self.Emax_xray_norm/self.E0_xray)**(1 + alphaX) - 1.0) / self.E0_xray - - return np.power(En/self.E0_xray, alphaX)/En * norm * np.heaviside(En - self.E0_xray, 0.5) - #do not cut at higher energies since they redshift into <2 keV band - - def SED_LyA(self, nu_in, pop = 0): #default pop set to zero so python doesn't complain, but must be 2 or 3 for this to work - "SED of our Lyman-alpha-continuum sources, normalized to integrate to 1 (int d nu SED(nu), so SED is number per units energy (as opposed as E*SED, what was for Xrays) " - - nucut = constants.freqLyB #above and below this freq different power laws - if pop == 2: - amps = np.array([0.68,0.32]) #Approx following the stellar spectra of BL05. Normalized to unity - indexbelow = 0.14 #if one of them zero worry about normalization - normbelow = (1.0 + indexbelow)/(1.0 - (constants.freqLyA/nucut)**(1 + indexbelow)) * amps[0] - indexabove = -8.0 - normabove = (1.0 + indexabove)/((constants.freqLyCont/nucut)**(1 + indexabove) - 1.0) * amps[1] - elif pop == 3: - amps = np.array([0.56,0.44]) #Approx following the stellar spectra of BL05. Normalized to unity - indexbelow = 1.29 #if one of them zero worry about normalization - normbelow = (1.0 + indexbelow)/(1.0 - (constants.freqLyA/nucut)**(1 + indexbelow)) * amps[0] - indexabove = 0.2 - normabove = (1.0 + indexabove)/((constants.freqLyCont/nucut)**(1 + indexabove) - 1.0) * amps[1] + zcenter: float = 6. + zwidth: float = 0.5 + + ### Flags for computing LFs and bias for available populations + RETURNLF: bool = True + RETURNBIAS: bool = False + SKIP_POPII: bool = False + SKIP_POPIII: bool = True + SKIP_TOT: bool = False + + ### General UVLF parameters + FLAG_COMPUTE_UVLF: bool = True + MUVcenters: np.ndarray | float = _field(default_factory=lambda: np.linspace(-23,-14,100)) + MUVwidths: np.ndarray | float = 0.5 + FLAG_RENORMALIZE_LUV: bool = False # Whether to renormalize the lognormal LUV with sigmaUV to recover or otherwise . Recommend False. + _kappaUV: float = _field(init=False) # in SFR/LUV. Set by zeus21 to the value from Madau+Dickinson14, fully degenerate with epsilon + sigmaUV: float = 0.5 + + ### PopIII UVLF parameters (main component) + UV_boost_III: float = 1. + _kappaUV_III: float = _field(init=False) # in SFR/LUV for PopIII. Set by zeus21 to be a factor UV_boost_III times more efficient than PopII. + sigmaUV_III: float = 0.5 + DUST_FLAG_III: bool = False + + ### PopIII UVLF parameters (ACH component) + UV_boost_III_ACH: float = 1. + _kappaUV_III_ACH: float = _field(init=False) # in SFR/LUV for PopIII. Set by zeus21 to be a factor UV_boost_III_ACH times more efficient than PopII. + sigmaUV_III_ACH: float = 0.5 + DUST_FLAG_III_ACH: bool = False + + ### Halpha LF parameters + log10LHacenters: np.ndarray | float = _field(default_factory=lambda: np.linspace(38,45,10)) + log10LHawidths: np.ndarray | float = 0.5 + FLAG_COMPUTE_HaLF: bool = False + + ### Dust parameters for UVLFs + DUST_FLAG: bool = True + DUST_model: str = 'Bouwens13' + HIGH_Z_DUST: bool = True + _zmaxdata: float = 8.0 + C0dust: float = 4.43 + C1dust: float = 1.99 #4.43, 1.99 is Meurer99; 4.54, 2.07 is Overzier01 + sigma_times_AUV_dust: float = 0. + + def __post_init__(self): + schema = { + "RETURNLF": (bool, None), + "RETURNBIAS": (bool, None), + "FLAG_COMPUTE_UVLF": (bool, None), + "FLAG_COMPUTE_HaLF": (bool, None), + "FLAG_RENORMALIZE_LUV": (bool, None), + "DUST_FLAG": (bool, None), + "HIGH_Z_DUST": (bool, None), + "DUST_model": (str, {"Bouwens13", "Zhao24"}), + } + validate_fields(self, schema) + + + # --- normalize MUV --- + if np.isscalar(self.zcenter): + self.MUVcenters = np.array(self.MUVcenters, dtype=float) else: - print("Must set pop to 2 or 3!") - - nulist = np.asarray([nu_in]) if np.isscalar(nu_in) else np.asarray(nu_in) + self.MUVcenters = np.atleast_1d(self.MUVcenters).astype(float) - result = np.zeros_like(nulist) - for inu, currnu in enumerate(nulist): - if (currnu=constants.freqLyCont): - result[inu] = 0.0 - elif (currnu < nucut): #between LyA and LyB - result[inu] = normbelow * (currnu/nucut)**indexbelow - elif (currnu >= nucut): #between LyB and Continuum - result[inu] = normabove * (currnu/nucut)**indexabove - else: - print("Error in SED_LyA, whats the frequency Kenneth?") + # --- normalize MUVwidth --- + if np.isscalar(self.MUVwidths): + # broadcast scalar to same length as zcenter + self.MUVwidths = np.full_like(self.MUVcenters, self.MUVwidths, dtype=float) + else: + self.MUVwidths = np.atleast_1d(self.MUVwidths).astype(float) + # --- consistency check --- + if self.MUVwidths.shape != self.MUVcenters.shape: + raise ValueError( + f"MUVwidth shape {self.MUVwidths.shape} does not match MUVcenter shape {self.MUVcenters.shape}" + ) - return result/nucut #extra 1/nucut because dnu, normalizes the integral - + # --- normalize logLHa --- + if np.isscalar(self.log10LHacenters): + self.log10LHacenters = np.array([self.log10LHacenters], dtype=float) + else: + self.log10LHacenters = np.atleast_1d(self.log10LHacenters).astype(float) - -###HAC: Original SED_LyA -# def SED_LyA(self, nu_in): -# "SED of our Lyman-alpha-continuum sources, normalized to integrate to 1 (int d nu SED(nu), so SED is number per units energy (as opposed as E*SED, what was for Xrays) " -# -# nucut = constants.freqLyB #above and below this freq different power laws -# amps = np.array([0.68,0.32]) #Approx following the stellar spectra of BL05. Normalized to unity -# -# indexbelow = 0.14 #if one of them zero worry about normalization -# normbelow = (1.0 + indexbelow)/(1.0 - (constants.freqLyA/nucut)**(1 + indexbelow)) * amps[0] -# indexabove = -8.0 -# normabove = (1.0 + indexabove)/((constants.freqLyCont/nucut)**(1 + indexabove) - 1.0) * amps[1] -# -# nulist = np.asarray([nu_in]) if np.isscalar(nu_in) else np.asarray(nu_in) -# -# result = np.zeros_like(nulist) -# for inu, currnu in enumerate(nulist): -# if (currnu=constants.freqLyCont): -# result[inu] = 0.0 -# elif (currnu < nucut): #between LyA and LyB -# result[inu] = normbelow * (currnu/nucut)**indexbelow -# elif (currnu >= nucut): #between LyB and Continuum -# result[inu] = normabove * (currnu/nucut)**indexabove -# else: -# print("Error in SED_LyA, whats the frequency Kenneth?") -# -# -# return result/nucut #extra 1/nucut because dnu, normalizes the integral + # --- normalize logHazwidth --- + if np.isscalar(self.log10LHawidths): + # broadcast scalar to same length as zcenter + self.log10LHawidths = np.full_like(self.log10LHacenters, self.log10LHawidths, dtype=float) + else: + self.log10LHawidths = np.atleast_1d(self.log10LHawidths).astype(float) + + # --- consistency check --- + if self.log10LHawidths.shape != self.log10LHacenters.shape: + raise ValueError( + f"log10Hawidth shape {self.log10LHawidths.shape} does not match log10Hacenter shape {self.log10LHacenters.shape}" + ) + + ### Parameters for SFR-to-LUV conversion + self._kappaUV = 1.15e-28 # SFR/LUV, value from Madau+Dickinson14, fully degenerate with epsilon + self._kappaUV_III = self._kappaUV / self.UV_boost_III # SFR/LUV for PopIII main component + self._kappaUV_III_ACH = self._kappaUV / self.UV_boost_III_ACH # SFR/LUV for PopIII ACH component + + +def validate_fields(obj, schema: dict): + """ Helper function to check whether the input parameters are set with the proper values. """ + for field, (expected_type, allowed_values) in schema.items(): + value = getattr(obj, field) + + if not isinstance(value, expected_type): + raise TypeError( + f"{field} must be of type {expected_type.__name__}, got {type(value).__name__}" + ) + + if allowed_values is not None and value not in allowed_values: + raise ValueError( + f"{field} must be one of {allowed_values}, got '{value}'" + ) diff --git a/zeus21/maps.py b/zeus21/maps.py index e8bcd4c..2c7ff04 100644 --- a/zeus21/maps.py +++ b/zeus21/maps.py @@ -1,125 +1,794 @@ """ - -Make maps! For fun and science - -Author: Julian B. Muñoz -UT Austin - August 2024 - +Make maps! For fun and science. + +Authors: zeus21 v2 collaboration - June 2026 + Emily Bregou ; + Hector Afonso G. Cruz ; + Sarah Libanore ; + Julian B. Muñoz ; + Yonny Sklansky ; + Emilie Thélie ; + Alessandra Venditti +arXiv:2302.08506, arXiv:2306.09403, arXiv:2407.18294, Sklansky et al. (in prep) """ from . import cosmology -from . import constants +from . import z21_utilities +from . import inputs +from . import T21coefficients +from . import correlations import numpy as np import powerbox as pbox from scipy.interpolate import interp1d -from pyfftw import empty_aligned as empty - - -class CoevalMaps: - "Class that calculates and keeps coeval maps, one z at a time." - - def __init__(self, T21_coefficients, Power_Spectrum, z, Lbox=600, Nbox=200, KIND=None, seed=1605): - 'the KIND flag determines the kind of map you make. Options are:' - 'KIND = 0, only T21 lognormal. OK approximation' - 'KIND = 1, density and T21 correlated. T21 has a gaussian and a lognormal component. Decent approximation' - 'KIND = 2, all maps' - 'KIND = 3, same as 2 but integrating over all R. Slow but most accurate' - - zlist = T21_coefficients.zintegral - _iz = min(range(len(zlist)), key=lambda i: np.abs(zlist[i]-z)) #pick closest z - self.T21global = T21_coefficients.T21avg[_iz] - self.Nbox = Nbox - self.Lbox = Lbox +from scipy.interpolate import InterpolatedUnivariateSpline as spline +from tqdm import trange +import time +from dataclasses import dataclass, field as _field, InitVar +import copy + + +@dataclass(kw_only=True) +class ReioMapsConfig: + """ + All arguments of reionization_maps that have default values + """ + input_boxlength: float = 300. + ncells: int = 300 + seed: int = 1234 + r_precision: float = 1. + Rs: list | np.ndarray | None = None + barrier: np.ndarray = None + PRINT_TIMER: bool = True + LOGNORMAL_DENSITY: bool = False + COMPUTE_DENSITY_AT_ALLZ: bool = False + COMPUTE_MASSWEIGHTED: bool = False + lowres_massweighting: int = 1 + COMPUTE_PARTIAL_IONIZATIONS: bool = False + COMPUTE_ZREION: bool = False + + + +class reionization_maps: + """ + Generates 3D maps of the reionization fields. + + Uses a density threshold barrier determined from a converged bubble mass function. With default parameters, the code takes about 20 minutes on laptop to run. + + Parameters + ---------- + CosmoParams: zeus21.Cosmo_Parameters class + Stores cosmology. + CoeffStructure: zeus21.get_T21_coefficients class + Stores sfrd and 21cm coefficients. + input_z: 1D np.array + The redshifts at which to compute output maps. Narrowed down later to select available redshifts from CoeffStructure.zintegral. + input_boxlength: float + Comoving physical side length of the box. Default is 300 cMpc. + ncells: int + Number of cells on a side. Default is 300 cells. + seed: int + Sets the predetermined generation of maps. Default is 1234. + r_precision: float + Allows to change the steps of the radii for faster computation. Default (and max) is 1, lower values make the computation faster at the cost of accuracy. + barrier: function + Input density barrier to be used as the threshold for map generation. Takes z value as input and returns np.array of shape. Default is None. + PRINT_TIMER: bool + Whether to print the time elapsed along the process. Default is True. + LOGNORMAL_DENSITY: bool + Whether to use lognormal (True) or Gaussian (False) density fields. Default is False. + COMPUTE_DENSITY_AT_ALLZ: bool + Whether to output the density field at all redshifts. If False, only the density at the lower input redshift is computed. If True, the computation time and memory usage dramatically increases. Default is False. + COMPUTE_MASSWEIGHTED: bool + Whether to compute the mass weighted ionized field and fraction. If True, COMPUTE_DENSITY_AT_ALLZ will be forced to True, thus increasing computation time dramatically. Default is False. + lowres_massweighting: int + Compute the mass-weighted ionized field and fraction more efficiently by using lower resolution density and ionized fields. Has to be >=1 and an integer. Default is 1. + COMPUTE_PARTIAL_IONIZATIONS: bool + Whether to compute the subpixel ionizations in the field and the ionized fractions. + + Attributes + ---------- + dx: float + Cell resolution of a side of the boxes. + z: 1D np.array + Redshifts at which the output maps are computed. Selected to be the closest to the input redshifts from the available ones in zeus21. + r: 1D np.array + Radii at which the density field is smoothed. Selected using r_precision from the available ones in zeus21. + z_of_density: float + Redshift at which the density is computed. + density: 3D np.array + Overdensity field at the lowest redshift asked by the user. + density_allz: 4D np.array + Overdensity field at all the redshifts asked by the user. First dimension correponds to redshifts. Only computed if COMPUTE_DENSITY_AT_ALLZ is True. + ion_field_allz: 4D np.array + Ionized fraction field at all the redshifts asked by the user. First dimension correponds to redshifts. + ion_frac: 1D np.array + Volume weighted ionized fraction at all the redshifts asked by the user. + ion_frac_massweighted: 1D np.array + Mass weighted ionized fraction at all the redshifts asked by the user. Only computed if COMPUTE_MASSWEIGHTED is True. + """ + + def __init__(self, CosmoParams, CoeffStructure, input_z, + input_boxlength=300., ncells=300, seed=1234, r_precision=1., Rs=None, barrier=None, + PRINT_TIMER=True, + LOGNORMAL_DENSITY=False, COMPUTE_DENSITY_AT_ALLZ=False, + COMPUTE_MASSWEIGHTED=False, lowres_massweighting=1, COMPUTE_PARTIAL_IONIZATIONS=False, + COMPUTE_ZREION=False + ): + #Measure time elapsed from start + self._start_time = time.time() + + ### boxes parameters + self.input_z = input_z + self.ncells = ncells + self.boxlength = input_boxlength + self.dx = self.boxlength/self.ncells + + # radii + if Rs is None: + default_len = len(CosmoParams._Rtabsmoo) + self.r_precision = r_precision + self.r = np.logspace(np.log10(self.dx * (3/4/np.pi)**(1/3)), np.log10(self.boxlength), int(default_len*self.r_precision)) + self._r_idx = np.arange(int(default_len*self.r_precision)) + else: + self.r_precision = r_precision + self.r = Rs + if self.r_precision > 1: + raise ValueError('r_precision cannot be greater than 1 if you input your own radii.') + self._r_idx = np.floor(np.arange(len(self.r), step=self.r_precision)).astype(int) + smallest_r = self.dx * (3/4/np.pi)**(1/3) + if self.r[0] < smallest_r: + print(f'WARNING: Your input radii are too small for the pixel size. The code will still run now.\nIn the future, for best performance and physical accuracy on this boxlength and ncells, the smallest smoothing radius should be no less than R=L/N * (4pi/3)^(-1/3), or approximately {smallest_r:.2f} cMpc.') + self.seed = seed - self.z = zlist[_iz] #will be slightly different from z input - - klist = Power_Spectrum.klist_PS - k3over2pi2 = klist**3/(2*np.pi**2) - - - if (KIND == 0): #just T21, ~gaussian - - P21 = Power_Spectrum.Deltasq_T21_lin[_iz]/k3over2pi2 - P21norminterp = interp1d(klist,P21/self.T21global**2,fill_value=0.0,bounds_error=False) + ### FLAGS + self.PRINT_TIMER = PRINT_TIMER + self.LOGNORMAL_DENSITY = LOGNORMAL_DENSITY + self.COMPUTE_DENSITY_AT_ALLZ = COMPUTE_DENSITY_AT_ALLZ + self._has_density = COMPUTE_DENSITY_AT_ALLZ + self.COMPUTE_MASSWEIGHTED = COMPUTE_MASSWEIGHTED + self.COMPUTE_PARTIAL_IONIZATIONS = COMPUTE_PARTIAL_IONIZATIONS + self.COMPUTE_PARTIAL_AND_MASSWEIGHTED = False + if self.COMPUTE_MASSWEIGHTED and self.COMPUTE_PARTIAL_IONIZATIONS: + self.COMPUTE_PARTIAL_AND_MASSWEIGHTED = True + self.COMPUTE_ZREION = COMPUTE_ZREION + if self.COMPUTE_MASSWEIGHTED or self.COMPUTE_PARTIAL_IONIZATIONS or self.COMPUTE_PARTIAL_AND_MASSWEIGHTED: + self.COMPUTE_DENSITY_AT_ALLZ = True + + ### selecting redshifts and radii from available redshifts + # redshifts + self._z_idx = np.arange(len(np.atleast_1d(input_z))) #z21_utilities.find_nearest_idx(CoeffStructure.zintegral, self.input_z) + self.z = np.atleast_1d(input_z) #CoeffStructure.zintegral[self._z_idx] + + ### generating the density field at the closest redshift to the lower one inputed + self.z_of_density = self.z[0] + self.density = self.generate_density(CosmoParams) + self.sig_corr = self.sigma_correction(CosmoParams) + self.density /= self.sig_corr #non-ergodicity correction + + ### smoothing the density field + self._k = self.compute_k() + self.density_smoothed_allr = self.smooth_density() + + ### evolving density + self.density_allz = np.empty((len(self.z), self.ncells, self.ncells, self.ncells), dtype=np.float32) + if self.COMPUTE_DENSITY_AT_ALLZ: + self.generate_density_allz(CosmoParams) + + ### generating the ionized field, and computing the ionized fraction + self.barrier = barrier + if self.barrier is None: + self.barrier = CoeffStructure.B(self.z, self.r) #BMF linear barrier + self.ion_field_allz, self.ion_frac = self.generate_xHII(CosmoParams) + + ### computing the mass weighted ionized fraction + + self._has_mw = False + self.lowres_massweighting = lowres_massweighting + if self.COMPUTE_MASSWEIGHTED: + self.compute_massweighted(CosmoParams, self.lowres_massweighting) + + self._has_p = False + if self.COMPUTE_PARTIAL_IONIZATIONS: + self.compute_partial(CosmoParams, CoeffStructure) + + self._has_mwp = False + if self.COMPUTE_PARTIAL_AND_MASSWEIGHTED: + self.compute_partial_massweighted(CosmoParams, CoeffStructure) + + if self.COMPUTE_ZREION: + self.zreion = self.compute_zreion_frombinaryxHII() + self.treion = self.compute_treion(CosmoParams) + + + if self.PRINT_TIMER: + z21_utilities.print_timer(self._start_time, text_before="Total computation time: ") + + + def generate_density(self, CosmoParams): + """ + Generates the initial density field at the lowest redshift. + + Parameters + ---------- + CosmoParams : CosmoParams class + + Returns + ------- + density_field : array + Three-dimensional delta field evaluated at self.z_of_density. + """ + if self.PRINT_TIMER: + start_time = time.time() + print("Generating density field...") + #generating matter power spectrum at the lowest redshift + klist = CosmoParams._klistCF + pk_matter = np.zeros_like(klist) + for i, k in enumerate(klist): + pk_matter[i] = CosmoParams.ClassCosmo.pk(k, self.z_of_density) + pk_spl = spline(np.log(klist), np.log(pk_matter)) + + #generating density map + if self.LOGNORMAL_DENSITY: + pb = pbox.LogNormalPowerBox(N=self.ncells, dim=3, pk=(lambda k: np.exp(pk_spl(np.log(k)))), boxlength=self.boxlength, seed=self.seed) + else: + pb = pbox.PowerBox(N=self.ncells, dim=3, pk=(lambda k: np.exp(pk_spl(np.log(k)))), boxlength=self.boxlength, seed=self.seed) + density_field = pb.delta_x().astype(np.float32, copy=False) + if self.PRINT_TIMER: + z21_utilities.print_timer(start_time, text_before=" done in ") + return density_field + + def generate_density_allz(self, CosmoParams): + """ + Evolves the density field to all redshifts using the linear growth factor. + + Parameters + ---------- + CosmoParams : CosmoParams class + + Returns + ------- + density_allz : array + Four-dimensional delta field. The first dimension is z. + """ + if self.PRINT_TIMER: + start_time = time.time() + print('Evolving density field...') + Dg = CosmoParams.growthint(self.z) + growthfactor_ratio = (Dg/Dg[0])[:, np.newaxis, np.newaxis, np.newaxis] + density_lastz = np.copy(self.density) + self.density_allz = density_lastz[np.newaxis]*growthfactor_ratio + if self.PRINT_TIMER: + z21_utilities.print_timer(start_time, text_before=" done in ") + + self._has_density = True + + return self.density_allz + + def compute_k(self): + """ + Computes the Fourier-space wavenumber grid for the box. + + Returns + ------- + k : array + Three-dimensional array of wavenumber magnitudes. + """ + klistfftx = np.fft.fftfreq(self.ncells,self.dx)*2*np.pi + k = np.sqrt(np.sum(np.meshgrid(klistfftx**2, klistfftx**2, klistfftx**2, indexing='ij'), axis=0)) + return k + + def smooth_density(self): + """ + Smooths the density field over all smoothing radii. + + Returns + ------- + density_smoothed_allr : array + Density field smoothed at each radius in self.r. + """ + if self.PRINT_TIMER: + start_time = time.time() + print("Smoothing density field...") + + #smooth by FFT convolution with a tophat + density_fft = np.fft.fftn(self.density) + density_smoothed_allr = np.array([z21_utilities.tophat_smooth(rr, self._k, density_fft) for rr in self.r]) + if self.PRINT_TIMER: + z21_utilities.print_timer(start_time, text_before=" done in ") + return density_smoothed_allr + + def sigma_correction(self, CosmoParams): + """ + Computes the non-ergodicity correction to the generated density variance. + + Parameters + ---------- + CosmoParams : CosmoParams class + + Returns + ------- + sigma_ratio : float + Ratio between the measured and theoretical sigma. + """ + sigma_ratio = np.std(self.density)/CosmoParams.ClassCosmo.sigma(self.r[0], self.z_of_density) + return sigma_ratio + + def generate_xHII(self, CosmoParams): + """ + Generates ionized fraction fields and volume-weighted ionized fractions. + + Parameters + ---------- + CosmoParams : CosmoParams class + + Returns + ------- + ion_field_allz : array + Ionized fraction field at each redshift. + ion_frac : array + Volume-weighted ionized fraction at each redshift. + """ + if self.PRINT_TIMER: + start_time = time.time() + print("Generating ionized field...") + ion_field_allz = np.zeros((len(self.z),self.ncells,self.ncells,self.ncells)) + ion_frac = np.zeros(len(self.z)) + + #choose iterator based on if the user wants to print progress or not. + iterator = trange(len(self.z)) if self.PRINT_TIMER else range(len(self.z)) + + for i in iterator: + curr_z_idx = self._z_idx[i] + ion_field = self.ionize(CosmoParams, curr_z_idx) + ion_field_allz[i] = ion_field + ion_frac[i] = np.sum(ion_field)/(self.ncells**3) + if self.PRINT_TIMER: + z21_utilities.print_timer(start_time, text_before=" done in ") + return ion_field_allz, ion_frac + + def ionize(self,CosmoParams, curr_z_idx): + """ + Computes the binary ionized field at a single redshift. + + Parameters + ---------- + CosmoParams : CosmoParams class + curr_z_idx : int + Index of the redshift at which to compute the ionized field. + + Returns + ------- + ion_field : array + Binary ionized field at curr_z. + """ + Dg0 = CosmoParams.growthint(self.z[0]) + Dg = CosmoParams.growthint(self.z[curr_z_idx]) + Dg0_Dg = Dg0/Dg + ion_field = np.any(self.density_smoothed_allr > (Dg0_Dg)*self.barrier[curr_z_idx, self._r_idx][:, None, None, None], axis=0) + + #Earlier versions of this code contained a spherize method in addition to this central pixel flagging, where spheres are ionized instead of just the central pixel. + #We found that central pixel flagging is generally more consistent with the bubble mass function than spherizing, so future versions will not include this. + + return ion_field + + def compute_massweighted(self, CosmoParams, lowres_massweighting=1): + """ + Computes the mass-weighted ionized field and ionized fraction. + + Parameters + ---------- + CosmoParams : CosmoParams class + lowres_massweighting : int, optional + Factor by which to downsample the density and ionized fields for mass weighting. Default is 1. + + Returns + ------- + ion_frac_massweighted : array + Mass-weighted ionized fraction at each redshift. + ion_field_massweighted_allz : array + Mass-weighted ionized field at each redshift. + """ + if not self._has_mw: + self.ion_frac_massweighted = np.empty(len(self.z)) + self.ion_field_massweighted_allz = np.empty_like(self.ion_field_allz) + if not self._has_density: + self.generate_density_allz(CosmoParams) + self.lowres_massweighting = lowres_massweighting + if self.lowres_massweighting < 1: + raise Exception('lowres_massweighting should be >=1.') + if not isinstance(self.lowres_massweighting, (int, np.int32, np.int64)): + raise Exception('lowres_massweighting should be an integer.') + d_allz = self.density_allz[:, ::self.lowres_massweighting, ::self.lowres_massweighting, ::self.lowres_massweighting] + ion_allz = self.ion_field_allz[:, ::self.lowres_massweighting, ::self.lowres_massweighting, ::self.lowres_massweighting] + if self.PRINT_TIMER: + start_time = time.time() + print("Computing mass-weighted field...") + #where the magic happens + self.ion_field_massweighted_allz = (1+d_allz) * ion_allz + if self.PRINT_TIMER: + print("Computing mass-weighted ionized fraction...") + self.ion_frac_massweighted = np.average(self.ion_field_massweighted_allz, axis=(1, 2, 3)) + + if self.PRINT_TIMER: + z21_utilities.print_timer(start_time, text_before=" done in ") + + self._has_mw = True + + return self.ion_frac_massweighted, self.ion_field_massweighted_allz + + def compute_partial(self, CosmoParams, CoeffStructure, r=None): + """ + Computes the partially ionized field and volume-weighted partially ionized fraction. + + Parameters + ---------- + CosmoParams : CosmoParams class + CoeffStructure : get_T21_coefficients class + r : float, optional + Smoothing radius in cMpc used to evaluate the prebarrier ionized fraction. Default is None, in which case self.r[0] is used. + + Returns + ------- + ion_frac_partial : array + Volume-weighted partially ionized fraction at each redshift. + ion_field_partial_allz : array + Partially ionized field at each redshift. + """ + if r is None: + r = self.r[0] + if not self._has_p: + self.ion_frac_partial = np.empty(len(self.z)) + self.ion_field_partial_allz = np.empty_like(self.ion_field_allz) + if not self._has_density: + self.generate_density_allz(CosmoParams) + sample_d = np.linspace(-5, 5, 51) + + if self.PRINT_TIMER: + start_time = time.time() + print("Computing partially ionized field...") + + #loop over each z. + out_shape = self.density.shape + iterator = trange(len(self.z)) if self.PRINT_TIMER else range(len(self.z)) + for i in iterator: + #evaluate sample grid, and then input the actual density field into an interpolator. + tempgrid = CoeffStructure.prebarrier_xHII_int_grid(sample_d, self.z[i], r) + + partialfield = np.interp(self.density.ravel(), sample_d, tempgrid).reshape(out_shape) + + np.abs(partialfield, out=partialfield)#abs just in case, beacuse negative numbers here are unphysical + #add partials to binaries and then clip to 1. + np.add(self.ion_field_allz[i], partialfield, out=self.ion_field_partial_allz[i]) + np.clip(self.ion_field_partial_allz[i], 0, 1, out=self.ion_field_partial_allz[i]) - pb = pbox.PowerBox( - N=self.Nbox, - dim=3, - pk = lambda k: P21norminterp(k), - boxlength = self.Lbox, - seed = self.seed - ) + if self.PRINT_TIMER: + print("Computing partial ionized fraction...") - self.T21map = self.T21global * (1 + pb.delta_x() ) - self.deltamap = None + self.ion_frac_partial = np.average(self.ion_field_partial_allz, axis=(1, 2, 3)) + if self.PRINT_TIMER: + z21_utilities.print_timer(start_time, text_before=" done in ") + self._has_p = True - elif (KIND == 1): - Pd = Power_Spectrum.Deltasq_d_lin[_iz,:]/k3over2pi2 - Pdinterp = interp1d(klist,Pd,fill_value=0.0,bounds_error=False) - + return self.ion_frac_partial, self.ion_field_partial_allz + + def compute_partial_massweighted(self, CosmoParams, CoeffStructure, r=None): + """ + Computes the mass-weighted partially ionized field and fraction. + + Parameters + ---------- + CosmoParams : CosmoParams class + CoeffStructure : get_T21_coefficients class + r : float, optional + Smoothing radius in cMpc used to evaluate the prebarrier ionized fraction. Default is None, in which case self.r[0] is used. + + Returns + ------- + ion_frac_partial_massweighted : array + Mass-weighted partially ionized fraction at each redshift. + ion_field_partial_massweighted_allz : array + Mass-weighted partially ionized field at each redshift. + """ + if not self._has_p: + self.compute_partial(CosmoParams, CoeffStructure, r) + + if not self._has_mwp: + self.ion_frac_partial_massweighted = np.empty(len(self.z)) + self.ion_field_partial_massweighted_allz = np.empty_like(self.ion_field_allz) + + if self.PRINT_TIMER: + start_time = time.time() + print("Computing mass-weighted partially ionized field...") + + #where the magic happens + iterator = trange(len(self.z)) if self.PRINT_TIMER else range(len(self.z)) + for i in iterator: + self.ion_field_partial_massweighted_allz[i] = (1+self.density_allz[i]) * self.ion_field_partial_allz[i] + + if self.PRINT_TIMER: + print("Computing mass-weighted partial ionized fraction...") + + iterator = trange(len(self.z)) if self.PRINT_TIMER else range(len(self.z)) + for i in iterator: + self.ion_frac_partial_massweighted[i] = np.average(self.ion_field_partial_massweighted_allz[i]) + + if self.PRINT_TIMER: + z21_utilities.print_timer(start_time, text_before=" done in ") + + self._has_mwp = True + + return self.ion_frac_partial_massweighted, self.ion_field_partial_massweighted_allz + + def compute_zreion_frombinaryxHII(self): + """ + Computes the redshift-of-reionization map from the binary ionized fraction field. + + Returns + ------- + zreion : array + Three-dimensional map of the z at which each cell is first ionized. + """ + if self.PRINT_TIMER: + start_time = time.time() + print("Computing zreion map...") + + vectorized_zlist = np.vectorize(lambda iz: self.z[iz]) + zreion = vectorized_zlist(np.argmin(self.ion_field_allz,axis=0)-1).reshape((self.ncells,self.ncells,self.ncells)) + + if self.PRINT_TIMER: + z21_utilities.print_timer(start_time, text_before=" done in ") + return zreion + + def compute_treion(self,CosmoParams): + """ + Computes the time-of-reionization map from the redshift-of-reionization map. + + Parameters + ---------- + CosmoParams : CosmoParams class + + Returns + ------- + treion : array + Three-dimensional map of the time at which each cell becomes ionized. + """ + if self.PRINT_TIMER: + start_time = time.time() + print("Computing treion map...") + + treion = cosmology.time_at_redshift(CosmoParams.ClassCosmo,self.zreion) + + if self.PRINT_TIMER: + z21_utilities.print_timer(start_time, text_before=" done in ") + return treion + + def _compute_ionfrac_from_zreion(self): + """ + Way to compute the volume ionized fraction from zreion. Currently not used but there if needed. + """ + zvalues = np.unique(self.zreion) + neutfrac = np.zeros(len(zvalues)) + for i in range(len(zvalues)): + neutfrac[i] = np.sum(self.zreiontvalues[i]) / self.ncells**3 + return 1-neutfrac, tvalues + + +@dataclass() +class T21_maps: + # arguments to pass + CosmoParams: InitVar[inputs.Cosmo_Parameters] + CoeffStructure: InitVar[T21coefficients.get_T21_coefficients] + PowerSpectra: InitVar[correlations.Power_Spectra] + input_z: np.ndarray + + # reionization + ReioMaps_config: ReioMapsConfig = _field(default_factory=ReioMapsConfig) + ReioMaps: reionization_maps = _field(init=False) + + # flag + USE_xHII_MAPS: bool = _field(default=True) + COMPUTE_TAU: bool = _field(default=False) + + # box params + input_boxlength: float = _field(default=300.) + ncells: int = _field(default=300) + seed: int = _field(default=1234) + input_Resolution: float = _field(default=0.5) + + # boxes + density: np.ndarray = _field(init=False) + xHI: np.ndarray = _field(init=False) + xHI_massweighted: np.ndarray = _field(init=False) + tau: np.ndarray = _field(init=False) + T21_lin: np.ndarray = _field(init=False) + T21_NL: np.ndarray = _field(init=False) + T21: np.ndarray = _field(init=False) + smooth_box: bool = _field(default=False) + xHI_smooth: np.ndarray = _field(init=False) + T21_smooth: np.ndarray = _field(init=False) + + # other attributes + _klist: np.ndarray = _field(init=False) + _k3over2pi2: np.ndarray = _field(init=False) + T21avg: np.ndarray = _field(init=False) + _Dsq_T21_lin: np.ndarray = _field(init=False) + _Dsq_T21: np.ndarray = _field(init=False) + _PdT21: np.ndarray = _field(init=False) + _Pd: np.ndarray = _field(init=False) + + + def __post_init__(self, CosmoParams, CoeffStructure, PowerSpectra): + """ + Generates 21cm maps. + + Parameters + ---------- + CosmoParams : CosmoParams class + CoeffStructure : get_T21_coefficients class + PowerSpectra : Power_Spectra class + + Returns + ------- + None + """ + + ### z and k + _iz = z21_utilities.find_nearest_idx(CoeffStructure.zlist, self.input_z) + self._klist = PowerSpectra.klist_PS + self._k3over2pi2 = self._klist**3/(2*np.pi**2) + + ### get T21 avg + if self.USE_xHII_MAPS: + # in this case, we will use the simulated xHI with reionization_maps + # so, we need to remove the xHI contribution from T21avg + self.T21avg = (CoeffStructure.T21avg / (CoeffStructure.xHI_avg + 1e-15))[_iz] + else: + self.T21avg = CoeffStructure.T21avg[_iz] + + ### get power spectra + self._Dsq_T21_lin = ((PowerSpectra.Deltasq_T21_lin[_iz].T / CoeffStructure.T21avg[_iz]**2) * self.T21avg**2).T + self._Dsq_T21 = ((PowerSpectra.Deltasq_T21[_iz].T / CoeffStructure.T21avg[_iz]**2) * self.T21avg**2).T + self._PdT21 = (PowerSpectra.Deltasq_dT21[_iz]/CoeffStructure.T21avg[_iz])* self.T21avg/self._k3over2pi2 + self._Pd = (PowerSpectra.Deltasq_d_lin[_iz,:])/self._k3over2pi2 + + ### generate densities + self.density, pbs = self.generate_density_pb() + + + ### map of the linear T21 fluctuation, better to use the cross to keep sign, at linear level same + self.T21_lin = self.generate_T21_lin(pbs) + + ### map of the nonlinear correction + # built as \sum_R [e^(gR dR) - gR dR]. Uncorrelatd with all dR so just a separate field! + # NOTE: its not guaranteed to work, excess power can be negative in some cases! Not for each component xa, Tk, but yes for T21 + self.T21_NL = self.generate_T21_NL() + + ### add T21 lin and nonlin correction together + self.T21 = self.T21_lin + self.T21_NL + + if self.USE_xHII_MAPS: + ### generate xHII + self.ReioMaps_config.input_boxlength = self.input_boxlength + self.ReioMaps_config.ncells = self.ncells + self.ReioMaps_config.seed = self.seed + if self.COMPUTE_TAU: + self.ReioMaps = reionization_maps(CosmoParams, CoeffStructure, CoeffStructure.zintegral, **vars(self.ReioMaps_config)) + + ### include ionization + if self.ReioMaps_config.COMPUTE_PARTIAL_IONIZATIONS: + self.xHI_massweighted = (1. - self.ReioMaps.ion_frac_partial_massweighted) + else: + self.xHI_massweighted = (1. - self.ReioMaps.ion_frac_massweighted) + # !!! COMPUTE TAU + self.tau = CoeffStructure.tau_reio(CosmoParams, CoeffStructure.zintegral, self.xHI_massweighted) + + if self.ReioMaps_config.COMPUTE_PARTIAL_IONIZATIONS: + self.xHI = (1. - self.ReioMaps.ion_field_partial_allz[_iz]) + else: + self.xHI = (1. - self.ReioMaps.ion_field_allz[_iz]) + + else: + self.ReioMaps = reionization_maps(CosmoParams, CoeffStructure, self.input_z, **vars(self.ReioMaps_config)) + if self.ReioMaps_config.COMPUTE_PARTIAL_IONIZATIONS: + self.xHI = (1. - self.ReioMaps.ion_field_partial_allz) + else: + self.xHI = (1. - self.ReioMaps.ion_field_allz) + + self.T21 *= self.xHI + + self.T21[np.isnan(self.T21)] = 0. + + if self.smooth_box: + Resolution = max(self.input_Resolution, self.input_boxlength/self.ncells) + if self.USE_xHII_MAPS: + self.xHI_smooth = z21_utilities.smooth_box(self.xHI, Resolution, self.input_boxlength, self.ncells) + self.T21_smooth = z21_utilities.smooth_box(self.T21[0], Resolution, self.input_boxlength, self.ncells) + + + def generate_density_pb(self): + """ + Generates density fields using PowerBox. + + Returns + ------- + density : array + Density field at each redshift. + pbs : list + PowerBox objects that generate the density fields. + """ + density = np.zeros((len(self.input_z),self.ncells,self.ncells,self.ncells)) + pbs = [] + for iz, z in enumerate(self.input_z): + Pd_spl = spline(np.log(self._klist), np.log(self._Pd[iz])) # density at min z pb = pbox.PowerBox( - N=self.Nbox, - dim=3, - pk = lambda k: Pdinterp(k), - boxlength = self.Lbox, - seed = self.seed + N=self.ncells, + dim=3, + pk = lambda k: np.exp(Pd_spl(np.log(k))), + boxlength = self.input_boxlength, + seed = self.seed ) - - self.deltamap = pb.delta_x() #density map, basis of this KIND of approach - - #then we make a map of the linear T21 fluctuation, better to use the cross to keep sign, at linear level same - PdT21 = Power_Spectrum.Deltasq_dT21[_iz]/k3over2pi2 - - powerratioint = interp1d(klist,PdT21/Pd,fill_value=0.0,bounds_error=False) - - - deltak = pb.delta_k() - - powerratio = powerratioint(pb.k()) - T21lin_k = powerratio * deltak - self.T21maplin= self.T21global + powerboxCtoR(pb,mapkin = T21lin_k) - - #now make a nonlinear correction, built as \sum_R [e^(gR dR) - gR dR]. Uncorrelatd with all dR so just a separate field! - #NOTE: its not guaranteed to work, excess power can be negative in some cases! Not for each component xa, Tk, but yes for T21 - excesspower21 = (Power_Spectrum.Deltasq_T21[_iz,:]-Power_Spectrum.Deltasq_T21_lin[_iz,:])/k3over2pi2 - - lognormpower = interp1d(klist,excesspower21/self.T21global**2,fill_value=0.0,bounds_error=False) - #G or logG? TODO revisit - pbe = pbox.LogNormalPowerBox( - N=self.Nbox, - dim=3, - pk = lambda k: lognormpower(k), - boxlength = self.Lbox, + density[iz] = pb.delta_x() + pbs.append(pb) + return density, pbs + + def generate_T21_lin(self, pbs): + """ + Generates the linear 21cm temperature field. + + Parameters + ---------- + pbs : list + PowerBox objects. + + Returns + ------- + T21_lin : array + Linear 21cm brightness temperature field at each redshift. + """ + T21_lin = np.zeros((len(self.input_z),self.ncells,self.ncells,self.ncells)) + for iz, z in enumerate(self.input_z): + pb = pbs[iz] + powerratio_spl = spline(self._klist, self._PdT21[iz]/self._Pd[iz]) #cross can be negative, so can't interpolate over log values + powerratio = powerratio_spl(pb.k()) + T21lin_k = powerratio * pb.delta_k() + T21_lin[iz] = self.T21avg[iz] + z21_utilities.powerboxCtoR(pb, mapkin = T21lin_k) + pbs.append(pb) + + return T21_lin + + def generate_T21_NL(self): + """ + Generates the nonlinear correction to the 21cm temperature field. + + Returns + ------- + T21_NL : array + Nonlinear 21cm brightness temperature correction field at each redshift. + """ + T21_NL = np.zeros((len(self.input_z),self.ncells,self.ncells,self.ncells)) + for iz, z in enumerate(self.input_z): + excesspower21 = (self._Dsq_T21[iz]-self._Dsq_T21_lin[iz])/self._k3over2pi2 + lognormpower = interp1d(self._klist, excesspower21/self.T21avg[iz]**2, fill_value=0.0, bounds_error=False) + pbe = pbox.LogNormalPowerBox( #G or logG? TODO revisit + N=self.ncells, + dim=3, + pk = lambda k: lognormpower(k), + boxlength = self.input_boxlength, seed = self.seed+1 # uncorrelated ) - - self.T21mapNL = self.T21global*pbe.delta_x() - - #and finally, just add them together! - self.T21map = self.T21maplin + self.T21mapNL - - - - else: - print('ERROR, KIND not implemented yet!') - - - - -def powerboxCtoR(pbobject,mapkin = None): - 'Function to convert a complex field to real 3D (eg density, T21...) on the powerbox notation' - 'Takes a powerbox object pbobject, and a map in k space (mapkin), or otherwise assumes its pbobject.delta_k() (tho in that case it should be delta_x() so...' - - realmap = empty((pbobject.N,) * pbobject.dim, dtype='complex128') - if (mapkin is None): - realmap[...] = pbobject.delta_k() - else: - realmap[...] = mapkin - realmap[...] = pbobject.V * pbox.dft.ifft(realmap, L=pbobject.boxlength, a=pbobject.fourier_a, b=pbobject.fourier_b)[0] - realmap = np.real(realmap) - - return realmap \ No newline at end of file + T21_NL[iz] = self.T21avg[iz] * pbe.delta_x() + return T21_NL diff --git a/zeus21/reionization.py b/zeus21/reionization.py new file mode 100644 index 0000000..16c3ae2 --- /dev/null +++ b/zeus21/reionization.py @@ -0,0 +1,937 @@ +""" +Models reionization using an analogy of a halo mass function to ionized bubbles. + +Authors: zeus21 v2 collaboration - June 2026 + Emily Bregou ; + Hector Afonso G. Cruz ; + Sarah Libanore ; + Julian B. Muñoz ; + Yonny Sklansky ; + Emilie Thélie ; + Alessandra Venditti +arXiv:2302.08506, arXiv:2306.09403, arXiv:2407.18294, Sklansky et al. (in prep) +""" + +from . import z21_utilities +from . import cosmology +from . import constants +import numpy as np +from scipy.integrate import cumulative_trapezoid +from scipy.interpolate import interp1d +from scipy.interpolate import RegularGridInterpolator +from scipy.interpolate import UnivariateSpline +from scipy.special import erfc +from tqdm import trange + + +class reionization_global: + """ + Computes the global reionization history and bubble mass function (BMF). + + Parameters + ---------- + CosmoParams : CosmoParams class + AstroParams : AstroParams class + HMFintclass : HMFinterp class + z_Init : Z_init class + SFRD_Init : SFRD_class class + PRINT_SUCCESS : bool, optional + Whether to print convergence status for BMF iteration. + Default is True. + + Attributes + ---------- + PRINT_SUCCESS : bool + Whether to print convergence status messages. + zlist : array + Redshift array. + Rs : array + Smoothing radii array matching the rest of the code, in cMpc. + Rs_BMF : array + Bubble radii only used for the BMF, in cMpc. + ds_array : array + Sample overdensity values used to compute the ionization barrier. + gamma : array + Linear coefficient multiplying delta in niondot fit. + gamma2 : array + Quadratic coefficient multiplying delta in niondot fit. + sigma : array + Matter fluctuation on (zlist, Rs) grid. + gamma_int : RegularGridInterpolator + Interpolator for gamma as a function of z and log R. + gamma2_int : RegularGridInterpolator + Interpolator for gamma2 as a function of z and log R. + sigma_BMF : array + Matter fluctuation evaluated on (zlist, Rs_BMF) grid. + sigma_int : RegularGridInterpolator + Interpolator for sigma as a function of z and log R. + trec0 : float + Recombination time normalization at z=0. + trec : array + Recombination time evaluated on zlist, in years. + trec_int : interp1d + Interpolator for recombination time as a function of z. + niondot_avg : array + Average ionizing photon production rate as a function of z. + niondot_avg_int : interp1d + Interpolator for the average ionizing photon production rate. + ion_frac : array + Global ionized fraction as a function of z. + ion_frac_initial : array + Initial guess of the global ionized fraction before BMF convergence, from Madau equation. + nion_norm : array + Normalization factor for niondot fit to match the average niondot, evaluated on (zlist, Rs) grid. + nion_norm_int : RegularGridInterpolator + Interpolator for nion_norm as a function of z and log R. + prebarrier_xHII : array + Ionized fraction evaluated on delta, z, and R before computing the barrier. + barrier : array + Density barrier threshold for ionization as a function of z and R. + barrier_initial : array + Initial barrier before BMF convergence. + barrier_int : RegularGridInterpolator + Interpolator for the barrier as a function of z and log R. + prebarrier_xHII_int : RegularGridInterpolator + Interpolator for prebarrier_xHII as a function of delta, z, and log R. + R_linear_sigma_fit_idx : int + Index of the smoothing radius closest to AstroParams.R_linear_sigma_fit_input. + R_linear_sigma_fit : float + Radius used as the initial guess of the peak scale for the linear barrier fit, in cMpc. + BMF : array + BMF evaluated on (zlist, Rs_BMF) grid. + BMF_initial : array + Initial BMF based on first guess values before convergence. + peakRofz : array + Peak bubble radius as a function of z, in cMpc. + peakRofz_int : interp1d + Interpolator for peak bubble radius as a function of z. + + compute_prebarrier_xHII : method + Computes the ionized fraction before solving for the barrier. + compute_barrier : method + Computes the density barrier threshold for ionization. + nion_normalization : method + Computes the normalization factor for the niondot fit. + nrec : method + Computes the cumulative number of recombinations over delta and z. + niondot_delta_r : method + Computes the delta and R-dependent ionizing photon production rate. + nion_delta_r_int : method + Computes the cumulative number of ionizing photons produced since the maximum redshift. + Madau_Q : method + Computes the global ionized fraction by solving the Madau equation. + B_1 : method + Computes the slope term of the linear ionization barrier. + B_0 : method + Computes the y-intercept term of the linear ionization barrier. + B : method + Computes the linear ionization barrier as a function of z and R. + dlogsigma_dlogR : method + Computes dlogsigma/dlogR. + VRdn_dR : method + Computes the volume-weighted BMF. Integrating this quantity gives the global xHII. + Rdn_dR : method + Computes the number-weighted BMF. + BMF_peak_R : method + Finds the radius at which the BMF peaks. + monotonic_after_peak : method + Enforces monotonic growth of the bubble size peak with time. + analytic_Q : method + Analytically integrate the BMF to compute global xHII. + converge_BMF : method + Iteratively updates the ionization barrier, BMF, and xHII until convergence. + interpR : method + Evaluates any interpolator at fixed z and an array of R. + interpz : method + Evaluates any interpolator at an array of z and fixed R. + sigmaR_int : method + Interpolates sigma at fixed z and an array of R. + sigmaz_int : method + Interpolates sigma at an array of z and fixed R. + barrierR_int : method + Interpolates the ionization barrier at fixed z and an array of R. + barrierz_int : method + Interpolates the ionization barrier at an array of z and fixed R. + gammaR_int : method + Interpolates gamma at at fixed z and an array of R. + gammaz_int : method + Interpolates gamma at an array of z and fixed R. + gamma2R_int : method + Interpolates gamma2 at at fixed z and an array of R. + gamma2z_int : method + Interpolates gamma2 at an array of z and fixed R. + nion_normR_int : method + Interpolates nion_norm at at fixed z and an array of R. + nion_normz_int : method + Interpolates nion_norm at an array of z and fixed R. + interp_zR : method + Evaluates any RegularGridInterpolator on z and R arrays. + sigma_zR_int : method + Interpolates sigma on z and R arrays. + barrier_zR_int : method + Interpolates the barrier on z and R arrays. + gamma_zR_int : method + Interpolates gamma on z and R arrays. + gamma2_zR_int : method + Interpolates gamma2 on z and R arrays. + nion_norm_zR_int : method + Interpolates nion_norm on z and R arrays. + prebarrier_xHII_int_grid : method + Evaluates prebarrier xHII on a delta field at fixed z and R. + """ + def __init__(self, CosmoParams, AstroParams, HMFintclass, z_Init, SFRD_Init, PRINT_SUCCESS=True): + + #initializing values and interpolators that will be used in the reionization calculations. The ones that depend on the BMF (ion_frac, barrier, peakRofz) are initialized but will be updated if AstroParams.FLAG_BMF_converge is True. + self.PRINT_SUCCESS = PRINT_SUCCESS + self.zlist = z_Init.zintegral + self.Rs = CosmoParams._Rtabsmoo + #initialize separate R array for the BMF focused on the relevant range of bubble sizes. + self.Rs_BMF = np.logspace(np.log10(AstroParams.Rbub_min), np.log10(self.Rs[-1]), 100) + self.ds_array = np.linspace(-1, 5, 101) + + self._r_array, self._z_array = np.meshgrid(self.Rs, self.zlist, sparse=True, indexing='ij') + self._rb_array, self._z_array = np.meshgrid(self.Rs_BMF, self.zlist, sparse=True, indexing='ij') + + self.gamma = SFRD_Init.gamma_niondot_II_index2D ### TODO maybe not store them as attributes? + self.gamma2 = SFRD_Init.gamma2_niondot_II_index2D ### TODO maybe not store them as attributes? + self.sigma = HMFintclass.sigmaRintlog((np.log(self._r_array), self._z_array)).T + + self.zr = [self.zlist, np.log(self.Rs)] + self.gamma_int = RegularGridInterpolator(self.zr, self.gamma, bounds_error = False, fill_value = None) + self.gamma2_int = RegularGridInterpolator(self.zr, self.gamma2, bounds_error = False, fill_value = None) + + self.sigma_BMF = HMFintclass.sigmaRintlog((np.log(self._rb_array), self._z_array)).T #might need to make a new interpolator for different R range + self.zr_BMF = [self.zlist, np.log(self.Rs_BMF)] + self.sigma_int = RegularGridInterpolator(self.zr_BMF, self.sigma_BMF, bounds_error = False, fill_value = None) + + self.trec0 = 1/(constants.alphaB * cosmology.n_H(CosmoParams,0) * AstroParams.clumping) #seconds + self.trec = self.trec0/(1+self.zlist)**3/constants.yrTos #years + self.trec_int = interp1d(self.zlist, self.trec, bounds_error = False, fill_value = None) + + self.niondot_avg = SFRD_Init.niondot_avg_II ### TODO maybe not store them as attributes? + self.niondot_avg_int = interp1d(self.zlist, self.niondot_avg, bounds_error = False, fill_value = None) + + #solving Madau equation to get an initial xHII + self.ion_frac = np.fmin(1, self.Madau_Q(CosmoParams, self.zlist)) + self.ion_frac_initial = np.copy(self.ion_frac) + + zr_mesh = np.meshgrid(np.arange(len(self.Rs)), np.arange(len(self.zlist))) + self.nion_norm = self.nion_normalization(zr_mesh[1], zr_mesh[0]) + self.nion_norm_int = RegularGridInterpolator(self.zr, self.nion_norm, bounds_error = False, fill_value = None) + + #using initial xHII to compute initial barrier + self.prebarrier_xHII = np.empty((len(self.ds_array), len(self.zlist), len(self.Rs))) + self.barrier = self.compute_barrier(CosmoParams, AstroParams, self.ion_frac, self.zlist, self.Rs) + self.barrier_initial = np.copy(self.barrier) + self.barrier_int = RegularGridInterpolator(self.zr, self.barrier, bounds_error = False, fill_value = None) + + self.dzr = [self.ds_array, self.zlist, np.log(self.Rs)] + self.prebarrier_xHII_int = RegularGridInterpolator(self.dzr, self.prebarrier_xHII, bounds_error = False, fill_value = None) #allow extrapolation + + self.R_linear_sigma_fit_idx = z21_utilities.find_nearest_idx(self.Rs_BMF, AstroParams.R_linear_sigma_fit_input)[0] + self.R_linear_sigma_fit = self.Rs_BMF[self.R_linear_sigma_fit_idx] + + #fake bubble mass function to impose peak around R_linear_sigma_fit for the initial linear barriers + #looks something like [0, 0, ..., 1, ..., 0, 0]*(number of redshifts) + self.BMF = np.repeat([np.eye(len(self.Rs_BMF))[self.R_linear_sigma_fit_idx]], len(self.zlist), axis=0) + + self.peakRofz = np.array([self.BMF_peak_R(z) for z in self.zlist]) + #ensuring that the peak bubble size is monotonically increasing with time. + self.peakRofz = self.monotonic_after_peak(self.peakRofz) + self.peakRofz_int = interp1d(self.zlist, self.peakRofz, bounds_error = False, fill_value = None) + + #first computation of BMF using the initial guesses + self.BMF = self.VRdn_dR(self.zlist, self.Rs_BMF) + self.BMF_initial = np.copy(self.BMF) + + #xHII from analytic integral of BMF + self.ion_frac = np.nan_to_num(self.analytic_Q(CosmoParams, self.zlist)) + + #ensure that if the barrier is negative at the largest smoothing scale (i.e. the whole box is ionized), then xHII is 1. + self.ion_frac[self.barrier[:, -1]<=0] = 1 + + #converge the BMF interatively + if AstroParams.FLAG_BMF_converge: + self.converge_BMF(CosmoParams, AstroParams, self.ion_frac) + + + def compute_prebarrier_xHII(self, CosmoParams, ion_frac, z, R): + """ + Computes the ionized fraction before solving for the barrier. + + Parameters + ---------- + CosmoParams : CosmoParams class + ion_frac : array + Global xHII at z. + z : array + Redshifts. + R : array + Smoothing radii in cMpc. + + Returns + ------- + prebarrier_xHII : array + Ionized fraction evaluated over sample delta, z, and R before solving for the barrier. + """ + nion_values = self.nion_delta_r_int(CosmoParams, z, R) #Shape (nd, nz, nR) + nrec_values = self.nrec(CosmoParams, ion_frac, z)[:, :, None] #Shape (nd, nz) * (1, 1, nR) + + prebarrier_xHII = nion_values / (1 + nrec_values) + + return prebarrier_xHII + + def compute_barrier(self, CosmoParams, AstroParams, ion_frac, z, R): + """ + Computes the density barrier threshold for ionization by finding when nion surpasses nH+nrec. + + Parameters + ---------- + CosmoParams : CosmoParams class + AstroParams : AstroParams class + ion_frac : array + Global xHII at z. + z : array + Redshifts. + R : array + Smoothing radii in cMpc. + + Returns + ------- + barrier : array + Density threshold for ionization as a function of z and R. + """ + zarg = np.argsort(z) + z = z[zarg] + ion_frac = ion_frac[zarg] + + self.prebarrier_xHII = self.compute_prebarrier_xHII( + CosmoParams, ion_frac, z, R + ) + + total_values = np.log10(self.prebarrier_xHII + 1e-10) + # Expected shape: (len(self.ds_array), len(self.zlist), len(R)) + + crosses = np.diff(np.sign(total_values), axis=0) != 0 + # Shape: (len(self.ds_array) - 1, len(self.zlist), len(R)) + + #first time nion surpasses nH+nrec along the delta axis + has_crossing = crosses.any(axis=0) + first_idx = np.argmax(crosses, axis=0) + # Shape: (len(self.zlist), len(R)) + + y0 = np.take_along_axis( + total_values[:-1, :, :], + first_idx[None, :, :], + axis=0, + )[0] + + y1 = np.take_along_axis( + total_values[1:, :, :], + first_idx[None, :, :], + axis=0, + )[0] + + x0 = self.ds_array[first_idx] + x1 = self.ds_array[first_idx + 1] + + #interpolate between grid values to find more precise barrier. + with np.errstate(divide="ignore", invalid="ignore"): + barrier = x0 - y0 * (x1 - x0) / (y1 - y0) + + barrier = np.where(has_crossing, barrier, np.nan) + + growth = ( + CosmoParams.growthint(self.zlist) + / CosmoParams.growthint(self.zlist[0]) + ) + + #the barrier is defined in terms of the overdensity linearly extrapolated to zlist[0], so we need to multiply by the growth factor to get the correct barrier at each redshift. + barrier = barrier * growth[:, None] + + #the barrier is an unreachable number above zmax, ensuring that there are no bubbles. + barrier[self.zlist > AstroParams.ZMAX_REION] = 100 + + return barrier + + def nion_normalization(self, z, R): + """ + Computes the normalization factor for the niondot fit. + + Parameters + ---------- + z : int or array + Redshift-grid index or indices. + R : int or array + Radius-grid index or indices. + + Returns + ------- + nion_norm : float or array + Normalization for niondot. + """ + return 1/np.sqrt(1-2*self.gamma2[z, R]*self.sigma[z, R]**2)*np.exp(self.gamma[z, R]**2 * self.sigma[z, R]**2 / (2-4*self.gamma2[z, R]*self.sigma[z, R]**2)) + + def nrec(self, CosmoParams, ion_frac, z, d_array=None): + """ + Vectorized computation of nrec over an array of overdensities d_array. + + Parameters + ---------- + CosmoParams: CosmoParams class + Stores cosmology. + ion_frac: 1D np.array + The ionized fraction over all redshifts. + z: array + Redshifts + d_array : array, optional + Sample delta values. + Default is None, in which case self.ds_array is used. + + Returns + ---------- + nrecs: 2D np.array + The total number of recombinations at each overdensity for a certain ionized fraction history at each redshift. The first dimension is densities, the second dimension is redshifts. + """ + zarg = np.argsort(z) #sort just in case + z = z[zarg] + ion_frac = ion_frac[zarg] + + if d_array is None: + d_array = self.ds_array + + #reverse the inputs to make the integral easier to compute + z_rev = z[::-1] + Hz_rev = cosmology.Hubinvyr(CosmoParams, z_rev) + trec_rev = self.trec_int(z_rev) + ion_frac_rev = ion_frac[::-1] + + denom = -1 / (1 + z_rev) / Hz_rev / trec_rev + integrand_base = denom * ion_frac_rev + Dg = CosmoParams.growthint(z_rev) #growth factor + + nrecs = cumulative_trapezoid(integrand_base*(1+d_array[:, np.newaxis]*Dg/Dg[-1]), x=z_rev, initial=0) #(1+delta) rather than (1+delta)^2 because nrec and nion are per hydrogen atom + + #TODO: nonlinear recombinations/higher order + + nrecs = nrecs[:, ::-1] #reverse back to increasing z order + return nrecs + + def niondot_delta_r(self, CosmoParams, z, R, d_array=None): + """ + Compute niondot over an array of overdensities d_array for a given R. + + Parameters + ---------- + CosmoParams: CosmoParams class + Stores cosmology. + z: array + Redshifts + R: float + Radius value (cMpc) + d_array : array, optional + Sample delta values. + Default is None, in which case self.ds_array is used. + + Output + ---------- + niondot: 2D np.array + The rates of ionizing photon production. The first dimension is densities, the second dimension is redshifts. + """ + + z1d = np.copy(z) + R1d = np.copy(R) + + z = z[None, :, None] + R = R[None, None, :] + + if d_array is None: + d_array = self.ds_array[:, None, None] + + d_array = d_array * CosmoParams.growthint(z) / CosmoParams.growthint(z1d[0]) + + gamma = self.gamma_zR_int(z1d[:, None], R1d[None, :])[None, :, :] + gamma2 = self.gamma2_zR_int(z1d[:, None], R1d[None, :])[None, :, :] + nion_norm = self.nion_norm_zR_int(z1d[:, None], R1d[None, :])[None, :, :] + + #niondot fit with gammas and normalization + exp_term = np.exp(gamma * d_array + gamma2 * d_array**2) + niondot = (self.niondot_avg_int(z) / nion_norm) * exp_term + + return niondot + + def nion_delta_r_int(self, CosmoParams, z, R, d_array=None): + """ + Vectorized computation of nion over an array of overdensities d_array for a given R. + + Parameters + ---------- + CosmoParams: CosmoParams class + Stores cosmology. + z: array + Redshifts + R: float + Radius value (cMpc) + d_array : array, optional + Sample delta values. + Default is None, in which case self.ds_array is used. + + Output + ---------- + nion: 2D np.array + The total number of ionizing photons produced since z=zmax. The first dimension is densities, the second dimension is redshifts. + """ + + z.sort() #sort if not sorted + + if d_array is None: + d_array = self.ds_array[:, None, None] + + #reverse the inputs to make the integral easier to compute + z_rev = z[::-1] + Hz_rev = cosmology.Hubinvyr(CosmoParams, z_rev) + + niondot_values = self.niondot_delta_r(CosmoParams, z, R, d_array) + + #cumulatively integrate over all time + integrand = -1 / (1 + z_rev[None, :, None]) / Hz_rev[None, :, None] * niondot_values[:, ::-1] + nion = cumulative_trapezoid(integrand, x=z_rev, initial=0, axis=1)[:, ::-1] #reverse back to increasing z order + + return nion + + def Madau_Q(self, CosmoParams, z): + """ + Computes the global ionized fraction by solving the Madau equation. + + Parameters + ---------- + CosmoParams : CosmoParams class + z : float or array + Redshifts + + Returns + ------- + Q : float or array + Global xHII evaluated at z. + """ + + z = np.atleast_1d(z) #accepts scalar or array + z_arr = np.geomspace(z, self.zlist[-1], len(self.zlist)) + dtdz = 1/cosmology.Hubinvyr(CosmoParams, z_arr)/(1 + z_arr) + tau0 = self.trec0 * np.sqrt(CosmoParams.OmegaM) * cosmology.Hubinvyr(CosmoParams, 0) / constants.yrTos + exp = np.exp(2/3/tau0 * (np.power(1 + z, 3/2) - np.power(1 + z_arr, 3/2))) #switched order around to be correct (typo in paper) + + niondot_avgs = self.niondot_avg_int(z_arr) + integrand = dtdz * niondot_avgs * exp + + return np.trapezoid(integrand, x = z_arr, axis = 0) + + def B_1(self, z): + """ + Computes the slope term of the linear ionization barrier. + + Parameters + ---------- + z : float or array + Redshifts + + Returns + ------- + B1 : array + Slope term of the linear barrier as a function of z. + """ + z = np.atleast_1d(z) + #compute slope near the peak of the BMF + R_pivot = self.peakRofz_int(z) + sigmax = np.diagonal(self.sigma_zR_int(z[:, None], (R_pivot*1.1)[None, :])) + sigmin = np.diagonal(self.sigma_zR_int(z[:, None], (R_pivot*0.9)[None, :])) + barriermax = np.diagonal(self.barrier_zR_int(z[:, None], (R_pivot*1.1)[None, :])) + barriermin = np.diagonal(self.barrier_zR_int(z[:, None], (R_pivot*0.9)[None, :])) + return (barriermax - barriermin)/(sigmax**2 - sigmin**2) + + def B_0(self, z): + """ + Computes the y-intercept term of the linear ionization barrier. + + Parameters + ---------- + z : float or array + Redshifts + + Returns + ------- + B0 : array + Intercept term of the linear barrier as a function of z. + """ + z = np.atleast_1d(z) + R_pivot = self.peakRofz_int(z) + sigmin = np.diagonal(self.sigma_zR_int(z[:, None], (R_pivot*0.9)[None, :])) + barriermin = np.diagonal(self.barrier_zR_int(z[:, None], (R_pivot*0.9)[None, :])) + return barriermin - sigmin**2 * self.B_1(z) + + def B(self, z, R, sig=None): + """ + Computes the linear ionization barrier as a function of z and R. + + Parameters + ---------- + z : float or array + Redshifts + R : float or array + Radii in cMpc + sig : array, optional + Matter fluctuation sigma(z, R). + Default is None, in which case sigma is interpolated internally. + + Returns + ------- + B : array + Linear barrier as a function of z and R. + """ + z = np.atleast_1d(z) + if sig is None: + R = np.atleast_1d(R) + sig = self.sigma_zR_int(z[:, None], R[None, :]) + B0 = self.B_0(z) + B1 = self.B_1(z) + return B0[:, None] + B1[:, None]*sig**2 + + def dlogsigma_dlogR(self, z, R, sig): + """ + Computes dlogsigma/dlogR. + + Parameters + ---------- + z : float or array + Redshifts + R : array + Bubble radii in cMpc. + sig : array + Matter fluctuation sigma(z, R). + + Returns + ------- + dlogsigma_dlogR : array + Logarithmic derivative dlog(sigma)/dlog(R). + """ + return np.gradient(np.log(sig), np.log(R), axis=1) + + def VRdn_dR(self, z, R): + """ + Computes the volume-weighted BMF. Integrating this quantity gives the global xHII. + + Parameters + ---------- + z : float or array + Redshifts + R : array + Bubble radii in cMpc. + + Returns + ------- + VRdn_dR : array + Volume-weighted BMF evaluated over redshift and radius. + """ + z = np.atleast_1d(z) + sig = self.sigma_zR_int(z[:, None], R[None, :]) + B0 = self.B_0(z) + B1 = self.B_1(z) + return np.sqrt(2/np.pi) * np.abs(self.dlogsigma_dlogR(z, R, sig)) * np.abs(B0[:, None])/sig * np.exp(-(B0[:, None]+B1[:, None]*sig**2)**2/2/sig**2) + + def Rdn_dR(self, z, R): + """ + Computes the number-weighted BMF. + + Parameters + ---------- + z : float or array + Redshifts + R : array + Bubble radii in cMpc. + + Returns + ------- + Rdn_dR : array + Number-weighted bubble mass function evaluated over redshift and radius. + """ + return self.VRdn_dR(z, R)*3/(4*np.pi*R[None, :]**3) + + def BMF_peak_R(self, z, fit_window=5, max_bubble=100, min_bubble=0.5): + """ + Finds the radius at which the BMF peaks. + + Parameters + ---------- + z : float + Redshift + fit_window : int, optional + Number of points on each side of the coarse peak to use for the spline fit. Default is 5. + max_bubble : float, optional + Maximum allowed bubble radius in cMpc. Default is 100. + min_bubble : float, optional + Minimum allowed bubble radius in cMpc. Default is 0.5. + + Returns + ------- + peak_R : float + Peak bubble radius in cMpc. + """ + min_bubble = np.max([self.Rs[1], min_bubble]) + + iz = z21_utilities.find_nearest_idx(self.zlist, z)[0] + + R = self.Rs_BMF + y = self.BMF[iz] + + #keep only finite positive values + good = np.isfinite(y) & (y > 0) & np.isfinite(R) + if not np.any(good): + return min_bubble + + R_good = R[good] + y_good = y[good] + + #coarse peak index + ir_peak = np.argmax(y_good) + + #fit spline around the peak to get more precise. + #find where derivative = 0. If too close to edge, return bounds. + i_lo = max(0, ir_peak - fit_window) + i_hi = min(len(R_good), ir_peak + fit_window + 1) + + R_window = R_good[i_lo:i_hi] + y_window = y_good[i_lo:i_hi] + + if len(R_window) < 5: + return np.clip(R_good[ir_peak], min_bubble, max_bubble) + + x = np.log(R_window) + ly = np.log(y_window) + + #get more exact fit between the points using a spline. + spline_fit = UnivariateSpline(x, ly, k=4, s=0) + roots = spline_fit.derivative().roots() + + d2 = spline_fit.derivative(n=2) + + valid_roots = [ + root for root in roots + if x[0] <= root <= x[-1] and d2(root) < 0 + ] + + if len(valid_roots) == 0: + peak_R = R_good[ir_peak] + else: + x_peak_guess = np.log(R_good[ir_peak]) + x_peak = valid_roots[np.argmin(np.abs(np.array(valid_roots) - x_peak_guess))] + peak_R = np.exp(x_peak) + + #return peak within allowed bounds + return np.clip(peak_R, min_bubble, max_bubble) + + def monotonic_after_peak(self, x): + """ + Enforces monotonic growth of the bubble size peak with time. + + Parameters + ---------- + x : array + Input array. + + Returns + ------- + x : array + Array with values left of the peak forced to be non-decreasing. + """ + x = np.asarray(x).copy() + i_peak = np.nanargmax(x) + + x[i_peak:] = np.minimum.accumulate(x[i_peak:]) + + return x + + def analytic_Q(self, CosmoParams, z): + """ + Analytically integrate the BMF to compute global xHII. + Integrates down to some minimum sigma corresponding to the smallest relevant scale for bubbles. + Any smaller makes the integral discrepant with the BMF, and the linear barrier becomes a poor fit. + + Parameters + ---------- + CosmoParams : CosmoParams class + z : float or array + Redshifts + + Returns + ------- + Q : array + Ionized fraction obtained from the analytic integral of the BMF. + """ + z = np.atleast_1d(z) + Rmin = self.Rs_BMF[0] #fixing analytic to fit numeric (old version: 1e-10, arbitrarily small) + B0 = self.B_0(z) + B1 = self.B_1(z) + sigmin = CosmoParams.ClassCosmo.sigma(Rmin, z[0])*CosmoParams.growthint(z)/CosmoParams.growthint(z[0]) ### Faster to multiply sigma by the growth but there is a 0.2% error on the xHII_avg + ### TODO maybe add a flag to call ClassCosmo.sigma for every z + s2 = sigmin**2 + return 0.5*np.exp(-2*B0*B1)*erfc((B0-B1*s2)/np.sqrt(2*s2)) + 0.5*erfc((B0+B1*s2)/np.sqrt(2*s2)) + + def converge_BMF(self, CosmoParams, AstroParams, ion_frac_input): + """ + Iteratively updates the ionization barrier, BMF, and xHII until convergence. + + Parameters + ---------- + CosmoParams : CosmoParams class + AstroParams : AstroParams class + ion_frac_input : array + Initial ionized fraction used to begin the BMF convergence loop. + + Returns + ------- + None + """ + self.ion_frac = ion_frac_input + iterator = trange(AstroParams.max_iter) if self.PRINT_SUCCESS else range(AstroParams.max_iter) + for j in iterator: + ion_frac_prev = np.copy(self.ion_frac) + #need xHII for recombination calculation, which affects the barrier. + self.barrier = self.compute_barrier(CosmoParams, AstroParams, self.ion_frac, self.zlist, self.Rs) + self.barrier_int = RegularGridInterpolator(self.zr, self.barrier, bounds_error = False, fill_value = None) + + #update BMF and peaks. + self.BMF = self.VRdn_dR(self.zlist, self.Rs_BMF) + self.peakRofz = np.array([self.BMF_peak_R(z) for z in self.zlist]) + self.peakRofz = self.monotonic_after_peak(self.peakRofz) + self.peakRofz_int = interp1d(self.zlist, self.peakRofz, bounds_error = False, fill_value = None) + + #update xHII + self.ion_frac = np.nan_to_num(self.analytic_Q(CosmoParams, self.zlist)) + self.ion_frac[self.barrier[:, -1]<=0] = 1 + + #stop the loop if xHII hasn't changed much between iterations. + if np.allclose(ion_frac_prev, self.ion_frac, rtol=1e-1, atol=1e-2): + if self.PRINT_SUCCESS: + print(f"SUCCESS: BMF converged after {j+1} iteration{'s' if j > 0 else ''}.") + return + + print(f"WARNING: BMF didn't converge within {AstroParams.max_iter} iterations.") + + + #interpolators in z and R used in reionization.py + def interpR(self, z, R, func): + "Interpolator to find func(z, R), designed to take a single z but an array of R in cMpc" + _logR = np.log(R) + logRvec = np.asarray([_logR]) if np.isscalar(_logR) else np.asarray(_logR) + inarray = np.array([[z, LR] for LR in logRvec]) + return func(inarray) + + def interpz(self, z, R, func): + "Interpolator to find func(z, R), designed to take a single R in cMpc but an array of z" + zvec = np.asarray([z]) if np.isscalar(z) else np.asarray(z) + inarray = np.array([[zz, np.log(R)] for zz in zvec]) + return func(inarray) + + #all instances of different (z, R) interpolators, named explicitly for clarity in the code + def sigmaR_int(self, z, R): + return self.interpR(z, R, self.sigma_int) + def sigmaz_int(self, z, R): + return self.interpz(z, R, self.sigma_int) + + def barrierR_int(self, z, R): + return self.interpR(z, R, self.barrier_int) + def barrierz_int(self, z, R): + return self.interpz(z, R, self.barrier_int) + + def gammaR_int(self, z, R): + return self.interpR(z, R, self.gamma_int) + def gammaz_int(self, z, R): + return self.interpz(z, R, self.gamma_int) + + def gamma2R_int(self, z, R): + return self.interpR(z, R, self.gamma2_int) + def gamma2z_int(self, z, R): + return self.interpz(z, R, self.gamma2_int) + + def nion_normR_int(self, z, R): + return self.interpR(z, R, self.nion_norm_int) + def nion_normz_int(self, z, R): + return self.interpz(z, R, self.nion_norm_int) + + def interp_zR(self, z, R, func): + """ + Evaluate a RegularGridInterpolator defined on (z, logR). + + Accepts scalar, 1D, 2D, or ND z and R. + z and R are broadcast against each other. + + Examples + -------- + scalar z, vector R: + out.shape == R.shape + + vector z, scalar R: + out.shape == z.shape + + z[:, None], R[None, :]: + out.shape == (nz, nR) + """ + z = np.asarray(z, dtype=float) + R = np.asarray(R, dtype=float) + + z_b, R_b = np.broadcast_arrays(z, R) + + points = np.column_stack([ + z_b.ravel(), + np.log(R_b).ravel() + ]) + + out = func(points) + return out.reshape(z_b.shape) + + def sigma_zR_int(self, z, R): + return self.interp_zR(z, R, self.sigma_int) + + def barrier_zR_int(self, z, R): + return self.interp_zR(z, R, self.barrier_int) + + def gamma_zR_int(self, z, R): + return self.interp_zR(z, R, self.gamma_int) + + def gamma2_zR_int(self, z, R): + return self.interp_zR(z, R, self.gamma2_int) + + def nion_norm_zR_int(self, z, R): + return self.interp_zR(z, R, self.nion_norm_int) + + def prebarrier_xHII_int_grid(self, d, z, R): + """ + Evaluate prebarrier xHII on a density field d(x), + at fixed redshift z and smoothing radius R. + + Parameters + ---------- + d: np.ndarray + Density/overdensity field. Can be any shape (...). + z: float + Redshift. + R: float + Smoothing radius (cMpc). + + Output + ---------- + values: np.ndarray + xHII field with the same shape as d. + """ + + d = np.asarray(d, dtype=float) + + z_arr = np.full_like(d, float(z), dtype=float) + logr_arr = np.full_like(d, np.log(float(R)), dtype=float) + + #stack into points (..., 3) where last axis is (delta, z, logR) + points = np.stack([d, z_arr, logr_arr], axis=-1) + + values = self.prebarrier_xHII_int(points) + + return values \ No newline at end of file diff --git a/zeus21/sfrd.py b/zeus21/sfrd.py index 6819cc5..a04f4af 100644 --- a/zeus21/sfrd.py +++ b/zeus21/sfrd.py @@ -1,957 +1,1250 @@ """ -Bulk of the Zeus21 calculation. Compute sSFRD from cosmology, determines Lyman-alpha and X-ray fluxes, and evolves the cosmic-dawn IGM state (WF coupling and heating). From that we get the 21-cm global signal and the effective biases gammaR to determine the 21-cm power spectrum. - -Author: Julian B. Muñoz -UT Austin and Harvard CfA - January 2023 - -Edited by Hector Afonso G. Cruz -JHU - July 2024 - -Edited by Emily Bregou -UT Austin - October 2025 - -Edited by Sarah Libanore -BGU - July 2025 - +Bulk of the Zeus21 calculation. Compute SFRD from cosmology. + +Authors: zeus21 v2 collaboration - June 2026 + Emily Bregou ; + Hector Afonso G. Cruz ; + Sarah Libanore ; + Julian B. Muñoz ; + Yonny Sklansky ; + Emilie Thélie ; + Alessandra Venditti +arXiv:2302.08506, arXiv:2306.09403, arXiv:2407.18294, Sklansky et al. (in prep) """ from . import cosmology -from .xrays import Xray_class, sigma_HI, sigma_HeI from . import constants import numpy as np import astropy from astropy import units as u -from astropy import constants as const import scipy from scipy import interpolate -import pickle - - - -class get_T21_coefficients: - "Loops through SFRD integrals and obtains avg T21 and the coefficients for its power spectrum. Takes input zmin, which minimum z we integrate down to. It accounts for: \ - -Xray heating \ - -LyA coupling. \ - TODO: reionization/EoR" - def __init__(self, User_Parameters, Cosmo_Parameters, ClassCosmo, Astro_Parameters, HMF_interpolator, zmin = 10.0): +class Z_init: + """ + Initial redshift matrices for the calculation - ##################################################################################################### - ### STEP 0: Defining Constants and storage variables - - #define comoving distance quantities - self.Rtabsmoo = Cosmo_Parameters._Rtabsmoo - self.dlogRR = Cosmo_Parameters._dlogRR - - #define the integration redshifts, goes as log(z) (1+ doesn't change sampling much) - self.zmax_integral = constants.ZMAX_INTEGRAL - self.zmin = zmin - self._dlogzint_target = 0.02/User_Parameters.precisionboost - self.Nzintegral = np.ceil(1.0 + np.log(self.zmax_integral/self.zmin)/self._dlogzint_target).astype(int) - self.dlogzint = np.log(self.zmax_integral/self.zmin)/(self.Nzintegral-1.0) #exact value rather than input target above - self.zintegral = np.logspace(np.log10(self.zmin), np.log10(self.zmax_integral), self.Nzintegral) #note these are also the z at which we "observe", to share computational load + Parameters + ---------- + UserParams : UserParams class + CosmoParams : CosmoParams class + + Attributes + ---------- + dlogzint : array + Set the log step for the redshift binning, based on the required input + zintegral : array + Redshift array over which will be performed all integration and for which the output will be computed + zGreaterMatrix : matrix + Redshift associated with the distance at radius R. Dimension (z, R) + zGreaterMatrix_nonan : matrix + Redshift associated with the distance at radius R; when z > zmax_AstroBreak (50 by default in constants), we set z > 100 to prevent computing things where we don't trust the astrophysical model. Dimension (z, R) + """ + + def __init__(self, UserParams, CosmoParams): + + zmin_integral = UserParams.zmin + zmax_integral = constants.ZMAX_INTEGRAL - #define table of redshifts and distances - self.rGreaterMatrix = np.transpose([Cosmo_Parameters.chiofzint(self.zintegral)]) + self.Rtabsmoo - self.zGreaterMatrix = Cosmo_Parameters.zfofRint(self.rGreaterMatrix) + Nzintegral = np.ceil(1.0 + np.log(zmax_integral/zmin_integral)/UserParams.dlogzint_target).astype(int) - self.ztabRsmoo = np.nan_to_num(np.copy(self.zGreaterMatrix), nan = 100)#HAC: patch fix for now. Later, figure out how to reconcile zGreaterMatrix with zGreaterMatrix_nonan - if(Cosmo_Parameters.Flag_emulate_21cmfast == True): #they take the redshift to be at the midpoint of the two shells. In dr really. + self.dlogzint = np.log(zmax_integral/zmin_integral)/(Nzintegral-1.0) #exact value rather than input target above + self.zintegral = np.geomspace(zmin_integral, zmax_integral, Nzintegral) #note these are also the z at which we "observe", to share computational load + # define table of redshifts + rGreaterMatrix = np.transpose([CosmoParams.chiofzint(self.zintegral)]) + CosmoParams._Rtabsmoo + self.zGreaterMatrix = CosmoParams.zfofRint(rGreaterMatrix) + + if CosmoParams.Flag_emulate_21cmfast: + # 21cmFAST takes the redshift to be at the midpoint of the two shells + # TODO: HECTOR CHANGES self.zGreaterMatrix = np.append(self.zintegral.reshape(len(self.zGreaterMatrix), 1), self.zGreaterMatrix, axis = 1) - self.zGreaterMatrix = (self.zGreaterMatrix[:, 1:] + self.zGreaterMatrix[:, :-1])/2 - -# self.zGreaterMatrix[self.rGreaterMatrix > Cosmo_Parameters.chiofzint(50.0)] = 50.0 ###HAC: Check if I can actually comment this out or not - self.rGreaterMatrix[self.rGreaterMatrix > Cosmo_Parameters.chiofzint(50.0)] = Cosmo_Parameters.chiofzint(50.0) - - - self.ztabRsmoo = np.append(self.zintegral.reshape(len(self.ztabRsmoo), 1), self.ztabRsmoo, axis = 1)###HAC: no longer necessary! - self.ztabRsmoo = (self.ztabRsmoo[:, 1:] + self.ztabRsmoo[:, :-1])/2###HAC: no longer necessary! + self.zGreaterMatrix = (self.zGreaterMatrix[:, 1:] + self.zGreaterMatrix[:, :-1])/2 else: - self.zGreaterMatrix[self.rGreaterMatrix > Cosmo_Parameters.chiofzint(50.0)] = np.nan - self.rGreaterMatrix[self.rGreaterMatrix > Cosmo_Parameters.chiofzint(50.0)] = np.nan #replace z > 50 = np.nan so that nothing exceeds zmax = 50 - self.ztabRsmoo = np.nan_to_num(np.copy(self.zGreaterMatrix), nan = 100)#HAC: patch fix for now. Later, figure out how to reconcile zGreaterMatrix with zGreaterMatrix_nonan + self.zGreaterMatrix[rGreaterMatrix > CosmoParams.chiofzint(constants.zmax_AstroBreak)] = np.nan - zGreaterMatrix_nonan = np.nan_to_num(self.zGreaterMatrix, nan = 100) -# self.ztabRsmoo = np.zeros_like(self.SFRDbar2D) #z's that correspond to each Radius R around each zp #HAC: No longer needed - - ###HAC: added SFRD & J21LW variables for pop II and III stars TO BE DELETED (not needed) - self.SFRD_avg = np.zeros_like(self.zintegral) - self.SFRD_II_avg = np.zeros_like(self.zintegral) - self.SFRD_III_avg = np.zeros_like(self.zintegral) - self.J_21_LW_II = np.zeros_like(self.zintegral) - self.J_21_LW_III = np.zeros_like(self.zintegral) - - self.SFRDbar2D = np.zeros((self.Nzintegral, Cosmo_Parameters.NRs)) #SFR at z=zprime when averaged over a radius R (so up to a higher z) - - self.gamma_index2D = np.zeros_like(self.SFRDbar2D) #index of SFR ~ exp(\gamma delta) - self.gamma_II_index2D = np.zeros_like(self.SFRDbar2D) #index of SFR ~ exp(\gamma delta) - self.gamma_III_index2D = np.zeros_like(self.SFRDbar2D) #index of SFR ~ exp(\gamma delta) - - # SarahLibanore: gamma non linear for quadratic order - self.gamma2_II_index2D = np.zeros_like(self.SFRDbar2D) #index of SFR ~ exp(\gamma delta + \gamma_2 delta^2) - self.gamma2_III_index2D = np.zeros_like(self.SFRDbar2D) #index of SFR ~ exp(\gamma \delta + \gamma_2 \delta^2) - - self.niondot_avg = np.zeros_like(self.zintegral) #\dot nion at each z (int d(SFRD)/dM *fesc(M) dM)/rhobaryon - self.gamma_Niondot_index2D = np.zeros_like(self.SFRDbar2D) #index of SFR ~ exp(\gamma delta) - - - -# ###HAC: OLD, TO BE DELETED, added SFRD variables for pop II and III stars -# self.gamma_index2D_old = np.zeros_like(self.SFRDbar2D) #index of SFR ~ exp(\gamma delta) - - #EoR coeffs - self.sigmaofRtab = np.array([HMF_interpolator.sigmaR_int(self.Rtabsmoo, zz) for zz in self.zintegral]) #to be used in correlations.py, in get_bubbles() - - fesctab_II = fesc_II(Astro_Parameters, HMF_interpolator.Mhtab) #prepare fesc(M) table -- z independent for now so only once - fesctab_III = fesc_III(Astro_Parameters, HMF_interpolator.Mhtab) #PopIII prepare fesc(M) table -- z independent for now so only once - - #Xray coeffs - self.coeff1Xzp = np.zeros_like(self.zintegral) #zp-dependent coeff in Xray calculation - self.coeff2XzpRR = np.zeros_like(self.SFRDbar2D) #zp and R-dependent coeff in Xray calculation - self.Tk_avg = np.zeros_like(self.zintegral) #average kinetic temperature - - #LyA coeffs - self.coeff1LyAzp = np.zeros_like(self.zintegral) # Same but for LyA -# self.coeff2LyAzpRR = np.zeros_like(self.SFRDbar2D)# Same but for LyA - self.Jalpha_avg = np.zeros_like(self.zintegral) #avg Jalpha (we compute xa at the end) - _Jalpha_coeffs = np.zeros([constants.n_max_recycle-1,Cosmo_Parameters.NRs]) #the line recycled coeffs - - #and EPS factors - Nsigmad = 1.0 #how many sigmas we explore - Nds = 3 #how many deltas - SarahLibanore: changed to compute the non linear gamma - deltatab_norm = np.linspace(-Nsigmad,Nsigmad,Nds) - - #initialize Xrays - Xrays = Xray_class(User_Parameters, Cosmo_Parameters) - _Energylist = Astro_Parameters.Energylist - Nzinttau = np.floor(10*User_Parameters.precisionboost).astype(int) - - ##################################################################################################### - ### STEP 1: Recursive routine to compute average Pop II and III SFRDs with LW feedback - ### Will only perform 1 iteration; if Astro_Parameters.USE_LW_FEEDBACK = False, then inputs.py sets A_LW = 0.0 - zSFRDflat = np.geomspace(self.zmin, 50, 128) #extend to z = 50 for extrapolation purposes. Higher in z than self.zintegral - zSFRD, mArray = np.meshgrid(zSFRDflat, HMF_interpolator.Mhtab, indexing = 'ij', sparse = True) - - J21LW_interp = interpolate.interp1d(zSFRDflat, np.zeros_like(zSFRDflat), kind = 'linear', bounds_error = False, fill_value = 0,) #no LW background. Controls only Mmol() function, NOT the individual Pop II and III LW background - SFRD_II_avg = np.trapezoid(SFRD_II_integrand(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, mArray, zSFRD, zSFRD), HMF_interpolator.logtabMh, axis = 1) #never changes with J_LW - SFRD_II_interp = interpolate.interp1d(zSFRDflat, SFRD_II_avg, kind = 'cubic', bounds_error = False, fill_value = 0,) + self.zGreaterMatrix_nonan = np.nan_to_num(self.zGreaterMatrix, nan = 100) # prevent calculation where the astro model is not trusted - J21LW_II = 1e21 * J_LW(Astro_Parameters, Cosmo_Parameters, SFRD_II_avg, zSFRDflat, 2) #this never changes; only Pop III Quanties change - self.J_21_LW_II = interpolate.interp1d(zSFRDflat, J21LW_II, kind = 'cubic')(self.zintegral) #different from J21LW_interp - if Astro_Parameters.USE_POPIII == True: - SFRD_III_Iter_Matrix = [np.trapezoid(SFRD_III_integrand(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, mArray, J21LW_interp, zSFRD, zSFRD, ClassCosmo.pars['v_avg']), HMF_interpolator.logtabMh, axis = 1)] #changes with each iteration +class SFRD_class: + """ + Compute all quantities and methods associated with the star formation rate density and the astrophysical model + + Parameters + ---------- + UserParams : UserParams class + CosmoParams : CosmoParams class + AstroParams : AstroParams class + HMFinterp : HMFinterp class + z_Init : Z_init class, optional + Initial redshift matrices for the calculation (see sfrd.py for details). + Default is None. + + Attributes + ---------- + SFRD_II_interp : interpolator + Average SFRD for popII stars, interpolated over redshift. + J_21_LW_II : interpolator + Lyman-Werner flux from popII stars, units of erg/s/cm^2/Hz/s, interpolated over redshift + J21LW_interp_conv_avg : interpolator + Lyman-Werner flux iteratively computed to account for popIII contribution, interpolated over redshift + SFRD_III_cnvg_interp : : interpolator + Average SFRD for popIII stars, determines part-of and is affected by the LW flux; interpolated over redshift + J_21_LW_III : interpolator + Lyman-Werner flux, units of erg/s/cm^2/Hz/s from popIII stars, interpolated over redshift + SFRD_II_avg : array + Average SFRD for popII stars, units Msun/yr + SFRD_III_avg : array + Average SFRD for popIII stars, units Msun/yr + SFRD_avg : array + Total average SFRD, units Msun/yr + SFRDbar2D_II : matrix + Average SFRD for popII computed at z corresponding to each shell + SFRDbar2D_III : matrix + Average SFRD for popIII computed at z corresponding to each shell + fesctab_II : array + Escape fraction for popII, z-independent, as function of the halo mass + fesctab_III : array + Escape fraction for popIII, z-independent, as function of the halo mass + reio_integrand_II_interp : integrand + Number of ionizing photons produced by popII, interpolated in redshift + reio_integrand_II_interp : integrand + Number of ionizing photons produced by popIII, interpolated in redshift + niondot_avg_II : array + Number of ionizing photons produced by popII computed at the redshifts of the analysis + niondot_avg_III : array + Number of ionizing photons produced by popIII computed at the redshifts of the analysis + niondot_avg : array + Number of ionizing photons produced at the redshifts of the analysis + sigmaofRtab : matrix + Variance of the matter field on scales associated with the shells and at the observed rerdshift + Matom : method + Minimum mass for atomic-cooling halos at given redshift + Mmol_0 : method + Minimum mass for molecular halos without LW or VCB feedback + Mmol_vcb : method + Minimum mass for molecular halos without LW feedback + Mmol_LW : method + Minimum mass for molecular halos without VCB feedback + Mmol : method + Minimum mass for molecular halos with LW and VCB feedback + dMh_dt : method + Mass accretion rate, in units of M_sun/yr + fstar_ofz : method + Star formation efficiency generative function + fduty : method + Duty cycle to damp star formation in low- or high-mass halos or both + SFE_II : method + Star formation efficiency for popII stars + SFE_III : method + Star formation efficiency for popIII stars + SFE : method + Total star formation efficiency + SFR : method + Star formation rate + SRFD_integrand : method + Integrand to compute the star formation rate density + J_LW_21 : method + Mean cosmological LW background specific intensity + J_LW_Discrete : method + Radial kernel of the LW specific intensity before R integration + dSFRDIII_dJ : method + Response of the popIII star formation rate density to the LW background + fesc_II : method + Escape fraction of ionizing photons in halos hosting popII stars + fesc_III : method + Escape fraction of ionizing photons in halos hosting popIII stars + compute_sigmaR_nu : method + Compute the local mass function conditioned over the environment + compute_gamma : method + Compute linear and quadratic gamma exponents for the SFRD-delta (popII+popIII) and niondot-delta (only popII) lognormal approximations + gamma_II_index2D : array + Linear gamma exponent for SFRD in popII + gamma2_II_index2D : array + Quadratic gamma exponent for SFRD in popII + gamma_niondot_II_index2D : array + Linear gamma exponent for niondot in popII + gamma2_niondot_II_index2D : array + Quadratic gamma exponent for niondot in popII + gamma_III_index2D : array + Linear gamma exponent for SFRD in popIII + gamma2_III_index2D : array + Quadratic gamma exponent for SFRD in popIII + compute_numerical_der_gamma : method + Compute first and second numerical derivatives of an array wrt the other (used for SFRD and niondot wrt delta) + """ + + + @classmethod + def light_init(cls): # Light instantialization, in case we only need to access specific class methods without initializing the full class + obj = cls.__new__(cls) + return obj + + def __init__(self, UserParams, CosmoParams, AstroParams, HMFinterp, z_Init = None): + + # if z_Init is not provided, we initialize it here. This allows us to avoid redundant computations if they were already initialized in the parent class and passed as arguments. + if z_Init is None: + z_Init = Z_init(UserParams=UserParams, CosmoParams=CosmoParams) + + zSFRDflat = np.geomspace(UserParams.zmin, constants.zmax_AstroBreak, 128) # extend to z = constants.zmax_AstroBreak for extrapolation purposes. Higher in z than zInit.zintegral + zSFRD, mArray = np.meshgrid(zSFRDflat, HMFinterp.Mhtab, indexing = 'ij', sparse = True) # create redshift and halo mass matrices, dimension (z, Mh) + + init_J21LW_interp = interpolate.interp1d(zSFRDflat, np.zeros_like(zSFRDflat), kind = 'linear', bounds_error = False, fill_value = 0,) # initialize no LW background, used to compute Mmol() function, NOT the individual Pop II and III LW background + + SFRD_II_avg = np.trapezoid(self.SFRD_integrand(CosmoParams, AstroParams, HMFinterp, mArray, zSFRD, pop=2), HMFinterp.logtabMh, axis = 1) # average SFRD + self.SFRD_II_interp = interpolate.interp1d(zSFRDflat, SFRD_II_avg, kind = 'cubic', bounds_error = False, fill_value = 0,) + + J21LW_II = self.J_LW_21(CosmoParams, AstroParams, SFRD_II_avg, zSFRDflat, pop=2) # LW specific intensity from popII + self.J_21_LW_II = interpolate.interp1d(zSFRDflat, J21LW_II, kind = 'cubic')(z_Init.zintegral) # TODO: fix inconsistent naming conventions for J21LW + + if AstroParams.USE_POPIII: + + # initialize popIII SFRD, update iteratively to account for LW feedback + SFRD_III_Iter_Matrix = [np.trapezoid(self.SFRD_integrand(CosmoParams, AstroParams, HMFinterp, mArray, zSFRD, pop=3, vCB=CosmoParams.vcb_avg, J21LW_interp=init_J21LW_interp), HMFinterp.logtabMh, axis = 1)] + + errorTolerance = 0.001 # 0.1 percent accuracy --> TODO: allow to change tolerance? E.g. input of init function with default 0.001 - errorTolerance = 0.001 # 0.1 percent accuracy recur_iterate_Flag = True - while recur_iterate_Flag == True: - J21LW_III_iter = 1e21 * J_LW(Astro_Parameters, Cosmo_Parameters, SFRD_III_Iter_Matrix[-1], zSFRDflat, 3) - J21LW_interp = interpolate.interp1d(zSFRDflat, J21LW_II + J21LW_III_iter, kind = 'linear', fill_value = 0, bounds_error = False) + while recur_iterate_Flag: + + J21LW_III_iter = self.J_LW_21(CosmoParams, AstroParams, SFRD_III_Iter_Matrix[-1], zSFRDflat, pop=3) + loop_J21LW_interp = interpolate.interp1d(zSFRDflat, J21LW_II + J21LW_III_iter, kind = 'linear', fill_value = 0, bounds_error = False) - SFRD_III_avg_n = np.trapezoid(SFRD_III_integrand(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, mArray, J21LW_interp, zSFRD, zSFRD, ClassCosmo.pars['v_avg']), HMF_interpolator.logtabMh, axis = 1) + SFRD_III_avg_n = np.trapezoid(self.SFRD_integrand(CosmoParams, AstroParams, HMFinterp, mArray, zSFRD, pop=3, vCB=CosmoParams.vcb_avg, J21LW_interp= loop_J21LW_interp), HMFinterp.logtabMh, axis = 1) # correct through LW feedback SFRD_III_Iter_Matrix.append(SFRD_III_avg_n) if max(SFRD_III_Iter_Matrix[-1]/SFRD_III_Iter_Matrix[-2]) < 1.0 + errorTolerance and min(SFRD_III_Iter_Matrix[-1]/SFRD_III_Iter_Matrix[-2]) > 1.0 - errorTolerance: recur_iterate_Flag = False - self.J21LW_interp_conv_avg = J21LW_interp - SFRD_III_cnvg_interp = interpolate.interp1d(zSFRDflat, SFRD_III_Iter_Matrix[-1], kind = 'cubic', bounds_error = False, fill_value = 0) - self.J_21_LW_III = interpolate.interp1d(zSFRDflat, J21LW_III_iter, kind = 'cubic')(self.zintegral) + self.J21LW_interp_conv_avg = loop_J21LW_interp - elif Astro_Parameters.USE_POPIII == False: - self.SFRD_III_avg = np.zeros_like(self.zintegral) - SFRD_III_cnvg_interp = interpolate.interp1d(zSFRDflat, np.zeros_like(zSFRDflat), kind = 'cubic', bounds_error = False, fill_value = 0) + self.SFRD_III_cnvg_interp = interpolate.interp1d(zSFRDflat, SFRD_III_Iter_Matrix[-1], kind = 'cubic', bounds_error = False, fill_value = 0) # SFRD for popIII + self.J_21_LW_III = interpolate.interp1d(zSFRDflat, J21LW_III_iter, kind = 'cubic')(z_Init.zintegral) # LW flux from popIIII + + else: + self.SFRD_III_cnvg_interp = interpolate.interp1d(zSFRDflat, np.zeros_like(zSFRDflat), kind = 'cubic', bounds_error = False, fill_value = 0) - self.SFRD_II_avg = SFRD_II_interp(self.zintegral) - self.SFRD_III_avg = SFRD_III_cnvg_interp(self.zintegral) + self.SFRD_II_avg = self.SFRD_II_interp(z_Init.zintegral) + self.SFRD_III_avg = self.SFRD_III_cnvg_interp(z_Init.zintegral) self.SFRD_avg = self.SFRD_II_avg + self.SFRD_III_avg - if(Cosmo_Parameters.Flag_emulate_21cmfast==False): - self.SFRDbar2D_II = SFRD_II_interp(np.nan_to_num(self.zGreaterMatrix, nan = 100)) - self.SFRDbar2D_III = SFRD_III_cnvg_interp(np.nan_to_num(self.zGreaterMatrix, nan = 100)) + self.SFRDbar2D_II = self.SFRD_II_interp(np.nan_to_num(z_Init.zGreaterMatrix, nan = 100)) # dimension (z,R) + self.SFRDbar2D_III = self.SFRD_III_cnvg_interp(np.nan_to_num(z_Init.zGreaterMatrix, nan = 100)) # dimension (z,R) - elif(Cosmo_Parameters.Flag_emulate_21cmfast==True): ###HAC ACAUSAL: This accounts for the acausal Mmol effect in 21cmfast - zpTable, tempTable, mTable = np.meshgrid(self.zintegral, self.Rtabsmoo, HMF_interpolator.Mhtab, indexing = 'ij', sparse = True) - zppTable = self.zGreaterMatrix.reshape((len(self.zintegral), len(self.Rtabsmoo), 1)) + # Reionization + self.fesctab_II = self.fesc_II(AstroParams, HMFinterp.Mhtab) # prepare fesc(M) table -- z independent for now + self.fesctab_III = self.fesc_III(AstroParams, HMFinterp.Mhtab) # PopIII prepare fesc(M) table -- z independent for now - self.SFRDbar2D_II = np.trapezoid(SFRD_II_integrand(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, mTable, zppTable, zpTable), HMF_interpolator.logtabMh, axis = 2) - self.SFRDbar2D_III = np.trapezoid(SFRD_III_integrand(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, mTable, J21LW_interp, zppTable, zpTable, ClassCosmo.pars['v_avg']), HMF_interpolator.logtabMh, axis = 2) + # prepare integrand to compute number of ionizing photons + reio_integrand_II = self.SFRD_integrand(CosmoParams, AstroParams, HMFinterp, mArray, zSFRD, pop=2) + reio_integrand_III = self.SFRD_integrand(CosmoParams, AstroParams, HMFinterp, mArray, zSFRD, pop=3, vCB=CosmoParams.vcb_avg, J21LW_interp=init_J21LW_interp) + niondot_avg_II = AstroParams.N_ion_perbaryon_II/cosmology.rho_baryon(CosmoParams,0.) * np.trapezoid(reio_integrand_II * self.fesctab_II, HMFinterp.logtabMh, axis = 1) # number of ionizing photons produced by popII + niondot_avg_III = AstroParams.N_ion_perbaryon_III/cosmology.rho_baryon(CosmoParams,0.) * np.trapezoid(reio_integrand_III * self.fesctab_III, HMFinterp.logtabMh, axis = 1) # number of ionizing photons produced by popIIII - self.SFRDbar2D_II[np.isnan(self.SFRDbar2D_II)] = 0.0 - self.SFRDbar2D_III[np.isnan(self.SFRDbar2D_III)] = 0.0 - - - ##################################################################################################### - ### STEP 2: Broadcasted Prescription to Compute gammas - zArray, rArray, mArray, deltaNormArray = np.meshgrid(self.zintegral, self.Rtabsmoo, HMF_interpolator.Mhtab, deltatab_norm, indexing = 'ij', sparse = True) + self.reio_integrand_II_interp = interpolate.interp1d(zSFRDflat, niondot_avg_II, kind = 'cubic', bounds_error = False, fill_value = 0) + self.reio_integrand_III_interp = interpolate.interp1d(zSFRDflat, niondot_avg_III, kind = 'cubic', bounds_error = False, fill_value = 0) - rGreaterArray = np.zeros_like(zArray) + rArray + self.niondot_avg_II = self.reio_integrand_II_interp(z_Init.zintegral) + self.niondot_avg_III = self.reio_integrand_III_interp(z_Init.zintegral) + self.niondot_avg = self.niondot_avg_II + self.niondot_avg_III - rGreaterArray[Cosmo_Parameters.chiofzint(zArray) + rArray >= Cosmo_Parameters.chiofzint(50)] = np.nan - zGreaterArray = Cosmo_Parameters.zfofRint(Cosmo_Parameters.chiofzint(zArray) + rGreaterArray) + if not UserParams.DO_ONLY_GLOBAL: + # compute gamma coefficients required by the power spectrum (see correlations.py for detail) + self.sigmaofRtab = np.array([HMFinterp.sigmaR_int(CosmoParams._Rtabsmoo, zz) for zz in z_Init.zintegral]) - whereNotNans = np.invert(np.isnan(rGreaterArray)) + self.compute_gamma(CosmoParams, AstroParams, HMFinterp, z_Init.zintegral, CosmoParams._Rtabsmoo, HMFinterp.Mhtab, self.sigmaofRtab, self.fesctab_II) - sigmaR = np.zeros((len(self.zintegral), len(self.Rtabsmoo), 1, 1)) - sigmaR[whereNotNans] = HMF_interpolator.sigmaRintlog((np.log(rGreaterArray)[whereNotNans], zGreaterArray[whereNotNans])) - sigmaM = np.zeros((len(self.zintegral), len(self.Rtabsmoo), len(HMF_interpolator.Mhtab), 1)) ###HAC: Is this necessary? - sigmaM = HMF_interpolator.sigmaintlog((np.log(mArray), zGreaterArray)) + def Matom(self, z): + """ + Compute minimum mass for atomic-cooling halos - modSigmaSq = sigmaM**2 - sigmaR**2 - indexTooBig = (modSigmaSq <= 0.0) - modSigmaSq[indexTooBig] = np.inf #if sigmaR > sigmaM the halo does not fit in the radius R. Cut the sum - modSigma = np.sqrt(modSigmaSq) + Parameters + ---------- + z : float + Redshift - nu0 = Cosmo_Parameters.delta_crit_ST / sigmaM - nu0[indexTooBig] = 1.0 + Returns + ---------- + Matom : float + Minimum halo mass, in Msun + """ - dsigmadMcurr = HMF_interpolator.dsigmadMintlog((np.log(mArray),zGreaterArray)) ###HAC: Check this works when emulating 21cmFAST - dlogSdMcurr = (dsigmadMcurr*sigmaM*2.0)/(modSigmaSq) + Matom = 3.3e7 * pow((1.+z)/(21.),-3./2) - deltaArray = deltaNormArray * sigmaR - # sMax = 0.3 - # deltaArray[Nsigmad * sigmaR > 1.0] = deltaNormArray * sMax + return Matom - modd = Cosmo_Parameters.delta_crit_ST - deltaArray - nu = modd / modSigma - #PS_HMF~ delta/sigma^3 *exp(-delta^2/2sigma^2) * consts(of M including dsigma^2/dm) - if(Cosmo_Parameters.Flag_emulate_21cmfast==False): - #Normalized PS(d)/ at each mass. 21cmFAST instead integrates it and does SFRD(d)/ - # last 1+delta product converts from Lagrangian to Eulerian - EPS_HMF_corr = (nu/nu0) * (sigmaM/modSigma)**2.0 * np.exp(-Cosmo_Parameters.a_corr_EPS * (nu**2-nu0**2)/2.0 ) * (1.0 + deltaArray) - integrand_II = EPS_HMF_corr * SFRD_II_integrand(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, mArray, zGreaterArray, zGreaterArray) - - # SarahLibanore: compute quantities in Lagrangian space to get gamma in Lagrangian space - EPS_HMF_corr_Lag = (nu/nu0) * (sigmaM/modSigma)**2.0 * np.exp(-Cosmo_Parameters.a_corr_EPS * (nu**2-nu0**2)/2.0 ) - integrand_II_Lag = EPS_HMF_corr_Lag * SFRD_II_integrand(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, mArray, zGreaterArray, zGreaterArray) + def Mmol_0(self, z): + """ + Compute minimum mass for molecular-cooling halos without LW or VCB feedback - elif(Cosmo_Parameters.Flag_emulate_21cmfast==True): #as 21cmFAST, use PS HMF, integrate and normalize at the end - PS_HMF_corr = cosmology.PS_HMF_unnorm(Cosmo_Parameters, HMF_interpolator.Mhtab.reshape(len(HMF_interpolator.Mhtab),1),nu,dlogSdMcurr) * (1.0 + deltaArray) - integrand_II = PS_HMF_corr * SFR_II(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, mArray, zGreaterArray, zGreaterArray) * mArray - - else: - print("ERROR: Need to set FLAG_EMULATE_21CMFAST at True or False in the self.gamma_index2D calculation.") + Parameters + ---------- + z : float + Redshift - ######## - # Compute SFRD quantities - SFRD_II_dR = np.trapezoid(integrand_II, HMF_interpolator.logtabMh, axis = 2) - # SarahLibanore: to compute reionization - niondot_II_dR = np.trapezoid(integrand_II*fesctab_II[None, None, :, None], HMF_interpolator.logtabMh, axis = 2) + Returns + ---------- + Mmol_0 : float + Minimum halo mass, in Msun + """ - # SarahLibanore: compute quantities in Lagrangian space to get gamma in Lagrangian space - SFRD_II_dR_Lag = np.trapezoid(integrand_II_Lag, HMF_interpolator.logtabMh, axis = 2) - niondot_II_dR_Lag = np.trapezoid(integrand_II_Lag*fesctab_II[None, None, :, None], HMF_interpolator.logtabMh, axis = 2) + Mmol_0 = 3.3e7 * (1.+z)**(-1.5) - ### - if Astro_Parameters.USE_POPIII == True: - if(Cosmo_Parameters.Flag_emulate_21cmfast==False): - integrand_III = EPS_HMF_corr * SFRD_III_integrand(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, mArray, J21LW_interp, zGreaterArray, zGreaterArray, ClassCosmo.pars['v_avg']) - elif(Cosmo_Parameters.Flag_emulate_21cmfast==True): - integrand_III = PS_HMF_corr * SFR_III(Astro_Parameters, Cosmo_Parameters, ClassCosmo, HMF_interpolator, mArray, J21LW_interp, zGreaterArray, zGreaterArray, ClassCosmo.pars['v_avg']) * mArray + return Mmol_0 - SFRD_III_dR = np.trapezoid(integrand_III, HMF_interpolator.logtabMh, axis = 2) - # SarahLibanore: reionization - niondot_III_dR = np.trapezoid(integrand_III*fesctab_III[None, None, :, None], HMF_interpolator.logtabMh, axis = 2) - else: - SFRD_III_dR = np.zeros_like(SFRD_II_dR) - - #compute gammas - # SarahLibanore: extend gamma computation to reionization, Lagrangian space and to second order - midpoint = deltaArray.shape[-1]//2 #midpoint of deltaArray at delta = 0 + def Mmol_vcb(self, CosmoParams, AstroParams, z, vCB): + """ + Compute minimum mass for molecular-cooling halos without LW feedback - self.gamma_II_index2D = np.log(SFRD_II_dR[:,:,midpoint+1]/SFRD_II_dR[:,:,midpoint-1]) / (deltaArray[:,:,0,midpoint+1] - deltaArray[:,:,0,midpoint-1]) - self.gamma_II_index2D[np.isnan(self.gamma_II_index2D)] = 0.0 + Parameters + ---------- + CosmoParams : CosmoParams class + AstroParams : AstroParams class + z : float + Redshift + vCB : float + Baryon-DM relative velocity - self.gamma_niondot_II_index2D = np.log(niondot_II_dR[:,:,midpoint+1]/niondot_II_dR[:,:,midpoint-1]) / (deltaArray[:,:,0,midpoint+1] - deltaArray[:,:,0,midpoint-1]) - self.gamma_niondot_II_index2D[np.isnan(self.gamma_niondot_II_index2D)] = 0.0 + Returns + ---------- + Mmol_vcb : float + Minimum halo mass, in Msun + """ - # Lagrangian - self.gamma_II_index2D_Lag = np.log(SFRD_II_dR_Lag[:,:,midpoint+1]/SFRD_II_dR_Lag[:,:,midpoint-1]) / (deltaArray[:,:,0,midpoint+1] - deltaArray[:,:,0,midpoint-1]) - self.gamma_II_index2D_Lag[np.isnan(self.gamma_II_index2D_Lag)] = 0.0 + mmolBase = self.Mmol_0(z) + vcbFeedback = pow(1 + AstroParams.A_vcb * vCB / CosmoParams.sigma_vcb, AstroParams.beta_vcb) - self.gamma_niondot_II_index2D_Lag = np.log(niondot_II_dR_Lag[:,:,midpoint+1]/niondot_II_dR_Lag[:,:,midpoint-1]) / (deltaArray[:,:,0,midpoint+1] - deltaArray[:,:,0,midpoint-1]) - self.gamma_niondot_II_index2D_Lag[np.isnan(self.gamma_niondot_II_index2D_Lag)] = 0.0 + Mmol_vcb = mmolBase * vcbFeedback - #compute second-order derivative gammas by computing two first-order derivatives #TODO: functionalize derivatives - der1_II = np.log(SFRD_II_dR[:,:,midpoint]/SFRD_II_dR[:,:,midpoint-1])/(deltaArray[:,:,0,midpoint] - deltaArray[:,:,0,midpoint-1]) #ln(y2/y1)/(x2-x1) - der2_II = np.log(SFRD_II_dR[:,:,midpoint+1]/SFRD_II_dR[:,:,midpoint])/(deltaArray[:,:,0,midpoint+1] - deltaArray[:,:,0,midpoint]) #ln(y3/y2)/(x3-x2) - self.gamma2_II_index2D = (der2_II - der1_II)/(deltaArray[:,:,0,midpoint+1] - deltaArray[:,:,0,midpoint-1]) #second derivative: (der2-der1)/((x3-x1)/2) - self.gamma2_II_index2D[np.isnan(self.gamma2_II_index2D)] = 0.0 - - der1_niondot_II = np.log(niondot_II_dR[:,:,midpoint]/niondot_II_dR[:,:,midpoint-1])/(deltaArray[:,:,0,midpoint] - deltaArray[:,:,0,midpoint-1]) #ln(y2/y1)/(x2-x1) - der2_niondot_II = np.log(niondot_II_dR[:,:,midpoint+1]/niondot_II_dR[:,:,midpoint])/(deltaArray[:,:,0,midpoint+1] - deltaArray[:,:,0,midpoint]) #ln(y3/y2)/(x3-x2) - self.gamma2_niondot_II_index2D = (der2_niondot_II - der1_niondot_II)/(deltaArray[:,:,0,midpoint+1] - deltaArray[:,:,0,midpoint-1]) #second derivative: (der2-der1)/((x3-x1)/2) - self.gamma2_niondot_II_index2D[np.isnan(self.gamma2_niondot_II_index2D)] = 0.0 - - # Lagrangian - der1_II_Lag = np.log(SFRD_II_dR_Lag[:,:,midpoint]/SFRD_II_dR_Lag[:,:,midpoint-1])/(deltaArray[:,:,0,midpoint] - deltaArray[:,:,0,midpoint-1]) #ln(y2/y1)/(x2-x1) - der2_II_Lag = np.log(SFRD_II_dR_Lag[:,:,midpoint+1]/SFRD_II_dR_Lag[:,:,midpoint])/(deltaArray[:,:,0,midpoint+1] - deltaArray[:,:,0,midpoint]) #ln(y3/y2)/(x3-x2) - self.gamma2_II_index2D_Lag = (der2_II_Lag - der1_II_Lag)/(deltaArray[:,:,0,midpoint+1] - deltaArray[:,:,0,midpoint-1]) #second derivative: (der2-der1)/((x3-x1)/2) - self.gamma2_II_index2D_Lag[np.isnan(self.gamma2_II_index2D_Lag)] = 0.0 - - der1_niondot_II_Lag = np.log(niondot_II_dR_Lag[:,:,midpoint]/niondot_II_dR_Lag[:,:,midpoint-1])/(deltaArray[:,:,0,midpoint] - deltaArray[:,:,0,midpoint-1]) #ln(y2/y1)/(x2-x1) - der2_niondot_II_Lag = np.log(niondot_II_dR_Lag[:,:,midpoint+1]/niondot_II_dR_Lag[:,:,midpoint])/(deltaArray[:,:,0,midpoint+1] - deltaArray[:,:,0,midpoint]) #ln(y3/y2)/(x3-x2) - self.gamma2_niondot_II_index2D_Lag = (der2_niondot_II_Lag - der1_niondot_II_Lag)/(deltaArray[:,:,0,midpoint+1] - deltaArray[:,:,0,midpoint-1]) #second derivative: (der2-der1)/((x3-x1)/2) - self.gamma2_niondot_II_index2D_Lag[np.isnan(self.gamma2_niondot_II_index2D_Lag)] = 0.0 + return Mmol_vcb - if Astro_Parameters.USE_POPIII == True: - self.gamma_III_index2D = np.log(SFRD_III_dR[:,:,-1]/SFRD_III_dR[:,:,0]) / (deltaArray[:,:,0,-1] - deltaArray[:,:,0,0]) - self.gamma_III_index2D[np.isnan(self.gamma_III_index2D)] = 0.0 - # SarahLibanore: reionization - self.gamma_niondot_III_index2D = np.log(niondot_III_dR[:,:,midpoint+1]/niondot_III_dR[:,:,midpoint-1]) / (deltaArray[:,:,0,midpoint+1] - deltaArray[:,:,0,midpoint-1]) - self.gamma_niondot_III_index2D[np.isnan(self.gamma_niondot_III_index2D)] = 0.0 + def Mmol_LW(self, AstroParams, J21LW_interp, z): + """ + Compute minimum mass for molecular halos without VCB feedback - else: - self.gamma_III_index2D = np.zeros_like(self.gamma_II_index2D) - # SarahLibanore: reionization - self.gamma_niondot_III_index2D = np.zeros_like(self.gamma_niondot_II_index2D) + Parameters + ---------- + AstroParams : AstroParams class + J21LWinterp : interpolator + Interpolator of the LW flux, function of z + z : float + Redshift - ##################################################################################################### - ### STEP 3: Computing lambdas in velocity anisotropies - ### Because we found the SFRD vcb dependence to be delta independent, we compute quantities below for a variety of R's and delta_R = 0 - - if Astro_Parameters.USE_POPIII == True: - self.vcb_expFitParams = np.zeros((len(self.zintegral),len(self.Rtabsmoo), 4)) #for the 4 exponential parameters - - if Cosmo_Parameters.USE_RELATIVE_VELOCITIES == True: + Returns + ---------- + Mmol_LW : float + Minimum halo mass, in Msun + """ - v_avg0 = ClassCosmo.pars['v_avg'] - vAvg_array = v_avg0 * np.array([0.2, 0.7, 1, 1.25, 2.0]) - etaTilde_array = 3 * vAvg_array**2 / ClassCosmo.pars['sigma_vcb']**2 + mmolBase = self.Mmol_0(z) + lwFeedback = 1 + AstroParams.A_LW*pow(J21LW_interp(z), AstroParams.beta_LW) - zArray, rArray, mArray, velArray = np.meshgrid(self.zintegral, self.Rtabsmoo, HMF_interpolator.Mhtab, vAvg_array, indexing = 'ij', sparse = True) + Mmol_LW = mmolBase * lwFeedback - rGreaterArray = np.zeros_like(zArray) + rArray + return Mmol_LW - rGreaterArray[Cosmo_Parameters.chiofzint(zArray) + rArray >= Cosmo_Parameters.chiofzint(50)] = np.nan - zGreaterArray = Cosmo_Parameters.zfofRint(Cosmo_Parameters.chiofzint(zArray) + rGreaterArray) - whereNotNans = np.invert(np.isnan(rGreaterArray)) + def Mmol(self, CosmoParams, AstroParams, J21LW_interp, z, vCB): + """ + Compute minimum mass for molecular halos with LW and VCB feedback - sigmaR = np.zeros((len(self.zintegral), len(self.Rtabsmoo), 1, 1)) - sigmaR[whereNotNans] = HMF_interpolator.sigmaRintlog((np.log(rGreaterArray)[whereNotNans], zGreaterArray[whereNotNans])) + Parameters + ---------- + CosmoParams : CosmoParams class + AstroParams : AstroParams class + J21LWinterp : interpolator or False + Interpolator of the LW flux, function of z. If False, no LW feedback. + z : float + Redshift + vCB : float or False + Baryon-DM relative velocity. If False, no feedback from streaming velocitites. - sigmaM = np.zeros((len(self.zintegral), len(self.Rtabsmoo), len(HMF_interpolator.Mhtab), 1)) ###HAC: Is this necessary? - sigmaM = HMF_interpolator.sigmaintlog((np.log(mArray), zGreaterArray)) + Returns + ---------- + Mmol : float + Minimum halo mass, in Msun + """ - modSigmaSq = sigmaM**2 - sigmaR**2 - indexTooBig = (modSigmaSq <= 0.0) - modSigmaSq[indexTooBig] = np.inf #if sigmaR > sigmaM the halo does not fit in the radius R. Cut the sum - modSigma = np.sqrt(modSigmaSq) + Mmol = self.Mmol_0(z) - nu0 = Cosmo_Parameters.delta_crit_ST / sigmaM - nu0[indexTooBig] = 1.0 + if vCB is not False: + vcbFeedback = pow(1 + AstroParams.A_vcb * vCB / CosmoParams.sigma_vcb, AstroParams.beta_vcb) + Mmol = Mmol * vcbFeedback - dsigmadMcurr = HMF_interpolator.dsigmadMintlog((np.log(mArray),zGreaterArray)) ###HAC: Check this works when emulating 21cmFAST - dlogSdMcurr = (dsigmadMcurr*sigmaM*2.0)/(modSigmaSq) + if J21LW_interp is not False: + lwFeedback = 1 + AstroParams.A_LW*pow(J21LW_interp(z), AstroParams.beta_LW) + Mmol = Mmol * lwFeedback - deltaZero = np.zeros_like(sigmaR) - # sMax = 0.3 - # deltaArray[Nsigmad * sigmaR > 1.0] = deltaNormArray * sMax + # TODO: added option to turn off vCB/LW feedback entirely by putting the input to False, we may consider removing duplicating functions without feedback (Mmol_0, Mmol_vcb, Mmol_LW) + option to pass None and get the CosmoParams.vcb_avg and SFRD.J21LW_interp_conv_avg within the method instead, to avoid having to deal with this externally? E.g. in compute_pop_LFbias_binned(); note that CosmoParams is note needed unless we are computing the vcb feedback, so it could be made an optional parameter as well + # TODO: refs for atomic/molecular-cooling mass calculation functions - modd = Cosmo_Parameters.delta_crit_ST - deltaZero - nu = modd / modSigma + return Mmol - #PS_HMF~ delta/sigma^3 *exp(-delta^2/2sigma^2) * consts(of M including dsigma^2/dm) - if(Cosmo_Parameters.Flag_emulate_21cmfast==False): - #Normalized PS(d)/ at each mass. 21cmFAST instead integrates it and does SFRD(d)/ - # last 1+delta product converts from Lagrangian to Eulerian - EPS_HMF_corr = (nu/nu0) * (sigmaM/modSigma)**2.0 * np.exp(-Cosmo_Parameters.a_corr_EPS * (nu**2-nu0**2)/2.0 ) * (1.0 + deltaZero) - integrand_III = EPS_HMF_corr * SFRD_III_integrand(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, mArray, J21LW_interp, zGreaterArray, zGreaterArray, velArray) - - elif(Cosmo_Parameters.Flag_emulate_21cmfast==True): #as 21cmFAST, use PS HMF, integrate and normalize at the end -# PS_HMF_corr = cosmology.PS_HMF_unnorm(Cosmo_Parameters, HMF_interpolator.Mhtab.reshape(len(HMF_interpolator.Mhtab),1),nu,dlogSdMcurr) * (1.0 + deltaZero) - PS_HMF_corr = cosmology.PS_HMF_unnorm(Cosmo_Parameters, HMF_interpolator.Mhtab.reshape(len(HMF_interpolator.Mhtab),1), nu, dlogSdMcurr) * (1.0 + deltaZero) - integrand_III = PS_HMF_corr * SFR_III(Astro_Parameters, Cosmo_Parameters, ClassCosmo, HMF_interpolator, mArray, J21LW_interp, zGreaterArray, zGreaterArray, velArray) * mArray - - else: - print("ERROR: Need to set FLAG_EMULATE_21CMFAST at True or False in the self.gamma_index2D calculation.") - SFRD_III_dR_V = np.trapezoid(integrand_III, HMF_interpolator.logtabMh, axis = 2) - - SFRDIII_Ratio = SFRD_III_dR_V / SFRD_III_dR_V[:,:,len(vAvg_array)//2].reshape((len(self.zintegral), len(self.Rtabsmoo), 1)) - SFRDIII_Ratio[np.isnan(SFRDIII_Ratio)] = 0.0 - - #temporarily turning off divide warnings; will turn them on again after exponential fitting routine - divideErr = np.seterr(divide = 'ignore') - divideErr2 = np.seterr(invalid = 'ignore') + def dMh_dt(self, CosmoParams, AstroParams, HMFinterp, massVector, z): + """ + Compute halo mass accretion rate, in units of M_sun/yr + + Parameters + ---------- + CosmoParams : CosmoParams class + AstroParams : AstroParams class + HMFinterp : HMFinterp class + massVector : array + Halo masses + z : array + Redshift + + Returns + ---------- + Mhdot : array + Halo mass accretion rate + """ + + if not CosmoParams.Flag_emulate_21cmfast: # GALLUMI-like + if AstroParams.accretion_model == "exp": # Exponential accretion + dMhdz = massVector * constants.ALPHA_accretion_exponential - ###HAC: The next few lines fits for rho(z, v) / rhoavg = Ae^-b tilde(eta) + Ce^-d tilde(eta). - ### To expedite the computation, instead of using scipy.optimize.curve_fit, I choose two points where one - ### exponential dominates to fit for C and d, subtract Ce^-d tilde(eta) from rho(z, v) / rhoavg, then fit for A and b + elif AstroParams.accretion_model == "EPS": # EPS accretion - dParams = -1 * np.log(SFRDIII_Ratio[:,:,-1]/SFRDIII_Ratio[:,:,-2]) / (etaTilde_array[-1]-etaTilde_array[-2]) - cParams = np.exp(np.log(SFRDIII_Ratio[:,:,-1]) + dParams * etaTilde_array[-1]) - - SFRDIII_RatioNew = SFRDIII_Ratio - cParams.reshape(*cParams.shape, 1) * np.exp(-1 * dParams.reshape(*dParams.shape, 1)* etaTilde_array.reshape(1,1,*etaTilde_array.shape) ) - bParams = -1 * np.log(SFRDIII_RatioNew[:,:,0]/SFRDIII_RatioNew[:,:,1]) / (etaTilde_array[0]-etaTilde_array[1]) - aParams = np.exp(np.log(SFRDIII_RatioNew[:,:,0]) + bParams * etaTilde_array[0]) + Mh2 = massVector* constants.EPSQ_accretion + indexMh2low = Mh2 < massVector.flatten()[0] + Mh2[indexMh2low] = massVector.flatten()[0] - divideErr = np.seterr(divide = 'warn') - divideErr2 = np.seterr(invalid = 'warn') + sigmaMh = HMFinterp.sigmaintlog((np.log(massVector), z)) + sigmaMh2 = HMFinterp.sigmaintlog((np.log(Mh2), z)) + sigmaMh2[np.full_like(sigmaMh2, fill_value=True, dtype = bool) * indexMh2low] = 1e99 - self.vcb_expFitParams[:,:,0] = aParams - self.vcb_expFitParams[:,:,1] = bParams - self.vcb_expFitParams[:,:,2] = cParams - self.vcb_expFitParams[:,:,3] = dParams - - self.vcb_expFitParams[np.isnan(self.vcb_expFitParams)] = 0.0 - - - ##################################################################################################### - ### STEP 4: LW correction to Pop III gammas - if Astro_Parameters.USE_POPIII == True: - if Astro_Parameters.USE_LW_FEEDBACK == True: - #get the zero-lag correlation function (zero distance separation) - xi_RR_CF_zerolag = np.copy(ClassCosmo.pars['xi_RR_CF'][:,:,0]) - - #compute LW coefficients for Pop II and III stars - coeff1LWzp_II, coeff2LWzpRR_II = J_LW_Discrete(Astro_Parameters, Cosmo_Parameters, ClassCosmo, self.zintegral, 2, self.Rtabsmoo, SFRD_II_interp, SFRD_III_cnvg_interp) - coeff1LWzp_III, coeff2LWzpRR_III = J_LW_Discrete(Astro_Parameters, Cosmo_Parameters, ClassCosmo, self.zintegral, 3, self.Rtabsmoo, SFRD_II_interp, SFRD_III_cnvg_interp) - - # Corrections WITH Rmax smoothing - deltaGamma_R = 1 / np.transpose([SFRD_III_cnvg_interp(self.zintegral)]) - deltaGamma_R *= np.array([dSFRDIII_dJ(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, J21LW_interp, np.array([self.zintegral]).T, np.array([self.zintegral]).T, ClassCosmo.pars['v_avg'])]).T - deltaGamma_R = deltaGamma_R * (coeff1LWzp_II * coeff2LWzpRR_II * self.gamma_II_index2D + coeff1LWzp_III * coeff2LWzpRR_III * self.gamma_III_index2D) * 1e21 - - #choose only max of r and R; since growth factors cancel out, none are used here - xi_R_maxrR = np.tril(np.ones_like(xi_RR_CF_zerolag)) * np.transpose([np.diag(xi_RR_CF_zerolag)]) - xi_R_maxrR = xi_R_maxrR + np.triu(xi_RR_CF_zerolag, k = 1) - - self.deltaGamma_R_Matrix = xi_R_maxrR.reshape(len(self.Rtabsmoo), 1, len(self.Rtabsmoo)) * (deltaGamma_R * self.dlogRR * self.Rtabsmoo).reshape(1, len(self.zintegral), len(self.Rtabsmoo)) - deltaGamma_R_z = np.transpose( np.sum(self.deltaGamma_R_Matrix, axis = 2) / np.transpose([np.diagonal(xi_RR_CF_zerolag[:,:])]) ) - deltaGamma_R_z[ self.gamma_III_index2D == 0 ] = 0 #don't correct gammas if gammas are zero - self.deltaGamma_R_z = deltaGamma_R_z - self.gamma_III_index2D += deltaGamma_R_z #correct Pop III gammas with LW correction factor + growth = cosmology.growth(CosmoParams,z) + dzgrow = z*0.01 + dgrowthdz = (cosmology.growth(CosmoParams,z+dzgrow) - cosmology.growth(CosmoParams,z-dzgrow))/(2.0 * dzgrow) + dMhdz = - massVector * np.sqrt(2/np.pi)/np.sqrt(sigmaMh2**2 - sigmaMh**2) *dgrowthdz/growth * CosmoParams.delta_crit_ST + + elif(AstroParams.accretion_model == 'RP16'): # Fitting function to Rodríguez-Puebla+16 N-body simulations (eq. 11, dynamically averaged parameters from table 2) + a = (1+z)**-1 + beta = 10**(2.73-(1.828*a)+(0.654*a**2)) + alpha = 1 + (0.329*a) - (0.206*a**2) + + # Factors of h are accounted for to give units of M_sun/year for halo masses in units of M_sun: + Mhdot = beta * (massVector/1e12)**alpha * cosmology.Hub(CosmoParams, z) / (100*CosmoParams.h_fid) + else: + print("ERROR! Have to choose an accretion model in AstroParams (accretion_model)") - ##################################################################################################### - ### STEP 5: Lyman-Alpha Anisotropies - - #Makes heavy use of broadcasting to make computations faster - #3D cube will be summed over one axis. Dimensions are (z,R,n) = (64, 45, 21) + return -1 + + Mhdot = dMhdz*cosmology.Hubinvyr(CosmoParams,z)*(1.0+z) + + else: # 21cmfast-like + Mhdot = massVector/AstroParams.tstar*cosmology.Hubinvyr(CosmoParams,z) - self.coeff1LyAzp = (1+self.zintegral)**2/(4*np.pi) + return Mhdot - nuLYA = np.geomspace(constants.freqLyA, constants.freqLyCont, 128) - sedLYAII_interp = interpolate.interp1d(nuLYA, Astro_Parameters.SED_LyA(nuLYA, pop = 2), kind = 'linear', bounds_error = False, fill_value = 0) #interpolate LyA SED - n_recArray = np.arange(0,constants.n_max_recycle-1 ) - zpCube, rCube, n_recCube = np.meshgrid(self.zintegral, self.Rtabsmoo, n_recArray, indexing='ij', sparse=True) #for broadcasting purposes - n_lineCube = n_recCube + 2 - zmax_lineCube = (1+zpCube) * (1 - pow(1+n_lineCube,-2.0))/(1-pow(n_lineCube,-2.0) ) - 1.0 #maximum redshift Lyman series photons can redshift before falling into a Ly-n resonance + def fstar_ofz(self, CosmoParams, z, massVector, eps, dlog10eps, zpiv, Mc, alphastar, betastar, fstarmax): + """ + Compute star formation efficiency as function of z -- by changing the parameters, the user can run both popII and popIII + + Parameters + ---------- + CosmoParams : CosmoParams class + z : float + Redshift + massVector : array + Halo masses + eps : float + Star formation efficiency at pivot redshift and mass + dlog10eps : float + Logaritmic redshift evolution + zpiv : float + Pivot reference redshift + Mc : float + Pivot reference halo mass + alphastar : float + Power-law coefficient + betastar : float + Second power-law coefficient + fstarmax : float + Cap + + Returns + ---------- + fstar : array + Star formation efficiency + """ + + epsstar_ofz = eps * 10**(dlog10eps * (z-zpiv)) + + if CosmoParams.Flag_emulate_21cmfast: + # 21cmFAST-like + fstar = CosmoParams.OmegaB/CosmoParams.OmegaM * np.clip(epsstar_ofz\ + /(pow(massVector/Mc, -alphastar)), 0, fstarmax) - nu_linezpCube = constants.freqLyCont * (1 - (1.0/n_lineCube)**2) - zGreaterCube = zGreaterMatrix_nonan.reshape(len(self.zintegral), len(self.Rtabsmoo), 1) - nu_lineRRCube = nu_linezpCube * (1.+zGreaterCube)/(1+zpCube) - - eps_alphaRR_II_Cube = Astro_Parameters.N_alpha_perbaryon_II/Cosmo_Parameters.mu_baryon_Msun * sedLYAII_interp(nu_lineRRCube) - - #the last nonzero index of the array is overestimated since only part of the spherical shell is within zmax_line. Correct by by dz/Delta z - weights_recCube = np.heaviside(zmax_lineCube - zGreaterCube, 0.0) - index_first0_weightsCube = np.where(np.diff(weights_recCube, axis = 1) == -1) #find index of last nonzero value. equals zero if two consecutive elements are 1 or 0, and -1 if two consecutive elements are [1,0] - i0Z, i0R, i0N = index_first0_weightsCube - weights_recCube[i0Z, i0R, i0N] *= (zmax_lineCube[i0Z, 0, i0N] - zGreaterCube[i0Z, i0R, 0])/ (zGreaterCube[i0Z, i0R+1, 0] - zGreaterCube[i0Z, i0R, 0]) - - Jalpha_II = np.array(constants.fractions_recycle)[:len(n_recArray)].reshape(1,1,len(n_recArray)) * weights_recCube * eps_alphaRR_II_Cube #just resizing f_recycle; it is length 29,we only consider up to n=22 - LyAintegral_II = np.sum(Jalpha_II,axis=2) #sum over axis 2, over all possible n transitions - self.coeff2LyAzpRR_II = self.Rtabsmoo * self.dlogRR * self.SFRDbar2D_II * LyAintegral_II/ constants.yrTos/constants.Mpctocm**2 - - if Astro_Parameters.USE_POPIII == True: - sedLYAIII_interp = interpolate.interp1d(nuLYA, Astro_Parameters.SED_LyA(nuLYA, pop = 3), kind = 'linear', bounds_error = False, fill_value = 0) - eps_alphaRR_III_Cube = Astro_Parameters.N_alpha_perbaryon_III/Cosmo_Parameters.mu_baryon_Msun * sedLYAIII_interp(nu_lineRRCube) - - Jalpha_III = np.array(constants.fractions_recycle)[:len(n_recArray)].reshape(1,1,len(n_recArray)) * weights_recCube * eps_alphaRR_III_Cube - LyAintegral_III = np.sum(Jalpha_III,axis=2) - self.coeff2LyAzpRR_III = self.Rtabsmoo * self.dlogRR * self.SFRDbar2D_III * LyAintegral_III/ constants.yrTos/constants.Mpctocm**2 else: - self.coeff2LyAzpRR_III = np.zeros_like(self.coeff2LyAzpRR_II) + # GALLUMI-like + fstar = CosmoParams.OmegaB/CosmoParams.OmegaM * np.clip(2.0 * epsstar_ofz\ + /(pow(massVector/Mc,-alphastar) + pow(massVector/Mc,-betastar)), 0, fstarmax) + return fstar - ##################################################################################################### - ### STEP 6: X-ray Anisotropies - - zGreaterCube = zGreaterMatrix_nonan.reshape(len(self.zintegral), len(self.Rtabsmoo), 1, 1) #redefine this just for x-ray routine - - self.coeff1Xzp = -2/3 * self.zintegral * self.dlogzint / cosmology.Hubinvyr(Cosmo_Parameters,self.zintegral) / (1+self.zintegral) * (1+self.zintegral)**2 - self.coeff1Xzp = self.coeff1Xzp / (1+self.zintegral)**2 * constants.yrTos #this accounts for adiabatic cooling. compensated by the inverse at the end - zpCube, rCube, eCube, zPPCube = np.meshgrid(self.zintegral, self.Rtabsmoo, _Energylist, np.arange(Nzinttau), indexing='ij', sparse=True) - currentEnergyTable = eCube * (1+zGreaterCube) / (1+zpCube) - SEDCube = Astro_Parameters.SED_XRAY(currentEnergyTable, pop = 2) - SEDCube_III = Astro_Parameters.SED_XRAY(currentEnergyTable, pop = 3) + def fduty(self, CosmoParams, AstroParams, massVector, z, lower_cutoff=None, upper_cutoff=None, is_sharp_cutoff=False, vCB=None, J21LW_interp=None): + """ + Compute duty fraction to damp star formation + + Parameters + ---------- + CosmoParams : CosmoParams class + AstroParams : AstroParams class + massVector : array + Halo masses + z : float + Redshift + lower_cutoff : str or float or None + Apply cutoff at the low-mass end; if None, no cutoff; if str == "Mmol" or "Matom", cutoff at the molecular/atomic-cooling limit respectively; if float, custom cutoff at user-provided value + upper_cutoff : str or float or None + Apply cutoff at the high-mass end; if None, no cutoff; if str == "Matom", cutoff at the atomic-cooling limit; if float, custom cutoff at user-provided value + is_sharp_cutoff : bool + If true, apply sharp cutoff; exponential cutoff otherwise + vCB : float or None + Baryon-DM relative velocity (None by default: only matters for molecular-cooling limit computation for Pop IIIs) + J21LW_interp : interpolator or None + Interpolator of the LW flux, function of z (None by default: only matters for molecular-cooling limit computation for Pop IIIs) + + Returns + ---------- + fduty : array + Duty cycle + """ + + # Low-mass end cutoff + if lower_cutoff is not None: + if lower_cutoff == "Mmol": + Mlow = self.Mmol(CosmoParams, AstroParams, J21LW_interp, z, vCB) + elif lower_cutoff == "Matom": + Mlow = self.Matom(z) + else: + Mlow = lower_cutoff - ######## Broadcasted routine to find X-ray optical depths, modeled after but does not use xrays.optical_depth - zPPCube = np.array([np.linspace(np.transpose([self.zintegral]), self.zGreaterMatrix, Nzinttau, axis = 2)]) - zPPCube = zPPCube.reshape(len(self.zintegral), len(self.Rtabsmoo), 1, Nzinttau) #to have 4D dimensions, default shape = (64,45, 1, 10) + if is_sharp_cutoff: + fduty_low = np.heaviside(massVector - Mlow, 0.5) + else: + fduty_low = np.exp(-Mlow/massVector) + else: + fduty_low = 1. - ePPCube = eCube * (1+ zPPCube) / (1+zpCube) #E'' = E(1+z'')/(1+z) - sigmatot = Xrays.atomfractions[0] * sigma_HI(ePPCube) - sigmatot += Xrays.atomfractions[1] * sigma_HeI(ePPCube) + # High-mass end cutoff + if upper_cutoff is not None: + if upper_cutoff == "Matom": + Mup = self.Matom(z) + else: + Mup = upper_cutoff - opticalDepthIntegrand = 1 / cosmology.HubinvMpc(Cosmo_Parameters, zPPCube) / (1+zPPCube) * sigmatot * cosmology.n_H(Cosmo_Parameters, zPPCube) * constants.Mpctocm #this uses atom fractions of 1 for HI and x_He for HeI -# opticalDepthIntegrand = 1 / cosmology.HubinvMpc(Cosmo_Parameters, zPPCube) / (1+zPPCube) * sigmatot * cosmology.n_baryon(Cosmo_Parameters, zPPCube) * constants.Mpctocm - tauCube = np.trapezoid(opticalDepthIntegrand, zPPCube, axis = 3) + if is_sharp_cutoff: + fduty_up = np.heaviside(Mup - massVector, 0.5) + else: + fduty_up = np.exp(-massVector/Mup) + else: + fduty_up = 1. - indextautoolarge = np.array(tauCube>=Xrays.TAUMAX) - tauCube[indextautoolarge] = Xrays.TAUMAX + fduty = fduty_low * fduty_up + + return fduty + + + def SFE_II(self, CosmoParams, AstroParams, massVector, z): + """ + Star formation efficiency for popII stars + + Parameters + ---------- + CosmoParams : CosmoParams class + AstroParams : AstroParams class + massVector : array + Halo masses + z : float + Redshift + + Returns + ---------- + SFE : array + Star formation efficiency + """ + + fstarM = self.fstar_ofz(CosmoParams, z, massVector, + AstroParams.epsstar, AstroParams.dlog10epsstardz, AstroParams._zpivot, + AstroParams.Mc, AstroParams.alphastar, AstroParams.betastar, AstroParams.fstarmax) - if Cosmo_Parameters.Flag_emulate_21cmfast == False: - weights_X_zCube = np.exp(-tauCube) - elif Cosmo_Parameters.Flag_emulate_21cmfast == True: - weights_X_zCube = np.heaviside(1.0 - tauCube, 0.5) + if not AstroParams.FLAG_MTURN_FIXED: + fduty = self.fduty(CosmoParams, AstroParams, massVector, z, lower_cutoff="Matom", upper_cutoff=None, is_sharp_cutoff=AstroParams.FLAG_MTURN_SHARP) else: - print("Error, choose a correct XRAY_OPACITY_MODEL") - - SEDCube = SEDCube[:,:,:,0] #rescale dimensions of energy and SED cubes back to 3D, so we can integrate over energy - SEDCube_III = SEDCube_III[:,:,:,0] #rescale dimensions of energy and SED cubes back to 3D, so we can integrate over energy - - eCube = eCube[:,:,:,0] - ######## end of optical depth routine + fduty = self.fduty(CosmoParams, AstroParams, massVector, z, lower_cutoff=AstroParams.Mturn_fixed, upper_cutoff=None, is_sharp_cutoff=AstroParams.FLAG_MTURN_SHARP) - JX_coeffsCube = SEDCube * weights_X_zCube - JX_coeffsCube_III = SEDCube_III * weights_X_zCube + SFE = fstarM * fduty - sigma_times_en = Xrays.atomfractions[0] * sigma_HI(eCube) * (eCube - Xrays.atomEnIon[0]) - sigma_times_en += Xrays.atomfractions[1] * sigma_HeI(eCube) * (eCube - Xrays.atomEnIon[1]) - sigma_times_en /= np.sum(Xrays.atomfractions)#to normalize per baryon, instead of per Hydrogen nucleus - #HI and HeII separate. Notice Energy (and not Energy'), since they get absorbed at the zp frame - - xrayEnergyTable = np.sum(JX_coeffsCube * sigma_times_en * eCube * Astro_Parameters.dlogEnergy,axis=2) - self.coeff2XzpRR_II = np.nan_to_num(self.Rtabsmoo * self.dlogRR * self.SFRDbar2D_II * xrayEnergyTable * (1.0/constants.Mpctocm**2.0) * constants.normLX_CONST, nan = 0) + return SFE + + + def SFE_III(self, CosmoParams, AstroParams, massVector, z, vCB, J21LW_interp): + """ + Star formation efficiency for popIII stars. + + By default, this computes a single main Pop III component with a low-mass cutoff at the molecular-cooling limit and a user-controlled high-mass cutoff Mup_III. Setting Mup_III="Matom" (default) in the AstroParams recovers standard minihalo-only behavior, with high-mass cutoff at the atomic-cooling limit. + + If AstroParams.DETACH_III_ACH is True, main component is forced to stop at the atomic-cooling limit and a detached atomic-cooling-halo component with independent parameters is added between Matom and Mup_III. + + Parameters + ---------- + CosmoParams : CosmoParams class + AstroParams : AstroParams class + massVector : array + Halo masses + z : float + Redshift + vCB : float or None + Baryon-DM relative velocity + J21LW_interp : interpolator or None + Interpolator of the LW flux, function of z + + Returns + ---------- + SFE_tot : array + Star formation efficiency + """ - if Astro_Parameters.USE_POPIII == True: - xrayEnergyTable_III = np.sum(JX_coeffsCube_III * sigma_times_en * eCube * Astro_Parameters.dlogEnergy,axis=2) - self.coeff2XzpRR_III = np.nan_to_num(self.Rtabsmoo * self.dlogRR * self.SFRDbar2D_III * xrayEnergyTable_III * (1.0/constants.Mpctocm**2.0) * constants.normLX_CONST, nan = 0) + # Main component + fstarM = self.fstar_ofz(CosmoParams, z, massVector, + AstroParams.epsstar_III, AstroParams.dlog10epsstardz_III, AstroParams._zpivot_III, + AstroParams.Mc_III, AstroParams.alphastar_III, AstroParams.betastar_III, AstroParams.fstarmax) + detach_ACH = AstroParams.DETACH_III_ACH + if detach_ACH: + # If detached ACH component, high-mass cutoff of the main component forced at the ACH limit + Mup_main = "Matom" else: - self.coeff2XzpRR_III = np.zeros_like(self.coeff2XzpRR_II) - + # Main component cut at the user-defined high-mass cutoff - note that this has to be put to "Matom" in order to recover standard behaviour, with main component limited to the MC minihalo regime + Mup_main = AstroParams.Mup_III + fduty = self.fduty(CosmoParams, AstroParams, massVector, z, lower_cutoff="Mmol", upper_cutoff=Mup_main, is_sharp_cutoff=False, vCB=vCB, J21LW_interp=J21LW_interp) # TODO: Do we want to allow the cut-off to be sharp? + SFE = fstarM * fduty + + # Detached ACH component with custom parameters, added to the MC minihalo component + # TODO: for now, the case with detached Pop III ACH component is isolated here, but we could integrate it in the general case below by implementing a separate poulation type, e.g. 3.II? This could be extended to other population types e.g. different morphological types etc... + if detach_ACH: + if AstroParams.Mup_III == "Matom": + print("WARNING: AstroParams.DETACH_III_ACH = True but AstroParams.Mup_III == 'Matom'. Ignoring atomic-cooling Pop III component") # In this case, both low- and high-mass cutoff for the ACH component would be at the ACH limit, so this component is ignored + else: + fstarM_ACH = self.fstar_ofz(CosmoParams, z, massVector, + AstroParams.epsstar_III_ACH, AstroParams.dlog10epsstardz_III_ACH, AstroParams._zpivot_III_ACH, + AstroParams.Mc_III_ACH, AstroParams.alphastar_III_ACH, AstroParams.betastar_III_ACH, AstroParams.fstarmax) + fduty_ACH = self.fduty(CosmoParams, AstroParams, massVector, z, lower_cutoff="Matom", upper_cutoff=AstroParams.Mup_III, is_sharp_cutoff=False) + SFE += fstarM_ACH * fduty_ACH + + return SFE + + + def SFE(self, CosmoParams, AstroParams, massVector, z, pop, vCB=None, J21LW_interp=None): + """ + Total star formation efficiency + + Parameters + ---------- + CosmoParams : CosmoParams class + AstroParams : AstroParams class + massVector : array + Halo masses + z : float + Redshift + pop : int + Which population (2 for popII or 3 for popIII) + vCB : float or None + Baryon-DM relative velocity (None by default: only matters for molecular-cooling limit computation for Pop IIIs) + J21LW_interp : interpolator or None + Interpolator of the LW flux, function of z (None by default: only matters for molecular-cooling limit computation for Pop IIIs) + + Returns + ---------- + SFE : array + Star formation efficiency for the input population + """ + + if pop == 3 and not AstroParams.USE_POPIII: + return 0 # Skip whole routine if NOT using popIII stars --> TODO: here we may want to raise a ValueError instead... - ##################################################################################################### - ### STEP 7: Non-Linear Correction Factors - #correct for nonlinearities in <(1+d)SFRD>, only if doing nonlinear stuff. We're assuming that (1+d)SFRD ~ exp(gamma*d), so the "Lagrangian" gamma was gamma-1. We're using the fact that for a lognormal variable X = log(Z), with Z=\gamma \delta, = exp(\gamma^2 \sigma^2/2). + if pop == 3 and AstroParams.USE_POPIII: + SFE = self.SFE_III(CosmoParams, AstroParams, massVector, z, vCB, J21LW_interp) + elif pop == 2: + SFE = self.SFE_II(CosmoParams, AstroParams, massVector, z) + - if(User_Parameters.C2_RENORMALIZATION_FLAG==True): + return SFE + - # SarahLibanore: compute using Lagrangian gammas and include quadratic case - if Astro_Parameters.quadratic_SFRD_lognormal: - _corrfactorEulerian_II = (1+(self.gamma_II_index2D_Lag-2*self.gamma2_II_index2D_Lag)*self.sigmaofRtab**2)/(1-2*self.gamma2_II_index2D_Lag*self.sigmaofRtab**2) + def SFR(self, CosmoParams, AstroParams, HMFinterp, massVector, z, pop, vCB=None, J21LW_interp=None): + """ + Star formation rate in Msun/yr for given population + + Parameters + ---------- + CosmoParams : CosmoParams class + AstroParams : AstroParams class + HMFinterp : HMFinterp class + massVector : array + Halo masses + z : float + Redshift + pop : int + Which population (2 for popII or 3 for popIII) + vCB : float or None + Baryon-DM relative velocity (None by default: only matters for molecular-cooling limit computation for Pop IIIs) + J21LW_interp : interpolator or None + Interpolator of the LW flux, function of z (None by default: only matters for molecular-cooling limit computation for Pop IIIs) + + Returns + ---------- + SFR : array + Star formation rate + """ + + SFR = self.dMh_dt(CosmoParams, AstroParams, HMFinterp, massVector, z) * self.SFE(CosmoParams, AstroParams, massVector, z, pop, vCB, J21LW_interp) + + return SFR + + + def SFRD_integrand(self, CosmoParams, AstroParams, HMFinterp, massVector, z, pop, vCB=None, J21LW_interp=None): + """ + Integrand for the star formation rate density for a given population + + Parameters + ---------- + CosmoParams : CosmoParams class + AstroParams : AstroParams class + HMFinterp : HMFinterp class + massVector : array + Halo masses + z : float + Redshift + pop : int + Which population (2 for popII or 3 for popIII) + vCB : float or None + Baryon-DM relative velocity (None by default: only matters for molecular-cooling limit computation for Pop IIIs) + J21LW_interp : interpolator or None + Interpolator of the LW flux, function of z (None by default: only matters for molecular-cooling limit computation for Pop IIIs) + + Returns + ---------- + integrand : array + Integrand to be used in the main class + """ + + HMF_curr = np.exp(HMFinterp.logHMFint((np.log(massVector), z))) + SFRtab_curr = self.SFR(CosmoParams, AstroParams, HMFinterp, massVector, z, pop, vCB, J21LW_interp) + integrand = HMF_curr * SFRtab_curr * massVector + + return integrand + - else: - _corrfactorEulerian_II = 1.0 + self.gamma_II_index2D_Lag*self.sigmaofRtab**2 - - _corrfactorEulerian_II=_corrfactorEulerian_II.T - _corrfactorEulerian_II[0:Cosmo_Parameters.indexminNL] = _corrfactorEulerian_II[Cosmo_Parameters.indexminNL] #for R CosmoParams.chiofzint(constants.zmax_AstroBreak)] = CosmoParams.chiofzint(constants.zmax_AstroBreak) #c ut down so that nothing exceeds zmax where we do not trust the astrophysical model + zTable = CosmoParams.zfofRint(rTable) + + zMax = np.transpose([constants.redshiftFactor_Visbal*(1+z)-1]) + rMax = CosmoParams.chiofzint(zMax) - fion = 0.4 * np.exp(-cosmology.xefid(Cosmo_Parameters, self.zintegral)/0.2)#partial ionization from Xrays. Fit to Furlanetto&Stoever - atomEnIonavg = (Xrays.atomfractions[0] * Xrays.atomEnIon[0] + Xrays.atomfractions[1] * Xrays.atomEnIon[1]) / (Xrays.atomfractions[0] + Xrays.atomfractions[1] ) #to turn this ratio into one over n_b instead of n_H + c1 = (1+z)**2/4/np.pi - self.coeff_Gammah_Tx_II = -Astro_Parameters.L40_xray * constants.ergToK * (1.0+self.zintegral)**2 - self.coeff_Gammah_Tx_III = -Astro_Parameters.L40_xray_III * constants.ergToK * (1.0+self.zintegral)**2 #convert from one to the other, last factors accounts for adiabatic cooling. compensated by the inverse at zp in coeff1Xzp. Minus because integral goes from low to high z, but we'll be summing from high to low everywhere. + if pop == 3: + Nlw = AstroParams.N_LW_III + + elif pop == 2: + Nlw = AstroParams.N_LW_II - self.Gammaion_II = self.coeff_Gammah_Tx_II *constants.KtoeV * self._GammaXray_II * fion/atomEnIonavg * 3/2 - self.Gammaion_III = self.coeff_Gammah_Tx_III *constants.KtoeV * self._GammaXray_III * fion/atomEnIonavg * 3/2 #atomEnIonavg makes it approximate. No adiabatic cooling (or recombinations) so no 1+z factors. Extra 3/2 bc temperature has a 2/3 + c2r = SFRD_interp_input(zTable) + + c2r *= Nlw * Elw / constants.deltaNulw / constants.mprotoninMsun * 0.5*(1 - np.tanh((rTable - rMax)/10)) * (1 /u.yr/u.Mpc**2).to(1/u.s/u.cm**2).value # smooth tanh cutoff, smoother function within 2-3% agreement with J_LW() - #TODO: Improve model for xe + RK = np.transpose([c1]), c2r - self.xe_avg_ad = cosmology.xefid(Cosmo_Parameters, self.zintegral) - self.xe_avg = self.xe_avg_ad + np.cumsum((self.Gammaion_II+self.Gammaion_III)[::-1])[::-1] - if(Cosmo_Parameters.Flag_emulate_21cmfast==True): - self.xe_avg = 2e-4 * np.ones_like(self.Gammaion_II) #we force this when we emualte 21cmdast to compare both codes on the same footing - self.xe_avg = np.fmin(self.xe_avg, 1.0-1e-9) + return RK - #and heat from Xrays - self._fheat = pow(self.xe_avg,0.225) - self.coeff1Xzp*=self._fheat #since this is what we use for the power spectrum (and not Gammaheat) we need to upate it - self.Gammaheat_II = self._GammaXray_II * self._fheat - self.Gammaheat_III = self._GammaXray_III * self._fheat - #Computing avg kinetic temperature as sum of adiabatic & xray temperature - self.Tk_xray = self.coeff_Gammah_Tx_II * np.cumsum(self.Gammaheat_II[::-1])[::-1] + self.coeff_Gammah_Tx_III * np.cumsum(self.Gammaheat_III[::-1])[::-1]#in K, cumsum reversed because integral goes from high to low z. Only heating part - self.Tk_ad = cosmology.Tadiabatic(Cosmo_Parameters, self.zintegral) - if(Cosmo_Parameters.Flag_emulate_21cmfast==True): - self.Tk_ad*=0.95 #they use recfast, so their 'cosmo' temperature is slightly off - self.Tk_avg = self.Tk_ad + self.Tk_xray + def dSFRDIII_dJ(self,CosmoParams, AstroParams, HMFinterp, z, vCB, J21LW_interp): + """ + Response of the popIII star formation rate density to the LW background + Parameters + ---------- + CosmoParams : CosmoParams class + AstroParams : AstroParams class + HMFinterp : HMFinterp class + z : float + Redshift + vCB : bool + Include contribution from baryon-CDM relative velocity (popIII) or not (popII) + J21LW_interp : bool + Include contribution from LW feedback (popIII) or not (popII) - # LyA stuff to find components of Salpha correction factor - self.Jalpha_avg = self.coeff1LyAzp*np.sum(self.coeff2LyAzpRR_II + self.coeff2LyAzpRR_III,axis=1) #units of 1/(cm^2 s Hz sr) - self.T_CMB = cosmology.Tcmb(ClassCosmo, self.zintegral) + Returns + ---------- + integral : array + Response integrated over the mass array + """ - _tau_GP = 3./2. * cosmology.n_H(Cosmo_Parameters,self.zintegral) * constants.Mpctocm / cosmology.HubinvMpc(Cosmo_Parameters,self.zintegral) * (constants.wavelengthLyA/1e7)**3 * constants.widthLyAcm * (1.0 - self.xe_avg) #~3e5 at z=6 -# _tau_GP = 3./2.*Cosmo_Parameters.f_H * cosmology.n_baryon(Cosmo_Parameters,self.zintegral)*constants.Mpctocm/cosmology.HubinvMpc(Cosmo_Parameters,self.zintegral) * (constants.wavelengthLyA/1e7)**3 * constants.widthLyAcm * (1.0 - self.xe_avg) #~3e5 at z=6 - if(Cosmo_Parameters.Flag_emulate_21cmfast==True): - _tau_GP/=Cosmo_Parameters.f_H #for some reason they multiuply by N0 (all baryons) and not NH0. + Mh = HMFinterp.Mhtab + HMF_curr = np.exp(HMFinterp.logHMFint((np.log(Mh), z))) - _xiHirata = pow(_tau_GP*1e-7,1/3.)*pow(self.Tk_avg,-2./3) - _factorxi = (1.0 + 2.98394*_xiHirata + 1.53583 * _xiHirata**2 + 3.8528 * _xiHirata**3) + SFRtab_currIII = self.SFR(CosmoParams, AstroParams, HMFinterp, HMFinterp.Mhtab, z, pop=3, vCB = vCB, J21LW_interp=J21LW_interp) + integrand_III = HMF_curr * SFRtab_currIII * HMFinterp.Mhtab + integrand_III *= AstroParams.A_LW * AstroParams.beta_LW * J21LW_interp(z)**(AstroParams.beta_LW - 1) + integrand_III *= -1 * self.Mmol_vcb(CosmoParams, AstroParams, z, CosmoParams.vcb_avg)/ HMFinterp.Mhtab - #prefactor without the Salpha correction from Hirata2006 - if(Cosmo_Parameters.Flag_emulate_21cmfast==True): - self._coeff_Ja_xa_0 = 1.66e11/(1+self.zintegral) #They use a fixed (and slightly ~10% off) value. - else: - self._coeff_Ja_xa_0 = 8.0*np.pi*(constants.wavelengthLyA/1e7)**2 * constants.widthLyA * constants.Tstar_21/(9.0*constants.A10_21*self.T_CMB) #units of (cm^2 s Hz sr), convert from Ja to xa. should give 1.81e11/(1+self.zintegral) for Tcmb_0=2.725 K - - self.coeff_Ja_xa = self._coeff_Ja_xa_0 * Salpha_exp(self.zintegral, self.Tk_avg, self.xe_avg) - self.xa_avg = self.coeff_Ja_xa * self.Jalpha_avg - self.invTcol_avg = 1.0 / self.Tk_avg - self._invTs_avg = (1.0/self.T_CMB+self.xa_avg*self.invTcol_avg)/(1+self.xa_avg) - if(User_Parameters.FLAG_WF_ITERATIVE==True): #iteratively find Tcolor and Ts. Could initialize one to zero, but this should converge faster - ### iteration routine to find Tcolor and Ts - _invTs_tryfirst = 1.0/self.T_CMB - while(np.sum(np.fabs(_invTs_tryfirst/self._invTs_avg - 1.0))>0.01): #no more than 1% error total - _invTs_tryfirst = self._invTs_avg + integral = np.trapezoid(integrand_III, HMFinterp.logtabMh) - #update xalpha - _Salphatilde = (1.0 - 0.0632/self.Tk_avg + 0.116/self.Tk_avg**2 - 0.401/self.Tk_avg*self._invTs_avg + 0.336*self._invTs_avg/self.Tk_avg**2)/_factorxi - self.coeff_Ja_xa = self._coeff_Ja_xa_0 * _Salphatilde - self.xa_avg = self.coeff_Ja_xa * self.Jalpha_avg + return integral - #and Tcolor^-1 - self.invTcol_avg = 1.0/self.Tk_avg + constants.gcolorfactorHirata * 1.0/self.Tk_avg * (_invTs_tryfirst - 1.0/self.Tk_avg) - #and finally Ts^-1 - self._invTs_avg = (1.0/self.T_CMB+self.xa_avg * self.invTcol_avg)/(1+self.xa_avg) + def fesc_II(self,AstroParams, Mh): + """ + Escape fraction of ionizing photons in halos hosting popII stars + Parameters + ---------- + AstroParams : AstroParams class + Mh : array + Halo masses + Returns + ---------- + fesc : array + Escape fraction + """ + + fesc = np.fmin(1.0, AstroParams.fesc10 * pow(Mh/1e10,AstroParams.alphaesc) ) + + return fesc + + + def fesc_III(self,AstroParams, Mh): + """ + Escape fraction of ionizing photons in halos hosting popIII stars + + Parameters + ---------- + AstroParams : AstroParams class + Mh : array + Halo masses - ##################################################################################################### - ### STEP 9: Reionization - _trec0 = 1.0/(constants.alphaB * cosmology.n_H(Cosmo_Parameters,0) *(1 + Cosmo_Parameters.x_He) * Astro_Parameters._clumping)#t_recombination at z=0, in sec -# _trec0 = 1.0/(constants.alphaB * cosmology.n_baryon(Cosmo_Parameters,0) * Astro_Parameters._clumping)#t_recombination at z=0, in sec - _recexp = 1.0/(_trec0 * np.sqrt(Cosmo_Parameters.OmegaM) * cosmology.Hubinvyr(Cosmo_Parameters,0) / constants.yrTos)# = 1/(_trec0 * H0 * sqrt(OmegaM) ), dimless. Assumes matter domination and constant clumping. Can be modified to power-law clumping changing the powerlaw below from 3/2 + Returns + ---------- + fesc : array + Escape fraction + """ - self.coeffQzp = self.dlogzint*self.zintegral/cosmology.Hubinvyr(Cosmo_Parameters,self.zintegral)/(1+self.zintegral) #Deltaz * dt/dz. Units of 1/yr, inverse of niondot + fesc = np.fmin(1.0, AstroParams.fesc7_III * pow(Mh/1e7,AstroParams.alphaesc_III) ) - ###HAC: Added N_ion rate contribution from Pop II and III stars. Note that I am using rho_b(z=0) because it's a comoving volume - zArray, mArray = np.meshgrid(self.zintegral, HMF_interpolator.Mhtab, indexing = 'ij', sparse = True) + return fesc + - integrand_II_table = SFRD_II_integrand(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, mArray, zArray, zArray) - integrand_III_table = SFRD_III_integrand(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, mArray, J21LW_interp, zArray, zArray, ClassCosmo.pars['v_avg']) + def compute_sigmaR_nu(self, CosmoParams, HMFinterp, z_array, R_array, Mh_array, dorv_array, dorv): + """ + Compute the local mass function conditioned over the environment - self.niondot_avg_II = Astro_Parameters.N_ion_perbaryon_II/cosmology.rho_baryon(Cosmo_Parameters,0.) * np.trapezoid(integrand_II_table * fesctab_II, HMF_interpolator.logtabMh, axis = 1) - self.niondot_avg_III = Astro_Parameters.N_ion_perbaryon_II/cosmology.rho_baryon(Cosmo_Parameters,0.) * np.trapezoid(integrand_III_table * fesctab_III, HMF_interpolator.logtabMh, axis = 1) - self.niondot_avg = self.niondot_avg_II + self.niondot_avg_III + Parameters + ---------- + CosmoParams : CosmoParams class + HMFinterp : HMFinterp class + z_array : array + Redshifts + R_array : array + Shell radii + Mh_array : array + Halo masses + dorv_array : array + Input values of either the denisty or velocity field + dorv : str + Compute the output wrt the density field ("delta") or the velocity field ("vel") + + Returns + ---------- + HMF_corr : array + Local HMF in Eulerian space + mArray : array + Halo masses, dimension (z,R,Mh,delta or v) + zGreaterArray : array + Redshifts of the sources, dimension (z,R,Mh,delta or v) + out : array + Either delta_R (if dorv == delta) or velocity, dimension (z,R,Mh,delta or v) + """ + + zArray, rArray, mArray, dorvNormArray = np.meshgrid(z_array, R_array, Mh_array, dorv_array, indexing = 'ij', sparse = True) # reshape - if(Cosmo_Parameters.Flag_emulate_21cmfast==False): #regular calculation, integrating over time and accounting for recombinations in the exponent + rGreaterArray = np.zeros_like(zArray) + rArray + rGreaterArray[CosmoParams.chiofzint(zArray) + rArray >= CosmoParams.chiofzint(constants.zmax_AstroBreak)] = np.nan + zGreaterArray = CosmoParams.zfofRint(CosmoParams.chiofzint(zArray) + rGreaterArray) # redshift of the source - self.Qfactrecomb = np.exp(-2/3 * _recexp * pow(1+self.zintegral,3/2)) - self.Qion_avg = 1/self.Qfactrecomb*np.cumsum(self.coeffQzp[::-1] * self.Qfactrecomb[::-1] * self.niondot_avg[::-1])[::-1] + whereNotNans = np.invert(np.isnan(rGreaterArray)) - if(Cosmo_Parameters.Flag_emulate_21cmfast==True): #21cmfast instead uses nion (rather than niondot and integrating). We can emulate that here. there nion = niondot * t_star/H(z) [see Park+19]. In that case we can iteratively solve for Q=nion - nrecom(Q), where nrecom = int dt Q/t_recom to correct for recombinations. Easier than ODE. + sigmaR = np.zeros((len(z_array), len(R_array), 1, 1)) + sigmaR[whereNotNans] = HMFinterp.sigmaRintlog((np.log(rGreaterArray)[whereNotNans], zGreaterArray[whereNotNans])) # mass field variance on R (environment scale) - #self._nion = np.cumsum(self.coeffQzp[::-1] * self.niondot_avg[::-1])[::-1] - self._nion = self.niondot_avg * Astro_Parameters.tstar/cosmology.Hubinvyr(Cosmo_Parameters,self.zintegral) - self._Q0iteration = self._nion #0th iteration has no recombinations - self.trec = _trec0/(1+self.zintegral)**3/constants.yrTos #in yr at each time t - self._Q1iteration = 0.0 - while(np.sum(np.abs(self._Q1iteration-self._Q0iteration))>0.001): - self._Q1iteration = self._Q0iteration - self._nrecombinations = np.cumsum(self.coeffQzp[::-1] * (self._Q1iteration/self.trec)[::-1])[::-1] #coeffQzp = dt/dz as before - self._Q0iteration = self._nion - self._nrecombinations - self.Qion_avg = self._Q0iteration + sigmaM = HMFinterp.sigmaintlog((np.log(mArray), zGreaterArray)) # mass field variance on Mh + modSigmaSq = sigmaM**2 - sigmaR**2 + indexTooBig = (modSigmaSq <= 0.0) + modSigmaSq[indexTooBig] = np.inf #if sigmaR > sigmaM the halo does not fit in the radius R. Cut the sum + modSigma = np.sqrt(modSigmaSq) - #common to both methods. - self.Qion_avg = np.fmin(1.0, self.Qion_avg) - self._xHII_avg = self.Qion_avg + (1.0 - self.Qion_avg) * self.xe_avg #accounts for partial ionization, small effect - self.xHI_avg = (1.0 - self._xHII_avg) - self.xHI_avg = np.fmin(1.0, self.xHI_avg) - - - ##################################################################################################### - ### STEP 10: Compute the 21cm Global Signal - self.T21avg = cosmology.T021(Cosmo_Parameters,self.zintegral) * self.xa_avg/(1.0 + self.xa_avg) * (1.0 - self.T_CMB * self.invTcol_avg) * self.xHI_avg - + # variables of the EPS theory + nu0 = CosmoParams.delta_crit_ST / sigmaM + nu0[indexTooBig] = 1.0 + + dsigmadMcurr = HMFinterp.dsigmadMintlog((np.log(mArray),zGreaterArray)) ###HAC: Check this works when emulating 21cmFAST + dlogSdMcurr = (dsigmadMcurr*sigmaM*2.0)/(modSigmaSq) + + if dorv == "delta": + deltaArray = dorvNormArray * sigmaR + elif dorv == "vel": + deltaArray = np.zeros_like(sigmaR) # deltaZero + + modd = CosmoParams.delta_crit_ST - deltaArray + nu = modd / modSigma + if not CosmoParams.Flag_emulate_21cmfast: + # EPS_HMF corrected with (1+delta) for Eulerian space + HMF_corr = (nu/nu0) * (sigmaM/modSigma)**2.0 * np.exp(-CosmoParams.a_corr_EPS * (nu**2-nu0**2)/2.0 ) * (1.0 + deltaArray) + + else: + # as 21cmFAST, use PS HMF, integrate and normalize at the end + HMF_corr = cosmology.PS_HMF_unnorm(CosmoParams, Mh_array.reshape(len(Mh_array),1),nu,dlogSdMcurr) * (1.0 + deltaArray) + + if dorv == "delta": + out = deltaArray + elif dorv == "vel": + out = dorvNormArray + return HMF_corr, mArray, zGreaterArray, out + + + def compute_gamma(self, CosmoParams, AstroParams, HMFinterp, z_array, R_array, Mh_array, input_sigmaofRtab, fesctab_II): + """ + Compute linear and quadratic gamma exponents for the SFRD-delta (popII+popIII) and niondot-delta (onlypopII) lognormal approximations + Parameters + ---------- + CosmoParams : CosmoParams class + AstroParams : AstroParams class + HMFinterp : HMFinterp class + z_array : array + Redshifts + R_array : array r + Shell radii + Mh_array : array + Halo masses + input_sigmaofRtab : array + Variance of the matter field smoothed over R + fesctab_II : + Escape fraction for popII stars + + Returns + ---------- + Empty + """ + + Nsigmad = 1.0 # how many sigmas we explore + Nds = 3 # how many deltas + + deltatab_norm = np.linspace(-Nsigmad,Nsigmad,Nds) + HMF_corr, mArray, zGreaterArray, deltaArray = self.compute_sigmaR_nu(CosmoParams, HMFinterp, z_array, R_array, Mh_array, deltatab_norm, "delta") # compute local HMF + + # PS_HMF~ delta/sigma^3 *exp(-delta^2/2sigma^2) * consts(of M including dsigma^2/dm) + if not CosmoParams.Flag_emulate_21cmfast: + + # Normalized PS(d)/ at each mass + integrand_II = HMF_corr * self.SFRD_integrand(CosmoParams, AstroParams, HMFinterp, mArray, zGreaterArray, pop=2) + if AstroParams.USE_POPIII: + integrand_III = HMF_corr * self.SFRD_integrand(CosmoParams, AstroParams, HMFinterp, mArray, zGreaterArray, pop=3, vCB=CosmoParams.vcb_avg,J21LW_interp=self.J21LW_interp_conv_avg) + + else: + # 21cmFAST uses PS HMF, integrates and normalizes as SFRD(d)/ + integrand_II = HMF_corr * self.SFR(CosmoParams, AstroParams, HMFinterp, mArray, zGreaterArray, pop=2) * mArray + if AstroParams.USE_POPIII: + integrand_III = HMF_corr * self.SFR(CosmoParams, AstroParams, HMFinterp, mArray, zGreaterArray, pop=3, vCB=CosmoParams.vcb_avg,J21LW_interp=self.J21LW_interp_conv_avg) * mArray + + # Local popII SFRD + SFRD_II_dR = np.trapezoid(integrand_II, HMFinterp.logtabMh, axis = 2) + + # Local popII niondot + niondot_II_dR = np.trapezoid(integrand_II*fesctab_II[None, None, :, None], HMFinterp.logtabMh, axis = 2) + + if AstroParams.USE_POPIII: + # Local popIII SFRD + SFRD_III_dR = np.trapezoid(integrand_III, HMFinterp.logtabMh, axis = 2) + else: + SFRD_III_dR = np.zeros_like(SFRD_II_dR) + + # compute all required gammas + self.gamma_II_index2D = self.compute_numerical_der_gamma(SFRD_II_dR, deltaArray, 1) + self.gamma2_II_index2D = self.compute_numerical_der_gamma(SFRD_II_dR, deltaArray, 2) + self.gamma_niondot_II_index2D = self.compute_numerical_der_gamma(niondot_II_dR, deltaArray, 1) -def tau_reio(Cosmo_Parameters, T21_coefficients): - "Returns the optical depth to reionization given a model. It assumes xHI=1 for z zmini) + self.gamma2_niondot_II_index2D = self.compute_numerical_der_gamma(niondot_II_dR, deltaArray, 2) - _zlistlowz = np.linspace(0,T21_coefficients.zmin,100) - - _nelistlowz = cosmology.n_H(Cosmo_Parameters,_zlistlowz)*(1.0 + Cosmo_Parameters.x_He + Cosmo_Parameters.x_He * np.heaviside(constants.zHeIIreio - _zlistlowz,0.5)) -# _nelistlowz = cosmology.n_baryon(Cosmo_Parameters,_zlistlowz)*(Cosmo_Parameters.f_H + Cosmo_Parameters.f_He + Cosmo_Parameters.f_He * np.heaviside(constants.zHeIIreio - _zlistlowz,0.5)) - _distlistlowz = 1.0/cosmology.HubinvMpc(Cosmo_Parameters,_zlistlowz)/(1+_zlistlowz) - _lowzint = constants.sigmaT * np.trapezoid(_nelistlowz*_distlistlowz,_zlistlowz) * constants.Mpctocm + if AstroParams.USE_POPIII: + self.gamma_III_index2D = self.compute_numerical_der_gamma(SFRD_III_dR, deltaArray, 1) + self.gamma2_III_index2D = self.compute_numerical_der_gamma(SFRD_III_dR, deltaArray, 2) + else: + self.gamma_III_index2D = np.zeros_like(self.gamma_II_index2D) + self.gamma2_III_index2D = np.zeros_like(self.gamma2_II_index2D) - _zlisthiz = T21_coefficients.zintegral - - _nelistlhiz = cosmology.n_H(Cosmo_Parameters,_zlisthiz) * (1 + Cosmo_Parameters.x_He) * (1.0 - T21_coefficients.xHI_avg) -# _nelistlhiz = cosmology.n_baryon(Cosmo_Parameters,_zlisthiz) * (1.0 - T21_coefficients.xHI_avg) - _distlisthiz = 1.0/cosmology.HubinvMpc(Cosmo_Parameters,_zlisthiz)/(1+_zlisthiz) - - _hizint = constants.sigmaT * np.trapezoid(_nelistlhiz*_distlisthiz,_zlisthiz) * constants.Mpctocm - - return(_lowzint + _hizint) - -def Matom(z): - "Returns Matom as a function of z" - return 3.3e7 * pow((1.+z)/(21.),-3./2) - -###HAC: Added Mmol split by contributions with no, vcb, and LW feecback -def Mmol_0(z): - "Returns Mmol as a function of z WITHOUT LW or VCB feedback" - return 3.3e7 * (1.+z)**(-1.5) - -def Mmol_vcb(Astro_Parameters, Cosmo_Parameters, z, vCB): - "Returns Mmol as a function of z WITHOUT LW feedback" - mmolBase = Mmol_0(z) - vcbFeedback = pow(1 + Astro_Parameters.A_vcb * vCB / Cosmo_Parameters.sigma_vcb, Astro_Parameters.beta_vcb) - return mmolBase * vcbFeedback - -def Mmol_LW(Astro_Parameters, J21LW_interp, z): - "Returns Mmol as a function of z WITHOUT VCB feedback" - mmolBase = Mmol_0(z) - lwFeedback = 1 + Astro_Parameters.A_LW*pow(J21LW_interp(z), Astro_Parameters.beta_LW) - return mmolBase * lwFeedback - -def Mmol(Astro_Parameters, Cosmo_Parameters, J21LW_interp, z, vCB): - "Returns Mmol as a function of z WITH LW AND VCB feedback" - mmolBase = Mmol_0(z) - vcbFeedback = pow(1 + Astro_Parameters.A_vcb * vCB / Cosmo_Parameters.sigma_vcb, Astro_Parameters.beta_vcb) - lwFeedback = 1 + Astro_Parameters.A_LW*pow(J21LW_interp(z), Astro_Parameters.beta_LW) - - return mmolBase * vcbFeedback * lwFeedback - - -#fstar = Mstardot/Mhdot, parametrizes as you wish -def fstarofz(Astro_Parameters, Cosmo_Parameters, z, Mhlist): - epsstar_ofz = Astro_Parameters.epsstar * 10**(Astro_Parameters.dlog10epsstardz * (z-Astro_Parameters._zpivot) ) - if Cosmo_Parameters.Flag_emulate_21cmfast == False: - return Cosmo_Parameters.OmegaB/Cosmo_Parameters.OmegaM * np.clip(2.0 * epsstar_ofz\ - /(pow(Mhlist/Astro_Parameters.Mc,- Astro_Parameters.alphastar) + pow(Mhlist/Astro_Parameters.Mc,- Astro_Parameters.betastar) ), 0, 1) - elif Cosmo_Parameters.Flag_emulate_21cmfast == True: - return Cosmo_Parameters.OmegaB/Cosmo_Parameters.OmegaM * np.clip(epsstar_ofz /(pow(Mhlist/Astro_Parameters.Mc,- Astro_Parameters.alphastar)), 0, 1) + # LW correction to Pop III gammas + if AstroParams.USE_POPIII and AstroParams.USE_LW_FEEDBACK: - -###HAC: Added fstar for PopIII -def fstarofz_III(Astro_Parameters, Cosmo_Parameters, z, Mhlist): - epsstar_ofz_III = Astro_Parameters.fstar_III * 10**(Astro_Parameters.dlog10epsstardz_III * (z-Astro_Parameters._zpivot_III) ) - if Cosmo_Parameters.Flag_emulate_21cmfast == False: - return 2 * Cosmo_Parameters.OmegaB/Cosmo_Parameters.OmegaM * epsstar_ofz_III\ - /(pow(Mhlist/Astro_Parameters.Mc_III, -Astro_Parameters.alphastar_III) + pow(Mhlist/Astro_Parameters.Mc_III, -Astro_Parameters.betastar_III)) - elif Cosmo_Parameters.Flag_emulate_21cmfast == True: - return Cosmo_Parameters.OmegaB/Cosmo_Parameters.OmegaM * epsstar_ofz_III/(pow(Mhlist/Astro_Parameters.Mc_III, -Astro_Parameters.alphastar_III)) - - -def J_LW(Astro_Parameters, Cosmo_Parameters, sfrdIter, z, pop): - #specific intensity, units of erg/s/cm^2/Hz/sr - #for units to work, c must be in Mpc/s and proton mass in solar masses - #and convert from 1/Mpc^2 to 1/cm^2 - - Elw = (constants.Elw_eV * u.eV).to(u.erg).value - deltaNulw = constants.deltaNulw #Hz - speedLight = constants.c_Mpcs - massProton = constants.mprotoninMsun - redshiftFactor = 1.04 #max amount LW photons can redshift before being scattered, as in Visbal+1402.0882 - - if pop == 3: - Nlw = Astro_Parameters.N_LW_III - elif pop == 2: - Nlw = Astro_Parameters.N_LW_II + # get the zero-lag correlation function (zero distance separation) + xi_RR_CF_zerolag = np.copy(CosmoParams.ClassCosmo.pars['xi_RR_CF'][:,:,0]) - zIntMatrix = np.linspace(z, redshiftFactor*(1+z)-1, 20) - - sfrdIterMatrix_LW = interpolate.interp1d(z, sfrdIter, kind = 'linear', bounds_error=False, fill_value=0)(zIntMatrix) - - if(Cosmo_Parameters.Flag_emulate_21cmfast==True):##HAC ACAUSAL: This if statement allows for acausal Mmol - sfrdIterMatrix_LW = sfrdIter * np.ones_like(zIntMatrix) #HAC: This fixes J_LW(z) = int SFRD(z) dz' such that no z' dependence in the integral (for some reason 21cmFAST does this). Delete when comparing J_LW() with Visbal+14 and Mebane+17 + #compute LW coefficients for Pop II and III stars + coeff1LWzp_II, coeff2LWzpRR_II = self.J_LW_Discrete(CosmoParams, AstroParams, z_array, 2, R_array, self.SFRD_II_interp) + coeff1LWzp_III, coeff2LWzpRR_III = self.J_LW_Discrete(CosmoParams, AstroParams, z_array, 3, R_array, self.SFRD_III_cnvg_interp) + + # Corrections WITH Rmax smoothing + deltaGamma_R = 1 / np.transpose([self.SFRD_III_cnvg_interp(z_array)]) + deltaGamma_R *= np.array([self.dSFRDIII_dJ(CosmoParams, AstroParams, HMFinterp, np.array([z_array]).T, vCB=CosmoParams.vcb_avg, J21LW_interp=self.J21LW_interp_conv_avg)]).T + + deltaGamma_R = deltaGamma_R * (coeff1LWzp_II * coeff2LWzpRR_II * self.gamma_II_index2D + coeff1LWzp_III * coeff2LWzpRR_III * self.gamma_III_index2D) * 1e21 + + #choose only max of r and R; since growth factors cancel out, none are used here + xi_R_maxrR = np.tril(np.ones_like(xi_RR_CF_zerolag)) * np.transpose([np.diag(xi_RR_CF_zerolag)]) + xi_R_maxrR = xi_R_maxrR + np.triu(xi_RR_CF_zerolag, k = 1) + + self.deltaGamma_R_Matrix = xi_R_maxrR.reshape(len(R_array), 1, len(R_array)) * (deltaGamma_R * CosmoParams._dlogRR * R_array).reshape(1, len(z_array), len(R_array)) + self.deltaGamma_R_z = np.transpose( np.sum(self.deltaGamma_R_Matrix, axis = 2) / np.transpose([np.diagonal(xi_RR_CF_zerolag[:,:])]) ) + self.deltaGamma_R_z[ self.gamma_III_index2D == 0 ] = 0 #don't correct gammas if gammas are zero + self.gamma_III_index2D += self.deltaGamma_R_z #correct Pop III gammas with LW correction factor + + # Non-Linear Correction Factors to convert from Lagrangian to Eulerian space and to normalize the integral of the SFRD (see sec 3A in 2507.15922) + gamma_II_index2D_Lag = self.gamma_II_index2D - 1. + gamma_III_Lagrangian = self.gamma_III_index2D - 1. + if AstroParams.quadratic_SFRD_lognormal: + gamma2_II_index2D_Lag = self.gamma2_II_index2D + 1/2. + _corrfactorEulerian_II = (1+(gamma_II_index2D_Lag-2*gamma2_II_index2D_Lag)*self.sigmaofRtab**2)/(1-2*gamma2_II_index2D_Lag*self.sigmaofRtab**2) + if AstroParams.USE_POPIII: + gamma2_III_Lagrangian = self.gamma2_III_index2D + 1/2. + _corrfactorEulerian_III = (1+(gamma_III_Lagrangian-2*gamma2_III_Lagrangian)*self.sigmaofRtab**2)/(1-2*gamma2_III_Lagrangian*self.sigmaofRtab**2) + else: + _corrfactorEulerian_III = np.zeros_like(_corrfactorEulerian_II) + else: + _corrfactorEulerian_II = 1.0 + gamma_II_index2D_Lag * input_sigmaofRtab**2 + if AstroParams.USE_POPIII: + _corrfactorEulerian_III = 1.0 + gamma_III_Lagrangian*self.sigmaofRtab**2 + else: + _corrfactorEulerian_III = np.zeros_like(_corrfactorEulerian_II) - integrandLW = speedLight / 4 / np.pi - integrandLW *= (1+z)**2 / cosmology.Hubinvyr(Cosmo_Parameters,zIntMatrix) -# integrandLW *= (1+z)**3 / cosmology.Hubinvyr(Cosmo_Parameters,zIntMatrix) / (1+zIntMatrix) #HAC: delete this and comment above back in!!! - integrandLW *= Nlw * Elw / massProton / deltaNulw - integrandLW = integrandLW * sfrdIterMatrix_LW * (1 /u.Mpc**2).to(1/u.cm**2).value #broadcasting doesn't like augmented assignment operations (like *=) for some reason - return np.trapezoid(integrandLW, x = zIntMatrix, axis = 0) + self._corrfactorEulerian_II=_corrfactorEulerian_II.T + self._corrfactorEulerian_II[0:CosmoParams.indexminNL] = self._corrfactorEulerian_II[CosmoParams.indexminNL] #for Rz, but in general it should not - z2 = z - duty_matom_component = np.exp(-Mh/Matom(z2)) - fduty_III = np.exp(-Mmol(Astro_Parameters, Cosmo_Parameters, J21LW_interp, z2, vCB)/Mh) * duty_matom_component - - fstarM_III = fstarofz_III(Astro_Parameters, Cosmo_Parameters, z, Mh) - fstarM_III = np.fmin(fstarM_III, Astro_Parameters.fstarmax) - - return dMh_dt(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, Mh, z) * fstarM_III * fduty_III - - -def dMh_dt(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, massVector, z): - 'Mass accretion rate, in units of M_sun/yr' - Mh = massVector - - if(Astro_Parameters.astromodel == False): #GALLUMI-like - if(Astro_Parameters.accretion_model == False): #exponential accretion - dMhdz = massVector * constants.ALPHA_accretion_exponential - - elif(Astro_Parameters.accretion_model == True): #EPS accretion - - Mh2 = Mh * constants.EPSQ_accretion - indexMh2low = Mh2 < Mh.flatten()[0] - Mh2[indexMh2low] = Mh.flatten()[0] - - sigmaMh = HMF_interpolator.sigmaintlog((np.log(Mh), z)) - sigmaMh2 = HMF_interpolator.sigmaintlog((np.log(Mh2), z)) - sigmaMh2[np.full_like(sigmaMh2, fill_value=True, dtype = bool) * indexMh2low] = 1e99 - - growth = cosmology.growth(Cosmo_Parameters,z) - dzgrow = z*0.01 - dgrowthdz = (cosmology.growth(Cosmo_Parameters,z+dzgrow) - cosmology.growth(Cosmo_Parameters,z-dzgrow))/(2.0 * dzgrow) - dMhdz = - Mh * np.sqrt(2/np.pi)/np.sqrt(sigmaMh2**2 - sigmaMh**2) *dgrowthdz/growth * Cosmo_Parameters.delta_crit_ST - else: - print("ERROR! Have to choose an accretion model in Astro_Parameters (accretion_model)") - Mhdot = dMhdz*cosmology.Hubinvyr(Cosmo_Parameters,z)*(1.0+z) - return Mhdot + print('Check derivation order for gammas') + return 0 - elif(Astro_Parameters.astromodel == True): #21cmfast-like - return Mh/Astro_Parameters.tstar*cosmology.Hubinvyr(Cosmo_Parameters,z) - else: - print('ERROR, MODEL is not defined') - + darr1_darr2[np.isnan(darr1_darr2)] = 0.0 -def J_LW_Discrete(Astro_Parameters, Cosmo_Parameters, ClassCosmo, z, pop, rGreater, SFRD_II_interp, SFRD_III_cnvg_interp): - #specific intensity, units of erg/s/cm^2/Hz/sr - #for units to work, c must be in Mpc/s and proton mass in solar masses - #and convert from 1/Mpc^2 to 1/cm^2 - - Elw = (constants.Elw_eV * u.eV).to(u.erg).value - deltaNulw = constants.deltaNulw - massProton = constants.mprotoninMsun - redshiftFactor = 1.04 #max amount LW photons can redshift before being scattered, as in Visbal+1402.0882 - - rTable = np.transpose([Cosmo_Parameters.chiofzint(z)]) + rGreater - rTable[rTable > Cosmo_Parameters.chiofzint(50)] = Cosmo_Parameters.chiofzint(50) #cut down so that nothing exceeds zmax = 50 - zTable = Cosmo_Parameters.zfofRint(rTable) - - ##HAC ACAUSAL: The below if statement allows for acausal Mmol - if(Cosmo_Parameters.Flag_emulate_21cmfast==True): - zTable = np.array([z]).T * np.ones_like(rTable) #HAC: This fixes J_LW(z) = int SFRD(z) dz' such that no z' dependence in the integral (for some reason 21cmFAST does this). Delete when comparing J_LW() with Visbal+14 and Mebane+17 - - zMax = np.transpose([redshiftFactor*(1+z)-1]) - rMax = Cosmo_Parameters.chiofzint(zMax) - - c1 = (1+z)**2/4/np.pi - - if pop == 3: - Nlw = Astro_Parameters.N_LW_III - c2r = SFRD_III_cnvg_interp(zTable) - elif pop == 2: - Nlw = Astro_Parameters.N_LW_II - c2r = SFRD_II_interp(zTable) + return darr1_darr2 + + +class PopIII_relvel: + """ + Pre-compute velocity–dependent Pop III star formation suppression (see sec 4A in arXiv:2407.18294) + + Parameters + ---------- + UserParams : UserParams class + CosmoParams : CosmoParams class + AstroParams : AstroParams class + HMFinterp : HMFinterp class + z_Init : Z_init class, optional + Initial redshift matrices for the calculation (see sfrd.py for details). + Default is None. + SFRD_Init : SFRD_class class, optional + Initial star formation rate density for the calculation (see sfrd.py for details). + Default is None. + + Attributes + ---------- + vcb_expFitParams : float + Coefficients to approximate SFRD(vCB) / SFRD + + """ + def __init__(self, UserParams, CosmoParams, AstroParams, HMFinterp, z_Init = None, SFRD_Init = None): + + # if z_Init and SFRD_Init are not provided, we initialize them here. This allows us to avoid redundant computations if they were already initialized in the parent class and passed as arguments. + if z_Init is None: + z_Init = Z_init(UserParams, CosmoParams) + + if SFRD_Init is None: + SFRD_Init = SFRD_class(UserParams, CosmoParams, AstroParams, HMFinterp, z_Init) + + + if AstroParams.USE_POPIII: + self.vcb_expFitParams = np.zeros((len(z_Init.zintegral),len(CosmoParams._Rtabsmoo), 4)) #for the 4 exponential parameters -# c2r *= Nlw * Elw / deltaNulw / massProton * (1 - np.heaviside(rTable - rMax, 1)) * (1 /u.yr/u.Mpc**2).to(1/u.s/u.cm**2).value #hard Heaviside cutoff, leads to instabilities & discontinuities - c2r *= Nlw * Elw / deltaNulw / massProton * 0.5*(1 - np.tanh((rTable - rMax)/10)) * (1 /u.yr/u.Mpc**2).to(1/u.s/u.cm**2).value #smooth tanh cutoff, smoother function within 2-3% agreement with J_LW() - return np.transpose([c1]), c2r - -def dSFRDIII_dJ(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, J21LW_interp, z, z2, vCB): - Mh = HMF_interpolator.Mhtab - HMF_curr = np.exp(HMF_interpolator.logHMFint((np.log(Mh), z))) - SFRtab_currIII = SFR_III(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, HMF_interpolator.Mhtab, J21LW_interp, z, z2, vCB) - integrand_III = HMF_curr * SFRtab_currIII * HMF_interpolator.Mhtab - integrand_III *= Astro_Parameters.A_LW * Astro_Parameters.beta_LW * J21LW_interp(z)**(Astro_Parameters.beta_LW - 1) - integrand_III *= -1 * Mmol_vcb(Astro_Parameters, Cosmo_Parameters, z, Cosmo_Parameters.vcb_avg)/ HMF_interpolator.Mhtab - return np.trapezoid(integrand_III, HMF_interpolator.logtabMh) - - -def fesc_II(Astro_Parameters, Mh): - "f_escape for a halo of mass Mh [Msun] given Astro_Parameters" #The pivot scale here for Pop II stars is at 1e10 solar masses - return np.fmin(1.0, Astro_Parameters.fesc10 * pow(Mh/1e10,Astro_Parameters.alphaesc) ) - -def fesc_III(Astro_Parameters, Mh): - "f_escape for a PopIII halo of mass Mh [Msun] given Astro_Parameters" #The pivot scale here for Pop III stars is at 1e7 solar masses - return np.fmin(1.0, Astro_Parameters.fesc7_III * pow(Mh/1e7,Astro_Parameters.alphaesc_III) ) - -def vFit_2(vel2, aVel, bVel, cVel, dVel): - #fitting 2 exponentials to SFRD(z | \delta_r, v_cb) / SFRD(z | \delta_r, v_avg) - vel = 3*vel2 - return aVel * np.exp(-bVel * vel) + cVel* np.exp(-dVel * vel) - -#Kept for reference purposes. Does not correct x_alpha as a function of Ts iteratively, but some old works don't either so this allows for comparison. Only used if FLAG_WF_ITERATIVE == False -def Salpha_exp(z, T, xe): - "correction from Eq 55 in astro-ph/0608032, Tk in K evaluated for the IGM where there is small reionization (xHI~1 and xe<<1) during LyA coupling era" - tau_GP_noreio = 3e5*pow((1+z)/7,3./2.)*(1-xe) - gamma_Sobolev = 1.0/tau_GP_noreio - return np.exp( - 0.803 * pow(T,-2./3.) * pow(1e-6/gamma_Sobolev,-1.0/3.0)) + if CosmoParams.USE_RELATIVE_VELOCITIES: + + v_avg0 = CosmoParams.ClassCosmo.pars['v_avg'] + vAvg_array = v_avg0 * np.array([0.2, 0.7, 1, 1.25, 2.0]) + etaTilde_array = 3 * vAvg_array**2 / CosmoParams.ClassCosmo.pars['sigma_vcb']**2 + HMF_corr, mArray, zGreaterArray, velArray = SFRD_Init.compute_sigmaR_nu(CosmoParams, HMFinterp, z_Init.zintegral, CosmoParams._Rtabsmoo, HMFinterp.Mhtab, vAvg_array, 'vel') # local HMF + + # PS_HMF~ delta/sigma^3 *exp(-delta^2/2sigma^2) * consts(of M including dsigma^2/dm) + if not CosmoParams.Flag_emulate_21cmfast: + # Normalized PS(d)/ at each mass + integrand_III = HMF_corr * SFRD_Init.SFRD_integrand(CosmoParams, AstroParams, HMFinterp, mArray, zGreaterArray, pop=3, vCB=velArray, J21LW_interp=SFRD_Init.J21LW_interp_conv_avg) + + else: + # 21cmFAST uses PS HMF, integrates and normalizes as SFRD(d)/ + integrand_III = HMF_corr * SFRD_Init.SFR(CosmoParams, AstroParams, HMFinterp, mArray, zGreaterArray, pop=3, vCB=velArray, J21LW_interp=SFRD_Init.J21LW_interp_conv_avg) * mArray + + SFRD_III_dR_V = np.trapezoid(integrand_III, HMFinterp.logtabMh, axis = 2) # local SFRD corrected by vCB + + SFRDIII_Ratio = SFRD_III_dR_V / SFRD_III_dR_V[:,:,len(vAvg_array)//2].reshape((len(z_Init.zintegral), len(CosmoParams._Rtabsmoo), 1)) + SFRDIII_Ratio[np.isnan(SFRDIII_Ratio)] = 0.0 + + # temporarily turning off divide warnings; will turn them on again after exponential fitting routine + divideErr = np.seterr(divide = 'ignore') + divideErr2 = np.seterr(invalid = 'ignore') + + ### TODO: The next few lines fits for rho(z, v) / rhoavg = Ae^-b tilde(eta) + Ce^-d tilde(eta). + ### To expedite the computation, instead of using scipy.optimize.curve_fit, I choose two points where one + ### exponential dominates to fit for C and d, subtract Ce^-d tilde(eta) from rho(z, v) / rhoavg, then fit for A and b + dParams = -1 * np.log(SFRDIII_Ratio[:,:,-1]/SFRDIII_Ratio[:,:,-2]) / (etaTilde_array[-1]-etaTilde_array[-2]) + + cParams = np.exp(np.log(SFRDIII_Ratio[:,:,-1]) + dParams * etaTilde_array[-1]) + + SFRDIII_RatioNew = SFRDIII_Ratio - cParams.reshape(*cParams.shape, 1) * np.exp(-1 * dParams.reshape(*dParams.shape, 1)* etaTilde_array.reshape(1,1,*etaTilde_array.shape) ) + + bParams = -1 * np.log(SFRDIII_RatioNew[:,:,0]/SFRDIII_RatioNew[:,:,1]) / (etaTilde_array[0]-etaTilde_array[1]) + aParams = np.exp(np.log(SFRDIII_RatioNew[:,:,0]) + bParams * etaTilde_array[0]) + + divideErr = np.seterr(divide = 'warn') + divideErr2 = np.seterr(invalid = 'warn') + + self.vcb_expFitParams[:,:,0] = aParams + self.vcb_expFitParams[:,:,1] = bParams + self.vcb_expFitParams[:,:,2] = cParams + self.vcb_expFitParams[:,:,3] = dParams + + self.vcb_expFitParams[np.isnan(self.vcb_expFitParams)] = 0.0 + diff --git a/zeus21/wrappers.py b/zeus21/wrappers.py new file mode 100644 index 0000000..7b46c77 --- /dev/null +++ b/zeus21/wrappers.py @@ -0,0 +1,30 @@ +# TO BE DONE! +# SL: for now I just move here the cosmo_wrapper from the cosmology.py, but we will need to add more wrapper functions here in the future. + +""" +Wrappers to run zeus21 modules more easily. + +Authors: zeus21 v2 collaboration - June 2026 + Emily Bregou ; + Hector Afonso G. Cruz ; + Sarah Libanore ; + Julian B. Muñoz ; + Yonny Sklansky ; + Emilie Thélie ; + Alessandra Venditti +arXiv:2302.08506, arXiv:2306.09403, arXiv:2407.18294, Sklansky et al. (in prep) +""" + +from .inputs import Cosmo_Parameters +from .cosmology import HMF_interpolator + +def cosmo_wrapper(User_Parameters): + """ + Wrapper function for all the cosmology. It takes Cosmo_Parameters_Input and returns: + Cosmo_Parameters, Class_Cosmo, Correlations, HMF_interpolator + """ + + CosmoParams = Cosmo_Parameters(User_Parameters) + HMFintclass = HMF_interpolator(User_Parameters,CosmoParams) + + return CosmoParams, HMFintclass diff --git a/zeus21/xrays.py b/zeus21/xrays.py deleted file mode 100644 index 78cdaa1..0000000 --- a/zeus21/xrays.py +++ /dev/null @@ -1,138 +0,0 @@ -""" - -Xray structure, helper functions, and definitions - -Author: Julian B. Muñoz -UT Austin and Harvard CfA - January 2023 - -Edited by Hector Afonso G. Cruz -JHU - July 2024 -""" - -import numpy as np -from . import constants -from .cosmology import n_H, HubinvMpc - - -class Xray_class: - "Class containing the X-ray functions that we want to pass to main calculation" - - def __init__(self, User_Parameters, Cosmo_Parameters): - - self.atomfractions = np.array([1,Cosmo_Parameters.x_He]) #fraction of baryons in HI and HeI, assumed to just be the avg cosmic -# self.atomfractions = np.array([Cosmo_Parameters.f_H,Cosmo_Parameters.f_He]) #fraction of baryons in HI and HeI, assumed to just be the avg cosmic - self.atomEnIon = np.array([constants.EN_ION_HI, constants.EN_ION_HeI]) #threshold energies for each, in eV - self.TAUMAX=100. #max optical depth, cut to 0 after to avoid overflows - - - def optical_depth(self, User_Parameters, Cosmo_Parameters, En,z,zp): - "Function that calculates the optical depth for a photon of energy En/eV from z to zp" - Nzinttau = np.floor(10*User_Parameters.precisionboost).astype(int) - #surprisingly it converges very quickly, since things are smooth functions of nu/z. Warning, make sure to tweak if SED is not a powerlaw! - - Envec = np.asarray([En]) if np.isscalar(En) else np.asarray(En) - - zinttau = np.linspace(z,zp,Nzinttau) - - - Eninttautab = np.outer((1+zinttau)/(1+z) , Envec) - - sigmatot = self.atomfractions[0] * sigma_HI(Eninttautab) - sigmatot += self.atomfractions[1] * sigma_HeI(Eninttautab) - sigmatot = sigmatot.T #to broadcast below - # divided by factor of H(z')(1+z') because of variable of integration change from proper distance to redshift - integrand = 1.0/HubinvMpc(Cosmo_Parameters, zinttau)/(1+zinttau) * sigmatot * n_H(Cosmo_Parameters, zinttau) * constants.Mpctocm -# integrand = 1.0/HubinvMpc(Cosmo_Parameters, zinttau)/(1+zinttau) * sigmatot * n_baryon(Cosmo_Parameters, zinttau) * constants.Mpctocm - taulist = np.trapezoid(integrand, zinttau, axis=1) - - #OLD: kept for reference only. - # taulist = 1.0*np.zeros_like(Envec) - # for iE, Energy in enumerate(Envec): - # Eninttau = (1+zinttau)/(1+z) * Energy - # sigmatot = self.atomfractions[0] * sigma_HI(Eninttau) - # sigmatot += self.atomfractions[1] * sigma_HeI(Eninttau) - # #we ignore HeII since it's a small correction (Pritchard and Furlanetto 06) - # - # integrand = 1.0/HubinvMpc(Cosmo_Parameters, zinttau)/(1+zinttau) * sigmatot * n_baryon(Cosmo_Parameters, zinttau) * constants.Mpctocm - # - # taulist[iE] = np.trapezoid(integrand, zinttau) - - indextautoolarge = np.array(taulist>=self.TAUMAX) - taulist [indextautoolarge] = self.TAUMAX - return taulist - - - - - def opacity_Xray(self, User_Parameters, Cosmo_Parameters, En,z,zp): - "Returns opacity, see optical_depth() for the hard calculation." - - XRAY_OPACITY_MODEL = Cosmo_Parameters.Flag_emulate_21cmfast - #important, 0 = standard, 1=21cmfast-like (step at tau=1) - - - if(XRAY_OPACITY_MODEL==0): #0 is standard/regular. - return np.exp(-self.optical_depth(User_Parameters, Cosmo_Parameters,En,z,zp)) - elif (XRAY_OPACITY_MODEL==1): #1 is 21cmFAST-like (step-wise exp(-tau), either 1 or 0) - return np.heaviside(1.0 - self.optical_depth(User_Parameters, Cosmo_Parameters,En,z,zp), 0.5) - else: - print('ERROR, choose a correct XRAY_OPACITY_MODEL') - - - def lambda_Xray_com(self, Cosmo_Parameters, En,z): - "Returns the mean free path in cMpc of an Xray of energy En/eV near z. Unused but good cross check" - - sigmatot = self.atomfractions[0] * sigma_HI(En) - sigmatot += self.atomfractions[1] * sigma_HeI(En) - return (1.0/(sigmatot * n_H(Cosmo_Parameters,z))/constants.Mpctocm*(1+z) ) -# return (1.0/(sigmatot * n_baryon(Cosmo_Parameters,z))/constants.Mpctocm*(1+z) ) - - - - -def sigma_HI(Energyin): - "cross section for Xray absorption for neutral HI, from astro-ph/9601009 and takes Energy in eV and returns cross sec in cm^2" - E0 = 4.298e-1 - sigma0 = 5.475e4 - ya = 3.288e1 - P = 2.963 - yw = 0.0 - y0 = 0.0 - y1 = 0.0 - - Energy = Energyin - - warning_lowE_HIXray = np.heaviside(13.6 - Energy, 0.5) - if(np.sum(warning_lowE_HIXray) > 0): - print('ERROR! Some energies for Xrays below HI threshold in sigma_HI. Too low!') - - - x = Energy/E0 - y0 - y = np.sqrt(x**2 + y1**2) - Fy = ((x-1.0)**2 + yw**2) * y**(0.5*P - 5.5) * (1.0+np.sqrt(y/ya))**(-P) - - return sigma0 * constants.sigma0norm * Fy - - - -def sigma_HeI(Energyin): - "same as sigma_HI but for HeI, parameters are:" - E0 = 13.61 - sigma0 = 9.492e2 - ya = 1.469 - P = 3.188 - yw = 2.039 - y0 = 4.434e-1 - y1 = 2.136 - - Energy = Energyin - warning_lowE_HeIXray = np.heaviside(25. - Energy, 0.5) - if(np.sum(warning_lowE_HeIXray) > 0): - print('ERROR! Some energies for Xrays below HeI threshold in sigma_HeI. Too low!') - - - x = Energy/E0 - y0 - y = np.sqrt(x**2 + y1**2) - Fy = ((x-1.0)**2 + yw**2) * y**(0.5*P - 5.5) * (1.0+np.sqrt(y/ya))**(-P) - - return sigma0 * constants.sigma0norm * Fy diff --git a/zeus21/z21_utilities.py b/zeus21/z21_utilities.py new file mode 100644 index 0000000..7a84fb2 --- /dev/null +++ b/zeus21/z21_utilities.py @@ -0,0 +1,484 @@ +""" +Helper functions to be used across zeus21. + +Authors: zeus21 v2 collaboration - June 2026 + Emily Bregou ; + Hector Afonso G. Cruz ; + Sarah Libanore ; + Julian B. Muñoz ; + Yonny Sklansky ; + Emilie Thélie ; + Alessandra Venditti +arXiv:2302.08506, arXiv:2306.09403, arXiv:2407.18294, Sklansky et al. (in prep) +""" + +import numpy as np +import powerbox as pbox +from pyfftw import empty_aligned as empty +import time +import gc + +from . import constants +from scipy.stats import lognorm +import mcfit + + +try: + from numba import jit, njit + HAS_NUMBA = True +except ImportError: + HAS_NUMBA = False + def jit(*args, **kwargs): + return lambda func: func + njit = lambda func: func + + +def powerboxCtoR(pbobject,mapkin = None): + """ + Converts a complex field to real 3D (eg density, T21...) on the powerbox notation. + + Parameters + ---------- + pbobject: powerbox.PowerBox + PowerBox object + mapkin: np.ndarray + Map of the field in k space. Default is None. + Otherwise assumes its pbobject.delta_k() (although in that case it should be directly pbobject.delta_x()). + + Returns + ---------- + realmap: np.ndarray + Real 3D field + """ + + realmap = empty((pbobject.N,) * pbobject.dim, dtype='complex128') + if (mapkin is None): + realmap[...] = pbobject.delta_k() + else: + realmap[...] = mapkin + realmap[...] = pbobject.V * pbox.dft.ifft(realmap, L=pbobject.boxlength, a=pbobject.fourier_a, b=pbobject.fourier_b)[0] + realmap = np.real(realmap) + + return realmap + +def tophat_smooth(rr, ks, dk): + """ + Top-hat smoothing. + + Parameters + ---------- + rr: np.ndarray + Array of radii. + ks: np.ndarray + Array of wave numbers. + dk: np.ndarray + Field to be smoothed in Fourier space. + + Returns + ---------- + np.ndarray + Smoothed field in real space. + """ + x = ks * rr + 1e-5 + win_k = 3/(x**3) * (np.sin(x) - x*np.cos(x)) + deltakfilt = dk * win_k + return np.real(np.fft.ifftn(deltakfilt)) + + + + +def _WinTH(k,R): + """ + 3D top-hat window function. + + Parameters + ---------- + k: np.ndarray + Array of wave numbers. + R: np.ndarray + Array of radii. + + Returns + ---------- + np.ndarray + 3D window function. + """ + x = k * R + return 3.0/x**2 * (np.sin(x)/x - np.cos(x)) + +def _WinTH1D(k,R): + """ + 1D top-hat window function. + + Parameters + ---------- + k: np.ndarray + Array of wave numbers. + R: np.ndarray + Array of radii. + + Returns + ---------- + np.ndarray + 1D window function. + """ + x = k * R + return np.sin(x)/x + +def _WinG(k,R): + """ + Gaussian window function. + + Parameters + ---------- + k: np.ndarray + Array of wave numbers. + R: np.ndarray + Array of radii. + + Returns + ---------- + np.ndarray + Window function. + """ + x = k * R * constants.RGauss_factor + return np.exp(-x**2/2.0) + +def Window(k, R, WINDOWTYPE="TOPHAT"): + """ + Window function. + + Parameters + ---------- + k: np.ndarray + Array of wave numbers. + R: np.ndarray + Array of radii. + WINDOWTYPE: str + Which window function to return. Default is TOPHAT. Can also be GAUSS or TOPHAT1D. + + Returns + ---------- + np.ndarray + Window function. + """ + if WINDOWTYPE == 'TOPHAT': + return _WinTH(k, R) + elif WINDOWTYPE == 'GAUSS': + return _WinG(k, R) + elif WINDOWTYPE == 'TOPHAT1D': + return _WinTH1D(k, R) + else: + print('ERROR in Window. Wrong type') + + + + + +def find_nearest_idx(array, values): + """ + Finds the nearest indices for some values inside another array. + + Parameters + ---------- + array: np.ndarray + Array from which to find the indices. + values: np.ndarray + Values for which we are searching the indices in array. + + Returns + ---------- + np.ndarray + Array of indices. + """ + array = np.atleast_1d(array) + values = np.atleast_1d(values) + idx = [] + for i in range(len(values)): + idx.append((np.abs(array - values[i])).argmin()) + return np.unique(idx) + +def print_timer(start_time, text_before="", text_after=""): + """ + Prints the duration since an initial time. + + Parameters + ---------- + start_time: time.time() + Initial time. + text_before: str + Text to print in front of the timer. Default is "". + text_after: str + Text to print after the timer. Default is "". + """ + elapsed_time = time.time() - start_time + mins = int(elapsed_time//60) + secs = int(elapsed_time - mins*60) + print(f"{text_before}{mins}min {secs}s{text_after}") + +def v2r(v): + """ + Computes the radius from a volume assuming a sphericity. + + Parameters + ---------- + v: float | np.ndarray + Volume of the object. + + Returns + ---------- + float | np.ndarray + Radius of the object. + """ + return (3/4/np.pi * v)**(1/3) + +def r2v(r): + """ + Computes the volume of a sphere of radius r. + + Parameters + ---------- + r: float | np.ndarray + Radius. + + Returns + ---------- + float | np.ndarray + Volume. + """ + return 4/3 * np.pi * r**3 + +def delete_class_attributes(class_instance): # delete all attributes of the class instance + """ + Properly deallocates all the attributes of a class instance. Calls the garbage collector. + Useful when we want to deallocate an instance + (doing del cls will not deallocate the attributes instantly as long as the garbage collector hasn't run). + + Parameters + ---------- + class_instance: cls instance + Class instance. + """ + for attr in list(class_instance.__dict__): + delattr(class_instance, attr) + gc.collect() + + + +# SarahLibanore +# PDFs for the SFH + +@njit +def pdf_log_transform(y_values, pdf_y_values): + """ + Get PDF of X = ln(Y) given Y values and their PDF values + + Uses the transformation rule: f_X(x) = f_Y(y) * |dy/dx| + where x = ln(y), so dy/dx = y. So: f_X(x) = f_Y(y) * y + """ + y_values = np.asarray(y_values) + pdf_y_values = np.asarray(pdf_y_values) + + # Remove any y <= 0 values (can't take log) + y_clean = np.fmax(1e-9, y_values) # Avoid log(0) or log(negative) + pdf_y_clean = np.fmax(1e-50, pdf_y_values) # Avoid zero PDF values + + # Transform: x = ln(y) + x_values = np.log(y_clean) + + # Apply transformation rule: f_X(x) = f_Y(y) * y + pdf_x_values = pdf_y_clean * y_clean + return x_values, pdf_x_values + +@njit +def lognormal_pdf(y, mu, sigma): + """ + Vectorized lognormal PDF(y) + """ + y = np.asarray(y) + result = np.zeros_like(y) + y_pos = np.fmax(1e-9, y) + result = (1 / (y_pos * sigma * np.sqrt(2 * np.pi))) * \ + np.exp(-0.5 * ((np.log(y_pos) - mu) / sigma)**2) + + return result + +@njit +def normal_pdf(y, mu, sigma,dimy=None): + """ + Vectorized normal PDF(y). dimy is the number of dimensions for mu and sigma. + """ + y = np.asarray(y) + mu = np.asarray(mu) + sigma = np.asarray(sigma) + if dimy is not None: + for i in range(dimy): + mu = mu[:, None] + sigma = sigma[:, None] + + return np.exp(-0.5 * ((y - mu) / sigma)**2) / (sigma * np.sqrt(2 * np.pi)) + + +def pdf_fft_convolution(mu1, sigma1, mu2, sigma2, highp=0.99): + """ + FFT convolution method to compute the PDF of the sum of two lognormal distributions + Uses the convolution theorem: convolution in real space = multiplication in Fourier space + mu1, sigma1: parameters of the first lognormal distribution + mu2, sigma2: parameters of the second lognormal distribution + returns: + y_array: the range of y values for which the PDF is computed + pdf_values: the PDF values at those y values + """ + + q_low = 1.0-highp # 0.1% quantile + q_high = highp # 99.9% quantile + + y1_low = lognorm.ppf(q_low, s=sigma1, scale=np.exp(mu1)) + y2_low = lognorm.ppf(q_low, s=sigma2, scale=np.exp(mu2)) + y1_high = lognorm.ppf(q_high, s=sigma1, scale=np.exp(mu1)) + y2_high = lognorm.ppf(q_high, s=sigma2, scale=np.exp(mu2)) + + y_min = max(1., y1_low + y2_low) + y_max = 2.*(y1_high + y2_high) + n_points = int(y_max/y_min) #to ensure we capture the resolution + n_points = max(n_points, 64) # Ensure at least 64 points + + # Increase points for small sigmas (to capture narrow peaks) and wide ones (for sampling) + min_sigma = min(sigma1, sigma2) + if min_sigma < 0.5: + n_points = int(n_points * (2 / (min_sigma+0.4))) # Scale inversely with sigma + # Ensure n_points is power of 2 for efficient FFT + n_points = int(2 ** np.ceil(np.log2(n_points))) + + if (n_points < 4097): + # Create uniform grid for FFT + y_uniform = np.linspace(0.0, y_max, n_points).flatten() + dy = y_uniform[1] - y_uniform[0] + + # Compute PDFs on uniform grid (vectorized) + pdf1_grid = lognormal_pdf(y_uniform, mu1, sigma1) + pdf2_grid = lognormal_pdf(y_uniform, mu2, sigma2) + + norm1 = np.trapezoid(pdf1_grid, y_uniform) + norm2 = np.trapezoid(pdf2_grid, y_uniform) + pdf1_grid /= norm1 + pdf2_grid /= norm2 + + # FFT convolution - this is where the magic happens! Convolution in real space = multiplication in Fourier space + fft1 = np.fft.fft(pdf1_grid) + fft2 = np.fft.fft(pdf2_grid) + fft_conv = fft1 * fft2 # Element-wise multiplication + + # Inverse FFT to get back to real space + pdf_conv = np.real(np.fft.ifft(fft_conv)) * dy + + y_uniform, pdf_conv = y_uniform[1:], pdf_conv[1:] #to remove y=0 which is annoying for log transform + else: + n_points = 10 #direct integration, few points are enough to get the mean and rms, but can crank up if wanted + y_uniform = np.geomspace(y_min, y_max, n_points).flatten() + n_points_integral = 333 + yintegralgrid = np.geomspace(y_min, y_max, n_points_integral).flatten() + pdf1_grid = lognormal_pdf(yintegralgrid, mu1, sigma1) + pdf_conv = np.zeros_like(y_uniform) + YminusYintegralgrid = y_uniform[:, np.newaxis] - yintegralgrid[np.newaxis, :] + pdf2_grid = lognormal_pdf(YminusYintegralgrid, mu2, sigma2) + pdf_conv = np.trapezoid(pdf1_grid[None,:] * pdf2_grid * np.heaviside(YminusYintegralgrid, 0.5), x=yintegralgrid) + + return y_uniform, np.maximum(pdf_conv, 0) + + +def sigma_log10(sigmaquantity, meanquantity): + """ + Returns the sigma(log10) for a given quantity with mean and sigma in linear units + """ + return np.sqrt(np.log((sigmaquantity/meanquantity)**2+1.))/np.log(10) + +def mean_log10(sigmaquantity, meanquantity): + """ + Returns the mean(log10) for a given quantity with mean and sigma in linear units + """ + return np.log10(meanquantity)- 1/2 * np.log10(1 + sigmaquantity**2/meanquantity**2) + + +def get_Pk_from_xi(rsinput, xiinput): + """ + Generic Fourier Transform, returns Pk from an input Corr Func xi. kPf should be the same as _klistCF + + Parameters + ---------- + rsinput : array + Array of Rs used to evaluate xiinput + xiinput: matrix + Matrix of values you are Fourier Transforming. Dimension (z, R) + + Returns + ---------- + kPf: list + List of wavenumbers + Pf: matrix + Resultant Fourier Transform of xiinput. Dimension (z, k) + + """ + + kPf, Pf = mcfit.xi2P(rsinput, l=0, lowring=True)(xiinput, extrap=False) + + return kPf, Pf + + +def get_list_PS(CosmoParams, xi_list, zlisttoconvert): + """ + Returns the power spectrum given a list of CFs (xi_list) evaluated at z=zlisttoconvert as input + + Parameters + ---------- + xi_list : matrix + list of correlation functions + zlisttoconvert: array + which redshifts xi_list is evaluated at + + Returns + ---------- + _Pk_list: matrix + Matrix of power spectra. Dimension (z, K) + + """ + _Pk_list = [] + + for izp,zp in enumerate(zlisttoconvert): + + _kzp, _Pkzp = get_Pk_from_xi(CosmoParams.rlist_CF,xi_list[izp]) + _Pk_list.append(_Pkzp) + + return np.array(_Pk_list) + + +def smooth_box(box, Resolution, input_boxlength, ncells): + """ + Smooth box + + Parameters + ---------- + box : matrix + Box + Resolution : float + Resolution over which you want to smooth + input_boxlength : int + Box size + ncells : int + Number of cells per side + + Returns + ---------- + box_smooth : matrix + Smoothed box + """ + + box_fft = np.fft.fftn(box) + + klistfftx = np.fft.fftfreq(box.shape[0],input_boxlength/ncells)*2*np.pi + + klist3Dfft = np.sqrt(np.sum(np.meshgrid(klistfftx**2, klistfftx**2, klistfftx**2, indexing='ij'), axis=0)) + + box_smooth = np.array(tophat_smooth(Resolution, klist3Dfft, box_fft)) + + return box_smooth \ No newline at end of file