diff --git a/aniso_xiEta_modifications.ipynb b/aniso_xiEta_modifications.ipynb new file mode 100644 index 0000000..6ac1145 --- /dev/null +++ b/aniso_xiEta_modifications.ipynb @@ -0,0 +1,416 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "688c31dd", + "metadata": {}, + "source": [ + "This notebook shows the effect of $\\xi_\\eta$ anisotropy on the 21-cm power spectrum, controlled by the `USE_ANISO_XI_ETA` flag in this modification.\n", + "\n", + "All other parameter values (except `precisionboost`) are left at default." + ] + }, + { + "cell_type": "markdown", + "id": "0580c594", + "metadata": {}, + "source": [ + "## Module imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "aca446c6", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy.interpolate import interp1d\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "from matplotlib.lines import Line2D\n", + "\n", + "from cycler import cycler\n", + "from types import SimpleNamespace\n", + "import zeus21 as zeus21_hack\n", + "\n", + "plt.rc('text', usetex=True)\n", + "plt.rc('font', family='serif', size=12)" + ] + }, + { + "cell_type": "markdown", + "id": "2a1c1872", + "metadata": {}, + "source": [ + "## Zeus setup" + ] + }, + { + "cell_type": "markdown", + "id": "a6a384ad", + "metadata": {}, + "source": [ + "Takes a few minutes to run." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "aff3a5c4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/yuranzhang/Zeus21_prod/zeus21/sfrd.py:568: RuntimeWarning: invalid value encountered in log\n", + " darr1_darr2 = np.log(arr1[:,:,midpoint+1]/arr1[:,:,midpoint-1]) / (arr2[:,:,0,midpoint+1] - arr2[:,:,0,midpoint-1])\n", + "/Users/yuranzhang/Zeus21_prod/zeus21/sfrd.py:572: RuntimeWarning: invalid value encountered in log\n", + " der1_II = np.log(arr1[:,:,midpoint]/arr1[:,:,midpoint-1])/(arr2[:,:,0,midpoint] - arr2[:,:,0,midpoint-1]) #ln(y2/y1)/(x2-x1)\n" + ] + } + ], + "source": [ + "UserParams = zeus21_hack.User_Parameters(precisionboost=1.2)\n", + "\n", + "# Base code\n", + "CosmoParams_base = zeus21_hack.Cosmo_Parameters(UserParams=UserParams,\n", + " USE_RELATIVE_VELOCITIES=True)\n", + "AstroParams_base = zeus21_hack.Astro_Parameters(CosmoParams=CosmoParams_base,\n", + " USE_POPIII=True)\n", + "HMFinterp_base = zeus21_hack.HMF_interpolator(User_Parameters=UserParams, \n", + " Cosmo_Parameters=CosmoParams_base)\n", + "Coeffs_base = zeus21_hack.get_T21_coefficients(UserParams, CosmoParams_base, \n", + " AstroParams_base, HMFinterp_base)\n", + "PS_base = zeus21_hack.Power_Spectra(UserParams, CosmoParams_base, AstroParams_base, Coeffs_base)\n", + "\n", + "# With anisotropic xi_eta\n", + "CosmoParams_aniso = zeus21_hack.Cosmo_Parameters(UserParams=UserParams,\n", + " USE_RELATIVE_VELOCITIES=True,\n", + " USE_ANISO_XI_ETA=True)\n", + "AstroParams_aniso = zeus21_hack.Astro_Parameters(CosmoParams=CosmoParams_aniso,\n", + " USE_POPIII=True)\n", + "HMFinterp_aniso = zeus21_hack.HMF_interpolator(User_Parameters=UserParams, \n", + " Cosmo_Parameters=CosmoParams_aniso)\n", + "Coeffs_aniso = zeus21_hack.get_T21_coefficients(UserParams, CosmoParams_aniso, \n", + " AstroParams_aniso, HMFinterp_aniso)\n", + "PS_aniso = zeus21_hack.Power_Spectra(UserParams, CosmoParams_aniso, AstroParams_aniso, Coeffs_aniso)" + ] + }, + { + "cell_type": "markdown", + "id": "04a20c89", + "metadata": {}, + "source": [ + "## Plot \\& compare" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b191096d", + "metadata": {}, + "outputs": [], + "source": [ + "rlist = CosmoParams_base._Rtabsmoo\n", + "zlist = Coeffs_base.zintegral\n", + "klist = PS_base.klist_PS\n", + "R1choose = 0\n", + "R2choose = 0" + ] + }, + { + "cell_type": "markdown", + "id": "79678937", + "metadata": {}, + "source": [ + "### $\\xi_\\eta(r)$" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "48d3f0e5", + "metadata": {}, + "outputs": [ + { + "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": [ + "xiEta_base = CosmoParams_base.xiEta_RR_CF[R1choose, R2choose, :]\n", + "xiEta_para = CosmoParams_aniso.xiEtaPara_RR_CF[R1choose, R2choose, :]\n", + "xiEta_perp = CosmoParams_aniso.xiEtaPerp_RR_CF[R1choose, R2choose, :]\n", + "\n", + "rdense = np.linspace(rlist[0], rlist[-1], 300)\n", + "xiEta_base_interp = interp1d(rlist, xiEta_base, axis=0, kind='cubic')\n", + "xiEta_para_interp = interp1d(rlist, xiEta_para, axis=0, kind='cubic')\n", + "xiEta_perp_interp = interp1d(rlist, xiEta_perp, axis=0, kind='cubic')\n", + "xiEta_base_dense = xiEta_base_interp(rdense)\n", + "xiEta_para_dense = xiEta_para_interp(rdense)\n", + "xiEta_perp_dense = xiEta_perp_interp(rdense)\n", + "\n", + "colors = mpl.colormaps[\"viridis\"](np.linspace(0.0, 0.8, 3))\n", + "mpl.rcParams[\"axes.prop_cycle\"] = cycler(color=colors)\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "ax.plot(rdense, xiEta_base_dense, lw=1, label=r'$\\xi_{\\eta}^{\\mathrm{base}}$')\n", + "ax.plot(rdense, xiEta_para_dense, ls='--', lw=1, label=r'$\\xi_{\\eta}^{\\parallel}$')\n", + "ax.plot(rdense, xiEta_perp_dense, ls='--', lw=1, label=r'$\\xi_{\\eta}^{\\perp}$')\n", + "ax.set_xlim(1e-1, 1e3)\n", + "ax.set_ylim(-1e-2, 1)\n", + "ax.set_xscale('log')\n", + "ax.set_yscale('symlog', linthresh=1e-4)\n", + "ax.set_xlabel(r'$r$ [Mpc]')\n", + "ax.set_ylabel(r'$\\xi_{\\eta}(r)$')\n", + "ax.grid(True, alpha=0.3)\n", + "ax.legend(fontsize=12, frameon=False, loc='lower left')" + ] + }, + { + "cell_type": "markdown", + "id": "ebfb90a0", + "metadata": {}, + "source": [ + "### Temperatures (no difference)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d181b017", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colors = mpl.colormaps[\"Paired\"].colors\n", + "mpl.rcParams[\"axes.prop_cycle\"] = cycler(color=colors)\n", + "\n", + "Ts_base = 1.0/Coeffs_base._invTs_avg\n", + "Ts_aniso = 1.0/Coeffs_aniso._invTs_avg\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 5), sharex=True, gridspec_kw={'height_ratios':[2,1]})\n", + "\n", + "ax1.plot(zlist, Coeffs_base.Tk_avg, lw=1, label=r'$T_k^{\\rm base}$')\n", + "ax1.plot(zlist, Coeffs_aniso.Tk_avg, ls='--', lw=1, label=r'$T_k^{\\rm aniso}$')\n", + "\n", + "ax1.plot(zlist, Ts_base, lw=1, label=r'$T_S^{\\rm base}$')\n", + "ax1.plot(zlist, Ts_aniso, ls='--', lw=1, label=r'$T_S^{\\rm aniso}$')\n", + "\n", + "ax1.plot(zlist, Coeffs_base.T_CMB, label=r'$T_{\\rm CMB}^{\\rm base}$')\n", + "ax1.plot(zlist, Coeffs_aniso.T_CMB, ls='--', label=r'$T_{\\rm CMB}^{\\rm aniso}$')\n", + "\n", + "ax1.set_ylabel(r'Temperatures [K]')\n", + "ax1.set_yscale('log')\n", + "ax1.tick_params(direction='in')\n", + "ax1.legend(fontsize=12, frameon=False)\n", + "\n", + "ax2.plot(zlist, (Ts_base - Ts_aniso) / Ts_aniso, 'g', ls='--', label=r'$T_S$')\n", + "ax2.plot(zlist, (Coeffs_aniso.Tk_avg - Coeffs_base.Tk_avg) / Coeffs_base.Tk_avg, 'c', ls='-.', label=r'$T_k$')\n", + "ax2.plot(zlist, (Coeffs_base.T_CMB - Coeffs_aniso.T_CMB) / Coeffs_aniso.T_CMB, 'm', ls=':', label=r'$T_{\\rm CMB}$')\n", + "ax2.axhline(0, color='gray', lw=0.5)\n", + "ax2.set_yscale('symlog', linthresh=1e-7)\n", + "ax2.set_ylabel('frac diff')\n", + "ax2.set_xlabel(r'$z$')\n", + "ax2.tick_params(direction='in')\n", + "ax2.legend(fontsize=12, frameon=False, loc='lower right')\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "223627d8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "z_targets = np.array([10.0, 12.0, 15.0, 17.0, 18.7, 21.0])\n", + "z_idx = [int(np.argmin(np.abs(zlist - z))) for z in z_targets]\n", + "z_samples = zlist[z_idx]\n", + "\n", + "interp_base = interp1d(zlist, PS_base.Deltasq_T21, axis=0, bounds_error=False, fill_value=\"extrapolate\")\n", + "interp_aniso = interp1d(zlist, PS_aniso.Deltasq_T21, axis=0, bounds_error=False, fill_value=\"extrapolate\")\n", + "\n", + "P21_base = interp_base(z_samples)\n", + "P21_aniso = interp_aniso(z_samples)\n", + "frac_diff = (P21_base - P21_aniso) / P21_base\n", + "\n", + "def smooth_1d(values, window=7):\n", + " window = max(3, int(window))\n", + " if window % 2 == 0:\n", + " window += 1\n", + " kernel = np.ones(window) / window\n", + " return np.convolve(values, kernel, mode=\"same\")\n", + "\n", + "# colors = mpl.colormaps[\"plasma\"].resampled(len(z_samples))\n", + "colors = mpl.colormaps[\"plasma\"](np.linspace(0.0, 0.9, len(z_samples)))\n", + "\n", + "fig, (ax_top, ax_bot) = plt.subplots(2, 1, sharex=True, \n", + " figsize=(7, 7), \n", + " gridspec_kw={\"height_ratios\": [1, 1]})\n", + "\n", + "k_mask = (klist > 5e-3) & (klist < 2.0)\n", + "k_trim = klist[k_mask]\n", + "k_dense = np.geomspace(k_trim.min(), k_trim.max(), 300)\n", + "\n", + "for i, z in enumerate(z_samples):\n", + " color = colors[i]\n", + " p1 = P21_base[i][k_mask]\n", + " p2 = P21_aniso[i][k_mask]\n", + " fd = frac_diff[i][k_mask]\n", + "\n", + " interp_logp1 = interp1d(np.log10(k_trim), np.log10(p1), bounds_error=False, fill_value=\"extrapolate\")\n", + " interp_logp2 = interp1d(np.log10(k_trim), np.log10(p2), bounds_error=False, fill_value=\"extrapolate\")\n", + " interp_fd = interp1d(np.log10(k_trim), fd, bounds_error=False, fill_value=\"extrapolate\")\n", + "\n", + " logp1_dense = interp_logp1(np.log10(k_dense))\n", + " logp2_dense = interp_logp2(np.log10(k_dense))\n", + " fd_dense = interp_fd(np.log10(k_dense))\n", + "\n", + " p1_smooth = 10**smooth_1d(logp1_dense, window=6)\n", + " p2_smooth = 10**smooth_1d(logp2_dense, window=6)\n", + " fd_smooth = smooth_1d(fd_dense, window=8)\n", + "\n", + " ax_top.loglog(k_dense, p1_smooth, lw=1, color=color)\n", + " ax_top.loglog(k_dense, p2_smooth, ls=\"--\", lw=1, color=color)\n", + " ax_bot.semilogx(k_dense, fd_smooth, lw=1, color=color)\n", + "\n", + "ax_top.set_ylim(5e-2, 2e2)\n", + "ax_top.set_xlim(1e-2, 1)\n", + "ax_top.set_ylabel(r\"$\\Delta^2_{21}(k)$ [mK$^2$]\")\n", + "ax_bot.set_xlabel(r\"$k$ [1/Mpc]\")\n", + "ax_bot.set_ylabel(r\"(approx$-$aniso)/aniso\")\n", + "ax_bot.axhline(0.0, color=\"0.3\", lw=0.8)\n", + "\n", + "ax_top.grid(True, which=\"both\", axis=\"x\", alpha=0.2)\n", + "ax_top.grid(True, which=\"major\", axis=\"y\", alpha=0.2)\n", + "ax_bot.grid(True, which=\"both\", alpha=0.2)\n", + "\n", + "z_handles = [Line2D([0], [0], color=colors[i], lw=1, ls=\"-\") for i in range(len(z_samples))]\n", + "z_labels = [rf\"$z$={z:.2f}\" for z in z_samples]\n", + "style_handles = [\n", + " Line2D([0], [0], color=\"0.2\", lw=1, ls=\"-\"),\n", + " Line2D([0], [0], color=\"0.2\", lw=1, ls=\"--\"),\n", + "]\n", + "style_labels = [\"base\", \"aniso\"]\n", + "\n", + "legend_z = ax_top.legend(\n", + " z_handles, z_labels, fontsize=12, frameon=False, loc=\"lower right\",\n", + " bbox_to_anchor=(1.0, 0.0), borderaxespad=0.2, handlelength=2.0\n", + " )\n", + "ax_top.add_artist(legend_z)\n", + "ax_top.legend(\n", + " style_handles, style_labels, fontsize=12, frameon=False, loc=\"lower right\",\n", + " bbox_to_anchor=(0.8, 0.0), borderaxespad=0.2, handlelength=2.0\n", + " )\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "21ff2769", + "metadata": {}, + "source": [ + "## `cosmo_wrapper` changes" + ] + }, + { + "cell_type": "markdown", + "id": "e91c14a2", + "metadata": {}, + "source": [ + "`cosmo_wrapper` has been edited based on the existing docstring. It now takes keyword arguments as well as the original `User_Parameters`; any kwarg that appears in the `Cosmo_Parameters` fields will be passed to `Cosmo_Parameters`. This ensures that all user-defined cosmo parameters and flags are respected by the wrapper. It now returns `ClassCosmo` alongside the original `CosmoParams` and `HMFintclass`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "35df9656", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "0.02242\n", + "\n" + ] + } + ], + "source": [ + "UserParams_w = zeus21_hack.User_Parameters(precisionboost=1.2)\n", + "\n", + "CosmoParams_w, ClassCosmo_w, HMFintclass_w = zeus21_hack.cosmo_wrapper(UserParams_w,\n", + " omegab=0.02242,\n", + " USE_RELATIVE_VELOCITIES=True)\n", + "print(CosmoParams_w.USE_RELATIVE_VELOCITIES)\n", + "print(ClassCosmo_w.omega_b)\n", + "print(HMFintclass_w.sigmaintlog)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.10.0)", + "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": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/requirements.txt b/requirements.txt index 9b133ad..bb61983 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,7 @@ pytest numpy>=2.0 scipy +matplotlib mcfit numexpr astropy diff --git a/zeus21/correlations.py b/zeus21/correlations.py index 5e4e096..2d4e2d2 100644 --- a/zeus21/correlations.py +++ b/zeus21/correlations.py @@ -770,12 +770,15 @@ def get_all_corrs_IIxIII(self, Cosmo_Parameters, T21_coefficients): return 1 - def get_xi_Sum_2ExpEta(self, xiEta, etaCoeff1, etaCoeff2): + def get_xi_Sum_2ExpEta(self, xiEtaPara, xiEtaPerp, 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 velocity correlation with xiEta anisotropy, i.e., with xiEtePara and xiEtaPerp separated + At call site, if isotropic version is desired, isotropic xiEta is passed in for xiEtaPara and xiEtaPerp is set to None """ aa, bb, cc, dd = etaCoeff1 @@ -791,8 +794,18 @@ def get_xi_Sum_2ExpEta(self, xiEta, etaCoeff1, etaCoeff2): cfDG = ne.evaluate('cc * ff / normDD / normGG') chDK = ne.evaluate('cc * hh / normDD / normKK') - #The below involves horribly long writing, but breaking this into pieces makes for slightly longer computation time - xiNumerator = ne.evaluate('afBG * (1 / (1 - 6*bb * gg * xiEta / ((1+2*bb)*(1+2*gg)))**(3/2) - 1) + ahBK * (1 / (1 - 6*bb * kk * xiEta / ((1+2*bb)*(1+2*kk)))**(3/2) - 1) + cfDG * (1 / (1 - 6*dd * gg * xiEta / ((1+2*dd)*(1+2*gg)))**(3/2) - 1) + chDK * (1 / (1 - 6*dd * kk * xiEta / ((1+2*dd)*(1+2*kk)))**(3/2) - 1)') + if xiEtaPerp is None: + xiEta = xiEtaPara + #The below involves horribly long writing, but breaking this into pieces makes for slightly longer computation time + xiNumerator = ne.evaluate('afBG * (1 / (1 - 6*bb * gg * xiEta / ((1+2*bb)*(1+2*gg)))**(3/2) - 1) + ahBK * (1 / (1 - 6*bb * kk * xiEta / ((1+2*bb)*(1+2*kk)))**(3/2) - 1) + cfDG * (1 / (1 - 6*dd * gg * xiEta / ((1+2*dd)*(1+2*gg)))**(3/2) - 1) + chDK * (1 / (1 - 6*dd * kk * xiEta / ((1+2*dd)*(1+2*kk)))**(3/2) - 1)') + else: + xiNumerator = ne.evaluate( + 'afBG * (1 / ((1 - 6*bb*gg*xiEtaPara/((1+2*bb)*(1+2*gg))) * (1 - 6*bb*gg*xiEtaPerp/((1+2*bb)*(1+2*gg)))**2)**(1/2) - 1)' + ' + ahBK * (1 / ((1 - 6*bb*kk*xiEtaPara/((1+2*bb)*(1+2*kk))) * (1 - 6*bb*kk*xiEtaPerp/((1+2*bb)*(1+2*kk)))**2)**(1/2) - 1)' + ' + cfDG * (1 / ((1 - 6*dd*gg*xiEtaPara/((1+2*dd)*(1+2*gg))) * (1 - 6*dd*gg*xiEtaPerp/((1+2*dd)*(1+2*gg)))**2)**(1/2) - 1)' + ' + chDK * (1 / ((1 - 6*dd*kk*xiEtaPara/((1+2*dd)*(1+2*kk))) * (1 - 6*dd*kk*xiEtaPerp/((1+2*dd)*(1+2*kk)))**2)**(1/2) - 1)' + ) + xiDenominator = ne.evaluate('afBG + ahBK + cfDG + chDK') xiTotal = ne.evaluate('xiNumerator / xiDenominator') @@ -807,10 +820,18 @@ def get_all_corrs_III(self, User_Parameters, Cosmo_Parameters, T21_coefficients) corrdNL = self._corrdNL + if Cosmo_Parameters.USE_ANISO_XI_ETA and Cosmo_Parameters.USE_RELATIVE_VELOCITIES: + corrEtaParaNL = Cosmo_Parameters.xiEtaPara_RR_CF[np.ix_(self._iRnonlinear,self._iRnonlinear)] + corrEtaParaNL[0:Cosmo_Parameters.indexminNL,0:Cosmo_Parameters.indexminNL] = corrEtaParaNL[Cosmo_Parameters.indexminNL,Cosmo_Parameters.indexminNL] + corrEtaParaNL = corrEtaParaNL.reshape(1, *corrEtaParaNL.shape) - corrEtaNL = Cosmo_Parameters.xiEta_RR_CF[np.ix_(self._iRnonlinear,self._iRnonlinear)] - corrEtaNL[0:Cosmo_Parameters.indexminNL,0:Cosmo_Parameters.indexminNL] = corrEtaNL[Cosmo_Parameters.indexminNL,Cosmo_Parameters.indexminNL] - corrEtaNL = corrEtaNL.reshape(1, *corrEtaNL.shape) + corrEtaPerpNL = Cosmo_Parameters.xiEtaPerp_RR_CF[np.ix_(self._iRnonlinear,self._iRnonlinear)] + corrEtaPerpNL[0:Cosmo_Parameters.indexminNL,0:Cosmo_Parameters.indexminNL] = corrEtaPerpNL[Cosmo_Parameters.indexminNL,Cosmo_Parameters.indexminNL] + corrEtaPerpNL = corrEtaPerpNL.reshape(1, *corrEtaPerpNL.shape) + else: + corrEtaNL = Cosmo_Parameters.xiEta_RR_CF[np.ix_(self._iRnonlinear,self._iRnonlinear)] + corrEtaNL[0:Cosmo_Parameters.indexminNL,0:Cosmo_Parameters.indexminNL] = corrEtaNL[Cosmo_Parameters.indexminNL,Cosmo_Parameters.indexminNL] + corrEtaNL = corrEtaNL.reshape(1, *corrEtaNL.shape) _coeffTx_units = T21_coefficients.coeff_Gammah_Tx_III #includes -10^40 erg/s/SFR normalizaiton and erg/K conversion factor @@ -836,7 +857,10 @@ def get_all_corrs_III(self, User_Parameters, Cosmo_Parameters, T21_coefficients) expGammaCorr = ne.evaluate('exp(gammaCorrdNL) - 1') # equivalent to np.exp(gammaTimesCorrdNL)-1.0 if Cosmo_Parameters.USE_RELATIVE_VELOCITIES == True: - etaCorr_xa = self.get_xi_Sum_2ExpEta(corrEtaNL, vcbCoeffsR1, vcbCoeffsR2) + if Cosmo_Parameters.USE_ANISO_XI_ETA: + etaCorr_xa = self.get_xi_Sum_2ExpEta(corrEtaParaNL, corrEtaPerpNL, vcbCoeffsR1, vcbCoeffsR2) + else: + etaCorr_xa = self.get_xi_Sum_2ExpEta(corrEtaNL, None, 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: totalCorr = ne.evaluate('expGammaCorr - gammaCorrdNL') ###TO DO (linearized VCB flucts): - etaCorr_xa_lin #note that the Taylor expansion of the cross-term is 0 to linear order @@ -867,7 +891,11 @@ def get_all_corrs_III(self, User_Parameters, Cosmo_Parameters, T21_coefficients) coeffsXaTxALL = coeffzp2Tx * coeffmatrixxaTx corrdNLBIG = corrdNL[:,:, np.newaxis, :, :] - corrEtaNLBIG = corrEtaNL[:,:, np.newaxis, :, :] + if Cosmo_Parameters.USE_ANISO_XI_ETA and Cosmo_Parameters.USE_RELATIVE_VELOCITIES: + corrEtaParaNLBIG = corrEtaParaNL[:,:, np.newaxis, :, :] + corrEtaPerpNLBIG = corrEtaPerpNL[:,:, np.newaxis, :, :] + else: + corrEtaNLBIG = corrEtaNL[:,:, np.newaxis, :, :] vcbCoeffsR1 = vcbCoeffsR1[:,:,:,:,:] vcbCoeffsR2 = np.transpose(vcbCoeffsR1, (0,3,4,1,2)) @@ -878,13 +906,20 @@ def get_all_corrs_III(self, User_Parameters, Cosmo_Parameters, T21_coefficients) for ir in range(len(Cosmo_Parameters._Rtabsmoo)): corrdNL = corrdNLBIG[:,:,:,:,ir] - corrEtaNL = corrEtaNLBIG[:,:,:,:,ir] + if Cosmo_Parameters.USE_ANISO_XI_ETA and Cosmo_Parameters.USE_RELATIVE_VELOCITIES: + corrEtaParaNL = corrEtaParaNLBIG[:,:,:,:,ir] + corrEtaPerpNL = corrEtaPerpNLBIG[:,:,:,:,ir] + else: + corrEtaNL = corrEtaNLBIG[:,:,:,:,ir] gammaCorrdNL = ne.evaluate('gammamatrixR1R2 * corrdNL') expGammaCorrdNL = ne.evaluate('exp(gammaCorrdNL) - 1') if Cosmo_Parameters.USE_RELATIVE_VELOCITIES == True: - etaCorr_Tx = self.get_xi_Sum_2ExpEta(corrEtaNL, vcbCoeffsR1, vcbCoeffsR2) + if Cosmo_Parameters.USE_ANISO_XI_ETA: + etaCorr_Tx = self.get_xi_Sum_2ExpEta(corrEtaParaNL, corrEtaPerpNL, vcbCoeffsR1, vcbCoeffsR2) + else: + etaCorr_Tx = self.get_xi_Sum_2ExpEta(corrEtaNL, None, 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: totalCorr = ne.evaluate('expGammaCorrdNL - gammaCorrdNL') ###TO DO (linearized VCB flucts): - etaCorr_xa_lin #note that the Taylor expansion of the cross-term is 0 to linear order @@ -943,4 +978,4 @@ def get_Pk_from_xi(self, rsinput, xiinput): kPf, Pf = mcfit.xi2P(rsinput, l=0, lowring=True)(xiinput, extrap=False) - return kPf, Pf \ No newline at end of file + return kPf, Pf diff --git a/zeus21/cosmology.py b/zeus21/cosmology.py index 3ae4bc7..0b994e7 100644 --- a/zeus21/cosmology.py +++ b/zeus21/cosmology.py @@ -14,20 +14,38 @@ import numpy as np from scipy.interpolate import RegularGridInterpolator +from scipy.interpolate import interp1d from . import constants from .inputs import Cosmo_Parameters -def cosmo_wrapper(User_Parameters): +from dataclasses import fields + +Cosmo_Param_Fields = tuple( + f.name for f in fields(Cosmo_Parameters) + if f.init and f.name != "UserParams" +) + +def cosmo_wrapper(User_Parameters, **kwargs): """ - Wrapper function for all the cosmology. It takes Cosmo_Parameters_Input and returns: - Cosmo_Parameters, Class_Cosmo, Correlations, HMF_interpolator + Wrapper function for all the cosmology. + It takes User_Parameters plus keyword arguments and returns: + Cosmo_Parameters, Class_Cosmo, HMF_interpolator """ - - CosmoParams = Cosmo_Parameters(User_Parameters) + cosmo_kwargs = { + name: value + for name, value in kwargs.items() + if name in Cosmo_Param_Fields + } + for name in kwargs: + if name not in Cosmo_Param_Fields: + print(f"cosmo_wrapper: ignoring unsupported kwarg '{name}'") + + CosmoParams = Cosmo_Parameters(UserParams=User_Parameters, **cosmo_kwargs) + ClassCosmo = CosmoParams.ClassCosmo HMFintclass = HMF_interpolator(User_Parameters,CosmoParams) - return CosmoParams, HMFintclass + return CosmoParams, ClassCosmo, HMFintclass diff --git a/zeus21/inputs.py b/zeus21/inputs.py index 10aee95..8bad82a 100644 --- a/zeus21/inputs.py +++ b/zeus21/inputs.py @@ -225,6 +225,7 @@ class Cosmo_Parameters: # Flags Flag_emulate_21cmfast: bool = False USE_RELATIVE_VELOCITIES: bool = False + USE_ANISO_XI_ETA: bool = False HMF_CHOICE: str = "ST" @@ -292,6 +293,7 @@ def __post_init__(self, UserParams): schema = { "Flag_emulate_21cmfast": (bool, None), "USE_RELATIVE_VELOCITIES": (bool, None), + "USE_ANISO_XI_ETA": (bool, None), "HMF_CHOICE": (str, {'ST','Yung'}), } validate_fields(self, schema) @@ -450,10 +452,19 @@ def runclass(self): 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) + if self.USE_ANISO_XI_ETA: + k_eta_para, P_eta_para = mcfit.xi2P(rVelIntp, l=0, lowring = True)(6 * (psi0 + psi2)**2, extrap = False) + k_eta_perp, P_eta_perp = mcfit.xi2P(rVelIntp, l=0, lowring = True)(6 * (psi0 - psi2/2.0)**2, extrap = False) + + ClassCosmo.pars['k_eta_para'] = k_eta_para[P_eta_para > 0] + ClassCosmo.pars['P_eta_para'] = P_eta_para[P_eta_para > 0] + ClassCosmo.pars['k_eta_perp'] = k_eta_perp[P_eta_perp > 0] + ClassCosmo.pars['P_eta_perp'] = P_eta_perp[P_eta_perp > 0] + else: + 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] + 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") @@ -483,9 +494,17 @@ def run_correlations(self): ###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') + if self.USE_ANISO_XI_ETA: + P_eta_para_interp = interp1d(self.ClassCosmo.pars['k_eta_para'], self.ClassCosmo.pars['P_eta_para'], bounds_error = False, fill_value = 0) + P_eta_perp_interp = interp1d(self.ClassCosmo.pars['k_eta_perp'], self.ClassCosmo.pars['P_eta_perp'], bounds_error = False, fill_value = 0) + self._PkEtaParaCF = P_eta_para_interp(self._klistCF) + self._PkEtaPerpCF = P_eta_perp_interp(self._klistCF) + self.xiEtaPara_RR_CF = self.get_xi_R1R2(field = 'vcb_para') + self.xiEtaPerp_RR_CF = self.get_xi_R1R2(field = 'vcb_perp') + else: + 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._PkEtaCF = np.zeros_like(self._PklinCF) self.xiEta_RR_CF = np.zeros_like(self.xi_RR_CF) @@ -503,7 +522,13 @@ def get_xi_R1R2 (self, field = None): _PkRR = np.array([[self._PklinCF]]) * windowR1 * windowR2 elif field == 'vcb': _PkRR = np.array([[self._PkEtaCF]]) * windowR1 * windowR2 + elif field == 'vcb_para': + _PkRR = np.array([[self._PkEtaParaCF]]) * windowR1 * windowR2 + elif field == 'vcb_perp': + _PkRR = np.array([[self._PkEtaPerpCF]]) * windowR1 * windowR2 else: + if self.USE_ANISO_XI_ETA: + raise ValueError('field has to be either delta, vcb_para or vcb_perp in get_xi_R1R2') raise ValueError('field has to be either delta or vcb in get_xi_R1R2') self.rlist_CF, xi_RR_CF = self._xif(_PkRR, extrap = False)