diff --git a/__pycache__/glass_glxy.cpython-313.pyc b/__pycache__/glass_glxy.cpython-313.pyc new file mode 100644 index 0000000..844ea02 Binary files /dev/null and b/__pycache__/glass_glxy.cpython-313.pyc differ diff --git a/glass_glxy.py b/glass_glxy.py new file mode 100644 index 0000000..6f0fe71 --- /dev/null +++ b/glass_glxy.py @@ -0,0 +1,216 @@ +#%% +import healpy as hp +import matplotlib.pyplot as plt +import numpy as np +import camb +from cosmology import Cosmology +import glass +import glass.ext.camb +from tqdm import tqdm + +#%% +def glxy_simulation(h, Oc, Ob, + shell_spacing=200.0, + random_seed=42): + ''' + Configs and Params + ''' + # creating a numpy random number generator for sampling + rng = np.random.default_rng(random_seed) + # basic parameters of the simulation + nside = lmax = 256 + # set up CAMB parameters for matter angular power spectrum + pars = camb.set_params( + H0=100 * h, + omch2=Oc * h**2, + ombh2=Ob * h**2, + NonLinear=camb.model.NonLinear_both, + ) + # get the cosmology from CAMB + cosmo = Cosmology.from_camb(pars) + + ''' + Matter sector + ''' + # shells of 200 Mpc in comoving distance spacing + zb = glass.distance_grid(cosmo, 0.0, 3.0, dx=shell_spacing) + # linear window functions for shells + shells = glass.linear_windows(zb) + # compute the angular matter power spectra of the shells with CAMB + cls = glass.ext.camb.matter_cls(pars, lmax, shells) + # apply discretisation to the full set of spectra: + # - HEALPix pixel window function (`nside=nside`) + # - maximum angular mode number (`lmax=lmax`) + # - number of correlated shells (`ncorr=3`) + cls = glass.discretized_cls(cls, nside=nside, lmax=lmax, ncorr=3) + # set up lognormal fields for simulation + fields = glass.lognormal_fields(shells) + # compute Gaussian spectra for lognormal fields from discretised spectra + gls = glass.solve_gaussian_spectra(fields, cls) + # generator for lognormal matter fields + matter = glass.generate(fields, gls, nside, ncorr=3, rng=rng) + + ''' + Lensing sector + ''' + # this will compute the convergence field iteratively + convergence = glass.MultiPlaneConvergence(cosmo) + + ''' + Galaxies sector + ''' + # galaxy density (using 1/100 of the expected galaxy number density for Stage-IV) + n_arcmin2 = 0.3 + # true redshift distribution following a Smail distribution + z = np.arange(0.0, 3.0, 0.01) + dndz = glass.smail_nz(z, z_mode=0.9, alpha=2.0, beta=1.5) + dndz *= n_arcmin2 + # distribute dN/dz over the radial window functions + ngal = glass.partition(z, dndz, shells) + # compute tomographic redshift bin edges with equal density + nbins = 10 + zbins = glass.equal_dens_zbins(z, dndz, nbins=nbins) + # photometric redshift error + sigma_z0 = 0.03 + # constant bias parameter for all shells + bias = 1.2 + # ellipticity standard deviation as expected for a Stage-IV survey + sigma_e = 0.27 + + ''' + Survey visibility mask + ''' + vis = glass.vmap_galactic_ecliptic(nside) + # checking the mask: + # hp.mollview(vis, title="Stage IV Space Survey-like Mask", unit="Visibility") + # plt.show() + + ''' + Simulation + ''' + # we will store the catalogue as a structured numpy array, initially empty + catalogue = np.empty( + 0, + dtype=[ + ("RA", float), + ("DEC", float), + ("Z_TRUE", float), + ("PHZ", float), + ("ZBIN", int), + ("G1", float), + ("G2", float), + ], + ) + # simulate the matter fields in the main loop, and build up the catalogue + for i, delta_i in tqdm(enumerate(matter)): + # compute the lensing maps for this shell + convergence.add_window(delta_i, shells[i]) + kappa_i = convergence.kappa + gamm1_i, gamm2_i = glass.shear_from_convergence(kappa_i) + # generate galaxy positions from the matter density contrast + for gal_lon, gal_lat, gal_count in glass.positions_from_delta( + ngal[i], + delta_i, + bias, + vis, + rng=rng, + ): + # generate random redshifts over the given shell + gal_z = glass.redshifts(gal_count, shells[i], rng=rng) + # generator photometric redshifts using a Gaussian model + gal_phz = glass.gaussian_phz(gal_z, sigma_z0, rng=rng) + # attach tomographic bin IDs to galaxies, based on photometric redshifts + gal_zbin = np.digitize(gal_phz, np.unique(zbins)) - 1 + # generate galaxy ellipticities from the chosen distribution + gal_eps = glass.ellipticity_intnorm(gal_count, sigma_e, rng=rng) + # apply the shear fields to the ellipticities + gal_she = glass.galaxy_shear( + gal_lon, + gal_lat, + gal_eps, + kappa_i, + gamm1_i, + gamm2_i, + ) + # make a mini-catalogue for the new rows + rows = np.empty(gal_count, dtype=catalogue.dtype) + rows["RA"] = gal_lon + rows["DEC"] = gal_lat + rows["Z_TRUE"] = gal_z + rows["PHZ"] = gal_phz + rows["ZBIN"] = gal_zbin + rows["G1"] = gal_she.real + rows["G2"] = gal_she.imag + # add the new rows to the catalogue + catalogue = np.append(catalogue, rows) + + print(f"Total number of galaxies sampled: {len(catalogue):,}") + #print(catalogue) + return ([h, Oc, Ob], catalogue, [z, dndz, sigma_z0, zbins, nbins, n_arcmin2]) + + +#%% +def plot_redshift_catalogue(catalogue, redshift_params): + ''' + Check + ''' + # extract redshift parameters from simulation instance + z = redshift_params[0] + dndz = redshift_params[1] + sigma_z0 = redshift_params[2] + zbins = redshift_params[3] + nbins = redshift_params[4] + n_arcmin2 = redshift_params[5] + # split dndz using the same Gaussian error model assumed in the sampling + tomo_nz = glass.tomo_nz_gausserr(z, + dndz, + sigma_z0, + zbins) + + # redshift distribution of tomographic bins & input distributions + plt.figure() + plt.title("redshifts in catalogue") + plt.ylabel("dN/dz - normalised") + plt.xlabel("z") + for i in range(nbins): + in_bin = catalogue["ZBIN"] == i + plt.hist( + catalogue["Z_TRUE"][in_bin], + histtype="stepfilled", + edgecolor="none", + alpha=0.5, + bins=50, + density=1, + label=f"cat. bin {i}", + ) + for i in range(nbins): + plt.plot(z, (tomo_nz[i] / n_arcmin2) * nbins, alpha=0.5, label=f"inp. bin {i}") + plt.plot(z, dndz / n_arcmin2 * nbins, ls="--", c="k") + plt.legend(ncol=2) + plt.show() + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/glass_glxy_samples.ipynb b/glass_glxy_samples.ipynb new file mode 100644 index 0000000..002e278 --- /dev/null +++ b/glass_glxy_samples.ipynb @@ -0,0 +1,181 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ad662637", + "metadata": {}, + "source": [ + "# GLASS simulation data demo\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0977c830", + "metadata": {}, + "outputs": [], + "source": [ + "from glass_glxy import * #glxy_simulation, plot_reshift_catalogue" + ] + }, + { + "cell_type": "markdown", + "id": "52fef705", + "metadata": {}, + "source": [ + "## 1. Prior Parameters: $\\bm{\\theta}\\sim P(\\bm{\\theta})$" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2c323272", + "metadata": {}, + "outputs": [], + "source": [ + "def prior_param_samples(h_range, Oc_range, Ob_range, type, n_samples):\n", + " assert isinstance(h_range, tuple) and len(h_range) == 2, \"h_range must be a tuple of (min, max)\"\n", + " assert isinstance(Oc_range, tuple) and len(Oc_range) == 2, \"Oc_range must be a tuple of (min, max)\"\n", + " assert isinstance(Ob_range, tuple) and len(Ob_range) == 2, \"Ob_range must be a tuple of (min, max)\"\n", + " assert type in [\"uniform\", \"normal\"], \"type must be either 'uniform' or 'normal'\"\n", + " assert isinstance(n_samples, int) and n_samples > 0, \"n_samples must be a positive integer\"\n", + " if type == \"uniform\":\n", + " h_samples = np.random.uniform(h_range[0], h_range[1], n_samples)\n", + " Oc_samples = np.random.uniform(Oc_range[0], Oc_range[1], n_samples)\n", + " Ob_samples = np.random.uniform(Ob_range[0], Ob_range[1], n_samples)\n", + " samples = np.vstack((h_samples, Oc_samples, Ob_samples)).T\n", + " elif type == \"normal\":\n", + " cov = np.diag([h_range[1] - h_range[0], Oc_range[1] - Oc_range[0], Ob_range[1] - Ob_range[0]])**2 / 12\n", + " mean = [(h_range[0] + h_range[1]) / 2, (Oc_range[0] + Oc_range[1]) / 2, (Ob_range[0] + Ob_range[1]) / 2]\n", + " samples = np.random.multivariate_normal(mean, cov, n_samples)\n", + " return samples\n", + "\n", + "\n", + "h_range = (0.6, 0.8)\n", + "Oc_range = (0.2, 0.4)\n", + "Ob_range = (0.03, 0.05)\n", + "n_samples = 2\n", + "uniform_samples = prior_param_samples(h_range, Oc_range, Ob_range, \"uniform\", n_samples)\n", + "normal_samples = prior_param_samples(h_range, Oc_range, Ob_range, \"normal\", n_samples)\n" + ] + }, + { + "cell_type": "markdown", + "id": "b1466de4", + "metadata": {}, + "source": [ + "## 2. Simulated Data: $\\bm{x}|\\bm{\\theta}$" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "85c56f8e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "33it [00:32, 1.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of galaxies sampled: 22,277,493\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "37it [00:38, 1.04s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of galaxies sampled: 22,276,447\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "cosmo_param_samples = []\n", + "catalogue_samples = []\n", + "redshift_param_samples = []\n", + "for i in range(uniform_samples.shape[0]):\n", + " h, Oc, Ob = uniform_samples[i]\n", + " sim = glxy_simulation(h, Oc, Ob)\n", + " cosmo_param_samples.append(sim[0])\n", + " catalogue_samples.append(sim[1])\n", + " redshift_param_samples.append(sim[2])\n", + "\n", + "# print(cosmo_param_samples[1])\n", + "# print(catalogue_samples[1])\n", + "# print(type(catalogue_samples[1][0]))\n", + "# print(redshift_param_samples[1])" + ] + }, + { + "cell_type": "markdown", + "id": "a44041b2", + "metadata": {}, + "source": [ + "Perform redshift catalogue check" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2d4c5dd4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_redshift_catalogue(catalogue_samples[0], redshift_param_samples[0])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/sbi+harmonic.ipynb b/sbi+harmonic.ipynb index 7506620..d7a2c2b 100644 --- a/sbi+harmonic.ipynb +++ b/sbi+harmonic.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ "import matplotlib.pyplot as plt\n", "from scipy.stats import multivariate_normal, norm\n", "from scipy.linalg import eigh\n", - "import harmonic as hm\n", + "# import harmonic as hm\n", "from nautilus import Prior, Sampler\n", "from sbi.inference import NLE\n", "from sbi.inference.posteriors.posterior_parameters import VIPosteriorParameters\n", @@ -1135,7 +1135,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "base", "language": "python", "name": "python3" }, @@ -1149,7 +1149,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.11" + "version": "3.13.5" } }, "nbformat": 4,