diff --git a/README.md b/README.md index 9e1a8a6..aab2e3e 100644 --- a/README.md +++ b/README.md @@ -27,6 +27,21 @@ The following optional dependencies are needed for some of the foreground modell * `multiprocessing` * `GPy` +INSTALLATION IN A CONDA ENVIRONMENT + +conda create -n fast-env python=3.8 pyccl=2.3.0 scikit-learn scikit-image healpy lmfit -c bccp -c conda-forge -y + +git clone https://github.com/philbull/FastBox.git + +cd Fastbox + +python setup.py install + +If want to use katbeam model: + +pip install git+https://github.com/ska-sa/katbeam.git + + ## Current features - Gaussian and log-normal density fields for any cosmology diff --git a/docs/power.md b/docs/power.md new file mode 100644 index 0000000..d33ba8c --- /dev/null +++ b/docs/power.md @@ -0,0 +1 @@ +::: fastbox.power diff --git a/examples/example_box.py b/examples/example_box.py index 576e348..0b067a5 100644 --- a/examples/example_box.py +++ b/examples/example_box.py @@ -3,8 +3,6 @@ import numpy as np import pylab as plt from fastbox.box import CosmoBox, default_cosmo -from nbodykit.algorithms.fftpower import FFTPower -from nbodykit.lab import ArrayMesh from numpy import fft import sys @@ -29,7 +27,7 @@ plt.colorbar() plt.show() -sys.exit(0) +#sys.exit(0) # Trim negative values @@ -65,7 +63,7 @@ plt.show() -sys.exit(0) +#sys.exit(0) # Gaussian box with beam smoothing and foreground cut transfer_fn = lambda k_perp, k_par: \ @@ -103,7 +101,7 @@ plt.show() -sys.exit(0) +#sys.exit(0) # Log-normal box @@ -127,7 +125,7 @@ #plt.errorbar(re_k, re_pk, yerr=re_stddev, fmt=".", color='r') plt.plot(re_k, re_pk, 'r.', label="P(k) from density field") plt.plot(lnre_k, lnre_pk, 'gx', label="P(k) from log-normal") -plt.xscale('log') +plt.xscale('log')ples plt.yscale('log') plt.legend(loc='lower left', frameon=False) diff --git a/examples/example_endtoend.py b/examples/example_endtoend.py index 4a3c6b7..070c29f 100644 --- a/examples/example_endtoend.py +++ b/examples/example_endtoend.py @@ -11,10 +11,10 @@ import fastbox from fastbox.box import CosmoBox, default_cosmo from fastbox.foregrounds import ForegroundModel -from nbodykit.lab import ArrayMesh -from nbodykit.algorithms.fftcorr import FFTCorr -from nbodykit.algorithms.fftpower import FFTPower -import time, sys +#from nbodykit.algorithms.fftpower import FFTPower +#import time, sys +from fastbox.beams import KatBeamModel +import time #------------------------------------------------------------------------------- # Realise density field in redshift space @@ -72,27 +72,32 @@ print("\t(2) Adding foregrounds complete (%3.3f sec)" % (time.time()-t0)) +#------------------------------------------------------------------------------- +# Beam convolution +#------------------------------------------------------------------------------- +print("(3) Beam convolving...") + +freqs = box.freq_array(redshift=0.8) +kbm = KatBeamModel(box, model='L') +data_cube = kbm.convolve_fft(data_cube,pol='I') + +print("Beam convolved succesfully!") #------------------------------------------------------------------------------- # Add noise #------------------------------------------------------------------------------- -print("(3) Adding noise...") +print("(4) Adding noise...") t0 = time.time() # Generate homogeneous radiometer noise noise_model = fastbox.noise.NoiseModel(box) noise_cube = noise_model.realise_radiometer_noise(Tinst=18., tp=2., fov=1., - Ndish=64) # FIXME: Long integration time! + Ndish=64) # Add to data cube data_cube += noise_cube print("\t(3) Adding noise complete (%3.3f sec)" % (time.time()-t0)) -#------------------------------------------------------------------------------- -# Beam convolution -#------------------------------------------------------------------------------- - -# FIXME #------------------------------------------------------------------------------- @@ -109,7 +114,6 @@ cleaned_cube8, U_fg, amp_fg = fastbox.filters.pca_filter(data_cube, nmodes=12, return_filter=True) -#cleaned_cube = data_cube # FIXME print("\t(4) Cleaning foregrounds complete (%3.3f sec)" % (time.time()-t0)) #------------------------------------------------------------------------------- @@ -196,26 +200,7 @@ plt.xscale('log') plt.yscale('log') -#plt.show() +plt.show() #sys.exit(0) -# Plot correlation functions and vanilla theoretical prediction -plt.figure() -plt.subplot(111) -r = corr_true['r'] -h = box.cosmo['h'] - -rr = np.linspace(2., 200., 300) -xi = ccl.correlation_multipole(box.cosmo, a=box.scale_factor, l=0, s=rr, beta=0.) - -plt.subplot(111) -plt.plot(rr, rr**2. * xi * tracer.signal_amplitude()**2., 'k-') -plt.plot(r, r**2. * corr_true['corr'], 'r.', label="True field") -plt.plot(r, r**2. * corr_proc4['corr'], 'bx', label="4 modes") -plt.plot(r, r**2. * corr_proc4_hp['corr'], 'ys', label="4 modes (high-pass)") -plt.plot(r, r**2. * corr_proc8['corr'], 'g+', label="4 modes") -plt.xlabel("r", fontsize=16) -plt.ylabel(r"$r^2 \xi(r)$", fontsize=16) - -plt.show() diff --git a/examples/gal_tracer.ipynb b/examples/gal_tracer.ipynb new file mode 100644 index 0000000..87ca726 --- /dev/null +++ b/examples/gal_tracer.ipynb @@ -0,0 +1,383 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2b17e4a4-d0cb-48ab-a41b-db56b78af532", + "metadata": {}, + "source": [ + "# GALAXY SIMULATION" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1d12d7a5-4b75-486c-b6cb-fff27a952162", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import numpy.fft as fft\n", + "import pylab as plt\n", + "import pyccl as ccl\n", + "#plt.rcParams[\"figure.figsize\"] = (14,7)\n", + "import fastbox\n", + "from fastbox.box import CosmoBox, default_cosmo\n", + "from fastbox.foregrounds import ForegroundModel\n", + "\n", + "import time, sys" + ] + }, + { + "cell_type": "markdown", + "id": "46573b1d-770b-4792-8681-06abb2b02746", + "metadata": {}, + "source": [ + "## Galaxy catalog" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "24db3e9b-3a39-43c8-b1f8-aa3118f126b7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generated 999814 galaxies.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " /home/bruno/anaconda3/envs/fast-test/lib/python3.8/site-packages/pyccl/pk2d.py:207: RuntimeWarning:divide by zero encountered in log\n" + ] + } + ], + "source": [ + "box = CosmoBox(cosmo=default_cosmo, box_scale=(1e3,1e3,1e3), nsamp=64, \n", + " redshift=0.8, realise_now=False)\n", + "box.realise_density()\n", + "\n", + "delta_field = box.lognormal(box.delta_x)\n", + "\n", + "# Initialise the Galaxy Tracer\n", + "mean_density = 1e-3 # galaxies per Mpc^3\n", + "bias = 1.5\n", + "\n", + "gal_tracer = fastbox.tracers.GalaxyTracer(box=box, vol_density=mean_density, bias=bias)\n", + "\n", + "# Generate the coordinate catalogue\n", + "galaxy_coordinates = gal_tracer.generate_catalogue(delta_field)\n", + "\n", + "print(f\"Generated {len(galaxy_coordinates)} galaxies.\")" + ] + }, + { + "cell_type": "markdown", + "id": "2a51d198-708f-4d21-8410-6bd4dfb65674", + "metadata": {}, + "source": [ + "## MASS ASSIGNMENT" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f9942207-176e-433a-b64f-5b90c762741b", + "metadata": {}, + "outputs": [], + "source": [ + "# You can start a mesh from scratch, I.E, generating a gal simulation internally\n", + "ngp_mesh = gal_tracer.generate_mesh(delta_field, method='NGP',overdensity=False)\n", + "cic_mesh = gal_tracer.generate_mesh(delta_field, method='CIC',overdensity=False)\n", + "\n", + "# Or you can pass the catalog previously generated\n", + "\n", + "mesh = gal_tracer._assign_ngp(galaxy_coordinates)" + ] + }, + { + "cell_type": "markdown", + "id": "f77f3dc8-4d62-4ba6-afc6-4fa24a71145a", + "metadata": {}, + "source": [ + "### sliceplot" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "72e7f9f2-abbc-4e55-acb9-b78db1eb4810", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "slice_ngp = ngp_mesh[:,:,0]\n", + "slice_cic = cic_mesh[:,:,0]\n", + "\n", + "vmin = min(slice_ngp.min(), slice_cic.min())\n", + "vmax = max(slice_ngp.max(), slice_cic.max())\n", + "\n", + "# 2. Create the side-by-side subplots\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n", + "\n", + "# 3. Plot NGP\n", + "im1 = axes[0].matshow(slice_ngp, vmin=vmin, vmax=vmax)\n", + "axes[0].set_title('NGP Mass Assignment', pad=15)\n", + "\n", + "# 4. Plot CIC\n", + "im2 = axes[1].matshow(slice_cic, vmin=vmin, vmax=vmax)\n", + "axes[1].set_title('CIC Mass Assignment', pad=15)\n", + "\n", + "# 5. Add a single shared colorbar\n", + "# Using fraction and pad helps keep the colorbar scaled correctly with the plots\n", + "fig.colorbar(im1, ax=axes.ravel().tolist(), fraction=0.02, pad=0.04, label='Density Counts')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "020d984f-7acc-4be9-9a58-b3dd5cc60e87", + "metadata": {}, + "source": [ + "### pdf" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b5e2c169-2633-4413-a54b-ca426b6bcee8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(ngp_mesh.flatten(),bins=50, density=True,label='NGP')\n", + "plt.hist(cic_mesh.flatten(),bins=50, density=True,alpha=0.5,label='CIC')\n", + "plt.legend()\n", + "plt.xlim(-1,30)\n", + "plt.grid()\n", + "plt.ylabel('pdf')\n", + "plt.xlabel('# density')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e7ff5573-eb58-4d3f-bf6b-94389744dab1", + "metadata": {}, + "source": [ + "## RSD" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "704a7628-6417-4bcb-bc7c-2d5003892693", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " /home/bruno/anaconda3/envs/fast-test/lib/python3.8/site-packages/fastbox-0.0.9-py3.8.egg/fastbox/box.py:254: RuntimeWarning:invalid value encountered in true_divide\n", + " /home/bruno/anaconda3/envs/fast-test/lib/python3.8/site-packages/fastbox-0.0.9-py3.8.egg/fastbox/box.py:255: RuntimeWarning:invalid value encountered in true_divide\n", + " /home/bruno/anaconda3/envs/fast-test/lib/python3.8/site-packages/fastbox-0.0.9-py3.8.egg/fastbox/box.py:256: RuntimeWarning:invalid value encountered in true_divide\n" + ] + } + ], + "source": [ + "galaxy_coordinates_rsd = gal_tracer.apply_rsd(galaxy_coordinates, sigma_nl=120) # APP FOG AND KAISER" + ] + }, + { + "cell_type": "markdown", + "id": "2d820db7-3912-464d-b67f-01781238a2fd", + "metadata": {}, + "source": [ + "### (1/ngal)dn/dz" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6c8d8c40-6933-4642-9dbf-d20d06ab99bd", + "metadata": {}, + "outputs": [], + "source": [ + "red_bins = 1420/box.freq_array()-1\n", + "dz = np.abs(np.gradient(red_bins))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cb61c2c8-83cb-4e95-ab58-f3dd37aa500c", + "metadata": {}, + "outputs": [], + "source": [ + "mesh_rsd = gal_tracer._assign_ngp(galaxy_coordinates_rsd)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a9849c48-832c-4dc8-9f27-8f8ad96fb1ae", + "metadata": {}, + "outputs": [], + "source": [ + "dndz_ngal = np.sum(mesh,axis=(0,1))/(galaxy_coordinates.shape[0]*dz)\n", + "dndz_ngal_rsd = np.sum(mesh_rsd,axis=(0,1))/(galaxy_coordinates_rsd.shape[0]*dz)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7e7f984b-15a0-475f-a3c1-d3aa8de683e4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(red_bins,dndz_ngal, label='No RSD')\n", + "plt.plot(red_bins,dndz_ngal_rsd, label='RSD')\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.ylabel(r'$\\frac{1}{N_{\\rm gal}}\\,\\frac{dn}{dz}$')\n", + "plt.xlabel('redshift')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "253c90f1-3146-45cb-980e-b56a915d7201", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "slice_no = mesh[:,0,:]\n", + "slice_rsd = mesh_rsd[:,0,:]\n", + "\n", + "vmin = min(slice_no.min(), slice_rsd.min())\n", + "vmax = max(slice_no.max(), slice_rsd.max())\n", + "\n", + "# 2. Create the side-by-side subplots\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n", + "\n", + "# 3. Plot NGP\n", + "im1 = axes[0].matshow(slice_no, vmin=vmin, vmax=vmax)\n", + "axes[0].set_title('No rsd', pad=15)\n", + "axes[0].set_ylabel('x')\n", + "axes[0].set_xlabel('z')\n", + "\n", + "# 4. Plot CIC\n", + "im2 = axes[1].matshow(slice_rsd, vmin=vmin, vmax=vmax)\n", + "axes[1].set_title('rsd', pad=15)\n", + "axes[1].set_ylabel('x')\n", + "axes[1].set_xlabel('z')\n", + "\n", + "# 5. Add a single shared colorbar\n", + "fig.colorbar(im1, ax=axes.ravel().tolist(), fraction=0.02, pad=0.04, label='Density Counts')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d90c50db-6869-42a1-ae73-b080a1425b8c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.matshow(mesh[:,0,:]-mesh_rsd[:,0,:],cmap='RdBu')\n", + "plt.colorbar(label='mesh - mesh_rsd')\n", + "plt.xlabel('z')\n", + "plt.ylabel('x')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5caa6c86-4edf-47d5-895f-95ffed846a56", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.8.20" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/power_example.ipynb b/examples/power_example.ipynb new file mode 100644 index 0000000..e3d8159 --- /dev/null +++ b/examples/power_example.ipynb @@ -0,0 +1,305 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d3bfbe78-3e95-4e0b-859f-75c6730af24d", + "metadata": {}, + "source": [ + "# POWER SPECTRUM EXAMPLE" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ece8cd7a-6a87-4a5a-98eb-103d27b21404", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import numpy.fft as fft\n", + "import pylab as plt\n", + "import pyccl as ccl\n", + "#plt.rcParams[\"figure.figsize\"] = (14,7)\n", + "import fastbox\n", + "from fastbox.box import CosmoBox, default_cosmo\n", + "from fastbox.foregrounds import ForegroundModel\n", + "import fastbox.power as power\n", + "\n", + "#from power import *\n", + "import time, sys\n" + ] + }, + { + "cell_type": "markdown", + "id": "1b12f65b-9aee-4220-8434-bd07427b5360", + "metadata": {}, + "source": [ + "## GENERATE DENSITY FIELD" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9c114691-2ce8-4313-8567-9ee5ee618e31", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " /home/bruno/anaconda3/envs/fast-test/lib/python3.8/site-packages/pyccl/pk2d.py:207: RuntimeWarning:divide by zero encountered in log\n" + ] + } + ], + "source": [ + "box = CosmoBox(cosmo=default_cosmo, box_scale=(1e3,1e3,1e3), nsamp=64, \n", + " redshift=0.8, realise_now=False)\n", + "_ = box.realise_density()" + ] + }, + { + "cell_type": "markdown", + "id": "1ad531fd-10b3-4e0f-b2d4-1f975ca41f1b", + "metadata": {}, + "source": [ + "## HI TRACER" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d82fa3b5-13dc-4ae6-a8b0-0b88207543ca", + "metadata": {}, + "outputs": [], + "source": [ + "tracer = fastbox.tracers.HITracer(box)\n", + "delta_hi = box.delta_x * tracer.bias_HI()\n", + "\n", + "delta_hi_ln = box.lognormal(delta_hi)\n", + "hi_cube = tracer.signal_amplitude() * (1. + delta_hi_ln)" + ] + }, + { + "cell_type": "markdown", + "id": "ff782c7f-91b0-4e41-ad27-ceeb79a7dc0a", + "metadata": {}, + "source": [ + "## GENERATE GALAXY CATALOG" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "474309af-149c-4840-8be9-bb45e80c2a3a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generated 999161 galaxies.\n" + ] + } + ], + "source": [ + "# Initialise the Galaxy Tracer\n", + "mean_density = 1e-3 # galaxies per Mpc^3\n", + "bias_g = 1.5\n", + "\n", + "delta_g = box.delta_x * bias_g\n", + "\n", + "delta_g_ln = box.lognormal(delta_g)\n", + "\n", + "mean_density = 1e-3\n", + "gal_tracer = fastbox.tracers.GalaxyTracer(box=box, vol_density=mean_density, bias=bias_g)\n", + "\n", + "galaxy_coordinates = gal_tracer.generate_catalogue(delta_g_ln)\n", + "\n", + "print(f\"Generated {len(galaxy_coordinates)} galaxies.\")\n", + "\n", + "mesh_g = gal_tracer._assign_cic(galaxy_coordinates,overdensity=True)" + ] + }, + { + "cell_type": "markdown", + "id": "e1a07a38-37e8-4db0-83f0-b74a06e02cb3", + "metadata": {}, + "source": [ + "## POWER SPECTRUM " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "01870922-2bf6-4564-952e-6413783857a8", + "metadata": {}, + "outputs": [], + "source": [ + "# INITIALIZE POWER OBJECT\n", + "power = power.Power(box)" + ] + }, + { + "cell_type": "markdown", + "id": "9219e427-d4e1-4ed8-8160-dbce01bc1b14", + "metadata": {}, + "source": [ + "### estimate power spectrum" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c5aee231-563b-416a-bd3e-a13cd3af36a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating auto-correlation power spectrum...\n", + "DONE\n", + "Calculating auto-correlation power spectrum...\n", + "DONE\n", + "Calculating cross-correlation power spectrum\n", + "DONE\n" + ] + } + ], + "source": [ + "k, pk_g, _ = power.unweighted_power(mesh_g)\n", + "k, pk_hi, _ = power.unweighted_power(hi_cube)\n", + "k, pk_X, _ = power.unweighted_power(hi_cube, mesh_g)" + ] + }, + { + "cell_type": "markdown", + "id": "781bd761-a012-414f-ac59-613d467f4dbf", + "metadata": {}, + "source": [ + "### Calculate theoretical predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8406a17b-c68a-454b-9dc7-10e35df159c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DONE\n", + "DONE\n", + "DONE\n" + ] + } + ], + "source": [ + "km,pkm = power.matter_power_spectrum(k,rsd=False,sigma_nl=0)\n", + "\n", + "k, pk_hi_obs, std_hi_obs = power.model_obs_power_IM(k, \n", + " pkm, \n", + " bias=tracer.bias_HI(),\n", + " Tb = tracer.signal_amplitude(),\n", + " sigdeg=0, \n", + " rsd = False,\n", + " sigma_nl=0)\n", + "\n", + "k, pk_gal_obs, std_gal_obs = power.model_obs_power_gal(k, \n", + " pkm, \n", + " bias=bias_g,\n", + " MAS='CIC', \n", + " rsd = False,\n", + " sigma_nl=0)\n", + "k, pk_X_obs, std_X_obs = power.model_obs_power_CC(k, \n", + " pkm, \n", + " bias_HI=tracer.bias_HI(),\n", + " bias_gal = bias_g,\n", + " Tb = tracer.signal_amplitude(),\n", + " sigdeg=0,\n", + " MAS = 'CIC',\n", + " rsd = False,\n", + " sigma_nl=0)" + ] + }, + { + "cell_type": "markdown", + "id": "dbfb284d-fd40-4369-afc9-67efc5d56332", + "metadata": {}, + "source": [ + "### plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "aca57226-f8d5-4599-8541-07586da779ab", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(k, pk_hi, label='HI',color='b')\n", + "plt.plot(k, pk_hi_obs,linestyle='--',color='b')\n", + "plt.plot(k, pk_g-1/mean_density, label='Gal', color='r')\n", + "plt.plot(k, pk_gal_obs, linestyle='--',color='r')\n", + "plt.plot(k, pk_X, label='HIxGal',color='g')\n", + "plt.plot(k, pk_X_obs,linestyle='--',color='g')\n", + "plt.loglog()\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.ylabel('P(k)')\n", + "plt.xlabel(r'$k \\ [\\mathrm{Mpc}^{-1}]$')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "671ea2be-fc9d-43ce-8d04-3bbbb313e383", + "metadata": {}, + "source": [ + "We can omit aliasing, grid correction, and channelization from the forward model because they represent physical or observational effects that simply do not occur in your specific simulation architecture. First, aliasing is unnecessary because our mock generates the linear density field directly on the finite Fourier grid; this naturally band-limits the signal, meaning no theoretical power is generated beyond the Nyquist frequency to fold back into the box. Second, the grid correction (voxel window function) is unneeded because the simulation point-samples the field exactly at cell centers rather than volume-averaging the continuous field, meaning no top-hat suppression takes place. Finally, channelization damping is not required because our cube is natively generated on a discrete 3D spatial grid—including the line-of-sight $z$-direction—from the start, rather than being built from continuous frequency observations that must be integrated into discrete channels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea29f882-9828-4c04-a590-6bbd2bc3899f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.8.20" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/fastbox/__init__.py b/fastbox/__init__.py index 159468a..666dbfd 100644 --- a/fastbox/__init__.py +++ b/fastbox/__init__.py @@ -1,3 +1,3 @@ from .box import CosmoBox -from . import analysis, beams, box, filters, forecast, foregrounds, halos, inpaint, noise, plot, tracers, utils, voids +from . import analysis, beams, box, filters, forecast, foregrounds, halos, inpaint, noise, plot, tracers, utils, voids, power diff --git a/fastbox/power.py b/fastbox/power.py new file mode 100644 index 0000000..8538060 --- /dev/null +++ b/fastbox/power.py @@ -0,0 +1,607 @@ +import numpy as np +import numpy.fft as fft +import pyccl as ccl + +class Power(object): + def __init__(self, box): + """ + An object to manage power spectrum estimation. + + Parameters: + box (CosmoBox): + Object containing a simulation box. + """ + self.box = box + + def get_window_correction_grid(self, method=None): + """ + Calculates the 3D squared window function |W(k)|^2 for mass assignment correction. + + It implements equation 18 of https://arxiv.org/abs/astro-ph/0409240 + + Parameters: + ----------- + method : 'CIC' of 'NGP' + Method to consider + + Returns: + -------- + W2_k : ndarray + Window function. + + """ + if method is None or method.upper() not in ['NGP', 'CIC']: + raise ValueError("Valid methods: 'NGP' and 'CIC'") + + p = 1 if method.upper() == 'NGP' else 2 + + # 1. Get the 1D sinc function along one axis + w1d = np.sinc(np.fft.fftfreq(self.box.N)) + + # 2. Square the 1D window + w1d_sq = w1d**2 + + # 3. Broadcast to 3D axes (X, Y, Z) + Wx2 = w1d_sq[:, None, None] + Wy2 = w1d_sq[None, :, None] + Wz2 = w1d_sq[None, None, :] + + # 4. Combine and raise to the assignment scheme power + W2_k = (Wx2 * Wy2 * Wz2)**p + + return W2_k + + def unweighted_power(self, delta_x1, delta_x2=None): + """ + Calculates the 1D spherically averaged auto or cross power spectrum. + + Parameters: + ----------- + delta_x1 : ndarray + overdensity mesh + delta_x2 : ndarray + Second overdensity mesh. Default None. If not None, returns cross-power + + Returns: + -------- + kc : ndarray + Center of k bins + vals : ndarray + power spectrum (auto or cross, depending on delta_x2) + stddev : + simple calculation of standard deviation of the estimator + """ + if delta_x1 is None: + raise ValueError('Need to specify field delta_x1') + + # FFT of the first field + delta_k1 = fft.fftn(delta_x1) + + # --------------------------------------------------------- + # AUTO-CORRELATION + # --------------------------------------------------------- + if delta_x2 is None: + print('Calculating auto-correlation power spectrum...') + pk = delta_k1 * np.conj(delta_k1) + pk = pk.real / self.box.boxfactor + + # --------------------------------------------------------- + # CROSS-CORRELATION + # --------------------------------------------------------- + else: + print('Calculating cross-correlation power spectrum') + delta_k2 = fft.fftn(delta_x2) + + # Cross power is the real part of (delta_1 * conj(delta_2)) + pk = delta_k1 * np.conj(delta_k2) + pk = pk.real / self.box.boxfactor + + + # --------------------------------------------------------- + # K-BINNING + # --------------------------------------------------------- + L_max = max(self.box.Lx, self.box.Ly, self.box.Lz) + k_min = 2.0 * np.pi / L_max + k_bin = k_min + + L_min = min(self.box.Lx, self.box.Ly, self.box.Lz) + k_nyq = np.pi * self.box.N / L_min + + bins = np.arange(k_min, k_nyq + k_bin, k_bin) + kc = 0.5 * (bins[1:] + bins[:-1]) + + vals = np.zeros(kc.size) + stddev = np.zeros(kc.size) + + idxs = np.digitize(self.box.k.flatten(), bins) + pk_flat = pk.flatten() + + for i in range(1, bins.size): + ii = (idxs == i) + if np.any(ii): + vals[i-1] = np.mean(pk_flat[ii]) + stddev[i-1] = np.std(pk_flat[ii]) / np.sqrt(np.sum(ii)) + else: + vals[i-1] = np.nan + stddev[i-1] = np.nan + print('DONE') + + return kc, vals, stddev + + def model_obs_power_IM(self, km, pkm, bias, Tb, sigdeg, rsd=True, sigma_nl=120): + """ + Computes the theoretical observational power spectrum by forward-modeling + the theoretical matter power spectrum on the 3D FFT grid. + Includes Beam smoothing, Channel smoothing, FFT Discretization, and RSD. + + Parameters: + ----------- + km, pkm : array_like + The 1D theoretical matter power spectrum (wavenumbers and power). + bias : float + The linear bias factor (b) + Tb : float + The mean brightness temperature (signal amplitude) + sigdeg : float + Standard deviation of the Gaussian beam in degrees. Set 0 to ignore beam. + rsd : Bool + Set True to apply Kaiser and FoG effects. + sigma_nl : float + Velocity dispersion in km/s for Finger of God damping. Set 0 to ignore FoG. + + Returns: + -------- + kc, pk_model, stddev : ndarray + The binned, forward-modeled 1D power spectrum. + """ + + # ======================================================= + # 1. Calculate Physical Scales & Cosmology (R_beam, R_chan, R_nl) + # ======================================================= + z = self.box.redshift + scale_factor = 1.0 / (1.0 + z) + h = self.box.cosmo['h'] + Hz = 100. * h * ccl.h_over_h0(self.box.cosmo, scale_factor) + + # Transverse Beam Scale + if sigdeg > 0: + chi_mpc = ccl.comoving_radial_distance(self.box.cosmo, scale_factor) + R_beam = np.radians(sigdeg) * (chi_mpc * h) + else: + R_beam = 0.0 + ''' + # LoS Channel Scale + if channel: + freqs = self.box.freq_array() + dnu_mhz = np.abs(np.mean(np.diff(freqs))) + nu_21 = 1420.40575177 + c_kms = 299792.458 + R_chan = (c_kms * (1.0 + z)**2) / (Hz * nu_21) * dnu_mhz * h + else: + R_chan = 0.0 + + # RSD Linear Growth Rate (f) and FoG Scale (R_nl) + if rsd: + f = ccl.growth_rate(self.box.cosmo, scale_factor) + if sigma_nl > 0: + # Convert km/s to comoving Mpc/h + R_nl = (sigma_nl * (1.0 + z) / Hz) * h + else: + R_nl = 0.0 + ''' + # ======================================================= + # 2. Create the exact 3D Fourier Grid + # ======================================================= + kx = 2.0 * np.pi * np.fft.fftfreq(self.box.N, d=self.box.Lx / self.box.N) + ky = 2.0 * np.pi * np.fft.fftfreq(self.box.N, d=self.box.Ly / self.box.N) + kz = 2.0 * np.pi * np.fft.fftfreq(self.box.N, d=self.box.Lz / self.box.N) + + kx3d = kx[:, None, None] + ky3d = ky[None, :, None] + kz3d = kz[None, None, :] + + # k_perp (X, Y) and k_parallel (Z) + k_perp_sq = kx3d**2 + ky3d**2 + k_par_sq = kz3d**2 + k_mag_sq = k_perp_sq + k_par_sq + k_mag = np.sqrt(k_mag_sq) + + # ======================================================= + # 3. Evaluate 3D Theory & Apply RSD + Damping Factors + # ======================================================= + # Interpolate the theoretical 1D P(k) onto the 3D grid + pk_3d = np.interp(k_mag.flatten(), km, pkm).reshape(k_mag.shape) + + # --- RSD (Kaiser + FoG) --- + if rsd: + # Safely calculate mu^2 = k_parallel^2 / k_mag^2 (avoid division by zero at k=0) + # FIXED: Used np.zeros_like(k_mag_sq) to match the broadcast shape + mu_sq = np.divide(k_par_sq, k_mag_sq, out=np.zeros_like(k_mag_sq), where=(k_mag_sq != 0)) + + # Kaiser Factor + rsd_factor = (bias + f * mu_sq)**2 + + # FoG Damping (Gaussian velocity dispersion) + if sigma_nl > 0: + D2_fog = np.exp(-k_par_sq * (R_nl**2)) + else: + D2_fog = 1.0 + + pk_3d = pk_3d * rsd_factor * D2_fog + else: + # No RSD: just use isotropic bias + pk_3d = pk_3d * (bias**2) + + + pk_3d = pk_3d * (Tb**2) + + # --- Observational Damping --- + if sigdeg > 0: + D2_beam = np.exp(-k_perp_sq * (R_beam**2)) + else: + D2_beam = 1.0 + ''' + if channel: + k_par = np.sqrt(k_par_sq) + D2_chan = (np.sinc((k_par * R_chan) / (2.0 * np.pi)))**2 + else: + D2_chan = 1.0 + ''' + # Combine all effects + pk_3d_obs = pk_3d * D2_beam# * D2_chan + + # ======================================================= + # 4. Bin the 3D Model into 1D k-shells + # ======================================================= + L_max = max(self.box.Lx, self.box.Ly, self.box.Lz) + k_min = 2.0 * np.pi / L_max + k_bin = k_min + + L_min = min(self.box.Lx, self.box.Ly, self.box.Lz) + k_nyq = np.pi * self.box.N / L_min + + bins = np.arange(k_min, k_nyq + k_bin, k_bin) + kc = 0.5 * (bins[1:] + bins[:-1]) + + vals = np.zeros(kc.size) + stddev = np.zeros(kc.size) + + idxs = np.digitize(k_mag.flatten(), bins) + pk_flat = pk_3d_obs.flatten() + + for i in range(1, bins.size): + ii = (idxs == i) + if np.any(ii): + vals[i-1] = np.mean(pk_flat[ii]) + stddev[i-1] = np.std(pk_flat[ii]) / np.sqrt(np.sum(ii)) + else: + vals[i-1] = np.nan + stddev[i-1] = np.nan + print('DONE') + + return kc, vals, stddev + + def model_obs_power_gal(self, km, pkm, bias, MAS='NGP', rsd=True, sigma_nl=120): + """ + Computes the theoretical observational power spectrum for galaxies + by forward-modeling the matter power spectrum on the 3D FFT grid. + Includes Mass Assignment Scheme (MAS) window and RSD. + + Parameters: + ----------- + km, pkm : array_like + The 1D theoretical matter power spectrum (wavenumbers and power). + bias : float + The linear bias factor (b) + MAS : 'NGP' or 'CIC' or None + mass assignment method to correct. + rsd : Bool + Set True to apply Kaiser and FoG effects. + sigma_nl : float + Velocity dispersion in km/s for Finger of God damping. Set 0 to ignore FoG. + + Returns: + -------- + kc, pk_model, stddev : ndarray + The binned, forward-modeled 1D power spectrum. + """ + + # ======================================================= + # 1. Create the exact 3D Fourier Grid + # ======================================================= + kx = 2.0 * np.pi * np.fft.fftfreq(self.box.N, d=self.box.Lx / self.box.N) + ky = 2.0 * np.pi * np.fft.fftfreq(self.box.N, d=self.box.Ly / self.box.N) + kz = 2.0 * np.pi * np.fft.fftfreq(self.box.N, d=self.box.Lz / self.box.N) + + kx3d = kx[:, None, None] + ky3d = ky[None, :, None] + kz3d = kz[None, None, :] + + # k_perp (X, Y) and k_parallel (Z) + k_perp_sq = kx3d**2 + ky3d**2 + k_par_sq = kz3d**2 + k_mag_sq = k_perp_sq + k_par_sq + k_mag = np.sqrt(k_mag_sq) + + # ======================================================= + # 2. Evaluate 3D Theory & Apply RSD + # ======================================================= + # Interpolate the theoretical 1D P(k) onto the 3D grid + pk_3d = np.interp(k_mag.flatten(), km, pkm).reshape(k_mag.shape) + + # Cosmology for RSD + z = self.box.redshift + scale_factor = 1.0 / (1.0 + z) + + if rsd: + h = self.box.cosmo['h'] + Hz = 100. * h * ccl.h_over_h0(self.box.cosmo, scale_factor) + f = ccl.growth_rate(self.box.cosmo, scale_factor) + + if sigma_nl > 0: + R_nl = (sigma_nl * (1.0 + z) / Hz) * h + else: + R_nl = 0.0 + + # Safely calculate mu^2 + mu_sq = np.divide(k_par_sq, k_mag_sq, out=np.zeros_like(k_mag_sq), where=(k_mag_sq != 0)) + + # Kaiser Factor and FoG + rsd_factor = (bias + f * mu_sq)**2 + D2_fog = np.exp(-k_par_sq * (R_nl**2)) if sigma_nl > 0 else 1.0 + + pk_3d = pk_3d * rsd_factor * D2_fog + else: + # If no RSD, just apply the isotropic isotropic clustering + pk_3d = pk_3d * (bias**2) + + # ======================================================= + # 3. Apply Discretization Window (MAS) + # ======================================================= + if MAS is not None: + # Fixed: Removed self.N argument to match your earlier definition + W2_k = self.get_window_correction_grid(method=MAS) + pk_3d = pk_3d * W2_k + + # ======================================================= + # 4. Bin the 3D Model into 1D k-shells + # ======================================================= + L_max = max(self.box.Lx, self.box.Ly, self.box.Lz) + k_min = 2.0 * np.pi / L_max + k_bin = k_min + + L_min = min(self.box.Lx, self.box.Ly, self.box.Lz) + k_nyq = np.pi * self.box.N / L_min + + bins = np.arange(k_min, k_nyq + k_bin, k_bin) + kc = 0.5 * (bins[1:] + bins[:-1]) + + vals = np.zeros(kc.size) + stddev = np.zeros(kc.size) + + idxs = np.digitize(k_mag.flatten(), bins) + pk_flat = pk_3d.flatten() + + for i in range(1, bins.size): + ii = (idxs == i) + if np.any(ii): + vals[i-1] = np.mean(pk_flat[ii]) + stddev[i-1] = np.std(pk_flat[ii]) / np.sqrt(np.sum(ii)) + else: + vals[i-1] = np.nan + stddev[i-1] = np.nan + print('DONE') + + return kc, vals, stddev + + def model_obs_power_CC(self, km, pkm, bias_HI, bias_gal, Tb, sigdeg, MAS='NGP', rsd=True, sigma_nl=120): + """ + Computes the theoretical Cross-Correlation power spectrum by forward-modeling + the matter power spectrum on the 3D FFT grid. + + Parameters: + ----------- + km, pkm : array_like + The 1D theoretical matter power spectrum (wavenumbers and power). + bias_HI : float + The linear bias factor of the Intensity Mapping tracer. + bias_gal : float + The linear bias factor of the Galaxy tracer. + Tb : float + The mean brightness temperature (signal amplitude). + sigdeg : float + Standard deviation of the Gaussian beam in degrees. + MAS : 'NGP' or 'CIC' or None + Mass assignment method used. + rsd : Bool + Set True to apply Kaiser and FoG effects. + sigma_nl : float + Velocity dispersion in km/s for Finger of God damping. + """ + import pyccl as ccl + import numpy as np + + # ======================================================= + # 1. Calculate Physical Scales & Cosmology + # ======================================================= + z = self.box.redshift + scale_factor = 1.0 / (1.0 + z) + h = self.box.cosmo['h'] + Hz = 100. * h * ccl.h_over_h0(self.box.cosmo, scale_factor) + + # Transverse Beam Scale + if sigdeg > 0: + chi_mpc = ccl.comoving_radial_distance(self.box.cosmo, scale_factor) + R_beam = np.radians(sigdeg) * (chi_mpc * h) + else: + R_beam = 0.0 + + # Calculate RSD parameters + if rsd: + f = ccl.growth_rate(self.box.cosmo, scale_factor) + if sigma_nl > 0: + R_nl = (sigma_nl * (1.0 + z) / Hz) * h + else: + R_nl = 0.0 + + # ======================================================= + # 2. Create the exact 3D Fourier Grid + # ======================================================= + kx = 2.0 * np.pi * np.fft.fftfreq(self.box.N, d=self.box.Lx / self.box.N) + ky = 2.0 * np.pi * np.fft.fftfreq(self.box.N, d=self.box.Ly / self.box.N) + kz = 2.0 * np.pi * np.fft.fftfreq(self.box.N, d=self.box.Lz / self.box.N) + + kx3d = kx[:, None, None] + ky3d = ky[None, :, None] + kz3d = kz[None, None, :] + + k_perp_sq = kx3d**2 + ky3d**2 + k_par_sq = kz3d**2 + k_mag_sq = k_perp_sq + k_par_sq + k_mag = np.sqrt(k_mag_sq) + + # ======================================================= + # 3. Evaluate 3D Theory & Apply RSD + Damping Factors + # ======================================================= + pk_3d = np.interp(k_mag.flatten(), km, pkm).reshape(k_mag.shape) + + # --- RSD (Kaiser + FoG) --- + if rsd: + mu_sq = np.divide(k_par_sq, k_mag_sq, out=np.zeros_like(k_mag_sq), where=(k_mag_sq != 0)) + + # Cross-Correlation Kaiser Factor: (b_HI + f*mu^2) * (b_gal + f*mu^2) + rsd_factor = (bias_HI + f * mu_sq) * (bias_gal + f * mu_sq) + + D2_fog = np.exp(-k_par_sq * (R_nl**2)) if sigma_nl > 0 else 1.0 + + pk_3d = pk_3d * rsd_factor * D2_fog + else: + # No RSD: just use isotropic cross-bias + pk_3d = pk_3d * (bias_HI * bias_gal) + + # Apply single Temperature scaling for Cross-Correlation + pk_3d = pk_3d * Tb + + # --- Observational Damping --- + D2_beam = np.exp(-k_perp_sq * (R_beam**2)) if sigdeg > 0 else 1.0 + + # Cross-power uses exactly one power of the beam (sqrt(D2_beam) = B_beam) + pk_3d_obs = pk_3d * np.sqrt(D2_beam) + + # Discretization Window + if MAS is not None: + # Retain TWO powers of pixelization for Cross-Correlation, applied directly to pk_3d_obs + W2_k = self.get_window_correction_grid(method=MAS) + pk_3d_obs = pk_3d_obs * np.sqrt(W2_k) + + # ======================================================= + # 4. Bin the 3D Model into 1D k-shells + # ======================================================= + L_max = max(self.box.Lx, self.box.Ly, self.box.Lz) + k_min = 2.0 * np.pi / L_max + k_bin = k_min + + L_min = min(self.box.Lx, self.box.Ly, self.box.Lz) + k_nyq = np.pi * self.box.N / L_min + + bins = np.arange(k_min, k_nyq + k_bin, k_bin) + kc = 0.5 * (bins[1:] + bins[:-1]) + + vals = np.zeros(kc.size) + stddev = np.zeros(kc.size) + + idxs = np.digitize(k_mag.flatten(), bins) + pk_flat = pk_3d_obs.flatten() + + for i in range(1, bins.size): + ii = (idxs == i) + if np.any(ii): + vals[i-1] = np.mean(pk_flat[ii]) + stddev[i-1] = np.std(pk_flat[ii]) / np.sqrt(np.sum(ii)) + else: + vals[i-1] = np.nan + stddev[i-1] = np.nan + print('DONE') + + return kc, vals, stddev + + + + + + + def matter_power_spectrum(self, k, rsd=False, sigma_nl=0): + """ + Calculate the theoretical nonlinear power spectrum for the given + cosmological parameters, using CCL. Does not depend on the realisation. + + Parameters: + ----------- + k : array_like + k values to evaluate power spectrum + rsd : Bool + Set True to consider Kaiser and FoG + sigma_nl : float + non-linear velocity. Set 0 to not consider FoG + + Returns: + -------- + k, pk (array_like): + Wavenumbers, from 10^-3.5 to 10^1, in Mpc^-1, and the + theoretical nonlinear power spectrum, in (Mpc)^3. + """ + + + # 1. Calculate the real-space matter power spectrum P_m(k) + pk = ccl.nonlin_matter_power(self.box.cosmo, k=k, a=self.box.scale_factor) + + if rsd is False: + return k, pk + + elif rsd is True: + # 2. Cosmology & Redshift parameters + z = (1.0 / self.box.scale_factor) - 1.0 + + # Linear growth rate (f) + f = ccl.growth_rate(self.box.cosmo, self.box.scale_factor) + + # Hubble parameter at z in km/s/Mpc + Hz = 100. * self.box.cosmo['h'] * ccl.h_over_h0(self.box.cosmo, self.box.scale_factor) + + # 3. Finger of God (FoG) Scale + # Since k is in Mpc^-1, R_nl must be in Mpc + if sigma_nl > 0: + R_nl = (sigma_nl * (1.0 + z)) / Hz + else: + R_nl = 0.0 + + # 4. Spherically Average over mu (angle to the line of sight) + n_mu = 200 + mu = np.linspace(0, 1, n_mu) + + # Create a 2D grid for k and mu combinations + k_grid, mu_grid = np.meshgrid(k, mu, indexing='ij') + + # Broadcast 1D P_m(k) to the 2D grid shape: (len(k), len(mu)) + pk_grid = pk[:, None] + + # Kaiser effect: (b + f*mu^2)^2. For pure matter, bias b = 1.0 + kaiser_factor = (1.0 + f * (mu_grid**2))**2 + + # FoG Damping: exp(-(k * mu * R_nl)^2) + if sigma_nl > 0: + fog_damping = np.exp(-((k_grid * mu_grid * R_nl)**2)) + else: + fog_damping = 1.0 + + # Combine to get the 2D Redshift-Space Power Spectrum + pk_2d = pk_grid * kaiser_factor * fog_damping + + # Integrate (average) over mu to get the 1D spherically averaged P(k) + pk_1d = np.trapz(pk_2d, x=mu, axis=1) + + return k, pk_1d + + else: + raise ValueError('RSD option must be either True or False') + \ No newline at end of file diff --git a/fastbox/tracers.py b/fastbox/tracers.py index 3333e3f..2c683c6 100644 --- a/fastbox/tracers.py +++ b/fastbox/tracers.py @@ -162,3 +162,210 @@ def Omega_HI(self, redshift=None, formula='powerlaw'): return (self.OmegaHI0 / 0.000486) \ * (4.8304e-04 + 3.8856e-04*z - 6.5119e-05*z**2.) + + + +class GalaxyTracer: + def __init__(self, box, vol_density, bias=1.0): + """ + Initialise the Galaxy Tracer. + + Parameters: + ----------- + box : fastbox.Box + The FastBox instance containing the density field and grid parameters. + vol_density : float + The mean volumetric density of galaxies in Mpc^-3. + bias : float + The linear galaxy bias factor. + """ + self.box = box + self.vol_density = vol_density + self.bias = bias + + def generate_catalogue(self, density_field): + """ + Generates a discrete galaxy catalogue via Poisson sampling. + """ + voxel_vol = (self.box.Lx * self.box.Ly * self.box.Lz) / \ + (self.box.N * self.box.N * self.box.N) + + expected_gals = self.vol_density * voxel_vol * (1.0 + density_field) + expected_gals[expected_gals < 0] = 0.0 + + N_gals_per_voxel = np.random.poisson(expected_gals) + + voxel_indices = np.where(N_gals_per_voxel > 0) + counts = N_gals_per_voxel[voxel_indices] + + dx = self.box.Lx / self.box.N + dy = self.box.Ly / self.box.N + dz = self.box.Lz / self.box.N + + x_base = voxel_indices[0] * dx + y_base = voxel_indices[1] * dy + z_base = voxel_indices[2] * dz + + x_base_exp = np.repeat(x_base, counts) + y_base_exp = np.repeat(y_base, counts) + z_base_exp = np.repeat(z_base, counts) + + total_gals = np.sum(counts) + x_gal = x_base_exp + np.random.uniform(0, dx, total_gals) + y_gal = y_base_exp + np.random.uniform(0, dy, total_gals) + z_gal = z_base_exp + np.random.uniform(0, dz, total_gals) + + galaxy_positions = np.vstack((x_gal, y_gal, z_gal)).T + + return galaxy_positions + + def generate_mesh(self, density_field, method='NGP',overdensity=False): + """ + Generates a discrete galaxy catalogue and assigns it to a density mesh. + """ + positions = self.generate_catalogue(density_field) + + method = method.upper() + if method == 'NGP': + return self._assign_ngp(positions,overdensity) + elif method == 'CIC': + return self._assign_cic(positions,overdensity) + else: + raise ValueError(f"Mass assignment method '{method}' not recognised. Use 'NGP' or 'CIC'.") + + def _assign_ngp(self, positions, overdensity=False): + """Internal method for Nearest Grid Point mass assignment.""" + edges_x = np.linspace(0, self.box.Lx, self.box.N + 1) + edges_y = np.linspace(0, self.box.Ly, self.box.N + 1) + edges_z = np.linspace(0, self.box.Lz, self.box.N + 1) + + pos = np.asarray(positions) + x = pos[:, 0] % self.box.Lx + y = pos[:, 1] % self.box.Ly + z = pos[:, 2] % self.box.Lz + + grid, _ = np.histogramdd((x, y, z), bins=(edges_x, edges_y, edges_z)) + + if overdensity==True: + mean_counts = np.mean(grid) + grid = (grid / mean_counts) - 1.0 + + return grid + + def _assign_cic(self, positions, overdensity=False): + """Internal method for Cloud-in-Cell mass assignment.""" + pos = np.asarray(positions) + N_total_cells = self.box.N * self.box.N * self.box.N + + dx = self.box.Lx / self.box.N + dy = self.box.Ly / self.box.N + dz = self.box.Lz / self.box.N + + u = (pos[:, 0] / dx - 0.5) % self.box.N + v = (pos[:, 1] / dy - 0.5) % self.box.N + w = (pos[:, 2] / dz - 0.5) % self.box.N + + i0 = np.floor(u).astype(int) + j0 = np.floor(v).astype(int) + k0 = np.floor(w).astype(int) + + i1 = (i0 + 1) % self.box.N + j1 = (j0 + 1) % self.box.N + k1 = (k0 + 1) % self.box.N + + dwx = u - i0 + dwy = v - j0 + dwz = w - k0 + + twx = 1.0 - dwx + twy = 1.0 - dwy + twz = 1.0 - dwz + + w000 = twx * twy * twz + w100 = dwx * twy * twz + w010 = twx * dwy * twz + w110 = dwx * dwy * twz + w001 = twx * twy * dwz + w101 = dwx * twy * dwz + w011 = twx * dwy * dwz + w111 = dwx * dwy * dwz + + def flat_idx(i, j, k): + return i * (self.box.N * self.box.N) + j * self.box.N + k + + grid_flat = ( + np.bincount(flat_idx(i0, j0, k0), weights=w000, minlength=N_total_cells) + + np.bincount(flat_idx(i1, j0, k0), weights=w100, minlength=N_total_cells) + + np.bincount(flat_idx(i0, j1, k0), weights=w010, minlength=N_total_cells) + + np.bincount(flat_idx(i1, j1, k0), weights=w110, minlength=N_total_cells) + + np.bincount(flat_idx(i0, j0, k1), weights=w001, minlength=N_total_cells) + + np.bincount(flat_idx(i1, j0, k1), weights=w101, minlength=N_total_cells) + + np.bincount(flat_idx(i0, j1, k1), weights=w011, minlength=N_total_cells) + + np.bincount(flat_idx(i1, j1, k1), weights=w111, minlength=N_total_cells) + ) + + grid = grid_flat.reshape((self.box.N, self.box.N, self.box.N)) + if overdensity==True: + mean_counts = np.mean(grid) + grid = (grid / mean_counts) - 1.0 + + return grid + + def apply_rsd(self, positions, sigma_nl=120.0): + """ + Applies Redshift Space Distortions (RSD) to discrete galaxy coordinates. + Calculates both the coherent large-scale infall (Kaiser effect) and + non-linear dispersion (Fingers of God). + + Parameters: + ----------- + positions : ndarray + (N, 3) array of real-space galaxy coordinates. + sigma_nl : float + Velocity dispersion for FoG in km/s. Set to 0 to disable FoG. + + Returns: + -------- + positions_rsd : ndarray + (N, 3) array of redshift-space galaxy coordinates. + """ + positions_rsd = np.copy(positions) + N_gals = positions_rsd.shape[0] + + # 1. Calculate Expansion Rate H(z) in km/s/Mpc + Hz = 100. * self.box.cosmo['h'] * ccl.h_over_h0(self.box.cosmo, self.box.scale_factor) + + # Array to accumulate total line-of-sight velocity for each galaxy (in km/s) + vel_shift_kms = np.zeros(N_gals) + + # 2. Kaiser Effect (Coherent velocities from linear theory) + # Generate Fourier-space velocity field + vel_k = self.box.realise_velocity(delta_x=self.box.delta_x, inplace=True) + # Inverse FFT to get real-space radial velocity (z-direction) + vel_z = fft.ifftn(vel_k[2]).real + + # Map galaxies to their nearest grid cells to extract local velocity + dx = self.box.Lx / self.box.N + dy = self.box.Ly / self.box.N + dz = self.box.Lz / self.box.N + + i = np.floor((positions[:, 0] % self.box.Lx) / dx).astype(int) + j = np.floor((positions[:, 1] % self.box.Ly) / dy).astype(int) + k = np.floor((positions[:, 2] % self.box.Lz) / dz).astype(int) + + vel_shift_kms += vel_z[i, j, k] + + # 3. Fingers of God (Incoherent thermal velocities) + if sigma_nl > 0.0: + vel_shift_kms += sigma_nl * np.random.normal(loc=0.0, scale=1.0, size=N_gals) + + # 4. Apply Spatial Displacement to Z-Coordinates + # Shift in comoving Mpc = velocity / H(z) + dz_mpc = vel_shift_kms / Hz + positions_rsd[:, 2] = (positions_rsd[:, 2] + dz_mpc) % self.box.Lz + + return positions_rsd + + + + diff --git a/fastbox/voids.py b/fastbox/voids.py index a14f995..19b496d 100644 --- a/fastbox/voids.py +++ b/fastbox/voids.py @@ -3,7 +3,7 @@ import numpy as np import scipy.interpolate from skimage.segmentation import watershed -import skimage.future.graph as graph +import skimage.graph as graph import time diff --git a/setup.py b/setup.py index 7296065..acc6756 100644 --- a/setup.py +++ b/setup.py @@ -19,7 +19,7 @@ 'numpy>=1.18', 'scipy>=1.5', 'matplotlib>=2.2', - 'sklearn', + 'scikit-learn', 'pyccl' ], 'extras_require': {'fgextras': ['healpy', 'lmfit', 'multiprocessing', 'GPy']}, @@ -30,4 +30,3 @@ if __name__ == '__main__': setup(**setup_args) -