mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-10-19 07:25:06 +02:00
f7285b2600
* Add GoF calculation * Add import * Add base flow * Add reading of ndata * Update nb * Update plotting * Update script * Update plots * Updaet plo * Add script * Update nb * Update nb * Update script * Update script * Update nb * Remove imports * Improve labelling * Improve flow calibration * Add bulk flow plots * Update flow * Update scrit * Calculate more radial steps * Update bulk * Update script * Update nb
776 lines
142 KiB
Plaintext
776 lines
142 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Calibrating the velocity field against observations "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Copyright (C) 2024 Richard Stiskalek\n",
|
|
"# This program is free software; you can redistribute it and/or modify it\n",
|
|
"# under the terms of the GNU General Public License as published by the\n",
|
|
"# Free Software Foundation; either version 3 of the License, or (at your\n",
|
|
"# option) any later version.\n",
|
|
"#\n",
|
|
"# This program is distributed in the hope that it will be useful, but\n",
|
|
"# WITHOUT ANY WARRANTY; without even the implied warranty of\n",
|
|
"# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General\n",
|
|
"# Public License for more details.\n",
|
|
"#\n",
|
|
"# You should have received a copy of the GNU General Public License along\n",
|
|
"# with this program; if not, write to the Free Software Foundation, Inc.,\n",
|
|
"# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n",
|
|
"import numpy as np\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import jax\n",
|
|
"from numpyro.infer import MCMC, NUTS, init_to_median\n",
|
|
"import corner\n",
|
|
"from getdist import plots\n",
|
|
"from scipy.stats import multivariate_normal\n",
|
|
"\n",
|
|
"import csiborgtools\n",
|
|
"\n",
|
|
"from flow_calibration import *\n",
|
|
"\n",
|
|
"%load_ext autoreload\n",
|
|
"%autoreload 2\n",
|
|
"%matplotlib inline\n",
|
|
"\n",
|
|
"paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## LOS density & radial velocity plots "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# fpath = \"/mnt/extraspace/rstiskalek/catalogs/A2.h5\"\n",
|
|
"fpath = \"/mnt/extraspace/rstiskalek/catalogs/PV_compilation_Supranta2019.hdf5\"\n",
|
|
"\n",
|
|
"loader_carrick = csiborgtools.flow.DataLoader(\"Carrick2015\", \"LOSS\", fpath, paths, ksmooth=0)\n",
|
|
"loader_csiborg = csiborgtools.flow.DataLoader(\"csiborg1\", \"LOSS\", fpath, paths, ksmooth=0)\n",
|
|
"loader_csiborg2 = csiborgtools.flow.DataLoader(\"csiborg2_main\", \"LOSS\", fpath, paths, ksmooth=0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# ks = [115, 53, 77, 105, 26, 61, 86, 29, 80, 21]\n",
|
|
"ks = [19, 8, 15, 0, 16, 6, 48, 38, 26, 44]\n",
|
|
"# ks = [19]\n",
|
|
"# ks = np.random.choice(50, 10, replace=False)\n",
|
|
"\n",
|
|
"# k = 6\n",
|
|
"for k in []:\n",
|
|
" fig, axs = plt.subplots(2, 1, figsize=(7, 7), sharex=True)\n",
|
|
" # Get rid of vertical spacing\n",
|
|
" fig.subplots_adjust(wspace=0)\n",
|
|
"\n",
|
|
" # Plot CSiBORG\n",
|
|
" for i in range(loader_csiborg.los_density.shape[1]):\n",
|
|
" axs[0].plot(loader_csiborg.rdist, loader_csiborg.los_density[k, i, :], alpha=0.1, color=\"black\")\n",
|
|
" axs[1].plot(loader_csiborg.rdist, loader_csiborg.los_radial_velocity[k, i, :], alpha=0.1, color=\"black\")\n",
|
|
"\n",
|
|
" # CSiBORG1\n",
|
|
" axs[0].plot(loader_csiborg.rdist, loader_csiborg.los_density[k, :, :].mean(axis=0), color=\"red\", label=\"CSiBORG1\")\n",
|
|
" axs[1].plot(loader_csiborg.rdist, loader_csiborg.los_radial_velocity[k, :, :].mean(axis=0), color=\"red\")\n",
|
|
"\n",
|
|
" # CSiBORG2\n",
|
|
" axs[0].plot(loader_csiborg2.rdist, loader_csiborg2.los_density[k, :, :].mean(axis=0), color=\"violet\", label=\"CSiBORG2\")\n",
|
|
" axs[1].plot(loader_csiborg2.rdist, loader_csiborg2.los_radial_velocity[k, :, :].mean(axis=0), color=\"violet\")\n",
|
|
"\n",
|
|
" # Plot Carrick+2015\n",
|
|
" axs[0].plot(loader_carrick.rdist, loader_carrick.los_density[k, 0, :], color=\"blue\", label=\"Carrick+2015\")\n",
|
|
" axs[1].plot(loader_carrick.rdist, loader_carrick.los_radial_velocity[k, 0, :] * 0.43, color=\"blue\")\n",
|
|
"\n",
|
|
"\n",
|
|
" # for i in range(2):\n",
|
|
" # label = \"SN\"\n",
|
|
" # rdist = loader_csiborg.cat[\"r_hMpc\"][k]\n",
|
|
" # axs[i].axvline(rdist, color=\"violet\", linestyle=\"--\",\n",
|
|
" # zorder=0, label=label)\n",
|
|
"\n",
|
|
" axs[1].set_xlabel(r\"$r ~ [\\mathrm{Mpc} / h]$\")\n",
|
|
" axs[0].set_ylabel(r\"$\\rho_{\\rm LOS} / \\langle \\rho_{\\rm matter} \\rangle$\")\n",
|
|
" axs[1].set_ylabel(r\"$v_{\\rm LOS} ~ [\\mathrm{km/s}]$\")\n",
|
|
"\n",
|
|
" axs[0].set_yscale(\"log\")\n",
|
|
"\n",
|
|
" axs[0].legend(loc=\"upper right\")\n",
|
|
" axs[0].set_xlim(0, 200)\n",
|
|
"\n",
|
|
" fig.tight_layout(w_pad=0, h_pad=0)\n",
|
|
" fig.savefig(f\"../plots/LOSS_los_{k}.png\", dpi=500, bbox_inches=\"tight\")\n",
|
|
"\n",
|
|
" fig.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Test running a model"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fpath_data = \"/mnt/extraspace/rstiskalek/catalogs/PV_compilation_Supranta2019.hdf5\"\n",
|
|
"# fpath_data = \"/mnt/extraspace/rstiskalek/catalogs/A2.h5\"\n",
|
|
"\n",
|
|
"simname = \"Carrick2015\"\n",
|
|
"catalogue = \"LOSS\"\n",
|
|
"loader = csiborgtools.flow.DataLoader(simname, 0, catalogue, fpath_data, paths, ksmooth=0)\n",
|
|
"get_model_kwargs = {\"zcmb_max\": 0.06}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Running HMC"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = csiborgtools.flow.get_model(loader, **get_model_kwargs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"kernel = NUTS(model, init_strategy=init_to_median(num_samples=100))\n",
|
|
"mcmc = MCMC(kernel, num_warmup=250, num_samples=2500)\n",
|
|
"\n",
|
|
"rng_key = jax.random.PRNGKey(5)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model_kwargs = {\"sample_alpha\": True}\n",
|
|
"mcmc.run(rng_key, **model_kwargs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"mcmc.print_summary()\n",
|
|
"samples = mcmc.get_samples(group_by_chain=False)\n",
|
|
"print(csiborgtools.numpyro_gof(model, mcmc, len(loader.cat), model_kwargs))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"Vmag = np.sqrt(samples[\"Vext_x\"]**2 + samples[\"Vext_y\"]**2 + samples[\"Vext_z\"]**2)\n",
|
|
"\n",
|
|
"V = np.vstack([samples[\"Vext_x\"], samples[\"Vext_y\"], samples[\"Vext_z\"]]).T\n",
|
|
"V = csiborgtools.cartesian_to_radec(V)\n",
|
|
"\n",
|
|
"l, b = csiborgtools.flow.radec_to_galactic(V[:, 1], V[:, 2])\n",
|
|
"\n",
|
|
"print(f\"|V| = {np.mean(Vmag)} +- {np.std(Vmag)}\")\n",
|
|
"print(f\"l = {np.mean(l)} +- {np.std(l)}\")\n",
|
|
"print(f\"b = {np.mean(b)} +- {np.std(b)}\")\n",
|
|
"print(f\"beta = {np.mean(samples['beta'])} +- {np.std(samples['beta'])}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"if \"alpha\" in samples:\n",
|
|
" data = np.vstack([samples[\"alpha\"], samples[\"beta\"], l, b, Vmag]).T\n",
|
|
" labels = [r\"$\\alpha$\", r\"$\\beta$\", r\"$l$\", r\"$b$\", r\"$|\\bf{V}_{\\rm ext}|$\"]\n",
|
|
"else:\n",
|
|
" data = np.vstack([samples[\"beta\"], l, b, Vmag]).T\n",
|
|
" labels = [r\"$\\beta$\", r\"$l$\", r\"$b$\", r\"$|\\bf{V}_{\\rm ext}|$\"]\n",
|
|
"\n",
|
|
"fig = corner.corner(data, labels=labels, show_titles=True, title_fmt=\".3f\", title_kwargs={\"fontsize\": 12}, smooth=1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Vizualize the results"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Functions to read in the results"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"data, names, gof = read_samples(\"2MTF\", \"Carrick2015\", 0)\n",
|
|
"\n",
|
|
"fig = corner.corner(data, labels=names_to_latex(names, True), show_titles=True,\n",
|
|
" title_fmt=\".3f\", title_kwargs={\"fontsize\": 12}, smooth=1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### LOSS-only comparison"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"LOSS_Carrick_0 = read_samples(\"LOSS\", \"Carrick2015\", 0, return_MCsamples=True)\n",
|
|
"LOSS_Carrick_1 = read_samples(\"LOSS\", \"Carrick2015\", 1, return_MCsamples=True)\n",
|
|
"\n",
|
|
"LOSS_CB1_0 = read_samples(\"LOSS\", \"csiborg1\", 0, return_MCsamples=True)\n",
|
|
"LOSS_CB1_1 = read_samples(\"LOSS\", \"csiborg1\", 1, return_MCsamples=True)\n",
|
|
"\n",
|
|
"LOSS_CB2_0 = read_samples(\"LOSS\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
|
|
"LOSS_CB2_1 = read_samples(\"LOSS\", \"csiborg2_main\", 1, return_MCsamples=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"X = [\n",
|
|
" LOSS_Carrick_0,\n",
|
|
" # LOSS_Carrick_1,\n",
|
|
" # LOSS_CB1_0,\n",
|
|
" LOSS_CB1_1,\n",
|
|
" LOSS_CB2_0,\n",
|
|
" LOSS_CB2_1,\n",
|
|
" ]\n",
|
|
"\n",
|
|
"# params = [\"l\", \"b\", \"Vmag\", \"beta\"]\n",
|
|
"params = None\n",
|
|
"\n",
|
|
"g = plots.get_subplot_plotter()\n",
|
|
"g.settings.figure_legend_frame = False\n",
|
|
"g.settings.alpha_filled_add = 0.75\n",
|
|
"# g.settings.title_limit_fontsize = 14\n",
|
|
"g.triangle_plot(X, params=params, filled=True, legend_loc='upper right', )\n",
|
|
"g.export(f\"../plots/LOSS_comparison.png\", dpi=500,)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Foundation-only comparison"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"FOUNDATION_Carrick_0 = read_samples(\"Foundation\", \"Carrick2015\", 0, return_MCsamples=True)\n",
|
|
"FOUNDATION_Carrick_1 = read_samples(\"Foundation\", \"Carrick2015\", 1, return_MCsamples=True)\n",
|
|
"\n",
|
|
"FOUNDATION_CB1_0 = read_samples(\"Foundation\", \"csiborg1\", 0, return_MCsamples=True)\n",
|
|
"FOUNDATION_CB1_1 = read_samples(\"Foundation\", \"csiborg1\", 1, return_MCsamples=True)\n",
|
|
"\n",
|
|
"FOUNDATION_CB2_0 = read_samples(\"Foundation\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
|
|
"FOUNDATION_CB2_1 = read_samples(\"Foundation\", \"csiborg2_main\", 1, return_MCsamples=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"X = [\n",
|
|
" FOUNDATION_Carrick_0,\n",
|
|
" # FOUNDATION_Carrick_1,\n",
|
|
" # FOUNDATION_CB1_0,\n",
|
|
" FOUNDATION_CB1_1,\n",
|
|
" FOUNDATION_CB2_0,\n",
|
|
" FOUNDATION_CB2_1,\n",
|
|
" ]\n",
|
|
"\n",
|
|
"g = plots.get_subplot_plotter()\n",
|
|
"g.settings.figure_legend_frame = False\n",
|
|
"g.settings.alpha_filled_add = 0.75\n",
|
|
"# g.settings.title_limit_fontsize = 14\n",
|
|
"g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
|
|
"g.export(f\"../plots/FOUNDATION_comparison.png\", dpi=500,)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### PantheonPlus-only comparison"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"PANTHEONP_Carrick_0 = read_samples(\"Pantheon+\", \"Carrick2015\", 0, return_MCsamples=True)\n",
|
|
"PANTHEONP_Carrick_1 = read_samples(\"Pantheon+\", \"Carrick2015\", 1, return_MCsamples=True)\n",
|
|
"\n",
|
|
"PANTHEONP_CB1_0 = read_samples(\"Pantheon+\", \"csiborg1\", 0, return_MCsamples=True)\n",
|
|
"PANTHEONP_CB1_1 = read_samples(\"Pantheon+\", \"csiborg1\", 1, return_MCsamples=True)\n",
|
|
"\n",
|
|
"# STILL RUNNING\n",
|
|
"# PANTHEONP_CB2_0 = read_samples(\"Pantheon+\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
|
|
"PANTHEONP_CB2_1 = read_samples(\"Pantheon+\", \"csiborg2_main\", 1, return_MCsamples=True)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"X = [\n",
|
|
" PANTHEONP_Carrick_0,\n",
|
|
" # PANTHEONP_Carrick_1,\n",
|
|
" # PANTHEONP_CB1_0,\n",
|
|
" PANTHEONP_CB1_1,\n",
|
|
" # PANTHEONP_CB2_0,\n",
|
|
" PANTHEONP_CB2_1,\n",
|
|
" ]\n",
|
|
"\n",
|
|
"g = plots.get_subplot_plotter()\n",
|
|
"g.settings.figure_legend_frame = False\n",
|
|
"g.settings.alpha_filled_add = 0.75\n",
|
|
"# g.settings.title_limit_fontsize = 14\n",
|
|
"g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
|
|
"g.export(f\"../plots/PANTHEONP_comparison.png\", dpi=500,)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### 2MTF comparison"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"TWOMTF_Carrick_0 = read_samples(\"2MTF\", \"Carrick2015\", 0, return_MCsamples=True)\n",
|
|
"TWOMTF_Carrick_1 = read_samples(\"2MTF\", \"Carrick2015\", 1, return_MCsamples=True)\n",
|
|
"\n",
|
|
"TWOMTF_CB1_0 = read_samples(\"2MTF\", \"csiborg1\", 0, return_MCsamples=True)\n",
|
|
"TWOMTF_CB1_1 = read_samples(\"2MTF\", \"csiborg1\", 1, return_MCsamples=True)\n",
|
|
"\n",
|
|
"TWOMTF_CB2_0 = read_samples(\"2MTF\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
|
|
"TWOMTF_CB2_1 = read_samples(\"2MTF\", \"csiborg2_main\", 1, return_MCsamples=True)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"X = [\n",
|
|
" TWOMTF_Carrick_0,\n",
|
|
" # TWOMTF_Carrick_1,\n",
|
|
" # TWOMTF_CB1_0,\n",
|
|
" TWOMTF_CB1_1,\n",
|
|
" TWOMTF_CB2_0,\n",
|
|
" TWOMTF_CB2_1,\n",
|
|
" ]\n",
|
|
"\n",
|
|
"g = plots.get_subplot_plotter()\n",
|
|
"g.settings.figure_legend_frame = False\n",
|
|
"g.settings.alpha_filled_add = 0.75\n",
|
|
"# g.settings.title_limit_fontsize = 14\n",
|
|
"g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
|
|
"g.export(f\"../plots/2MTF_comparison.png\", dpi=500,)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### SFI galaxies"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"SFIGAL_Carrick_0 = read_samples(\"SFI_gals\", \"Carrick2015\", 0, return_MCsamples=True)\n",
|
|
"SFIGAL_Carrick_1 = read_samples(\"SFI_gals\", \"Carrick2015\", 1, return_MCsamples=True)\n",
|
|
"\n",
|
|
"# SFIGAL_CB1_0 = read_samples(\"SFI_gals\", \"csiborg1\", 0, return_MCsamples=True)\n",
|
|
"# SFIGAL_CB1_1 = read_samples(\"SFI_gals\", \"csiborg1\", 1, return_MCsamples=True)\n",
|
|
"\n",
|
|
"SFIGAL_CB2_0 = read_samples(\"SFI_gals\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
|
|
"SFIGAL_CB2_1 = read_samples(\"SFI_gals\", \"csiborg2_main\", 1, return_MCsamples=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"X = [\n",
|
|
" SFIGAL_Carrick_0,\n",
|
|
" # SFIGAL_Carrick_1,\n",
|
|
" # SFIGAL_CB1_0,\n",
|
|
" # SFIGAL_CB1_1,\n",
|
|
" # SFIGAL_CB2_0,\n",
|
|
" SFIGAL_CB2_1,\n",
|
|
" ]\n",
|
|
"\n",
|
|
"g = plots.get_subplot_plotter()\n",
|
|
"g.settings.figure_legend_frame = False\n",
|
|
"g.settings.alpha_filled_add = 0.75\n",
|
|
"# g.settings.title_limit_fontsize = 14\n",
|
|
"g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
|
|
"g.export(f\"../plots/SFI_gals_comparison.png\", dpi=500,)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### External flow consistency"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Carrick2015"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"X = [\n",
|
|
" # LOSS_Carrick_0,\n",
|
|
" # FOUNDATION_Carrick_0,\n",
|
|
" PANTHEONP_Carrick_0,\n",
|
|
" TWOMTF_Carrick_0,\n",
|
|
" SFIGAL_Carrick_0,\n",
|
|
" ]\n",
|
|
"\n",
|
|
"params = [\"Vmag\", \"l\", \"b\", \"beta\"]\n",
|
|
"g = plots.get_subplot_plotter()\n",
|
|
"g.settings.figure_legend_frame = False\n",
|
|
"g.settings.alpha_filled_add = 0.75\n",
|
|
"# g.settings.title_limit_fontsize = 14\n",
|
|
"g.triangle_plot(X, params=params, filled=True, legend_loc='upper right',)\n",
|
|
"g.export(f\"../plots/Carrick2015_external_flow.png\", dpi=500,)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### CSiBORG1"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"X = [\n",
|
|
" # LOSS_CB1_1,\n",
|
|
" # FOUNDATION_CB1_1,\n",
|
|
" PANTHEONP_CB1_1,\n",
|
|
" TWOMTF_CB1_1,\n",
|
|
" # SFIGAL_CB1_1,\n",
|
|
" ]\n",
|
|
"\n",
|
|
"params = [\"Vmag\", \"l\", \"b\", \"beta\"]\n",
|
|
"g = plots.get_subplot_plotter()\n",
|
|
"g.settings.figure_legend_frame = False\n",
|
|
"g.settings.alpha_filled_add = 0.75\n",
|
|
"# g.settings.title_limit_fontsize = 14\n",
|
|
"g.triangle_plot(X, params=params, filled=True, legend_loc='upper right',)\n",
|
|
"g.export(f\"../plots/CB1_external_flow.png\", dpi=500,)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### CSiBORG2"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"X = [\n",
|
|
" # LOSS_CB2_1,\n",
|
|
" # FOUNDATION_CB2_1,\n",
|
|
" PANTHEONP_CB2_1,\n",
|
|
" TWOMTF_CB2_1,\n",
|
|
" SFIGAL_CB2_1,\n",
|
|
" ]\n",
|
|
"\n",
|
|
"params = [\"Vmag\", \"l\", \"b\", \"beta\"]\n",
|
|
"g = plots.get_subplot_plotter()\n",
|
|
"g.settings.figure_legend_frame = False\n",
|
|
"g.settings.alpha_filled_add = 0.75\n",
|
|
"# g.settings.title_limit_fontsize = 14\n",
|
|
"g.triangle_plot(X, params=params, filled=True, legend_loc='upper right',)\n",
|
|
"g.export(f\"../plots/CB2_external_flow.png\", dpi=500,)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"Reading LOSS fitted to csiborg2_main with ksmooth = 0.\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Reading off enclosed velocity from R = 3.383 Mpc / h.\n",
|
|
"BIC = 758.646942 +- 7.900863\n",
|
|
"AIC = 739.526703 +- 7.900863\n",
|
|
"logZ = -350.513582 +- 2.973401\n",
|
|
"Removed no burn in\n",
|
|
"\n",
|
|
"Reading Foundation fitted to csiborg2_main with ksmooth = 0.\n",
|
|
"Reading off enclosed velocity from R = 3.383 Mpc / h.\n",
|
|
"BIC = 1862.151703 +- 5.628656\n",
|
|
"AIC = 1833.948944 +- 5.628656\n",
|
|
"logZ = -902.754858 +- 2.689162\n",
|
|
"Removed no burn in\n",
|
|
"\n",
|
|
"Reading 2MTF fitted to csiborg2_main with ksmooth = 0.\n",
|
|
"Reading off enclosed velocity from R = 3.383 Mpc / h.\n",
|
|
"BIC = 19248.799609 +- 38.583873\n",
|
|
"AIC = 19202.635547 +- 38.583873\n",
|
|
"logZ = -9598.394336 +- 19.251815\n",
|
|
"Removed no burn in\n",
|
|
"\n",
|
|
"Reading SFI_gals fitted to csiborg2_main with ksmooth = 0.\n",
|
|
"Reading off enclosed velocity from R = 3.383 Mpc / h.\n",
|
|
"BIC = 28779.863770 +- 42.922014\n",
|
|
"AIC = 28730.453613 +- 42.922014\n",
|
|
"logZ = -14356.845068 +- 21.362802\n",
|
|
"Removed no burn in\n",
|
|
"\n",
|
|
"Reading LOSS fitted to csiborg1 with ksmooth = 0.\n",
|
|
"Reading off enclosed velocity from R = 3.3885 Mpc / h.\n",
|
|
"BIC = 775.752704 +- 5.665501\n",
|
|
"AIC = 756.632465 +- 5.665501\n",
|
|
"logZ = -358.973275 +- 2.379893\n",
|
|
"Removed no burn in\n",
|
|
"\n",
|
|
"Reading Foundation fitted to csiborg1 with ksmooth = 0.\n",
|
|
"Reading off enclosed velocity from R = 3.3885 Mpc / h.\n",
|
|
"BIC = 1874.444986 +- 5.030578\n",
|
|
"AIC = 1846.242227 +- 5.030578\n",
|
|
"logZ = -909.946525 +- 2.405679\n",
|
|
"Removed no burn in\n",
|
|
"\n",
|
|
"Reading Pantheon+ fitted to csiborg1 with ksmooth = 0.\n",
|
|
"Reading off enclosed velocity from R = 3.3885 Mpc / h.\n",
|
|
"BIC = 10205.162612 +- 16.905323\n",
|
|
"AIC = 10159.971206 +- 16.905323\n",
|
|
"logZ = -5071.152702 +- 8.458132\n",
|
|
"Removed no burn in\n",
|
|
"\n",
|
|
"Reading 2MTF fitted to csiborg1 with ksmooth = 0.\n",
|
|
"Reading off enclosed velocity from R = 3.3885 Mpc / h.\n",
|
|
"BIC = 19922.607596 +- 33.988735\n",
|
|
"AIC = 19876.443533 +- 33.988735\n",
|
|
"logZ = -9934.180538 +- 17.010780\n",
|
|
"Removed no burn in\n",
|
|
"\n",
|
|
"Reading SFI_gals fitted to csiborg1 with ksmooth = 0.\n",
|
|
"Reading off enclosed velocity from R = 3.3885 Mpc / h.\n",
|
|
"BIC = 29402.591449 +- 39.610700\n",
|
|
"AIC = 29353.181293 +- 39.610700\n",
|
|
"logZ = -14665.444152 +- 19.734507\n",
|
|
"Removed no burn in\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"k = 0\n",
|
|
"LG = 0\n",
|
|
"# CSiBORG2\n",
|
|
"LOSS_CB2_LG = read_samples(\"LOSS\", \"csiborg2_main\", k, return_MCsamples=True,subtract_LG_velocity=LG)\n",
|
|
"FOUNDATION_CB2_LG = read_samples(\"Foundation\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
|
|
"# PANTHEON_CB2_LG = read_samples(\"Pantheon+\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=0)\n",
|
|
"TWOMTF_CB2_LG = read_samples(\"2MTF\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
|
|
"SFIGAL_CB2_LG = read_samples(\"SFI_gals\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
|
|
"\n",
|
|
"# CSiBORG1\n",
|
|
"LOSS_CB1_LG = read_samples(\"LOSS\", \"csiborg1\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
|
|
"FOUNDATION_CB1_LG = read_samples(\"Foundation\", \"csiborg1\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
|
|
"PANTHEON_CB1_LG = read_samples(\"Pantheon+\", \"csiborg1\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
|
|
"TWOMTF_CB1_LG = read_samples(\"2MTF\", \"csiborg1\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
|
|
"SFIGAL_CB1_LG = read_samples(\"SFI_gals\", \"csiborg1\", k, return_MCsamples=True, subtract_LG_velocity=LG)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Removed no burn in\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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",
|
|
"text/plain": [
|
|
"<Figure size 600x600 with 6 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"params = [\"Vmag\", \"l\", \"b\"]\n",
|
|
"CMB = MCSamples(samples=multivariate_normal([627, 276, 30], [22, 3, 3]).rvs(size=20000),\n",
|
|
" names=params, labels=names_to_latex(params, True), label=\"CMB\")\n",
|
|
"\n",
|
|
"X = [\n",
|
|
" # LOSS_CB1_LG,\n",
|
|
" FOUNDATION_CB1_LG,\n",
|
|
" PANTHEON_CB1_LG,\n",
|
|
" TWOMTF_CB1_LG,\n",
|
|
" SFIGAL_CB1_LG,\n",
|
|
" # LOSS_CB2_0,\n",
|
|
" # LOSS_CB2_LG,\n",
|
|
" # FOUNDATION_CB2,\n",
|
|
" # FOUNDATION_CB2_LG,\n",
|
|
" # FOUNDATION_CB1_LG,\n",
|
|
" # PANTHEON_CB2,\n",
|
|
" # PANTHEON_CB2_LG,\n",
|
|
" # TWOMTF_CB1,\n",
|
|
" # TWOMTF_CB1_LG,\n",
|
|
" # TWOMTF_CB2,\n",
|
|
" # TWOMTF_CB2_LG,\n",
|
|
" # SFIGAL_CB2,\n",
|
|
" # SFIGAL_CB2_LG,\n",
|
|
" CMB,\n",
|
|
" ]\n",
|
|
"\n",
|
|
"g = plots.get_subplot_plotter()\n",
|
|
"g.settings.figure_legend_frame = False\n",
|
|
"g.settings.alpha_filled_add = 0.75\n",
|
|
"# g.settings.title_limit_fontsize = 14\n",
|
|
"g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n",
|
|
"g.export(f\"../plots/CB1_dipole.png\", dpi=500,)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "venv_csiborg",
|
|
"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": "3.11.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|