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https://github.com/Richard-Sti/csiborgtools.git
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rstiskalek
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336
notebooks/flow/void_test.ipynb
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336
notebooks/flow/void_test.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Copyright (C) 2024 Richard Stiskalek\n",
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"# This program is free software; you can redistribute it and/or modify it\n",
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"# under the terms of the GNU General Public License as published by the\n",
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"# Free Software Foundation; either version 3 of the License, or (at your\n",
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"# option) any later version.\n",
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"#\n",
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"# This program is distributed in the hope that it will be useful, but\n",
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"# WITHOUT ANY WARRANTY; without even the implied warranty of\n",
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"# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General\n",
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"# Public License for more details.\n",
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"#\n",
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"# You should have received a copy of the GNU General Public License along\n",
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"# with this program; if not, write to the Free Software Foundation, Inc.,\n",
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"# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n",
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"from os.path import exists\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from corner import corner\n",
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"from getdist import plots\n",
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"import scienceplots\n",
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"from os.path import exists\n",
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"import seaborn as sns\n",
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"\n",
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"\n",
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"from reconstruction_comparison import *\n",
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"\n",
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"%matplotlib inline\n",
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"\n",
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"paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)\n",
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"fdir = \"/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Quick checks"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"catalogue = \"CF4_TFR_i\"\n",
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"simname = \"IndranilVoid_exp\"\n",
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"zcmb_max=0.05\n",
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"sample_beta = None\n",
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"no_Vext = True\n",
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"\n",
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"fname = paths.flow_validation(\n",
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" fdir, simname, catalogue, inference_method=\"bayes\",\n",
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" sample_alpha=False, sample_beta=sample_beta,\n",
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" no_Vext=no_Vext, zcmb_max=zcmb_max)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = samples_to_getdist(get_samples(fname, False), \"Test\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"params = [\"rLG\", \"sigma_v\"]\n",
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"\n",
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"\n",
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"# params = [\"beta\", f\"a_{catalogue}\", f\"b_{catalogue}\", f\"e_mu_{catalogue}\"]\n",
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"# params = [\"Vmag\", \"l\", \"b\", \"sigma_v\", \"beta\", f\"mag_cal_{catalogue}\", f\"alpha_cal_{catalogue}\", f\"beta_cal_{catalogue}\", f\"e_mu_{catalogue}\"]\n",
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"\n",
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"with plt.style.context(\"science\"):\n",
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" g = plots.get_subplot_plotter()\n",
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" g.settings.figure_legend_frame = False\n",
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" g.settings.alpha_filled_add = 0.75\n",
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" \n",
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" g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n",
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" plt.gcf().suptitle(catalogue_to_pretty(catalogue), y=1.025)\n",
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" plt.gcf().tight_layout()\n",
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" plt.gcf().show()\n",
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" # plt.gcf().savefig(f\"../../plots/method_comparison_{simname}_{catalogue}.png\", dpi=500, bbox_inches='tight')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# catalogue = [\"LOSS\", \"Foundation\"]\n",
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"catalogue = \"CF4_TFR_i\"\n",
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"simname = \"IndranilVoid_exp\"\n",
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"zcmb_max = 0.05\n",
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"sample_alpha = False\n",
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"\n",
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"fname = paths.flow_validation(\n",
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" fdir, simname, catalogue, inference_method=\"mike\",\n",
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" sample_mag_dipole=True,\n",
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" sample_beta=False,\n",
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" sample_alpha=sample_alpha, zcmb_max=zcmb_max)\n",
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"\n",
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"\n",
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"samples = get_samples(fname, convert_Vext_to_galactic=True)\n",
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"\n",
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"samples, labels, keys = samples_for_corner(samples)\n",
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"fig = corner(samples, labels=labels, show_titles=True,\n",
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" title_kwargs={\"fontsize\": 12}, smooth=1)\n",
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"# fig.savefig(\"../../plots/test.png\", dpi=250)\n",
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"fig.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Paper plots"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 1. No $V_{\\rm ext}$ and no $\\beta$"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for simname in [\"IndranilVoid_exp\", \"IndranilVoid_gauss\", \"IndranilVoid_mb\"]:\n",
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" X = []\n",
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" for catalogue in [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]:\n",
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"\n",
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" fname = paths.flow_validation(\n",
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" fdir, simname, catalogue, inference_method=\"bayes\",\n",
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" sample_alpha=False, sample_beta=None,\n",
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" no_Vext=True, zcmb_max=0.05)\n",
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"\n",
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" X_i = samples_to_getdist(get_samples(fname, False), catalogue_to_pretty(catalogue))\n",
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" X.append(X_i)\n",
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"\n",
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" params = [\"rLG\", \"sigma_v\"]\n",
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" with plt.style.context(\"science\"):\n",
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" g = plots.get_subplot_plotter()\n",
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" g.settings.figure_legend_frame = False\n",
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" g.settings.alpha_filled_add = 0.75\n",
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"\n",
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" g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n",
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" plt.gcf().suptitle(simname_to_pretty(simname), y=1.025)\n",
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" plt.gcf().tight_layout()\n",
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" plt.gcf().show()\n",
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" plt.gcf().savefig(f\"../../plots/void_{simname}_noVext_nobeta.png\", dpi=500, bbox_inches='tight')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 2. No $V_{\\rm ext}$ but sampling $\\beta$"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for simname in [\"IndranilVoid_exp\", \"IndranilVoid_gauss\", \"IndranilVoid_mb\"]:\n",
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" X = []\n",
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" for catalogue in [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]:\n",
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"\n",
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" fname = paths.flow_validation(\n",
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" fdir, simname, catalogue, inference_method=\"bayes\",\n",
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" sample_alpha=False, sample_beta=True,\n",
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" no_Vext=True, zcmb_max=0.05)\n",
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"\n",
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" X_i = samples_to_getdist(get_samples(fname, False), catalogue_to_pretty(catalogue))\n",
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" X.append(X_i)\n",
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"\n",
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" params = [\"rLG\", \"sigma_v\", \"beta\"]\n",
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" with plt.style.context(\"science\"):\n",
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" g = plots.get_subplot_plotter()\n",
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" g.settings.figure_legend_frame = False\n",
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" g.settings.alpha_filled_add = 0.75\n",
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"\n",
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" g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n",
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" plt.gcf().suptitle(simname_to_pretty(simname), y=1.025)\n",
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" plt.gcf().tight_layout()\n",
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" plt.gcf().show()\n",
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" plt.gcf().savefig(f\"../../plots/void_{simname}_noVext_beta.png\", dpi=500, bbox_inches='tight')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 3. Yes $V_{\\rm ext}$ and no $\\beta$ "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for simname in [\"IndranilVoid_exp\", \"IndranilVoid_gauss\", \"IndranilVoid_mb\"]:\n",
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" X = []\n",
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" for catalogue in [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]:\n",
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"\n",
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" fname = paths.flow_validation(\n",
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" fdir, simname, catalogue, inference_method=\"bayes\",\n",
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" sample_alpha=False, sample_beta=False,\n",
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" no_Vext=None, zcmb_max=0.05)\n",
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"\n",
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" X_i = samples_to_getdist(get_samples(fname, False), catalogue_to_pretty(catalogue))\n",
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" X.append(X_i)\n",
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"\n",
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" params = [\"rLG\", \"sigma_v\", \"Vx\", \"Vy\", \"Vz\"]\n",
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" with plt.style.context(\"science\"):\n",
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" g = plots.get_subplot_plotter()\n",
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" g.settings.figure_legend_frame = False\n",
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" g.settings.alpha_filled_add = 0.75\n",
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"\n",
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" g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n",
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" plt.gcf().suptitle(simname_to_pretty(simname), y=1.025)\n",
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" plt.gcf().tight_layout()\n",
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" plt.gcf().show()\n",
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" plt.gcf().savefig(f\"../../plots/void_{simname}_Vext_nobeta.png\", dpi=500, bbox_inches='tight')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 4. Yes $V_{\\rm ext}$ and yes $\\beta$"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for simname in [\"IndranilVoid_exp\", \"IndranilVoid_gauss\", \"IndranilVoid_mb\"]:\n",
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" X = []\n",
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" for catalogue in [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]:\n",
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"\n",
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" fname = paths.flow_validation(\n",
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" fdir, simname, catalogue, inference_method=\"bayes\",\n",
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" sample_alpha=False, sample_beta=True,\n",
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" no_Vext=None, zcmb_max=0.05)\n",
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"\n",
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" X_i = samples_to_getdist(get_samples(fname, False), catalogue_to_pretty(catalogue))\n",
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" X.append(X_i)\n",
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"\n",
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" params = [\"rLG\", \"sigma_v\", \"beta\", \"Vx\", \"Vy\", \"Vz\"]\n",
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" with plt.style.context(\"science\"):\n",
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" g = plots.get_subplot_plotter()\n",
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" g.settings.figure_legend_frame = False\n",
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" g.settings.alpha_filled_add = 0.75\n",
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"\n",
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" g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n",
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" plt.gcf().suptitle(simname_to_pretty(simname), y=1.025)\n",
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" plt.gcf().tight_layout()\n",
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" plt.gcf().show()\n",
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" plt.gcf().savefig(f\"../../plots/void_{simname}_Vext_beta.png\", dpi=500, bbox_inches='tight')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "venv_csiborg",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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