mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-10-19 00:15:06 +02:00
219 lines
598 KiB
Plaintext
219 lines
598 KiB
Plaintext
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{
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"cells": [
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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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"# Calibrating the velocity field against observations "
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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": 95,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The autoreload extension is already loaded. To reload it, use:\n",
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" %reload_ext autoreload\n"
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]
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}
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],
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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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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"import csiborgtools\n",
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"\n",
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"from flow_bulk 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"
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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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"## Enclosed overdensity"
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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": 136,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"plt.figure()\n",
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"cols = plt.rcParamsDefault['axes.prop_cycle'].by_key()['color']\n",
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"\n",
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"r, overdensity = read_enclosed_density(\"csiborg1\")\n",
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"c = cols[0]\n",
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"for i in range(len(overdensity)):\n",
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" plt.plot(r, overdensity[i], c=c, lw=0.25, zorder=0, ls=\"dotted\")\n",
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"plt.plot(r, np.median(overdensity, axis=0), c=c, lw=2, label=\"CB1\")\n",
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"\n",
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"\n",
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"r, overdensity = read_enclosed_density(\"csiborg2_main\")\n",
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"c = cols[1]\n",
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"for i in range(len(overdensity)):\n",
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" plt.plot(r, overdensity[i], c=c, lw=0.25, ls=\"dotted\")\n",
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"plt.plot(r, np.median(overdensity, axis=0), c=c, lw=2, label=\"CB2\")\n",
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"\n",
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"\n",
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"r, overdensity = read_enclosed_density(\"csiborg2_random\")\n",
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"c = cols[2]\n",
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"ymin = np.percentile(overdensity, 16, axis=0)\n",
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"ymed = np.median(overdensity, axis=0)\n",
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"ymax = np.percentile(overdensity, 84, axis=0)\n",
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"plt.fill_between(r, ymin, ymax, color=c, alpha=0.35, label=\"Random\", zorder=-1)\n",
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"\n",
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"plt.xlabel(r\"$R ~ [\\mathrm{Mpc} / h]$\")\n",
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"plt.ylabel(r\"$\\delta_{\\rm enc} (r \\leq R)$\")\n",
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"plt.legend()\n",
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"\n",
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"plt.xlim(0, r.max())\n",
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"plt.ylim(-0.15, 0.075)\n",
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"plt.axhline(0, c='k', ls='--', lw=1)\n",
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"plt.tight_layout()\n",
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"plt.savefig(\"../plots/enclosed_density.png\", dpi=450)\n",
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"plt.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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"## Enclosed bulk flow"
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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": 141,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1400x500 with 3 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, axs = plt.subplots(1, 3, figsize=(14, 5))\n",
|
||
|
|
"cols = plt.rcParamsDefault['axes.prop_cycle'].by_key()['color']\n",
|
||
|
|
"thin_line_kwargs = {\"lw\": 0.3, \"zorder\": 0, \"ls\": \"dotted\"}\n",
|
||
|
|
"\n",
|
||
|
|
"# CSiBORG1\n",
|
||
|
|
"r, Vmag, l, b = read_enclosed_flow(\"csiborg1\")\n",
|
||
|
|
"c = cols[0]\n",
|
||
|
|
"for i in range(len(Vmag)):\n",
|
||
|
|
" axs[0].plot(r, Vmag[i], c=c, **thin_line_kwargs)\n",
|
||
|
|
" axs[1].plot(r, l[i], c=c, **thin_line_kwargs)\n",
|
||
|
|
" axs[2].plot(r, b[i], c=c, **thin_line_kwargs)\n",
|
||
|
|
"\n",
|
||
|
|
"axs[0].plot(r, np.median(Vmag, axis=0), c=c, lw=2, label=\"CB1\")\n",
|
||
|
|
"axs[1].plot(r, np.median(l, axis=0), c=c, lw=2)\n",
|
||
|
|
"axs[2].plot(r, np.median(b, axis=0), c=c, lw=2)\n",
|
||
|
|
"\n",
|
||
|
|
"# CSiBORG2\n",
|
||
|
|
"r, Vmag, l, b = read_enclosed_flow(\"csiborg2_main\")\n",
|
||
|
|
"c = cols[1]\n",
|
||
|
|
"for i in range(len(Vmag)):\n",
|
||
|
|
" axs[0].plot(r, Vmag[i], c=c, **thin_line_kwargs)\n",
|
||
|
|
" axs[1].plot(r, l[i], c=c, **thin_line_kwargs)\n",
|
||
|
|
" axs[2].plot(r, b[i], c=c, **thin_line_kwargs)\n",
|
||
|
|
"\n",
|
||
|
|
"axs[0].plot(r, np.median(Vmag, axis=0), c=c, lw=2, label=\"CB2\")\n",
|
||
|
|
"axs[1].plot(r, np.median(l, axis=0), c=c, lw=2)\n",
|
||
|
|
"axs[2].plot(r, np.median(b, axis=0), c=c, lw=2)\n",
|
||
|
|
"\n",
|
||
|
|
"# Random\n",
|
||
|
|
"r, Vmag, l, b = read_enclosed_flow(\"csiborg2_random\")\n",
|
||
|
|
"c = cols[2]\n",
|
||
|
|
"ymin = np.percentile(Vmag, 16, axis=0)\n",
|
||
|
|
"ymax = np.percentile(Vmag, 84, axis=0)\n",
|
||
|
|
"ymed = np.median(Vmag, axis=0)\n",
|
||
|
|
"axs[0].fill_between(r, ymin, ymax, color=c, alpha=0.35, zorder=-1,\n",
|
||
|
|
" label=\"Random\")\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"axs[0].legend()\n",
|
||
|
|
"for i in range(3):\n",
|
||
|
|
" axs[i].set_xlabel(r\"$R ~ [\\mathrm{Mpc} / h]$\")\n",
|
||
|
|
" axs[i].set_xlim(0, r.max())\n",
|
||
|
|
"\n",
|
||
|
|
"axs[0].set_ylim(0)\n",
|
||
|
|
"axs[0].set_ylabel(r\"$V_{\\rm enc} (r \\leq R) ~ [\\mathrm{km} / \\mathrm{s}]$\")\n",
|
||
|
|
"axs[1].set_ylabel(r\"$l_{\\rm enc} (r \\leq R) ~ [\\mathrm{deg}]$\")\n",
|
||
|
|
"axs[2].set_ylabel(r\"$b_{\\rm enc} (r \\leq R) ~ [\\mathrm{deg}]$\")\n",
|
||
|
|
"axs[1].set_ylim(0, 360)\n",
|
||
|
|
"\n",
|
||
|
|
"fig.tight_layout()\n",
|
||
|
|
"fig.savefig(\"../plots/enclosed_flow.png\", dpi=450)\n",
|
||
|
|
"fig.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"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
|
||
|
|
}
|