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Added notebooks to produce paper plots #152

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b5f2e48
added notebooks to produce paper plots
Apr 4, 2025
5f8b885
added e/b plots, cleanup
LisaGoh Apr 8, 2025
f479d04
Merge branch 'CosmoStat:develop' into plotting_notebooks
LisaGoh Apr 8, 2025
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248 changes: 248 additions & 0 deletions cosmo_inference/notebooks/2D_cosmic_shear_paper_plots/ psf_leakage.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PSF Leakage Plots\n",
"\n",
"This notebook creates all the plots of the PSF leakage: the scale-dependent $\\alpha$ parameter, $\\rho$ and $\\tau$ statistics (from Guerrini et al. 2024) as well as the covariance matrix of the $\\tau_0$ ad $\\tau_2$ data vectors."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib\n",
"from astropy.io import fits\n",
"import matplotlib.pyplot as plt\n",
"import uncertainties\n",
"import yaml\n",
"import os\n",
"import numpy as np\n",
"\n",
"plt.rcParams.update({'font.size': 25,'figure.figsize':[12,7]})\n",
"plt.rcParams.update({\"text.usetex\": True})\n",
"plt.rc('mathtext', fontset='stix')\n",
"plt.rc('font', family='serif')\n",
"\n",
"# Define path to data files (NOTE: YOU MUST HAVE RAN THE cosmo_val.py PIPELINE!)\n",
"cosmoval_data_dir = \"/n23data1/n06data/lgoh/scratch/UNIONS/sp_validation/notebooks/cosmo_val/output\"\n",
"ver = \"SP_v1.4.5\"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot the $\\alpha(\\theta)$ leakage"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Read in the text file of the 2PCFs\n",
"\n",
"alpha_fname = f\"{cosmoval_data_dir}/leakage_{ver}/alpha_leakage.txt\"\n",
"print(f\"Reading xi_sys's from {alpha_fname}\")\n",
"\n",
"alphas = np.loadtxt(alpha_fname)\n",
"theta = alphas[:,0]\n",
"alpha = alphas[:,1]\n",
"var_alpha = alphas[:,2]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# Plot the xi_+sys\n",
"ax1 = plt.subplot(111)\n",
"ax1.tick_params(axis='both', which='both', direction='in', length=6, width=1,\n",
" top=True, bottom=True, left=True, right=True)\n",
"ax1.yaxis.minorticks_on()\n",
"\n",
"ax1.errorbar(theta, alpha, yerr=var_alpha, \n",
" fmt='o', markersize=4, capsize=2, capthick=1.5, ls = 'solid', lw=1.8,\n",
" color='darkviolet')\n",
"ax1.axhline(0,ls='dashed',lw=1, color='grey')\n",
"ax1.text(0.85, 0.15, r'$1-1$', transform=ax1.transAxes,\n",
" bbox=dict(facecolor='white', edgecolor='black', boxstyle='round', pad=0.5))\n",
"ax1.set_xscale('log')\n",
"ax1.set_xlabel(r'$\\theta$ [arcmin]')\n",
"ax1.set_ylabel(r'$\\alpha(\\theta)$')\n",
"# plt.savefig('plots/alpha_sys_%s.pdf' %ver,bbox_inches='tight')\n",
"plt.show()\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot the $\\tau(\\theta)$ statistics"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tau_stats_fname = f\"{cosmoval_data_dir}/rho_tau_stats/tau_stats_{ver}.fits\"\n",
"print(f\"Reading tau stats from {tau_stats_fname}\")\n",
"tau_stats_hdu = fits.open(tau_stats_fname)\n",
"\n",
"theta = tau_stats_hdu[1].data['theta']\n",
"\n",
"tau_0_p = tau_stats_hdu[1].data['tau_0_p']\n",
"vartau_0_p = tau_stats_hdu[1].data['vartau_0_p']\n",
"\n",
"tau_2_p = tau_stats_hdu[1].data['tau_2_p']\n",
"vartau_2_p = tau_stats_hdu[1].data['vartau_2_p']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Plot the xi_plus\n",
"ax1 = plt.subplot(111)\n",
"ax1.tick_params(axis='both', which='both', direction='in', length=6, width=1,\n",
" top=True, bottom=True, left=True, right=True)\n",
"ax1.yaxis.minorticks_on()\n",
"\n",
"# ax1.errorbar(theta, tau_0_p*1e4, yerr=np.sqrt(vartau_0_p)*1e4, \n",
"# fmt='o', markersize=6, capsize=2, capthick=1.5, ls = 'solid', lw=1.8,\n",
"# color='royalblue')\n",
"ax1.plot(theta, tau_0_p*1e4, marker='o', markersize=4, ls = 'solid', lw=1.8, color=\"royalblue\")\n",
"ax1.fill_between(theta, (tau_0_p-np.sqrt(vartau_0_p))*1e4, (tau_0_p+np.sqrt(vartau_0_p))*1e4, color=\"powderblue\", alpha=0.7)\n",
"ax1.text(0.85 , 0.88, r'$1-1$', transform=ax1.transAxes,\n",
" bbox=dict(facecolor='white', edgecolor='black', boxstyle='round', pad=0.5))\n",
"ax1.axhline(0,ls='dashed',lw=1.5, color='grey')\n",
"# ax1.axvspan(0,10,color='gray', alpha=0.3)\n",
"# ax1.axvspan(150,200,color='gray', alpha=0.3)\n",
"ax1.set_xscale('log')\n",
"ax1.set_xlabel(r'$\\theta$ [arcmin]')\n",
"ax1.set_ylabel(r'$\\tau_{0,+}\\times 10^4$')\n",
"# plt.savefig('plots/tau0_%s.pdf' %ver,bbox_inches='tight')\n",
"plt.show()\n",
"\n",
"# Plot the xi_minus\n",
"ax2 = plt.subplot(111)\n",
"ax2.tick_params(axis='both', which='both', direction='in', length=6, width=1,\n",
" top=True, bottom=True, left=True, right=True)\n",
"ax2.yaxis.minorticks_on()\n",
"\n",
"# ax2.errorbar(theta, tau_2_p*1e4, yerr=np.sqrt(vartau_2_p)*1e4, \n",
"# fmt='o', markersize=6, capsize=2, capthick=1.5, ls = 'solid', lw=1.8,\n",
"# color='orangered')\n",
"ax2.plot(theta, tau_2_p*1e4, marker='o', markersize=4, ls = 'solid', lw=1.8, color=\"orangered\")\n",
"ax2.fill_between(theta, (tau_2_p-np.sqrt(vartau_2_p))*1e4, (tau_2_p+np.sqrt(vartau_2_p))*1e4, color=\"pink\", alpha=0.7)\n",
"ax2.text(0.85, 0.88, r'$1-1$', transform=ax2.transAxes,\n",
" bbox=dict(facecolor='white', edgecolor='black', boxstyle='round', pad=0.5))\n",
"ax2.axhline(0,ls='dashed',lw=1.5, color='grey')\n",
"# ax2.axvspan(0,10,color='gray', alpha=0.3)\n",
"# ax2.axvspan(150,200,color='gray', alpha=0.3)\n",
"ax2.set_xscale('log')\n",
"ax2.set_xlabel(r'$\\theta$ [arcmin]')\n",
"ax2.set_ylabel(r'$\\tau_{2,+}\\times 10^4$')\n",
"# plt.savefig('plots/tau2_%s.pdf' %ver,bbox_inches='tight')\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot the $\\tau$ covariance matrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tau_covmat_file = f\"{cosmoval_data_dir}/rho_tau_stats/cov_tau_{ver}_th.npy\"\n",
"print(f\"Reading tau covmat from {tau_covmat_file}\")\n",
"tau_cov = np.load(tau_covmat_file)\n",
"ndata = len(tau_cov[0])\n",
"tau_cov = tau_cov[:int(2*ndata/3),:int(2*ndata/3)]\n",
"cmap = 'coolwarm'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ndata = len(tau_cov[0])\n",
"pp_norm = np.zeros((ndata,ndata))\n",
"for i in range(ndata):\n",
" for j in range(ndata):\n",
" pp_norm[i][j] = tau_cov[i][j]/ np.sqrt(tau_cov[i][i]*tau_cov[j][j])\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(1, 1, 1) \n",
"extent = (0, ndata, ndata, 0)\n",
"im3 = ax.imshow(pp_norm, cmap=cmap, vmin=-1, vmax=1, extent=extent)\n",
"\n",
"plt.axvline(x=int(ndata/2),color='black',linewidth=1.0)\n",
"plt.axhline(y=int(ndata/2),color='black',linewidth=1.0)\n",
"\n",
"fig.colorbar(im3, orientation='vertical')\n",
"ticks = np.arange(0,ndata+10,10)\n",
"tick_labels = np.array([0,10,0,10,20])\n",
"ax.set_xticks(ticks,labels= tick_labels)\n",
"ax.set_yticks(ticks,labels= tick_labels)\n",
"ax.text(int(ndata/4), ndata+5, r'$\\tau_{0,+}(\\theta)$')\n",
"ax.text(3*int(ndata/4), ndata+5, r'$\\tau_{2,+}(\\theta)$')\n",
"ax.text(-11, int(ndata/4), r'$\\tau_{0,+}(\\theta)$')\n",
"ax.text(-11, 3*int(ndata/4), r'$\\tau_{2,+}(\\theta)$')\n",
"\n",
"# plt.savefig(\"plots/tau_covmat.pdf\", bbox_inches='tight')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "sp-validation",
"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": "3.9.21"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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