comparison_plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9bb02469-5056-471c-a187-062107478b81",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4994b4c8-4154-4c03-bb5a-8ec86f01aa81",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "font = {}\n",
    "font = {'family': 'Serif',\n",
    "        'size'   : '12'}\n",
    "\n",
    "matplotlib.rc('font', **font)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62088012-0a4e-4700-969e-c4494b319f28",
   "metadata": {},
   "source": [
    "# Comparing Mean Shear vs PSF g1 & g2, PSF T, Tgal, SNR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7f6e9983-0aad-4b17-aacd-9d16fc62314b",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def is_number(s):\n",
    "    try:\n",
    "        float(s)\n",
    "        return True\n",
    "    except ValueError:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f700dbc-4b59-4e8f-afe3-ba8361b7e9ea",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def cleanup(file):\n",
    "    remove = ['[','\\n']\n",
    "    f = file.read()\n",
    "    for char in remove:\n",
    "        f = f.replace(char, '')\n",
    "    f=f.split(']')\n",
    "    \n",
    "    fnew=[]\n",
    "    for data in f:\n",
    "        datai = []\n",
    "        for i in np.asarray(data.split(' ')):\n",
    "            if is_number(i):\n",
    "                datai.append(float(i))\n",
    "        fnew.append(np.array(datai))\n",
    "    return fnew\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8a068c9-acc1-4059-8f00-af642de25a45",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# KiDS\n",
    "file = open('../TXPipe/data/kids/outputs/g_psf_g_out.txt','r')\n",
    "\n",
    "k_psfg = cleanup(file)\n",
    "\n",
    "file = open('../TXPipe/data/kids/outputs/g_psf_T_out.txt','r')\n",
    "\n",
    "kpsfT = cleanup(file)\n",
    "\n",
    "file = open('../TXPipe/data/kids/outputs/g_T_out.txt','r')\n",
    "\n",
    "kT = cleanup(file)\n",
    "\n",
    "file = open('../TXPipe/data/kids/outputs/g_snr_out.txt','r')\n",
    "\n",
    "ksnr = cleanup(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "107660a8-b2c2-4346-8038-7b3bfc8afdf1",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# DESY1\n",
    "file = open('../TXPipe/data/desy1/outputs/g_psf_g_out.txt','r')\n",
    "\n",
    "y1_psfg = cleanup(file)\n",
    "\n",
    "file = open('../TXPipe/data/desy1/outputs/g_psf_T_out.txt','r')\n",
    "\n",
    "y1psfT = cleanup(file)\n",
    "\n",
    "file = open('../TXPipe/data/desy1/outputs/g_T_out.txt','r')\n",
    "\n",
    "y1T = cleanup(file)\n",
    "\n",
    "file = open('../TXPipe/data/desy1/outputs/g_snr_out.txt','r')\n",
    "\n",
    "y1snr = cleanup(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bdf7958-dd2f-4d55-b2db-852a585219f6",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# DESY3\n",
    "file = open('../TXPipe/data/desy3/outputs/g_psf_g_out.txt','r')\n",
    "\n",
    "y3_psfg = cleanup(file)\n",
    "\n",
    "file = open('../TXPipe/data/desy3/outputs/g_psf_T_out.txt','r')\n",
    "\n",
    "y3psfT = cleanup(file)\n",
    "\n",
    "file = open('../TXPipe/data/desy3/outputs/g_T_out.txt','r')\n",
    "\n",
    "y3T = cleanup(file)\n",
    "\n",
    "file = open('../TXPipe/data/desy3/outputs/g_snr_out.txt','r')\n",
    "\n",
    "y3snr = cleanup(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56342929-7f96-4fe3-b9c0-8e84caca097f",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# HSCY1\n",
    "file = open('../TXPipe/data/hscy1/outputs/g_psf_g_out.txt','r')\n",
    "\n",
    "hsc_psfg = cleanup(file)\n",
    "\n",
    "file = open('../TXPipe/data/hscy1/outputs/g_psf_T_out.txt','r')\n",
    "\n",
    "hscpsfT = cleanup(file)\n",
    "\n",
    "file = open('../TXPipe/data/hscy1/outputs/g_T_out.txt','r')\n",
    "\n",
    "hscT = cleanup(file)\n",
    "\n",
    "file = open('../TXPipe/data/hscy1/outputs/g_snr_out.txt','r')\n",
    "\n",
    "hscsnr = cleanup(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a397701-03bf-4355-bf27-3c4c9140caf3",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(nrows=4, ncols=2, sharex=True,figsize=(20, 10))\n",
    "ax1, ax2, ax3, ax4, ax5, ax6,ax7,ax8 = ax.flatten()\n",
    "\n",
    "#just to compare y1 and y3..\n",
    "#fig, ax = plt.subplots(nrows=2, ncols=2, sharex=True,figsize=(8, 6),layout='constrained')\n",
    "#ax1, ax2, ax3, ax4,= ax.flatten()\n",
    "\n",
    "#ax1.set_title('DESY1')\n",
    "ax1.errorbar(y1_psfg[0],y1_psfg[2],y1_psfg[6], color='firebrick',linestyle='None',marker='o',mfc='None',capsize=3)\n",
    "ax1.errorbar(y1_psfg[0],y1_psfg[4],y1_psfg[8], color='royalblue',linestyle='None',marker='o',mfc='None',capsize=3)\n",
    "ax1.plot(np.arange(-0.03,0.03,0.005),np.zeros(len(np.arange(-0.03,0.03,0.005))),color='black',linestyle='dashed')\n",
    "ax1.plot(y1_psfg[0],y1_psfg[10],color='firebrick',label=r'L: m = $(-3.56 \\pm 0.63) \\times 10^{-2}$')\n",
    "ax1.plot(y1_psfg[0],y1_psfg[12],color='royalblue',label=r'L: m = $(-2.95 \\pm 5.50) \\times 10^{-3}$')\n",
    "ax1.set_ylim([-0.002,0.002])\n",
    "ax1.set_ylabel(r'$\\langle$ g $\\rangle$')\n",
    "\n",
    "#ax2.set_title('DESY1')\n",
    "ax2.errorbar(y1_psfg[1],y1_psfg[3],y1_psfg[7], color='firebrick',linestyle='None',marker='o',mfc='None',capsize=3,label='g1')\n",
    "ax2.errorbar(y1_psfg[1],y1_psfg[5],y1_psfg[9], color='royalblue',linestyle='None',marker='o',mfc='None',capsize=3,label='g2')\n",
    "ax2.plot(np.arange(-0.03,0.03,0.005),np.zeros(len(np.arange(-0.03,0.03,0.005))),color='black',linestyle='dashed')\n",
    "g2fit1 = ax2.plot(y1_psfg[1],y1_psfg[11],color='firebrick')#,label=r'R: m = $(6.42 \\pm 4.65) \\times 10^{-3}$')\n",
    "g2fit2 = ax2.plot(y1_psfg[1],y1_psfg[13],color='royalblue')#,label=r'R: m = $(-3.45 \\pm 0.55) \\times 10^{-2}$')\n",
    "ax2.yaxis.set_tick_params(labelleft=False)\n",
    "ax2.set_ylim([-0.002,0.002])\n",
    "ax2.text(0.9, 0.1, 'DESY1', horizontalalignment='center', verticalalignment='center', transform=ax2.transAxes)\n",
    "ax2.legend(loc='upper right')\n",
    "#g1h,g1fits = ax1.get_legend_handles_labels()\n",
    "#g2h,g2fits = ax2.get_legend_handles_labels()\n",
    "#ax2.legend(g1h+g2h, g1fits+g2fits, loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "\n",
    "#ax3.set_title('DESY3')\n",
    "ax3.errorbar(y3_psfg[0],y3_psfg[2],y3_psfg[6], color='firebrick',linestyle='None',marker='o',mfc='None',capsize=3)\n",
    "ax3.errorbar(y3_psfg[0],y3_psfg[4],y3_psfg[8], color='royalblue',linestyle='None',marker='o',mfc='None',capsize=3)\n",
    "ax3.plot(np.arange(-0.03,0.03,0.005),np.zeros(len(np.arange(-0.03,0.03,0.005))),color='black',linestyle='dashed')\n",
    "ax3.plot(y3_psfg[0],y3_psfg[10],color='firebrick',label=r'L: m = $(-3.24 \\pm 4.44) \\times 10^{-3}$')\n",
    "ax3.plot(y3_psfg[0],y3_psfg[12],color='royalblue',label=r'L: m = $(-3.30 \\pm 2.53) \\times 10^{-3}$')\n",
    "\n",
    "ax3.set_ylim([-0.002,0.002])\n",
    "ax3.set_ylabel(r'$\\langle$ g $\\rangle$')\n",
    "#ax1.set_xlabel('PSF g1')\n",
    "\n",
    "#ax4.set_title('DESY3')\n",
    "ax4.errorbar(y3_psfg[1],y3_psfg[3],y3_psfg[7], color='firebrick',linestyle='None',marker='o',mfc='None',capsize=3)\n",
    "ax4.errorbar(y3_psfg[1],y3_psfg[5],y3_psfg[9], color='royalblue',linestyle='None',marker='o',mfc='None',capsize=3)\n",
    "ax4.plot(np.arange(-0.03,0.03,0.005),np.zeros(len(np.arange(-0.03,0.03,0.005))),color='black',linestyle='dashed')\n",
    "ax4.plot(y3_psfg[1],y3_psfg[11],color='firebrick',label=r'R: m = $(-7.24 \\pm 3.20) \\times 10^{-3}$')\n",
    "ax4.plot(y3_psfg[1],y3_psfg[13],color='royalblue',label=r'R: m = $(-3.58 \\pm 3.85) \\times 10^{-3}$')\n",
    "ax4.yaxis.set_tick_params(labelleft=False)\n",
    "ax4.text(0.9, 0.1, 'DESY3', horizontalalignment='center', verticalalignment='center', transform=ax4.transAxes)\n",
    "\n",
    "#ax2.set_yticks([])\n",
    "ax4.set_ylim([-0.002,0.002])\n",
    "#g1h,g1fits = ax3.get_legend_handles_labels()\n",
    "#g2h,g2fits = ax4.get_legend_handles_labels()\n",
    "#ax4.legend(g1h+g2h, g1fits+g2fits, loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "#ax2.set_xlabel('PSF g2')\n",
    "\n",
    "#ax5.set_title('HSCY1')\n",
    "ax5.errorbar(hsc_psfg[0],hsc_psfg[2]/2,hsc_psfg[6]/2, color = 'firebrick',linestyle='None',marker='o',mfc='None',capsize=3)\n",
    "ax5.errorbar(hsc_psfg[0],hsc_psfg[4]/2,hsc_psfg[8]/2, color='royalblue',linestyle='None',marker='o',mfc='None',capsize=3)\n",
    "ax5.plot(np.arange(-0.03,0.03,0.005),np.zeros(len(np.arange(-0.03,0.03,0.005))),color='black',linestyle='dashed')\n",
    "ax5.plot(hsc_psfg[0],hsc_psfg[10]/2,color='firebrick',label=r'L: m = $(-9.55 \\pm 3.81) \\times 10^{-3}$')\n",
    "ax5.plot(hsc_psfg[0],hsc_psfg[12]/2,color='royalblue',label=r'L: m = $(-1.54 \\pm 0.51) \\times 10^{-2}$')\n",
    "\n",
    "ax5.set_ylim([-0.002,0.002])\n",
    "ax5.set_ylabel(r'$\\langle$ g $\\rangle$')\n",
    "#ax3.set_xlabel('PSF g1')\n",
    "\n",
    "#ax6.set_title('HSCY1')\n",
    "ax6.errorbar(hsc_psfg[1],hsc_psfg[3]/2,hsc_psfg[7]/2, color = 'firebrick',linestyle='None',marker='o',mfc='None',capsize=3)\n",
    "ax6.errorbar(hsc_psfg[1],hsc_psfg[5]/2,hsc_psfg[9]/2, color='royalblue',linestyle='None',marker='o',mfc='None',capsize=3)\n",
    "ax6.plot(np.arange(-0.03,0.03,0.005),np.zeros(len(np.arange(-0.03,0.03,0.005))),color='black',linestyle='dashed')\n",
    "ax6.plot(hsc_psfg[1],hsc_psfg[11]/2,color='firebrick',label=r'R: m = $(1.87 \\pm 0.65) \\times 10^{-2}$')\n",
    "ax6.plot(hsc_psfg[1],hsc_psfg[13]/2,color='royalblue',label=r'R: m = $(-8.70 \\pm 5.45) \\times 10^{-3}$')\n",
    "ax6.yaxis.set_tick_params(labelleft=False)\n",
    "ax6.text(0.9, 0.1, 'HSCY1', horizontalalignment='center', verticalalignment='center', transform=ax6.transAxes)\n",
    "\n",
    "#ax4.set_yticks([])\n",
    "ax6.set_ylim([-0.002,0.002])\n",
    "#g1h,g1fits = ax5.get_legend_handles_labels()\n",
    "#g2h,g2fits = ax6.get_legend_handles_labels()\n",
    "#ax6.legend(g1h+g2h, g1fits+g2fits, loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "#ax4.set_xlabel('PSF g2')\n",
    "\n",
    "\n",
    "#ax7.set_title('KiDS-1000')\n",
    "ax7.errorbar(k_psfg[0],k_psfg[2],k_psfg[6], color='firebrick',linestyle='None',marker='o',mfc='None',capsize=3)\n",
    "ax7.errorbar(k_psfg[0],k_psfg[4],k_psfg[8], color='royalblue',linestyle='None',marker='o',mfc='None',capsize=3)\n",
    "ax7.plot(np.arange(-0.03,0.03,0.005),np.zeros(len(np.arange(-0.03,0.03,0.005))),color='black',linestyle='dashed')\n",
    "ax7.plot(k_psfg[0],k_psfg[10],color='firebrick',label=r'L: m = $(-4.11 \\pm 0.21) \\times 10^{-2}$')\n",
    "ax7.plot(k_psfg[0],k_psfg[12],color='royalblue',label=r'L: m = $(-1.16 \\pm 2.95) \\times 10^{-3}$')\n",
    "\n",
    "ax7.set_ylim([-0.002,0.002])\n",
    "ax7.set_ylabel(r'$\\langle$ g $\\rangle$')\n",
    "ax7.set_xlabel('PSF g1')\n",
    "\n",
    "#ax8.set_title('KiDS-1000')\n",
    "ax8.errorbar(k_psfg[1],k_psfg[3],k_psfg[7], color='firebrick',linestyle='None',marker='o',mfc='None',capsize=3)\n",
    "ax8.errorbar(k_psfg[1],k_psfg[5],k_psfg[9], color='royalblue',linestyle='None',marker='o',mfc='None',capsize=3)\n",
    "ax8.plot(np.arange(-0.03,0.03,0.005),np.zeros(len(np.arange(-0.03,0.03,0.005))),color='black',linestyle='dashed')\n",
    "ax8.plot(k_psfg[1],k_psfg[11],color='firebrick',label=r'R: m = $(-4.41 \\pm 2.47) \\times 10^{-3}$')\n",
    "ax8.plot(k_psfg[1],k_psfg[13],color='royalblue',label=r'R: m = $(-5.36 \\pm 0.26) \\times 10^{-2}$')\n",
    "ax8.yaxis.set_tick_params(labelleft=False)\n",
    "ax8.text(0.9, 0.1, 'KiDS-1000', horizontalalignment='center', verticalalignment='center', transform=ax8.transAxes)\n",
    "\n",
    "#ax6.set_yticks([])\n",
    "ax8.set_ylim([-0.002,0.002])\n",
    "ax8.set_xlabel('PSF g2')\n",
    "#g1h,g1fits = ax7.get_legend_handles_labels()\n",
    "#g2h,g2fits = ax8.get_legend_handles_labels()\n",
    "#ax8.legend(g1h+g2h, g1fits+g2fits, loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "fig.subplots_adjust(wspace=0,hspace=0)\n",
    "fig.show()\n",
    "fig.savefig('plots/comp_psfg.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "511a3e23-c8d4-4059-ba01-e30dfb7081da",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(nrows=4, ncols=1, sharex=True,figsize=(10, 10))\n",
    "ax1, ax2, ax3, ax4 = ax.flatten()\n",
    "#just to compare y1 and y3\n",
    "#fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True,figsize=(8, 6),layout='constrained')\n",
    "#ax1, ax2= ax.flatten()\n",
    "\n",
    "ax1.errorbar(y1T[0][4:],y1T[1][4:],y1T[3][4:], color='firebrick',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3,label = 'g1')\n",
    "ax1.errorbar(y1T[0][4:],y1T[2][4:],y1T[4][4:], color='royalblue',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3,label = 'g2')\n",
    "ax1.plot(np.arange(0.1,2,0.01),np.zeros(len(np.arange(0.1,2,0.01))),color='black',linestyle='dashed')\n",
    "\n",
    "ax1.plot(y1T[0][4:],y1T[5][4:],color='firebrick')#,label=r'm = $(-5.84 \\pm 0.21) \\times 10^{-5}$')\n",
    "ax1.plot(y1T[0][4:],y1T[6][4:],color='royalblue')#,label=r'm = $(2.44 \\pm 3.20) \\times 10^{-4}$')\n",
    "#ax1.set_xscale('log')\n",
    "ax1.set_ylim([-0.0015,0.0015])\n",
    "ax1.set_ylabel(r'$\\langle$ g $\\rangle$')\n",
    "ax1.text(0.9, 0.1, 'DESY1', horizontalalignment='center', verticalalignment='center', transform=ax1.transAxes)\n",
    "ax1.legend(loc='upper right')\n",
    "\n",
    "\n",
    "ax2.errorbar(y3T[0][4:],y3T[1][4:],y3T[3][4:], color='firebrick',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3)\n",
    "ax2.errorbar(y3T[0][4:],y3T[2][4:],y3T[4][4:], color='royalblue',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3)\n",
    "ax2.plot(np.arange(0.1,2,0.01),np.zeros(len(np.arange(0.1,2,0.01))),color='black',linestyle='dashed')\n",
    "\n",
    "ax2.plot(y3T[0][4:],y3T[5][4:],color='firebrick',label=r'm = $(-8.14 \\pm 1.50) \\times 10^{-4}$')\n",
    "ax2.plot(y3T[0][4:],y3T[6][4:],color='royalblue',label=r'm = $(2.53 \\pm 1.25) \\times 10^{-4}$')\n",
    "ax2.set_ylabel(r'$\\langle$ g $\\rangle$')\n",
    "#ax2.set_xscale('log')\n",
    "#ax2.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "ax2.set_ylim([-0.0015,0.0015])\n",
    "ax2.set_xlabel(r'T$_{gal}$')\n",
    "ax2.text(0.9, 0.1, 'DESY3', horizontalalignment='center', verticalalignment='center', transform=ax2.transAxes)\n",
    "\n",
    "\n",
    "\n",
    "ax3.errorbar(hscT[0],hscT[1]/2,hscT[3]/2, color = 'firebrick',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3)\n",
    "ax3.errorbar(hscT[0],hscT[2]/2,hscT[4]/2, color = 'royalblue',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3)\n",
    "ax3.plot(np.arange(0.1,2,0.01),np.zeros(len(np.arange(0.1,2,0.01))),color='black',linestyle='dashed')\n",
    "\n",
    "ax3.plot(hscT[0],hscT[5]/2,color='firebrick',label=r'm = $(1.05 \\pm 0.56) \\times 10^{-3}$')\n",
    "ax3.plot(hscT[0],hscT[6]/2,color='royalblue',label=r'm = $(-3.04 \\pm 4.32) \\times 10^{-4}$')\n",
    "#ax3.set_xscale('log')\n",
    "ax3.set_ylim([-0.0015,0.0015])\n",
    "ax3.set_ylabel(r'$\\langle$ g $\\rangle$')\n",
    "ax3.text(0.9, 0.1, 'HSCY1', horizontalalignment='center', verticalalignment='center', transform=ax3.transAxes)\n",
    "\n",
    "#ax3.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "#ax2.set_xlabel('T')\n",
    "\n",
    "ax4.errorbar(kT[0],kT[1],kT[3], color='firebrick',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3)\n",
    "ax4.errorbar(kT[0],kT[2],kT[4], color='royalblue',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3)\n",
    "ax4.plot(np.arange(0.1,2,0.01),np.zeros(len(np.arange(0.1,2,0.01))),color='black',linestyle='dashed')\n",
    "\n",
    "ax4.plot(kT[0],kT[5],color='firebrick',label=r'm = $(-1.33 \\pm 0.15) \\times 10^{-3}$')\n",
    "ax4.plot(kT[0],kT[6],color='royalblue',label=r'm = $(-1.56 \\pm 1.09) \\times 10^{-4}$')\n",
    "#ax4.set_xscale('log')\n",
    "ax4.set_ylim([-0.0015,0.0015])\n",
    "ax4.set_ylabel(r'$\\langle$ g $\\rangle$')\n",
    "ax4.set_xlabel(r'T$_{gal}$ [arcsec$^2$]')\n",
    "ax4.text(0.9, 0.1, 'KiDS-1000', horizontalalignment='center', verticalalignment='center', transform=ax4.transAxes)\n",
    "\n",
    "#ax4.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "fig.subplots_adjust(wspace=0,hspace=0)\n",
    "fig.savefig('./plots/comp_Tgal.pdf')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62daf3bf-b0bf-47fd-9012-3a7c99b9d359",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(nrows=4, ncols=1, sharex=True,figsize=(10, 10))\n",
    "ax1, ax2, ax3,ax4 = ax.flatten()\n",
    "\n",
    "ax1.errorbar(y1psfT[0],y1psfT[1],y1psfT[3], color='firebrick',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3,label = 'g1')\n",
    "ax1.errorbar(y1psfT[0],y1psfT[2],y1psfT[4], color='royalblue',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3,label = 'g2')\n",
    "ax1.plot(np.arange(0.05,0.38,0.01),np.zeros(len(np.arange(0.05,0.38,0.01))),color='black',linestyle='dashed')\n",
    "\n",
    "ax1.plot(y1psfT[0],y1psfT[5],color='firebrick')#,label=r'm = $(-4.98 \\pm 2.50) \\times 10^{-3}$')\n",
    "ax1.plot(y1psfT[0],y1psfT[6],color='royalblue')#,label=r'm = $(-7.26 \\pm 6.02) \\times 10^{-3}$')\n",
    "#ax1.set_xscale('log')\n",
    "ax1.set_ylim([-0.002,0.002])\n",
    "ax1.set_ylabel(r'$\\langle$ g $\\rangle$')\n",
    "ax1.legend(loc='upper right')\n",
    "ax1.text(0.9, 0.1, 'DESY1', horizontalalignment='center', verticalalignment='center', transform=ax1.transAxes)\n",
    "\n",
    "\n",
    "ax2.errorbar(y3psfT[0],y3psfT[1],y3psfT[3], color='firebrick',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3)\n",
    "ax2.errorbar(y3psfT[0],y3psfT[2],y3psfT[4], color='royalblue',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3)\n",
    "ax2.plot(np.arange(0.05,0.38,0.01),np.zeros(len(np.arange(0.05,0.38,0.01))),color='black',linestyle='dashed')\n",
    "ax2.plot(y3psfT[0],y3psfT[5],color='firebrick',label=r'm = $(5.88 \\pm 4.83) \\times 10^{-3}$')\n",
    "ax2.plot(y3psfT[0],y3psfT[6],color='royalblue',label=r'm = $(2.45 \\pm 0.51) \\times 10^{-4}$')\n",
    "#ax1.set_xscale('log')\n",
    "#ax2.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "ax2.set_ylim([-0.002,0.002])\n",
    "ax2.set_ylabel(r'$\\langle$ g $\\rangle$')\n",
    "ax2.text(0.9, 0.1, 'DESY3', horizontalalignment='center', verticalalignment='center', transform=ax2.transAxes)\n",
    "\n",
    "\n",
    "ax3.errorbar(hscpsfT[0],hscpsfT[1]/2,hscpsfT[3]/2, color = 'firebrick',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3)\n",
    "ax3.errorbar(hscpsfT[0],hscpsfT[2]/2,hscpsfT[4]/2, color = 'royalblue',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3)\n",
    "ax3.plot(np.arange(0.05,0.38,0.01),np.zeros(len(np.arange(0.05,0.38,0.01))),color='black',linestyle='dashed')\n",
    "\n",
    "ax3.plot(hscpsfT[0],hscpsfT[5]/2,color='firebrick',label=r'm = $(6.65 \\pm 5.05) \\times 10^{-3}$')\n",
    "ax3.plot(hscpsfT[0],hscpsfT[6]/2,color='royalblue',label=r'm = $(1.03 \\pm 6.00) \\times 10^{-3}$')\n",
    "#ax2.set_xscale('log')\n",
    "ax3.set_ylim([-0.002,0.002])\n",
    "ax3.set_ylabel(r'$\\langle$ g $\\rangle$')\n",
    "#ax2.set_xlabel('psfT')\n",
    "#ax3.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "ax3.text(0.9, 0.1, 'HSCY1', horizontalalignment='center', verticalalignment='center', transform=ax3.transAxes)\n",
    "\n",
    "\n",
    "ax4.errorbar(kpsfT[0],kpsfT[1],kpsfT[3], color='firebrick',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3)\n",
    "ax4.errorbar(kpsfT[0],kpsfT[2],kpsfT[4], color='royalblue',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3)\n",
    "ax4.plot(np.arange(0.05,0.38,0.01),np.zeros(len(np.arange(0.05,0.38,0.01))),color='black',linestyle='dashed')\n",
    "\n",
    "ax4.plot(kpsfT[0],kpsfT[5],color='firebrick',label=r'm = $(-6.29 \\pm 1.59) \\times 10^{-3}$')\n",
    "ax4.plot(kpsfT[0],kpsfT[6],color='royalblue',label=r'm = $(2.24 \\pm 1.27) \\times 10^{-3}$')\n",
    "#ax4.set_yscale('symlog')\n",
    "ax4.set_ylim([-0.002,0.002])\n",
    "ax4.set_ylabel(r'$\\langle$ g $\\rangle$')\n",
    "ax4.set_xlabel(r'PSF T [arcsec$^2$]')\n",
    "#ax4.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "ax4.text(0.9, 0.1, 'KiDS-1000', horizontalalignment='center', verticalalignment='center', transform=ax4.transAxes)\n",
    "fig.subplots_adjust(wspace=0,hspace=0)\n",
    "fig.savefig('./plots/comp_PSFT.pdf')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c617ceb5-c239-43cf-a2b3-e3016bea7cd7",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(nrows=4, ncols=1, sharex=True,figsize=(10, 10))\n",
    "ax1, ax2, ax3,ax4 = ax.flatten()\n",
    "#just to compare y1 and y3.\n",
    "#fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True,figsize=(8, 6),layout='constrained')\n",
    "#ax1, ax2 = ax.flatten()\n",
    "\n",
    "\n",
    "ax1.errorbar(y1snr[0],y1snr[1],y1snr[3], color='firebrick',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3,label = 'g1')\n",
    "ax1.errorbar(y1snr[0],y1snr[2],y1snr[4], color='royalblue',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3,label = 'g2')\n",
    "ax1.plot(range(10,160),np.zeros(len(range(10,160))),color='black', linestyle='dashed')\n",
    "ax1.plot(y1snr[0],y1snr[5],color='firebrick')#,label=r'm = $(-1.35 \\pm 1.55) \\times 10^{-6}$')\n",
    "ax1.plot(y1snr[0],y1snr[6],color='royalblue')#,label=r'm = $(8.17 \\pm 0.19) \\times 10^{-7}$')\n",
    "ax1.set_ylim([-0.0015,0.0015])\n",
    "ax1.set_ylabel(r'$\\langle$ g $\\rangle$')\n",
    "ax1.legend(loc='upper right')\n",
    "ax2.text(0.9, 0.1, 'DESY1', horizontalalignment='center', verticalalignment='center', transform=ax1.transAxes)\n",
    "\n",
    "\n",
    "\n",
    "ax2.errorbar(y3snr[0],y3snr[1],y3snr[3], color='firebrick',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3)\n",
    "ax2.errorbar(y3snr[0],y3snr[2],y3snr[4], color='royalblue',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3)\n",
    "ax2.plot(range(10,160),np.zeros(len(range(10,160))),color='black', linestyle='dashed')\n",
    "ax2.plot(y3snr[0],y3snr[5],color='firebrick',label=r'm = $(-2.87 \\pm 1.90) \\times 10^{-6}$')\n",
    "ax2.plot(y3snr[0],y3snr[6],color='royalblue',label=r'm = $(-1.47 \\pm 1.42) \\times 10^{-6}$')\n",
    "ax2.set_ylim([-0.0015,0.0015])\n",
    "ax2.set_ylabel(r'$\\langle$ g $\\rangle$')\n",
    "ax2.text(0.9, 0.1, 'DESY3', horizontalalignment='center', verticalalignment='center', transform=ax2.transAxes)\n",
    "#ax2.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "ax3.errorbar(hscsnr[0],hscsnr[1]/2,hscsnr[3]/2, color = 'firebrick',linestyle='None',marker='o',markersize=4,mfc='None',capsize=3)\n",
    "ax3.errorbar(hscsnr[0],hscsnr[2]/2,hscsnr[4]/2, color = 'royalblue',linestyle='None',marker='o',markersize=4,mfc='None',capsize=3)\n",
    "ax3.plot(range(10,160),np.zeros(len(range(10,160))),color='black', linestyle='dashed')\n",
    "\n",
    "ax3.plot(hscsnr[0],hscsnr[5]/2,color='firebrick',label=r'm = $(-2.7 \\pm 0.21) \\times 10^{-7}$')\n",
    "ax3.plot(hscsnr[0],hscsnr[6]/2,color='royalblue',label=r'm = $(4.11 \\pm 0.03) \\times 10^{-8}$') ######CHECK THIS ONE\n",
    "ax3.set_ylim([-0.0015,0.0015])\n",
    "ax3.set_ylabel(r'$\\langle$ g $\\rangle$')\n",
    "ax3.text(0.9, 0.1, 'HSCY1', horizontalalignment='center', verticalalignment='center', transform=ax3.transAxes)\n",
    "#ax3.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "ax4.errorbar(ksnr[0],ksnr[1],ksnr[3], color='firebrick',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3)\n",
    "ax4.errorbar(ksnr[0],ksnr[2],ksnr[4], color='royalblue',linestyle='None',marker='o',mfc='None',markersize=4,capsize=3)\n",
    "ax4.plot(range(10,160),np.zeros(len(range(10,160))),color='black', linestyle='dashed')\n",
    "\n",
    "ax4.plot(ksnr[0],ksnr[5],color='firebrick',label=r'm = $(9.02 \\pm 5.91) \\times 10^{-6}$')\n",
    "ax4.plot(ksnr[0],ksnr[6],color='royalblue',label=r'm = $(-1.04 \\pm 0.22) \\times 10^{-6}$')\n",
    "#ax3.set_xscale('log')\n",
    "ax4.set_ylim([-0.0015,0.0015])\n",
    "ax4.set_ylabel(r'$\\langle$ g $\\rangle$')\n",
    "ax4.set_xlabel('SNR')\n",
    "ax4.text(0.9, 0.1, 'KiDS-1000', horizontalalignment='center', verticalalignment='center', transform=ax4.transAxes)\n",
    "#ax4.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "fig.subplots_adjust(wspace=0,hspace=0)\n",
    "fig.savefig('plots/comp_snr.pdf')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c0eeaaa-9789-42ac-bba6-b9413aa523be",
   "metadata": {},
   "source": [
    "# Comparing Rowe Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0811692b-d4ba-4242-9d2c-0a0d0d7e9ecd",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y1rowe = h5py.File('../TXPipe/data/desy1/outputs/rowe_stats.hdf5','r')\n",
    "hscrowe = h5py.File('../TXPipe/data/hscy1/outputs/rowe_stats.hdf5','r')\n",
    "#hscrowe = h5py.File('../TXPipe/data/hscXMM/outputs/rowe_stats.hdf5','r')\n",
    "krowe = h5py.File('../TXPipe/data/kids/outputs/rowe_stats.hdf5','r')\n",
    "y3rowe= h5py.File('../TXPipe/data/desy3/outputs/rowe_stats.hdf5','r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4f5f44e-974b-4f1e-a053-52720e0d64ba",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(6, sharex=True,figsize=(5,15))\n",
    "for i in range(6):\n",
    "    axs[i].errorbar(np.array(y1rowe[f'rowe_statistics/rowe_{i}_PSF-reserved/theta']),\n",
    "                    np.abs(np.array(y1rowe[f'rowe_statistics/rowe_{i}_PSF-reserved/xi_plus'])),\n",
    "                    np.array(y1rowe[f'rowe_statistics/rowe_{i}_PSF-reserved/xi_err']),capsize=3,color='royalblue',label='DES-Y1')\n",
    "    axs[i].errorbar(np.array(y3rowe[f'rowe_statistics/rowe_{i}_PSF-reserved/theta']),\n",
    "                        np.abs(np.array(y3rowe[f'rowe_statistics/rowe_{i}_PSF-reserved/xi_plus'])),\n",
    "                        np.array(y3rowe[f'rowe_statistics/rowe_{i}_PSF-reserved/xi_err']),capsize=3,color='darkblue',label='DES-Y3')\n",
    "    axs[i].errorbar(np.array(hscrowe[f'rowe_statistics/rowe_{i}_PSF-reserved/theta']),\n",
    "                        np.abs(np.array(hscrowe[f'rowe_statistics/rowe_{i}_PSF-reserved/xi_plus']))/2,\n",
    "                        np.array(hscrowe[f'rowe_statistics/rowe_{i}_PSF-reserved/xi_err'])/2,capsize=3,color='grey',label='HSC-Y1')\n",
    "    axs[i].errorbar(np.array(krowe[f'rowe_statistics/rowe_{i}_PSF-reserved/theta']),\n",
    "                        np.abs(np.array(krowe[f'rowe_statistics/rowe_{i}_PSF-reserved/xi_plus'])),\n",
    "                        np.array(krowe[f'rowe_statistics/rowe_{i}_PSF-reserved/xi_err']),capsize=3,color='firebrick',label='KiDS-1000')\n",
    "    axs[i].set_xscale('log')\n",
    "    #plt.ylim([9e-5, 2.5e-3])\n",
    "    axs[i].set_yscale('log')\n",
    "    axs[i].set_ylabel(rf'$\\rho_{i}$',fontsize=10)\n",
    "    axs[i].tick_params(axis='both', which='major', labelsize=10)\n",
    "axs[1].legend(fontsize=10, bbox_to_anchor=[1.02,0.7], loc='lower right',framealpha=1)\n",
    "axs[5].set_xlabel(r'$\\theta$ [arcmin]',fontsize=10)\n",
    "fig.subplots_adjust(wspace=0,hspace=0)\n",
    "plt.savefig('plots/rowes.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6afcb253-9b5d-46b3-b9ad-6c0daa544b8f",
   "metadata": {},
   "source": [
    "# Tau Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "723bf39a-4935-45e9-8c44-df021615cc56",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "desy1tau = h5py.File('../TXPipe/data/desy1/outputs/tau_stats.hdf5','r')\n",
    "desy3tau = h5py.File('../TXPipe/data/desy3/outputs/tau_stats.hdf5','r')\n",
    "hscy1tau = h5py.File('../TXPipe/data/hscy1/outputs/tau_stats.hdf5','r')\n",
    "kidstau = h5py.File('../TXPipe/data/kids/outputs/tau_stats.hdf5','r')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1c393d03-98f4-4dec-9e79-81423bcdecd7",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fits = {}\n",
    "fits['desy1tau0'] = (desy1tau['tau_statistics/bestfits_PSF-reserved/alpha'][()]*y1rowe['rowe_statistics/rowe_0_PSF-reserved/xi_plus'][:]\n",
    "                     +desy1tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*y1rowe['rowe_statistics/rowe_2_PSF-reserved/xi_plus'][:]\n",
    "                     +desy1tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*y1rowe['rowe_statistics/rowe_5_PSF-reserved/xi_plus'][:])\n",
    "\n",
    "fits['desy3tau0'] = (desy3tau['tau_statistics/bestfits_PSF-reserved/alpha'][()]*y3rowe['rowe_statistics/rowe_0_PSF-reserved/xi_plus'][:]\n",
    "                     +desy3tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*y3rowe['rowe_statistics/rowe_2_PSF-reserved/xi_plus'][:]\n",
    "                     +desy3tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*y3rowe['rowe_statistics/rowe_5_PSF-reserved/xi_plus'][:])\n",
    "\n",
    "fits['hscy1tau0'] = ((hscy1tau['tau_statistics/bestfits_PSF-reserved/alpha'][()]*hscrowe['rowe_statistics/rowe_0_PSF-reserved/xi_plus'][:])/2\n",
    "                     +(hscy1tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*hscrowe['rowe_statistics/rowe_2_PSF-reserved/xi_plus'][:])/2\n",
    "                     +(hscy1tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*hscrowe['rowe_statistics/rowe_5_PSF-reserved/xi_plus'][:])/2)\n",
    "\n",
    "fits['kidstau0'] = (kidstau['tau_statistics/bestfits_PSF-reserved/alpha'][()]*krowe['rowe_statistics/rowe_0_PSF-reserved/xi_plus'][:]\n",
    "                     +kidstau['tau_statistics/bestfits_PSF-reserved/beta'][()]*krowe['rowe_statistics/rowe_2_PSF-reserved/xi_plus'][:]\n",
    "                     +kidstau['tau_statistics/bestfits_PSF-reserved/beta'][()]*krowe['rowe_statistics/rowe_5_PSF-reserved/xi_plus'][:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cf583353-74e2-4a5f-8607-f2184bb26974",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fits['desy1tau2'] = (desy1tau['tau_statistics/bestfits_PSF-reserved/alpha'][()]*y1rowe['rowe_statistics/rowe_2_PSF-reserved/xi_plus'][:]\n",
    "                     +desy1tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*y1rowe['rowe_statistics/rowe_1_PSF-reserved/xi_plus'][:]\n",
    "                     +desy1tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*y1rowe['rowe_statistics/rowe_4_PSF-reserved/xi_plus'][:])\n",
    "\n",
    "fits['desy3tau2'] = (desy3tau['tau_statistics/bestfits_PSF-reserved/alpha'][()]*y3rowe['rowe_statistics/rowe_2_PSF-reserved/xi_plus'][:]\n",
    "                     +desy3tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*y3rowe['rowe_statistics/rowe_1_PSF-reserved/xi_plus'][:]\n",
    "                     +desy3tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*y3rowe['rowe_statistics/rowe_4_PSF-reserved/xi_plus'][:])\n",
    "\n",
    "fits['hscy1tau2'] = ((hscy1tau['tau_statistics/bestfits_PSF-reserved/alpha'][()]*hscrowe['rowe_statistics/rowe_2_PSF-reserved/xi_plus'][:])/2\n",
    "                     +(hscy1tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*hscrowe['rowe_statistics/rowe_1_PSF-reserved/xi_plus'][:])/2\n",
    "                     +(hscy1tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*hscrowe['rowe_statistics/rowe_4_PSF-reserved/xi_plus'][:])/2)\n",
    "\n",
    "fits['kidstau2'] = (kidstau['tau_statistics/bestfits_PSF-reserved/alpha'][()]*krowe['rowe_statistics/rowe_2_PSF-reserved/xi_plus'][:]\n",
    "                     +kidstau['tau_statistics/bestfits_PSF-reserved/beta'][()]*krowe['rowe_statistics/rowe_1_PSF-reserved/xi_plus'][:]\n",
    "                     +kidstau['tau_statistics/bestfits_PSF-reserved/beta'][()]*krowe['rowe_statistics/rowe_4_PSF-reserved/xi_plus'][:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7dc96d93-35cb-43c2-81f2-8947e1ab0b7b",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fits['desy1tau5'] = (desy1tau['tau_statistics/bestfits_PSF-reserved/alpha'][()]*y1rowe['rowe_statistics/rowe_5_PSF-reserved/xi_plus'][:]\n",
    "                     +desy1tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*y1rowe['rowe_statistics/rowe_4_PSF-reserved/xi_plus'][:]\n",
    "                     +desy1tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*y1rowe['rowe_statistics/rowe_3_PSF-reserved/xi_plus'][:])\n",
    "\n",
    "fits['desy3tau5'] = (desy3tau['tau_statistics/bestfits_PSF-reserved/alpha'][()]*y3rowe['rowe_statistics/rowe_5_PSF-reserved/xi_plus'][:]\n",
    "                     +desy3tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*y3rowe['rowe_statistics/rowe_4_PSF-reserved/xi_plus'][:]\n",
    "                     +desy3tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*y3rowe['rowe_statistics/rowe_3_PSF-reserved/xi_plus'][:])\n",
    "\n",
    "fits['hscy1tau5'] = ((hscy1tau['tau_statistics/bestfits_PSF-reserved/alpha'][()]*hscrowe['rowe_statistics/rowe_5_PSF-reserved/xi_plus'][:])/2\n",
    "                     +(hscy1tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*hscrowe['rowe_statistics/rowe_4_PSF-reserved/xi_plus'][:])/2\n",
    "                     +(hscy1tau['tau_statistics/bestfits_PSF-reserved/beta'][()]*hscrowe['rowe_statistics/rowe_3_PSF-reserved/xi_plus'][:])/2)\n",
    "\n",
    "fits['kidstau5'] = (kidstau['tau_statistics/bestfits_PSF-reserved/alpha'][()]*krowe['rowe_statistics/rowe_5_PSF-reserved/xi_plus'][:]\n",
    "                     +kidstau['tau_statistics/bestfits_PSF-reserved/beta'][()]*krowe['rowe_statistics/rowe_4_PSF-reserved/xi_plus'][:]\n",
    "                     +kidstau['tau_statistics/bestfits_PSF-reserved/beta'][()]*krowe['rowe_statistics/rowe_3_PSF-reserved/xi_plus'][:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dc391cb1-3480-4592-afd3-37b99cac50f3",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "nb    = len(desy1tau['tau_statistics/tau_PSF-reserved/theta'][:])\n",
    "\n",
    "covdesy1 = desy1tau['tau_statistics/tau_PSF-reserved/cov'][:]\n",
    "covdesy3 = desy3tau['tau_statistics/tau_PSF-reserved/cov'][:]\n",
    "covhscy1 = (hscy1tau['tau_statistics/tau_PSF-reserved/cov'][:])/2\n",
    "covkids = kidstau['tau_statistics/tau_PSF-reserved/cov'][:]\n",
    "\n",
    "errs  = {'desy1_0': np.diag(covdesy1[int(0*nb):int(1*nb),int(0*nb):int(1*nb)])**0.5,\n",
    "         'desy1_2': np.diag(covdesy1[int(1*nb):int(2*nb),int(1*nb):int(2*nb)])**0.5,\n",
    "         'desy1_5': np.diag(covdesy1[int(2*nb):int(3*nb),int(2*nb):int(3*nb)])**0.5,\n",
    "         'desy3_0': np.diag(covdesy3[int(0*nb):int(1*nb),int(0*nb):int(1*nb)])**0.5,\n",
    "         'desy3_2': np.diag(covdesy3[int(1*nb):int(2*nb),int(1*nb):int(2*nb)])**0.5,\n",
    "         'desy3_5': np.diag(covdesy3[int(2*nb):int(3*nb),int(2*nb):int(3*nb)])**0.5,\n",
    "         'hscy1_0': np.diag(covhscy1[int(0*nb):int(1*nb),int(0*nb):int(1*nb)])**0.5,\n",
    "         'hscy1_2': np.diag(covhscy1[int(1*nb):int(2*nb),int(1*nb):int(2*nb)])**0.5,\n",
    "         'hscy1_5': np.diag(covhscy1[int(2*nb):int(3*nb),int(2*nb):int(3*nb)])**0.5,\n",
    "         'kids_0': np.diag(covkids[int(0*nb):int(1*nb),int(0*nb):int(1*nb)])**0.5,\n",
    "         'kids_2': np.diag(covkids[int(1*nb):int(2*nb),int(1*nb):int(2*nb)])**0.5,\n",
    "         'kids_5': np.diag(covkids[int(2*nb):int(3*nb),int(2*nb):int(3*nb)])**0.5,\n",
    "        }\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60f29472-f09e-440b-9a64-4e64e017140d",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "i=0\n",
    "fig, ax = plt.subplots(nrows=3, ncols=1, sharex=True,figsize=(10, 10))\n",
    "ax1, ax2, ax3= ax.flatten()\n",
    "ax1.errorbar(desy1tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "             desy1tau[f'tau_statistics/tau_PSF-reserved/tau{i}'][:],\n",
    "             errs[f'desy1_{i}'],\n",
    "             color='royalblue',marker='o',mfc='None',ls='None',capsize=3,label='DES-Y1')\n",
    "ax1.plot(desy1tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "         fits[f'desy1tau{i}'],\n",
    "         color='royalblue',marker='None')\n",
    "\n",
    "ax1.errorbar(desy3tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "             desy3tau[f'tau_statistics/tau_PSF-reserved/tau{i}'][:],\n",
    "             errs[f'desy3_{i}'],\n",
    "             color='darkblue',marker='o',mfc='None',ls='None',capsize=3,label='DES-Y3')\n",
    "\n",
    "ax1.plot(desy3tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "         fits[f'desy3tau{i}'],\n",
    "         color='darkblue',marker='None')\n",
    "\n",
    "ax1.errorbar(hscy1tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "            ((hscy1tau[f'tau_statistics/tau_PSF-reserved/tau{i}'][:])/2),#*0.1,\n",
    "             (errs[f'hscy1_{i}']),#*0.1,\n",
    "             color='grey',marker='o',mfc='None',ls='None',capsize=3,label=r'HSC-Y1 $[\\times10^{-1}]$')\n",
    "ax1.plot(hscy1tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "         (fits[f'hscy1tau{i}']),#*0.1,\n",
    "         color='grey',marker='None')\n",
    "\n",
    "ax1.errorbar(kidstau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "             kidstau[f'tau_statistics/tau_PSF-reserved/tau{i}'][:],\n",
    "             errs[f'kids_{i}'],\n",
    "             color='firebrick',marker='o',mfc='None',ls='None',capsize=3,label='KiDS-1000')\n",
    "ax1.plot(kidstau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "         fits[f'kidstau{i}'],\n",
    "         color='firebrick',marker='None')\n",
    "\n",
    "ax1.set_xscale('log')\n",
    "#ax.set_xlabel(r'$\\theta$ [arcmin]')\n",
    "ax1.set_ylabel(rf'$\\tau_{i}+$')\n",
    "ax1.set_ylim([-9e-6,10e-6])\n",
    "ax1.set_yscale('symlog')\n",
    "\n",
    "ax1.set_title(r\"$\\times 10^{-6}$\", loc = 'left', fontsize=12)\n",
    "\n",
    "ax1.set_yticks(ticks=[-9e-6,-8e-6,-7e-6,-6e-6,-5e-6,-4e-6,-3e-6,-2e-6,-1e-6,0,\n",
    "                     1e-6,2e-6,3e-6,4e-6,5e-6,6e-6,7e-6,8e-6,9e-6,10e-6],\n",
    "              labels=[-9,'','','',-5,'','','','',0,'','','','',5,'','','','',10])\n",
    "ax1.set_xlim([4e-1,3e2])\n",
    "\n",
    "pos = ax1.get_position()\n",
    "ax1.set_position([pos.x0, pos.y0, pos.width * 0.9, pos.height])\n",
    "ax1.legend(loc='upper left',fontsize=12,ncol=4)#, bbox_to_anchor =(1.4, 0.5))\n",
    "\n",
    "#=======================================================================\n",
    "i=2\n",
    "\n",
    "\n",
    "ax2.errorbar(desy1tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "             desy1tau['tau_statistics/tau_PSF-reserved/theta'][:]*desy1tau[f'tau_statistics/tau_PSF-reserved/tau{i}'][:],\n",
    "             desy1tau['tau_statistics/tau_PSF-reserved/theta'][:]*errs[f'desy1_{i}'],\n",
    "             color='royalblue',marker='o',mfc='None',ls='None',capsize=3,label=r'DES-Y1')\n",
    "ax2.plot(desy1tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "         desy1tau['tau_statistics/tau_PSF-reserved/theta'][:]*fits[f'desy1tau{i}'],\n",
    "         color='royalblue',marker='None')\n",
    "\n",
    "ax2.errorbar(desy3tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "             desy3tau['tau_statistics/tau_PSF-reserved/theta'][:]*desy3tau[f'tau_statistics/tau_PSF-reserved/tau{i}'][:],\n",
    "             desy3tau['tau_statistics/tau_PSF-reserved/theta'][:]*errs[f'desy3_{i}'],\n",
    "             color='darkblue',marker='o',mfc='None',ls='None',capsize=3,label='DES-Y3')\n",
    "\n",
    "ax2.plot(desy3tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "         desy3tau['tau_statistics/tau_PSF-reserved/theta'][:]*fits[f'desy3tau{i}'],\n",
    "         color='darkblue',marker='None')\n",
    "\n",
    "ax2.errorbar(hscy1tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "            (hscy1tau['tau_statistics/tau_PSF-reserved/theta'][:]*((hscy1tau[f'tau_statistics/tau_PSF-reserved/tau{i}'][:])/2)),#*0.01,\n",
    "             (hscy1tau['tau_statistics/tau_PSF-reserved/theta'][:]*errs[f'hscy1_{i}']),#*0.01,\n",
    "             color='grey',marker='o',mfc='None',ls='None',capsize=3,label=r'HSC-Y1')# [$\\times 10^{-2}$]')\n",
    "ax2.plot(hscy1tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "         (hscy1tau['tau_statistics/tau_PSF-reserved/theta'][:]*fits[f'hscy1tau{i}']),#*0.01,\n",
    "         color='grey',marker='None')\n",
    "\n",
    "ax2.errorbar(kidstau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "             kidstau['tau_statistics/tau_PSF-reserved/theta'][:]*kidstau[f'tau_statistics/tau_PSF-reserved/tau{i}'][:],\n",
    "             kidstau['tau_statistics/tau_PSF-reserved/theta'][:]*errs[f'kids_{i}'],\n",
    "             color='firebrick',marker='o',mfc='None',ls='None',capsize=3,label='KiDS-1000')\n",
    "ax2.plot(kidstau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "         kidstau['tau_statistics/tau_PSF-reserved/theta'][:]*fits[f'kidstau{i}'],\n",
    "         color='firebrick',marker='None')\n",
    "\n",
    "ax2.set_xscale('log')\n",
    "#ax.set_xlabel(r'$\\theta$ [arcmin]')\n",
    "ax2.set_ylabel(rf'$\\theta \\times \\tau_{i}+$')\n",
    "ax2.set_xlim([4e-1,3e2])\n",
    "ax2.set_ylim([-3e-6,1.0e-5]) \n",
    "ax2.set_yscale('symlog')\n",
    "#ax2.set_title(r\"$\\times 10^{-6}$\", loc = 'left',fontsize=12)\n",
    "ax2.set_yticks(ticks=[-3e-6,-2e-6,-2e-6,0,\n",
    "                     1e-6,2e-6,3e-6,4e-6,5e-6,6e-6,7e-6,8e-6,9e-6,10e-6],\n",
    "              labels=[-3,'','',0,'','','','',5,'','','','',10])\n",
    "\n",
    "pos = ax2.get_position()\n",
    "ax2.set_position([pos.x0, pos.y0, pos.width * 0.9, pos.height])\n",
    "ax2.legend(loc='upper left', fontsize=12,ncol=4)\n",
    "#=====================================================================\n",
    "i=5\n",
    "\n",
    "\n",
    "ax3.errorbar(desy1tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "             (desy1tau['tau_statistics/tau_PSF-reserved/theta'][:]*desy1tau[f'tau_statistics/tau_PSF-reserved/tau{i}'][:]),#*0.01,\n",
    "             (desy1tau['tau_statistics/tau_PSF-reserved/theta'][:]*errs[f'desy1_{i}']),#*0.01,\n",
    "             color='royalblue',marker='o',mfc='None',ls='None',capsize=3,label=r'DES-Y1[$\\times 10^{-2}$]')\n",
    "ax3.plot(desy1tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "         (desy1tau['tau_statistics/tau_PSF-reserved/theta'][:]*fits[f'desy1tau{i}']),#*0.01,\n",
    "         color='royalblue',marker='None')\n",
    "\n",
    "ax3.errorbar(desy3tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "             desy3tau['tau_statistics/tau_PSF-reserved/theta'][:]*desy3tau[f'tau_statistics/tau_PSF-reserved/tau{i}'][:],\n",
    "             desy3tau['tau_statistics/tau_PSF-reserved/theta'][:]*errs[f'desy3_{i}'],\n",
    "             color='darkblue',marker='o',mfc='None',ls='None',capsize=3,label='DES-Y3')\n",
    "\n",
    "ax3.plot(desy3tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "         desy3tau['tau_statistics/tau_PSF-reserved/theta'][:]*fits[f'desy3tau{i}'],\n",
    "         color='darkblue',marker='None')\n",
    "\n",
    "ax3.errorbar(hscy1tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "             (hscy1tau['tau_statistics/tau_PSF-reserved/theta'][:]*((hscy1tau[f'tau_statistics/tau_PSF-reserved/tau{i}'][:])/2)),#*0.01,\n",
    "             (hscy1tau['tau_statistics/tau_PSF-reserved/theta'][:]*errs[f'hscy1_{i}']),#*0.01,\n",
    "             color='grey',marker='o',mfc='None',ls='None',capsize=3,label=r'HSC-Y1')# [$\\times 10^{-2}$]')\n",
    "ax3.plot(hscy1tau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "         (hscy1tau['tau_statistics/tau_PSF-reserved/theta'][:]*fits[f'hscy1tau{i}']),#*0.01,\n",
    "         color='grey',marker='None')\n",
    "\n",
    "ax3.errorbar(kidstau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "             kidstau['tau_statistics/tau_PSF-reserved/theta'][:]*kidstau[f'tau_statistics/tau_PSF-reserved/tau{i}'][:],\n",
    "             kidstau['tau_statistics/tau_PSF-reserved/theta'][:]*errs[f'kids_{i}'],\n",
    "             color='firebrick',marker='o',mfc='None',ls='None',capsize=3,label='KiDS-1000')\n",
    "ax3.plot(kidstau['tau_statistics/tau_PSF-reserved/theta'][:],\n",
    "         kidstau['tau_statistics/tau_PSF-reserved/theta'][:]*fits[f'kidstau{i}'],\n",
    "         color='firebrick',marker='None')\n",
    "\n",
    "ax3.set_xscale('log')\n",
    "#ax.set_xlabel(r'$\\theta$ [arcmin]')\n",
    "ax3.set_ylabel(rf'$\\theta \\times \\tau_{i}+$')\n",
    "ax3.set_xlim([4e-1,3e2])\n",
    "\n",
    "\n",
    "ax3.set_ylim([-7e-7,10e-7]) \n",
    "ax3.set_yscale('symlog')\n",
    "#ax.set_title(r\"$\\times 10^{-6}$\", loc = 'left',fontsize=12)\n",
    "ax3.set_yticks(ticks=[-7e-7,-6e-7,-5e-7,-4e-7,-3e-7,-2e-7,-1e-7,0,\n",
    "                     1e-7,2e-7,3e-7,4e-7,5e-7,6e-7,7e-7,8e-7,9e-7,10e-7],\n",
    "              labels = [-7,'',-5,'','','','',0,'','','','',5,'','','','',10])\n",
    "\n",
    "\n",
    "pos = ax3.get_position()\n",
    "ax3.set_position([pos.x0, pos.y0, pos.width * 0.9, pos.height])\n",
    "ax3.legend(loc='upper left',fontsize=12,ncol=4)\n",
    "#ax2.legend(fontsize=12, bbox_to_anchor=[1.02,0.75], loc='lower right',framealpha=1)\n",
    "\n",
    "\n",
    "#plt.tight_layout()\n",
    "plt.savefig(f'./plots/taus.pdf',bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a009bc2-ce40-43a7-98e9-87765019e2bf",
   "metadata": {},
   "source": [
    "# Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57169d99-8b3a-47bb-a65c-ed88493b2776",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y1 = h5py.File('/global/cfs/cdirs/lsst/groups/WL/projects/txpipe-sys-tests/des-y1/shear_catalog_desy1_masked.h5','r')\n",
    "y3 = h5py.File('/global/cfs/cdirs/lsst/groups/WL/projects/txpipe-sys-tests/des-y3/shear_catalog_desy3_unmasked_withfakez.hdf5','r')\n",
    "hscy1 = h5py.File('/global/cfs/cdirs/lsst/groups/WL/projects/txpipe-sys-tests/hscy1/shear_cat_FULLCAT.h5','r')\n",
    "kids = h5py.File('/global/cfs/cdirs/lsst/groups/WL/projects/txpipe-sys-tests/kids/shear_catalog_kids1000.hdf5','r')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce690e44-4638-48d3-ad76-b52139a075cd",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#so we need to make cuts. \n",
    "cutT = y1['shear/mcal_T'][:]>0\n",
    "cutpsfT = y1['shear/mcal_psf_T_mean'][:]>0\n",
    "cutpsfg1 = y1['shear/mcal_psf_g1'][:]>-1\n",
    "cutpsfg2 = y1['shear/mcal_psf_g2'][:]>-1\n",
    "cutsnr = y1['shear/mcal_s2n'][:] > 10\n",
    "cutratio = (y1['shear/mcal_T'][:]/y1['shear/mcal_psf_T_mean'][:]) > 0.5\n",
    "cutall = cutT & cutpsfT & cutpsfg1 & cutpsfg2 & cutsnr & cutratio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "495efa9a-1a8a-4c02-bb44-4b260ad7a2b9",
   "metadata": {},
   "source": [
    "figures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "41502d91-b872-4e30-908f-7ddc0c2f25b3",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3,2, sharex=False,figsize=(10,15))\n",
    "ax1,ax2,ax3,ax4,ax5,ax6 = axs.flatten()\n",
    "\n",
    "ax1.hist(y1['shear/mcal_psf_g1'][:][cutall],bins=100,range=[-0.1,0.1],label='DES-Y1',histtype='step',color='royalblue',density=True)\n",
    "ax1.hist(y3['shear/mcal_psf_g1'][:],bins=100,range=[-0.1,0.1],label='DES-Y3',histtype='step',color='darkblue',density=True)\n",
    "ax1.hist(hscy1['shear/psf_g1'][:]/2,bins=100,range=[-0.1,0.1],label='HSC-Y1',histtype='step',color='slategrey',density=True)\n",
    "ax1.hist(kids['shear/psf_g1'][:],bins=100,range=[-0.1,0.1],label='KiDS-1000',histtype='step',color='firebrick',density=True)\n",
    "ax1.set_xlabel('PSF g1')\n",
    "\n",
    "ax2.hist(y1['shear/mcal_psf_g2'][:][cutall],bins=100,range=[-0.1,0.1],label='DES-Y1',histtype='step',color='royalblue',density=True)\n",
    "ax2.hist(y3['shear/mcal_psf_g2'][:],bins=100,range=[-0.1,0.1],label='DES-Y3',histtype='step',color='darkblue',density=True)\n",
    "ax2.hist(hscy1['shear/psf_g2'][:]/2,bins=100,range=[-0.1,0.1],label='HSC-Y1',histtype='step',color='slategrey',density=True)\n",
    "ax2.hist(kids['shear/psf_g2'][:],bins=100,range=[-0.1,0.1],label='KiDS-1000',histtype='step',color='firebrick',density=True)\n",
    "ax2.set_xlabel('PSF g2')\n",
    "\n",
    "ax3.hist(y1['shear/mcal_psf_T_mean'][:][cutall],bins=150,range=[0,0.8],label='DES-Y1',histtype='step',color='royalblue',density=True)\n",
    "ax3.hist(y3['shear/mcal_psf_T_mean'][:],bins=150,range=[0,0.8],label='DES-Y3',histtype='step',color='darkblue',density=True)\n",
    "ax3.hist(hscy1['shear/psf_T_mean'][:],bins=150,range=[0,0.8],label='HSC-Y1',histtype='step',color='slategrey',density=True)\n",
    "ax3.hist(kids['shear/psf_T_mean'][:],bins=150,range=[0,0.8],label='KiDS-1000',histtype='step',color='firebrick',density=True)\n",
    "ax3.set_xlabel(r'$T_{PSF}$ [arcsec$^2$]')\n",
    "\n",
    "ax4.hist(y1['shear/mcal_psf_T_mean'][:][cutall]/y1['shear/mcal_T'][:][cutall],bins=150,range=[0,3],label='DES-Y1',histtype='step',color='royalblue',density=True)\n",
    "ax4.hist(y3['shear/mcal_psf_T_mean'][:]/y3['shear/mcal_T'][:],bins=150,range=[0,3],label='DES-Y3',histtype='step',color='darkblue',density=True)\n",
    "ax4.hist(hscy1['shear/psf_T_mean'][:]/hscy1['shear/T'][:],bins=150,range=[0,3],label='HSC-Y1',histtype='step',color='slategrey',density=True)\n",
    "ax4.hist(kids['shear/psf_T_mean'][:]/kids['shear/T'][:],bins=150,range=[0,3],label='KiDS-1000',histtype='step',color='firebrick',density=True)\n",
    "ax4.set_xlabel(r'$T_{PSF}/T_{gal}$ [arcsec$^2$]')\n",
    "ax4.legend(loc='center right')\n",
    "\n",
    "\n",
    "ax5.hist(y1['shear/mcal_s2n'][:][cutall],bins=150,range=[0,500],label='DES-Y1',histtype='step',color='royalblue',density=True)\n",
    "ax5.hist(y3['shear/mcal_s2n'][:],bins=150,range=[0,500],label='DES-Y3',histtype='step',color='darkblue',density=True)\n",
    "ax5.hist(hscy1['shear/s2n'][:][hscy1['shear/s2n'][:]>10],bins=150,range=[0,500],label='HSC-Y1',histtype='step',color='slategrey',density=True)\n",
    "ax5.hist(kids['shear/s2n'][:],bins=150,range=[0,500],label='KiDS-1000',histtype='step',color='firebrick',density=True)\n",
    "ax5.set_xlabel('SNR')\n",
    "\n",
    "\n",
    "ax6.hist(y1['shear/mcal_T'][:][cutall],bins=150,range=[0,3],label='DES-Y1',histtype='step',color='royalblue',density=True)\n",
    "ax6.hist(y3['shear/mcal_T'][:],bins=150,range=[0,3],label='DES-Y3',histtype='step',color='darkblue',density=True)\n",
    "ax6.hist(hscy1['shear/T'][:],bins=150,range=[0,3],label='HSC-Y1',histtype='step',color='slategrey',density=True)\n",
    "ax6.hist(kids['shear/T'][:],bins=150,range=[0,3],label='KiDS-1000',histtype='step',color='firebrick',density=True)\n",
    "ax6.set_xlabel(r'$T_{gal}$ [arcsec$^2$]')\n",
    "\n",
    "plt.savefig('plots/survey_hists.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d52a8681-4d68-4bd3-8a55-5af4bbbe8772",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y1.close()\n",
    "y3.close()\n",
    "hscy1.close()\n",
    "kids.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1efa8d07-9f66-4bf3-ae68-6e44efd082a4",
   "metadata": {},
   "source": [
    "star catalog histograms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b5f470b5-b47b-4de3-bc88-15d5efb13b7f",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "desy1 = h5py.File('/global/cfs/cdirs/lsst/groups/WL/projects/txpipe-sys-tests/des-y1/DES_psf_catalog.hdf5','r')\n",
    "desy3 = h5py.File('/global/cfs/cdirs/lsst/groups/WL/projects/txpipe-sys-tests/des-y3/DES_psf_y3_catalog.hdf5','r')\n",
    "hscy1 = h5py.File('/global/cfs/cdirs/lsst/groups/WL/projects/txpipe-sys-tests/hscy1/star_cat_FULLCAT.h5','r')\n",
    "kids = h5py.File('/global/cfs/cdirs/lsst/groups/WL/projects/txpipe-sys-tests/kids/kids1000_psf_catalog.h5','r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "079c2942-74dd-4f96-9940-4cbd80565b94",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KeysViewHDF5 ['calib_psf_reserved', 'calib_psf_used', 'dec', 'extendedness', 'fov_x', 'fov_y', 'measured_T', 'measured_T_n', 'measured_e1', 'measured_e2', 'model_T', 'model_e1', 'model_e2', 'ra']>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kids['stars'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d0c15754-c91f-4b73-bf01-ffb4d3c3fa8b",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(3,1, sharex=True,figsize=(10,15))\n",
    "ax1,ax2,ax3 = axs.flatten()\n",
    "\n",
    "ax1.hist((desy1['stars/measured_T'][:]- desy1['stars/model_T'][:])/desy1['stars/model_T'][:],bins=150,range=[-0.1,0.1],histtype='step',color='royalblue',density=True)\n",
    "ax1.hist((desy3['stars/measured_T'][:]- desy3['stars/model_T'][:])/desy3['stars/model_T'][:],bins=150,range=[-0.1,0.1],histtype='step',color='darkblue',density=True)\n",
    "ax1.hist((hscy1['stars/measured_T'][:]- hscy1['stars/model_T'][:])/hscy1['stars/model_T'][:],bins=150,range=[-0.1,0.1],histtype='step',color='slategrey',density=True)\n",
    "ax1.hist((kids['stars/measured_T'][:]- kids['stars/model_T'][:])/kids['stars/model_T'][:],bins=150,range=[-0.1,0.1],histtype='step',color='firebrick',density=True)\n",
    "ax1.set_xlabel(r'$dT_{PSF}/T_{PSF}$')\n",
    "\n",
    "ax2.hist((desy1['stars/measured_e1'][:]- desy1['stars/model_e1'][:]),bins=150,range=[-0.1,0.1],histtype='step',color='royalblue',label='DES-Y1',density=True)\n",
    "ax2.hist((desy3['stars/measured_e1'][:]- desy3['stars/model_e1'][:]),bins=150,range=[-0.1,0.1],histtype='step',color='darkblue',label='DES-Y3',density=True)\n",
    "ax2.hist((hscy1['stars/measured_e1'][:]- hscy1['stars/model_e1'][:]),bins=150,range=[-0.1,0.1],histtype='step',color='slategrey',label='HSC_Y1',density=True)\n",
    "ax2.hist((kids['stars/measured_e1'][:]- kids['stars/model_e1'][:]),bins=150,range=[-0.1,0.1],histtype='step',color='firebrick',label='KiDS-1000',density=True)\n",
    "ax2.set_xlabel(r'$de_1$')\n",
    "ax2.legend(loc='center right')\n",
    "\n",
    "ax3.hist((desy1['stars/measured_e2'][:]- desy1['stars/model_e2'][:]),bins=150,range=[-0.1,0.1],histtype='step',color='royalblue',density=True)\n",
    "ax3.hist((desy3['stars/measured_e2'][:]- desy3['stars/model_e2'][:]),bins=150,range=[-0.1,0.1],histtype='step',color='darkblue',density=True)\n",
    "ax3.hist((hscy1['stars/measured_e2'][:]- hscy1['stars/model_e2'][:]),bins=150,range=[-0.1,0.1],histtype='step',color='slategrey',density=True)\n",
    "ax3.hist((kids['stars/measured_e2'][:]- kids['stars/model_e2'][:]),bins=150,range=[-0.1,0.1],histtype='step',color='firebrick',density=True)\n",
    "ax3.set_xlabel(r'$de_2$')\n",
    "\n",
    "plt.savefig('plots/psf_hists.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40c2dc7a-0196-4ad5-b366-5f49307b05fa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "desc-python",
   "language": "python",
   "name": "desc-python"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}