extract_clus_profile_pyproffit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Remember always to check the vignetted-corrected, bkg-subtracted images first before making sb profiles!\n",
    "(the image creating script is at the end of this file)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/stage/headat/yanling/xmm/reduction_230227/merged/2023-05-11'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# import pyproffit\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from astropy.io import fits\n",
    "\n",
    "workpath = '/stage/headat/yanling/xmm/reduction_230227/merged/2023-05-11'\n",
    "os.chdir(workpath)\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def DoubleBeta(x, beta, rc1, rc2, ratio, norm, const):\n",
    "    out = np.power(10,norm) * (np.power(1 + (x / rc1) **2, -3 * beta + 0.5) + ratio * np.power(1 + (x / rc2) **2, -3 * beta + 0.5)) + np.power(10,const)\n",
    "    return out\n",
    "def BetaModel(x, beta, rc, norm,const):\n",
    "    out = norm * np.power(1. + (x / rc) ** 2, -3. * beta + 0.5) + const\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta = 42.068108; rc1 = 10.888732/60; rc2 =  30.13499/60; ratio = 0.23852488; norm = 3.9734867e-05; const = 4.24e-07\n",
    "x = np.logspace(-3, 1, 100)\n",
    "y = DoubleBeta(x, beta, rc1, rc2, ratio, norm, const)\n",
    "\n",
    "plt.plot(x,y)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datapath = '/Users/eusracenorth/Documents/suzaku_A222-223/profile_230302'\n",
    "radec_lst = {'A222':[24.3941937, -12.9933436], \"A223\":[24.4830446, -12.8203439]}\n",
    "doublebeta_para = {'0.5-7':[0.6, 1, 7, 0.23852488, -4, -6],\n",
    " \"0.7-1.2\":[0.6, 1, 7, 0.2, -5, -6]}\n",
    "for ins in [ 'xmm']:#,'xmm','chandra'\n",
    "    for band in ['0.5-7']:#, \n",
    "        for clus in ['A222',  'A223']:\n",
    "            ra = radec_lst[clus][0]\n",
    "            dec = radec_lst[clus][1]\n",
    "            print(f'{ins}, {band}, {clus}, {ra}, {dec}')\n",
    "            path = f'{datapath}/{ins}'\n",
    "            if ins=='chandra':\n",
    "                # reg = f'{path}/wavdetect_merge_clean_withexp_src_fk5.reg'\n",
    "                img = f'{path}/images/{band}_band1_thresh.fits'\n",
    "                # exp = f'{path}/images/0.5-7_band1_thresh_srcmsk_expmap.fits'\n",
    "                exp = f'{path}/images/{band}_band1_thresh_srcmsk_{clus}filamsk_expmap_edgemsk.fits'\n",
    "                bkg = f'{path}/images/{band}_band1_nxb_scaled.fits'\n",
    "            elif ins=='xmm':\n",
    "                # reg = f'{path}/EPIC_src_edit_man_fk5.reg'\n",
    "                if band =='0.5-7':\n",
    "                    img = f'{path}/{band}/EPIC_cts.fits'\n",
    "                    # exp = f'{path}/{band}/EPIC_exp_srcmsk.fits'\n",
    "                    exp = f'{path}/{band}/EPIC_exp_{clus}filamsk_srcmsk.fits'\n",
    "                    bkg = f'{path}/{band}/EPIC_bkg.fits'\n",
    "                elif band =='0.7-1.2':\n",
    "                    img = f'{path}/{band}/EPIC_cts_700-1200.fits'\n",
    "                    exp = f'{path}/{band}/EPIC_exp_700-1200_{clus}filamsk_srcmsk.fits'\n",
    "                    bkg = f'{path}/{band}/EPIC_bkg_700-1200.fits'\n",
    "\n",
    "            # # for nw region only\n",
    "            # exp = f'{path}/0.5-7/EPIC_exp_A223_nw_xmm_exp.fits'\n",
    "            dat=pyproffit.Data(imglink=img,explink=exp,bkglink=bkg)\n",
    "            # fig = plt.figure(figsize=(6,6))\n",
    "            # s1=plt.subplot(221)\n",
    "            # plt.imshow(np.log10(dat.img),aspect='auto')\n",
    "            # print(dat.img.max(), dat.img.min())\n",
    "            # print(dat.bkg.max(), dat.bkg.min())\n",
    "            # print((dat.img-dat.bkg).min(), (dat.img-dat.bkg).max())\n",
    "            # s2=plt.subplot(222)\n",
    "            # plt.imshow(dat.exposure,aspect='auto')\n",
    "            \n",
    "            prof=pyproffit.Profile(dat, center_choice='custom_fk5', center_ra = ra, center_dec =dec, maxrad=20., binsize=6, binning='log')\n",
    "            prof.SBprofile(ellipse_ratio=1)#, angle_low=80, angle_high=140)\n",
    "\n",
    "            # ## Beta\n",
    "            # mod=pyproffit.Model(pyproffit.BetaModel)\n",
    "            # fitobj=pyproffit.Fitter(model=mod, method='cstat', profile=prof, beta=0.6, rc=1, norm=-2, bkg=-2, fitlow=0., fithigh=10.)\n",
    "\n",
    "            ### Double Beta\n",
    "            mod=pyproffit.Model(pyproffit.DoubleBeta)\n",
    "            beta, rc1,rc2, ratio, norm, bkg = doublebeta_para[band]\n",
    "            fitobj=pyproffit.Fitter(model=mod, method='cstat', profile=prof, beta = beta, rc1 = rc1, rc2 =  rc2, ratio = ratio, norm = norm, bkg=bkg, fitlow=0.1, fithigh=20.)\n",
    "\n",
    "\n",
    "            fitobj.Migrad()\n",
    "            prof.Plot(model=mod)\n",
    "            plt.title(f'{ins}, {band}, {clus}, {ra}, {dec}')\n",
    "\n",
    "            # # inspecting results\n",
    "            # # fitobj.minuit.minos()\n",
    "            # #fitobj.minuit.draw_mncontour('beta', 'rc', cl=(0.68, 0.9, 0.99))\n",
    "            os.makedirs(f'{datapath}/png', exist_ok=True)\n",
    "            plt.savefig(f'{datapath}/png/A223_nw_{ins}_{clus}_{band}.pdf', dpi=300)\n",
    "            # prof.Save(outfile=f\"/Users/eusracenorth/Documents/suzaku_A222-223/profile_230302/results_230305/{clus}_{ins}_{band}_doublebeta_profile_inclfila.fits\", model=mod)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot only emos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datapath = '/Users/eusracenorth/Documents/suzaku_A222-223/profile_230302/results_230422'\n",
    "radec_lst = {'A222':[24.3941937, -12.9933436], \"A223\":[24.4830446, -12.8203439]}\n",
    "doublebeta_para = {\"700-1200\":[0.6, 1, 7, 0.2, -3, -4], \"1200-2000\":[0.6, 1, 7, 0.2, -3, -4], \"2000-5000\":[0.6, 1, 7, 0.2, -2, -4], \"5000-7000\":[0.6, 1, 7, 0.2, -2, -4]}\n",
    "for ins in ['EMOS', 'EPIC']:#,'xmm','chandra'\n",
    "    for band in ['700-1200', '1200-2000', '2000-5000', '5000-7000']:#, \n",
    "        for clus in ['A222',  'A223']:\n",
    "            ra = radec_lst[clus][0]\n",
    "            dec = radec_lst[clus][1]\n",
    "            img = f'{ins}_cts_{band}.img'\n",
    "            exp = f'{ins}_exp_{band}_srcmsk_{clus}filamsk_expmap.fits'\n",
    "            bkg = f'{ins}_bkg_{band}.img'      \n",
    "\n",
    "            dat=pyproffit.Data(imglink=img, explink=exp, bkglink=bkg)\n",
    "            prof=pyproffit.Profile(dat, center_choice='custom_fk5', center_ra = ra, center_dec =dec, maxrad=20., binsize=6, binning='log')\n",
    "            prof.SBprofile(ellipse_ratio=1)#, angle_low=80, angle_high=140)\n",
    "            \n",
    "            ### Double Beta\n",
    "            mod=pyproffit.Model(pyproffit.DoubleBeta)\n",
    "            beta, rc1,rc2, ratio, norm, bkg = doublebeta_para[band]\n",
    "            fitobj=pyproffit.Fitter(model=mod, method='cstat', profile=prof, beta = beta, rc1 = rc1, rc2 =  rc2, ratio = ratio, norm = norm, bkg=bkg, fitlow=0.1, fithigh=20.)\n",
    "            \n",
    "            fitobj.Migrad()\n",
    "            prof.Plot(model=mod)\n",
    "            plt.title(f'{ins}, {band}, {clus}, {ra}, {dec}')\n",
    "\n",
    "            # # inspecting results\n",
    "            # # fitobj.minuit.minos()\n",
    "            # #fitobj.minuit.draw_mncontour('beta', 'rc', cl=(0.68, 0.9, 0.99))\n",
    "            os.makedirs(f'{datapath}/png', exist_ok=True)\n",
    "            plt.savefig(f'{datapath}/png/{ins}_{clus}_{band}.pdf', dpi=300)\n",
    "            prof.Save(outfile=f\"/Users/eusracenorth/Documents/suzaku_A222-223/profile_230302/results_230422/{clus}_{ins}_{band}_doublebeta_profile_inclfila.fits\", model=mod)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot obs and ins separately\n",
    "1. mask the exp edge with 10arcmin circle centered on aimpoint\n",
    "2. do the fitting and plot separately\n",
    "3. merge\n",
    "4. fit and plot the merged images\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# make masks for expmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "today = '2023-05-11'\n",
    "workpath = f'/stage/headat/yanling/xmm/reduction_230227/merged/2023-05-11'\n",
    "# os.makedirs(f'{workpath}/masks', exist_ok=True)\n",
    "# os.system(f'cp {workpath}/../2023-05-04/masks/*reg {workpath}/masks')\n",
    "f = open(f'{workpath}/masks/make_masks_{today}.sh', 'w')\n",
    "for obs in [101,201]:\n",
    "    for ins in ['EMOS1', 'EMOS2', 'EPN']: # \n",
    "        for type in ['cts', 'bkg', 'exp']:\n",
    "            files = glob(f'{workpath}/{ins}_exp_obs{obs}*.img')\n",
    "            for file in files:\n",
    "                f.write(f\"ftimgcalc {workpath}/masks/{file.split('/')[-1]}_edgemsk.fits 'regfilter(\\\"{workpath}/masks/{ins}_obs{obs}_edgemsk.reg\\\",A.P1,A.P2) ? (1):(0)' a={file} clobber=yes\\n\")\n",
    "                f.write(f\"ftimgcalc {workpath}/masks/{file.split('/')[-1]}_srcmsk.fits 'regfilter(\\\"{workpath}/masks/EPIC_src_edit_man_fk5_1e-6.reg\\\",A.P1,A.P2) ? (0):(1)' a={file} clobber=yes\\n\")\n",
    "                for clus in ['A222', 'A223']:\n",
    "                    f.write(f\"ftimgcalc {workpath}/masks/{file.split('/')[-1]}_{clus}_filamsk.fits 'regfilter(\\\"{workpath}/masks/{clus}_filamsk_xmm.reg\\\",A.P1,A.P2) ? (0):(1)' a={file} clobber=yes\\n\")\n",
    "\n",
    "f.close()\n",
    "\n",
    "# sh masks/make_masks*.sh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mask edge, filamsk, sources from the data, bkg, exp maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "\n",
    "today = '2023-05-11'\n",
    "f = open(f'masks/mask_data_bkg_{today}.sh', 'w')\n",
    "f.write('#!/bin/bash\\n')\n",
    "for obs in [101,201]:\n",
    "    for ins in ['EMOS1', 'EMOS2', 'EPN']: # \n",
    "        for type in ['cts', 'bkg', 'exp']:\n",
    "            files = glob(f'{ins}_{type}_obs{obs}*.img')\n",
    "            for file in files:\n",
    "                f.write(f\"farith masks/{file.split('/')[-1]}_edgemsk.fits {file} {file.split('/')[-1].split('.')[0]}_{today}_edgemsk.fits '*' clobber=yes\\n\")\n",
    "                f.write(f\"farith {file.split('/')[-1].split('.')[0]}_{today}_edgemsk.fits masks/{file.split('/')[-1]}_srcmsk.fits {file.split('/')[-1].split('.')[0]}_{today}_edgemsk_srcmsk.fits '*' clobber=yes\\n\")\n",
    "                for clus in ['A222', 'A223']:\n",
    "                    f.write(f\"farith masks/{file.split('/')[-1]}_{clus}_filamsk.fits {file.split('/')[-1].split('.')[0]}_{today}_edgemsk_srcmsk.fits {file.split('/')[-1].split('.')[0]}_{today}_edgemsk_srcmsk_{clus}_filamsk.fits '*' clobber=yes\\n\")\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scale PN expmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from PIMMS\n",
    "PN_scaling  = {'700-1200':[2.137, 7.92] , '700-2000':[2.024, 6.374] , \n",
    "'700-7000': [0.9817,2.916], '2000-7000': [0.5469, 1.474], '5000-7000': [2.547, 8.651], \n",
    "'2000-5000': [0.7015, 1.797], '1200-7000': [0.812, 2.181], '1200-2000': [1.937, 5.183]}\n",
    "\n",
    "PN_scales = {}\n",
    "for band in PN_scaling.keys():\n",
    "    PN_scales[band] = PN_scaling[band][1]/PN_scaling[band][0]\n",
    "print(PN_scales)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('scale_PN_expmap.sh', 'w')\n",
    "for obs in [101,201]:\n",
    "    files = glob(f'EPN_exp_obs{obs}_*-*_{today}*.fits')\n",
    "    for file in files:\n",
    "        f.write(f'fcarith {file} {PN_scales[band]} {file.split(\".\")[0]}_scaled.fits \"*\" clobber=yes\\n')\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use zhtools to merge exp images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(f'merge_inst_zhtools.sh', 'w')\n",
    "date2 = '2023-05-05'\n",
    "for band in ['700-1200', '1200-2000', '2000-5000', '5000-7000']:\n",
    "    for type in ['edgemsk', 'edgemsk_srcmsk', 'edgemsk_srcmsk_A222_filamsk', 'edgemsk_srcmsk_A223_filamsk']:\n",
    "        for file in files:\n",
    "            f.write(f'addimages EMOS1_exp_obs101_{band}_{date2}_{today}_{type}.fits EMOS1_exp_obs201_{band}_{date2}_{today}_{type}.fits EMOS2_exp_obs101_{band}_{date2}_{today}_{type}.fits EMOS2_exp_obs201_{band}_{date2}_{today}_{type}.fits EMOS_exp_{band}_{today}_{type}.fits\\n')\n",
    "            f.write(f'addimages EPN_exp_obs101_{band}_{today}_{type}_scaled.fits EPN_exp_obs201_{band}_{today}_{type}_scaled.fits EPN_exp_{band}_{today}_{type}.fits\\n')\n",
    "            f.write(f'addimages EMOS_exp_{band}_{today}_{type}.fits EPN_exp_{band}_{today}_{type}.fits EPIC_exp_{band}_{today}_{type}.fits\\n')\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "use zhtools to merge bands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(f'merge_bands_zhtools.sh', 'w')\n",
    "for type in ['edgemsk', 'edgemsk_srcmsk', 'edgemsk_srcmsk_A222_filamsk', 'edgemsk_srcmsk_A223_filamsk']:\n",
    "    for inst in ['EMOS', 'EPN', 'EPIC']:\n",
    "        for type in ['counts', 'bkg']:\n",
    "            f.write(f'addimages {inst}_{type}_1200-2000_{today}.fits {inst}_{type}_2000-5000_{today}.fits {inst}_{type}_5000-7000_{today}.fits {inst}_{type}_1200-7000_{today}.fits expand=no\\n')\n",
    "            f.write(f'addimages {inst}_{type}_700-1200_{today}.fits {inst}_{type}_1200-2000_{today}.fits {inst}_{type}_700-2000_{today}.fits expand=no\\n')\n",
    "            f.write(f'addimages {inst}_{type}_2000-5000_{today}.fits {inst}_{type}_5000-7000_{today}.fits {inst}_{type}_2000-7000_{today}.fits expand=no\\n')\n",
    "f.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(f'merge_bands_zhtools_exp.sh', 'w')\n",
    "for type in ['edgemsk', 'edgemsk_srcmsk', 'edgemsk_srcmsk_A222_filamsk', 'edgemsk_srcmsk_A223_filamsk']:\n",
    "    for inst in ['EMOS', 'EPN', 'EPIC']:\n",
    "        for type in ['exp']:\n",
    "            for type1 in ['edgemsk', 'edgemsk_srcmsk', 'edgemsk_srcmsk_A222_filamsk', 'edgemsk_srcmsk_A223_filamsk']:\n",
    "                f.write(f'addimages {inst}_{type}_1200-2000_{today}_{type1}.fits {inst}_{type}_2000-5000_{today}_{type1}.fits {inst}_{type}_5000-7000_{today}_{type1}.fits {inst}_{type}_1200-7000_{today}_{type1}.fits expand=no\\n')\n",
    "                f.write(f'addimages {inst}_{type}_700-1200_{today}_{type1}.fits {inst}_{type}_1200-2000_{today}_{type1}.fits {inst}_{type}_700-2000_{today}_{type1}.fits expand=no\\n')\n",
    "                f.write(f'addimages {inst}_{type}_2000-5000_{today}_{type1}.fits {inst}_{type}_5000-7000_{today}_{type1}.fits {inst}_{type}_2000-7000_{today}_{type1}.fits expand=no\\n')\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "alter background file name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bkgfiles = glob('*scaled*.img')\n",
    "for file in bkgfiles:\n",
    "    if '-' in file:\n",
    "        band = file.split('_')[-2]\n",
    "        obs = file.split('_')[5]\n",
    "        if 'mos1' in file:\n",
    "            os.system(f'cp {file} EMOS1_bkg_{obs}_{band}.fits')\n",
    "        elif 'mos2' in file:\n",
    "            os.system(f'cp {file} EMOS2_bkg_{obs}_{band}.fits')\n",
    "        elif 'pn' in file:\n",
    "            band = file.split('_')[-3]\n",
    "            obs = file.split('_')[5]\n",
    "            os.system(f'cp {file} EPN_bkg_{obs}_{band}.fits')\n",
    "        else:\n",
    "            raise ValueError('wrong types!')\n",
    "\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot obs, ins, bands separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "today = '2023-05-05'\n",
    "maxrad = 10.\n",
    "# datapath = '/Users/eusracenorth/Documents/suzaku_A222-223/profile_230302/results_230422'\n",
    "# datapath = f'/Users/eusracenorth/Documents/suzaku_A222-223/xmm_data/merged/{today}'\n",
    "os.chdir(datapath)\n",
    "radec_lst = {'A222':[24.3941937, -12.9933436], \"A223\":[24.4830446, -12.8203439]}\n",
    "doublebeta_para = {\"700-1200\":[0.6, 1, 7, 0.2, -2, -4], \"1200-2000\":[0.6, 1, 7, 0.2, -1, -4], \"2000-5000\":[0.6, 1, 7, 0.2, -2, -4], \"5000-7000\":[0.6, 1, 7, 0.2, -2, -4]}\n",
    "for ins in ['EMOS1', 'EMOS2', 'EPN']:#,'xmm','chandra'\n",
    "    for obs in ['101', '201']:\n",
    "        for band in ['700-1200', '700-7000']:#, '1200-2000', '2000-5000', '5000-7000']:#, \n",
    "            for clus in ['A222',  'A223']:\n",
    "                ra = radec_lst[clus][0]\n",
    "                dec = radec_lst[clus][1]\n",
    "                # img = f'{ins}_counts_obs{obs}_{band}.img'\n",
    "                # exp = f'{ins}_exp_obs{obs}_edgemsk_srcmsk_{clus}_filamsk.fits'\n",
    "                # bkg = f'{ins}_bkg_obs{obs}_{band}.fits'\n",
    "                img = f'{ins}_counts_obs{obs}_{band}_{today}.img'\n",
    "                exp = f'{ins}_exp_obs{obs}_{band}_{today}_edgemsk_srcmsk.fits'\n",
    "                # exp = f'{ins}_exp_obs{obs}_{band}_{today}_edgemsk_srcmsk_{clus}_filamsk.fits'\n",
    "                bkg = glob(f'../2023-05-07-bkg/{ins}_obs{obs}_bkg_clean_2023-05-07_{band}_scaled*.img')[0]\n",
    "\n",
    "                dat=pyproffit.Data(imglink=img, explink=exp, bkglink=bkg)\n",
    "                prof=pyproffit.Profile(dat, center_choice='custom_fk5', center_ra = ra, center_dec =dec, maxrad=maxrad, binsize=6, binning='log')\n",
    "                prof.SBprofile(ellipse_ratio=1)#, angle_low=80, angle_high=140)\n",
    "                \n",
    "                ### Double Beta\n",
    "                mod=pyproffit.Model(pyproffit.DoubleBeta)\n",
    "                beta, rc1,rc2, ratio, norm, bkg = doublebeta_para[band]\n",
    "                fitobj=pyproffit.Fitter(model=mod, method='cstat', profile=prof, beta = beta, rc1 = rc1, rc2 =  rc2, ratio = ratio, norm = norm, bkg=bkg, fitlow=0.1, fithigh=maxrad)\n",
    "                \n",
    "                fitobj.Migrad()\n",
    "                prof.Plot(model=mod)\n",
    "                plt.title(f'{ins}, {band}, {clus}, {ra}, {dec}\\n {mod.parnames} \\n {mod.params}  ')\n",
    "\n",
    "                # # inspecting results\n",
    "                # # fitobj.minuit.minos()\n",
    "                # #fitobj.minuit.draw_mncontour('beta', 'rc', cl=(0.68, 0.9, 0.99))\n",
    "                os.makedirs(f'{datapath}/png/{int(maxrad)}am/{today}', exist_ok=True)\n",
    "                os.makedirs(f'{datapath}/profiles/{int(maxrad)}am/{today}', exist_ok=True)\n",
    "                plt.savefig(f'{datapath}/png/{int(maxrad)}am/{today}/{ins}_{clus}_{band}_{obs}_inclfila.png')\n",
    "                prof.Save(outfile=f\"{datapath}/profiles/{int(maxrad)}am/{today}/{clus}_{ins}_{band}_obs{obs}_doublebeta_profile_inclfila.fits\", model=mod)\n",
    "\n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot merged obs and ins of combined bands"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "merge masked exp maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# error: can't use astropy fits or farith to sum up, only can use farith or emosaic. \n",
    "# Since they can't align coordinates of different observations and instruments. \n",
    "# The data image array size will also be different.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "create profiles for merged or separate bands, merged obs and merged instruments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPIC_bkg_700-1200_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  354.490737413121 279.07162092803856\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 1294                       │              Nfcn = 991              │\n",
      "│ EDM = 3.1 (Goal: 0.0002)         │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           ABOVE call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │APPROXIMATE│  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │   6.32    │   0.30    │            │            │         │         │       │\n",
      "│ 1 │ rc1   │   2.92    │   0.09    │            │            │         │         │       │\n",
      "│ 2 │ rc2   │   12.6    │    0.4    │            │            │         │         │       │\n",
      "│ 3 │ ratio │   0.304   │   0.006   │            │            │         │         │       │\n",
      "│ 4 │ norm  │  -2.240   │   0.008   │            │            │         │         │       │\n",
      "│ 5 │ bkg   │  -4.299   │   0.021   │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬───────────────────────────────────────────────────────┐\n",
      "│       │     beta      rc1      rc2    ratio     norm      bkg │\n",
      "├───────┼───────────────────────────────────────────────────────┤\n",
      "│  beta │   0.0928    0.026     0.11 -0.06e-3 -0.12e-3   0.5e-3 │\n",
      "│   rc1 │    0.026  0.00786    0.032        0 -0.15e-3        0 │\n",
      "│   rc2 │     0.11    0.032    0.142 -0.20e-3 -0.24e-3  -0.2e-3 │\n",
      "│ ratio │ -0.06e-3        0 -0.20e-3 3.55e-05 -0.04e-3 -0.01e-3 │\n",
      "│  norm │ -0.12e-3 -0.15e-3 -0.24e-3 -0.04e-3 6.18e-05  0.02e-3 │\n",
      "│   bkg │   0.5e-3        0  -0.2e-3 -0.01e-3  0.02e-3 0.000441 │\n",
      "└───────┴───────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 1293.5 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 25.3628\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: FITSFixedWarning: RADECSYS= 'FK5 ' / World coord. system for this file \n",
      "the RADECSYS keyword is deprecated, use RADESYSa. [astropy.wcs.wcs]\n",
      "WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 54269.336065 from DATE-OBS.\n",
      "Set MJD-END to 54270.141181 from DATE-END'. [astropy.wcs.wcs]\n",
      "/home/yanling/.local/lib/python3.9/site-packages/pyproffit/fitting.py:58: VisibleDeprecationWarning: make_func_code is deprecated: Use of `func_code` attribute to declare parameters is deprecated. Use `_parameters` instead, which is a dict of parameter names to limits.\n",
      "  self.func_code = iminuit.util.make_func_code(iminuit.util.describe(self.model)[1:])\n",
      "/home/yanling/.local/lib/python3.9/site-packages/iminuit/util.py:1297: VisibleDeprecationWarning: Using the `func_code` attribute to dynamically declare parameter names is deprecated, use the attribute `_parameters` instead (a dict of strings to limits)\n",
      "  warnings.warn(\n",
      "WARNING: VerifyWarning: Keyword name 'ROT_ANGLE' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created. [astropy.io.fits.card]\n",
      "WARNING: VerifyWarning: Keyword name 'ELL_RATIO' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created. [astropy.io.fits.card]\n",
      "WARNING: VerifyWarning: Card is too long, comment will be truncated. [astropy.io.fits.card]\n",
      "/home/yanling/.local/lib/python3.9/site-packages/pyproffit/models.py:59: RuntimeWarning: overflow encountered in power\n",
      "  c2 = np.power(10., bkg)\n",
      "/home/yanling/.local/lib/python3.9/site-packages/pyproffit/fitting.py:74: RuntimeWarning: overflow encountered in square\n",
      "  chi2 = np.sum((self.y[reg][nonz] - ym[reg][nonz])**2/self.dy[reg][nonz]**2)\n",
      "/home/yanling/.local/lib/python3.9/site-packages/pyproffit/models.py:56: RuntimeWarning: overflow encountered in power\n",
      "  comp1 = np.power(1. + (x / rc1) ** 2, -3. * beta + 0.5)\n",
      "/home/yanling/.local/lib/python3.9/site-packages/pyproffit/models.py:57: RuntimeWarning: overflow encountered in power\n",
      "  comp2 = np.power(1. + (x / rc2) ** 2, -3. * beta + 0.5)\n",
      "/home/yanling/.local/lib/python3.9/site-packages/pyproffit/fitting.py:74: RuntimeWarning: overflow encountered in divide\n",
      "  chi2 = np.sum((self.y[reg][nonz] - ym[reg][nonz])**2/self.dy[reg][nonz]**2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPIC_bkg_700-1200_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  276.5272248028996 434.76259126509797\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 782.1                      │              Nfcn = 857              │\n",
      "│ EDM = 4.04e+70 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ ABOVE EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │    0e3    │    7e3    │            │            │         │         │       │\n",
      "│ 1 │ rc1   │    0e3    │    9e3    │            │            │         │         │       │\n",
      "│ 2 │ rc2   │    0e3    │    5e3    │            │            │         │         │       │\n",
      "│ 3 │ ratio │    7e3    │    4e3    │            │            │         │         │       │\n",
      "│ 4 │ norm  │   -0e3    │    9e3    │            │            │         │         │       │\n",
      "│ 5 │ bkg   │   -0e3    │    9e3    │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬───────────────────────────────────────────────────────┐\n",
      "│       │     beta      rc1      rc2    ratio     norm      bkg │\n",
      "├───────┼───────────────────────────────────────────────────────┤\n",
      "│  beta │ 5.37e+07   0.01e9 -0.037e9       -0       -0        0 │\n",
      "│   rc1 │   0.01e9 7.92e+07  0.008e9       -0        0       -0 │\n",
      "│   rc2 │ -0.037e9  0.008e9 2.83e+07       -0 -0.003e9  0.003e9 │\n",
      "│ ratio │       -0       -0       -0 1.79e+07        0       -0 │\n",
      "│  norm │       -0        0 -0.003e9        0 8.02e+07        0 │\n",
      "│   bkg │        0       -0  0.003e9       -0        0 8.03e+07 │\n",
      "└───────┴───────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 782.09 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 15.3351\n",
      "EPIC_bkg_1200-7000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  354.490737413121 279.07162092803856\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 425.6                      │             Nfcn = 1141              │\n",
      "│ EDM = 9.62e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           ABOVE call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │APPROXIMATE│NOT pos. def.│ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │ 4.0548e-1 │ 0.0000e-1 │            │            │         │         │       │\n",
      "│ 1 │ rc1   │ 3.8207e-1 │ 0.0000e-1 │            │            │         │         │       │\n",
      "│ 2 │ rc2   │-8.8301e-2 │ 0.0000e-2 │            │            │         │         │       │\n",
      "│ 3 │ ratio │  -1.8553  │  0.0000   │            │            │         │         │       │\n",
      "│ 4 │ norm  │  -1.6764  │  0.0000   │            │            │         │         │       │\n",
      "│ 5 │ bkg   │ -1.0611e1 │ 0.0000e1  │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "Best fit chi-squared: 425.589 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 8.34488\n",
      "EPIC_bkg_1200-7000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  276.5272248028996 434.76259126509797\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 100.5                      │              Nfcn = 908              │\n",
      "│ EDM = 0.000187 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │   0.71    │   0.14    │            │            │         │         │       │\n",
      "│ 1 │ rc1   │    1.6    │    0.7    │            │            │         │         │       │\n",
      "│ 2 │ rc2   │   0.54    │   0.17    │            │            │         │         │       │\n",
      "│ 3 │ ratio │    5.0    │    2.3    │            │            │         │         │       │\n",
      "│ 4 │ norm  │   -2.52   │   0.21    │            │            │         │         │       │\n",
      "│ 5 │ bkg   │   -3.91   │   0.04    │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬─────────────────────────────────────────────────┐\n",
      "│       │    beta     rc1     rc2   ratio    norm     bkg │\n",
      "├───────┼─────────────────────────────────────────────────┤\n",
      "│  beta │  0.0194   0.096   0.023   0.247  -0.023  0.0051 │\n",
      "│   rc1 │   0.096   0.503   0.122     1.5   -0.14  0.0229 │\n",
      "│   rc2 │   0.023   0.122    0.03   0.360  -0.034  0.0056 │\n",
      "│ ratio │   0.247     1.5   0.360    5.27   -0.47  0.0471 │\n",
      "│  norm │  -0.023   -0.14  -0.034   -0.47  0.0429 -0.0046 │\n",
      "│   bkg │  0.0051  0.0229  0.0056  0.0471 -0.0046 0.00198 │\n",
      "└───────┴─────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 100.513 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 1.97084\n",
      "EPIC_bkg_2000-7000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  354.490737413121 279.07162092803856\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 100.5                      │              Nfcn = 988              │\n",
      "│ EDM = 0.00204 (Goal: 0.0002)     │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           ABOVE call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │APPROXIMATE│  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │    60     │    40     │            │            │         │         │       │\n",
      "│ 1 │ rc1   │    10     │     4     │            │            │         │         │       │\n",
      "│ 2 │ rc2   │    40     │    13     │            │            │         │         │       │\n",
      "│ 3 │ ratio │   0.303   │   0.023   │            │            │         │         │       │\n",
      "│ 4 │ norm  │  -2.323   │   0.026   │            │            │         │         │       │\n",
      "│ 5 │ bkg   │  -3.744   │   0.029   │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬─────────────────────────────────────────────────────────────┐\n",
      "│       │      beta       rc1       rc2     ratio      norm       bkg │\n",
      "├───────┼─────────────────────────────────────────────────────────────┤\n",
      "│  beta │  1.38e+03       132    0.47e3  237.4e-3 -395.9e-3  293.4e-3 │\n",
      "│   rc1 │       132      12.7        45   21.0e-3  -39.9e-3   24.5e-3 │\n",
      "│   rc2 │    0.47e3        45       163   69.9e-3 -139.5e-3   83.9e-3 │\n",
      "│ ratio │  237.4e-3   21.0e-3   69.9e-3   0.00054   -0.5e-3         0 │\n",
      "│  norm │ -395.9e-3  -39.9e-3 -139.5e-3   -0.5e-3  0.000652         0 │\n",
      "│   bkg │  293.4e-3   24.5e-3   83.9e-3         0         0   0.00087 │\n",
      "└───────┴─────────────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 100.465 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 1.9699\n",
      "EPIC_bkg_2000-7000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  276.5272248028996 434.76259126509797\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 73.99                      │              Nfcn = 982              │\n",
      "│ EDM = 3.61e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           ABOVE call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │APPROXIMATE│NOT pos. def.│ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │ 6.6848e-1 │ 0.0000e-1 │            │            │         │         │       │\n",
      "│ 1 │ rc1   │  1.3618   │  0.0000   │            │            │         │         │       │\n",
      "│ 2 │ rc2   │ -4.605e-1 │ 0.000e-1  │            │            │         │         │       │\n",
      "│ 3 │ ratio │  4.1065   │  0.0000   │            │            │         │         │       │\n",
      "│ 4 │ norm  │  -2.5957  │  0.0000   │            │            │         │         │       │\n",
      "│ 5 │ bkg   │  -3.8452  │  0.0000   │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "Best fit chi-squared: 73.9942 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 1.45087\n",
      "EPIC_bkg_700-2000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  354.490737413121 279.07162092803856\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 2945                       │              Nfcn = 981              │\n",
      "│ EDM = 4.84e+04 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           ABOVE call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │APPROXIMATE│  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │   0.622   │   0.034   │            │            │         │         │       │\n",
      "│ 1 │ rc1   │    2.7    │    0.4    │            │            │         │         │       │\n",
      "│ 2 │ rc2   │   0.58    │   0.07    │            │            │         │         │       │\n",
      "│ 3 │ ratio │    4.5    │    0.9    │            │            │         │         │       │\n",
      "│ 4 │ norm  │   -2.77   │   0.08    │            │            │         │         │       │\n",
      "│ 5 │ bkg   │ -9.2593e1 │ 0.0000e1  │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬─────────────────────────────────────────────────┐\n",
      "│       │    beta     rc1     rc2   ratio    norm     bkg │\n",
      "├───────┼─────────────────────────────────────────────────┤\n",
      "│  beta │ 0.00113  0.0147  0.0024  0.0293 -0.0025  0.0000 │\n",
      "│   rc1 │  0.0147   0.193   0.032    0.39  -0.033    0.00 │\n",
      "│   rc2 │  0.0024   0.032 0.00546   0.064  -0.006   0.000 │\n",
      "│ ratio │  0.0293    0.39   0.064   0.799  -0.067     0.0 │\n",
      "│  norm │ -0.0025  -0.033  -0.006  -0.067 0.00576   0.000 │\n",
      "│   bkg │  0.0000    0.00   0.000     0.0   0.000       0 │\n",
      "└───────┴─────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 2944.69 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 57.7391\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yanling/.local/lib/python3.9/site-packages/pyproffit/models.py:58: RuntimeWarning: overflow encountered in power\n",
      "  n2 = np.power(10., norm)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPIC_bkg_700-2000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  276.5272248028996 434.76259126509797\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 865.2                      │              Nfcn = 825              │\n",
      "│ EDM = 2.04e+14 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ ABOVE EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │     0     │    80     │            │            │         │         │       │\n",
      "│ 1 │ rc1   │     0     │    80     │            │            │         │         │       │\n",
      "│ 2 │ rc2   │    -0     │    80     │            │            │         │         │       │\n",
      "│ 3 │ ratio │   437.5   │    1.0    │            │            │         │         │       │\n",
      "│ 4 │ norm  │    -0     │    80     │            │            │         │         │       │\n",
      "│ 5 │ bkg   │    -0     │    80     │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬───────────────────────────────────────────────────────┐\n",
      "│       │     beta      rc1      rc2    ratio     norm      bkg │\n",
      "├───────┼───────────────────────────────────────────────────────┤\n",
      "│  beta │ 6.68e+03      0e3      0e3      0.4     -0e3      0e3 │\n",
      "│   rc1 │      0e3 6.68e+03      0e3      1.6     -0e3      0e3 │\n",
      "│   rc2 │      0e3      0e3 6.68e+03     -0.2     -0e3     -0e3 │\n",
      "│ ratio │      0.4      1.6     -0.2        1     -1.4      1.2 │\n",
      "│  norm │     -0e3     -0e3     -0e3     -1.4 6.68e+03     -0e3 │\n",
      "│   bkg │      0e3      0e3     -0e3      1.2     -0e3 6.68e+03 │\n",
      "└───────┴───────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 865.156 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 16.9638\n",
      "EMOS_bkg_700-1200_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  354.490737413121 279.07162092803856\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 1120                       │              Nfcn = 992              │\n",
      "│ EDM = 3.11 (Goal: 0.0002)        │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           ABOVE call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │APPROXIMATE│  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │   11.8    │    0.9    │            │            │         │         │       │\n",
      "│ 1 │ rc1   │   16.7    │    0.7    │            │            │         │         │       │\n",
      "│ 2 │ rc2   │   3.93    │   0.17    │            │            │         │         │       │\n",
      "│ 3 │ ratio │   4.04    │   0.10    │            │            │         │         │       │\n",
      "│ 4 │ norm  │  -2.745   │   0.006   │            │            │         │         │       │\n",
      "│ 5 │ bkg   │   -13.6   │    1.2    │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬─────────────────────────────────────────────────────────────┐\n",
      "│       │      beta       rc1       rc2     ratio      norm       bkg │\n",
      "├───────┼─────────────────────────────────────────────────────────────┤\n",
      "│  beta │     0.737       0.6     0.145    -0.003 -0.969e-3      -1.0 │\n",
      "│   rc1 │       0.6     0.483     0.118     0.001 -1.282e-3      -0.8 │\n",
      "│   rc2 │     0.145     0.118    0.0298    -0.001 -0.321e-3    -0.190 │\n",
      "│ ratio │    -0.003     0.001    -0.001    0.0104 -0.284e-3     0.005 │\n",
      "│  norm │ -0.969e-3 -1.282e-3 -0.321e-3 -0.284e-3  3.43e-05  0.809e-3 │\n",
      "│   bkg │      -1.0      -0.8    -0.190     0.005  0.809e-3       1.4 │\n",
      "└───────┴─────────────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 1119.67 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 21.9542\n",
      "EMOS_bkg_700-1200_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  276.5272248028996 434.76259126509797\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 984.7                      │              Nfcn = 995              │\n",
      "│ EDM = 0.154 (Goal: 0.0002)       │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           ABOVE call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │APPROXIMATE│  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │   1.27    │   0.08    │            │            │         │         │       │\n",
      "│ 1 │ rc1   │   3.25    │   0.20    │            │            │         │         │       │\n",
      "│ 2 │ rc2   │   1.04    │   0.06    │            │            │         │         │       │\n",
      "│ 3 │ ratio │   5.96    │   0.31    │            │            │         │         │       │\n",
      "│ 4 │ norm  │  -2.768   │   0.022   │            │            │         │         │       │\n",
      "│ 5 │ bkg   │  -15.278  │   0.009   │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬───────────────────────────────────────────────────────┐\n",
      "│       │     beta      rc1      rc2    ratio     norm      bkg │\n",
      "├───────┼───────────────────────────────────────────────────────┤\n",
      "│  beta │  0.00645    0.015   0.0045    0.002  -0.5e-3 -0.51e-3 │\n",
      "│   rc1 │    0.015   0.0409   0.0117     0.02  -2.4e-3 -0.84e-3 │\n",
      "│   rc2 │   0.0045   0.0117  0.00353   0.0050  -0.7e-3 -0.27e-3 │\n",
      "│ ratio │    0.002     0.02   0.0050   0.0972  -6.4e-3  1.48e-3 │\n",
      "│  norm │  -0.5e-3  -2.4e-3  -0.7e-3  -6.4e-3 0.000493 -0.08e-3 │\n",
      "│   bkg │ -0.51e-3 -0.84e-3 -0.27e-3  1.48e-3 -0.08e-3 8.63e-05 │\n",
      "└───────┴───────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 984.668 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 19.3072\n",
      "EMOS_bkg_1200-7000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  354.490737413121 279.07162092803856\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 130.8                      │              Nfcn = 989              │\n",
      "│ EDM = 2.3e+62 (Goal: 0.0002)     │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           ABOVE call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │APPROXIMATE│  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │   7.79    │   0.21    │            │            │         │         │       │\n",
      "│ 1 │ rc1   │   13.8    │    0.4    │            │            │         │         │       │\n",
      "│ 2 │ rc2   │   3.37    │   0.12    │            │            │         │         │       │\n",
      "│ 3 │ ratio │   3.95    │   0.21    │            │            │         │         │       │\n",
      "│ 4 │ norm  │  -2.606   │   0.017   │            │            │         │         │       │\n",
      "│ 5 │ bkg   │ -1.8606e1 │    nan    │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬─────────────────────────────────────────────────────────────┐\n",
      "│       │      beta       rc1       rc2     ratio      norm       bkg │\n",
      "├───────┼─────────────────────────────────────────────────────────────┤\n",
      "│  beta │     0.046      0.08     0.021      0.00  -1.81e-3   0.4e-15 │\n",
      "│   rc1 │      0.08      0.17     0.044      0.02  -5.51e-3   0.9e-15 │\n",
      "│   rc2 │     0.021     0.044    0.0134     0.000  -1.44e-3         0 │\n",
      "│ ratio │      0.00      0.02     0.000    0.0436  -1.73e-3         0 │\n",
      "│  norm │  -1.81e-3  -5.51e-3  -1.44e-3  -1.73e-3  0.000274         0 │\n",
      "│   bkg │   0.4e-15   0.9e-15         0         0         0 -1.78e-15 │\n",
      "└───────┴─────────────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 130.826 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 2.56521\n",
      "EMOS_bkg_1200-7000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  276.5272248028996 434.76259126509797\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 137.6                      │             Nfcn = 1086              │\n",
      "│ EDM = 2.12e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           ABOVE call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │APPROXIMATE│NOT pos. def.│ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │  1.0096   │  0.0000   │            │            │         │         │       │\n",
      "│ 1 │ rc1   │  3.0474   │  0.0000   │            │            │         │         │       │\n",
      "│ 2 │ rc2   │ 8.9316e-1 │ 0.0000e-1 │            │            │         │         │       │\n",
      "│ 3 │ ratio │  7.7232   │  0.0000   │            │            │         │         │       │\n",
      "│ 4 │ norm  │  -2.6967  │  0.0000   │            │            │         │         │       │\n",
      "│ 5 │ bkg   │ -1.4231e1 │ 0.0000e1  │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "Best fit chi-squared: 137.645 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 2.69892\n",
      "EMOS_bkg_2000-7000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  354.490737413121 279.07162092803856\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 129                        │             Nfcn = 1135              │\n",
      "│ EDM = nan (Goal: 0.0002)         │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ ABOVE EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │ 4.8843e-1 │    nan    │            │            │         │         │       │\n",
      "│ 1 │ rc1   │ 6.5356e-1 │    nan    │            │            │         │         │       │\n",
      "│ 2 │ rc2   │ 3.2982e-3 │    nan    │            │            │         │         │       │\n",
      "│ 3 │ ratio │ -5.7251e2 │    nan    │            │            │         │         │       │\n",
      "│ 4 │ norm  │  -2.0489  │    nan    │            │            │         │         │       │\n",
      "│ 5 │ bkg   │ -2.5377e3 │    nan    │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬─────────────────────────────────────┐\n",
      "│       │  beta   rc1   rc2 ratio  norm   bkg │\n",
      "├───────┼─────────────────────────────────────┤\n",
      "│  beta │   nan   nan   nan   nan   nan   nan │\n",
      "│   rc1 │   nan   nan   nan   nan   nan   nan │\n",
      "│   rc2 │   nan   nan   nan   nan   nan   nan │\n",
      "│ ratio │   nan   nan   nan   nan   nan   nan │\n",
      "│  norm │   nan   nan   nan   nan   nan   nan │\n",
      "│   bkg │   nan   nan   nan   nan   nan   nan │\n",
      "└───────┴─────────────────────────────────────┘\n",
      "Best fit chi-squared: 129.035 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 2.53009\n",
      "EMOS_bkg_2000-7000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  276.5272248028996 434.76259126509797\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 79.74                      │              Nfcn = 606              │\n",
      "│ EDM = 3.32e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │APPROXIMATE│NOT pos. def.│   FORCED   │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │   0.519   │   0.012   │            │            │         │         │       │\n",
      "│ 1 │ rc1   │   0.51    │   0.05    │            │            │         │         │       │\n",
      "│ 2 │ rc2   │   -0.0    │    0.6    │            │            │         │         │       │\n",
      "│ 3 │ ratio │     0     │    50     │            │            │         │         │       │\n",
      "│ 4 │ norm  │   -1.89   │   0.07    │            │            │         │         │       │\n",
      "│ 5 │ bkg   │  -0.01e3  │  0.16e3   │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬───────────────────────────────────────────────────────┐\n",
      "│       │     beta      rc1      rc2    ratio     norm      bkg │\n",
      "├───────┼───────────────────────────────────────────────────────┤\n",
      "│  beta │ 0.000145  0.48e-3  0.24e-3 69.86e-3 -0.37e-3  3.41e-3 │\n",
      "│   rc1 │  0.48e-3  0.00265   0.0013   0.6300  -0.0030   0.0063 │\n",
      "│   rc2 │  0.24e-3   0.0013    0.325    25.78   -0.001     0.00 │\n",
      "│ ratio │ 69.86e-3   0.6300    25.78 2.24e+03   -0.935        0 │\n",
      "│  norm │ -0.37e-3  -0.0030   -0.001   -0.935  0.00429   -0.003 │\n",
      "│   bkg │  3.41e-3   0.0063     0.00        0   -0.003 2.46e+04 │\n",
      "└───────┴───────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 79.7427 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 1.56358\n",
      "EMOS_bkg_700-2000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  354.490737413121 279.07162092803856\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 1292                       │              Nfcn = 988              │\n",
      "│ EDM = 1.57 (Goal: 0.0002)        │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           ABOVE call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │APPROXIMATE│  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │    8.5    │    0.6    │            │            │         │         │       │\n",
      "│ 1 │ rc1   │   14.0    │    0.6    │            │            │         │         │       │\n",
      "│ 2 │ rc2   │   3.31    │   0.14    │            │            │         │         │       │\n",
      "│ 3 │ ratio │   4.02    │   0.09    │            │            │         │         │       │\n",
      "│ 4 │ norm  │  -2.718   │   0.006   │            │            │         │         │       │\n",
      "│ 5 │ bkg   │   -8.4    │    0.5    │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬─────────────────────────────────────────────────────────────┐\n",
      "│       │      beta       rc1       rc2     ratio      norm       bkg │\n",
      "├───────┼─────────────────────────────────────────────────────────────┤\n",
      "│  beta │     0.325      0.31     0.076    -0.001 -0.492e-3     -0.19 │\n",
      "│   rc1 │      0.31     0.307     0.074     0.002 -0.884e-3     -0.18 │\n",
      "│   rc2 │     0.076     0.074    0.0186    -0.001 -0.221e-3    -0.042 │\n",
      "│ ratio │    -0.001     0.002    -0.001   0.00895 -0.264e-3     0.002 │\n",
      "│  norm │ -0.492e-3 -0.884e-3 -0.221e-3 -0.264e-3  3.18e-05  0.082e-3 │\n",
      "│   bkg │     -0.19     -0.18    -0.042     0.002  0.082e-3     0.239 │\n",
      "└───────┴─────────────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 1292.27 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 25.3387\n",
      "EMOS_bkg_700-2000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  276.5272248028996 434.76259126509797\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 1139                       │              Nfcn = 981              │\n",
      "│ EDM = 5.16 (Goal: 0.0002)        │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           ABOVE call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │APPROXIMATE│  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │   1.01    │   0.04    │            │            │         │         │       │\n",
      "│ 1 │ rc1   │   2.62    │   0.13    │            │            │         │         │       │\n",
      "│ 2 │ rc2   │   0.86    │   0.04    │            │            │         │         │       │\n",
      "│ 3 │ ratio │   6.07    │   0.26    │            │            │         │         │       │\n",
      "│ 4 │ norm  │  -2.733   │   0.021   │            │            │         │         │       │\n",
      "│ 5 │ bkg   │  -14.69   │   0.07    │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬───────────────────────────────────────────────────────┐\n",
      "│       │     beta      rc1      rc2    ratio     norm      bkg │\n",
      "├───────┼───────────────────────────────────────────────────────┤\n",
      "│  beta │   0.0014   0.0049   0.0012   0.0066  -0.6e-3   0.0002 │\n",
      "│   rc1 │   0.0049    0.018   0.0044    0.027  -2.3e-3    0.002 │\n",
      "│   rc2 │   0.0012   0.0044   0.0013   0.0065  -0.7e-3   0.0009 │\n",
      "│ ratio │   0.0066    0.027   0.0065   0.0649  -4.8e-3    0.010 │\n",
      "│  norm │  -0.6e-3  -2.3e-3  -0.7e-3  -4.8e-3 0.000432  -0.9e-3 │\n",
      "│   bkg │   0.0002    0.002   0.0009    0.010  -0.9e-3  0.00535 │\n",
      "└───────┴───────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 1139.2 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 22.3372\n",
      "EPN_bkg_700-1200_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  354.490737413121 279.07162092803856\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 870.8                      │              Nfcn = 981              │\n",
      "│ EDM = 0.0275 (Goal: 0.0002)      │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           ABOVE call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │APPROXIMATE│  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │    7.9    │    2.7    │            │            │         │         │       │\n",
      "│ 1 │ rc1   │    3.9    │    0.7    │            │            │         │         │       │\n",
      "│ 2 │ rc2   │   -15.3   │    2.9    │            │            │         │         │       │\n",
      "│ 3 │ ratio │   0.515   │   0.019   │            │            │         │         │       │\n",
      "│ 4 │ norm  │  -2.511   │   0.014   │            │            │         │         │       │\n",
      "│ 5 │ bkg   │  -3.882   │   0.006   │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬─────────────────────────────────────────────────────────────┐\n",
      "│       │      beta       rc1       rc2     ratio      norm       bkg │\n",
      "├───────┼─────────────────────────────────────────────────────────────┤\n",
      "│  beta │      7.48       2.0        -8    8.7e-3 -13.27e-3   4.97e-3 │\n",
      "│   rc1 │       2.0     0.558      -2.1    2.4e-3  -3.96e-3   1.30e-3 │\n",
      "│   rc2 │        -8      -2.1      8.24   -7.9e-3  13.73e-3  -4.88e-3 │\n",
      "│ ratio │    8.7e-3    2.4e-3   -7.9e-3  0.000372  -0.24e-3   0.01e-3 │\n",
      "│  norm │ -13.27e-3  -3.96e-3  13.73e-3  -0.24e-3  0.000208  -0.01e-3 │\n",
      "│   bkg │   4.97e-3   1.30e-3  -4.88e-3   0.01e-3  -0.01e-3  4.06e-05 │\n",
      "└───────┴─────────────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 870.786 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 17.0742\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: FITSFixedWarning: 'datfix' made the change 'Set DATEREF to '1998-01-01' from MJDREF.\n",
      "Set MJD-OBS to 54269.378148 from DATE-OBS.\n",
      "Set MJD-END to 54270.138345 from DATE-END'. [astropy.wcs.wcs]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPN_bkg_700-1200_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  276.5272248028996 434.76259126509797\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 1156                       │              Nfcn = 383              │\n",
      "│ EDM = 0.000109 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │   0.564   │   0.007   │            │            │         │         │       │\n",
      "│ 1 │ rc1   │   0.594   │   0.020   │            │            │         │         │       │\n",
      "│ 2 │ rc2   │  -0.000   │   0.012   │            │            │         │         │       │\n",
      "│ 3 │ ratio │   -6.27   │   0.25    │            │            │         │         │       │\n",
      "│ 4 │ norm  │  -2.018   │   0.012   │            │            │         │         │       │\n",
      "│ 5 │ bkg   │  -3.987   │   0.008   │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬───────────────────────────────────────────────────────┐\n",
      "│       │     beta      rc1      rc2    ratio     norm      bkg │\n",
      "├───────┼───────────────────────────────────────────────────────┤\n",
      "│  beta │ 4.99e-05  0.13e-3       -0 -0.23e-3 -0.06e-3  0.04e-3 │\n",
      "│   rc1 │  0.13e-3 0.000381 -0.01e-3  -0.4e-3 -0.20e-3  0.09e-3 │\n",
      "│   rc2 │       -0 -0.01e-3 0.000155 -0.79e-3  0.01e-3 -0.01e-3 │\n",
      "│ ratio │ -0.23e-3  -0.4e-3 -0.79e-3   0.0637 -0.22e-3 -0.14e-3 │\n",
      "│  norm │ -0.06e-3 -0.20e-3  0.01e-3 -0.22e-3 0.000138 -0.03e-3 │\n",
      "│   bkg │  0.04e-3  0.09e-3 -0.01e-3 -0.14e-3 -0.03e-3 7.02e-05 │\n",
      "└───────┴───────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 1156.01 for 57 bins and 50 d.o.f\n",
      "Reduced chi-squared: 23.1203\n",
      "EPN_bkg_1200-7000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  354.490737413121 279.07162092803856\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 154.8                      │              Nfcn = 982              │\n",
      "│ EDM = 0.0139 (Goal: 0.0002)      │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           ABOVE call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │APPROXIMATE│  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │    12     │     7     │            │            │         │         │       │\n",
      "│ 1 │ rc1   │    4.9    │    1.5    │            │            │         │         │       │\n",
      "│ 2 │ rc2   │    20     │     6     │            │            │         │         │       │\n",
      "│ 3 │ ratio │   0.53    │   0.05    │            │            │         │         │       │\n",
      "│ 4 │ norm  │   -2.40   │   0.04    │            │            │         │         │       │\n",
      "│ 5 │ bkg   │  -3.520   │   0.012   │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬───────────────────────────────────────────────────────┐\n",
      "│       │     beta      rc1      rc2    ratio     norm      bkg │\n",
      "├───────┼───────────────────────────────────────────────────────┤\n",
      "│  beta │       47     10.4       40  -0.0144  -0.0053  5.33e-3 │\n",
      "│   rc1 │     10.4     2.31      9.1  -0.0048  -0.0027  0.94e-3 │\n",
      "│   rc2 │       40      9.1     35.8  -0.0277  -0.0008  2.43e-3 │\n",
      "│ ratio │  -0.0144  -0.0048  -0.0277   0.0029  -0.0018  0.07e-3 │\n",
      "│  norm │  -0.0053  -0.0027  -0.0008  -0.0018  0.00131 -0.04e-3 │\n",
      "│   bkg │  5.33e-3  0.94e-3  2.43e-3  0.07e-3 -0.04e-3 0.000141 │\n",
      "└───────┴───────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 154.803 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 3.03535\n",
      "EPN_bkg_1200-7000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  276.5272248028996 434.76259126509797\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 171.7                      │              Nfcn = 945              │\n",
      "│ EDM = 4.23e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │   1.02    │   0.17    │            │            │         │         │       │\n",
      "│ 1 │ rc1   │    2.1    │    0.4    │            │            │         │         │       │\n",
      "│ 2 │ rc2   │   -0.54   │   0.13    │            │            │         │         │       │\n",
      "│ 3 │ ratio │    4.0    │    0.5    │            │            │         │         │       │\n",
      "│ 4 │ norm  │   -2.44   │   0.07    │            │            │         │         │       │\n",
      "│ 5 │ bkg   │  -3.558   │   0.011   │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬───────────────────────────────────────────────────────┐\n",
      "│       │     beta      rc1      rc2    ratio     norm      bkg │\n",
      "├───────┼───────────────────────────────────────────────────────┤\n",
      "│  beta │   0.0286    0.065   -0.019    0.018   -0.007  0.85e-3 │\n",
      "│   rc1 │    0.065    0.159   -0.048     0.07   -0.021  1.56e-3 │\n",
      "│   rc2 │   -0.019   -0.048   0.0159   -0.011    0.007 -0.47e-3 │\n",
      "│ ratio │    0.018     0.07   -0.011    0.259   -0.018 -0.40e-3 │\n",
      "│  norm │   -0.007   -0.021    0.007   -0.018  0.00433 -0.08e-3 │\n",
      "│   bkg │  0.85e-3  1.56e-3 -0.47e-3 -0.40e-3 -0.08e-3 0.000122 │\n",
      "└───────┴───────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 171.695 for 57 bins and 50 d.o.f\n",
      "Reduced chi-squared: 3.4339\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yanling/.local/lib/python3.9/site-packages/pyproffit/profextract.py:949: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n",
      "  fig = plt.figure(figsize=figsize)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPN_bkg_2000-7000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  354.490737413121 279.07162092803856\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 92.89                      │              Nfcn = 982              │\n",
      "│ EDM = 0.00203 (Goal: 0.0002)     │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           ABOVE call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │APPROXIMATE│  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │    14     │    17     │            │            │         │         │       │\n",
      "│ 1 │ rc1   │     6     │     4     │            │            │         │         │       │\n",
      "│ 2 │ rc2   │    22     │    14     │            │            │         │         │       │\n",
      "│ 3 │ ratio │   0.53    │   0.09    │            │            │         │         │       │\n",
      "│ 4 │ norm  │   -2.61   │   0.06    │            │            │         │         │       │\n",
      "│ 5 │ bkg   │  -3.527   │   0.016   │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬───────────────────────────────────────────────────────┐\n",
      "│       │     beta      rc1      rc2    ratio     norm      bkg │\n",
      "├───────┼───────────────────────────────────────────────────────┤\n",
      "│  beta │      293       62   0.24e3   -0.037   -0.059 29.32e-3 │\n",
      "│   rc1 │       62     13.2       51   -0.009   -0.020  5.09e-3 │\n",
      "│   rc2 │   0.24e3       51      202   -0.082   -0.037 17.08e-3 │\n",
      "│ ratio │   -0.037   -0.009   -0.082  0.00868   -0.005  0.08e-3 │\n",
      "│  norm │   -0.059   -0.020   -0.037   -0.005  0.00368 -0.02e-3 │\n",
      "│   bkg │ 29.32e-3  5.09e-3 17.08e-3  0.08e-3 -0.02e-3 0.000255 │\n",
      "└───────┴───────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 92.8933 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 1.82144\n",
      "EPN_bkg_2000-7000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  276.5272248028996 434.76259126509797\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 118                        │              Nfcn = 973              │\n",
      "│ EDM = 2.81e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │   0.75    │   0.07    │            │            │         │         │       │\n",
      "│ 1 │ rc1   │   1.18    │   0.15    │            │            │         │         │       │\n",
      "│ 2 │ rc2   │   0.021   │   0.006   │            │            │         │         │       │\n",
      "│ 3 │ ratio │   6.6e3   │   1.8e3   │            │            │         │         │       │\n",
      "│ 4 │ norm  │   -2.36   │   0.04    │            │            │         │         │       │\n",
      "│ 5 │ bkg   │  -3.575   │   0.014   │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬─────────────────────────────────────────────────────────────┐\n",
      "│       │      beta       rc1       rc2     ratio      norm       bkg │\n",
      "├───────┼─────────────────────────────────────────────────────────────┤\n",
      "│  beta │   0.00542     0.010  0.406e-3   105.623   -0.0008   0.53e-3 │\n",
      "│   rc1 │     0.010    0.0235  0.831e-3   210.103   -0.0036   0.74e-3 │\n",
      "│   rc2 │  0.406e-3  0.831e-3  3.26e-05  7.174079 -0.095e-3  0.036e-3 │\n",
      "│ ratio │   105.623   210.103  7.174079  3.25e+06  -23.4117  10.72496 │\n",
      "│  norm │   -0.0008   -0.0036 -0.095e-3  -23.4117   0.00159   0.03e-3 │\n",
      "│   bkg │   0.53e-3   0.74e-3  0.036e-3  10.72496   0.03e-3  0.000208 │\n",
      "└───────┴─────────────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 117.966 for 57 bins and 50 d.o.f\n",
      "Reduced chi-squared: 2.35933\n",
      "EPN_bkg_700-2000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  354.490737413121 279.07162092803856\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 973.4                      │              Nfcn = 990              │\n",
      "│ EDM = 0.0294 (Goal: 0.0002)      │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│         INVALID Minimum          │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           ABOVE call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │APPROXIMATE│  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │    7.5    │    1.7    │            │            │         │         │       │\n",
      "│ 1 │ rc1   │    3.8    │    0.5    │            │            │         │         │       │\n",
      "│ 2 │ rc2   │   14.9    │    1.9    │            │            │         │         │       │\n",
      "│ 3 │ ratio │   0.515   │   0.021   │            │            │         │         │       │\n",
      "│ 4 │ norm  │  -2.487   │   0.014   │            │            │         │         │       │\n",
      "│ 5 │ bkg   │  -3.857   │   0.006   │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬─────────────────────────────────────────────────────────────┐\n",
      "│       │      beta       rc1       rc2     ratio      norm       bkg │\n",
      "├───────┼─────────────────────────────────────────────────────────────┤\n",
      "│  beta │      2.95      0.84       3.2   -3.8e-3  -0.94e-3  1.693e-3 │\n",
      "│   rc1 │      0.84     0.239      0.92   -1.1e-3  -0.52e-3  0.441e-3 │\n",
      "│   rc2 │       3.2      0.92      3.53   -5.5e-3  -0.76e-3  1.570e-3 │\n",
      "│ ratio │   -3.8e-3   -1.1e-3   -5.5e-3  0.000429  -0.26e-3  0.010e-3 │\n",
      "│  norm │  -0.94e-3  -0.52e-3  -0.76e-3  -0.26e-3  0.000202 -0.005e-3 │\n",
      "│   bkg │  1.693e-3  0.441e-3  1.570e-3  0.010e-3 -0.005e-3  3.49e-05 │\n",
      "└───────┴─────────────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 973.357 for 58 bins and 51 d.o.f\n",
      "Reduced chi-squared: 19.0854\n",
      "EPN_bkg_700-2000_2023-05-11.fits\n",
      "Corresponding pixels coordinates:  276.5272248028996 434.76259126509797\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 1293                       │              Nfcn = 354              │\n",
      "│ EDM = 4e-05 (Goal: 0.0002)       │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ beta  │   0.564   │   0.007   │            │            │         │         │       │\n",
      "│ 1 │ rc1   │   0.594   │   0.019   │            │            │         │         │       │\n",
      "│ 2 │ rc2   │   0.000   │   0.005   │            │            │         │         │       │\n",
      "│ 3 │ ratio │   -40.8   │    1.8    │            │            │         │         │       │\n",
      "│ 4 │ norm  │  -1.994   │   0.010   │            │            │         │         │       │\n",
      "│ 5 │ bkg   │  -3.961   │   0.008   │            │            │         │         │       │\n",
      "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌───────┬─────────────────────────────────────────────────────────────┐\n",
      "│       │      beta       rc1       rc2     ratio      norm       bkg │\n",
      "├───────┼─────────────────────────────────────────────────────────────┤\n",
      "│  beta │  4.78e-05   0.12e-3 -0.002e-3  -2.26e-3  -0.05e-3   0.04e-3 │\n",
      "│   rc1 │   0.12e-3  0.000349 -0.014e-3  -7.24e-3  -0.18e-3   0.09e-3 │\n",
      "│   rc2 │ -0.002e-3 -0.014e-3   2.8e-05  3.905e-3  0.015e-3 -0.001e-3 │\n",
      "│ ratio │  -2.26e-3  -7.24e-3  3.905e-3      3.36   3.89e-3  -1.85e-3 │\n",
      "│  norm │  -0.05e-3  -0.18e-3  0.015e-3   3.89e-3  0.000111  -0.03e-3 │\n",
      "│   bkg │   0.04e-3   0.09e-3 -0.001e-3  -1.85e-3  -0.03e-3  6.59e-05 │\n",
      "└───────┴─────────────────────────────────────────────────────────────┘\n",
      "Best fit chi-squared: 1292.91 for 57 bins and 50 d.o.f\n",
      "Reduced chi-squared: 25.8581\n"
     ]
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1300x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1300x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1300x1000 with 0 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1300x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1300x1000 with 0 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1300x1000 with 0 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1300x1000 with 0 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1300x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1300x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1300x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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",
      "text/plain": [
       "<Figure size 1300x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "today = '2023-05-11'\n",
    "maxrad = 20.\n",
    "radec_lst = {'A222':[24.3941937, -12.9933436], \"A223\":[24.4830446, -12.8203439]}\n",
    "doublebeta_para = {\"700-1200\":[0.6, 1, 7, 0.2, -2, -4], \"1200-2000\":[0.6, 1, 7, 0.2, -2, -4.2], \"2000-5000\":[0.6, 1, 7, 0.2, -2, -4], \n",
    "\"5000-7000\":[0.6, 1, 7, 0.2, -4, -4], \"700-7000\":[0.6, 1, 7, 0.2, -2, -2.5], \"700-2000\":[0.6, 1, 7, 0.2, -2, -4],\n",
    "\"2000-7000\":[0.6, 1, 7, 0.2, -2.5, -4] , \"1200-7000\":[0.6, 1, 7, 0.2, -2, -4]}\n",
    "for ins in ['EPIC', 'EMOS', 'EPN']:#,'xmm','chandra'\n",
    "        # for band in ['700-1200', '1200-2000', '2000-5000', '5000-7000', '700-7000', \"700-2000\", \"2000-7000\", \"1200-7000\"]:#, \n",
    "        for band in ['700-1200', '1200-7000', '2000-7000', '700-2000']:\n",
    "            for clus in ['A222', 'A223']:\n",
    "                ra = radec_lst[clus][0]\n",
    "                dec = radec_lst[clus][1]\n",
    "\n",
    "                img = f'{ins}_counts_{band}_{today}.fits'\n",
    "                exp = f'{ins}_exp_{band}_{today}_edgemsk_srcmsk_{clus}_filamsk.fits'\n",
    "                # exp = f'{ins}_exp_{band}_{today}_edgemsk_srcmsk.fits'\n",
    "                bkg = f'{ins}_bkg_{band}_{today}.fits'\n",
    "                print(bkg)\n",
    "                dat=pyproffit.Data(imglink=img, explink=exp, bkglink=bkg)\n",
    "                prof=pyproffit.Profile(dat, center_choice='custom_fk5', center_ra = ra, center_dec =dec, maxrad=maxrad, binsize=10, binning='log')\n",
    "                prof.SBprofile(ellipse_ratio=1)#, angle_low=80, angle_high=140)\n",
    "                \n",
    "                ### Double Beta\n",
    "                mod=pyproffit.Model(pyproffit.DoubleBeta)\n",
    "                beta, rc1,rc2, ratio, norm, bkg = doublebeta_para[band]\n",
    "                fitobj=pyproffit.Fitter(model=mod, method='chi2', profile=prof, beta = beta, rc1 = rc1, rc2 =  rc2, ratio = ratio, norm = norm, bkg=bkg, fitlow=0.1, fithigh=maxrad)\n",
    "                \n",
    "                fitobj.Migrad()\n",
    "                prof.Plot(model=mod)\n",
    "                plt.title(f'{ins}, {band}, {clus}, {ra}, {dec}\\n {mod.parnames} \\n {mod.params}  ')\n",
    "\n",
    "                # # inspecting results\n",
    "                # # fitobj.minuit.minos()\n",
    "                # #fitobj.minuit.draw_mncontour('beta', 'rc', cl=(0.68, 0.9, 0.99))\n",
    "                os.makedirs(f'{workpath}/png/{int(maxrad)}am/{today}', exist_ok=True)\n",
    "                os.makedirs(f'{workpath}/profiles/{int(maxrad)}am/{today}', exist_ok=True)\n",
    "                plt.savefig(f'{workpath}/png/{int(maxrad)}am/{today}/{ins}_{clus}_{band}_noclfila.png')\n",
    "                prof.Save(outfile=f\"{workpath}/profiles/{int(maxrad)}am/{today}/{clus}_{ins}_{band}_doublebeta_profile_noclfila.fits\", model=mod)\n",
    "\n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mask strange chip in A222 profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "today = '2023-05-08'\n",
    "workpath = f'/Users/eusracenorth/Documents/suzaku_A222-223/xmm_data/merged/2023-05-07_merge_obs_inst_bands'\n",
    "os.chdir(workpath)\n",
    "f = open(f'{workpath}/masks/mask_eschipgap_big_A222_nofilamsk_{today}.sh', 'w')\n",
    "f.write('#!/bin/bash\\n')\n",
    "for inst in ['EMOS', 'EPIC', 'EPN']:\n",
    "    files = glob(f'{workpath}/{inst}_exp_*_edgemsk_srcmsk.fits')\n",
    "    for file in files:\n",
    "        f.write(f\"ftimgcalc masks/{file.split('/')[-1].split('.')[0]}_bigeschipgapmsk.fits 'regfilter(\\\"masks/A222_eschip_gap_big.reg\\\",A.P1,A.P2) ? (0):(1)' a={file} clobber=yes\\n\")\n",
    "        f.write(f\"farith masks/{file.split('/')[-1].split('.')[0]}_bigeschipgapmsk.fits {file} {file.split('/')[-1].split('.')[0]}_bigeschipgapmasked.fits '*' clobber=yes\\n\")\n",
    "f.close()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def basic_figure_style():\n",
    "    SMALL_SIZE = 5*4                                       \n",
    "    MEDIUM_SIZE = 8*2\n",
    "    BIGGER_SIZE = 12*2\n",
    "\n",
    "    # plt.rcParams.update({\n",
    "    # \"text.usetex\": True,\n",
    "    # \"font.family\": \"sans-serif\",\n",
    "    # \"font.sans-serif\": \"Helvetica\",})\n",
    "    plt.rc('font', size=BIGGER_SIZE, family='serif')          # controls default text sizes\n",
    "    plt.rc('axes', titlesize=MEDIUM_SIZE)                     # fontsize of the axes title\n",
    "    plt.rc('axes', labelsize=MEDIUM_SIZE)                    # fontsize of the x and y labels\n",
    "    plt.rc('xtick', labelsize=MEDIUM_SIZE, direction='out')    # fontsize of the tick labels\n",
    "    plt.rc('ytick', labelsize=MEDIUM_SIZE, direction='out')    # fontsize of the tick labels\n",
    "    plt.rc('legend', fontsize=MEDIUM_SIZE)                    # legend fontsize\n",
    "    plt.rc('figure', titlesize=BIGGER_SIZE)                  # fontsize of the figure title\n",
    "    plt.rc('lines', linewidth=4) \n",
    "    plt.rc('axes', grid=True) #\n",
    "    plt.rc('grid', alpha=0.7) #\n",
    "    plt.rc('xtick', top=True)\n",
    "    plt.rc('ytick', right=True)\n",
    "    plt.rc('axes.formatter', use_mathtext=True, min_exponent=4, useoffset=False)\n",
    "    plt.rc('figure', figsize='6, 6')                         # size of the figure, used to be '4, 3' in inches\n",
    "    ######################################################\n",
    "basic_figure_style()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "cb = ['#0173b2', '#de8f05', '#029e73', '#d55e00', '#cc78bc', '#ca9161', '#fbafe4', '#949494', '#ece133', '#56b4e9']\n",
    "path = '/Users/eusracenorth/Documents/suzaku_A222-223/profile_230302/results_230305'\n",
    "xmmfiles = glob(f'{path}/*chan*0.7-1.2*doublebeta*10am*')\n",
    "xmmfiles = np.sort(xmmfiles)\n",
    "print(xmmfiles)\n",
    "fig, axs = plt.subplots(2, len(xmmfiles), sharex=True, figsize=(len(xmmfiles)*10,8),gridspec_kw={'height_ratios': [3, 1]})\n",
    "fig.subplots_adjust(hspace=0,wspace=0.2)\n",
    "fig.align_ylabels()\n",
    "for i, fname in enumerate(xmmfiles):\n",
    "    hdul = fits.open(fname)\n",
    "    dat = hdul[1].data \n",
    "    mdl = hdul[2].data\n",
    "    model = DoubleBeta(dat['RADIUS'], mdl['VALUE'][0], mdl['VALUE'][1], mdl['VALUE'][2],  mdl['VALUE'][3],  mdl['VALUE'][4],  mdl['VALUE'][5])\n",
    "    axs[0,i].errorbar(dat['RADIUS'], dat['SB'], yerr=dat['ERR_SB'], xerr=dat['WIDTH'], c = 'k', label = 'data', alpha=0.8,fmt = 'o')\n",
    "    axs[0,i].plot(dat['RADIUS'], model, label = 'model', c = 'b')\n",
    "    axs[0,i].plot(dat['RADIUS'], dat['BKG'], label = 'bkg', c = 'g')\n",
    "    \n",
    "    chi = (dat['SB'] - model) / dat['ERR_SB']\n",
    "    axs[1,i].errorbar(dat['RADIUS'], chi, yerr=np.ones(len(dat['RADIUS'])),  c = 'k', label = 'data', alpha=0.8, fmt='o')\n",
    "    axs[1,i].axhline(0, c='b', linestyle = '--')\n",
    "\n",
    "    # axs[0,i].set_title(f'{fname.split(\"/\")[-1]}')\n",
    "    axs[0,i].set_yscale('log')\n",
    "    # axs[0,i].set_ylim(1e-7,0.01)\n",
    "    axs[1,i].set_yscale('linear')\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range(len(xmmfiles)):\n",
    "        axs[i,j].minorticks_on()\n",
    "        axs[i,j].tick_params(length=20, width=1, which='major', direction='in', right=True, top=True)\n",
    "        axs[i,j].tick_params(length=10, width=1, which='minor', direction='in', right=True, top=True)\n",
    "        axs[i,j].set_xlabel('Radius [arcmin]')\n",
    "        axs[0,j].set_ylabel('SB [$\\\\rm cts/s/cm^2/arcmin^2$]')\n",
    "        axs[1,j].set_ylabel('$\\chi$')\n",
    "axs[0,1].legend(frameon=False)\n",
    "plt.xscale('log')\n",
    "plt.yscale('linear')\n",
    "\n",
    "# plt.savefig(f'{path}/pdf/{fnasplit(\"/\")[-1].split(\".fits\")[0]}_A222left_A223right_sbprofile.pdf', dpi=300)\n",
    "# print(f'{fname.split(\"/\")[-1].splime.t(\".fits\")[0]}_A222left_A223right_sbprofile.pdf has been saved!')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "make camel-like plot \n",
    "https://arxiv.org/pdf/2110.06224.pdf "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('dark_background')\n",
    "\n",
    "from glob import glob\n",
    "today = '2023-05-11'\n",
    "chandra_path = '/stage/headat/yanling/chandra/results_230305'\n",
    "xmm_path = f'{workpath}/profiles/10am/{today}'\n",
    "savepath = f'{workpath}/png/camelplot'\n",
    "os.makedirs(savepath, exist_ok=True)\n",
    "\n",
    "cb = ['#0173b2', '#de8f05', '#029e73', '#d55e00', '#cc78bc', '#ca9161', '#fbafe4', '#949494', '#ece133', '#56b4e9']\n",
    "chandra_Aeff = 100\n",
    "\n",
    "\n",
    "def DoubleBeta_wobkg(x, beta, rc1, rc2, ratio, norm):\n",
    "    out = np.power(10,norm) * (np.power(1 + (x / rc1) **2, -3 * beta + 0.5) + ratio * np.power(1 + (x / rc2) **2, -3 * beta + 0.5))\n",
    "    return out\n",
    "fig, ax = plt.subplots(figsize = (16,8))\n",
    "\n",
    "\n",
    "\n",
    "## load xmm data\n",
    "\n",
    "# xmm_skybg = np.array([0.0033413481427292846, 0.002563827590320338])\n",
    "# skybg = xmm_skybg.min()#/3.08\n",
    "# skybg = (1.77e-3+2*5.485e-4)/5*3/3.08\n",
    "# plt.axhline(skybg)\n",
    "x_mdl = np.arange(-10,11.5+10+0.01,0.01)\n",
    "xmm_Aeff = 1 #400\n",
    "fname = glob(f'{xmm_path}/*A222*EPIC*700-1200*doublebeta_profile_noclfila.fits')[0]\n",
    "hdul = fits.open(fname)\n",
    "dat = hdul[1].data \n",
    "mdl = hdul[2].data\n",
    "a222mdl = DoubleBeta_wobkg(np.arange(0,10,0.01), mdl['VALUE'][0], mdl['VALUE'][1], mdl['VALUE'][2],  mdl['VALUE'][3],  mdl['VALUE'][4])\n",
    "y_a222mdl = np.concatenate((a222mdl[::-1], a222mdl))/xmm_Aeff\n",
    "x_a222mdl = np.arange(-10,10, 0.01)\n",
    "# plt.plot(x_a222mdl, y_a222mdl, c = 'g', alpha = 0.5)\n",
    "msk = dat['RADIUS'] < 6\n",
    "# plt.errorbar(dat['RADIUS'][msk], (dat['SB'][msk]-skybg)/xmm_Aeff, yerr=dat['ERR_SB'][msk]/xmm_Aeff, xerr=dat['WIDTH'][msk], c = cb[-5],fmt = 'o')\n",
    "\n",
    "# plt.axhline(skybg/xmm_Aeff, label = '0', c = cb[0])\n",
    "fname = glob(f'{xmm_path}/*A222*EPIC*700-1200*doublebeta_profile_inclfila.fits')[0]\n",
    "hdul = fits.open(fname)\n",
    "dat = hdul[1].data \n",
    "mdl = hdul[2].data\n",
    "msk = dat['RADIUS'] < 6\n",
    "plt.errorbar(dat['RADIUS'][msk], (dat['SB'][msk]-np.power(10, mdl['VALUE'][-1]))/xmm_Aeff, yerr=dat['ERR_SB'][msk]/xmm_Aeff, linewidth=2,xerr=dat['WIDTH'][msk], c = cb[1],fmt = '.')\n",
    "xmm_Aeff = 1 #400\n",
    "fname = glob(f'{xmm_path}/*A223*EPIC*700-1200*doublebeta_profile_noclfila.fits')[0]\n",
    "hdul = fits.open(fname)\n",
    "dat = hdul[1].data \n",
    "mdl = hdul[2].data\n",
    "a223mdl = DoubleBeta_wobkg(np.arange(0,10,0.01), mdl['VALUE'][0], mdl['VALUE'][1], mdl['VALUE'][2],  mdl['VALUE'][3],  mdl['VALUE'][4])\n",
    "y_a223mdl = np.concatenate((a223mdl[::-1], a223mdl))/xmm_Aeff\n",
    "x_a223mdl = np.arange(11.5-10,11.5+10, 0.01)\n",
    "# plt.plot(x_a223mdl, y_a223mdl, c = 'g')\n",
    "y_mdl = np.zeros(x_mdl.shape)\n",
    "y_mdl[(x_mdl>(x_a222mdl[0]-0.01))&(x_mdl<=x_a222mdl[-1])] = y_a222mdl\n",
    "y_mdl[(x_mdl>=x_a223mdl[0])&(x_mdl<(x_a223mdl[-1]+0.01))] += y_a223mdl\n",
    "plt.plot(x_mdl, y_mdl,c = cb[1], linestyle = '-', label = 'XMM-Newton sum mdl')\n",
    "msk = dat['RADIUS'] < 6\n",
    "\n",
    "# plt.errorbar(11.5-dat['RADIUS'][msk], (dat['SB'][msk]-skybg)/xmm_Aeff, yerr=dat['ERR_SB'][msk]/xmm_Aeff, xerr=dat['WIDTH'][msk], c = cb[-5],fmt = 'o', label = 'xmm_wofila')\n",
    "\n",
    "# plt.axhline(skybg/xmm_Aeff, label = '1', c = cb[1])\n",
    "fname = glob(f'{xmm_path}/*A223*EPIC*700-1200*doublebeta_profile_inclfila.fits')[0]\n",
    "hdul = fits.open(fname)\n",
    "dat = hdul[1].data \n",
    "mdl = hdul[2].data\n",
    "msk = dat['RADIUS'] < 6\n",
    "plt.errorbar(11.5-dat['RADIUS'][msk], (dat['SB'][msk]-np.power(10, mdl['VALUE'][-1]))/xmm_Aeff, yerr=dat['ERR_SB'][msk]/xmm_Aeff, xerr=dat['WIDTH'][msk], c = cb[1],linewidth=2, label = 'XMM-Newton data',fmt = '.')\n",
    "\n",
    "### load chandra models\n",
    "fname = glob(f'{chandra_path}/*A222*chan*0.7-1.2*doublebeta*10am*')[0]\n",
    "hdul = fits.open(fname)\n",
    "dat = hdul[1].data \n",
    "mdl = hdul[2].data\n",
    "\n",
    "# plt.errorbar(dat['RADIUS'], dat['SB']-np.power(10,mdl['VALUE'][5]), yerr=dat['ERR_SB'], xerr=dat['WIDTH'], c = cb[-2], fmt = 'o')\n",
    "\n",
    "a222mdl = DoubleBeta_wobkg(np.arange(0,10,0.01), mdl['VALUE'][0], mdl['VALUE'][1], mdl['VALUE'][2],  mdl['VALUE'][3],  mdl['VALUE'][4])\n",
    "y_a222mdl = np.concatenate((a222mdl[::-1], a222mdl))\n",
    "x_a222mdl = (-10,10)\n",
    "\n",
    "\n",
    "fname = glob(f'{chandra_path}/*A223*chan*0.7-1.2*doublebeta*10am*')[0]\n",
    "hdul = fits.open(fname)\n",
    "dat = hdul[1].data \n",
    "mdl = hdul[2].data\n",
    "# plt.errorbar(6 - dat['RADIUS'], dat['SB']-np.power(10,mdl['VALUE'][5]), yerr=dat['ERR_SB'], xerr=dat['WIDTH'], label = 'Chandra', c = cb[-2],fmt = 'o')\n",
    "\n",
    "a223mdl = DoubleBeta_wobkg(np.arange(0,10,0.01), mdl['VALUE'][0], mdl['VALUE'][1], mdl['VALUE'][2],  mdl['VALUE'][3],  mdl['VALUE'][4])\n",
    "y_a223mdl = np.concatenate((a223mdl[::-1], a223mdl))\n",
    "x_a223mdl = (11.5-10,11.5+10)\n",
    "\n",
    "x_mdl = np.arange(-10,11.5+10+0.01,0.01)\n",
    "y_mdl = np.zeros(x_mdl.shape)\n",
    "y_mdl[(x_mdl>x_a222mdl[0])&(x_mdl<=x_a222mdl[1])] = y_a222mdl\n",
    "# plt.plot(x_mdl, y_mdl* chandra_Aeff,c = 'r', alpha = 0.5)\n",
    "y_mdl = np.zeros(x_mdl.shape)\n",
    "y_mdl[(x_mdl>=x_a223mdl[0])&(x_mdl<x_a223mdl[1])] = y_a223mdl\n",
    "# plt.plot(x_mdl, y_mdl* chandra_Aeff,c = 'r', alpha = 0.5)\n",
    "y_mdl = np.zeros(x_mdl.shape)\n",
    "y_mdl[(x_mdl>x_a222mdl[0])&(x_mdl<=x_a222mdl[1])] = y_a222mdl\n",
    "y_mdl[(x_mdl>=x_a223mdl[0])&(x_mdl<x_a223mdl[1])] += y_a223mdl\n",
    "plt.plot(x_mdl, y_mdl* chandra_Aeff, c = 'r', linestyle = '-', label = 'Chandra sum mdl')\n",
    "\n",
    "## load chandra data\n",
    "# chan_skybg = np.array([1.563939922864141e-06, 1.7019209655102093e-06])\n",
    "# skybg = chan_skybg.min()#/3.08 ##3.08 for0.7-1.2 keV\n",
    "# skybg = 2.723e-4/5*3/3.08\n",
    "# plt.axhline(skybg)\n",
    "fname = glob(f'{chandra_path}/*A222*chandra*0.7-1.2*doublebeta_profile.fits')[0]\n",
    "hdul = fits.open(fname)\n",
    "dat = hdul[1].data \n",
    "mdl = hdul[2].data\n",
    "msk = dat['RADIUS'] < 6\n",
    "# plt.errorbar(dat['RADIUS'][msk], (dat['SB'])[msk]* chandra_Aeff-skybg, yerr=dat['ERR_SB'][msk]* chandra_Aeff, xerr=dat['WIDTH'][msk], c = cb[-2],fmt = 'o')\n",
    "\n",
    "fname = glob(f'{chandra_path}/*A222*chandra*0.7-1.2*doublebeta_profile_inclfila.fits')[0]\n",
    "hdul = fits.open(fname)\n",
    "dat = hdul[1].data \n",
    "mdl = hdul[2].data\n",
    "msk = dat['RADIUS'] < 6\n",
    "plt.errorbar(dat['RADIUS'][msk], (dat['SB'])[msk]* chandra_Aeff-np.power(10, mdl['VALUE'][-1]), yerr=dat['ERR_SB'][msk]* chandra_Aeff, xerr=dat['WIDTH'][msk],linewidth=2, c = cb[-4],fmt = '.')\n",
    "\n",
    "fname = glob(f'{chandra_path}/*A223*chandra*0.7-1.2*doublebeta_profile.fits')[0]\n",
    "hdul = fits.open(fname)\n",
    "dat = hdul[1].data \n",
    "mdl = hdul[2].data\n",
    "msk = dat['RADIUS'] < 6\n",
    "# plt.errorbar(11.5-dat['RADIUS'][msk], (dat['SB'])[msk]* chandra_Aeff-skybg, yerr=dat['ERR_SB'][msk]* chandra_Aeff, xerr=dat['WIDTH'][msk], c = cb[-2],label = 'chandra_wofila',fmt = 'o')\n",
    "\n",
    "fname = glob(f'{chandra_path}/*A223*chandra*0.7-1.2*doublebeta_profile_inclfila.fits')[0]\n",
    "hdul = fits.open(fname)\n",
    "dat = hdul[1].data \n",
    "mdl = hdul[2].data\n",
    "msk = dat['RADIUS'] < 6\n",
    "plt.errorbar(11.5-dat['RADIUS'][msk], (dat['SB'])[msk]* chandra_Aeff-np.power(10, mdl['VALUE'][-1]), yerr=dat['ERR_SB'][msk]* chandra_Aeff, xerr=dat['WIDTH'][msk], c = cb[-4],linewidth=2, label = 'Chandra data ',fmt = '.')\n",
    "plt.ylim(1e-6,1)\n",
    "plt.xlim(-8,20)\n",
    "plt.text(10.5,3e-4, 'A223')\n",
    "plt.text(-1,3e-4, 'A222')\n",
    "plt.axvline(4, c= cb[-1])\n",
    "plt.axvline(4+3.5, c = cb[-1], label = 'filabox')\n",
    "plt.yscale('log')\n",
    "plt.ylabel('$S$ ($\\\\rm cts\\ s^{-1}\\ arcmin^{-2}$)')\n",
    "plt.xlabel('Radius (arcmin)')\n",
    "plt.legend()\n",
    "plt.xticks(np.arange(-10, 11.5+10,1.5))\n",
    "plt.title('0.7-1.2 keV', fontsize=24)\n",
    "plt.savefig(f'{savepath}/camel-poster.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.power(10, mdl['VALUE'[-1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot vignetted-corrected, bkg-subtracted plots for sanity check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scale PN exp map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PN_scaling  = {'700-1200': , '700-2000': , \n",
    "'700-7000': , '2000-7000': , '5000-7000': , \n",
    "'2000-5000': , '1200-7000': , '1200-2000': }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "make maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datapath2 = f'/Users/eusracenorth/Documents/suzaku_A222-223/xmm_data/merged/2023-05-07_merge_obs_inst_bands'\n",
    "datapath1 = f'/Users/eusracenorth/Documents/suzaku_A222-223/xmm_data/merged/2023-05-10'\n",
    "savepath = f'{datapath1}/images'\n",
    "os.makedirs(savepath, exist_ok=True)\n",
    "\n",
    "today = '2023-05-10'\n",
    "maxrad = 10.\n",
    "os.chdir(datapath1)\n",
    "os.system(f'cp {datapath2}/*_exp*_edgemsk_srcmsk.fits {datapath1}')\n",
    "radec_lst = {'A222':[24.3941937, -12.9933436], \"A223\":[24.4830446, -12.8203439]}\n",
    "doublebeta_para = {\"700-1200\":[0.6, 1, 7, 0.2, -2, -3], \"1200-2000\":[0.6, 1, 7, 0.2, -2, -3.2], \"2000-5000\":[0.6, 1, 7, 0.2, -2, -3], \n",
    "\"5000-7000\":[0.6, 1, 7, 0.2, -3, -3], \"700-7000\":[0.6, 1, 7, 0.2, -2, -2.5], \"700-2000\":[0.6, 1, 7, 0.2, -2, -3],\n",
    "\"2000-7000\":[0.6, 1, 7, 0.2, -2.5, -3] , \"1200-7000\":[0.6, 1, 7, 0.2, -2, -3]}\n",
    "f = open(f'{savepath}/create_images.sh', 'w')\n",
    "\n",
    "for ins in ['EPIC', 'EMOS','EPN']:#,'xmm','chandra'\n",
    "        for band in ['700-1200', '1200-2000', '2000-5000', '5000-7000', '700-7000', \"700-2000\", \"2000-7000\", \"1200-7000\"]:#, \n",
    "            for clus in ['A222', 'A223']:\n",
    "                ra = radec_lst[clus][0]\n",
    "                dec = radec_lst[clus][1]\n",
    "\n",
    "                img = f'{ins}_cts_{band}_{today}.img'\n",
    "                # exp = f'{ins}_exp_{band}_{today}_edgemsk_srcmsk_{clus}_filamsk_bigeschipgapmasked.fits'\n",
    "                # exp = f'{ins}_exp_{band}_{today}_edgemsk_srcmsk_{clus}_filamsk.fits'\n",
    "                exp = f'{ins}_exp_{band}_*_edgemsk_srcmsk.fits'\n",
    "                bkg = glob(f'{ins}_bkg_{band}_{today}.img')[0]\n",
    "                f.write(f'farith {img} {bkg} {img.split(\".\")[0]}_net.fits \"-\" clobber=yes\\n')\n",
    "                f.write(f'fimgtrim infile={img.split(\".\")[0]}_net.fits threshlo=0 threshup=I const_lo=0 outfile={img.split(\".\")[0]}_net_trimmed.fits clobber=yes\\n')\n",
    "                f.write(f'farith {img.split(\".\")[0]}_net_trimmed.fits {exp} {img.split(\".\")[0]}_net_ctr.fits \"/\" clobber=yes\\n')\n",
    "                f.write(f'fimgtrim infile={img.split(\".\")[0]}_net_ctr.fits threshlo=0 threshup=I const_lo=0 outfile={img.split(\".\")[0]}_net_ctr_trimmed_nogapmsk.fits clobber=yes\\n')\n",
    "f.close()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "imshow and save the figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from astropy.visualization import astropy_mpl_style\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.pyplot as plt\n",
    "# plt.style.use(astropy_mpl_style)\n",
    "plt.style.use('default')\n",
    "from astropy.utils.data import get_pkg_data_filename\n",
    "from astropy.wcs import WCS\n",
    "from astropy.convolution import convolve\n",
    "from astropy.convolution import Gaussian2DKernel\n",
    "from astropy.io import fits\n",
    "import numpy as np\n",
    "kernel = Gaussian2DKernel(x_stddev=1)\n",
    "# 'EPN_corr_counts'\n",
    "#EMOS1_obs101_bkg_clean_2023-05-04_1200-2000_scaled.img\n",
    "data_dir = '/stage/headat/yanling/xmm/reduction_230227/101'\n",
    "bkg_dir = '/stage/headat/yanling/xmm/fwc_dat'\n",
    "title = 'EPN_obs201_bkg_clean_2023-05-10_2000-5000_scaled_corr'\n",
    "file = f\"{bkg_dir}/{title}\"\n",
    "hdu = fits.open(f'{file}.img')[0]\n",
    "image_data = hdu.data\n",
    "print(image_data.shape)\n",
    "fig = plt.figure(figsize = (6,6))\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], projection=WCS(f'{file}.img'))\n",
    "ra = ax.coords[0]\n",
    "dec = ax.coords[1]\n",
    "# plt.grid(color='grey', ls='dashed')\n",
    "plt.grid('off')\n",
    "ra.set_axislabel('RA')\n",
    "dec.set_axislabel('Dec')\n",
    "image_data = np.where(np.isfinite(np.log10(image_data)), image_data, np.nan)\n",
    "astropy_conv = convolve(image_data, kernel)\n",
    "plt.title(title)\n",
    "plt.imshow(astropy_conv[50:650,50:550], cmap='gnuplot2', norm = colors.LogNorm(), origin='lower')\n",
    "plt.colorbar()\n",
    "plt.savefig(f'{file}.png')\n",
    "plt.show()\n",
    "plt.clf()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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