Reduce-Generic-IXPE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cooked-captain",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Load Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "angry-negotiation",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from astropy.io import fits\n",
    "from scipy.optimize import curve_fit,minimize\n",
    "from glob import glob\n",
    "from scipy.interpolate import InterpolatedUnivariateSpline\n",
    "import re\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "552890a9-906d-4d43-82a9-01d6d4172a15",
   "metadata": {},
   "outputs": [],
   "source": [
    "doorbbary=True   # Barycentric correction of pulsar orbit\n",
    "dolocalbary=True # Barycentric correction of spacecraft orbit; requires HEASOFT barycorr\n",
    "if doorbbary:\n",
    "    SOURCENAME='herx1' # For GBM PSR data \n",
    "else:\n",
    "    NU=0.8079475 # guess for PSR frequency\n",
    "    NUDOT=0\n",
    "OBSID='02004001'\n",
    "MODF='FTP/caldb/data/ixpe/gpd/cpf/modfact/ixpe_d%d_20170101_mfact_02.fits'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6304b002-24f4-44ad-9d3e-d345527be20f",
   "metadata": {},
   "source": [
    "# Setup  Directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b7614795-2642-400b-9532-c1788d7efe88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.system('[ -d output ] || mkdir output ; [ -d figures ] || mkdir figures ; [ -d data ] || mkdir data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6129d488-d36d-4801-9e00-7a92a8e546df",
   "metadata": {},
   "source": [
    "# Setup Data and Modulation Files in Data Directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8fe00e0e-886b-4bdf-bb33-fdb96baee8f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "DIRECTORY='FTP/ixpe/data/obs/%s//%s/' % (OBSID[0:2],OBSID)\n",
    "SCIHEADER='/home/idies/workspace/headata'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3dd06a7-c563-463d-8463-e913a69c004f",
   "metadata": {},
   "source": [
    "## Set up Raw Data Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a40b4940-8c0c-45c4-bf09-80ae9b7968d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "rawdatafilelist=sorted(glob('data/ixpe*_det?_evt2_*.fits*'))\n",
    "if len(rawdatafilelist)<3:\n",
    "    print('get data files')\n",
    "    # if you are on sciserver create symbolic links to the data, otherwise download the manifest and then files\n",
    "    if os.path.isdir('%s/%s/event_l2/' % (SCIHEADER,DIRECTORY)):\n",
    "        os.system('cd data ; for ii in %s/%s/event_l2/*.fits.gz ; do ln -s $ii ; done' % (SCIHEADER,DIRECTORY))\n",
    "    else:\n",
    "        os.system('cd data ; wget -nH -nv --cut-dirs=8 https://heasarc.gsfc.nasa.gov/%s/event_l2/' % DIRECTORY)\n",
    "        with open('data/index.html','r') as f:\n",
    "            for line in f:\n",
    "                res=re.search('href=\"ixpe.+fits\\.gz\"',line)\n",
    "                if res is not None:\n",
    "                    os.system('cd data  ; wget -nH -nv --cut-dirs=8 https://heasarc.gsfc.nasa.gov/%s/event_l2/%s' % (DIRECTORY,line[res.start()+6:res.end()-1]))\n",
    "        os.remove('data/index.html')\n",
    "    rawdatafilelist=sorted(glob('data/ixpe*_det?_evt2_*.fits*'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "296fe295-de25-4990-afa4-5d428783ceeb",
   "metadata": {},
   "source": [
    "## Perform Spacecraft barycentering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "73d32a98-f3e6-48eb-a532-526286033d34",
   "metadata": {},
   "outputs": [],
   "source": [
    "if dolocalbary:\n",
    "    orbfilelist=sorted(glob('data/ixpe*orb_*fits*'))\n",
    "    if len(orbfilelist)<1:\n",
    "        print('get orb files')\n",
    "        # if you are on sciserver create symbolic links to the data, otherwise download the manifest and then files\n",
    "        if os.path.isdir('%s/%s/hk/' % (SCIHEADER,DIRECTORY)):\n",
    "            os.system('cd data ; for ii in %s/%s/hk/*orb*.fits.gz ; do ln -s $ii ; done' % (SCIHEADER,DIRECTORY))\n",
    "        else:\n",
    "            os.system('cd data ; wget -nH -nv --cut-dirs=8 https://heasarc.gsfc.nasa.gov/%s/hk/' % DIRECTORY)\n",
    "            with open('data/index.html','r') as f:\n",
    "                for line in f:\n",
    "                    res=re.search('href=\"ixpe.+orb.+fits\\.gz\"',line)\n",
    "                    if res is not None:\n",
    "                        os.system('cd data  ; wget -nH -nv --cut-dirs=8 https://heasarc.gsfc.nasa.gov/%s/hk/%s' % (DIRECTORY,line[res.start()+6:res.end()-1]))\n",
    "            os.remove('data/index.html')\n",
    "    orbfilelist=sorted(glob('data/ixpe*orb_*fits*'))\n",
    "    datafilelist=sorted(glob('data/ixpe*_det?_evt2_*_bary.fits*'))\n",
    "    if len(datafilelist)<3:\n",
    "        print('perform barycentric correction')\n",
    "        for ff in rawdatafilelist:\n",
    "            os.system('HEADASNOQUERY= HEADASPROMPT=/dev/null barycorr %s %s %s' % (ff,ff.replace('.fits.gz','_bary.fits.gz'),','.join(orbfilelist)))\n",
    "    datafilelist=sorted(glob('data/ixpe*_det?_evt2_*_bary.fits*'))\n",
    "else:\n",
    "    datafilelist=rawdatafilelist\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0ef50f5-e062-451f-90c1-8296f7be7563",
   "metadata": {},
   "source": [
    "## Setup modulation files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a3fc821f-0b42-48bc-83d0-698d53a5cd78",
   "metadata": {},
   "outputs": [],
   "source": [
    "modfilelist=sorted(glob('data/ixpe*_mfact*.fits*'))\n",
    "if len(modfilelist)<3:\n",
    "    print('get modf files')\n",
    "    # if you are on sciserver create symbolic links to the data, otherwise download the files\n",
    "    if os.path.exists('%s/%s' % (SCIHEADER,MODF.replace('%d','1'))):\n",
    "        os.system('cd data ; for ii in %s/%s ; do ln -s $ii ; done' % (SCIHEADER,MODF.replace('%d','?')))\n",
    "    else:\n",
    "        for i in range(1,4):\n",
    "            os.system((('cd data ; wget -nH -nv --cut-dirs=8 https://heasarc.gsfc.nasa.gov/%s' % MODF)) % i)\n",
    "    modfilelist=sorted(glob('data/ixpe*_mfact*.fits*'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4102a0b-d3ea-419b-8c22-cc977d8d1e7f",
   "metadata": {},
   "source": [
    "# Definition of RVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "documented-latin",
   "metadata": {},
   "outputs": [],
   "source": [
    "def angfunk(alpha,beta,phase):\n",
    "    tanhalfC=np.tan(phase*np.pi)\n",
    "    halfamb=np.radians(alpha-beta)/2\n",
    "    halfapb=np.radians(alpha+beta)/2\n",
    "    halfAmB=np.arctan2(np.sin(halfamb),np.sin(halfapb)*tanhalfC)\n",
    "    halfApB=np.arctan2(np.cos(halfamb),np.cos(halfapb)*tanhalfC)\n",
    "    return (halfAmB-halfApB)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00e17cbb-07ab-4a7f-9f34-1e512fee0890",
   "metadata": {},
   "source": [
    "# Load Data and Modulation Fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "collaborative-thriller",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 data/ixpe02004001_det1_evt2_v01_bary.fits.gz\n",
      "1 data/ixpe02004001_det2_evt2_v01_bary.fits.gz\n",
      "2 data/ixpe02004001_det3_evt2_v01_bary.fits.gz\n"
     ]
    }
   ],
   "source": [
    "nphotondu=np.zeros(4).astype(int)\n",
    "for i,f in enumerate(datafilelist): \n",
    "    print(i,f)\n",
    "    with fits.open(modfilelist[i]) as hdu:\n",
    "        modlist=hdu[1].data['SPECRESP']\n",
    "    with fits.open(f) as hdu:\n",
    "        # hdu[1].data['PI']=pislope[i]*hdu[1].data['PI']+pioffset[i]\n",
    "        if (i==0):\n",
    "            MJDREFI=hdu[0].header['MJDREFI']\n",
    "            MJDREFF=hdu[0].header['MJDREFF']\n",
    "            LIVETIME=hdu[0].header['LIVETIME']\n",
    "            evtlist=hdu[1].data\n",
    "            moddata=np.transpose(modlist[np.clip(hdu[1].data['PI']-25,0,len(modlist)-1)])\n",
    "            detector=np.full(len(hdu[1].data),i+1)\n",
    "        else:\n",
    "            evtlist=np.append(evtlist,hdu[1].data)\n",
    "            moddata=np.concatenate((moddata,np.transpose(modlist[np.clip(hdu[1].data['PI']-25,0,len(modlist)-1)])))\n",
    "            detector=np.concatenate((detector,np.full(len(hdu[1].data),i+1)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "transparent-papua",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "301.63663 286.92328\n",
      "301.67972 286.5232\n",
      "301.68073 286.51266\n",
      "301.68088 286.51227\n",
      "301.68088 286.51227\n"
     ]
    }
   ],
   "source": [
    "xc, yc=300, 300\n",
    "for _i in range(5):\n",
    "    rr=np.hypot(evtlist['X']-xc,evtlist['Y']-yc)\n",
    "    ok=(evtlist['PI']>49) & (evtlist['PI']<=200) & (rr<50) \n",
    "    xc,yc=np.mean(evtlist['X'][ok]),np.mean(evtlist['Y'][ok])\n",
    "    print(xc,yc)\n",
    "rr=np.hypot(evtlist['X']-xc,evtlist['Y']-yc)\n",
    "ok=(evtlist['PI']>49) & (evtlist['PI']<=200) & (rr<20) \n",
    "back=(evtlist['PI']>49) & (evtlist['PI']<=200) & (rr>30)  & (rr<50)\n",
    "evtback=evtlist[back]\n",
    "evtlist=evtlist[ok]\n",
    "modback=moddata[back]\n",
    "moddata=moddata[ok]\n",
    "detback=detector[back]\n",
    "detector=detector[ok]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "premium-remedy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3303573a90>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(evtlist['X'],evtlist['Y'],bins=100)\n",
    "plt.show()\n",
    "plt.plot(evtlist['X'],evtlist['Y'],',')\n",
    "plt.plot(evtback['X'],evtback['Y'],',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2a3eeda4-3f50-446b-bb6d-f1091614f7a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.007906091241875133"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(evtback)/len(evtlist)/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "proof-mitchell",
   "metadata": {},
   "outputs": [],
   "source": [
    "pispec=np.histogram(evtlist['PI'],bins=150)[0]\n",
    "np.save('output/pispec',pispec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "sixth-saskatchewan",
   "metadata": {},
   "outputs": [],
   "source": [
    "backpispec=np.histogram(evtback['PI'],bins=150)[0]*20**2/(50**2-30**2)\n",
    "np.save('output/backpispec',backpispec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "minus-duplicate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 -0.02140812673438465 0.0736804327826248 0.0767275313385643\n",
      "2 -0.0024519747801810448 0.08642464985447634 0.08645942564458477\n",
      "3 -0.01345737110357219 0.08338576005084894 0.08446470159940787\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,4):\n",
    "    _q,_u=np.mean((evtlist['Q']/moddata)[detector==i]),np.mean((evtlist['U']/moddata)[detector==i])\n",
    "    print(i,_q,_u,np.hypot(_q,_u))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71bd1464-1608-4413-ac29-63eb80b51a62",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Barycentring with GBM Orbital Elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "43573827-28d2-4486-a989-17c564f5aa70",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "if doorbbary:\n",
    "    os.system('[ -f %s.fits.gz ] || curl https://gammaray.nsstc.nasa.gov/gbm/science/pulsars/lightcurves/%s.fits.gz --output %s.fits.gz' % (SOURCENAME,SOURCENAME,SOURCENAME))\n",
    "    with fits.open('%s.fits.gz' % SOURCENAME)  as hdu:\n",
    "        orbitdata=hdu['Orbital Elements'].data\n",
    "        spindata=hdu['PSR_HIST'].data\n",
    "    day=3600*24\n",
    "    TPIOVER2O=orbitdata['BINARYEPOCH']-2400000.5\n",
    "    PORB=orbitdata['PBINARY']*day\n",
    "    PORBDOT=orbitdata['PBDOT']\n",
    "    ECCENTRICITY=orbitdata['ECCENTRICITY']\n",
    "    OMEGA=orbitdata['PERIAPSE']\n",
    "    ASINIOVERC=orbitdata['AXSINI']\n",
    "    NUORB=1/PORB\n",
    "    NUORBDOT=-1/PORB/PORB*PORBDOT\n",
    "    met0=evtlist['TIME'].min()\n",
    "    MJDSTART=MJDREFF+MJDREFI+met0/day\n",
    "    NU=np.interp(MJDSTART,spindata['BARYTIME'],spindata['FREQUENCY'])\n",
    "    NUDOT=np.interp(MJDSTART,spindata['BARYTIME'],spindata['FDOT'])\n",
    "    TREFNU=MJDSTART\n",
    "    torb=((MJDREFF+MJDREFI)+evtlist['TIME']/day-TPIOVER2O)*day\n",
    "    phaseorb=(0.25+torb*(NUORB+torb*NUORBDOT/2))                    \n",
    "    orbtime=ASINIOVERC*((np.sin(2*np.pi*phaseorb)-np.sin(np.pi/2))-\n",
    "                    0.5*ECCENTRICITY*(np.sin(4*np.pi*phaseorb-np.radians(OMEGA))-\n",
    "                                      np.sin(np.pi/2-np.radians(OMEGA))))\n",
    "    deltat=evtlist['TIME']-orbtime-met0\n",
    "    freqtst=NU\n",
    "    if NUDOT==0:\n",
    "        _ii=spindata['BARYTIME']>59000\n",
    "        nufunk=InterpolatedUnivariateSpline(spindata['BARYTIME'][_ii],spindata['FREQUENCY'][_ii],k=2)\n",
    "        nudd=nufunk.derivatives(MJDSTART)\n",
    "        NU=nudd[0]\n",
    "        NUDOT=nudd[1]/day\n",
    "        NUDDOT=nudd[2]/day/day\n",
    "    else:\n",
    "        NUDDOT=0\n",
    "\n",
    "else:\n",
    "    deltat=evtlist['TIME']-evtlist['TIME'].min()\n",
    "phdata=np.mod(deltat*(NU+0*0.5*deltat*(NUDOT*deltat*NUDDOT*0.3333333)),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ba7dd383-153b-4683-bda1-d8e7f7d21419",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59978.81988397932,\n",
       " 0.8079475039414423,\n",
       " 4.143139582617706e-15,\n",
       " 0.8079474850576043,\n",
       " 244566.410694)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MJDSTART,NU,NUDOT,freqtst,LIVETIME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ec620e0d-48de-4b67-97d4-53b26a24e1b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bindata=plt.hist2d(phdata,deltat,bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4cdee69b-174c-4c8e-b23b-7101a87e409b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8079475039414423, 21.44425031542778)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NU,deltat.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5342d515-05cc-434c-bac4-0c02394f0a50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6686771538022659\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "toton=plt.hist(phdata,bins=100)\n",
    "print((toton[0].max()-toton[0].min())/(toton[0].max()+toton[0].min()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8b784660-7c37-4003-8557-178587ae4fba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(toton[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "715b184c-8160-410a-9667-75edd64dbbf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez('output/toton',np.array(toton,dtype=object))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interested-passport",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Unbinned Likelihood Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "resistant-governor",
   "metadata": {},
   "outputs": [],
   "source": [
    "qdata=evtlist['Q']*moddata\n",
    "udata=evtlist['U']*moddata\n",
    "\n",
    "def maxlikephase(param):\n",
    "    for _i in range(len(param)//2):\n",
    "        qloc[_i]=np.cos(2*param[2*_i])*np.tanh(param[2*_i+1])\n",
    "        uloc[_i]=np.sin(2*param[2*_i])*np.tanh(param[2*_i+1])\n",
    "#        qloc[_i]=param[2*_i]\n",
    "#        uloc[_i]=param[2*_i+1]\n",
    "\n",
    "    return(-np.sum(np.log(1+0.5*(qloc[phbin]*qdata+uloc[phbin]*udata))))\n",
    "\n",
    "def maxlikephaseok(param,_phb,_qd,_ud):\n",
    "    for _i in range(len(param)//2):\n",
    "        qloc[_i]=np.cos(2*param[2*_i])*np.tanh(param[2*_i+1])\n",
    "        uloc[_i]=np.sin(2*param[2*_i])*np.tanh(param[2*_i+1])\n",
    "#        qloc[_i]=param[2*_i]\n",
    "#        uloc[_i]=param[2*_i+1]\n",
    "\n",
    "    return(-np.sum(np.log(1+0.5*(qloc[_phb]*_qd+uloc[_phb]*_ud))))\n",
    "\n",
    "\n",
    "def maxlikephasequ(param):\n",
    "    for _i in range(len(param)//2):\n",
    "        qloc[_i]=np.tanh(param[2*_i])\n",
    "        uloc[_i]=np.tanh(param[2*_i+1])\n",
    "#        qloc[_i]=param[2*_i]\n",
    "#        uloc[_i]=param[2*_i+1]\n",
    "\n",
    "    return(-np.sum(np.log(1+0.5*(qloc[phbin]*qdata+uloc[phbin]*udata))))\n",
    "\n",
    "def maxlikefixed(param):\n",
    "    qloc=param[0]\n",
    "    uloc=param[1]\n",
    "    norm=np.hypot(qloc,uloc)\n",
    "    if norm>1:\n",
    "        qloc/=norm\n",
    "        uloc/=norm\n",
    "    \n",
    "    return(-np.sum(np.log(1+0.5*(qloc*qdata+uloc*udata))))\n",
    "\n",
    "def maxlikerotation(param):\n",
    "    ang=angfunk(param[1],param[2],phdata-param[4])+np.radians(param[3])\n",
    "    qloc=np.cos(2*ang)*np.tanh(param[0])\n",
    "    uloc=np.sin(2*ang)*np.tanh(param[0])\n",
    "    if (len(param)>5):\n",
    "        qloc+=np.tanh(param[5])\n",
    "        uloc+=np.tanh(param[6])\n",
    "    _res=-np.sum(np.log(1+0.5*(qloc*qdata+uloc*udata)))\n",
    "    # print(_res,param)\n",
    "    return _res\n",
    "def maxlikerotation_rand(param):\n",
    "    ang=angfunk(param[1],param[2],phdata-param[4])+np.radians(param[3])\n",
    "    qloc=np.cos(2*ang)*np.tanh(param[0])\n",
    "    uloc=np.sin(2*ang)*np.tanh(param[0])\n",
    "    if (len(param)>5):\n",
    "        qloc+=np.tanh(param[5])\n",
    "        uloc+=np.tanh(param[6])\n",
    "    return(-np.sum(np.log(1+0.5*(qloc*qrand+uloc*urand))))\n",
    "def maxlikerotation2(param):\n",
    "    ang=angfunk(param[1],param[2],phdata-param[4])+np.radians(param[3])\n",
    "    qloc=np.cos(2*ang)\n",
    "    uloc=np.sin(2*ang)\n",
    "    polfrac=np.tanh(param[0])\n",
    "    if (len(param)>5):\n",
    "        polfrac*=(1+param[5]*np.cos(2*np.pi*(phdata-param[6])))\n",
    "    qloc*=polfrac\n",
    "    uloc*=polfrac\n",
    "    if (len(param)>7):\n",
    "        qloc+=np.tanh(param[7])\n",
    "        uloc+=np.tanh(param[8])        \n",
    "    return(-np.sum(np.log(1+0.5*(qloc*qdata+uloc*udata))))\n",
    "def maxlikerotation2_ok(param,_phd,_qd,_ud):\n",
    "    ang=angfunk(param[1],param[2],_phd-param[4])+np.radians(param[3])\n",
    "    qloc=np.cos(2*ang)\n",
    "    uloc=np.sin(2*ang)\n",
    "    polfrac=np.tanh(param[0])\n",
    "    if (len(param)>5):\n",
    "        polfrac*=(1+param[5]*np.cos(2*np.pi*(_phd-param[6])))\n",
    "    qloc*=polfrac\n",
    "    uloc*=polfrac\n",
    "    if (len(param)>7):\n",
    "        qloc+=np.tanh(param[7])\n",
    "        uloc+=np.tanh(param[8]) \n",
    "    return(-np.sum(np.log(1+0.5*(qloc*_qd+uloc*_ud))))\n",
    "def maxlikerotation2frac(param):\n",
    "    ang=angfunk(param[1],param[2],phdata-param[4])+np.radians(param[3]+param[4]*tdata)\n",
    "    qloc=np.cos(2*ang)\n",
    "    uloc=np.sin(2*ang)\n",
    "    polfrac=np.tanh(param[0])\n",
    "    if (len(param)>5):\n",
    "        polfrac*=(1+param[5]*np.cos(2*np.pi*(phdata-param[6])))\n",
    "    qloc*=polfrac\n",
    "    uloc*=polfrac\n",
    "    if (len(param)>7):\n",
    "        qloc+=np.tanh(param[7])\n",
    "        uloc+=np.tanh(param[8])        \n",
    "    return(-np.sum(np.log(1+0.5*(qloc*qdata+uloc*udata))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "massive-joseph",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.savetxt('output/photondata.txt',[phdata,qdata,udata])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "temporal-adventure",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " final_simplex: (array([[-0.00800552,  0.08049365],\n",
       "       [-0.00793784,  0.08048935],\n",
       "       [-0.00795446,  0.08043259]]), array([-153.55928255, -153.559238  , -153.55922261]))\n",
       "           fun: -153.55928255076324\n",
       "       message: 'Optimization terminated successfully.'\n",
       "          nfev: 74\n",
       "           nit: 39\n",
       "        status: 0\n",
       "       success: True\n",
       "             x: array([-0.00800552,  0.08049365])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppp_fixed=minimize(maxlikefixed,np.random.normal(size=2)*np.array([0.3,0.3]),method='Nelder-Mead')\n",
    "ppp_fixed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bound-colon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " final_simplex: (array([[ 9.44709758e-02, -3.77613153e-02, -1.49366449e-02,\n",
       "        -1.33725530e+02, -1.54901793e+00],\n",
       "       [ 9.44683730e-02, -3.90429298e-02, -1.54440673e-02,\n",
       "        -1.33726005e+02, -1.54902454e+00],\n",
       "       [ 9.44669200e-02, -4.04092414e-02, -1.59842672e-02,\n",
       "        -1.33725532e+02, -1.54901630e+00],\n",
       "       [ 9.44685216e-02, -4.05732210e-02, -1.60497095e-02,\n",
       "        -1.33725875e+02, -1.54902079e+00],\n",
       "       [ 9.44679527e-02, -4.33447702e-02, -1.71456188e-02,\n",
       "        -1.33725673e+02, -1.54902298e+00],\n",
       "       [ 9.44685873e-02, -4.12380320e-02, -1.63120736e-02,\n",
       "        -1.33726146e+02, -1.54902059e+00]]), array([-208.46474104, -208.46474103, -208.46474089, -208.46474088,\n",
       "       -208.46474085, -208.46474084]))\n",
       "           fun: -208.46474103930012\n",
       "       message: 'Maximum number of function evaluations has been exceeded.'\n",
       "          nfev: 1000\n",
       "           nit: 625\n",
       "        status: 1\n",
       "       success: False\n",
       "             x: array([ 9.44709758e-02, -3.77613153e-02, -1.49366449e-02, -1.33725530e+02,\n",
       "       -1.54901793e+00])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppp_rot=minimize(maxlikerotation2,np.random.normal(size=5)*np.array([np.hypot(ppp_fixed.x[0],ppp_fixed.x[1]),50,50,90,1]),method='Nelder-Mead')\n",
    "if (ppp_rot.x[0]<0):\n",
    "    ppp_rot.x[0]*=-1\n",
    "    ppp_rot.x[3]+=90\n",
    "ppp_rot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "proud-prior",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " final_simplex: (array([[ 1.06480205e-01, -4.29700507e-02, -1.48942228e-02,\n",
       "        -1.33950441e+02, -1.53612007e+00,  4.19322494e-01,\n",
       "        -1.68557407e-01],\n",
       "       [ 1.06480282e-01, -4.29700679e-02, -1.48941838e-02,\n",
       "        -1.33950383e+02, -1.53612000e+00,  4.19322795e-01,\n",
       "        -1.68558019e-01],\n",
       "       [ 1.06480209e-01, -4.29701284e-02, -1.48941663e-02,\n",
       "        -1.33950445e+02, -1.53612077e+00,  4.19323059e-01,\n",
       "        -1.68557783e-01],\n",
       "       [ 1.06480265e-01, -4.29700954e-02, -1.48941734e-02,\n",
       "        -1.33950496e+02, -1.53612039e+00,  4.19324423e-01,\n",
       "        -1.68558408e-01],\n",
       "       [ 1.06480393e-01, -4.29700213e-02, -1.48941285e-02,\n",
       "        -1.33950394e+02, -1.53612034e+00,  4.19324575e-01,\n",
       "        -1.68557165e-01],\n",
       "       [ 1.06480352e-01, -4.29700086e-02, -1.48942176e-02,\n",
       "        -1.33950392e+02, -1.53612105e+00,  4.19323828e-01,\n",
       "        -1.68557979e-01],\n",
       "       [ 1.06480520e-01, -4.29700829e-02, -1.48941897e-02,\n",
       "        -1.33950469e+02, -1.53612061e+00,  4.19321562e-01,\n",
       "        -1.68557675e-01],\n",
       "       [ 1.06480292e-01, -4.29697752e-02, -1.48941165e-02,\n",
       "        -1.33950488e+02, -1.53612074e+00,  4.19327172e-01,\n",
       "        -1.68556835e-01]]), array([-230.61675113, -230.61675113, -230.61675113, -230.61675113,\n",
       "       -230.61675113, -230.61675113, -230.61675113, -230.61675113]))\n",
       "           fun: -230.61675113124932\n",
       "       message: 'Optimization terminated successfully.'\n",
       "          nfev: 561\n",
       "           nit: 345\n",
       "        status: 0\n",
       "       success: True\n",
       "             x: array([ 1.06480205e-01, -4.29700507e-02, -1.48942228e-02, -1.33950441e+02,\n",
       "       -1.53612007e+00,  4.19322494e-01, -1.68557407e-01])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppp_rot2=minimize(maxlikerotation2,np.concatenate((ppp_rot.x,np.random.normal(size=2))),method='Nelder-Mead')\n",
    "ppp_rot2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "divine-kelly",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " final_simplex: (array([[ 9.44683560e-02, -4.18684393e-02, -1.65613382e-02,\n",
       "        -1.33725357e+02, -1.54902260e+00],\n",
       "       [ 9.44685401e-02, -4.18681799e-02, -1.65614048e-02,\n",
       "        -1.33725423e+02, -1.54902312e+00],\n",
       "       [ 9.44685632e-02, -4.18682609e-02, -1.65613675e-02,\n",
       "        -1.33725445e+02, -1.54902261e+00],\n",
       "       [ 9.44682137e-02, -4.18683261e-02, -1.65614359e-02,\n",
       "        -1.33725404e+02, -1.54902254e+00],\n",
       "       [ 9.44683376e-02, -4.18682129e-02, -1.65612782e-02,\n",
       "        -1.33725392e+02, -1.54902115e+00],\n",
       "       [ 9.44679953e-02, -4.18684339e-02, -1.65613788e-02,\n",
       "        -1.33725366e+02, -1.54902248e+00]]), array([-208.46474099, -208.46474099, -208.46474099, -208.46474099,\n",
       "       -208.46474099, -208.46474099]))\n",
       "           fun: -208.46474099131513\n",
       "       message: 'Optimization terminated successfully.'\n",
       "          nfev: 189\n",
       "           nit: 104\n",
       "        status: 0\n",
       "       success: True\n",
       "             x: array([ 9.44683560e-02, -4.18684393e-02, -1.65613382e-02, -1.33725357e+02,\n",
       "       -1.54902260e+00])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppp_rot=minimize(maxlikerotation2,ppp_rot2.x[0:-2],method='Nelder-Mead')\n",
    "ppp_rot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "african-voluntary",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Binned Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "arctic-memphis",
   "metadata": {},
   "outputs": [],
   "source": [
    "qloc=np.zeros(14)\n",
    "uloc=np.zeros(14)\n",
    "phbin=(phdata//(1/14.0)).astype(int)\n",
    "ppp_phase=minimize(maxlikephase,np.random.uniform(size=28))\n",
    "ppp_phase_err=np.diag(ppp_phase.hess_inv)**0.5  \n",
    "tt=np.histogram(phdata,bins=14)[0]\n",
    "phlist=np.linspace(1./28,27./28,14)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "surface-adelaide",
   "metadata": {},
   "outputs": [],
   "source": [
    "ppp_phasequ=minimize(maxlikephasequ,np.random.uniform(size=28))\n",
    "ppp_phasequ_err=np.diag(ppp_phasequ.hess_inv)**.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ebb9d8fb-a608-485e-a633-18ee195861e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.03571429, 0.10714286, 0.17857143, 0.25      , 0.32142857,\n",
       "        0.39285714, 0.46428571, 0.53571429, 0.60714286, 0.67857143,\n",
       "        0.75      , 0.82142857, 0.89285714, 0.96428571]),\n",
       " 28,\n",
       " 28,\n",
       " 14)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phlist,len(ppp_phasequ.x),len(np.diag(ppp_phasequ.hess_inv)),len(tt-0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "distant-announcement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#    Phase        Q/I       EQ/I        U/I       EU/I     Counts\n",
      " 0.0357143  0.0428778  0.0205473   0.186143  0.0213772      50474 \n",
      "  0.107143 0.00464067  0.0209698  0.0981155  0.0204109      52516 \n",
      "  0.178571 -0.0261179   0.016329  0.0377245  0.0159812      88713 \n",
      "      0.25 -0.0817189  0.0124544 0.00711005    0.01337     137108 \n",
      "  0.321429 -0.0669715  0.0131076  0.0582348  0.0129167     130033 \n",
      "  0.392857 -0.0195726  0.0160935   0.084321  0.0143627      97009 \n",
      "  0.464286  0.0106664  0.0175752  0.0569486  0.0156542     101232 \n",
      "  0.535714  0.0259964  0.0181125  0.0462874  0.0187823      62833 \n",
      "  0.607143  0.0944383  0.0273639   0.139734  0.0255099      29923 \n",
      "  0.678571  0.0542189  0.0244731   0.107398  0.0246466      36556 \n",
      "      0.75  0.0776928  0.0223456   0.127444  0.0222653      48800 \n",
      "  0.821429  0.0139313   0.022976   0.164831  0.0216684      48891 \n",
      "  0.892857  0.0185637  0.0207594   0.128696  0.0207479      51126 \n",
      "  0.964286  0.0127976  0.0200154    0.13525  0.0195772      50956 \n"
     ]
    }
   ],
   "source": [
    "tt=np.histogram(phdata,bins=14)[0]\n",
    "phlist=np.linspace(1./28,27./28,14)\n",
    "with open('output/QU_phase.dat','w') as f:\n",
    "    print('#    Phase        Q/I       EQ/I        U/I       EU/I     Counts')\n",
    "    f.write('#    Phase        Q/I       EQ/I        U/I       EU/I     Counts\\n')\n",
    "\n",
    "    for r in zip(phlist,np.tanh(ppp_phasequ.x[::2]),np.diag(ppp_phasequ.hess_inv)[::2]**0.5/np.cosh(ppp_phasequ.x[::2])**2,\n",
    "                 np.tanh(ppp_phasequ.x[1::2]),np.diag(ppp_phasequ.hess_inv)[1::2]**0.5/np.cosh(ppp_phasequ.x[1::2])**2,tt):\n",
    "        print(('%10g '*6) % r)\n",
    "        f.write((('%10g '*6) % r)+'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "suburban-geography",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#    Phase         PD        EPD         PA        EPA     Counts\n",
      " 0.0357143   0.191017  0.0209484    38.5141     3.3566      50474 \n",
      "  0.107143  0.0982251  0.0198804     43.646    10.6486      52516 \n",
      "  0.178571  0.0458834  0.0155114     62.348    13.7882      88713 \n",
      "      0.25  0.0820276  0.0130134    87.5137    3.50334     137108 \n",
      "  0.321429  0.0887495  0.0128376    69.4958    4.54815     130033 \n",
      "  0.392857  0.0865628  0.0144624     51.534    4.78293      97009 \n",
      "  0.464286  0.0579389  0.0155428    39.6958    4.07438     101232 \n",
      "  0.535714   0.053088  0.0252015    30.3401    4.37934      62833 \n",
      "  0.607143   0.168654  0.0325911    27.9737     9.4833      29923 \n",
      "  0.678571   0.120308  0.0238998    31.6068    3.59178      36556 \n",
      "      0.75   0.149259  0.0210001    29.3163    6.17261      48800 \n",
      "  0.821429   0.165418  0.0278412    42.5844    3.15033      48891 \n",
      "  0.892857   0.130028  0.0203621     40.896    9.25027      51126 \n",
      "  0.964286   0.135854  0.0208199    42.2973     3.1185      50956 \n"
     ]
    }
   ],
   "source": [
    "with open('output/pol_phase.dat','w') as f:\n",
    "    print('#    Phase         PD        EPD         PA        EPA     Counts')\n",
    "    f.write('#    Phase         PD        EPD         PA        EPA     Counts\\n')\n",
    "    for r in zip((np.arange(len(ppp_phase.x)//2)+0.5)/(len(ppp_phase.x)//2),np.abs(np.tanh(ppp_phase.x[1::2])),ppp_phase_err[1::2]/np.cosh(ppp_phase.x[1::2])**2,np.mod(np.degrees(np.where(ppp_phase.x[1::2]>0,0,np.pi*0.5)+ppp_phase.x[::2]),180),\n",
    "        np.degrees(ppp_phase_err[::2]),tt):\n",
    "        print('%10g '*6 %r)\n",
    "        f.write(('%10g '*6 %r)+'\\n')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "powerful-language",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'U/I')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize=(6,4))\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.errorbar(np.tanh(ppp_phasequ.x[::2]),np.tanh(ppp_phasequ.x[1::2]),yerr=np.diag(ppp_phasequ.hess_inv)[1::2]**0.5/np.cosh(ppp_phasequ.x[1::2])**2,xerr=np.diag(ppp_phasequ.hess_inv)[::2]**0.5/np.cosh(ppp_phasequ.x[::2])**2,fmt='.')\n",
    "for _xx,_yy,_tt in zip(np.tanh(ppp_phasequ.x[::2]),np.tanh(ppp_phasequ.x[1::2]),np.linspace(0.05,0.95,10)):\n",
    "    plt.text(_xx-3e-2,_yy+1e-2,'%.2f' % _tt)\n",
    "plt.xlabel('Q/I')\n",
    "plt.ylabel('U/I')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "figured-writing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize=(7,4))\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.errorbar(np.tanh(ppp_phasequ.x[::2])*tt,np.tanh(ppp_phasequ.x[1::2])*tt,yerr=tt*np.diag(ppp_phasequ.hess_inv)[1::2]**0.5/np.cosh(ppp_phasequ.x[1::2])**2,xerr=tt*np.diag(ppp_phasequ.hess_inv)[::2]**0.5/np.cosh(ppp_phasequ.x[::2])**2,fmt='.')\n",
    "for _xx,_yy,_tt,_qq in zip(np.tanh(ppp_phasequ.x[::2])*tt,np.tanh(ppp_phasequ.x[1::2])*tt,phlist,tt):\n",
    "    plt.text(_xx-3e+1,_yy+1e+1,'%.2f' % (_tt))\n",
    "param=ppp_rot2.x\n",
    "ang=angfunk(param[1],param[2],phlist-param[4])+np.radians(param[3])\n",
    "polfrac=np.tanh(param[0])\n",
    "if (len(param)>5):\n",
    "    polfrac*=(1+param[5]*np.cos(2*np.pi*(phlist-param[6])))\n",
    "\n",
    "qloc=np.cos(2*ang)*polfrac*60000\n",
    "uloc=np.sin(2*ang)*polfrac*60000\n",
    "#plt.plot(qloc,uloc)  \n",
    "#plt.xlim(-18000,5000)\n",
    "#plt.ylim(0,12000)\n",
    "#plt.ylim(-0.1,0.3)\n",
    "plt.xlabel('Q [Counts]')\n",
    "plt.ylabel('U [Counts]')\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/QU.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "southeast-warrior",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "blank-permit",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plottwo(x,y,yerr=None,fmt=None,ax=None,**kwargs):\n",
    "    _x=np.concatenate((x,x+1))\n",
    "    _y=np.concatenate((y,y))\n",
    "    if fmt is None:\n",
    "        fmt='.'\n",
    "    if ax is None:\n",
    "        if yerr is None:\n",
    "            plt.plot(_x,_y,xerr=(_x[1]-_x[0])/2,fmt=fmt,**kwargs)\n",
    "        else:\n",
    "            _yerr=np.concatenate((yerr,yerr))\n",
    "            plt.errorbar(_x,_y,xerr=(_x[1]-_x[0])/2,yerr=_yerr,fmt=fmt,**kwargs)\n",
    "    else:\n",
    "        if yerr is None:\n",
    "            ax.plot(_x,_y,xerr=(_x[1]-_x[0])/2,fmt=fmt,**kwargs)\n",
    "        else:\n",
    "            _yerr=np.concatenate((yerr,yerr))\n",
    "            ax.errorbar(_x,_y,xerr=(_x[1]-_x[0])/2,yerr=_yerr,fmt=fmt,**kwargs)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "black-laugh",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cfunk(p,a,b,c):\n",
    "    return(a*np.cos((p-b)*2*np.pi)+c)\n",
    "\n",
    "cfit=curve_fit(cfunk,0.5*toton[1][:-1]+0.5*toton[1][1:],toton[0],sigma=(toton[0])**0.5)\n",
    "pfit=curve_fit(cfunk,(np.arange(len(ppp_phase.x)//2)+0.5)/10,100*np.abs(np.tanh(ppp_phase.x[1::2])),sigma=100*ppp_phase_err[1::2]/np.cosh(ppp_phase.x[1::2])**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "multiple-value",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6686771538022659\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "toton=plt.hist(phdata,bins=100)\n",
    "print((toton[0].max()-toton[0].min())/(toton[0].max()+toton[0].min()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "imperial-evolution",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6686771538022659 0.5040216248236846\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "_ph=np.linspace(0,2,201)\n",
    "plottwo(0.5*toton[1][:-1]+0.5*toton[1][1:],toton[0],(toton[0])**0.5,'.')\n",
    "#plt.plot(_ph,cfunk(_ph,*cfit[0]))\n",
    "plt.ylabel('Counts')\n",
    "plt.xlabel('Phase')\n",
    "print((toton[0].max()-toton[0].min())/(toton[0].max()+toton[0].min()),cfit[0][0]/cfit[0][2])\n",
    "plt.savefig('figures/intensity.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "indian-induction",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11235812308505995 0.007941361731190177\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plottwo((np.arange(len(ppp_phase.x)//2)+0.5)/(len(ppp_phase.x)//2),np.abs(np.tanh(ppp_phase.x[1::2])),ppp_phase_err[1::2]/np.cosh(ppp_phase.x[1::2])**2,'.')\n",
    "#plt.plot(_ph,cfunk(_ph,*pfit[0])/100)\n",
    "#plt.plot(_ph,np.abs(ppp_rot2.x[0]*(1+ppp_rot2.x[5]*np.cos(2*np.pi*(_ph-ppp_rot2.x[6])))))\n",
    "plt.ylabel('Polarization Degree')\n",
    "plt.xlabel('Phase')\n",
    "plt.savefig('figures/polar-deg.pdf')\n",
    "print(np.mean(np.abs(np.tanh(ppp_phase.x[1::2]))),np.sum((ppp_phase_err[1::2]/np.cosh(ppp_phase.x[1::2]))**2)**0.5/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "raised-bulgaria",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Alpha:                      179.957\n",
      "Beta:                       -0.0148942\n",
      "Mean Polarization Fraction: 0.10608\n",
      "Ampl Polarization Fraction: 0.0444816\n",
      "Peak Polarization Fraction: 0.831443\n",
      "Pole across Line of Sight:  0.96388\n",
      "Peak Flux:                  0.28505\n",
      "\n",
      "\n",
      "Alpha:                      179.958\n",
      "Beta:                       -0.0165613\n",
      "Mean Polarization Fraction: 0.0941883\n",
      "Pole across Line of Sight:  0.950977\n",
      "Peak Flux:                  0.28505\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shift=0\n",
    "\n",
    "\n",
    "plottwo((np.arange(len(ppp_phase.x)//2)+0.5)/(len(ppp_phase.x)//2),np.mod(np.degrees(np.where(ppp_phase.x[1::2]>0,0,np.pi*0.5)+ppp_phase.x[::2])-shift,180)+shift,np.degrees(ppp_phase_err[::2]),c='tab:orange')\n",
    "_ph=np.linspace(0,2,401)\n",
    "print('''\n",
    "Alpha:                      %g\n",
    "Beta:                       %g\n",
    "Mean Polarization Fraction: %g\n",
    "Ampl Polarization Fraction: %g\n",
    "Peak Polarization Fraction: %g\n",
    "Pole across Line of Sight:  %g\n",
    "Peak Flux:                  %g\n",
    "''' % ((ppp_rot2.x[1]%180,ppp_rot2.x[2])+tuple(np.mod(np.array([np.abs(np.tanh(ppp_rot2.x[0])),np.abs(ppp_rot2.x[5]*np.tanh(ppp_rot2.x[0])),\n",
    "                                                            ppp_rot2.x[6],\n",
    "                                                            ppp_rot2.x[4]+(0 if ppp_rot2.x[2]>0 else 0.5),\n",
    "                                                            (cfit[0][1] if cfit[0][0]>0 else cfit[0][1]-0.5)]),1))))\n",
    "print('''\n",
    "Alpha:                      %g\n",
    "Beta:                       %g\n",
    "Mean Polarization Fraction: %g\n",
    "Pole across Line of Sight:  %g\n",
    "Peak Flux:                  %g\n",
    "''' % (ppp_rot.x[1]%180,ppp_rot.x[2],np.abs(np.tanh(ppp_rot.x[0])),np.mod(ppp_rot.x[4]+(0 if ppp_rot.x[2]>0 else 0.5),1),\n",
    "                                                            np.mod(cfit[0][1] if cfit[0][0]>0 else cfit[0][1]-0.5,1)))\n",
    "if ppp_rot.x[0]>0:\n",
    "    plt.plot(_ph,np.degrees(np.mod((angfunk(ppp_rot.x[1],ppp_rot.x[2],_ph-ppp_rot.x[4])+np.radians(ppp_rot.x[3]-shift)),np.pi))+shift,label='Constant PD')\n",
    "else:\n",
    "    plt.plot(_ph,np.degrees(np.mod(np.pi/2+(angfunk(ppp_rot.x[1],ppp_rot.x[2],_ph-ppp_rot.x[4])+np.radians(ppp_rot.x[3]-shift)),np.pi))+shift,label='Constant PD')\n",
    "\n",
    "if ppp_rot2.x[0]>0:\n",
    "    plt.plot(_ph,np.degrees(np.mod((angfunk(ppp_rot2.x[1],ppp_rot2.x[2],_ph-ppp_rot2.x[4])+np.radians(ppp_rot2.x[3]-shift)),np.pi))+shift,label='Variable PD')\n",
    "else:\n",
    "    plt.plot(_ph,np.degrees(np.mod(np.pi/2+(angfunk(ppp_rot2.x[1],ppp_rot2.x[2],_ph-ppp_rot2.x[4])+np.radians(ppp_rot2.x[3]-shift)),np.pi))+shift,label='Variable PD')\n",
    "\n",
    "#plt.plot(_ph,np.degrees(np.mod((angfunk(medvalues1[1],medvalues1[2],_ph-medvalues1[4])+np.radians(medvalues1[3]-shift)),np.pi))+shift,c='tab:blue',label='MCMC1')\n",
    "#plt.plot(_ph,np.degrees(np.mod((angfunk(medvalues2[1],medvalues2[2],_ph-medvalues2[4])+np.radians(medvalues2[3]-shift)),np.pi))+shift,c='tab:orange',label='MCMC2')\n",
    "\n",
    "#plt.plot(_ph,np.degrees(np.mod((angfunk(60.8,16.8,_ph-0.272)+np.radians(45.2-shift)),np.pi))+shift,label='MCMC')\n",
    "    \n",
    "#plt.ylim(25,75)\n",
    "plt.ylabel('Polarization Angle')\n",
    "plt.xlabel('Phase')\n",
    "plt.legend()\n",
    "plt.savefig('figures/polar-angle.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ab12b5a0-f7b8-4b61-9ca4-7a85cae1923d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shift=0\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3, 1,sharex=True,figsize=(6,12))\n",
    "_ph=np.linspace(0,2,201)\n",
    "plottwo(0.5*toton[1][:-1]+0.5*toton[1][1:],toton[0],(toton[0])**0.5,'.',ax1)\n",
    "ax1.set_ylabel('Counts')\n",
    "plottwo((np.arange(len(ppp_phase.x)//2)+0.5)/(len(ppp_phase.x)//2),np.abs(np.tanh(ppp_phase.x[1::2])),ppp_phase_err[1::2]/np.cosh(ppp_phase.x[1::2])**2,'.',ax2)\n",
    "ax2.plot(_ph,np.abs(ppp_rot2.x[0]*(1+ppp_rot2.x[5]*np.cos(2*np.pi*(_ph-ppp_rot2.x[6])))),c='tab:green')\n",
    "ax2.set_ylabel('Polarization Degree')\n",
    "plottwo((np.arange(len(ppp_phase.x)//2)+0.5)/(len(ppp_phase.x)//2),np.mod(np.degrees(np.where(ppp_phase.x[1::2]>0,0,np.pi*0.5)+ppp_phase.x[::2])-shift,180)+shift,np.degrees(ppp_phase_err[::2]),'.',ax3)\n",
    "ax3.plot(_ph,np.degrees(np.mod((angfunk(ppp_rot.x[1],ppp_rot.x[2],_ph-ppp_rot.x[4])+np.radians(ppp_rot.x[3]-shift)),np.pi))+shift)\n",
    "\n",
    "\n",
    "ax3.set_ylabel('Polarization Angle')\n",
    "plt.xlabel('Phase')\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "plt.savefig('figures/triple.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unsigned-recommendation",
   "metadata": {},
   "source": [
    "# Energy Dependence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "indoor-hunger",
   "metadata": {},
   "outputs": [],
   "source": [
    "pibin=np.clip(((evtlist['PI']-50)//25).astype(int),0,4)\n",
    "def maxlikeenergy(param):\n",
    "    return(-np.sum(np.log(1+0.5*(np.tanh(param[2*pibin])*qrot+np.tanh(param[2*pibin+1])*urot))))\n",
    "def maxlikeenergy2(param):\n",
    "    return(-np.sum(np.log(1+0.5*(np.tanh(param[pibin])*qrot))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "complimentary-inside",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0020060404956114644 0.09584386956135163 0.09586486077267008 0.005793704833153907 0.004842637267803286\n"
     ]
    }
   ],
   "source": [
    "angdata=angfunk( ppp_rot.x[1],  ppp_rot.x[2], phdata-ppp_rot.x[4])+np.radians(ppp_rot.x[3])\n",
    "\n",
    "sangdata=np.sin(2*angdata)\n",
    "cangdata=np.cos(2*angdata)\n",
    "urot=evtlist['U']*cangdata-evtlist['Q']*sangdata\n",
    "qrot=evtlist['U']*sangdata+evtlist['Q']*cangdata\n",
    "um,qm=np.mean(urot/moddata),np.mean(qrot/moddata)\n",
    "print(um,qm,np.hypot(um,qm),np.std(qrot/moddata)/len(qrot)**0.5,np.std(qrot)/np.mean(moddata)/len(qrot)**0.5)\n",
    "urot*=moddata\n",
    "qrot*=moddata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "covered-medication",
   "metadata": {},
   "outputs": [],
   "source": [
    "ppp_spec_deg=minimize(maxlikeenergy2,np.random.uniform(size=5)-0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "worse-xerox",
   "metadata": {},
   "outputs": [],
   "source": [
    "ppp_spec_qu=minimize(maxlikeenergy,np.random.uniform(size=10)-0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "champion-narrow",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'U/I')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.errorbar(np.tanh(ppp_spec_qu.x[::2]),np.tanh(ppp_spec_qu.x[1::2]),yerr=np.diag(ppp_spec_qu.hess_inv)[1::2]**0.5/np.cosh(ppp_spec_qu.x[1::2])**2,xerr=np.diag(ppp_spec_qu.hess_inv)[::2]**0.5/np.cosh(ppp_spec_qu.x[::2])**2,fmt='.')\n",
    "for _xx,_yy,_tt in zip(np.tanh(ppp_spec_qu.x[::2]),np.tanh(ppp_spec_qu.x[1::2]),['2.5-3','3-4','4-5','5-6','6-8']):\n",
    "    plt.text(_xx-3e-2,_yy+1e-2,_tt)\n",
    "#plt.xlim(-0.29,0.29)\n",
    "#plt.ylim(-0.29,0.29)\n",
    "plt.xlabel('Q/I')\n",
    "plt.ylabel('U/I')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "american-korea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Q/I')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "_xxx=[2.5,3.5,4.5,5.5,7]\n",
    "plt.errorbar(_xxx,np.tanh(ppp_spec_deg.x),yerr=np.diag(ppp_spec_deg.hess_inv)**0.5/np.cosh(ppp_spec_deg.x)**2,fmt='.')\n",
    "plt.xlabel('Energy [keV]')\n",
    "plt.ylabel('Q/I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adaptive-deputy",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Bin optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "superb-jumping",
   "metadata": {},
   "outputs": [],
   "source": [
    "_ii=np.argsort(evtlist['PI'])\n",
    "pisort=evtlist['PI'][_ii]\n",
    "modsort=moddata[_ii]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "subtle-sharing",
   "metadata": {},
   "outputs": [],
   "source": [
    "modcum=np.cumsum(modsort)\n",
    "def mdpfunk(imin,imax):\n",
    "    return(4.29/((modcum[imax]-modcum[imin])/(imax-imin))/(imax-imin)**0.5)\n",
    "def mdppi(pimin,pimax):\n",
    "    _imin=np.interp(pimin,pisort,range(len(pisort))).astype(int)\n",
    "    _imax=np.interp(pimax,pisort,range(len(pisort))).astype(int)\n",
    "\n",
    "    return mdpfunk(_imin,_imax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "removed-brown",
   "metadata": {},
   "outputs": [],
   "source": [
    "modscum=np.cumsum(modsort**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "wrong-theory",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     0,  65619, 117275, 160490, 197808, 231182, 261210, 288645,\n",
       "       314123, 337919, 360262, 381389, 401399, 420424, 438612, 456104,\n",
       "       472904, 489094, 504783, 519978, 534720, 549037, 562927, 576410,\n",
       "       589574, 602391, 614955, 627265, 639289, 651150, 662736, 674082,\n",
       "       685187, 696119, 706926, 717485, 727882, 738139, 748183, 758111,\n",
       "       767851, 777528, 786953, 796322, 805499, 814602, 823543, 832316,\n",
       "       840995, 849524, 857827, 866130, 874246, 882171, 890025, 897606,\n",
       "       905130, 912403, 919576, 926550, 933315, 939928, 946351, 952576,\n",
       "       958605, 964449, 970115, 975629, 980979, 986169])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plist=np.interp(np.linspace(0,1,70),modscum/modscum[-1],np.arange(len(modcum))).astype(int)\n",
    "plist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "allied-gibraltar",
   "metadata": {},
   "outputs": [],
   "source": [
    "for ii in range(len(plist)-1):\n",
    "    _imin=plist[ii]\n",
    "    _imax=plist[ii+1]\n",
    "    _pmin,_pmax=pisort[_imin],pisort[_imax]\n",
    "    # print(ii,_pmin,_pmax,_pmin/25,_pmax/25,mdpfunk(plist[ii],plist[ii+1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "anticipated-surge",
   "metadata": {},
   "outputs": [],
   "source": [
    "pilist=np.concatenate((pisort[plist[0:65:5]],pisort[plist[65:]]))\n",
    "elist=pilist/25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "packed-novelty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 17)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pibin=np.digitize(evtlist['PI'],pilist)-1\n",
    "pibin.min(),pibin.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "revised-tension",
   "metadata": {},
   "outputs": [],
   "source": [
    "ppp_spec_deg=minimize(maxlikeenergy2,np.random.uniform(size=pibin.max()+1)-0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "afraid-danger",
   "metadata": {},
   "outputs": [],
   "source": [
    "ppp_spec_qu=minimize(maxlikeenergy,np.random.uniform(size=2*pibin.max()+2)-0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "appreciated-belly",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,6))\n",
    "plt.errorbar(np.tanh(ppp_spec_qu.x[::2]),np.tanh(ppp_spec_qu.x[1::2]),yerr=np.diag(ppp_spec_qu.hess_inv)[1::2]**0.5/np.cosh(ppp_spec_qu.x[1::2])**2,xerr=np.diag(ppp_spec_qu.hess_inv)[::2]**0.5/np.cosh(ppp_spec_qu.x[::2])**2,fmt='.')\n",
    "for _xx,_yy,_e0,_e1 in zip(np.tanh(ppp_spec_qu.x[::2]),np.tanh(ppp_spec_qu.x[1::2]),elist[:-1],elist[1:]):\n",
    "    plt.text(_xx-3e-2,_yy+1e-2,'%.1f-%.1f' % (_e0,_e1))\n",
    "#plt.xlim(-0.4,0.3)\n",
    "#plt.ylim(-0.3,0.3)\n",
    "plt.xlabel('Q/I')\n",
    "plt.ylabel('U/I')\n",
    "plt.savefig('figures/QU-energy_v1.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "copyrighted-scenario",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "\n",
    "plt.errorbar(0.5*(elist[:-1]+elist[1:]),np.tanh(ppp_spec_deg.x)[:-1],\n",
    "             yerr=(np.diag(ppp_spec_deg.hess_inv)**0.5/np.cosh(ppp_spec_deg.x)**2)[:-1],\n",
    "             xerr=0.5*(-elist[:-1]+elist[1:]),fmt='.')\n",
    "plt.xlim(2,8)\n",
    "\n",
    "plt.xlabel('Energy [keV]')\n",
    "plt.ylabel('Q/I')\n",
    "plt.savefig('figures/polar-energy.pdf')\n",
    "np.save('output/qoveri',[0.5*(elist[:-1]+elist[1:]),np.tanh(ppp_spec_deg.x)[:-1],\n",
    "             (np.diag(ppp_spec_deg.hess_inv)**0.5/np.cosh(ppp_spec_deg.x)**2)[:-1],\n",
    "             0.5*(-elist[:-1]+elist[1:])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "prospective-funeral",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#     Emid        Q/I       EQ/I   DeltaE/2\n",
      "      2.18    0.11089  0.0175013       0.18 \n",
      "      2.46   0.119648  0.0229664        0.1 \n",
      "      2.66  0.0677209  0.0180133        0.1 \n",
      "      2.86   0.053727  0.0168314        0.1 \n",
      "      3.06  0.0871951  0.0225665        0.1 \n",
      "      3.26  0.0675895  0.0174285        0.1 \n",
      "      3.48  0.0947418  0.0188528       0.12 \n",
      "      3.72  0.0830283   0.019801       0.12 \n",
      "         4  0.0959807  0.0185043       0.16 \n",
      "      4.34   0.119446  0.0176444       0.18 \n",
      "      4.76   0.107369  0.0167885       0.24 \n",
      "       5.3  0.0836069  0.0185973        0.3 \n",
      "      6.02   0.134283  0.0238329       0.42 \n",
      "      6.56   0.115942  0.0365134       0.12 \n",
      "       6.8  0.0712052   0.110471       0.12 \n",
      "       7.1   0.105707  0.0468743       0.18 \n",
      "      7.64  0.0922356  0.0496681       0.36 \n"
     ]
    }
   ],
   "source": [
    "with open('output/poldeg_energy.dat','w') as f:\n",
    "    print('#     Emid        Q/I       EQ/I   DeltaE/2')\n",
    "    f.write('#     Emid        Q/I       EQ/I   DeltaE/2\\n')\n",
    "\n",
    "    for r in zip(0.5*(elist[:-1]+elist[1:]),np.tanh(ppp_spec_deg.x)[:-1],\n",
    "                 (np.diag(ppp_spec_deg.hess_inv)**0.5/np.cosh(ppp_spec_deg.x)**2)[:-1],\n",
    "                 0.5*(-elist[:-1]+elist[1:])):\n",
    "        print(('%10g '*4) % r)\n",
    "        f.write((('%10g '*4) % r)+'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "included-transportation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#    Emid         Q/I       EQ/I        U/I       EU/I   DeltaE/2\n",
      "      2.18   0.110869  0.0172425  0.0130767  0.0305407       0.18 \n",
      "      2.46   0.119621  0.0240529 0.00964676  0.0252596        0.1 \n",
      "      2.66  0.0677126  0.0184015 -0.00630817  0.0265069        0.1 \n",
      "      2.86  0.0537287  0.0174096 0.00589821  0.0170906        0.1 \n",
      "      3.06  0.0871913  0.0161769 0.00088447  0.0193299        0.1 \n",
      "      3.26  0.0675844  0.0185848 -0.0021946  0.0194568        0.1 \n",
      "      3.48  0.0947421  0.0174105 -0.00118454  0.0178734       0.12 \n",
      "      3.72  0.0831774  0.0172639 -0.0281121  0.0194774       0.12 \n",
      "         4  0.0959645  0.0190012  0.0142667  0.0188117       0.16 \n",
      "      4.34   0.119446  0.0174952  0.0108495    0.01983       0.18 \n",
      "      4.76   0.107436  0.0164565  0.0224453  0.0172896       0.24 \n",
      "       5.3  0.0835892  0.0223554 -0.0323856   0.016486        0.3 \n",
      "      6.02   0.134262  0.0180437 -0.00878858  0.0263735       0.42 \n",
      "      6.56   0.116144  0.0433009  -0.027627  0.0971115       0.12 \n",
      "       6.8  0.0706441   0.110571  0.0614161   0.080427       0.12 \n",
      "       7.1   0.106313  0.0910925 -0.0558499  0.0371839       0.18 \n",
      "      7.64  0.0923352  0.0561874  0.0302785  0.0436872       0.36 \n"
     ]
    }
   ],
   "source": [
    "with open('output/QU_energy.dat','w') as f:\n",
    "    print('#    Emid         Q/I       EQ/I        U/I       EU/I   DeltaE/2')\n",
    "    f.write('#    Emid         Q/I       EQ/I        U/I       EU/I   DeltaE/2\\n')\n",
    "\n",
    "    for r in zip(0.5*(elist[:-1]+elist[1:]),np.tanh(ppp_spec_qu.x[::2]),np.diag(ppp_spec_qu.hess_inv)[::2]**0.5/np.cosh(ppp_spec_qu.x[::2])**2,\n",
    "                 np.tanh(ppp_spec_qu.x[1::2]),np.diag(ppp_spec_qu.hess_inv)[1::2]**0.5/np.cosh(ppp_spec_qu.x[1::2])**2,\n",
    "                0.5*(-elist[:-1]+elist[1:])):\n",
    "        print(('%10g '*6) % r)\n",
    "        f.write((('%10g '*6) % r)+'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "operating-harassment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(0.5*(elist[:-1]+elist[1:]),np.tanh(ppp_spec_qu.x)[:-2:2],\n",
    "             yerr=(np.diag(ppp_spec_qu.hess_inv)**0.5/np.cosh(ppp_spec_qu.x)**2)[:-2:2],\n",
    "             xerr=0.5*(-elist[:-1]+elist[1:]),fmt='.',label=r'$Q/I$')\n",
    "plt.errorbar(0.5*(elist[:-1]+elist[1:]),np.tanh(ppp_spec_qu.x)[1:-1:2],\n",
    "             yerr=(np.diag(ppp_spec_qu.hess_inv)**0.5/np.cosh(ppp_spec_qu.x)**2)[1:-1:2],\n",
    "             xerr=0.5*(-elist[:-1]+elist[1:]),fmt='.',label=r'$U/I$')\n",
    "plt.legend()\n",
    "plt.xlabel('Energy [keV]')\n",
    "plt.ylabel(r'$Q/I, U/I$')\n",
    "plt.savefig('figures/QU-energy_v2.pdf')"
   ]
  }
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