Reduce-CenX3.ipynb

{
  "nbformat_minor": 5, 
  "nbformat": 4, 
  "cells": [
    {
      "source": [
        "# Setup  Directories"
      ], 
      "cell_type": "markdown", 
      "id": "6304b002-24f4-44ad-9d3e-d345527be20f", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 1, 
      "outputs": [], 
      "source": [
        "!  [ -d output ] || mkdir output ; [ -d figures ] || mkdir figures"
      ], 
      "id": "b7614795-2642-400b-9532-c1788d7efe88", 
      "metadata": {}
    }, 
    {
      "source": [
        "# Load Packages"
      ], 
      "cell_type": "markdown", 
      "id": "cooked-captain", 
      "metadata": {
        "tags": []
      }
    }, 
    {
      "cell_type": "code", 
      "execution_count": 2, 
      "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"
      ], 
      "id": "angry-negotiation", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 3, 
      "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 (halfApB-halfAmB)\n", 
        "\n", 
        "def angfunk2(alpha,beta,phase):\n", 
        "    a=np.radians(alpha)\n", 
        "    b=np.radians(beta)\n", 
        "    sbeta=np.sin(b)\n", 
        "    p=phase*2*np.pi\n", 
        "    return np.arctan2(sbeta*np.sin(p),(np.cos(b)*np.sin(a)-sbeta*np.cos(a)*np.cos(p)))\n", 
        "def costheta(alpha,beta,phase):\n", 
        "    a=np.radians(alpha)\n", 
        "    b=np.radians(beta)\n", 
        "    return np.cos(a)*np.cos(b)+np.sin(a)*np.sin(b)*np.cos(phase*2*np.pi) "
      ], 
      "id": "documented-latin", 
      "metadata": {}
    }, 
    {
      "source": [
        "# Load Data and Modulation Fraction"
      ], 
      "cell_type": "markdown", 
      "id": "00e17cbb-07ab-4a7f-9f34-1e512fee0890", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 4, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "0 /home/idies/workspace/headata/FTP/ixpe/data/obs/01/01250201/event_l2/ixpe01250201_det1_evt2_v02.fits.gz\n", 
            "1 /home/idies/workspace/headata/FTP/ixpe/data/obs/01/01250201/event_l2/ixpe01250201_det2_evt2_v02.fits.gz\n", 
            "2 /home/idies/workspace/headata/FTP/ixpe/data/obs/01/01250201/event_l2/ixpe01250201_det3_evt2_v02.fits.gz\n"
          ]
        }
      ], 
      "source": [
        "# point this to wherever the IXPE Calb files are located\n", 
        "CALB_DIR='/home/idies/workspace/headata/FTP/caldb/data'\n", 
        "OBSID='01250201'\n", 
        "# point this to wherever the IXPE data files are located -- this is on sciserver.org\n", 
        "datafile='/home/idies/workspace/headata/FTP/ixpe/data/obs/%s/%s/event_l2/ixpe%s_det?_evt2_v02.fits.gz' % (OBSID[0:2],OBSID,OBSID)\n", 
        "#datafile='/home/idies/workspace/Temporary/heyl/scratch/1RXS/01003199/event_l2/*1.fits.gz'\n", 
        "#for i,f in enumerate(sorted(glob('01003299/event_l2/ixpe01003299_det?_evt2_v02_geocenter.fits.gz'))):\n", 
        "for i,f in enumerate(sorted(glob(datafile))): \n", 
        "    print(i,f)\n", 
        "    with fits.open('%s/ixpe/gpd/cpf/modfact/ixpe_d%d_20170101_mfact_02.fits' % (CALB_DIR,i+1)) 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"
      ], 
      "id": "collaborative-thriller", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 5, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "301.3189 304.74167\n", 
            "301.3597 304.88684\n", 
            "301.3612 304.89142\n", 
            "301.3612 304.89145\n", 
            "301.3612 304.89145\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"
      ], 
      "id": "transparent-papua", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 6, 
      "outputs": [
        {
          "output_type": "display_data", 
          "data": {
            "image/png": 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p83L6eA5XfI/rcHU89o4UyX1V51P2nxT/UNchxpQETGkbvtxnzQR6RuK0XuYz6RgurXIcpzbSdjnvVMttNQloSj8rwvhcvCns9t5CLc70CO40BRrn74Cl6nPjKJ4t7xCLN6IgCIJgZtqj3BsuoFFrmwmhNsP7vfa1Jv7qU0Yl19pl9lnKS9rpdWpXMh4hqUJloJJSdTKVADJ2dSD1kBlKb5mldOzdU+PPR6p9UjZy2hSvpYBUhnqSql6MXa8JyJQDcpbAe1RQlGWrHkhFLsa+kXqwyHtBmc8KANB32nFz6y/Wd0LOQJQC58w9q3cyfYBPfdY6OdXyNH2mzZylKb2qfodKe4ZyD4IgaCG7S7nvoL961i6u1YC3MIhzTFnbenG5QKE8kxmI9sUWytDyiMkl39JYftrdyR4yUp0CAIt2w2UxvkF6z/p7ZGIuMbwNnfZA2OY3xTHKRs4kbe7ic9SqWH7cy86v1kCvdYhtOTtR90/OJqRKploFEXmckeZX9pEULlGzLGehkWwMhTfFuHq+LU8a+R0pspEbhUYa96rZRkK5B0EQtJDFV+4lCcGcybcsm7upoL2J+QtsbcVFPeQ+IzlYTaGf2aGUYU6tawVuFdfIpADWNuhErSd+7nnlLl2xh0a7jkjzO1hNx7N0YqxCB1Ltb+bvc0feZ2VzT9S0nPnoSNYkqZhSxuK4zsZ4fLyiFLl0VNk0Cl9L1Sw/j4HyqpGKWrZT0arUEd5Mm0YRE5lQTp6mZtvPqOtagROZoE53YfjHSzLfzSbK8RWVCJyz/T2UexAEQQvZ8sediFaJ6EEi+hoRPUZEH6rev756PSSiQ+qYW4noKSJ6kojeOa/BB0EQBJPxmGXWAbyNmU8QUQ/AfUR0D4BHAbwbwB/JxkT0JgA3ALgcwPcD+DwRXcbMRrkcg5KQXHdVFGf+cUwTxDT99MzKg57ss8wyVs3OpMNMWgFdcUeOrzYPThqKPnSA02TTjnZxlOce7B0/ksPl9LxDkaZBbg+W8/d5KMLxO/30sxqsTnYfJRXEJNcU9ZjSduIZkQultUVOOUAdZi8XjeWCrzdVRN4ERDJZmFU1K5diAECumhPrZ25zcvKxmllQLrzK8zbg0mjR+EKp9V33uns2nPd9S+XOI05UL3vVP2bmx5n5yQmHXAfgE8y8zszfBvAUgKuLRhcEQRAU4VpQpZF0OwrgBwD8ATM/YDS/AMD94vUz1Xu6z8MADgPAKvb6RutNtGP9RWy4ElPxX/3cOKzwfkudZ0L6swuoGtUf5ZSdnmUsGTMLOcNJklal7fr7xAKr2JUoawD9PTKISQ7WSKcsu9BJq4S26a4LJbeStuvIOadUrtba3aaxU16/coVMXDeFq+VQjb2TS+5luUzKurMb2i0yk/ZAp3g+tS5eWNdopDWWPYgUBslMgKxEafnTmqm1nWrdO0vPnbd44bXhxVZXD8w8YOYrAVwI4GoiusJoPumu1a6WmW9n5kPMfKiHFddggyAIAh9TuUIy83EiuhfAtRjZ3CfxDICLxOsLATxbNLraAJpNiVma1tfEaTfL2tn1NQ4n24VrNkmpBhM7uOG6KMdjrg84FZpSedK2Pjwn/wecxLVsniNs7iqtQGpnH78/6Cm1Km3kIkK+o8SqtMFzMptQKlmocHlNWrnrYKrTyFlBrXs2xj4ULp3r6l4Ie3xXpi5W6wpdmbZA3k/t7ilnfmaeZGnfF/Z8tX7FibyW59Wulbnvi7pn8nkvTJWbqvXJ6UTq7Xy4f0u8iryB3zqPt8wBIjqv2t4D4BoATxiH3A3gBiJaIaJLAFwK4MGZRxoEQRC48Sj3gwDuqOzuHQB3MvNniejnAPwPAAcA/CURPczM72Tmx4joTgDfANAHcHOxp8xWzBgwNA3Z1LtaCef+4qoxZe1/pirJBwy5FZC7jJ8MtBGPiU5XO5ysakfHTR6TDk7KJQHTyl2amfur0nMm7X+g8nSdpqMcOEgqY7FPm7O76857JlS49JwZ9vLH59T+6LxyTOlzlnr+jD+fpRPp9KT2mZxGpU+mdXEDZJI3na5YPgsyIEkFJ1Hm+6JLIib0jaCoZJ3CWFPzBjTJMTmDmNyJBq01v20subfljzszPwLgqgnvfxrApzPH3AbgtplHFwRBEBSx+OkHLJzpB5JV+EI7e9NJg0y1njYcbyfeMt4TOdMKeBM6aXRZtwxSrQ9VKL30ipEqt78nHftAeLEMhbgcqqdYKnmWNaFX03a9l0Q76fSjIvMHcrlAPlraOSiZGYx3buxP20lHkO6pvH1f3ovuhrKlS6cVMQMZ7FE3Q5rIT41VvU5slswgpF3c8sqyksFJ55uMRwyAWgrpM+10umdpF7e+i0kd7Xzyv7Qcn++7bSn8RtbvGlb4kX4gCIKghcSPexAEQQvZ3WaZHHrxMjMds7vwLqI1vEBiLcZ4zSi5FANQaQXkWpmuB5pJHVCbEhvBSTLbo1xgY91OLJxK08tgWQU7iVi3vjCxDJS5JTHLJNkj03s2fM14uyNcA3lf2p90TyTDNaB7SvQtvlnaBVOalHov616EKUaYXnon01Yy3D9nygEAXhrfDJmAsrOZXshwZdwueWL04qU0P8gF7756LtbHtq3EFKOC42QwlVxsTeqzArYJKPNdqJlRCsyzVuBTto+SVASjDl1j8hLKPQiCoIUsvnL3LjJYrpAFrpE6sKFkAcZ73myVJyC9rlr4vGwmFLlU3dpFTSqnJCjKyDku1Zt+YuSikuHmJoNuhkq5y9eDZEFV9SEua1MsUg7VaYcrUskKdXWO8oVcmzzejgrwSfqQKn6YtiOpXuWmdsGU7XQsmljMlbMOUp89y3QJg3FDNf9Cd124BspFbdVOpjpIKmApd9bk2e8bib7kcyZVeH9yQrHRYGUO/Px3p/bdTAcoNsuUcEn6gcITNdufIpR7EARBC1l85d5Eop2SACejXRPpQrPqQIdC59SwZYO09uVSGGQCjgCY7m9JOL5S/0MR8GRMOpJgJZ0vSjIQNndp++YVZUtfGXfSOW8shbtLaed98Xpl71hdbm6m97ybGXwtU0Q/cw/XtJ1ZfN7aRn5yss29JlZ5cru+SraWpEiQBbqUnZpFlSoeiFTD64btOxfoBgAbYgoin2F9IXK4G/J7mk81XCOXUE+nUUhmwfnfhCTDtVyjciYbc1NcB9mn+EO5B0EQtJDFV+4Sb3J7TQPpCBqvfJ5JK2CeJ6mN6hyPDj/PqRytqGQIulRRSkGlYfZqltGdrPK0t4y0T8u0AhppW5eBRYO9al1h/1ht7t+/dma7ozJ9LZ07WQEtL6V2YXmcTL27ZylVteuD8dfp1OZ41nL8RLp4sLE23jd4RUdgiZmQsM3rlAjSG0feM52kTB6XbusZmPS+8RXrcM8e5bPlDZbTXl7Sbl87r8y2JtdHnLWOnXjTFGzRiW8MDaRTCeUeBEHQQnaXctc4k3R5aVydG31zLq2qNXaZYlUrG2nvtoojCG+FxHNGhaMnaivxsFHnFSqc+srmLvqkgfTG8D12ujpd8lqoaV5Jz7v3nLERerk7vo6D+15K2y2N7cKr3bEyfN1Kzfn8DN2an8mYJ05835ntzp7x+Prqsz4htlVWAQw3xqp+KFIT9FU9m54U1+LzZj0REOo/GYVeO8nNBNWMTnrS0Jp07TGKaxjnzbbTXl5G0RnWvvi5djPaxS3vG7dXTYm1YV5l9oIgCILdx+5W7iUK3bBlNV40N9N3DeuvdOYaTa+abj41cBZtc5epfc11ADGm5bwtPVF8WuRl7MI6IVhim98rPqul9N6uLo9nJ6/ZOw7tvGDv8aTdisj0dfne/3tm+/zuiaTdKR6vP5zTGc8KjvXTUNZ9S+MQ1W+efN2Z7dftS2cCJGYdL6zrAidiWyYzW0+apUU9coVaVH9DUUi8u5F+CJ2NjO+4TjwnUwOb6XulD3xeebJVjDt3jNFfghofiaxiZuSp5UefHVMDPvC536bC4iSh3IMgCFpI/LgHQRC0kN1tlinBmxJgzm5PSX+ynTXVLQ1cSpplEoJZx8uQ8ZW0FmoSuGRUFUoqNmnrjfSUE5eva5TmAqGWVjcn7wCwV7grXrByPNn3qu4rZ7Z/aOWZM9vnKRvIinSFFO8/ofIKdIW9aX3P2Kx1apAmBXh5Y3wPWaUwQHd8riQ/vH5s5aMlHx+d9/3UUOwTwV0bTtOG/kyl2Ucn90rGJ/q30mHIZF4D0Z/+HlhpC3LfTfUdnmdagUb6K1pszTcL5R4EQdBCtlTuRLQK4EsAVqr2n2Tm3yai8wH8bwAXA/h7AD/PzN8joosBPA7gyaqL+5n5fY2MtjRdZgNBTEU1T0vOa/3Vl26HlsK3+ssdpvtL0vzmw8d5abzYyCqIiXIBU0poyRS9Uq1rpS5T5cqGnU56jdL9Ubo4dtRK7rkiR+8BsX2ucrt7dWfsh/i94Tgo6l/0jiXtemLB7pmN889sb6iV4RPrYvajLjIXuGSmGpYpf1Xd2W7mcSLlPigrMSUurfoRToKYZCY3peJ74prXTmFqaon7vAFT83NXbGLhdTvxmGXWAbyNmU8QUQ/AfUR0D4B3A/gCM3+YiG4BcAuAD1THPM3MV85lxEEQBMGWeApkM8ZxF73qHwO4DsC/rd6/A8C9GP+4N4fXTbABdW4PI6McjDGZSiE3Xu26yHLGYM1cMkpEjzt7Hep4qYASt0g1hqW8PZWF4pdBMjrlr7atn2mXmvfBUoaK0w5UelidPuA0B5ZSl8SLhPI+Jk528dJy0u4lodZ74sQXdNMBPiYCkF4WFUQ2BumMJrnVg7xyl7VcdcGPtJCHmMVsKkUub5mws+v0A+naiUhB3dd+q6JDaQfXz88pMUDxzNSKcMjnrJt8qOlpcymtgXx9VUOdu1W8+H6zU6i7i3oY5ypxj9a4WhFRl4geBvA8gCPM/ACA1zHzcwBQ/f9accglRPRVIvoiEf14ps/DRPQQET20ifVJTYIgCIJCXN4yzDwAcCURnQfg00R0hdH8OQCvZ+ZjRPQWAH9ORJczcxL7zcy3A7gdAPbT+do1RTQsSLSjKSit5/5rW1q2LzfemitJJsWqJhO4VLPNJ2XxZJSMuo6eLvtQ71v3VyvWIfYNenKWlTaT5fQGQjTXvWomX7+2uW/0x4/1snA5+e7mq5J2e4U0vmDlO2JPqtz3d8aJv9Z4LEQe20xnCDLASSYYk94xQLomUHsMpEk7KQySNuutyYRt4/eXlHlbqnWpwnXQEslkYfJc+nsgyuIlal3bn+U+OfvUgYNJNJaltIX610FMmePMQD8jlW/TRTm8nnZFKUkMpjqKmY9jZH65FsB3ieggAFT/P1+1WWfmY9X2UQBPA7isaHRBEARBER5vmQMANpn5OBHtAXANgI8AuBvAjQA+XP3/GdH+BWYeENEbAFwK4FtTjSqrarfP5l6afmDmEnw63FsqaqmGLG+ZJDWwbzj1hE7yXvts/aTsrlKtW14wsrSeVvXpucRmbzy+zbV0lrG5b3xe6WP+sqqkPRADeV5UAulQmn5gVQy+KwZ4itP+jos+huKeyVTAAPDiSVU/UNA7Mdnmrr1lpAPO0prYofzSZfm8xCda29ITbxlxsk3Dv9xKVudU0zn/dXeKASCdTcrEeFbJyuTtBlKDGJaDxsvzOfGYZQ4CuIOIuhh9y+9k5s8S0ZcB3ElE7wXwjwCur9q/FcDvEFEfwADA+5j5hTmMPQiCIMjg8ZZ5BMBVE94/BuDtE96/C8BdjYxO04Q6byAJfhHOVL41BeSNPHWm0XUjVY+0qytf9uGKKKXXS69xKAsy9/JrB52+tLvmS+5JbxnaEEmwztlI2vVEatbn18bJvV7VW0vafbsz9gFYFe4oQ2OmcnwoFL5K//vo2kVnto9tjNutqqIe0ruHTilPGhleIESt9paR+xIfdR1QKotw9KX9PVXMlEv0NVRKeyO91x6YDYUvny3Lb5wN23yBJ1sj5fOcCbwaL7IdicOCIAjOXuLHPQiCoIUsTuKwWd0fC5LuTLVo6g1JLskJ7zS9uI+xAjlk0JEMEbfMOksiGGlJ3YckX7hKPyDMBTKIaZB6GiYmG11JKBnGmuhDVCYabKTnffGV8UKnzJ3+DyfPT9rtl/nXTx08s/1s99XZMQyF3eTFQbowemxjbAL6p1Pj7WMnz0naDUQOd216ElkQUtOLtjDIDAHiPuuEYNLlkazc6bngJB3OL49LzDcqSVeubmppyH4yXh1wJ69renfpJkwv7v62kVDuQRAELWRxlLvH/VH/VfaG+ieHzF5hyTqXO6GXRLoT1ty3MtXmrUUg4b5GyyoYKUm/alSyt9S6PFfOVRPAYK+soSr3GWPP7gEGq7ksWOn7aydESl3h7rjeTx/3vUsHzmzvF4ut/WH6GZzbG8vplzdFWgGVEOylzfF5n31p/5ntkydTl0msjfvvnlIzP9mlccs6IllYGviknkepvOVXTKfyzbk1TuOSKMkpdy+Wa6VzodRK272dar3ovNYMJFL+BkEQnL0sjnKfEbetu4GghEaQKQKshGBOssmTtAKS57LaJUrJkgcy/UB6Hd1T4zFt7BePmpolJEFNhmlVqtzEzfKkqkMqApyGe4Ry30zbPf2915zZPm91rNxP9dPZzqv3jIt6rG2O972ymS4erPfHinxdJBHTjxLn8vAiDVxKFLlS2px7ZCyRLFNGWy63RtrcHKwvUq7nbOYTjCXBSt7zFrowN20Lb7z4h/e6whUyCILg7GXxlXsDqS8lUuG6Fb6xjzpalmVK5tXsfyIRkhyTZbOXqtvwYqBMAFK9P6OdVOHSHNvzrykMliffQ606pbdHd12oc+VVk5STEyq+s5kPntroizQAvfSe0dL4vC+vjO3ie/ekgTrfE+kCOsK+r1MN9zdlyThxHcqbZ+l43uYulbeoM5KW3FOvvSXzaMNIJZBb27G8aiyFmlHrtbQCsj/Ly8sb3t9EosEctVKZvj6KlHwDwZah3IMgCFrIYir3Atu31wumCW8Z91994y+2OzFSTnnr43Nq3esPr2AZFr6cf0ykCmdVhCPxbV+R9n3diRiSXDpQaf6T5FlSrOnzykRfJ8cDTMaAdCYwEDOSl15Opwy0JKcMk71UAICFQk+KbvTVeRNbetpH7xVMRJbSA5CUMEychfRHmvn8tQcUycmKfC42837p2XUeQM0yCzxnplDWqb27Wb2aqm6VQnjW35I5J0IM5R4EQdBCFlO5C6T9uJFV6EJbVu6vdLGfu7CZk/AsqBUYyEWO9tL3kyIIUh3pohs5Jb+kxt2ZvK09YnS5tuRUwpa+JIpLbO5N23U3hPLqyPfTdvJWL7843u6rDLpLr9DEfd01pfCluMz50CNNiCYLhgy1DV+4/Uhbui4ysnRSjEnPToSpWpbP07OEJNmaHKsxU5PFVDrrm9l2pi1dxmTI51anBhbPsTlLLfhOF/8OzOgB13hRjzknLgzlHgRB0ELixz0IgqCFLLxZxjv1MYOYMtOx2jTLmYDIGlOJq6XbFdKLFRSVuDga17E0OeiIlAtmJ1nYTM+beLaJNUptbhmIEqPS3KBzkw+lSUTcpp5uJ/Ohyeh77VopPu7eycl9j8YxOekZcf6zkou1uj9pillay++TwV3ddd/3gLSZULg/JoFLVoUliTYLChMLr8vB6ogz6QYsTaF64VX2bTkgzOhOqHE6bZipRgoClxrP7W4Qyj0IgqCFLLxynydm5RfdNjcz0Ao/t2DrroCuFukyKlxXkU/6N5J5Ja+NhGBJelix8shqPDKlrg5pl0m7OutiAXmoFzYnJ0LSi7UyWdZgWexTM4EkSEpmm11J2yULxcbHI/d1pPrXx2QEYE89Zkm6XmNds7sh7q1SeR2xTwYx6ZmVDDqjvjiZWpDHRmYgejHUSgcsSWaw4vnR/RUoXk3jqt7dxfRunU27alpseSYiWiWiB4noa0T0GBF9qHr/fCI6QkR/V/3/anHMrUT0FBE9SUTvnOcFBEEQBHU8yn0dwNuY+QQR9QDcR0T3AHg3gC8w84eJ6BYAtwD4ABG9CcANAC4H8P0APk9ElzFzYZb+HcIIMEht88oOJxWR/Muu/8rnEoeVFO7Qx8lt7eIoMc5Lot7mcCXfByf2eKUuhZSV6pyU0l5K2uWLesj+u5uW1B5vbuwbn3f1eD75FhsqXob690UisqH+9sg4Lflx6I9emKqlOtfnkjMV3UdSN3UoVbxypV0XLolyptZXM0T5fEo7u57dyudErrEUJBsDkE85YMyITdt3aTGQWXGmJnf30QBbnpVHnKhe9qp/DOA6AHdU798B4Ger7esAfIKZ15n52wCeAnB1k4MOgiAIbFw2dyLqAjgK4AcA/AEzP0BEr2Pm5wCAmZ8jotOl5C8AcL84/JnqPT8zJs2x/rK7qSUJmhziXOtbKgcjcZhE2iFrNnapokzvm1yaAnXenCpTiof3jg3UiceFspmycAXhfjp2qRS1qk/aiWRh/XPG49OBOontW+zT6QekTXtJpB2ue8GIvjs+RZ54wVjfnoyKB9KEYLp83sqLQmnL86p7QYPJap36hueH8TkmDGVaAfU8em3z8nlMyuypdslakZE4LPk+qmc1p/gLlXCRDb9EnbsLcpRdh2tEzDxg5isBXAjgaiK6wmg+6VemdreI6DARPURED21ifcIhQRAEQSlTecsw83EiuhfAtQC+S0QHK9V+EMDzVbNnAFwkDrsQwLMT+rodwO0AsJ/Ob9ThszihT6FvexZ5jBWmb/mlJ8mZxMelfMqzHjLaW2ZDGLzlMdqfOZfgSZdxk0pRG4Yz7WpJq4QY7EobvjrXUM4ExL5BT/WX+ayWjI+wv2fch6ibXSNJWZBJaQwo1V1ToTKpmHbSF+sKfanO8zbyZN1DjUN6PSWfVd9pm64lqJOfgc9nm8UzR8pLp5a2YNyhetl8wrFc30W+6F6f+m0sFuTxljlAROdV23sAXAPgCQB3A7ixanYjgM9U23cDuIGIVojoEgCXAnhw5pEGQRAEbjzK/SCAOyq7ewfAncz8WSL6MoA7iei9AP4RwPUAwMyPEdGdAL4BoA/g5l3nKRMEQbDLIS6pTN4w++l8/hF6++SdBQuqVva2RuqrGufKHqPbZRZAa+kHpOlETomt+7IsMkFqk4825+TGo7NJnka5ViaLmepc0vwiMxLqxbyhyBcv97F240xymI+3h8tpO9ocTmw3UC6diRVJ9qfbicXLpHbrkvaZpIn7aikBNvPfuc5gsimms6nMMsKsIt0dawulMoujNMvohdHEbVdoMW2+GUzex30jnYGVITJX2WkKs0TWdOKt3rSdNOzu+Hn+5FFmPjRpX6QfCIIgaCGtTD9gukJa6ryJqk9FMw0jXUCGWrV52Z83kMMKdpKKTSo5qCpFkPn2lbqU26KiD6vZSS63OBlVgOTYu6o2aDJLEPezq90xkyAmsaC6mY4nyZEuFzzX0v4Gq8KNUwVqJf115IJqes+6J8W1yMdWKffknsn7rtNDrImB9CfXNQWQuijK2Z1W5Lnn06q16kV+r9RH37TS3s4EXurE+X3bHcQUBEEQ7D4WX7kXBDSZSYYMVetNt+tOLyzHW0s/kBmjHkNGNdZIVM9w8jYArAjl7a3jKt0ktQ1WBmBp9dafvF5Qc6cUijqx4VvDk4+CPq/8CITyrLmcJvdW2PrVZ5AsnWwKm3svbbd0cqym5XXoQKXkGvW9kDZyqdZ1cFIuaM0IYkoupG9kLJOfsT5P8lo+Z6oPK81v0q6BxGGZ73QjLo7GGObpntkEodyDIAhayOIr9wZIlLasyar/4jsDEbzpR0kKu5rCF2pVqt/aeTPnqgU+OQO3cl4N+l4kal1ms8qr5FoglAxQkSH32mMnCVxyJqOSylArbc6o0lqhCKHkZICUukfJzEKodW0vlymUE08hnZRNDuFkapyXyj0JTlLrColXjLxPtZlVZqZas7ln2hmzu2wAEpA+n9YaUG5GZxzShI28RMU3YptvOFDJIpR7EARBC9ldyt3pe27+hRV9FKcpMM7l7lMel7O/105mXJcMR+9YnjTiReJFpO3lGVWm/d+l8lo3XERy/vWaU7IGnS7xlpFztRmYTCSVt29jebz+QDlvESCZgSQeQEpeDjvjeyN92WtJ06zPWyjlZGagZzFJWgq5tqOVu/QjF31YZfGEt5Ce3cr4CnMWnB6U35cbq1UEp9Z/LgZlG/3cd8iubhHKPQiCoIXsLuWuKfCksf5im6rbWyorZ1OzjhGqzKsnap4piY3TSOKU5LkVNl19/6R6zXiVAEhUXs3TRyaJsjx4cufdUDMBS4V70Pds7dTkfcuZ6FwgXUdQ/XVUsevT6JTEuixgMkR5f+Wzqu3b8lk9ZRSqTgYi9lkRpXI86rlIFHpiI88X9TBVfQMR8t5iHTkvt8Zt6fUTT96eM6HcgyAIWkj8uAdBELSQ3W2W2SkMc1BuWmgt7iTow+W5xAJj7Twyb3cu6RegEjWJt+vx3uP+kige4zr0VN8bJJX0Yezzhr7nwvH1Aq0crzQpWQvDkhVV5FXmXJcpEHTOdjle43O0F9Az7zfwGSQujoYLr9m319ySfHaFicMK6qbGgmoQBEGwKwnlLjDTCuQWSg33zCZcLdPzZlz8FEnlG6vCUlLjVcfIy3S94n29bib712otp+ws1zi52KrDvQsUWtLDKVXOMRtAY5xHzoq0SvamirAcAXKLzbVavXLh1akarWpJuRmDkbxNpvnVqTuS/pKZipGsT/YxzUddEBjUeLWlBSSUexAEQQtpj3J3ukUWu0A1/Fc7O0uwziPVjJXkzHBDo5xa99pIlfJiaSTX99M500iCYeT1G0Ut3LOYwdhVU89iEuWZ61v3L10/9axI0vXeT8NGnpzXsM1bMzBBcm+tz8ogn6RLnTepHyzer6WvyKQpcK5lVSfP78sekp+lz2yP38a0vhah3IMgCFpIe5S7xLCDlyYJctvPvcFOJSQKTReeyNg1zcAsyxsjY9O17Oo6mVnuOG2fzQXUaDVYS5aWoT850MjyMDLVltwlr0MHWeWUvCr+YSp+iVVcIxlHPn1vtqiLN3FYKbnnrjZjyI3P8sTxqd/GE4JZKRG2MSGYly2/LUR0ERH9NRE9TkSPEdEvV++/mYi+TERfJ6K/IKL91fsXE9EaET1c/fvYvC8iCIIgSPFIiD6AX2PmrxDRuQCOEtERAH8M4NeZ+YtEdBOA3wDwn6tjnmbmK+cy4m3Cnda34eRjppdOUmhYzywyH6Vqx1LliXPVCpXkUhJr9exN7SpR7ZL+5TiUumSpgA2be7ZsoZ4hSFt/4vOuPVjgIhmFHIOVlM0qT+dNFGcpRfn555KN6XGU+KjXZnRlPuu+8+YV9Fxt6dY4FkStS7ZU7sz8HDN/pdp+GcDjAC4A8EYAX6qaHQHwnnkNMgiCIJiOqWzuRHQxgKsAPADgUQDvAvAZANcDuEg0vYSIvgrgJQAfZOa/mdDXYQCHAWAVe0vGvmP4Vb2ReleqRqnCaop8ch+1MeRUibNdzR4tC0UIlVfzvnH2kZ7M8EaRzXTBbTkOyyvEmmlIcr7YGnkukQytlmwtSa9r+IdzZqai21mRosk+Y5aQm01ZsyfznhUkwSLj+cl8l2oqu4Hi9TP7tjuTEy4K7tES0T4AdwF4PzO/BOAmADcT0VEA52Kc6+45AK9n5qsA/CqAPzttj5cw8+3MfIiZD/WwMut1BEEQBAKXcieiHkY/7B9n5k8BADM/AeAd1f7LAPxU9f46gPVq+ygRPQ3gMgAPNT76IAiCYCJb/rjTKHH4nwB4nJl/X7z/WmZ+nkZz0w8C+Fj1/gEALzDzgIjeAOBSAN+ay+i9NJz33cK7wMruBScxbfcu4CT79EKpsU8ic8wb+ddrCccyfSRmhZobp+jPyDOerdlpmcmcFYLIWhjOhM+zvn3yeuUx1vjUNSVmGutZyo3XMvPI5HLa9JJZQPcvrhpugiWuiw0sUHq/w+bC64Ivmlp4lPuPAfhFAF8nooer934TwKVEdHP1+lMA/rTafiuA3yGiPkZ+Bu9j5heaG3IQBEGwFVv+uDPzfVBeXoKPTmh/F0YmnKABUvWRV6G5hdeaMrQUdH4QsoN8O2sRTA7dWogsWcCqKW2Zoti4XlkhyXLry6W5tYKs5Hl1kJERxGTNXBJyC8rGQmlWndf6MOrJYrKq9S6Uut0TvQFDal/JjLuRIKYFZHct/wZBEAQu2pl+wMKpALzJx+rdT1YBtWO2KRGZrZQG2XbGidLX3qj1Ejey0nuUq11rpM1lw7acDaxSbocyyIoG4rzaDi7VuZWKwFkMw7SR51w8rTq28j5ZMwnjM83Zz7XrZ9Opd0v6c88mjLQmi6jiQ7kHQRC0kLNPuUusv8TmYYbK8wZl5I5RY8gpEUttmIrFOTspSbFgJlszlI17tjPj+NxrDPpcuWAnQ+2zYZtPPFWsgh9JhyoVgzfVQ7Je4PRGMbPreteA5HNrJO7LjaMwYKgk/YA5pgVJ31tCKPcgCIIWcnYrd80cQ41LbdqcEUe596uTufYl6srwOCn1+Z81UVMT6ZkthUu5FMo127zPj9yL1/e+iNJZmzN9rXdmmr1G7/eqYKZX2q7GLkszkKMdVxEEQRAkhHJvmJzNr8ROX+2UnXsHMfn4WrPZbfPmmHLHeY8pXBNJD7GKlWSuUd8X73kTm7ZT7dfsvYXHncabUM5SxgOfF5X5/JR89gbbmsp3wW3pXkK5B0EQtJD4cQ+CIGghYZbJ0YBJoJHp4jyniCV9T7HQlc1n30CSKQvO1Ea1pvNW7v1cNawic42mZkbJmIe8pqyGF79L86o3TVGwU/nJxtu72EQTyj0IgqCFhHL3UpCaYFsXgXI0oTycat1Wxr6AprK0xkYfmfFYmDOQksXlaQKGctdvnVfeW8PN0kypm3ORLVk0NbASjC3E92V0su071xwJ5R4EQdBCQrmXYCgqr+3SHdSU6aPk+O0+lzfgJRd0ZCVbKxqfoVYtZZizzZd+pqZCzShvc01AHFKcvtYxhnrD6Wd03hqn1r5iFd8SRe4llHsQBEELCeU+K940oA2X95uHapz1vKWBWt7Ar5wCbNxG3kDSqrS7KcoAFjwn5npG054fzv5KbOlz94hpiReMl1DuQRAELcRTIPsiAP8TwPcBGAK4nZk/SkRvxqgo9j4Afw/gF5j5peqYWwG8FyNr4C8x8+fmM/wFZMbkY14vgaYVkN/m7PSIUfu8qQ5ketjSe5Hruwm8HifZYwoZGoW03TbyJtLXFhS79s7GvDM/dx9ngTq38Pz69AH8GjP/IIAfBXAzEb0JwB8DuIWZfwjApwH8BgBU+24AcDmAawH8IRHNnkovCIIgcLPljzszP8fMX6m2XwbwOIALALwRwJeqZkcAvKfavg7AJ5h5nZm/DeApAFc3PfAgCIIgz1QLqkR0MYCrADwA4FEA7wLwGQDXA7ioanYBgPvFYc9U7+m+DgM4DACr2DvlsHcJBS6T/q59ZpTSPuZ53hpzNGe4F5CbWGzzHifPZaViSA4prMFbYEYpoTR7pNd8M5fnruW4jcJEtA/AXQDeX9nWb8LIRHMUwLkANk43nXB47e4z8+3MfIiZD/WwMv3IgyAIgiwu5U5EPYx+2D/OzJ8CAGZ+AsA7qv2XAfipqvkzGKt4ALgQwLNNDXjX0kAislkXa9tEI7nyBUnwlM6jPuPCnDuh2oS242PKFtC9119S+7eIwkCyqfoMADiUOxERgD8B8Dgz/754/7XV/x0AH8TIcwYA7gZwAxGtENElAC4F8GDTAw+CIAjyeJT7jwH4RQBfJ6KHq/d+E8ClRHRz9fpTAP4UAJj5MSK6E8A3MPK0uZnZrPh5dpJR4ab7n9eVraROZelMYBvrzrpdKzPHWP27E5s1gLUOMOuMpPZ+Zi2haUVemvZgW1P5nmVs+ePOzPdhsh0dAD6aOeY2ALfNMK4gCIJgBiL9wCIgFZWVAjZzjLfvqfpbAJt+aVBLE/3nzuU93pssy5tiwXtub7K1RcE9prCrT83Of4ODIAiCxgnlvuh4k1uVKJsmCmNYlPh9O23481ShTRSK8PrXlyRUKx2HhXsmNE+PrVDnjRLKPQiCoIWEct+teJW2V+FbKszrpVOa/rhADW6nl0XTpeBK+ihJvDYNRd4us87MgrkSyj0IgqCFxI97EARBCwmzTDBiHtPlnLmldPG2iVD1pDufaaekLq434Ko0v7k6yNfOS9OLpmGK2RFCuQdBELSQUO5tZ1FU0zzH0XAA1ryDokr6cC/eNl1DdQGD2wIf8UkFQRC0kFDuwdY0UXuzCUpSLhjKtdRtcGoM9VtT5LIeaskaQwNupnMJaAu2nVDuQRAELSSUe7A1pWptnsErVt8l520gyCrr6TJNGoVZbdqlNvJQ5K0jlHsQBEELCeUezI9Zk5nNu++G/bnnWpKutFBLKPKzllDuQRAELSSUe7BYLJran6b/3LmmmTGUFmGZ9vig9XgKZF9ERH9NRI8T0WNE9MvV+1cS0f1E9DARPUREV1fvX0xEa9X7DxPRx+wzBEEQBE3jUe59AL/GzF8honMBHCWiIwD+G4APMfM9RPST1et/Wx3zNDNfOY8BB0EQBFvjKZD9HIDnqu2XiehxABcAYAD7q2avAvDsvAYZBCZNu2o2vfDaRFBQmFuCKZnK5k5EFwO4CsADAN4P4HNE9HsYmXf+tWh6CRF9FcBLAD7IzH8zoa/DAA4DwCr2low9CIIgyEDMzhqLRPsAfBHAbcz8KSL67wC+yMx3EdHPAzjMzNcQ0QqAfcx8jIjeAuDPAVzOzC8Zff8/AP8w68UU8s8A/NMOndvLbhgjEONsmhhns7RxnP+cmQ9M2uH6cSeiHoDPAvgcM/9+9d6LAM5jZiYiAvAiM++fcOy9AH6dmR9yDnZbIaKHmPnQTo/DYjeMEYhxNk2Ms1nOtnF6vGUIwJ8AePz0D3vFswB+otp+G4C/q9ofIKJutf0GAJcC+NasAw2CIAj8eGzuPwbgFwF8nYgert77TQD/EcBHiWgJwClU9nMAbwXwO0TUBzAA8D5mfqHRUQdBEAQmHm+Z+wBQZvdbJrS/C8BdM45rO7l9pwfgYDeMEYhxNk2Ms1nOqnG6F1SDIAiC3UPklgmCIGgh8eMeBEHQQlr9475b8uIY43wzEX2ZiL5ORH9BRPvFMbcS0VNE9CQRvXMRx7kT95OIVonoQSL6WjXGD1Xvn09ER4jo76r/Xy2O2Yl7OdU4d/DZzI3z+ur1kIgOqWMW6X5OHOcC3s/fJaIniOgRIvo0EZ0njim7n8zc2n8ADgL4l9X2uQC+CeBNAP4KwL+r3v9JAPdW2xcDeHSBxvm3AH6iev8mAP+12n4TgK8BWAFwCYCnAXQXcJzbfj8xWvzfV233MIqm/lGMch/dUr1/C4CP7PC9nHacO/Vs5sb5gwDeCOBeAIdE+0W7n7lxLtr9fAeAper9jzTxfLZauTPzc8z8lWr7ZQALmRfHGOcbAXypanYEwHuq7esAfIKZ15n52wCeAnD1Ao5z2+ERJ6qXveofY3TP7qjevwPAz1bbO3Uvpx3njpAbJzM/zsxPTjhkoe6nMc4dwRjnXzFzv3r/fgAXVtvF97PVP+4SqufF+V0i+g6A3wNwq2h6CRF9lYi+SEQ/vsPjfBTAu6pd1wO4qNq+AMB3xGHPVO9tG85xAjtwP4moS6OYjOcBHGHmBwC8jkdJ8FD9/9qq+Y7dyynHCezQs5kZZ45Fu58Wi3o/bwJwT7VdfD/Pih93GuXFuQvA+3mU4+Y/AfgVZr4IwK9gFIELjLJfvp6ZrwLwqwD+jISdewfGeROAm4noKEZmkI3TTSccvm0+rVOMc0fuJzMPeJRy+kIAVxPRFUbzHbuXU45zx57NuJ/NYo2TiH4LozTrHz/91qQuPOdp/Y87jfLi3AXg48z8qertGwGc3v4/qKY51dTnWLV9FCP71mU7NU5mfoKZ38HMbwHwv6rxAKO/3lIdX4htMi1NM86dvJ/VOY9jZGu9FsB3iehgdQ0HMVJNwA7ey9N4xrnT93LCOHMs2v3MtVm4+0lENwL4aQC/wJXBHTPcz1b/uBPtjrw4uXES0Wur/zsAPgjg9Ir+3QBuIKIVIrqkGueDizbOnbif1TnPq7b3ALgGwBMY3bMbq2Y3AvhMtb1T93Kqce7gs5kbZ45Fu59W+4W5n0R0LYAPAHgXM78iDim/n55V1936D8C/wWgK8wiAh6t/P1m9fxSjVegHALylav8eAI9V738FwM/s8Dh/GSOPlG8C+DCqiOLqmN/CSG08icrzZ9HGuRP3E8APA/hqNcZHAfyX6v3XAPgCRn/IvwDg/B2+l1ONcwefzdw4fw4jVbkO4LsYZYxdxPs5cZwLeD+fwsi2fvp79bFZ72ekHwiCIGghrTbLBEEQnK3Ej3sQBEELiR/3IAiCFhI/7kEQBC0kftyDIAhaSPy4B0EQtJD4cQ+CIGgh/x/8edwuDeOM7wAAAABJRU5ErkJggg==\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }, 
        {
          "execution_count": 6, 
          "output_type": "execute_result", 
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8fecd3e760>]"
            ]
          }, 
          "metadata": {}
        }, 
        {
          "output_type": "display_data", 
          "data": {
            "image/png": 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\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "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'],',')"
      ], 
      "id": "premium-remedy", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 7, 
      "outputs": [], 
      "source": [
        "pispec=np.histogram(evtlist['PI'],bins=150)[0]\n", 
        "np.save('output/pispec',pispec)"
      ], 
      "id": "proof-mitchell", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 8, 
      "outputs": [], 
      "source": [
        "backpispec=np.histogram(evtback['PI'],bins=150)[0]*20**2/(50**2-30**2)\n", 
        "np.save('output/backpispec',backpispec)"
      ], 
      "id": "sixth-saskatchewan", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 9, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "1 -0.0026278792661364203 0.060483889717658945 0.060540950313117146\n", 
            "2 -0.014401953596030888 0.0627799050841303 0.06441065711320322\n", 
            "3 -0.016574992955302615 0.05172885169861504 0.05431946694809918\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))"
      ], 
      "id": "minus-duplicate", 
      "metadata": {}
    }, 
    {
      "source": [
        "# Barycentring First Try with GBM Orbital Elements"
      ], 
      "cell_type": "markdown", 
      "id": "71bd1464-1608-4413-ac29-63eb80b51a62", 
      "metadata": {
        "tags": []
      }
    }, 
    {
      "cell_type": "code", 
      "execution_count": 10, 
      "outputs": [], 
      "source": [
        "dobary=True\n", 
        "if dobary:\n", 
        "    ! [ -f cenx3.fits.gz ] || curl https://gammaray.nsstc.nasa.gov/gbm/science/pulsars/lightcurves/cenx3.fits.gz --output cenx3.fits.gz\n", 
        "    with fits.open('cenx3.fits.gz')  as hdu:\n", 
        "        orbitdata=hdu['Orbital Elements'].data\n", 
        "        spindata=hdu['PSR_HIST'].data\n", 
        "    day=3600*24\n", 
        "    TPIOVER2O=orbitdata['BINARYEPOCH']-2400000\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.cos(2*np.pi*phaseorb)-np.cos(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']-met0-orbtime\n", 
        "    freqtst=NU\n", 
        "else:\n", 
        "    deltat=evtlist['TIME']-evtlist['TIME'].min()\n", 
        "    freqtst=1/8.68832905"
      ], 
      "id": "43573827-28d2-4486-a989-17c564f5aa70", 
      "metadata": {}
    }, 
    {
      "source": [
        "## Lomb-Scargle"
      ], 
      "cell_type": "markdown", 
      "id": "choice-showcase", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 11, 
      "outputs": [], 
      "source": [
        "from astropy.timeseries import LombScargle\n", 
        "ls = LombScargle(deltat,np.ones(len(deltat)))\n", 
        "lsu = LombScargle(deltat,evtlist['U']*moddata)\n", 
        "lsq = LombScargle(deltat,evtlist['Q']*moddata)"
      ], 
      "id": "optional-feedback", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 12, 
      "outputs": [], 
      "source": [
        "freqq,powerq = lsq.autopower(minimum_frequency=freqtst*0.99,maximum_frequency=freqtst*1.01,samples_per_peak=50)\n", 
        "frequ,poweru = lsu.autopower(minimum_frequency=freqtst*0.99,maximum_frequency=freqtst*1.01,samples_per_peak=50)"
      ], 
      "id": "going-wallace", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 13, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "0.2085140076453364 2.1068330599283147e-06\n"
          ]
        }
      ], 
      "source": [
        "freq,power = ls.autopower(minimum_frequency=freqtst*0.99,maximum_frequency=freqtst*1.01,samples_per_peak=50)\n", 
        "freqmax=freq[np.argmax(power)]\n", 
        "print(freqmax,freqmax-freqtst)"
      ], 
      "id": "functional-complement", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 14, 
      "outputs": [
        {
          "execution_count": 14, 
          "output_type": "execute_result", 
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9070f5c1c0>]"
            ]
          }, 
          "metadata": {}
        }, 
        {
          "output_type": "display_data", 
          "data": {
            "image/png": "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\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "source": [
        "plt.plot(freq,power)\n", 
        "plt.axvline(freqmax,c='r')\n", 
        "plt.plot(frequ,poweru)\n", 
        "plt.plot(freqq,powerq)"
      ], 
      "id": "induced-recorder", 
      "metadata": {
        "tags": []
      }
    }, 
    {
      "cell_type": "code", 
      "execution_count": 15, 
      "outputs": [
        {
          "execution_count": 15, 
          "output_type": "execute_result", 
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f9070f0a130>"
            ]
          }, 
          "metadata": {}
        }, 
        {
          "output_type": "display_data", 
          "data": {
            "image/png": 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\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "source": [
        "plt.loglog(np.linspace(1,len(power),len(power))/len(power),-np.sort(-power),label='I')\n", 
        "plt.loglog(np.linspace(1,len(powerq),len(powerq))/len(powerq),-np.sort(-powerq),label='Q')\n", 
        "plt.loglog(np.linspace(1,len(poweru),len(poweru))/len(poweru),-np.sort(-poweru),label='U')\n", 
        "plt.legend()"
      ], 
      "id": "plastic-lesbian", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 16, 
      "outputs": [], 
      "source": [
        "_dt=deltat\n", 
        "def calcppow(xx,kmax=2):\n", 
        "    _fff=xx[0]\n", 
        "    _fdf=xx[1]\n", 
        "    phase=2*np.pi*(_dt*(_fff+_dt*_fdf*0.5))\n", 
        "    if kmax>1:\n", 
        "        sphase=np.sin(phase)\n", 
        "        cphase=np.cos(phase)\n", 
        "        snphase=sphase\n", 
        "        cnphase=cphase\n", 
        "        pppow=np.sum(snphase)**2+np.sum(cnphase)**2\n", 
        "        for _j in range(2,kmax+1):\n", 
        "            _snphase=snphase*cphase+cnphase*sphase\n", 
        "            cnphase=cnphase*cphase-snphase*sphase\n", 
        "            snphase=_snphase\n", 
        "            pppow+=np.sum(snphase)**2+np.sum(cnphase)**2\n", 
        "    else:\n", 
        "        pppow=np.sum(np.sin(phase))**2+np.sum(np.cos(phase))**2\n", 
        "    return(-pppow)  "
      ], 
      "id": "personalized-divorce", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 17, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "-287033032914.0908\n", 
            "-289282562372.2851 [2.08513437e-01 3.66936159e-12]\n", 
            "-289282562372.2851 [2.08513437e-01 3.66936159e-12]\n"
          ]
        }
      ], 
      "source": [
        "print(calcppow([freqmax,-3e-15]))\n", 
        "respow=minimize(calcppow,[freqmax,-3e-15],method='Nelder-Mead')\n", 
        "print(respow.fun,respow.x)\n", 
        "respow=minimize(calcppow,respow.x)\n", 
        "print(respow.fun,respow.x)"
      ], 
      "id": "corrected-importance", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 18, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "0.2085140076453364 [2.08513437e-01 3.66936159e-12]\n"
          ]
        }
      ], 
      "source": [
        "print(freqmax,respow.x)"
      ], 
      "id": "1df119dd-0d64-46ec-bd5c-2bfb55fe685c", 
      "metadata": {}
    }, 
    {
      "source": [
        "## Folding"
      ], 
      "cell_type": "markdown", 
      "id": "driven-frost", 
      "metadata": {
        "tags": []
      }
    }, 
    {
      "cell_type": "code", 
      "execution_count": 19, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "0.4925667756461662\n"
          ]
        }, 
        {
          "output_type": "display_data", 
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD4CAYAAADsKpHdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAWC0lEQVR4nO3db4zd1Z3f8fcnNmHdJhDCP7m2qWnxtmtQcZapazVtxYbV4tAHJhJonVbB3Vh1SkmblfYBkAcNFbIEUrNUKIWVsyAM2gYskhQ3i3dLYSldLTE7rAjGEJppoDBrCzsxS8hWUNn59sE93lwP1zN3/s+deb+kq/nd7/2dO+fI6H7mnPO7P1JVSJL0ofnugCRpYTAQJEmAgSBJagwESRJgIEiSmuXz3YGpOu+882rt2rXz3Q1JGijPP//8j6rq/F6vDWwgrF27luHh4fnuhiQNlCT/53SvuWQkSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgb4m8qStBCtveX3/+r49Tv+6Tz2ZPIMBEmaJd3hcDoLKTQMBEmapn4++AeBgSBJUzBTITDe+8z17GHCQEjyC8AzwJnt/Eer6itJbgP+JXC0nfrlqnq8tbkV2A6cAP5tVf1hq18BPACsAB4HvlRVleRM4EHgCuDHwK9X1eszNEZJmhGLZSZwOv1cZfQ+8KmquhzYAGxOsqm9dldVbWiPk2GwHtgKXApsBu5Jsqydfy+wA1jXHptbfTvwdlVdAtwF3DntkUmSJmXCQKiOn7anZ7RHjdNkC/BwVb1fVa8BI8DGJCuBs6rq2aoqOjOCa7va7G7HjwJXJcmkRyNJmrK+voeQZFmSF4AjwBNVtb+99MUkLya5P8k5rbYKeLOr+WirrWrHY+untKmq48A7wLk9+rEjyXCS4aNHj459WZI0DX1tKlfVCWBDko8B305yGZ3ln9vpzBZuB74KfB7o9Zd9jVNngte6+7EL2AUwNDQ03ixFkmbEYt836Dapq4yq6i+SPA1srqr/cLKe5OvAd9rTUWBNV7PVwKFWX92j3t1mNMly4Gzg2GT6JkmLzenCaLauPppwySjJ+W1mQJIVwK8C3297Aid9BnipHe8FtiY5M8nFdDaPn6uqw8C7STa1/YEbgMe62mxrx9cBT7V9BknSHOlnhrAS2N2uFPoQsKeqvpPkoSQb6CztvA58AaCqDibZA7wMHAduaktOADfy88tO97UHwH3AQ0lG6MwMtk5/aJI0NUtpmajbhIFQVS8Cn+hR/9w4bXYCO3vUh4HLetTfA66fqC+SpNnj3U4lScASvXXFIN+NUJJmy5IMBEkaa6nuG3RzyUiSBDhDcPlIkhpnCJIkwECQJDUGgiQJcA/hFO4nSEuLVxadyhmCJAkwECRJjUtGkpYUl4lOz0A4DfcTJC01LhlJkgADQZLUGAiSJMA9hL6M3YRyT0EaLG4k98cZgiQJMBAkSc2EgZDkF5I8l+R7SQ4m+fet/vEkTyT5Qft5TlebW5OMJHk1ydVd9SuSHGiv3Z0krX5mkkdafX+StbMwVknSOPqZIbwPfKqqLgc2AJuTbAJuAZ6sqnXAk+05SdYDW4FLgc3APUmWtfe6F9gBrGuPza2+HXi7qi4B7gLunP7QJEmTMeGmclUV8NP29Iz2KGALcGWr7waeBm5u9Yer6n3gtSQjwMYkrwNnVdWzAEkeBK4F9rU2t7X3ehT4WpK0373g+KU1SYtRX3sISZYleQE4AjxRVfuBC6vqMED7eUE7fRXwZlfz0VZb1Y7H1k9pU1XHgXeAc6cwHknSFPV12WlVnQA2JPkY8O0kl41zenq9xTj18dqc+sbJDjpLTlx00UXjdVnSEuelppM3qe8hVNVfJHmaztr/W0lWVtXhJCvpzB6g85f/mq5mq4FDrb66R727zWiS5cDZwLEev38XsAtgaGhoQSwnuXwkabHo5yqj89vMgCQrgF8Fvg/sBba107YBj7XjvcDWduXQxXQ2j59ry0rvJtnUri66YUybk+91HfDUQt0/kKTFqp8Zwkpgd7tS6EPAnqr6TpJngT1JtgNvANcDVNXBJHuAl4HjwE1tyQngRuABYAWdzeR9rX4f8FDbgD5G5yolSdIc6ucqoxeBT/So/xi46jRtdgI7e9SHgQ/sP1TVe7RAkSTND+9lJGnRcCN5egyEGeQGs6RB5r2MJEmAgSBJagwESRLgHsKscT9B0qBxhiBJApwhSBpwXmo6c5whSJIAA0GS1LhkNAfcYJY0CAwESQPHfYPZ4ZKRJAkwECRJjYEgSQLcQ5hzbjBLWqicIUiSAANBktS4ZCRpIHip6ewzEOaR+wmSFpIJl4ySrEnyR0leSXIwyZda/bYkf57khfa4pqvNrUlGkrya5Oqu+hVJDrTX7k6SVj8zySOtvj/J2lkYqyRpHP3sIRwHfquqfgnYBNyUZH177a6q2tAejwO017YClwKbgXuSLGvn3wvsANa1x+ZW3w68XVWXAHcBd05/aJKkyZgwEKrqcFX9WTt+F3gFWDVOky3Aw1X1flW9BowAG5OsBM6qqmerqoAHgWu72uxux48CV52cPUiS5sakrjJqSzmfAPa30heTvJjk/iTntNoq4M2uZqOttqodj62f0qaqjgPvAOf2+P07kgwnGT569Ohkui5JmkDfm8pJPgJ8E/jNqvpJknuB24FqP78KfB7o9Zd9jVNngtd+XqjaBewCGBoa+sDrg8wNZumDvLJobvU1Q0hyBp0w+L2q+hZAVb1VVSeq6mfA14GN7fRRYE1X89XAoVZf3aN+Spsky4GzgWNTGZAkaWr6ucoowH3AK1X12131lV2nfQZ4qR3vBba2K4cuprN5/FxVHQbeTbKpvecNwGNdbba14+uAp9o+gyRpjvSzZPRJ4HPAgSQvtNqXgc8m2UBnaed14AsAVXUwyR7gZTpXKN1UVSdauxuBB4AVwL72gE7gPJRkhM7MYOt0BiVJmrwJA6Gq/pjea/yPj9NmJ7CzR30YuKxH/T3g+on6slS4nyBpPvhN5QXOcJA0V7y5nSQJcIYgaYHxUtP54wxBkgQ4Qxgo7idImk3OECRJgIEgSWoMBEkS4B7CwHI/QYuJVxYtDAbCImA4SJoJLhlJkgADQZLUuGS0yLh8JGmqDIRFzHCQNBkGgqR54ZVFC4+BsEQ4W5A0ETeVJUmAgSBJalwyWoJcPpLUizMESRLQRyAkWZPkj5K8kuRgki+1+seTPJHkB+3nOV1tbk0ykuTVJFd31a9IcqC9dneStPqZSR5p9f1J1s7CWNXD2lt+/68ekpa2fpaMjgO/VVV/luSjwPNJngD+BfBkVd2R5BbgFuDmJOuBrcClwN8A/nuSX6yqE8C9wA7gu8DjwGZgH7AdeLuqLkmyFbgT+PWZHKgm5lKSZpN/dCx8EwZCVR0GDrfjd5O8AqwCtgBXttN2A08DN7f6w1X1PvBakhFgY5LXgbOq6lmAJA8C19IJhC3Abe29HgW+liRVVdMeoabEcJCWnkntIbSlnE8A+4ELW1icDI0L2mmrgDe7mo222qp2PLZ+SpuqOg68A5zb4/fvSDKcZPjo0aOT6bokaQJ9X2WU5CPAN4HfrKqftOX/nqf2qNU49fHanFqo2gXsAhgaGnL2MEf6neo7k5AGW1+BkOQMOmHwe1X1rVZ+K8nKqjqcZCVwpNVHgTVdzVcDh1p9dY96d5vRJMuBs4FjUxiP5tHpgsOgkAZDP1cZBbgPeKWqfrvrpb3Atna8DXisq761XTl0MbAOeK4tK72bZFN7zxvGtDn5XtcBT7l/sDh5VZO0cPUzQ/gk8DngQJIXWu3LwB3AniTbgTeA6wGq6mCSPcDLdK5QuqldYQRwI/AAsILOZvK+Vr8PeKhtQB+jc5WSFonTffi7cS0tLP1cZfTH9F7jB7jqNG12Ajt71IeBy3rU36MFiiRpfnjrCi0I7j8sTi4NDhYDQQuay0rS3DEQNDAMB2l2GQgaSP0sRRga0uQYCJJmlPsGg8vbX0uSAGcIWsTcc5AmxxmCJAlwhqAlwtmCNDEDQUuO4SD1ZiBoSfMb0tLPuYcgSQKcIUiaAX73YHEwEKQe3GfQUuSSkSQJMBAkSY2BIEkC3EOQJuR+Qm9uJC8+BoI0CYaDFrMJl4yS3J/kSJKXumq3JfnzJC+0xzVdr92aZCTJq0mu7qpfkeRAe+3uJGn1M5M80ur7k6yd4TFKkvrQzx7CA8DmHvW7qmpDezwOkGQ9sBW4tLW5J8mydv69wA5gXXucfM/twNtVdQlwF3DnFMciSZqGCZeMquqZSfzVvgV4uKreB15LMgJsTPI6cFZVPQuQ5EHgWmBfa3Nba/8o8LUkqaqaxDikOefykRab6Vxl9MUkL7YlpXNabRXwZtc5o622qh2PrZ/SpqqOA+8A5/b6hUl2JBlOMnz06NFpdF2SNNZUN5XvBW4Hqv38KvB5ID3OrXHqTPDaqcWqXcAugKGhIWcQWjCWymzBK4sWtykFQlW9dfI4ydeB77Sno8CarlNXA4dafXWPeneb0STLgbOBY1Ppl7QQjP3QXMwBocVlSktGSVZ2Pf0McPIKpL3A1nbl0MV0No+fq6rDwLtJNrWri24AHutqs60dXwc85f6BJM29CWcISb4BXAmcl2QU+ApwZZINdJZ2Xge+AFBVB5PsAV4GjgM3VdWJ9lY30rliaQWdzeR9rX4f8FDbgD5G5yoladEY9OUkl4mWjn6uMvpsj/J945y/E9jZoz4MXNaj/h5w/UT9kCTNLu9lJEkCvHWFNKcGZfnIZaKlyRmCJAlwhiDNm4U2W3BWIANBWgDmKxwMAXVzyUiSBDhDkBac2Z4tOCvQ6RgI0gBaaPsPWhwMBGkBm85f884ENFkGgjTg/ODXTHFTWZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkZsJASHJ/kiNJXuqqfTzJE0l+0H6e0/XarUlGkrya5Oqu+hVJDrTX7k6SVj8zySOtvj/J2hkeoySpD/3MEB4ANo+p3QI8WVXrgCfbc5KsB7YCl7Y29yRZ1trcC+wA1rXHyffcDrxdVZcAdwF3TnUwkqSpmzAQquoZ4NiY8hZgdzveDVzbVX+4qt6vqteAEWBjkpXAWVX1bFUV8OCYNiff61HgqpOzB0nS3JnqHsKFVXUYoP28oNVXAW92nTfaaqva8dj6KW2q6jjwDnBur1+aZEeS4STDR48enWLXJUm9zPSmcq+/7Guc+nhtPlis2lVVQ1U1dP7550+xi5KkXqYaCG+1ZSDazyOtPgqs6TpvNXCo1Vf3qJ/SJsly4Gw+uEQlSZplUw2EvcC2drwNeKyrvrVdOXQxnc3j59qy0rtJNrX9gRvGtDn5XtcBT7V9BknSHJrw/5iW5BvAlcB5SUaBrwB3AHuSbAfeAK4HqKqDSfYALwPHgZuq6kR7qxvpXLG0AtjXHgD3AQ8lGaEzM9g6IyOTJE3KhIFQVZ89zUtXneb8ncDOHvVh4LIe9fdogSJJmj9+U1mSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAHTDIQkryc5kOSFJMOt9vEkTyT5Qft5Ttf5tyYZSfJqkqu76le09xlJcneSTKdfkqTJm4kZwq9U1YaqGmrPbwGerKp1wJPtOUnWA1uBS4HNwD1JlrU29wI7gHXtsXkG+iVJmoTZWDLaAuxux7uBa7vqD1fV+1X1GjACbEyyEjirqp6tqgIe7GojSZoj0w2EAv5bkueT7Gi1C6vqMED7eUGrrwLe7Go72mqr2vHY+gck2ZFkOMnw0aNHp9l1SVK35dNs/8mqOpTkAuCJJN8f59xe+wI1Tv2DxapdwC6AoaGhnudIkqZmWjOEqjrUfh4Bvg1sBN5qy0C0n0fa6aPAmq7mq4FDrb66R12SNIemHAhJ/nqSj548Bn4NeAnYC2xrp20DHmvHe4GtSc5McjGdzePn2rLSu0k2tauLbuhqI0maI9NZMroQ+Ha7QnQ58J+r6g+S/CmwJ8l24A3geoCqOphkD/AycBy4qapOtPe6EXgAWAHsaw9J0hyaciBU1Q+By3vUfwxcdZo2O4GdPerDwGVT7Yskafr8prIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSs2ACIcnmJK8mGUlyy3z3R5KWmgURCEmWAf8J+DSwHvhskvXz2ytJWloWRCAAG4GRqvphVf0/4GFgyzz3SZKWlOXz3YFmFfBm1/NR4B+MPSnJDmBHe/rTJK9O8fedB/xoim0HlWNeGhzzEpA7pzXmv3m6FxZKIKRHrT5QqNoF7Jr2L0uGq2pouu8zSBzz0uCYl4bZGvNCWTIaBdZ0PV8NHJqnvkjSkrRQAuFPgXVJLk7yYWArsHee+yRJS8qCWDKqquNJvgj8IbAMuL+qDs7ir5z2stMAcsxLg2NeGmZlzKn6wFK9JGkJWihLRpKkeWYgSJKARR4IE90OIx13t9dfTPLL89HPmdTHmP95G+uLSf4kyeXz0c+Z1O9tT5L8/SQnklw3l/2bDf2MOcmVSV5IcjDJ/5jrPs6kPv67PjvJf03yvTbe35iPfs6kJPcnOZLkpdO8PvOfX1W1KB90Nqf/N/C3gA8D3wPWjznnGmAfne9BbAL2z3e/52DM/xA4px1/eimMueu8p4DHgevmu99z8O/8MeBl4KL2/IL57vcsj/fLwJ3t+HzgGPDh+e77NMf9T4BfBl46zesz/vm1mGcI/dwOYwvwYHV8F/hYkpVz3dEZNOGYq+pPqurt9vS7dL7zMcj6ve3JvwG+CRyZy87Nkn7G/M+Ab1XVGwBVNcjj7me8BXw0SYCP0AmE43PbzZlVVc/QGcfpzPjn12IOhF63w1g1hXMGyWTHs53OXxiDbMIxJ1kFfAb4nTns12zq59/5F4Fzkjyd5PkkN8xZ72ZeP+P9GvBLdL7QegD4UlX9bG66N29m/PNrQXwPYZb0czuMvm6ZMUD6Hk+SX6ETCP9oVns0+/oZ838Ebq6qE50/IAdeP2NeDlwBXAWsAJ5N8t2q+l+z3blZ0M94rwZeAD4F/G3giST/s6p+Mst9m08z/vm1mAOhn9thLLZbZvQ1niR/D/hd4NNV9eM56tts6WfMQ8DDLQzOA65Jcryq/suc9HDm9fvf9o+q6i+Bv0zyDHA5MIiB0M94fwO4ozqL6yNJXgP+LvDc3HRxXsz459diXjLq53YYe4Eb2m79JuCdqjo81x2dQROOOclFwLeAzw3oX4tjTTjmqrq4qtZW1VrgUeBfD3AYQH//bT8G/OMky5P8NTp3D35ljvs5U/oZ7xt0ZkMkuRD4O8AP57SXc2/GP78W7QyhTnM7jCT/qr3+O3SuOLkGGAH+L52/MgZWn2P+d8C5wD3tL+bjNcB3iuxzzItKP2OuqleS/AHwIvAz4Herqufliwtdn//GtwMPJDlAZynl5qoa6FtiJ/kGcCVwXpJR4CvAGTB7n1/eukKSBCzuJSNJ0iQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUvP/AbZ6OyvWAKtfAAAAAElFTkSuQmCC\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "source": [
        "phdata=np.mod(deltat*(respow.x[0]+deltat*respow.x[1]*0.5),1)\n", 
        "toton=plt.hist(phdata,bins=100)\n", 
        "print((toton[0].max()-toton[0].min())/(toton[0].max()+toton[0].min()))"
      ], 
      "id": "lined-bottle", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 20, 
      "outputs": [
        {
          "output_type": "display_data", 
          "data": {
            "image/png": 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\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "source": [
        "bindata=plt.hist2d(phdata,deltat,bins=100)"
      ], 
      "id": "postal-disposition", 
      "metadata": {}
    }, 
    {
      "source": [
        "This looks like the orbital elements need correction.  Let's correct by hand"
      ], 
      "cell_type": "markdown", 
      "id": "7defa4f7-8e25-4a53-afca-16520e0110cb", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 21, 
      "outputs": [
        {
          "execution_count": 21, 
          "output_type": "execute_result", 
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9070b52dc0>]"
            ]
          }, 
          "metadata": {}
        }, 
        {
          "output_type": "display_data", 
          "data": {
            "image/png": 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\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "source": [
        "bindata=plt.hist2d(phdata,deltat,bins=100)\n", 
        "plt.plot(-0.044*np.sin(4*np.pi*phaseorb+0.1)+0.4+2e-6*deltat,deltat,',')\n", 
        "plt.plot(np.modf(phaseorb)[0],deltat,',')"
      ], 
      "id": "d39a4aa5-a299-4578-8388-b9a41a5b1711", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 22, 
      "outputs": [
        {
          "output_type": "display_data", 
          "data": {
            "image/png": 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\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "source": [
        "phdata_trial=np.mod(deltat*(respow.x[0]+deltat*respow.x[1]*0.5-2e-6)+0.044*np.sin(4*np.pi*phaseorb+0.1)+np.where(deltat>200000,0.3,0)+np.where(deltat<40000,-0.3,0),1)\n", 
        "bindata=plt.hist2d(phdata_trial,deltat,bins=100)\n"
      ], 
      "id": "797987ec-14ff-45f0-8224-3a8d1645134d", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 23, 
      "outputs": [], 
      "source": [
        "phdata=phdata_trial"
      ], 
      "id": "64fa7ebc-d915-4e62-a534-aa87fc97e517", 
      "metadata": {}
    }, 
    {
      "source": [
        "# Unbinned Likelihood Analysis"
      ], 
      "cell_type": "markdown", 
      "id": "interested-passport", 
      "metadata": {
        "tags": []
      }
    }, 
    {
      "cell_type": "code", 
      "execution_count": 24, 
      "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))))"
      ], 
      "id": "resistant-governor", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 25, 
      "outputs": [], 
      "source": [
        "np.savetxt('output/photondata.txt',[phdata,qdata,udata])"
      ], 
      "id": "massive-joseph", 
      "metadata": {
        "tags": []
      }
    }, 
    {
      "cell_type": "code", 
      "execution_count": 26, 
      "outputs": [
        {
          "execution_count": 26, 
          "output_type": "execute_result", 
          "data": {
            "text/plain": [
              " final_simplex: (array([[-0.00612425,  0.05610781],\n", 
              "       [-0.00609078,  0.05608075],\n", 
              "       [-0.00611631,  0.0560585 ]]), array([-181.86012736, -181.86012641, -181.86011831]))\n", 
              "           fun: -181.8601273642228\n", 
              "       message: 'Optimization terminated successfully.'\n", 
              "          nfev: 71\n", 
              "           nit: 39\n", 
              "        status: 0\n", 
              "       success: True\n", 
              "             x: array([-0.00612425,  0.05610781])"
            ]
          }, 
          "metadata": {}
        }
      ], 
      "source": [
        "ppp_fixed=minimize(maxlikefixed,np.random.normal(size=2)*np.array([0.3,0.3]),method='Nelder-Mead')\n", 
        "ppp_fixed"
      ], 
      "id": "temporal-adventure", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 27, 
      "outputs": [
        {
          "execution_count": 27, 
          "output_type": "execute_result", 
          "data": {
            "text/plain": [
              " final_simplex: (array([[ 6.27044817e-02, -2.31097417e-04,  8.68337894e-05,\n", 
              "         2.25109453e+02,  1.79613222e+00],\n", 
              "       [-6.27044958e-02, -1.70024469e-04,  6.38859347e-05,\n", 
              "         1.35109454e+02,  1.79613214e+00],\n", 
              "       [-6.27044816e-02, -1.79461702e-04,  6.74319003e-05,\n", 
              "         1.35109464e+02,  1.79613217e+00],\n", 
              "       [-6.27044893e-02, -2.84296448e-04,  1.06823235e-04,\n", 
              "         1.35109457e+02,  1.79613216e+00],\n", 
              "       [-6.27044918e-02, -3.28339331e-04,  1.23372038e-04,\n", 
              "         1.35109461e+02,  1.79613217e+00],\n", 
              "       [-6.27044941e-02, -2.39106335e-04,  8.98432420e-05,\n", 
              "         1.35109453e+02,  1.79613224e+00]]), array([-223.9772857, -223.9772857, -223.9772857, -223.9772857,\n", 
              "       -223.9772857, -223.9772857]))\n", 
              "           fun: -223.9772856982913\n", 
              "       message: 'Optimization terminated successfully.'\n", 
              "          nfev: 843\n", 
              "           nit: 523\n", 
              "        status: 0\n", 
              "       success: True\n", 
              "             x: array([ 6.27044817e-02, -2.31097417e-04,  8.68337894e-05,  2.25109453e+02,\n", 
              "        1.79613222e+00])"
            ]
          }, 
          "metadata": {}
        }
      ], 
      "source": [
        "ppp_rot=minimize(maxlikerotation2,np.random.normal(size=5)*np.array([0.1,50,50,90,1]),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"
      ], 
      "id": "bound-colon", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 28, 
      "outputs": [
        {
          "execution_count": 28, 
          "output_type": "execute_result", 
          "data": {
            "text/plain": [
              " final_simplex: (array([[ 6.00396419e-02, -2.49864123e-04,  9.40995378e-05,\n", 
              "         2.25124510e+02,  1.80252390e+00,  2.31552658e-01,\n", 
              "        -1.14961829e+00],\n", 
              "       [ 6.00396682e-02, -2.49866168e-04,  9.41000352e-05,\n", 
              "         2.25124583e+02,  1.80252378e+00,  2.31551035e-01,\n", 
              "        -1.14961860e+00],\n", 
              "       [ 6.00396241e-02, -2.49863950e-04,  9.40996463e-05,\n", 
              "         2.25124518e+02,  1.80252380e+00,  2.31552730e-01,\n", 
              "        -1.14961688e+00],\n", 
              "       [ 6.00396177e-02, -2.49866986e-04,  9.41003567e-05,\n", 
              "         2.25124557e+02,  1.80252307e+00,  2.31550853e-01,\n", 
              "        -1.14961810e+00],\n", 
              "       [ 6.00397214e-02, -2.49866756e-04,  9.41002312e-05,\n", 
              "         2.25124520e+02,  1.80252323e+00,  2.31551030e-01,\n", 
              "        -1.14961700e+00],\n", 
              "       [ 6.00396870e-02, -2.49863580e-04,  9.40993763e-05,\n", 
              "         2.25124578e+02,  1.80252338e+00,  2.31552913e-01,\n", 
              "        -1.14961615e+00],\n", 
              "       [ 6.00395101e-02, -2.49862273e-04,  9.40984925e-05,\n", 
              "         2.25124565e+02,  1.80252338e+00,  2.31554084e-01,\n", 
              "        -1.14961889e+00],\n", 
              "       [ 6.00397105e-02, -2.49865232e-04,  9.41001157e-05,\n", 
              "         2.25124554e+02,  1.80252324e+00,  2.31551205e-01,\n", 
              "        -1.14961829e+00]]), array([-228.27463389, -228.27463389, -228.27463389, -228.27463389,\n", 
              "       -228.27463389, -228.27463389, -228.27463389, -228.27463389]))\n", 
              "           fun: -228.27463388951006\n", 
              "       message: 'Optimization terminated successfully.'\n", 
              "          nfev: 487\n", 
              "           nit: 305\n", 
              "        status: 0\n", 
              "       success: True\n", 
              "             x: array([ 6.00396419e-02, -2.49864123e-04,  9.40995378e-05,  2.25124510e+02,\n", 
              "        1.80252390e+00,  2.31552658e-01, -1.14961829e+00])"
            ]
          }, 
          "metadata": {}
        }
      ], 
      "source": [
        "ppp_rot2=minimize(maxlikerotation2,np.concatenate((ppp_rot.x,np.random.normal(size=2))),method='Nelder-Mead')\n", 
        "ppp_rot2"
      ], 
      "id": "proud-prior", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 29, 
      "outputs": [
        {
          "execution_count": 29, 
          "output_type": "execute_result", 
          "data": {
            "text/plain": [
              " final_simplex: (array([[ 6.27043851e-02, -2.48749980e-04,  9.34669469e-05,\n", 
              "         2.25109425e+02,  1.79613160e+00],\n", 
              "       [ 6.27043978e-02, -2.48750553e-04,  9.34670234e-05,\n", 
              "         2.25109509e+02,  1.79613134e+00],\n", 
              "       [ 6.27045726e-02, -2.48749504e-04,  9.34669500e-05,\n", 
              "         2.25109440e+02,  1.79613197e+00],\n", 
              "       [ 6.27045618e-02, -2.48750444e-04,  9.34669173e-05,\n", 
              "         2.25109353e+02,  1.79613265e+00],\n", 
              "       [ 6.27043481e-02, -2.48749315e-04,  9.34670868e-05,\n", 
              "         2.25109472e+02,  1.79613247e+00],\n", 
              "       [ 6.27044575e-02, -2.48749726e-04,  9.34671829e-05,\n", 
              "         2.25109326e+02,  1.79613259e+00]]), array([-223.9772857 , -223.9772857 , -223.9772857 , -223.9772857 ,\n", 
              "       -223.9772857 , -223.97728569]))\n", 
              "           fun: -223.97728569720599\n", 
              "       message: 'Optimization terminated successfully.'\n", 
              "          nfev: 179\n", 
              "           nit: 97\n", 
              "        status: 0\n", 
              "       success: True\n", 
              "             x: array([ 6.27043851e-02, -2.48749980e-04,  9.34669469e-05,  2.25109425e+02,\n", 
              "        1.79613160e+00])"
            ]
          }, 
          "metadata": {}
        }
      ], 
      "source": [
        "ppp_rot=minimize(maxlikerotation2,ppp_rot2.x[0:-2],method='Nelder-Mead')\n", 
        "ppp_rot"
      ], 
      "id": "divine-kelly", 
      "metadata": {}
    }, 
    {
      "source": [
        "# Binned Analysis"
      ], 
      "cell_type": "markdown", 
      "id": "african-voluntary", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 30, 
      "outputs": [], 
      "source": [
        "qloc=np.zeros(10)\n", 
        "uloc=np.zeros(10)\n", 
        "phbin=(phdata//0.1).astype(int)\n", 
        "ppp_phase=minimize(maxlikephase,np.random.uniform(size=20))\n", 
        "ppp_phase_err=np.diag(ppp_phase.hess_inv)**0.5    "
      ], 
      "id": "arctic-memphis", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 31, 
      "outputs": [], 
      "source": [
        "ppp_phasequ=minimize(maxlikephasequ,(np.arange(20)+1)*0.2)\n", 
        "ppp_phasequ_err=np.diag(ppp_phasequ.hess_inv)**.5"
      ], 
      "id": "surface-adelaide", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 32, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "#    Phase        Q/I       EQ/I        U/I       EU/I     Counts\n", 
            "      0.05   0.042176  0.0102605  0.0191112  0.0128878     306827 \n", 
            "      0.15  0.0144441  0.0103016  0.0075652   0.014033     236965 \n", 
            "      0.25 0.00498682  0.0104414  0.0265791  0.0100914     201665 \n", 
            "      0.35 -0.0682125  0.0160734  0.0988263  0.0134848     187796 \n", 
            "      0.45 -0.0501933  0.0108781    0.10532  0.0116016     165499 \n", 
            "      0.55 -0.0220746  0.0135137  0.0297856  0.0107601     132835 \n", 
            "      0.65 -0.0101819  0.0071835  0.0436161 0.00705831     136293 \n", 
            "      0.75 -0.0237692  0.0069664   0.061699 0.00753534     219803 \n", 
            "      0.85 -0.00506659 0.00966128   0.106281  0.0140362     320677 \n", 
            "      0.95  0.0436714 0.00979093          1 2.25583e-10     356951 \n"
          ]
        }
      ], 
      "source": [
        "tt=np.sum(toton[0].reshape((10,10)),axis=1)\n", 
        "phlist=np.linspace(0.05,0.95,10)\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')"
      ], 
      "id": "distant-announcement", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 33, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "#    Phase         PD        EPD         PA        EPA     Counts\n", 
            "      0.05  0.0463039 0.00988271    12.1883    7.81452     306827 \n", 
            "      0.15  0.0163053 0.00959612    13.8219    19.1204     236965 \n", 
            "      0.25  0.0270429  0.0112792    39.6868    13.8739     201665 \n", 
            "      0.35   0.120082  0.0160488    62.3072    3.37164     187796 \n", 
            "      0.45   0.116669  0.0119364    57.7408    3.18039     165499 \n", 
            "      0.55  0.0370738  0.0105256    63.2714    7.36632     132835 \n", 
            "      0.65  0.0447888 0.00782267      51.57     9.8883     136293 \n", 
            "      0.75  0.0661192  0.0118734    55.5345     3.5127     219803 \n", 
            "      0.85   0.106401 0.00914459    46.3647    2.17228     320677 \n", 
            "      0.95    0.10459  0.0102528    32.2757    2.58156     356951 \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", 
        "        "
      ], 
      "id": "suburban-geography", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 34, 
      "outputs": [
        {
          "execution_count": 34, 
          "output_type": "execute_result", 
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'U/I')"
            ]
          }, 
          "metadata": {}
        }, 
        {
          "output_type": "display_data", 
          "data": {
            "image/png": "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\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "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')"
      ], 
      "id": "powerful-language", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 35, 
      "outputs": [
        {
          "output_type": "display_data", 
          "data": {
            "image/png": 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\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "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')"
      ], 
      "id": "figured-writing", 
      "metadata": {}
    }, 
    {
      "source": [
        "# Plots"
      ], 
      "cell_type": "markdown", 
      "id": "southeast-warrior", 
      "metadata": {
        "tags": []
      }
    }, 
    {
      "cell_type": "code", 
      "execution_count": 36, 
      "outputs": [], 
      "source": [
        "def plottwo(x,y,yerr,fmt):\n", 
        "    _x=np.concatenate((x,x+1))\n", 
        "    _y=np.concatenate((y,y))\n", 
        "    _yerr=np.concatenate((yerr,yerr))\n", 
        "    plt.errorbar(_x,_y,xerr=(_x[1]-_x[0])/2,yerr=_yerr,fmt=fmt)"
      ], 
      "id": "blank-permit", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 37, 
      "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)"
      ], 
      "id": "black-laugh", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 38, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "0.6529669309767366\n"
          ]
        }, 
        {
          "output_type": "display_data", 
          "data": {
            "image/png": 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fofOc435sAe6qqrer6jlgFNiYZBVwWlU9UlUF3AFc0VVmb9u/B7j0eO9BkjR/ZrLK6LNJrgZGgC9U1evAauDRrnPGWuxnbX98nPb6IkBVHU3yBnAW8Or4H5hkB51eBuvWrZtB1TUZl6NKy890J5VvAT4EbAAOA19p8V6f7GuC+ERl3h2s2lNVw1U1vHLlyilVWJI0sWklhKp6uaqOVdXPga8BG9uhMWBt16lrgJdafE2P+AllkqwATqf/ISpJ0iyZVkJocwLHfRI4vgJpP7C1rRw6j87k8WNVdRh4M8mmNj9wNXBvV5ltbf9K4OE2zyBJmkeTziEk+TpwCXB2kjHgy8AlSTbQGdp5HvgMQFUdSrIPeBo4CuysqmPtUtfQWbF0KvBA2wBuBe5MMkqnZ7B1FtolzYhLb7UcTZoQqupTPcK3TnD+bmB3j/gIcFGP+FvAVZPVQwvHCWZpefCbypIkwIQgSWq826mmxOEjaemyhyBJAkwIkqTGhCBJApxD0AyMX6s/6HMKfvdAy509BEkSYEKQJDUOGWnWuCRVGmz2ECRJgAlBktSYECRJgHMImiODMp/gUlPpL9hDkCQBJgRJUjNpQkhyW5IjSZ7qip2Z5MEkP2ivZ3QduzbJaJJnk1zWFb84ycF27Kb2KE3a4zbvbvEDSYZmuY1aYEO77ntnk7R49dNDuB3YPC62C3ioqtYDD7X3JLmAziMwL2xlbk5ySitzC7CDznOW13ddczvwelWdD9wI3DDdxmjxMzlIi9ekCaGqvkPnWcfdtgB72/5e4Iqu+F1V9XZVPQeMAhuTrAJOq6pHqqqAO8aVOX6te4BLj/ceJEnzZ7qrjM6tqsMAVXU4yTktvhp4tOu8sRb7WdsfHz9e5sV2raNJ3gDOAl4d/0OT7KDTy2DdunXTrLoWi0FZiSQtF7O97LTXJ/uaID5RmXcHq/YAewCGh4d7nqPBNJ/JweEqqbfprjJ6uQ0D0V6PtPgYsLbrvDXASy2+pkf8hDJJVgCn8+4hKknSHJtuQtgPbGv724B7u+Jb28qh8+hMHj/WhpfeTLKpzQ9cPa7M8WtdCTzc5hkkSfNo0iGjJF8HLgHOTjIGfBm4HtiXZDvwAnAVQFUdSrIPeBo4CuysqmPtUtfQWbF0KvBA2wBuBe5MMkqnZ7B1VlqmgTUXw0cOE0mTmzQhVNWnTnLo0pOcvxvY3SM+AlzUI/4WLaFI480kOZgEpKnxm8qSJMCb22mA+Ilfmlv2ECRJgAlBktSYECRJgAlBktSYECRJgAlBktSYECRJgAlBktSYECRJgAlBktSYECRJgAlBktSYECRJgAlBktTMKCEkeT7JwSRPJBlpsTOTPJjkB+31jK7zr00ymuTZJJd1xS9u1xlNclN7zKYkaR7NRg/hl6tqQ1UNt/e7gIeqaj3wUHtPkgvoPB7zQmAzcHOSU1qZW4AddJ7BvL4dlyTNo7kYMtoC7G37e4EruuJ3VdXbVfUcMApsTLIKOK2qHqmqAu7oKiNJmiczTQgF/NckjyfZ0WLnVtVhgPZ6TouvBl7sKjvWYqvb/vi4JGkezfQRmh+rqpeSnAM8mOT7E5zba16gJoi/+wKdpLMDYN26dVOtqyRpAjPqIVTVS+31CPCHwEbg5TYMRHs90k4fA9Z2FV8DvNTia3rEe/28PVU1XFXDK1eunEnVJUnjTDshJPnLSX7x+D7wj4CngP3AtnbaNuDetr8f2Jrk/UnOozN5/FgbVnozyaa2uujqrjKSpHkykyGjc4E/bCtEVwB/UFV/lOS7wL4k24EXgKsAqupQkn3A08BRYGdVHWvXuga4HTgVeKBtkqR5NO2EUFU/BD7SI/5j4NKTlNkN7O4RHwEumm5dJEkz5zeVJUmACUGS1JgQJEmACUGS1JgQJEmACUGS1JgQJEmACUGS1JgQJEmACUGS1JgQJEmACUGS1JgQJEmACUGS1JgQJEmACUGS1JgQJEnAIkoISTYneTbJaJJdC10fSVpuFkVCSHIK8DvAJ4ALgE8luWBhayVJy8uiSAjARmC0qn5YVf8PuAvYssB1kqRlZcVCV6BZDbzY9X4M+LvjT0qyA9jR3v40ybPT/HlnA69Os+ygss3Lg21eBnLDjNr8V092YLEkhPSI1bsCVXuAPTP+YclIVQ3P9DqDxDYvD7Z5eZirNi+WIaMxYG3X+zXASwtUF0lalhZLQvgusD7JeUneB2wF9i9wnSRpWVkUQ0ZVdTTJZ4H/ApwC3FZVh+bwR8542GkA2eblwTYvD3PS5lS9a6hekrQMLZYhI0nSAjMhSJKAJZ4QJrsdRjpuasefTPJLC1HP2dRHm/9Fa+uTSf40yUcWop6zqd/bniT5O0mOJblyPus3F/ppc5JLkjyR5FCS/zbfdZxNffxen57kPyf5Xmvvry1EPWdTktuSHEny1EmOz/7fr6pakhudyen/Dfw14H3A94ALxp1zOfAAne9BbAIOLHS956HNfw84o+1/Yjm0ueu8h4H7gSsXut7z8O/8QeBpYF17f85C13uO2/sl4Ia2vxJ4DXjfQtd9hu3+h8AvAU+d5Pis//1ayj2Efm6HsQW4ozoeBT6YZNV8V3QWTdrmqvrTqnq9vX2Uznc+Blm/tz35DeAbwJH5rNwc6afN/xz4ZlW9AFBVg9zuftpbwC8mCfABOgnh6PxWc3ZV1XfotONkZv3v11JOCL1uh7F6GucMkqm2ZzudTxiDbNI2J1kNfBL43Xms11zq59/5rwNnJPl2kseTXD1vtZt9/bT3q8CH6Xyh9SDwuar6+fxUb8HM+t+vRfE9hDnSz+0w+rplxgDpuz1JfplOQvj7c1qjuddPm38b+GJVHet8gBx4/bR5BXAxcClwKvBIkker6n/NdeXmQD/tvQx4Avg48CHgwST/vap+Msd1W0iz/vdrKSeEfm6HsdRumdFXe5L8beD3gE9U1Y/nqW5zpZ82DwN3tWRwNnB5kqNV9Z/mpYazr9/f7Ver6s+BP0/yHeAjwCAmhH7a+2vA9dUZXB9N8hzwN4HH5qeKC2LW/34t5SGjfm6HsR+4us3WbwLeqKrD813RWTRpm5OsA74JfHpAPy2ON2mbq+q8qhqqqiHgHuBfDXAygP5+t+8F/kGSFUn+Ep27Bz8zz/WcLf209wU6vSGSnAv8DeCH81rL+Tfrf7+WbA+hTnI7jCS/3o7/Lp0VJ5cDo8D/pfMpY2D12eZ/A5wF3Nw+MR+tAb5TZJ9tXlL6aXNVPZPkj4AngZ8Dv1dVPZcvLnZ9/hv/W+D2JAfpDKV8saoG+pbYSb4OXAKcnWQM+DLwXpi7v1/eukKSBCztISNJ0hSYECRJgAlBktSYECRJgAlBktSYECRJgAlBktT8fxDr/cpKnc/wAAAAAElFTkSuQmCC\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "source": [
        "toton=plt.hist(phdata,bins=100)\n", 
        "print((toton[0].max()-toton[0].min())/(toton[0].max()+toton[0].min()))"
      ], 
      "id": "multiple-value", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 39, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "0.6529669309767366 0.42332375678904743\n"
          ]
        }, 
        {
          "output_type": "display_data", 
          "data": {
            "image/png": 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\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "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')"
      ], 
      "id": "imperial-evolution", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 40, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "0.06853759182935774 0.00350398903602702\n"
          ]
        }, 
        {
          "output_type": "display_data", 
          "data": {
            "image/png": 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\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "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)"
      ], 
      "id": "indian-induction", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 41, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "\n", 
            "Alpha:                      180\n", 
            "Beta:                       9.40995e-05\n", 
            "Mean Polarization Fraction: 0.0599676\n", 
            "Ampl Polarization Fraction: 0.0138857\n", 
            "Peak Polarization Fraction: 0.850382\n", 
            "Pole across Line of Sight:  0.802524\n", 
            "Peak Flux:                  0.997864\n", 
            "\n", 
            "\n", 
            "Alpha:                      180\n", 
            "Beta:                       9.34669e-05\n", 
            "Mean Polarization Fraction: 0.0626223\n", 
            "Pole across Line of Sight:  0.796132\n", 
            "Peak Flux:                  0.997864\n", 
            "\n"
          ]
        }, 
        {
          "output_type": "display_data", 
          "data": {
            "image/png": 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L9i7Q+Xo9ia2E/G2zWBQeRv8mAyo9Wswj2ld4LFFDXmLSoUPsytjFu1t0vt5TaeL3NttmMT88DEEY2GSg1dFU3YUPMS6oAW2KDU+vfJLMwswyV7VqpIhP+mMly22Z5GHj4qaVP7HwGO1GcEGzQQzOyWf6pukkZySXuaovti/t4/c2O35gfnRtusR3Ji7MvXc0V+uIjIBgAkf+l8kfXco/A4SXEl9i++bT+5OdGSlS7bH5Mkc3T3RQFOfWd76bpzpU+7/hkBd48L/nsSLMxqTlkyjafxtyyrmur7YvPeP3Jtmp7D6YyC4/W5W6eTxOw6607zKB6zMy+WbnN+T4/XraKlaPFPEpNhsF2+ewODyMAU0HEujn5UMhI+tRZ+CTPJhqnxXuiP8vp63iq+1Lz/i9yW/zWBxqL7s8oEkNuRXiooe5bftsEmyCqfMVM0Z8e9LwQY8YKeIr/tpAYslRcgiuOe2ry3UM2/wVP+bvYl34LD4aOZ4GEQ2Of+yr7UvP+L3Jjh9YHhlDq1qtqnwL/fGSD54iOILQoS/xn0MH+SNrH9M2TTvpY48YKeIrfpvL8tBQgvyCKjWa50Qe175EkOGv8diRTCgp5ImVkzHm77khfLV9aeL3FgXZ5O5eyPpAP/o27Gt1NNXrrIs5r+VQrsjO5aOtM/j1yMldPh4xUsQX/DqP5VExdKvbjdCAUKujqT61W9Cg30TuTjvM8r9WMPf3uSd97IvtSxO/t0heSGKgUITNcytxnolLp3JvdjHRNnhi5RPYjM3qiHzL0f0cOLyN3VJMn4Z9rI6m+vW8jdFhzWlfZOO5tc+WO4rMF2jit0CVysD+OpdlkbUI9Q+ha92urgnMSpF1iR74BPcdTiXpcBLf7PzG6oi8VpXa12/zWB5mv7bSp0ENTPz+AfgPe5XHUlJIzz/Ca+tfszoiS2ni9wbGQHICKyIi6V7v3Arn1fVaXa5jeJ1OdC8o5pXEl0nLS7M6It/x6zxWRNUhPiyelrVaWh2NazTqRvtO1zMmM4uvfv2KLYe3WB2RZTTxe4NDW9iXf5i9pqBmfg0/xs8PGfYqj6Wlk1uUzUvrXrI6It9QmEPx70tYFRxA34Z9PXOmrerS/zHuKAgk1ghPrJxMsc03hm+eShO/N9iVwIpQ+8U2b/0a7vTdkXFn0eK8O/hXRgazk2ez9uBa9wToy3YvYnOAIcsUe+31I6fbV2gtIi6ewgOp9gqeX/76pXsC9DA6jt8bJCewrFY8DSPq0jSqqdXRlOlM6rTDCXdHnn8fNyZ9yY824cmVT/DNhG+1rrorOa4f+YkfPev3tDqacpXWxirdvjqM4pINHzEzfyevr3+N2dfNIT4s3qVxexo94/d0hTkU/bGKNYGGPg36eOXX8ErfHRkYSujg53k45RC/Z+7hw60fujZAX2azwc75rIiuTYfYDkQHR1sdUaVVun2JIENf5uEjGRQV5/Hc2udcHKHncekZv4jsAbKAEqDYGNNdRGoDXwLNgD3AVcYY36mOVFl7lrMxUMg1JR7fv1+tddrbXEq/Jv0ZlLGOtze9xaXNL6VxZGMXRO3jUraSnpvKVlswt3p4+4LS21iV2ldsK5r0vIsbN/2XN/mJy1td7vF/X9XJHWf8FxljOhtjjk3BOBFIMMa0BhIc71VZkhNYHh5JgPjTo14Pq6OpkirfHXnpVB7MyMbfVsyU1VNOuuNSVZPkhawMDcHgvdePqty++t7LeKlNsxJ4atWT5BfnuzZQD2JFV89lwAzH6xnASAti8B67ElgeFUPn+C5EBEVYHU2VVenuyJim1O1zL3ekpbHsz2Us3LfQdQH6quSFLI+pR3RwNO3rtLc6miqrUvsKDCFo6Es8knKI/dl/8sGWD1wXoIdxdeI3wHwRWSciNzmW1TXGHABwPJd6VUVEbhKRRBFJTE1NdXGYHirjDw5nJLODGj6Mszy972RMYDytSuC5Nc+SV5xndUQ1R2Eutr0rWR7sT6/6vfD387c6IvdrNYCeLQZzSW4+721+l/1Z+62OyC1cnfj7GGO6AoOB20Wkn7MbGmOmG2O6G2O6x8W5t+68x6gBwzjPWEAwAUNe5JGUQ/yVc4D3t7xvdUQ1x94V/OZvI83mwycWAJc8zf1Hc/GzFfvMhV6nEr+IhIpIm8ru3Bjzl+M5BfgO6AEcEpH6jv3WB1Iqu1+fkZzA8qja1A6pTZvalf711xwtL6J7y6EMycnj/c3vsS9zn9UR1QzJC1keZu8+9Nbx+9UiuhH1+tzHzUfS+GXfLyzdv9TqiFyuwsQvIsOBjcA8x/vOIjLbie3CRSTy2GvgYmALMBsY61htLDCrSpHXdCXF2HYvYWVIEH0a9MFPfHzk7SVTuC+zgABbCc+unWp1NDVD8kKWx8RxVsxZPjeO/TS97uD6gHialcDUNc9QWFJodUQu5Uw2eRz7mXoGgDFmI/ahmBWpCywTkU3AGuAHY8w8YCowSER2AoMc79Wp/lzHdpNHuimid0MfPhs7Jqo+8RdM5Na0NBbvX8LifYud3rRKRctqusy/yDm8gw0U+m434okCgggc/DwPpabwR9Y+ZmydUfE2Dt7YvpxJ/MXGmKOV3bExZrcxppPj0d4Y87RjeZoxZoAxprXj+Uilo/YFyQksCwtFEN/+Gn6iHjdzTUhjWpQYpq55hoKSAqsj8l7JC1kTGkIxNt/u3z9RqwH0bjqQgbkFTE96mwPZB6yOyGWcSfxbROSfgL+ItBaR14EVLo5L7UpgRXQsZ9c5m9ohta2OxjP4BxA49EUeSknxueF31S55IcsjYwgNCKFLfBero/Eclz7D/2VkQ0khzyc+b3U0LuNM4r8TaA8UAJ8DmcC/XRiTyj1C1oENbPIr1q/hp2ram55nXc7FOXm8m/QOf2b/WeEmThfw8hU2GyT/wvLwcHrUO48g/yCrI/IctZrQoPc9TEhPZ8HeBaz8q+IuHG9sXxWWbDDG5AKPOB7KHXYvYnVIECUY/RpemkFP8H/T5rI0vIjn1z7PKxe9ckYF4jxqjlh3OLiJP4oy2W8iuE67EU/X+07GbfyEWTYbU1Y/zbcjvuPadxNLXdVb21eZiV9E5mC/AatUxpgRLolI2fv3I6OJCIygY1xHq6PxPJF1qXfBw9y08mleJYHlfy6nrC+vpRXwKq1yo09JXsiyUPtsWzVu/ubqEBhC8ODnmfjdddzuB59s/wRoW+qq3tq+yjvjf8FtUai/GYPZtZAVceGcV/88Av08vxFZ4twJXL/hI2aWZPLM6qf5dsLMUrssqlTAq6bbtZAVMfE0iqhHk6gmVkfjmc66mH5N+nPh0fX8d+M05lz7PXXD6562mre2rzL7+I0xi8t7uDNIn5L6K7/np3DAFGk3T3n8Awga8iITU1PZm7WPj7Z9VOpqVS7gVVMVZFG4bzVrArQbsUKXTOGBI5mUlBTwYuKLpa7ire3LmRu4NotI0imPpSLysojUcUeQPiU5geW+XqbBWU170fesK+ifm8/0TW9xMOdgqatVqYBXTbVnORuC/MgzJdq+KlK7OY173cUN6RnM3TO3zNngvLF9OTOqZy7wA3CN4zEHWAocBD50WWS+alcCy6Pr0Dy6OQ0iGlgdjecbNJkHsgqxlRTy/NqaO/yu2iQvtJf59gugR33vLPPtVn3/zXiiaWgTpqx6miJbkdURVQtnEn8fY8xDxpjNjscjwAXGmGdx7g5e5ayiPPL/WE5igJ7tOy0inoYXPMIN6enM3zufVQdWWR2RZ0tOYHlULbrEdyE8MNzqaDxfYCghlz7LA6kp7DqazBc7vrA6omrhTOKPEJHzjr0RkR7AscLwvjlFvavsXcG6ACjQuykrp/t4xoc0oVGJ4ZlVT1NUcvJZ2Zc39/LIIXVul/EHKRm/85uWaaicNoO5qEFf+uYXMW3DGxzOO3zSx97YvpxJ/BOAd0Xkd8dUiu8CNzoKrz3jyuB8TvJCloeFE+QXRLe63ayOxnv4BxA85CUmpqayO3MPn27/1OqIPNOuBFY4hnHqiUUliCCDn2XikQwKivN4KfElqyM6YxUmfmPMWmNMB6Az0NkY09EYs8YYk2OM+crlEfqSXfav4d3rdSc0INTqaLxLk/O4oM0o+uXm89+Nb3Io55DVEXmeZPtsbrGhsbSJ8eEy31VRpyVNe9zOuIwM5uyew7pD66yO6Iw4M6on2FGr53bgLhGZJCKTXB+ajzn6JweO/MZuirQoW1UNfJyJ2YUUl+TzYqLehnKSkmJKHGW+ezfojYhYHZH3Of8+brRFU98mPL3qKYpt3tvT7UxXzyzs8+QWAzknPFR12v0Ly8P0bsozEhFH4wseZUJ6BnP3zNMLvSf6M5Gt5HHUaP2nKgsKI3TwVB5MTWFnxi4+3/G51RFVmTOJv5Ex5mpjzHPGmBePPVwema/ZlcCKyBjqhtWlRXQLq6PxXt3H86+QpjQqMUxZ9dRpF3p91i77/SGC0KuBd12I9ChthtC/wfn0zS/kzQ1vkJrrnfOBO5P4V4hIB5dH4stsJRTv/oVVwYH0bdhXv4afCT9/Qoa+zEOpqfyeuZePt39sdUSeITmB5dGxtK/TnpgQ77nRyOOIIEOe5aEjmRQV5/GCl3YpOpP4+wLrRORXx127m0UkydWB+ZQ/17PZlkMWJTW2f9+tQ94an0u/tldyUW4eb22cVuYdvT4j9whHD2xgs19xjZ3Nza3tq3YLmvS6m/HpGfz4+49l3tHryZxJ/IOB1tjnzB0ODHM8O0VE/EVkg4h873hfW0QWiMhOx7Oefuz6mWVhYfiLPz0b9LQ6mpph4OM8mF2MrbiQ59Y+Z3U01tr9C6tCgrFh9PpRdenzb26QGBo6LvR62x29zgzn3HvsARzG/g1gWiWOcTew/YT3E4EEY0xrIMHx3rftWsCKqDp0iO1AVFCU1dHUDOGxNLzwMW7MsE+oseJPH540btdCVkRGExkYQYdY7bWtFoEhhAx5gYkpKSQf3c1n2z+zOqJKcWY4Z5CIjBSRr4ADwEDgLWd2LiKNgKHYb/o65jLg2EzGM4CRlQm4Jjhpxp6cNI4c2MhWP63GWe26jWNcaHOaFBueWf0UhSWFVkfkFie1L2MwyQtZFhZOzwa9CPCrcO4l5azWg7iw6QAuyCtk2gbvundEjCl9rhURGQSMAS4BfgG+BF43xjRzeuciX2O/uzcSuN8YM0xEMowxtU5YJ90Yc1p3j4jcBNwE0KRJk2579+519rAeo7RZoU6cscdP4KqYnfTxe5aH42JpXvAIoaYp4Jmz9nil/etY9ulQbq0Xx91d72ZChwlWR1RtnGlf/WJzeKjgNkY1rE/9ouuJKbF39Wj7qiYZf7D/vz0Z2SCWi5pdwvMXeFahQBFZZ4zpfury8s74fwJaAn2NMdcaY+YAtkoccBiQYoyp0i1uxpjpxpjuxpjucXFxVdmFRzp1xp6S3AwWhkURYKIIMY0tjKyGatSNvmdfxcCcPN7e+F8OZB+wOiKXOrV9BeQeZKmjzHdEyTlWhVVz1WpCo773cUN6OvO86N6R8s74uwCjgVHAbuALYJIxjlPSinYs8gxwHfYbv0KAKOBb4FzgQmPMARGpDywyxpR7/3j37t1NYmLpc156m1Nn7Pko5AXuaZRG/1bDebLPk1aHVzPlpHFgWndGxEXSp2l/XrnoFasjcplT29enjWbyWsAq8uLb8NVwrbDiEsUFFPy3FyPDiwiMacY3I74l0N8zZs6r9Bm/MWaDMeZBY0xL4HGgCxAkInMd3TDlcpRybuToGhoNLDTGXAvMBsY6VhuL/c5gn3HSjD2XxRDol0QmJZzf8HyrQ6u5wutQv/9/uDk9nYQ/Eli6f6nVEbnMSe1rXBdaHZ7FxgAb5zfS9uUyAcEED3mRh1JT+D1zT5mzwXkSZ4ZzYoxZboy5A2gIvAKcSQfhVGCQiOwEBjne+5TjM/bkLGFJWBj+4qd3U7pal+sZG9GKZsU2nln9NAUlBVZH5DLH21fxRlYECTbQEwtXa3kR/VoMpn9uAW9v8vwuRacS/zHGGJsx5idjzL8qud0iY8wwx+s0Y8wAY0xrx/ORyuyrRtn1M0ujYugS35XIoEiro6nZ/PwIHPIiD6ceZl/2n0xPmm51RK732zyWRkQSHRStwzjd4eKneTAzD0oKmbL6acrqRvcElUr8qvpE2DI5dGAdv/qV6Ndwd2nYlV7n/JNh2Tm8v/k9kjOSrY7IdYzB9ttPLAsLo0/DPvj7+VsdUc0X3ZAGFzzMbWlHWLR/MQl/JFgdUZk08Vuka/4aloUEA/o13K36P8b/5RjCbTYmr3gcm3F6oJpXaV68i60Fh0lHTyzcqsdNXBvRkrZF9i7FrMIsqyMqlVOJ31F2oYGINDn2cHVgNV33gpUsjYqhfnh9WtVqZXU4viOsNrUHPcl9hw+zIXUj3+z8xuqIXKJr/hqWhtmrcfZtoGUa3MbPn4Dhr/Of1MMczjvMa+tfszqiUjlz5+6dwCFgAfCD4/G9i+Oq0QJNIWcXrGOlVuO0RudrGBnXlXMLing58aXT5lCtCboVrGJJdB06xHWgVkgtq8PxLfU7ck63mxhzNIsvf/2STambrI7oNM6c8d8NtDHGtDfGdHA8Oro6sJrsnIINbAyBXEq4qPFFVofje0SQ4a/x2JFM8otyeG7Ns1ZHVK1iStIIs+1mq1+xti+rXDiRO22RxNsMk1c87nFF3JxJ/PuAo64OxJd0z1/FT+GRRASGc17986wOxzfVbkHz8x/kxvR05u6Zx7I/l1kdUbXpWrCaheH2u3UHNhlocTQ+Kiic8KEv83BqKjszdvHRVs8a2+9M4t8NLBKRh0Tk3mMPVwdWY9lsdCpYxcKwcM5v1I8g/yCrI/JdPW/nhpCmNC+28dTKyeQW5VodUbU4L28ZP4bH0KpWS5pFN7M6HN/VeiD9WwxlQG4+b22cxr6sfVZHdJwzif8P7P37QdiLrR17qKr4az17gnLJ9rfp2ZjV/AMIGvEG/0lN48+cg7yx8Y3TVrn67ZWlFkPzWLlHaFCcxOZgYYC2L+td+gwPZRXiX1LE5BWTTxvbb1X7cqYe/2RjzGTgJeDFE96rqtj6HfPDwhEToJNieIIGnenW7Sauzszik22fsDFlo9URnZkdP7A0LBgjMLCpJn7LRcRTd9AU7k1LY/XB1R4zisyZUT3niMgGYAuwVUTWiUh714dWA9lsmC3f8lNEFBG29oQFhlkdkQK4YCL32KKpbzM8tuwR8ovzrY6o6rbN4oeIGAJtsbSJKbf2oXKXzv/kyrq9OC+/kBfWPucR5Ryc6eqZDtxrjGnqqMx5H/COa8OqofatZkvBYdL9bUSWdLU6GnVMUBjhw19j8qFD7Mn6gzc3vml1RFWTl07W74tZF+JPpK2LDhP2FCLIiNeYfDQfW1E+j6/4j+XlHJxJ/OHGmF+OvTHGLALCXRZRTbblG36MikKMP5E2HRHrUVpcQM8O13NlZhYfbZ3hkWOvK/TrXH4ODaREDFEl3ayORp0oqgENBz3NvWlprDiwku92fWdpOE6N6hGRx0SkmePxKPC7qwPzdJW+KGMroXjbLH6MjCLC1hF//b/T8wyazL2mFvGOLp+CkoKTpzF0oypd9NvyDT/UqkOQLZ5Q09w1gamq63wNV9Xtxbn5hTy/5lkO5hy0rH05MwHneGAy9klUBFgCVKo6pwL2LGOVLZMjhNCopKfV0agTnJhgz5I7+U/KI9zqBxf89zkOHrB3yV351gra1oskMuTkCTY8ZgrDzAMc3LOYNY3rE1t0HoJ283iKE9tXTMlYJqbfyj+Dghj64VOkHRgMlN2+wDVtzJlRPenGmLuMMV2NMV2MMXcbY9z731NNsPkrvo+MJjIwkgibToHnqX4Lakea3zD+kZVNekEKYO+LtZmTpzX0OJu/Ym54CAaItulNgZ4q3T+WhLAbuSf9CFn5glXtq8wzfhF5xRjzbxGZw7HoTmCMGeHSyGqSgixyt37HwoZxDG1+CduSPGNaNmV32hlVcVcGvt2PX0KS2SvFYAIJCfTj1dFd6NY0xpogy2MMbPyM72Pi6BjXjuJ98VZHpE5wWvsyPbF9vp1ZKTtYI/0d7cvfre2rvDP+jx3PLwAvlvJQztryLQmBhjxsDGsxzOpoVEUCggm/YjqvZa0nvMk7RNXZyqcTenpm0gf4awO/ZSTzmxRr+/IGIvhd9gZv2PYS2+hdwmNX8dH47m5tX+XNubvO8bKzMWbxiQ+gc0U7FpEQEVkjIptEZKuITHYsry0iC0Rkp+PZQ/+aqtH6j5hZO56GEQ3oEt/F6miUM+p3olPv+7mjIAkT/zGHSjz47t1Nn/NdVBQBfgFc0uwSq6NRzgiPJf6yt3g2Zx1+cbNYceRTtx7emVE9Y0tZNs6J7QqA/saYTtj/o7hURHoCE4EEY0xrIMHxvuY6tI3fUzayJsDGqLOuxE907huv0fceeuU2p2N+IU+tnMxf2X9ZHdHpivLI3/w/ZkdFMbDJQGqH1LY6IuWslv3Jk8H8Iyub97e8z9qDa9126DKzkIiMcfTvNxeR2Sc8fgHSKtqxsct2vA10PAxwGTDDsXwGMPJMfgCPt+Fjvo6KIkD8GdlqpNXRqMrw82darQd5JK0AW1EuE5c84HHlddnyDfP9C8mkhKvaXGV1NKqSPo8cx9VHo2lSXMJDSx4kPd8942bKO/1cgb0vfwcn9+3fB1zqzM4dM3dtBFKABcaY1UBdY8wBAMdzqVeiROQmEUkUkcTU1FQnfxwPU5hD/sbPmBUVTf8mA4gNjbU6IlVJR/1rMzPiPh5LPcyG1E28vv51q0P6mzGw+m2+rB1Hs6hmdK/b3eqIVCUVSxDvRD/Ec2lHOZKbykNLJ7plOtAyR/UYY/YCe4EqDyI1xpQAnUWkFvCdiDg9jtEYMx17uQi6d+/uudPVl2fjZ8wJLOIoJYxuO9rqaFQV2Edk9IIFh1m/YwYfbP2AzvGd6d+kv9Whwf61bErfQVKDekxsO1pLNHih4+0rSZi44B6elBW8k/QON3e62aXHdaZIW08RWSsi2SJSKCIlIpJZmYMYYzKARdi/KRwSkfqOfdfH/m2g5rGVYFv1Jh/Viad9nfZ6Nubt+j/KA2GtaVdYxKNLH/KM2uorXmNGTB0iAyO5vNXlVkejzkTHq7jyrKsYkp3DtI1vsvrAapcezpkrjW8AY4CdQCgwAajw+66IxDnO9BGRUGAg9m6j2fx9wXgsMKvSUXuDX39kcf5B9kgJY9uP1bMxb+cfSPBVH/NiloHCXO5deJe1E7ek/sq+nXNJCA3iqjZXaaXXGkAGT+U/gY1pVlTMA4vuc2kVT6eGmBhjdgH+xpgSY8wHgDMTedYHfhGRJGAt9j7+74GpwCAR2QkMcryvWWw2zKKpvFMnjgbh9RnUdJDVEanqEFmPRld+zNTUI/yasYtHlz3ilv7YUi17hfdiYvD3C+CfZ//TmhhU9QoIJuyqj3n5aAGFBUe5O+F2l51cOJP4c0UkCNgoIs+JyD04UZ3TGJPkKPHQ0RhzjjHmCcfyNGPMAGNMa8fzkTP8Gcp0tOAo6w+td9Xuy7bje5Zk7mJzANzU8WYC/JwpiaS8QuMe9BvwNPelpbPgj5+tKeF8eBf7tn3NzIgwrjzrSuLD9E7dGqNWE1qM+oRnU4+wI30njy171CUlnJ1J/Nc51rsDyAEaA/+o9khcYMrKJ7hlwU1sTt3svoPaSrAtmsKbcXVpFNGIEa20skWN020c17cZzeVZ2UxPms4Pu39w7/EXPsFbMbUI8AtkQocJ7j22cr2mveh38YvceySd+X8sYN7vc6v9EM4k/n5AoDEm0zHt4r1A22qPxAX+73AatQvyuH3Bzew5usc9B133IT/k7mW7v+G2zrcR6Kd1eWoiGfwsj9XpSdf8fB5b9ggr/3LTnb37E9m260fmhIcw5ux/EhcW557jKvfqNJqxHW/kpUOpXJJT/d09ziT+14GlInL2CcueqPZIXCB24BNMzwIpyOTmn8aTkuviAUR56WT/8hQvxsbTIfYchrYY6trjKev4+RM46n1eC2hGs4J87k64k6TUJNces6QY2w/3MiW+LjHBMdzY8UbXHk9ZSvo/xqABU/Frd1m179uZxP879pr8X4vIlcdiqvZIXKFWE5pcO4tpGQVk5KZwy083cCTfZZcUYMEk3gw1HBHDw+c9ouUZarrAUKLHfMnbhRHUKczl1vk3sjN9p+uOt/YdZmXtYlOgH/d0v5eooCjXHUtZz88Puo0D/+rvNXAmMxljzHrgAuAmEXkB8K/2SFwlthXtx3zLq0dy+OPoHm748XrS8iqsOFF5v81nzbYv+TQqkqvaXMU5sVpz3yeE1Sbuujm8kxdCcEE2N84bx2/pv1X/cdKS+WvR0zwbF0fX+K6MaKnXjlTVOZP4j5VXOAxcgr3ejndltXod6HnV/3jzSA5/Zu5h/A/XcDDnYPXtP/MAR+fcwaN169Eksgn3dru33NW/vLmX58zcpM5cVH0aXf8D7+YG4Z+Xwb9+vLZ6BxQUF1D8v7E8UicSAoKZcv6Ucr9NavtSFXFmBq6hJ7y2GWP+zxjjfX0Yjbpz3jXfM+1oMYey9nPN7FFsS9t25vstyqfoy2u4L0I47O/PM+dP1ZtpfFFUfVqMncuMomii8rKYMG8si/YtOvP9GgPf38vzhftJDArg4Z6P0jCi4ZnvV/m08qpzvuJ4nnNKdc7ZIjLbbRFWp7rt6H7dXD7KC8YvN41xP1zDT7//VOXd+ZsibF+P58mC31kdEsR/ej9Oh7gO1Riw8iqR9Wg07idmBLWmWV42dy28k+kbp53ZOOyFT/Hx7ll8Fh3J9e2u1y4eVS18bwau2s05a/wvfBbWgdZ5Ody/5H4mLbq/0nfIBdvyuSv9aSalreS7yAhu7ngzl7Wq/qvvyssERxJ/zTfMaHIFQ7NzeH3Tf7n1x+sr3bUoxsa1mW/xQdLbPFcnhgFNBlTYhaiUs6S8sxER8QdmGGOudV9Ip+vevbtJTEys3p3abBStfJ1p61/lvcgw6gdFcXePiQxuObziujop21n/3tW8UjufDSHB3Nb5Nm7peIvW41EnMdt/4KsFd/NiZDB+/kHc1eVOrmx3HYEVjdLIPMCKadcwJ3IP30eEc0nTi3mm31S9J0RVmoisM8acViGy3MTv2PAnYLgxptBVwVXEJYn/mLRk1s25mamF+9gRHET74FiuOWc8A9uOIjQg9OR1U3aQtvoNvkqewwfRkRRJIE/2e1LnOVVly05l3/yJTE5ZwurQEBr5h3PNWVcyrOMN1AqpdfK6WQfJXfM232/5mGkRQRzx9+e2LrdzY4cb8ffznoF0ynOcSeJ/G+iKvapmzrHlxpiXqjvIsrg08QMYg23nAuYsf5J3SlLZGxhIkIHOfuE0D4gk3FZCTm4qO00+m4KDKREhprgDdUqu5rubtHtHVczsX8fSJU/wdvZ2koKD8TOG9hJK68AoahkozMtgd9FRNoQEk+fnR3RJI2KKr2XOTddYHbryYmUlfmeqh/3lePgBkdUdmEcQwe+si7nsrIsZnrKDxE0fsOjgatYXpTO3JJt8EYKD/WkZ0oxxTS9iWJureOQrL50VTFlCGnWj3z9n0S/zADs2fkDC/sWsyT/EYlsKmQKBgULT8IaMqHcug9uN4dmZBYiX3CepvE+Fid8YM9kdgXgKv/i29Bj0LD0qXFMTv6qCqPq07fcwbXmY28tZTXBT7R/lkypM/CISBzwAtAdCji03xnjA3HNKKaUqy5kbsT7FPnNWc2AysAf7xCpKKaW8kDOJv44x5j2gyBiz2BgzHujp4riUUkq5iDOJv8jxfEBEhopIF6BRRRuJSGMR+UVEtovIVhG527G8togsEJGdjueYM4hfKaVUJTmT+J8SkWjgPuB+4F3gHie2KwbuM8acjf0bwu0i0g6YCCQYY1oDCY73Siml3MSZUT3fO14exblJ1o9td4C/K3tmich2oCFwGXChY7UZwCLgQacjVkopdUbKTPwi8jr2EsylMsbc5exBRKQZ0AVYDdR1/KeAMeaAiJQ6U7SI3ATcBNCkSRNnD1UpV79tHzKnJWyVK2j7Up6qvDP+arlVVkQigG+AfxtjMp2tZ2OMmQ5MB/udu9URi1JKqXISvzFmxonvRSTSvthkO7tzEQnEnvQ/NcZ861h8SETqO8726wMunghXKaXUiSq8uCsi54jIBmALsE1E1olIeye2E+A9YPspdX1mA2Mdr8cCsyoftlJKqapyplbPdOBeY8wvACJyIfAO0LuC7foA1wGbRWSjY9nDwFTgKxG5AfgDuLL0zZVSSrmCM4k//FjSBzDGLBKR8Io2MsYsgzKrTA1wMj6llFLVzJnEv1tEHuPvGbmuBX53XUhKKaVcyZkbuMYDccC3jkcs8C9XBqWUUsp1yhvHHwLcArQCNmO/C7eorPWVUkp5h/LO+GcA3bEn/cHA826JSCmllEuV18ffzhjTAUBE3gPWuCckpZRSrlTeGf/xbh1jTLEbYlFKKeUG5Z3xdxKRTMdrAUId7wX7HbxRLo9OKaVUtSuvZIO/OwNRSinlHs4M51SlyMov4s+MPNbtTbc6FFUDaftSruTMDVw+61hZ3VNl5Rex7UAWAFe+tYK29SKJDAk8bT0tx6sqUlob0/alXE3P+KsgM//va902c/J7pc6Uti/lanrGX46yzqjW7U3nyrdWYDMQEujHq6O70K2pTh2sKq+0NqbtS7maT5/xV7UftVvTGNrWi6RRTCifTuipf5SqVNq+lKeq8Wf8Z9JPX14famRIIJEhgfpH6eNcdR1I25dyJZ8949d+VOVK2r6UJ6vxZ/zaT69cSduX8kY1PvGX5Vg/amZ+sf5Rqmqn7Ut5Mpd19YjI+yKSIiJbTlhWW0QWiMhOx7Olfw2RIYE0rBWqf5TKJbR9KU/lyj7+D4FLT1k2EUgwxrQGEhzvlVJKuZHLEr8xZglw5JTFl2Gv84/jeaSrjq+UUqp07h7VU9cYcwDA8Rxf1ooicpOIJIpIYmpqqtsCVEqpms5jh3MaY6YbY7obY7rHxcVZHY5SStUY7k78h0SkPoDjOcXNx1dKKZ/n7sQ/GxjreD0WmOXm4yullM9z5XDOz4GVQBsR2S8iNwBTgUEishMY5HivlFLKjVx2A5cxZkwZHw1w1TGVUkpVzGMv7iqllHINTfxKKeVjNPErpZSP0cSvlFI+RhO/Ukr5GE38SinlY7y2Hn9RURH79+8nPz+/yvu4vUsoANu3b3frtjVFSEgIjRo1IjDw9CkFlVKey2sT//79+4mMjKRZs2aISJX2EZSaDUDLuAi3blsTGGNIS0tj//79NG/e3OpwlFKV4LVdPfn5+dSpU6fKSV+dGRGhTp06Z/SNSyllDa894wfOOOn76tl6ddH/dMtX1ny8SlnNa8/4lVJKVY0m/jOQeugQo0ePpmXLlrRr144hQ4bw22+/Vdv+Z86cybZt26q8/Z49e/jss8/K/Cw0NJTOnTvTrl07brnlFmw22/HlXbp04eyzz6ZHjx7MmDGj1H0opbyTJv4qMsZw67gxXHjhhSQnJ7Nt2zamTJnCoUOHqu0Yrkz8AC1btmTjxo0kJSWxbds2Zs6ceXz5hg0b2L59O1988QUvv/wyH3zwQZXjUEp5Fq/u4z9u7kQ4uLl691mvAwwuu2r0qmVLCAwM5JZbbjm+rHPnzoD9P4UHHniAuXPnIiI8+uijXH311SxatIjHH3+c2NhYtmzZQrdu3fjkk08QESZOnMjs2bMJCAjg4osv5oorrmD27NksXryYp556im+++YaFCxcyffp0CgsLadWqFR9//DFhYWGMGzeOqKgoEhMTOXjwIM899xyjRo1i4sSJbN++nc6dOzN27FjuueeeUn+WgIAAevfuza5du+jatetJn7Vo0YKXXnqJ++67j3/9619n/ntVSlmuZiR+C/y2YxvtO3Up9bNvv/2WjRs3smnTJg4fPsy5555Lv379ANiwYQNbt26lQYMG9OnTh+XLl9OuXTu+++47duzYgYiQkZFBrVq1GDFiBMOGDWPUqFEA1KpVixtvvBGARx99lPfee48777wTgAMHDrBs2TJ27NjBiBEjGDVqFFOnTuWFF17g+++/L/dnyc3NJSEhgSeeeKLUz7t27cqOHTuq9HtSSnmempH4yzkzt8KyZcsYM2YM/v7+1K1blwsuuIC1a9cSFRVFjx49aNSoEWD/hrBnzx569uxJSEgIEyZMYOjQoQwbNqzU/W7ZsoVHH32UjIwMsrOzueSSS45/NnLkSPz8/GjXrp3T3U3Jycl07twZEeGyyy5j8ODB7Nmz57T1jDGV/yUopTxWzUj8Fmjd5mzmzZlZ6mflJcrg4ODjr/39/SkuLiYgIIA1a9aQkJDAF198wRtvvMHChQtP23bcuHHMnDmTTp068eGHH7Jo0aJS9+tsoj7Wx1+RDRs2cPbZZzu1T6WU57Pk4q6IXCoiv4rILhGZaEUMZ6rX+RdQWFjIO++8c3zZ2rVrWbx4Mf369ePLL7+kpKSE1NRUlixZQo8ePcrcV3Z2NkePHmXIkCG88sorx5NxZGQkWVlZx9fLysqifv36FBUV8emnn1YY46nbV8WePXu4//77j3cpKaW8n9sTv4j4A28Cg4F2wBgRaefuOM6UiDDtw89YsGABLVu2pH379jz++OM0aNCAyy+/nI4dO9KpUyf69+/Pc889R7169crcV1ZWFsOGDaNjx45ccMEFvPzyywCMHj2a559/ni5dupCcnMyTTz7Jeeedx6BBg2jbtm2FMXbs2JGAgAA6dep0fJ/OSE5OPj6c86qrruLOO+/UC7tK1SDi7v5bEekFPG6MucTx/iEAY8wzZW3TvXt3k5iYeNKy7du3W9r9kOzjtXqOsfrfoaa6+u2VgN79q86MiKwzxnQ/dbkVXT0NgX0nvN/vWHYSEblJRBJFJDE1NdVtwSmlVE1nReIvrcDLaV87jDHTjTHdjTHd4+Li3BCWUkr5BisS/36g8QnvGwF/WRCHUkr5JCsS/1qgtYg0F5EgYDQw24I4lFLKJ7l9HL8xplhE7gB+AvyB940xW90dh1JK+SpLbuAyxvwI/GjFsZVSytdpdc4q+ufIwSxZ+PNJy1555RVuu+02p/cxadIkfv7553LXufDCCzl1KCvAhx9+yB133OH0sRYtWkR0dPTx8fmTJ08+bXmbNm3o169fhbV9lFLeTUs2VNHwy6/kh5lf86+rRx5f9sUXX/D88887tX1JSUmZRdFc5fzzz+f7778nJyeHzp07H68JdGw5wMaNGxk5ciShoaEMGDDArfEppdyjRiT+Z9c8y44j1Vs9sm3ttjzY48EyP790+GW8NPUJCgoKCA4OZs+ePfz111/07duXW2+9lbVr15KXl8eoUaOOn103a9aM8ePHM3/+fO644w7mzZt3vPrmE088wZw5c8jLy6N37968/fbbx6c2/OSTT7jrrrvIzMzk/fffP638Q2pqKrfccgt//PEHYP/m0adPnzJjDw8Pp1u3biQnJxMfH3/SZ507d2bSpEm88cYbmviVqqG0q6eKYmrXoVOXbsybNw+wn+1fffXViAhPP/00iYmJJCUlsXjxYpKSko5vFxISwrJlyxg9evRJ+7vjjjtYu3YtW7ZsIS8v76TulpycHFasWMG0adMYP378abHcfffd3HPPPaxdu5ZvvvmGCRMmlBt7Wloaq1aton379qV+rmWYlarZasQZf3ln5q407PIr+eKLL7jsssv44osveP/99wH46quvmD59OsXFxRw4cIBt27bRsWNHAK6++upS9/XLL7/w3HPPkZuby5EjR2jfvj3Dhw8HYMyYMQD069ePzMxMMjIyTtr2559/PmmmrszMTLKysoiMjDxpvaVLl9KlSxf8/PyYOHEi7du3P6nC5zFahlmpmq1GJH6rDBo8jGcff5j169eTl5dH165d+f3333nhhRdYu3YtMTExjBs3jvz8/OPbhIeHn7af/Px8brvtNhITE2ncuDGPP/74Sdsc6/Ip673NZmPlypWEhoaWG++Jffnl0TLMStVs2tVzBsIjIrjwwgsZP3788bPyzMxMwsPDiY6O5tChQ8ydO7fC/RxL8rGxsWRnZ/P111+f9PmXX34J2Cd4iY6OJjo6+qTPL774Yt54443j752psV+WpKQknnzySW6//fYq70Mp5dn0jP8MjRkzhiuuuIIvvvgCgE6dOtGlSxfat29PixYtyr3IesyxKRU7dOhAs2bNOPfcc0/6PCYmht69ex+/uHuq1157jdtvv52OHTtSXFxMv379eOutt5z+GY51AeXm5hIfH89rr72mF3aVqsHcXpa5KjyxLLOy038HpTyXJ5VlVkopZSFN/Eop5WO8OvF7QzdVTaa/f6W8k9cm/pCQENLS0jT5WMQYQ1paGiEhIVaHopSqJK8d1dOoUSP279+PTstonZCQEBo1amR1GEqpSvLaxB8YGEjz5s2tDkMppbyO13b1KKWUqhpN/Eop5WM08SullI/xijt3RSQV2FvFzWOBw9UYTnXRuCpH46ocjatyPDUuOLPYmhpj4k5d6BWJ/0yISGJptyxbTeOqHI2rcjSuyvHUuMA1sWlXj1JK+RhN/Eop5WN8IfFPtzqAMmhclaNxVY7GVTmeGhe4ILYa38evlFLqZL5wxq+UUuoEmviVUsrHeHXiF5FLReRXEdklIhNL+VxE5DXH50ki0tXZbV0c1zWOeJJEZIWIdDrhsz0isllENopI4qnbujiuC0XkqOPYG0VkkrPbujiu/zshpi0iUiIitR2fueT3JSLvi0iKiGwp43Or2lZFcVnVtiqKy6q2VVFcbm9bjn03FpFfRGS7iGwVkbtLWcd1bcwY45UPwB9IBloAQcAmoN0p6wwB5gIC9ARWO7uti+PqDcQ4Xg8+Fpfj/R4g1qLf14XA91XZ1pVxnbL+cGChG35f/YCuwJYyPnd723IyLre3LSfjcnvbciYuK9qWY9/1ga6O15HAb+7MX958xt8D2GWM2W2MKQS+AC47ZZ3LgI+M3SqglojUd3Jbl8VljFlhjEl3vF0FuKO28Zn8zJb+vk4xBvi8mo5dJmPMEuBIOatY0bYqjMuituXM76sslv6+TuGWtgVgjDlgjFnveJ0FbAcanrKay9qYNyf+hsC+E97v5/RfXFnrOLOtK+M60Q3Y/1c/xgDzRWSdiNxUTTFVJq5eIrJJROaKSPtKbuvKuBCRMOBS4JsTFrvq91URK9pWZbmrbTnL3W3LaVa2LRFpBnQBVp/ykcvamNfW48f+9edUp45NLWsdZ7atKqf3LSIXYf/j7HvC4j7GmL9EJB5YICI7HGct7ohrPfbaHtkiMgSYCbR2cltXxnXMcGC5MebEMzhX/b4qYkXbcpqb25YzrGhblWFJ2xKRCOz/2fzbGJN56selbFItbcybz/j3A41PeN8I+MvJdZzZ1pVxISIdgXeBy4wxaceWG2P+cjynAN9h/1rnlriMMZnGmGzH6x+BQBGJdWZbV8Z1gtGc8lXchb+viljRtpxiQduqkEVtqzLc3rZEJBB70v/UGPNtKau4ro254sKFOx7Yv63sBprz9wWO9qesM5STL46scXZbF8fVBNgF9D5leTgQecLrFcClboyrHn/f1NcD+MPxu7P09+VYLxp7X224O35fjn02o+yLlW5vW07G5fa25WRcbm9bzsRlYdsS4CPglXLWcVkb89quHmNMsYjcAfyE/Sr3+8aYrSJyi+Pzt4AfsV8Z3wXkAv8qb1s3xjUJqANMExGAYmOvvlcX+M6xLAD4zBgzz41xjQJuFZFiIA8YbewtzerfF8DlwHxjTM4Jm7vs9yUin2MfiRIrIvuB/wCBJ8Tk9rblZFxub1tOxuX2tuVkXODmtuXQB7gO2CwiGx3LHsb+H7fL25iWbFBKKR/jzX38SimlqkATv1JK+RhN/Eop5WM08SullI/RxK+UUj5GE79SgKMq47EKjf8TkTARaVZWVUelvJkmfqXs8owxnY0x5wCFwC1WB6SUq2jiV+p0S4FWjtf+IvKOo2b6fBEJBRCRG0VkraPo2DeOIl+IyJWObw2bRGSJY5m/iDzvWD9JRG625sdSyk4Tv1InEJEA7HXsNzsWtQbeNMa0BzKAfziWf2uMOdcY0wl7Sd0bHMsnAZc4lo9wLLsBOGqMORc4F7hRRJq7/IdRqgya+JWyC3XcOp+IvY7Me47lvxtjNjper8Ne9wXgHBFZKiKbgWuAY2WGlwMfisiN2G+nB7gYuN6x/9XYSyq0dtlPolQFvLZWj1LVLM8Y0/nEBY46LQUnLCoBQh2vPwRGGmM2icg47PVgMMbcIiLnYS+wtVFEOmMvsnWnMeYn14WvlPP0jF+pqokEDjhK615zbKGItDTGrDbGTAIOYy+f+xP2AmWBjnXOEpFwK4JWCvSMX6mqegx7t81e7NcDIh3LnxeRYxOMJGAvmZuEvYtovdi/RqQCI90cr1LHaXVOpZTyMdrVo5RSPkYTv1JK+RhN/Eop5WM08SullI/RxK+UUj5GE79SSvkYTfxKKeVj/h/s4RjOUphtwAAAAABJRU5ErkJggg==\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "source": [
        "shift=0\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]),'.')\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", 
        "\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.ylim(25,75)\n", 
        "plt.ylabel('Polarization Angle')\n", 
        "plt.xlabel('Phase')\n", 
        "plt.legend()\n", 
        "plt.savefig('figures/polar-angle.pdf')"
      ], 
      "id": "raised-bulgaria", 
      "metadata": {}
    }, 
    {
      "source": [
        "# Energy Dependence"
      ], 
      "cell_type": "markdown", 
      "id": "unsigned-recommendation", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 42, 
      "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"
      ], 
      "id": "indoor-hunger", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 43, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "0.0044074963605380555 0.06753796790568499 0.06768163069103382 0.003646631851242423 0.0030847769887952094\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"
      ], 
      "id": "complimentary-inside", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 44, 
      "outputs": [], 
      "source": [
        "ppp_spec_deg=minimize(maxlikeenergy2,np.random.uniform(size=5)-0.5)"
      ], 
      "id": "covered-medication", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 45, 
      "outputs": [], 
      "source": [
        "ppp_spec_qu=minimize(maxlikeenergy,np.random.uniform(size=10)-0.5)"
      ], 
      "id": "worse-xerox", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 46, 
      "outputs": [
        {
          "execution_count": 46, 
          "output_type": "execute_result", 
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'U/I')"
            ]
          }, 
          "metadata": {}
        }, 
        {
          "output_type": "display_data", 
          "data": {
            "image/png": "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\n", 
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "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')"
      ], 
      "id": "champion-narrow", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 47, 
      "outputs": [
        {
          "execution_count": 47, 
          "output_type": "execute_result", 
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Q/I')"
            ]
          }, 
          "metadata": {}
        }, 
        {
          "output_type": "display_data", 
          "data": {
            "image/png": "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\n", 
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "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')"
      ], 
      "id": "american-korea", 
      "metadata": {}
    }, 
    {
      "source": [
        "# Bin optimization"
      ], 
      "cell_type": "markdown", 
      "id": "adaptive-deputy", 
      "metadata": {
        "tags": []
      }
    }, 
    {
      "cell_type": "code", 
      "execution_count": 48, 
      "outputs": [], 
      "source": [
        "_ii=np.argsort(evtlist['PI'])\n", 
        "pisort=evtlist['PI'][_ii]\n", 
        "modsort=moddata[_ii]"
      ], 
      "id": "superb-jumping", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 49, 
      "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)"
      ], 
      "id": "subtle-sharing", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 50, 
      "outputs": [], 
      "source": [
        "modscum=np.cumsum(modsort**2)"
      ], 
      "id": "removed-brown", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 51, 
      "outputs": [
        {
          "execution_count": 51, 
          "output_type": "execute_result", 
          "data": {
            "text/plain": [
              "array([      0,  152074,  265421,  358914,  439722,  511267,  576336,\n", 
              "        635829,  691062,  743093,  792303,  838945,  883990,  926747,\n", 
              "        967432, 1006285, 1043967, 1080990, 1116765, 1151151, 1184437,\n", 
              "       1217060, 1249246, 1280467, 1310760, 1341054, 1370687, 1398961,\n", 
              "       1427235, 1455170, 1482090, 1508597, 1535104, 1561353, 1586770,\n", 
              "       1611717, 1636665, 1661529, 1685090, 1708652, 1732214, 1755775,\n", 
              "       1779337, 1802899, 1824109, 1845314, 1866520, 1887725, 1908931,\n", 
              "       1930136, 1951176, 1970782, 1990060, 2009337, 2028615, 2047844,\n", 
              "       2065836, 2083508, 2101179, 2118759, 2135177, 2151489, 2167555,\n", 
              "       2182701, 2197822, 2212046, 2226180, 2239562, 2252800, 2265310])"
            ]
          }, 
          "metadata": {}
        }
      ], 
      "source": [
        "plist=np.interp(np.linspace(0,1,70),modscum/modscum[-1],np.arange(len(modcum))).astype(int)\n", 
        "plist"
      ], 
      "id": "wrong-theory", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 52, 
      "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]))"
      ], 
      "id": "allied-gibraltar", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 53, 
      "outputs": [], 
      "source": [
        "pilist=np.concatenate((pisort[plist[0:65:5]],pisort[plist[65:]]))\n", 
        "elist=pilist/25"
      ], 
      "id": "anticipated-surge", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 54, 
      "outputs": [
        {
          "execution_count": 54, 
          "output_type": "execute_result", 
          "data": {
            "text/plain": [
              "(0, 17)"
            ]
          }, 
          "metadata": {}
        }
      ], 
      "source": [
        "pibin=np.digitize(evtlist['PI'],pilist)-1\n", 
        "pibin.min(),pibin.max()"
      ], 
      "id": "packed-novelty", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 55, 
      "outputs": [], 
      "source": [
        "ppp_spec_deg=minimize(maxlikeenergy2,np.random.uniform(size=pibin.max()+1)-0.5)"
      ], 
      "id": "revised-tension", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 56, 
      "outputs": [], 
      "source": [
        "ppp_spec_qu=minimize(maxlikeenergy,np.random.uniform(size=2*pibin.max()+2)-0.5)"
      ], 
      "id": "afraid-danger", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 57, 
      "outputs": [
        {
          "output_type": "display_data", 
          "data": {
            "image/png": 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\n", 
            "text/plain": [
              "<Figure size 504x432 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "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')"
      ], 
      "id": "appreciated-belly", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 58, 
      "outputs": [
        {
          "output_type": "display_data", 
          "data": {
            "image/png": "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\n", 
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "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:])])"
      ], 
      "id": "copyrighted-scenario", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 59, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "#     Emid        Q/I       EQ/I   DeltaE/2\n", 
            "       2.2  0.0764106  0.0121109        0.2 \n", 
            "      2.52  0.0742603  0.0111881       0.12 \n", 
            "      2.74  0.0695722  0.0111753        0.1 \n", 
            "      2.94  0.0660105  0.0107367        0.1 \n", 
            "      3.14  0.0480172  0.0107275        0.1 \n", 
            "      3.34  0.0508733  0.0109387        0.1 \n", 
            "      3.54   0.082324  0.0114486        0.1 \n", 
            "      3.76  0.0747562  0.0109253       0.12 \n", 
            "      4.02  0.0704831  0.0110709       0.14 \n", 
            "      4.34  0.0630806  0.0106542       0.18 \n", 
            "      4.74  0.0527985   0.010838       0.22 \n", 
            "      5.24  0.0629835  0.0110607       0.28 \n", 
            "      5.94  0.0445247   0.010932       0.42 \n", 
            "      6.48  0.0281666  0.0239453       0.12 \n", 
            "      6.72  0.0529529  0.0258811       0.12 \n", 
            "      7.04    0.06105  0.0235153        0.2 \n", 
            "      7.62  0.0196785  0.0244128       0.38 \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')"
      ], 
      "id": "prospective-funeral", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 60, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "#    Emid         Q/I       EQ/I        U/I       EU/I   DeltaE/2\n", 
            "       2.2  0.0764047  0.0116218 0.00942071  0.0117507        0.2 \n", 
            "      2.52  0.0742715  0.0110629  0.0187167  0.0111243       0.12 \n", 
            "      2.74  0.0695629  0.0103345  0.0165411  0.0109952        0.1 \n", 
            "      2.94  0.0659783  0.0109933  0.0184169   0.010672        0.1 \n", 
            "      3.14  0.0480144  0.0101975 -0.0039438    0.01086        0.1 \n", 
            "      3.34  0.0508743  0.0107449 -0.000385424 0.00983497        0.1 \n", 
            "      3.54  0.0823278  0.0113891 -0.0029091  0.0112208        0.1 \n", 
            "      3.76  0.0747549  0.0108259 0.00155015  0.0115936       0.12 \n", 
            "      4.02   0.070449  0.0116531 -0.0154494  0.0106167       0.14 \n", 
            "      4.34  0.0630794  0.0102792 -0.00454255  0.0109404       0.18 \n", 
            "      4.74  0.0528208   0.011135 -0.00862167  0.0103391       0.22 \n", 
            "      5.24  0.0630093  0.0109127 -0.0045495  0.0110339       0.28 \n", 
            "      5.94  0.0445108   0.011436 -0.00566144  0.0109234       0.42 \n", 
            "      6.48  0.0281728  0.0215028 0.00283047  0.0240263       0.12 \n", 
            "      6.72  0.0529367  0.0235323 0.00258229  0.0240858       0.12 \n", 
            "      7.04  0.0608771  0.0209552 -0.0332824  0.0263086        0.2 \n", 
            "      7.62   0.019918  0.0206057  -0.045336  0.0220903       0.38 \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')"
      ], 
      "id": "included-transportation", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 61, 
      "outputs": [
        {
          "output_type": "display_data", 
          "data": {
            "image/png": 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\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "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')"
      ], 
      "id": "operating-harassment", 
      "metadata": {}
    }, 
    {
      "source": [
        "# Energy Dependence of Phase-Resolved"
      ], 
      "cell_type": "markdown", 
      "id": "nasty-michael", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 62, 
      "outputs": [
        {
          "output_type": "display_data", 
          "data": {
            "image/png": 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\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "source": [
        "oklow=evtlist['PI']<=5.5*25\n", 
        "totonl=plt.hist(phdata[oklow],bins=100)\n", 
        "totonh=plt.hist(phdata[~oklow],bins=10)"
      ], 
      "id": "qualified-snapshot", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 63, 
      "outputs": [
        {
          "execution_count": 63, 
          "output_type": "execute_result", 
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Phase')"
            ]
          }, 
          "metadata": {}
        }, 
        {
          "output_type": "display_data", 
          "data": {
            "image/png": 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\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "source": [
        "_ph=np.linspace(0,2,201)\n", 
        "plottwo(0.5*totonl[1][:-1]+0.5*totonl[1][1:],totonl[0],(totonl[0])**0.5,'.')\n", 
        "_ph=np.linspace(0,2,21)\n", 
        "plottwo(0.5*totonh[1][:-1]+0.5*totonh[1][1:],8*totonh[0],8*(totonh[0])**0.5,'.')\n", 
        "\n", 
        "#plt.plot(_ph,cfunk(_ph,*cfit[0]))\n", 
        "plt.ylabel('Counts')\n", 
        "plt.xlabel('Phase')"
      ], 
      "id": "static-australian", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 64, 
      "outputs": [], 
      "source": [
        "ppp_rot_low=minimize(maxlikerotation2_ok,ppp_rot2.x,method='Nelder-Mead',args=(phdata[oklow],qdata[oklow],udata[oklow]))\n", 
        "ppp_rot_high=minimize(maxlikerotation2_ok,ppp_rot2.x,method='Nelder-Mead',args=(phdata[~oklow],qdata[~oklow],udata[~oklow]))"
      ], 
      "id": "relevant-gazette", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 65, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stderr", 
          "text": [
            "/home/idies/miniconda3/envs/heasoft/lib/python3.8/site-packages/numpy/lib/npyio.py:518: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", 
            "  arr = np.asanyarray(arr)\n"
          ]
        }
      ], 
      "source": [
        "np.save(\"output/totonh.npy\",totonh)\n", 
        "np.save(\"output/totonl.npy\",totonl)\n", 
        "np.save('output/ppp_rot',(ppp_rot2,ppp_rot_low,ppp_rot_high))"
      ], 
      "id": "sixth-month", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 66, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "0.06853759182935774 0.00350398903602702\n"
          ]
        }, 
        {
          "output_type": "display_data", 
          "data": {
            "image/png": 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\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "source": [
        "_ph=np.linspace(0,2,201)\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,'.')\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])))),label='2-8 keV')\n", 
        "plt.plot(_ph,np.abs(ppp_rot_low.x[0]*(1+ppp_rot_low.x[5]*np.cos(2*np.pi*(_ph-ppp_rot_low.x[6])))),label='2-5.5 keV')\n", 
        "plt.plot(_ph,np.abs(ppp_rot_high.x[0]*(1+ppp_rot_high.x[5]*np.cos(2*np.pi*(_ph-ppp_rot_high.x[6])))),label='5.5-8 keV')\n", 
        "plt.ylabel('Polarization Degree')\n", 
        "plt.xlabel('Phase')\n", 
        "#plt.ylim(0.1,0.6)\n", 
        "plt.legend()\n", 
        "plt.savefig('figures/polar-deg-energy.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)"
      ], 
      "id": "boxed-spain", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 67, 
      "outputs": [
        {
          "output_type": "stream", 
          "name": "stdout", 
          "text": [
            "\n", 
            "Alpha:                      180\n", 
            "Beta:                       9.34669e-05\n", 
            "Mean Polarization Fraction: 0.0626223\n", 
            "Pole across Line of Sight:  0.796132\n", 
            "Peak Flux:                  0.997864\n", 
            "\n"
          ]
        }, 
        {
          "output_type": "display_data", 
          "data": {
            "image/png": 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\n", 
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          }, 
          "metadata": {
            "needs_background": "light"
          }
        }
      ], 
      "source": [
        "shift=0\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]),'.')\n", 
        "_ph=np.linspace(0,2,401)\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", 
        "\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='2-8 keV')\n", 
        "    plt.plot(_ph,np.degrees(np.mod((angfunk(ppp_rot_low.x[1],ppp_rot_low.x[2],_ph-ppp_rot_low.x[4])+np.radians(ppp_rot_low.x[3]-shift)),np.pi))+shift,label='2-5.5 keV')\n", 
        "    plt.plot(_ph,np.degrees(np.mod((angfunk(ppp_rot_high.x[1],ppp_rot_high.x[2],_ph-ppp_rot_high.x[4])+np.radians(ppp_rot_high.x[3]-shift)),np.pi))+shift,label='5.5-8 keV')\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)\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)\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)\n", 
        "    '''\n", 
        "#plt.ylim(25,75)\n", 
        "plt.ylabel('Polarization Angle')\n", 
        "plt.xlabel('Phase')\n", 
        "plt.legend()\n", 
        "plt.savefig('figures/polar-angle-energy.pdf')"
      ], 
      "id": "alleged-gravity", 
      "metadata": {}
    }, 
    {
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      "execution_count": 68, 
      "outputs": [], 
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      "execution_count": 69, 
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    {
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      "execution_count": 70, 
      "outputs": [], 
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      "metadata": {}
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    {
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      "execution_count": 71, 
      "outputs": [], 
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      "id": "6098d99f-e798-432e-a8f7-780f179692c6", 
      "metadata": {}
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    {
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      "execution_count": 72, 
      "outputs": [], 
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      "metadata": {}
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    {
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      "execution_count": 73, 
      "outputs": [], 
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      "id": "e940e78f-1b82-4bee-9bfe-2e3944fa28d8", 
      "metadata": {}
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    {
      "cell_type": "code", 
      "execution_count": 74, 
      "outputs": [], 
      "source": [], 
      "id": "1a58b935-953b-4741-984c-67a3abab9434", 
      "metadata": {}
    }, 
    {
      "cell_type": "code", 
      "execution_count": 75, 
      "outputs": [], 
      "source": [], 
      "id": "c23462cd-9b08-436d-b753-b635120ce332", 
      "metadata": {}
    }
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