manvr_bias_jogs.ipynb

{
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  "name": "",
  "signature": "sha256:8cf533123f7b20dd1852e451c1390464bfead4cf9f2fdbfe7f4d883890eae1b9"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "os.environ['SKA'] = '/Users/aldcroft/ska'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import Chandra.Maneuver\n",
      "from Quaternion import Quat\n",
      "import numpy as np\n",
      "import Ska.engarchive.fetch_eng as fetch\n",
      "from Ska.Matplotlib import plot_cxctime\n",
      "from kadi import events\n",
      "from Chandra.Time import date2secs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t_step = 100\n",
      "atts = Chandra.Maneuver.attitudes((0,0,0), (50,0,0), step=t_step)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from astropy.table import Table\n",
      "atts = Table(atts)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "atts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table id=\"table4468374160\"><thead><tr><th>time</th><th>q1</th><th>q2</th><th>q3</th><th>q4</th><th>pitch</th></tr></thead><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>79.6448299995</td></tr><tr><td>102.051303168</td><td>0.0</td><td>0.0</td><td>0.00299508215609</td><td>0.999995514731</td><td>79.9585509056</td></tr><tr><td>204.102606335</td><td>0.0</td><td>0.0</td><td>0.0166953142302</td><td>0.999860623528</td><td>81.3986732246</td></tr><tr><td>306.153909503</td><td>0.0</td><td>0.0</td><td>0.0417453735559</td><td>0.999128281947</td><td>84.0364058454</td></tr><tr><td>408.205212671</td><td>0.0</td><td>0.0</td><td>0.0780997259619</td><td>0.996945551575</td><td>87.874250167</td></tr><tr><td>510.256515838</td><td>0.0</td><td>0.0</td><td>0.125628760642</td><td>0.992077322843</td><td>92.9116830422</td></tr><tr><td>612.307819006</td><td>0.0</td><td>0.0</td><td>0.184043452839</td><td>0.98291810822</td><td>99.1400062206</td></tr><tr><td>714.359122174</td><td>0.0</td><td>0.0</td><td>0.248599127868</td><td>0.968606459623</td><td>106.08062827</td></tr><tr><td>816.410425341</td><td>0.0</td><td>0.0</td><td>0.305411669647</td><td>0.952220411482</td><td>112.246518443</td></tr><tr><td>918.461728509</td><td>0.0</td><td>0.0</td><td>0.350545006252</td><td>0.936545887072</td><td>117.186275203</td></tr><tr><td>1020.51303168</td><td>0.0</td><td>0.0</td><td>0.384415700646</td><td>0.923160099385</td><td>120.917062136</td></tr><tr><td>1122.56433484</td><td>0.0</td><td>0.0</td><td>0.407428265405</td><td>0.913237213734</td><td>123.462289297</td></tr><tr><td>1224.61563801</td><td>0.0</td><td>0.0</td><td>0.419901899903</td><td>0.907569498417</td><td>124.844606862</td></tr><tr><td>1326.66694118</td><td>0.0</td><td>-0.0</td><td>0.422618261741</td><td>0.906307787037</td><td>125.145027826</td></tr></table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<Table rows=14 names=('time','q1','q2','q3','q4','pitch')>\n",
        "array([(0.0, 0.0, 0.0, 0.0, 1.0, 79.64482999950958),\n",
        "       (102.05130316768518, 0.0, 0.0, 0.0029950821560936654, 0.9999955147313803, 79.95855090559664),\n",
        "       (204.10260633537035, 0.0, 0.0, 0.016695314230227146, 0.9998606235284766, 81.39867322455306),\n",
        "       (306.1539095030555, 0.0, 0.0, 0.04174537355592242, 0.9991282819471564, 84.03640584542424),\n",
        "       (408.2052126707407, 0.0, 0.0, 0.07809972596185852, 0.9969455515747501, 87.87425016695065),\n",
        "       (510.2565158384259, 0.0, 0.0, 0.12562876064198872, 0.9920773228431128, 92.91168304219721),\n",
        "       (612.307819006111, 0.0, 0.0, 0.18404345283908577, 0.9829181082201442, 99.14000622061279),\n",
        "       (714.3591221737962, 0.0, 0.0, 0.24859912786827806, 0.9686064596228602, 106.08062827003035),\n",
        "       (816.4104253414814, 0.0, 0.0, 0.3054116696467252, 0.9522204114823414, 112.24651844257147),\n",
        "       (918.4617285091666, 0.0, 0.0, 0.3505450062519828, 0.9365458870721697, 117.18627520268366),\n",
        "       (1020.5130316768518, 0.0, 0.0, 0.38441570064599234, 0.9231600993851775, 120.91706213561129),\n",
        "       (1122.564334844537, 0.0, 0.0, 0.407428265404698, 0.9132372137343719, 123.46228929747727),\n",
        "       (1224.615638012222, 0.0, 0.0, 0.4199018999033896, 0.9075694984173519, 124.84460686178467),\n",
        "       (1326.6669411799073, 0.0, -0.0, 0.42261826174069944, 0.9063077870366499, 125.14502782631419)], \n",
        "      dtype=[('time', '<f8'), ('q1', '<f8'), ('q2', '<f8'), ('q3', '<f8'), ('q4', '<f8'), ('pitch', '<f8')])"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "qs = [Quat([att['q1'], att['q2'], att['q3'], att['q4']]) for att in atts]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dqs = [q1 / q0 for q0, q1 in zip(qs[:-1], qs[1:])]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dt = np.diff(atts['time'])[0]\n",
      "dt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "102.05130316768518"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "q_biaserr = Quat(np.array([0.001, 0.00, 0]) * dt)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "qout = q_biaserr * dqs[0]\n",
      "for dq in dqs[1:]:\n",
      "    qout = q_biaserr * dq * qout "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "qout.equatorial"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "array([ 51.32666694,   0.        ,   0.        ])"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "atts[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import astropy\n",
      "astropy.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dat = fetch.Msidset(['aogbias1'], '2010:001:00:00:00', '2010:001:00:30:00', stat='5min')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "biaserr1 = (dat['aogbias1'].vals - dat['aogbias1'].vals[0]) * 180 / np.pi\n",
      "plot_cxctime(dat['aogbias1'].times, biaserr1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "(((matplotlib.dates.MinuteLocator,\n",
        "   {'byminute': [0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56]},\n",
        "   '%j:%H:%M',\n",
        "   matplotlib.dates.MinuteLocator,\n",
        "   {}),),\n",
        " <matplotlib.figure.Figure at 0x107e83c90>,\n",
        " <matplotlib.axes._subplots.AxesSubplot at 0x107e7a690>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sXBgdlJdfrsQtItJcqda8V62CAw6ArbaCv/+9IGGIiGRKUda8//AHWLoULrss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\nkQz6f5NxWQl6KfHnAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107e83c90>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dat['aogbias1'].unit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "'RADPS'"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dat['aogbias1'].vals * 180 / np.pi * 3600"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "array([-0.96689391, -0.96663272, -0.96647745, -0.96663648, -0.96644443], dtype=float32)"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dwells = events.dwells(pad=(-1200, -3600))\n",
      "date_intervals = dwells.intervals('2010:001', '2010:003')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "secs_intervals = date2secs(np.array(date_intervals).ravel()).reshape(-1, 2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dt_secs_intervals = secs_intervals - secs_intervals[0, 0]\n",
      "dt_secs_intervals"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "array([[      0.        ,   19294.71900004],\n",
        "       [  30680.34400004,   31065.81900007],\n",
        "       [  40799.14400005,   46067.72000003],\n",
        "       [  51192.64500004,   56299.27000004],\n",
        "       [  70036.24600005,   95092.44700009],\n",
        "       [ 100745.24700004,  100982.09800005],\n",
        "       [ 107547.148     ,  121084.39900011],\n",
        "       [ 129695.34900004,  172800.        ]])"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dts = dt_secs_intervals[:, 1] - dt_secs_intervals[:, 0]\n",
      "dts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "array([ 19294.71900004,    385.47500002,   5268.57599998,   5106.625     ,\n",
        "        25056.20100003,    236.85100001,  13537.25100011,  43104.65099996])"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cum_dts = np.cumsum(dts)\n",
      "cum_dts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "array([  19294.71900004,   19680.19400007,   24948.77000004,\n",
        "         30055.39500004,   55111.59600008,   55348.44700009,\n",
        "         68885.69800019,  111990.34900016])"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "manvrs = events.manvrs.filter('2010:001', '2010:005')\n",
      "manvrs[2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "<Manvr: start=2010:001:21:37:56.582 dur=1222 n_dwell=1 template=normal>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(20):\n",
      "    t0 = np.random.uniform(high=cum_dts[-1])\n",
      "    index = np.searchsorted(cum_dts, t0)\n",
      "    print(index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "7\n",
        "7\n",
        "0\n",
        "6\n",
        "6\n",
        "0\n",
        "6\n",
        "4\n",
        "3\n",
        "6\n",
        "6\n",
        "4\n",
        "7\n",
        "7\n",
        "6\n",
        "4\n",
        "2\n",
        "4\n",
        "7\n",
        "1\n"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}