Spiral Spotter Analysis

SpiralSpotterClassifyAnalysisClean.ipynb

{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Notes\n\nSubject sets from Ross, but not final set he provided due to time limitations.\n\n### Classify workflow\n\nFirst session: Spiral1Classify & Spiral2Classify subject sets.\n\nBetween sessions: Copied workflow and renamed old workflow Classify1.\n\nSecond session: Spiral1Classify & Spiral2Classify subject sets.\n\nFor output and plotting, classifications from session2 are added those from session1.\nPlots shown in session had low numbers due to bug (now fixed) where fractions were stored as integers.\n\n### Outputs\n\n* spiral_spotter_classify_data.fits - the collated classifications\n* spiral_spotter_classify_consensus.fits - a (poor) attempt at a consensus classification\n* spiral_spotter_classify_subjects.fits - the metadata for the subjects"
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "from panoptes_client import Panoptes, Project, Classification, Subject\nfrom astropy.table import Table, Column, hstack, join\nfrom collections import OrderedDict\nfrom datetime import datetime",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "%matplotlib inline\nimport matplotlib as mpl\nfrom matplotlib import pyplot as plt\n# better-looking plots\nplt.rcParams['font.family'] = 'serif'\nplt.rcParams['figure.figsize'] = (16.0, 8.0)\nplt.rcParams['font.size'] = 14",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "Panoptes.connect(username='uon', password='XXXXXXXXX')",
      "execution_count": 3,
      "outputs": [
        {
          "data": {
            "text/plain": "<panoptes_client.panoptes.Panoptes at 0x10d209b90>"
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "project = Project.find(slug='uon/spiral-spotter')",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "for w in project.links.workflows:\n    print(w.display_name, w.raw[u'classifications_count'])",
      "execution_count": 5,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "(u'SpArcFiRe', 551)\n(u'Classify', 3659)\n(u'Classify1', 2628)\n(u'SpArcFiRe1', 813)\n(u'SpArcFiReShort', 0)\n"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "classifications = []",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "len(classifications)",
      "execution_count": 7,
      "outputs": [
        {
          "data": {
            "text/plain": "0"
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "for w in project.links.workflows:\n    if w.display_name == u'Classify1':\n        classify_workflow = w\nworkflow_version = classify_workflow.raw[u'version']",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#last_seen_id = classifications[-1].id if len(classifications) > 0 else None\nlast_seen_id = None\nfor c in Classification.where(scope='project', project_id=project.id,\n                              workflow_id=classify_workflow.id,\n                              last_id=last_seen_id):\n    # only get classifications for latest version of the workflow\n    version = c.raw[u'metadata'][u'workflow_version']\n    if version == workflow_version:\n        classifications.append(c)",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "len(classifications)",
      "execution_count": 10,
      "outputs": [
        {
          "data": {
            "text/plain": "2624"
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "for w in project.links.workflows:\n    if w.display_name == u'Classify':\n        classify_workflow = w\nworkflow_version = classify_workflow.raw[u'version']",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#last_seen_id = classifications[-1].id if len(classifications) > 0 else None\nlast_seen_id = None\nfor c in Classification.where(scope='project', project_id=project.id,\n                                           workflow_id=classify_workflow.id,\n                                           last_id=last_seen_id):\n    # only get classifications for latest version of the workflow\n    version = c.raw[u'metadata'][u'workflow_version']\n    if version == workflow_version:\n        classifications.append(c)",
      "execution_count": 12,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "len(classifications)",
      "execution_count": 13,
      "outputs": [
        {
          "data": {
            "text/plain": "6283"
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "subjects = [int(c.raw['links']['subjects'][0]) for c in classifications]\ntask0_answer = [c.raw['annotations'][0]['value'] for c in classifications]",
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "indata = Table((subjects, task0_answer), names=('subject_id', 'narms'))",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "questions = ['narms']\nanswers = [[a['label'].lower() for a in classify_workflow.raw['tasks']['T0']['answers']]]",
      "execution_count": 16,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def collate_classifications(indata, questions, answers):\n    \"\"\"Reduce a GZ classification database dump to a table of vote fractions\"\"\"\n    outdata = Table()\n    outdata['subject_id'] = np.unique(indata['subject_id'])\n    qindex = 0\n    for c in indata.columns:\n        if not c.endswith('_id'):\n            q = questions[qindex]\n            outcols = Table()\n            for aindex, a in enumerate(answers[qindex]):\n                matches = indata[c] == aindex\n                matches = Column(matches).group_by(indata['subject_id'])\n                count = matches.groups.aggregate(np.sum)\n                name = '{}_{}'.format(q, a)\n                outcols[name] = count\n                if aindex == 0:\n                    total = count\n                else:\n                    total += count\n            name = '{}_total'.format(q)\n            outdata[name] = total\n            outdata = hstack([outdata, outcols])\n            qindex += 1\n    return outdata\n\ndef calculate_fractions(data, questions, answers):\n    \"\"\"Reduce a GZ classification database dump to a table of vote fractions\"\"\"\n    data = data.copy()\n    for qindex in range(len(questions)):\n        q = questions[qindex]\n        for aindex, a in enumerate(answers[qindex]):\n            name = '{}_{}'.format(q, a)\n            totalname = '{}_total'.format(q)\n            fracname = '{}_frac'.format(name)\n            data[fracname] = data[name] / data[totalname].astype(np.float)\n    return data",
      "execution_count": 17,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "data = collate_classifications(indata, questions, answers)",
      "execution_count": 18,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "data = calculate_fractions(data, questions, answers)",
      "execution_count": 19,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "data.write('spiral_spotter_classify_data.fits')",
      "execution_count": 20,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "data",
      "execution_count": 21,
      "outputs": [
        {
          "data": {
            "text/html": "&lt;Table length=500&gt;\n<table id=\"table4529024080\">\n<thead><tr><th>subject_id</th><th>narms_total</th><th>narms_1</th><th>narms_2</th><th>narms_3</th><th>narms_4</th><th>narms_5 or more</th><th>narms_none</th><th>narms_1_frac</th><th>narms_2_frac</th><th>narms_3_frac</th><th>narms_4_frac</th><th>narms_5 or more_frac</th><th>narms_none_frac</th></tr></thead>\n<thead><tr><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n<tr><td>8741181</td><td>14</td><td>1</td><td>11</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0.0714285714286</td><td>0.785714285714</td><td>0.142857142857</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n<tr><td>8741184</td><td>25</td><td>1</td><td>6</td><td>7</td><td>3</td><td>4</td><td>4</td><td>0.04</td><td>0.24</td><td>0.28</td><td>0.12</td><td>0.16</td><td>0.16</td></tr>\n<tr><td>8741187</td><td>8</td><td>0</td><td>4</td><td>0</td><td>0</td><td>0</td><td>4</td><td>0.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.5</td></tr>\n<tr><td>8741189</td><td>14</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>14</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr>\n<tr><td>8741191</td><td>8</td><td>1</td><td>0</td><td>0</td><td>1</td><td>2</td><td>4</td><td>0.125</td><td>0.0</td><td>0.0</td><td>0.125</td><td>0.25</td><td>0.5</td></tr>\n<tr><td>8741194</td><td>9</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>8</td><td>0.0</td><td>0.111111111111</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.888888888889</td></tr>\n<tr><td>8741197</td><td>13</td><td>2</td><td>2</td><td>0</td><td>0</td><td>0</td><td>9</td><td>0.153846153846</td><td>0.153846153846</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.692307692308</td></tr>\n<tr><td>8741198</td><td>16</td><td>3</td><td>3</td><td>1</td><td>0</td><td>0</td><td>9</td><td>0.1875</td><td>0.1875</td><td>0.0625</td><td>0.0</td><td>0.0</td><td>0.5625</td></tr>\n<tr><td>8741199</td><td>10</td><td>0</td><td>5</td><td>3</td><td>0</td><td>1</td><td>1</td><td>0.0</td><td>0.5</td><td>0.3</td><td>0.0</td><td>0.1</td><td>0.1</td></tr>\n<tr><td>8741202</td><td>13</td><td>1</td><td>4</td><td>0</td><td>0</td><td>0</td><td>8</td><td>0.0769230769231</td><td>0.307692307692</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.615384615385</td></tr>\n<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n<tr><td>8742220</td><td>11</td><td>0</td><td>0</td><td>1</td><td>2</td><td>0</td><td>8</td><td>0.0</td><td>0.0</td><td>0.0909090909091</td><td>0.181818181818</td><td>0.0</td><td>0.727272727273</td></tr>\n<tr><td>8742222</td><td>11</td><td>0</td><td>9</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0.0</td><td>0.818181818182</td><td>0.0</td><td>0.0</td><td>0.0909090909091</td><td>0.0909090909091</td></tr>\n<tr><td>8742224</td><td>15</td><td>2</td><td>10</td><td>2</td><td>0</td><td>0</td><td>1</td><td>0.133333333333</td><td>0.666666666667</td><td>0.133333333333</td><td>0.0</td><td>0.0</td><td>0.0666666666667</td></tr>\n<tr><td>8742225</td><td>11</td><td>1</td><td>4</td><td>0</td><td>0</td><td>0</td><td>6</td><td>0.0909090909091</td><td>0.363636363636</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.545454545455</td></tr>\n<tr><td>8742226</td><td>10</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>10</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr>\n<tr><td>8742227</td><td>29</td><td>0</td><td>6</td><td>23</td><td>0</td><td>0</td><td>0</td><td>0.0</td><td>0.206896551724</td><td>0.793103448276</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n<tr><td>8742229</td><td>12</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>11</td><td>0.0833333333333</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.916666666667</td></tr>\n<tr><td>8742232</td><td>37</td><td>6</td><td>11</td><td>4</td><td>1</td><td>3</td><td>12</td><td>0.162162162162</td><td>0.297297297297</td><td>0.108108108108</td><td>0.027027027027</td><td>0.0810810810811</td><td>0.324324324324</td></tr>\n<tr><td>8742235</td><td>9</td><td>0</td><td>2</td><td>1</td><td>2</td><td>2</td><td>2</td><td>0.0</td><td>0.222222222222</td><td>0.111111111111</td><td>0.222222222222</td><td>0.222222222222</td><td>0.222222222222</td></tr>\n<tr><td>8742236</td><td>13</td><td>2</td><td>7</td><td>0</td><td>0</td><td>0</td><td>4</td><td>0.153846153846</td><td>0.538461538462</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.307692307692</td></tr>\n</table>",
            "text/plain": "<Table length=500>\nsubject_id narms_total narms_1 ... narms_5 or more_frac narms_none_frac\n  int64       int64     int64  ...       float64            float64    \n---------- ----------- ------- ... -------------------- ---------------\n   8741181          14       1 ...                  0.0             0.0\n   8741184          25       1 ...                 0.16            0.16\n   8741187           8       0 ...                  0.0             0.5\n   8741189          14       0 ...                  0.0             1.0\n   8741191           8       1 ...                 0.25             0.5\n   8741194           9       0 ...                  0.0  0.888888888889\n   8741197          13       2 ...                  0.0  0.692307692308\n   8741198          16       3 ...                  0.0          0.5625\n   8741199          10       0 ...                  0.1             0.1\n   8741202          13       1 ...                  0.0  0.615384615385\n       ...         ...     ... ...                  ...             ...\n   8742220          11       0 ...                  0.0  0.727272727273\n   8742222          11       0 ...      0.0909090909091 0.0909090909091\n   8742224          15       2 ...                  0.0 0.0666666666667\n   8742225          11       1 ...                  0.0  0.545454545455\n   8742226          10       0 ...                  0.0             1.0\n   8742227          29       0 ...                  0.0             0.0\n   8742229          12       1 ...                  0.0  0.916666666667\n   8742232          37       6 ...      0.0810810810811  0.324324324324\n   8742235           9       0 ...       0.222222222222  0.222222222222\n   8742236          13       2 ...                  0.0  0.307692307692"
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.hist(data['narms_total'], range=(-0.5, 40.5), bins=41)\nplt.xlabel('total number of classifications')",
      "execution_count": 22,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.text.Text at 0x100535150>"
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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Vz3NNgEPBJFzO+x4277FjXHNyrPdrAGA9qirdPddf41cd1ltVr6qqe8zsvn+ST3f3LZks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\nRiecAgAAMDrhFAAAgNH9/xwIZ5HfIujbAAAAAElFTkSuQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0x112156a10>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def calculate_consensus(data,answers):\n    # simply take answer with more than half votes as consensus\n    # if no answer meets this criteria, then \"unclassified\"\n    # this isn't a great method, as votes are often spread among\n    # categories and there are lots of \"no arms\" votes\n    # SHOULD BE IMPROVED BEFORE USING AGAIN\n    outdata = Table()\n    outdata['subject_id'] = data['subject_id']\n    # identify majority arm number classification\n    outdata['narms_new'] = np.zeros(len(data), np.int8) - 1\n    for i, a in enumerate(answers[0]):\n        majority = data['narms_{}_frac'.format(a)] > 0.5\n        outdata['narms_new'][majority] = i  # remember this is one less than number of arms!\n    # identify majority bar classification\n    #outdata['unbarred_new'] = data['bar_no_frac'] > 0.5\n    return outdata",
      "execution_count": 23,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "consensus = calculate_consensus(data, answers)\nconsensus.write('spiral_spotter_classify_consensus.fits')",
      "execution_count": 24,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "consensus",
      "execution_count": 25,
      "outputs": [
        {
          "data": {
            "text/html": "&lt;Table length=500&gt;\n<table id=\"table4630308688\">\n<thead><tr><th>subject_id</th><th>narms_new</th></tr></thead>\n<thead><tr><th>int64</th><th>int8</th></tr></thead>\n<tr><td>8741181</td><td>1</td></tr>\n<tr><td>8741184</td><td>-1</td></tr>\n<tr><td>8741187</td><td>-1</td></tr>\n<tr><td>8741189</td><td>5</td></tr>\n<tr><td>8741191</td><td>-1</td></tr>\n<tr><td>8741194</td><td>5</td></tr>\n<tr><td>8741197</td><td>5</td></tr>\n<tr><td>8741198</td><td>5</td></tr>\n<tr><td>8741199</td><td>-1</td></tr>\n<tr><td>8741202</td><td>5</td></tr>\n<tr><td>...</td><td>...</td></tr>\n<tr><td>8742220</td><td>5</td></tr>\n<tr><td>8742222</td><td>1</td></tr>\n<tr><td>8742224</td><td>1</td></tr>\n<tr><td>8742225</td><td>5</td></tr>\n<tr><td>8742226</td><td>5</td></tr>\n<tr><td>8742227</td><td>2</td></tr>\n<tr><td>8742229</td><td>5</td></tr>\n<tr><td>8742232</td><td>-1</td></tr>\n<tr><td>8742235</td><td>-1</td></tr>\n<tr><td>8742236</td><td>1</td></tr>\n</table>",
            "text/plain": "<Table length=500>\nsubject_id narms_new\n  int64       int8  \n---------- ---------\n   8741181         1\n   8741184        -1\n   8741187        -1\n   8741189         5\n   8741191        -1\n   8741194         5\n   8741197         5\n   8741198         5\n   8741199        -1\n   8741202         5\n       ...       ...\n   8742220         5\n   8742222         1\n   8742224         1\n   8742225         5\n   8742226         5\n   8742227         2\n   8742229         5\n   8742232        -1\n   8742235        -1\n   8742236         1"
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.hist(consensus['narms_new'], range=(-1.5, 5.5), bins=7)\nplt.xlabel('Consensus narms-1 (-1 = no consensus, 5 = no arms)')",
      "execution_count": 26,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.text.Text at 0x1121a8910>"
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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dW1V3W2X9U6vq3Kr6QFW9t6qOXqXMiVX1iar6cFW9taqO2KwHAAAAwPayZlCtqnskWU6y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\nAAAAACgKiSoAAAAAoCgfVK45U2Ma25UAAAAASUVORK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0x113de0190>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "subject_table_columns = OrderedDict(subject_id=[])\nfor subject_set in classify_workflow.links.subject_sets:\n    for subject in subject_set.subjects():\n        subject_table_columns['subject_id'].append(subject.id)\n        for key, value in subject.metadata.items():\n            key = key.replace('!', '').replace('#', '')\n            try:\n                subject_table_columns[key].append(value)\n            except KeyError:\n                subject_table_columns[key] = [value]",
      "execution_count": 27,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "subject_table_columns.keys()",
      "execution_count": 28,
      "outputs": [
        {
          "data": {
            "text/plain": "['subject_id',\n u'dr8objid',\n u'LOGMSTAR_BALDRY06',\n u'REDSHIFT',\n u'CUR',\n u'gz2_filename',\n u'NARMS',\n u'dr7objid',\n u'REDNESS_BALDRY06',\n u'PETROMAG_MR',\n u'UNBARRED']"
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "subject_table_dtype = ('int', 'int', 'float', 'float', 'float', 'string','int8', 'int64', 'float', 'float', 'bool')",
      "execution_count": 29,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "subject_table = Table(subject_table_columns.values(), names=subject_table_columns.keys(), dtype=subject_table_dtype)",
      "execution_count": 30,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "subject_table.write('spiral_spotter_classify_subjects.fits')",
      "execution_count": 31,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "subject_table",
      "execution_count": 32,
      "outputs": [
        {
          "data": {
            "text/html": "&lt;Table length=500&gt;\n<table id=\"table4639358032\">\n<thead><tr><th>subject_id</th><th>dr8objid</th><th>LOGMSTAR_BALDRY06</th><th>REDSHIFT</th><th>CUR</th><th>gz2_filename</th><th>NARMS</th><th>dr7objid</th><th>REDNESS_BALDRY06</th><th>PETROMAG_MR</th><th>UNBARRED</th></tr></thead>\n<thead><tr><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>str27</th><th>int8</th><th>int64</th><th>float64</th><th>float64</th><th>bool</th></tr></thead>\n<tr><td>8741618</td><td>1237658299532116072</td><td>10.159062</td><td>0.0496067</td><td>1.4912205</td><td>1237658299532116072_gz2.png</td><td>1</td><td>587732578307211377</td><td>-0.64386535</td><td>-21.06387</td><td>True</td></tr>\n<tr><td>8741584</td><td>1237655473435378090</td><td>10.469826</td><td>0.0376687</td><td>2.2472763</td><td>1237655473435378090_gz2.png</td><td>1</td><td>587729752210473139</td><td>-0.024667978</td><td>-20.94769</td><td>True</td></tr>\n<tr><td>8741551</td><td>1237664296371290252</td><td>9.999989</td><td>0.0521262</td><td>1.6368484</td><td>1237664296371290252_gz2.png</td><td>2</td><td>587738575146385582</td><td>-0.4304061</td><td>-20.46155</td><td>True</td></tr>\n<tr><td>8741548</td><td>1237667212665618541</td><td>10.267259</td><td>0.0511654</td><td>1.9294319</td><td>1237667212665618541_gz2.png</td><td>1</td><td>587741491440713841</td><td>-0.25381303</td><td>-20.721529</td><td>True</td></tr>\n<tr><td>8741547</td><td>1237658206122868843</td><td>9.998817</td><td>0.0427254</td><td>1.5267658</td><td>1237658206122868843_gz2.png</td><td>1</td><td>587732484897964124</td><td>-0.5400114</td><td>-20.614185</td><td>True</td></tr>\n<tr><td>8741545</td><td>1237667550344773783</td><td>9.981161</td><td>0.0321238</td><td>1.8223057</td><td>1237667550344773783_gz2.png</td><td>2</td><td>587741829119869092</td><td>-0.237324</td><td>-20.15555</td><td>True</td></tr>\n<tr><td>8741543</td><td>1237665372258762795</td><td>10.091015</td><td>0.0367353</td><td>1.5409546</td><td>1237665372258762795_gz2.png</td><td>1</td><td>587739651033858248</td><td>-0.5644789</td><td>-20.825008</td><td>True</td></tr>\n<tr><td>8741540</td><td>1237661949719937104</td><td>10.195151</td><td>0.0410798</td><td>1.8125076</td><td>1237661949719937104_gz2.png</td><td>1</td><td>588017703471743070</td><td>-0.33855724</td><td>-20.704294</td><td>True</td></tr>\n<tr><td>8741538</td><td>1237661387604557914</td><td>10.184109</td><td>0.0400596</td><td>1.753542</td><td>1237661387604557914_gz2.png</td><td>1</td><td>587735666379653228</td><td>-0.3926201</td><td>-20.760197</td><td>True</td></tr>\n<tr><td>8741536</td><td>1237667323801305167</td><td>10.564913</td><td>0.0414366</td><td>1.8981285</td><td>1237667323801305167_gz2.png</td><td>2</td><td>587741602576400465</td><td>-0.41262436</td><td>-21.511223</td><td>True</td></tr>\n<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n<tr><td>8741782</td><td>1237665531177205923</td><td>10.148008</td><td>0.0398365</td><td>1.5657883</td><td>1237665531177205923_gz2.png</td><td>2</td><td>587739809952301201</td><td>-0.5644326</td><td>-20.934343</td><td>True</td></tr>\n<tr><td>8741780</td><td>1237663787418059231</td><td>10.291989</td><td>0.0537311</td><td>1.85853</td><td>1237663787418059231_gz2.png</td><td>1</td><td>587738066193154449</td><td>-0.33576417</td><td>-20.882395</td><td>True</td></tr>\n<tr><td>8741778</td><td>1237664836461723789</td><td>10.445495</td><td>0.04387</td><td>1.9334183</td><td>1237664836461723789_gz2.png</td><td>4</td><td>587739115236819099</td><td>-0.3282168</td><td>-21.159187</td><td>True</td></tr>\n<tr><td>8741777</td><td>1237665024901972003</td><td>10.524229</td><td>0.0333223</td><td>1.9453068</td><td>1237665024901972003_gz2.png</td><td>1</td><td>587739303677067303</td><td>-0.34915566</td><td>-21.342052</td><td>True</td></tr>\n<tr><td>8741774</td><td>1237651213362790404</td><td>10.457506</td><td>0.0520571</td><td>2.3814373</td><td>1237651213362790404_gz2.png</td><td>1</td><td>587725492137885700</td><td>0.114696026</td><td>-20.85217</td><td>True</td></tr>\n<tr><td>8741773</td><td>1237657590858711117</td><td>10.39655</td><td>0.0521593</td><td>2.026926</td><td>1237657590858711117_gz2.png</td><td>1</td><td>587731869633806410</td><td>-0.21360016</td><td>-20.908802</td><td>True</td></tr>\n<tr><td>8741770</td><td>1237654602101686300</td><td>10.577152</td><td>0.0314156</td><td>2.3002472</td><td>1237654602101686300_gz2.png</td><td>1</td><td>587728880876781605</td><td>-0.015305519</td><td>-21.188156</td><td>True</td></tr>\n<tr><td>8741766</td><td>1237668298216570908</td><td>10.474027</td><td>0.0511627</td><td>2.0669346</td><td>1237668298216570908_gz2.png</td><td>1</td><td>587742576991666358</td><td>-0.20677567</td><td>-21.046549</td><td>True</td></tr>\n<tr><td>8741763</td><td>1237668310559293763</td><td>10.430369</td><td>0.051124</td><td>2.0049782</td><td>1237668310559293763_gz2.png</td><td>0</td><td>587742589334389075</td><td>-0.2501831</td><td>-21.022785</td><td>True</td></tr>\n<tr><td>8741760</td><td>1237667549805412549</td><td>10.101999</td><td>0.0421525</td><td>1.5330448</td><td>1237667549805412549_gz2.png</td><td>1</td><td>587741828580507674</td><td>-0.577122</td><td>-20.862606</td><td>True</td></tr>\n</table>",
            "text/plain": "<Table length=500>\nsubject_id       dr8objid      LOGMSTAR_BALDRY06 ... PETROMAG_MR UNBARRED\n  int64           int64             float64      ...   float64     bool  \n---------- ------------------- ----------------- ... ----------- --------\n   8741618 1237658299532116072         10.159062 ...   -21.06387     True\n   8741584 1237655473435378090         10.469826 ...   -20.94769     True\n   8741551 1237664296371290252          9.999989 ...   -20.46155     True\n   8741548 1237667212665618541         10.267259 ...  -20.721529     True\n   8741547 1237658206122868843          9.998817 ...  -20.614185     True\n   8741545 1237667550344773783          9.981161 ...   -20.15555     True\n   8741543 1237665372258762795         10.091015 ...  -20.825008     True\n   8741540 1237661949719937104         10.195151 ...  -20.704294     True\n   8741538 1237661387604557914         10.184109 ...  -20.760197     True\n   8741536 1237667323801305167         10.564913 ...  -21.511223     True\n       ...                 ...               ... ...         ...      ...\n   8741782 1237665531177205923         10.148008 ...  -20.934343     True\n   8741780 1237663787418059231         10.291989 ...  -20.882395     True\n   8741778 1237664836461723789         10.445495 ...  -21.159187     True\n   8741777 1237665024901972003         10.524229 ...  -21.342052     True\n   8741774 1237651213362790404         10.457506 ...   -20.85217     True\n   8741773 1237657590858711117          10.39655 ...  -20.908802     True\n   8741770 1237654602101686300         10.577152 ...  -21.188156     True\n   8741766 1237668298216570908         10.474027 ...  -21.046549     True\n   8741763 1237668310559293763         10.430369 ...  -21.022785     True\n   8741760 1237667549805412549         10.101999 ...  -20.862606     True"
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "data = join(subject_table, consensus, keys='subject_id', join_type='left')",
      "execution_count": 33,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "data",
      "execution_count": 34,
      "outputs": [
        {
          "data": {
            "text/html": "&lt;Table length=500&gt;\n<table id=\"table4630191056\">\n<thead><tr><th>subject_id</th><th>dr8objid</th><th>LOGMSTAR_BALDRY06</th><th>REDSHIFT</th><th>CUR</th><th>gz2_filename</th><th>NARMS</th><th>dr7objid</th><th>REDNESS_BALDRY06</th><th>PETROMAG_MR</th><th>UNBARRED</th><th>narms_new</th></tr></thead>\n<thead><tr><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>str27</th><th>int8</th><th>int64</th><th>float64</th><th>float64</th><th>bool</th><th>int8</th></tr></thead>\n<tr><td>8741181</td><td>1237667429550981158</td><td>10.272118</td><td>0.0317634</td><td>1.6926174</td><td>1237667429550981158_gz2.png</td><td>1</td><td>587741708326076464</td><td>-0.49279952</td><td>-21.06437</td><td>True</td><td>1</td></tr>\n<tr><td>8741184</td><td>1237658493345071231</td><td>10.287678</td><td>0.0335551</td><td>1.7747154</td><td>1237658493345071231_gz2.png</td><td>4</td><td>587732772120166516</td><td>-0.4176538</td><td>-20.989128</td><td>True</td><td>-1</td></tr>\n<tr><td>8741187</td><td>1237660958108221730</td><td>10.398433</td><td>0.0413652</td><td>1.9228668</td><td>1237660958108221730_gz2.png</td><td>1</td><td>587735236883317016</td><td>-0.3184793</td><td>-21.059275</td><td>True</td><td>-1</td></tr>\n<tr><td>8741189</td><td>1237662199354097895</td><td>10.199307</td><td>0.0548665</td><td>1.6982117</td><td>1237662199354097895_gz2.png</td><td>2</td><td>587736478129193204</td><td>-0.4547012</td><td>-20.875439</td><td>True</td><td>5</td></tr>\n<tr><td>8741191</td><td>1237655123940606166</td><td>10.47119</td><td>0.0381976</td><td>2.081499</td><td>1237655123940606166_gz2.png</td><td>4</td><td>588010877692412107</td><td>-0.19101954</td><td>-21.023554</td><td>True</td><td>-1</td></tr>\n<tr><td>8741194</td><td>1237667783353434119</td><td>10.178134</td><td>0.0440799</td><td>1.3107681</td><td>1237667783353434119_gz2.png</td><td>1</td><td>587742062128529415</td><td>-0.83274555</td><td>-21.364363</td><td>True</td><td>5</td></tr>\n<tr><td>8741197</td><td>1237661418747985973</td><td>10.019936</td><td>0.035372</td><td>1.66819</td><td>1237661418747985973_gz2.png</td><td>1</td><td>587735697523081270</td><td>-0.40725255</td><td>-20.469849</td><td>True</td><td>5</td></tr>\n<tr><td>8741198</td><td>1237667735032103006</td><td>10.327075</td><td>0.0309005</td><td>1.8553371</td><td>1237667735032103006_gz2.png</td><td>2</td><td>587742013807198300</td><td>-0.35458732</td><td>-20.973211</td><td>True</td><td>5</td></tr>\n<tr><td>8741199</td><td>1237653664714850592</td><td>10.382954</td><td>0.0514696</td><td>1.7615585</td><td>1237653664714850592_gz2.png</td><td>2</td><td>587727943489945860</td><td>-0.47302866</td><td>-21.245523</td><td>True</td><td>-1</td></tr>\n<tr><td>8741202</td><td>1237668349211902107</td><td>10.165767</td><td>0.0526503</td><td>1.9403095</td><td>1237668349211902107_gz2.png</td><td>1</td><td>587742627986997432</td><td>-0.19773436</td><td>-20.453255</td><td>True</td><td>5</td></tr>\n<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n<tr><td>8742220</td><td>1237654601556558026</td><td>10.09313</td><td>0.0545778</td><td>1.710804</td><td>1237654601556558026_gz2.png</td><td>1</td><td>587728880331653454</td><td>-0.39553928</td><td>-20.593534</td><td>True</td><td>5</td></tr>\n<tr><td>8742222</td><td>1237657590851371141</td><td>10.272995</td><td>0.0515251</td><td>1.5746632</td><td>1237657590851371141_gz2.png</td><td>1</td><td>587731869626466431</td><td>-0.611146</td><td>-21.233131</td><td>True</td><td>1</td></tr>\n<tr><td>8742224</td><td>1237663547971207612</td><td>10.459953</td><td>0.0421434</td><td>1.8047352</td><td>1237663547971207612_gz2.png</td><td>1</td><td>587737826746302844</td><td>-0.4630425</td><td>-21.378235</td><td>True</td><td>1</td></tr>\n<tr><td>8742225</td><td>1237655499204657255</td><td>10.062539</td><td>0.0540473</td><td>1.5562973</td><td>1237655499204657255_gz2.png</td><td>1</td><td>587729777979752577</td><td>-0.5369804</td><td>-20.732141</td><td>True</td><td>5</td></tr>\n<tr><td>8742226</td><td>1237665367426400354</td><td>10.411082</td><td>0.0522286</td><td>2.19425</td><td>1237665367426400354_gz2.png</td><td>1</td><td>587739646201495658</td><td>-0.05259013</td><td>-20.820257</td><td>True</td><td>5</td></tr>\n<tr><td>8742227</td><td>1237667536936894657</td><td>10.280678</td><td>0.0405101</td><td>1.6598072</td><td>1237667536936894657_gz2.png</td><td>2</td><td>587741815711989787</td><td>-0.52943516</td><td>-21.133358</td><td>True</td><td>2</td></tr>\n<tr><td>8742229</td><td>1237662635830935814</td><td>10.113575</td><td>0.0534687</td><td>1.835413</td><td>1237662635830935814_gz2.png</td><td>3</td><td>587736914606031100</td><td>-0.27976632</td><td>-20.468973</td><td>True</td><td>5</td></tr>\n<tr><td>8742232</td><td>1237667733986345070</td><td>10.210649</td><td>0.0479483</td><td>1.7276382</td><td>1237667733986345070_gz2.png</td><td>4</td><td>587742012761440413</td><td>-0.43032336</td><td>-20.861237</td><td>True</td><td>-1</td></tr>\n<tr><td>8742235</td><td>1237661949727735904</td><td>10.468281</td><td>0.037738</td><td>2.0827732</td><td>1237661949727735904_gz2.png</td><td>4</td><td>588017703479541773</td><td>-0.18852067</td><td>-21.01261</td><td>True</td><td>-1</td></tr>\n<tr><td>8742236</td><td>1237661358614446146</td><td>10.435781</td><td>0.0337615</td><td>2.3911686</td><td>1237661358614446146_gz2.png</td><td>1</td><td>588017112366252055</td><td>0.13368583</td><td>-20.793823</td><td>True</td><td>1</td></tr>\n</table>",
            "text/plain": "<Table length=500>\nsubject_id       dr8objid      LOGMSTAR_BALDRY06 ... UNBARRED narms_new\n  int64           int64             float64      ...   bool      int8  \n---------- ------------------- ----------------- ... -------- ---------\n   8741181 1237667429550981158         10.272118 ...     True         1\n   8741184 1237658493345071231         10.287678 ...     True        -1\n   8741187 1237660958108221730         10.398433 ...     True        -1\n   8741189 1237662199354097895         10.199307 ...     True         5\n   8741191 1237655123940606166          10.47119 ...     True        -1\n   8741194 1237667783353434119         10.178134 ...     True         5\n   8741197 1237661418747985973         10.019936 ...     True         5\n   8741198 1237667735032103006         10.327075 ...     True         5\n   8741199 1237653664714850592         10.382954 ...     True        -1\n   8741202 1237668349211902107         10.165767 ...     True         5\n       ...                 ...               ... ...      ...       ...\n   8742220 1237654601556558026          10.09313 ...     True         5\n   8742222 1237657590851371141         10.272995 ...     True         1\n   8742224 1237663547971207612         10.459953 ...     True         1\n   8742225 1237655499204657255         10.062539 ...     True         5\n   8742226 1237665367426400354         10.411082 ...     True         5\n   8742227 1237667536936894657         10.280678 ...     True         2\n   8742229 1237662635830935814         10.113575 ...     True         5\n   8742232 1237667733986345070         10.210649 ...     True        -1\n   8742235 1237661949727735904         10.468281 ...     True        -1\n   8742236 1237661358614446146         10.435781 ...     True         1"
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "arm_labels = ('1 arm', '2 arms', '3 arms', '4 arms', '5+ arms')\nbar_labels = ('no bar', 'bar')\ncolors = ('red', 'blue', 'orange', 'magenta', 'green', 'redorange', 'purple', 'cyan')",
      "execution_count": 35,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def plot_arms(col='LOGMSTAR_BALDRY06', xlabel='log$_{10}$(stellar mass)', ymax=5, new=False, bins=10):\n    if new:\n        narms = 'narms_new'\n    else:\n        narms = 'NARMS'\n    fig, axarr = plt.subplots(2, 3)\n    for m in range(5):\n        ax = axarr.flat[m]\n        _, bins, _ = ax.hist(data[col], bins=bins,\n                             histtype='stepfilled', alpha=0.1, color='k', normed=True)\n        ax.vlines(np.median(data[col]), 0, 10, color='k', linestyle=':')\n        try:\n            _, bins, _ = ax.hist(data[col][data[narms] == m], bins=bins,\n                                 histtype='step', color=colors[m], label=arm_labels[m], normed=True)\n            ax.vlines(np.median(data[col][data[narms] == m]), 0, 10, color=colors[m], linestyle=':')\n        except ValueError:\n            pass\n        if m in [2, 3, 4]:\n            ax.set_xlabel(xlabel)\n        if m in [0, 3]:\n            ax.set_ylabel('frequency');\n        ax.legend()\n        ax.set_ylim(0, ymax)\n        ax.locator_params(nbins=5)\n    axarr.flat[-1].axis('off');",
      "execution_count": 36,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot_arms('CUR', '$u-r$ colour', 5, True)",
      "execution_count": 37,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "/usr/local/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n  if self._edgecolors == str('face'):\n"
        },
        {
          "data": {
            "image/png": 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nFlFhMAsAAAAAKDkMZhEJemYBJAE9swCSgJ5ZRIXBLAAAAACg5DCYRSTomQWQ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\nAElFTkSuQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0x113f47490>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot_arms('LOGMSTAR_BALDRY06', 'log$_{10}$(stellar mass)', 9, True)",
      "execution_count": 38,
      "outputs": [
        {
          "data": {
            "image/png": 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RIN+Vgerq6kAd3GQyqcbGxvACAgqPXFfiGhsb1bt3b1VwwwEUiUI/B7dnIbeH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\nSCT8uuuu63J9M/O6urqct0uu48WLFy9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8BdU7Lp84e1XJGDSsKnWaHMFVjzLXqZWcgytJ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\nSRoI/x83leMN/HVMfwAAAABJRU5ErkJggg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x11522db50>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot_arms('REDNESS_BALDRY06', 'colour', 4.0, True)",
      "execution_count": 39,
      "outputs": [
        {
          "data": {
            "image/png": 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gK9XQR9Yb5DoAXSHXAUiDauuZBQAAAACgZBSziAw9swDSgJ5ZAIlCzyxiJNL7zAIAAACI\nkb9dIeU2df/+u49JL1yy5fTl3Uxvt+sJ0uA9S48PKELPLICqQh8ZgDQg1yEyswdJe5wh1QwIb8xF\nd0r7z5RGHRfemEiEUnMdR2YBAAAAfGjc96V+g8Ibb+Vz4Y0FFKFnFpGhZxZAGtAzCyBR6JlFjFDM\nAgAAAACqDj2zAKoKfWQA0oBch8jMHiR96e1wTzNuPE7a49/pmcUWuM8sAAAAACB1KGYRGXpmAaQB\nPbMAEoWeWcQIxSwAAAAAoOoE6pk1s9Pd/WcViKd4nfRWANgCfWQA0oBch8jQM4sKqtR9Zqeb2baS\nbnf39/u6MgAAAAAAwhD0NOOFklZLmm1mvzazY8wsMX8tRDTomQWQBvTMAkgUemYRI0GL2ZPc/RZ3\nnyTpQkmHSvqLmV1mZh8vX3gAAAAAAGypT/eZNbNDJZ0u6QRJqyTNk/T/SbrT3XOhBEZvBYAu0EcG\nIA3IdYgMPbOooIrcZ9bMHjazXczsAjN7VdKjkoYoX8zuIulESWMk/bKvgQAAAAAAEFTQ04w/K+lN\nSdMk3SrpY+5+lLvPdvdmd3/X3X8oaY8yxYkEomcWQBrQMwsgUeiZRYwEvZrxMklfl/REd+eImNn3\nJW0IKzAAAAAAALoT9D6zh7v7IxWIp3id9FYA2AJ9ZADSgFyHyNAziwqqSM+spEFmNtfMvlu04u+Y\n2bVmNqCvKwcAAAAAoC+CFrNnS/qNpOuLpt0s6V1J14UdFNKBnlkAaUDPLIBEoWcWMRK0mDV3v9bd\nN/fEuvtad79U0tjyhAYAAAAAQNeC9szOc/cJ3bw3393Hhx4YvRUAukAfGYA0INchMvTMooIq1TP7\nspndZGZ7mFmm8NjTzH4u6cW+rhwAAAAAgL4IWsx+W9Lekl6W1FJ4vCRpr8J7QK/RMwsgDeiZBZAo\n9MwiRgLdZ9bdl0s62MwOkzSuMPmv7j63bJEBAAAAANCNQD2zPQ5gdqS7/yGkeIrHpbcCwBboIwOQ\nBuQ6RIaeWVRQqbku0JHZworqJH1c0mBJ7Ss0SZdLCr2YBQAAAACgO4F6Zs3sq5KWSZon6TFJjYXH\nXEn7lik2JBw9swDSgJ5ZAIlCzyxiJOgFoH4g6SuSdpBU4+6Z9oekJ8oWHQAAAAAAXQh6n9lH3P3w\nbt4b7O5rQg+M3goAXaCPDEAakOsQGXpmUUGVus/sg4UrGXfl2r6uHAAAAACAvghazB4r6T4ze9XM\nHjezue0PSZPLGB8SjJ5ZAGlAzyyARKFnFjES9GrGYyTN0IdXMe78HgAAAAAAFRO0Z/Zydz+/m/cu\ndPcfhh4YvRUAukAfGYA0INchMvTMooIq0jPbXSFbeC/0QhYAAAAAgJ4E7ZmVmU0zs2fNbH7h9Q/N\n7N/KFxqSjp5ZAGlAzyyARKFnFjESqJg1s29JukjSHEm5wuTbJH3OzL5bptgAAAAAAOhS0J7ZP0k6\nzt0/MLO57n5YYXqtpLnufkiglZkdJ+mbkgZIqpP0gaTvufsLXcxLbwWALVRLH1nQfEeuA9AVch0i\nQ88sKqhS95nNufsHnSe6e6vyySuoWyXd7u6Hu/uBkp6X9KiZDe/FGABQDW4V+Q5A8t0qch2AiAQt\nZvuZ2fjOE83s6F6ur9Hd7yp6PUPSMElH9HIcJAA9s0g48h0k0TOLxCPXpQ09s4iRoPeZbZD0RzN7\nXNKeZna7pLGS9pF0bNCVufs/d5rUVPjZm6O7ABB75DsAaUCuAxClQD2zkmRmYyWdJ+mTklzSC5Ku\ncPeX+7xysyMk3SdptLuv6PQevRUAtlAtfWSddZfvyHUAukKuQ2TomUUFlZrrgh6Zlbu/JGlqX1fU\nmZmZ8ldIvrBzIQsASUK+A5AG5DoAlRa4mO2Omd3k7n253+xlkha6+8zuZig+976+vl719fV9WA3i\nKpvNBuuvyGbpm02xxsZGNTY2Rh1GqXrMd+S6ZKuvb1RjY33UYSDmyHWoGguy0j7ZLSYH3q9DqoWd\n6wIVs2Z2i/KnFhcfAm5/fVRvV2pmZyvfczulp/n4hQDQeYfn4osvji6YPgiS78h1AMh1ANIg7FwX\n9D6z70h6UPni1ZUvgkdJ2lfS79z9a4FXaPYNSf8s6Qvu3mJmH5O0m7s/2mk+eisAbKGa+siC5Dty\nHYCukOsQGXpmUUGV6pm91d3P72LlYyWdEnRlZnaCpAuU773dN99aof0ljZD0aPdLIq5yuZw2bNhQ\nlrHr6upU+I4AVYd8lyzr10tf+lJrWcb+r//KaM89g94pD4gXcl3yuEvr16+TasPbB9umrVWWa1NN\naCMCeYGK2a4K2cL0l8zs071Y339LqpHUWDyMpOo6lwab5XI5LV26VDU1vU9P1157rc4666xux91j\njz3yxSw9s6hO5LsEaW11PfFERtdd906vl73qqr/qu98d1+V7l1yyk1atcgW/7TsQO+S6hHF3vfPO\nO1Ltui7fH/LWtVo5asv9t57260ZsbNI2bTmKWYQuaM/s6C4mD5J0sKSdg67M3fsHnRfVo6amRnV1\ndb1ern///t0ut379+lLDAiJFvkuemhrXMcf0/kjFrFnqdrkf/zgnsXuHKkauS6a67eqkfl3vo/Xv\n1/X+W0/7dRnOtEOZBD3NeFE305dI+vdwQkHanHPOOcFm5KgsgCp2992fjDoEAAjN2o91vf8WeL8O\nCFHQYvZpSf+qD69m7JLWuvvKskQFAAAAAEAPgjbpnOHui919UeGxmEIWpZoxY0awGTkyC6CKTZny\nQtQhAEBoBi3sev8t8H4dEKKgxez0IDOZ2e9KiAUAAAAAgECCnmZ8sJnN0YenGbfzTtP2DSUqpAI9\nswDSgJ5ZAElCzyziJGgxe7ukkyT9r6Q3lS9iR0s6XNIdktovPTsm5PgAAAAAANhC0NOMh0g6wN2/\n5u4Xuvv33f1ryt+aZwd3z7p7VvnCFgiEnlkAaUDPLIAkoWcWcRK0mN3V3d/oPNHdF0oaW/T6+2EF\nBgAAAABAd4IWs7uY2QGdJ5rZpySNDDckpAU9swDSgJ5ZAElCzyziJGjP7BWSnjKzJyUtLEzbXdKB\nkk4vR2AAAAAAAHQn0JFZd/+5pImS3lL+isX7SFoi6bPuPqt84SHJ6JkFkAb0zAJIEnpmESdBj8zK\n3Z+S9FQZY0G53XOP9KMfhTpkjbtGDBumtT//eajjjjz1VNmmTfkXS5dKDz5Y+qBr1kirV0sf/Wjp\nYxX753+Wzj033DEBAAAA9ChwMWtmO0o6WdL27n6pmR0uaYG7v1u26BCud9+VdtlF+t73Qhsy99pr\nGtDQoLV9WLan3ooBf/ub/LbbZMOG9T24zt59Vxo+PLzxJOlXv5IWLQp3TACJQs8sgCShZxZxEqiY\nNbN9Jc2V1CrpA0mXKt8z+1Mz+6q7/7l8ISJUO+8sfeYzoQ3ndXWhjbWFAw6QRowo3/hheOYZ6aWX\noo4CAAAASJ2gVzO+WtIp7j5c0tuS5O4/k/R55QtboNfomQWQBvTMAkgSemYRJ0GL2Vp3v7fzRHdf\nJKlfqBEBAAAAALAVQYvZHcysf+eJZjZE0s7hhoS04D6zANKAnlkASULPLOIk6AWgHpT0iJn9l6TB\nZjZJ0lhJ35L063IFBwAAAABAV4Iemb1I0mOSbpE0QdLDkq5SvpBtKE9oSDp6ZgGkAT2zAJKEnlnE\nSaAjs+7eKukiM7tM0scLk19z941liwwAAAAAgG4EvTVPTtJz7n6AJP7EjFDQMwsgDeiZBZAk9Mwi\nToKeZvx8oZAFAAAAACByQYvZV7u6mrEkmdkVIcaDFKFnFkAa0DMLIEnomUWc9OZqxr81s/+R9Kak\ntsJ0k3SkpPPKEBsAAACArqz+u/TElNCHtdyG0McEyqXbYtbMtpWUc/dmSTcXJn++i1m9HIEh+eiZ\nBZAG9MwCKIu2pvzPQ+4JddilS99UpmZbWTfv0zOLOOnpyOxcSbdJukHSU+5+cFczmdnccgQGAAAA\noAc120rb7xXqkC3vD9AAqwl1TKBceuqZbXb3GwrPP9bDfA+HGA9ShJ5ZAGlAzyyAJKFnFnHSUzE7\n2Mw+Wnj+cg/zHRFiPAAAAAAAbFVPpxn/t6TFZvkz5gv3mu0KPbPoE3pmAaQBPbMAkoSeWcRJt8Ws\nu880s99I2lXSNZLOlrrsBZ9ZptgAAAAAAOhSj/eZdfdF7v6YpMvd/TF3b+z8kHR5RSJF4tAzCyAN\n6JkFkCT0zCJOeixm27n77L68BwAAAABAOQQqZoFyoGcWQBrQMwsgSeiZRZxQzAIAAAAAqg7FLCJD\nzyyANKBnFkCS0DOLOOnp1jwAAKDM2tpMra3hjpnJ5B8AACQZxSwiQ88sgDToqWc2k5EOOaQm1PW1\ntUkzZ0pnnx3qsAAgiZ5ZxAvFLAAAEbnnniX62Mc+pn79+oU2JkUsACAtIjsJycz6m9kVZtZiZqOj\nigPRoWcWaUCuAz2zSANyXXrQM4s4iaSYNbMxkholjZAU7vlVABAT5DoAaUCuAxCVqI7M1kk6WdIt\nEa0fMUDPLFKAXAfuM4s0INelCD2ziJNIembd/W+SxGkolZXL5ZRrbVXLxo3hjdncrPA6vYBkIddF\nI5fLqbm5OdQxN250SQNCHRNICnJdNNxdnsupOcT9uvZxgWrBBaBSpLW1VevXrtWKpUtDG7Pfe+9p\nZB+XnTFjRrC/4mWzHJ0FEFhLS4uWLFmiTIj3plm3LiNpTJ+WnTLlBY7OAghda1ub2lpatDTE/Top\n/wfBngxaOKPLo7OB9+uAEFHMpkwmk1FdXV1o49Vut52sTDczfOutt5Rrbtb2q1Zp9eLFJY+XyWQ0\nfPhwDRjA0RUgyd59V3ryyYHaZpttQhtzwwaTmYU2XrtcLqe33nor1LHXrt1RLS3bSOof2pgA4ivM\n/bpycblWrlypJit9f67Y0KFDNXDgwFDHRHWhmEVkevrrnZnlj6pkMlp7zjmhNHdv3LiRU2eAFPjL\nXzI655yd9clPtoY67sEH9+3U5Z6OypZjJ7S1tZVcB6Bs+tIzW1NTq0wmE+oZMxs2bFBbW1to46E6\nxbqYzRadWlpfX6/6+vrIYkHl1dTUyGrCuyhiOY6qoPwaGxvV2NgYdRhlRa4L3/jxTfrFL9ZEHcZW\n1YSY49qR66oTuQ5JZsqfIRdmzitH/kT5hZ3rqqaYRfIE7a0YNGOG1tKDkVqdd3guvvji6IIpE3Jd\nstEziyDIdagW9MyiFGHnuqhuzdMZf0YGkAbkOgBpQK4DUBGRHJk1s36SHpc0WJJL+o2ZveXuX4gi\nHkQj6F/vOCqLakWug8R9ZpF85Lp04T6ziJOo7jPbIunAKNYNAJVCrgOQBuQ6AFGJy2nGSKEZM2YE\nmm9QwPkAII6mTHkh6hAAIDSDFna9XxZ0vw4IE8UsAAAAAKDqUMwiMvTMAkgDemYBJAk9s4gTilkA\nAAAAQNWhmEVk6JkFkAb0zAJIEnpmEScUswAAAACAqkMxi8jQMwsgDeiZBZAk9MwiTihmAQAAAABV\nh2IWkaFnFkAa0DMLIEnomUWcUMwCAAAAAKoOxSwiQ88sgDSgZxZAktAzizihmAUAAAAAVB2KWUSG\nnlkAaUDPLIAkoWcWcUIxCwAAAACoOhSziAw9swDSgJ5ZAElCzyzihGIWAAAAAFB1KGYRGXpmAaQB\nPbMAkoSeWcQJxSwAAAAAoOpQzCIy9MwCSAN6ZgEkCT2ziJPaqAMAKqmlpUVmFtp4mdZW1eRy/FUI\nQKy0teXU3Nwc6pg1NTWqrWW3AUB8tLW1hZ7rMpmM+vXrF+qYKB/+V0JkZsyYEeiveINmzAjl6KyZ\n6Z133il5nGKDV6zQji0tGhDqqACSZMqUFyp6dNbMtHbtWi1evCq0MXO5nIYNG6Zhw4aFNiaA6jRo\n4Ywuj85ubb8u07ZO1rIytDhq25r1/vJ1WlGzXWhjursGDx6sXXbZJbQxUV4Us0iN7bYLL9m1q6mp\nkYc+KgD0XU1NrQYMMA0cODC0MZuamuROtgPQN7nagdr+1e9r+1AHbdKGUVO1ZveLQhuypaVFuVwu\ntPFQfhSziAw9swDSgJ5ZAEnSl57ZVZ/4aehx1C25QTUtK0IfF9WFYhYAgIS55ppBuuWWutDGc3fV\n17fqtturPuNzAAAgAElEQVRCGxIAgJJRzCIyle6ZBYAoVLpn9uyz1+mUU9aHOuajj2b01FODQx0T\nQHXqa88sUA4UswAAJMiQITkNGRLumMOG0S8LAIgf7iiCyNAzCyAN6JkFkCTcZxZxQjELAAAAAKg6\nFLOIzIwZMwLNNyjgfAAQR1OmvBB1CAAQmkELu94vC7pfB4SJYhYAAAAAUHUoZhEZemYBpAE9swCS\nhJ5ZxAnFLAAAAACg6nBrHkQmMfeZdVdLS0vow/br1y/0MQFUXqXvM1s+5DqgzzatkjatDHVI27g0\n1PGCSvp9ZnO5HLmuilDMAiUwMzU1Nem9RYtCG9PdVVdXp1GjRoU2JhBXra1SW1u4Y5ZhHyT1MpmM\nWlpatGjRm6GO269fP40ZMybUMYFYeuMW6YWs1H9oaEPWSGrqNya08ZDfr1u/fr0WhbhfJ+Vz6G67\n7SYzC3VcUMwiQknoma3t10/9+vVTXV1daGO2trYql8uFNh4QZ+edJ82cKdWG+r9RrSZObA5zwJIk\n4ahsbW2tamtrQ8117q7m5vh8TkDZ7XaKtP/M0IZrbWnR2wsXamBoIwaT5J7Z2tpaDR48OPRx161b\nF/qYyKOYBQBE6sorpXPPDW+85uZNWrLkHUnhFV4AACB+KGZjau3atdqwYUOoYw7YsEFyD3XMUiSm\nZxZAn7W0tGjt2iYtXx5evmtra4vV2Q3J6ZkF0FctLS3atGGD1i1fHtqYuVwuklyX9J5ZVBeK2Zhq\namrS6tWrQ20Wr2lt1YBwz+UDgJLkcjlt3Lgx9D/ebbPNNqGOB+mRR7bR3nuPCG08d2nQoDa9GW4b\nLhBLOXc1NzeFnuu23XbbUMcDqg2VTYzV1NRowIABoY1XW1urTIwaz5PQMwugdJlMJtRcFzdJOCo7\naVKT5s9fFuqYq1ebjj56p1DHBOLMLBm5Lsk9s6g+kRSzZjZZ0vclbVT+Ymzfdvdno4gFAMqFXIek\n6N9f6t8/7DaV+LS9oDTkOgBRyVR6hWa2v6Q7JH3N3Q+VdLmkP5jZzpWOBdGaMWNGoPkGBZwPiBNy\nHdpNmfJC1CEAZUOuS59BC7veLwu6XweEqeLFrKTzJT3o7i9Jkrv/TtJySd+KIJaKaWxsjDqE0D35\n5JNRhxC6pG1TEr93VSSVuU5K3vcuaXlBSt42Je07V2XIdQmRtLwgJW+bkvadC0MUxewkSc90mvYX\nSUdEEEvFJPHL99RTT5W0fBx7ZkvdprhJ4veuiqQy10nJ+96Vmhfi2DNLrkOIyHUJETQvVFPPLLku\n+SraM2tmQyRtL+mdTm8tl3R0JWMJ08qVK9XS0tLjPOvWrdPyXlyOvampqdSwUMXcfavfqSDa2to6\njBPm1bHRvaTmutWrVwfKTb3Jd5s2bac6bgebWuvXZ/S5z5V+a5GFC12PP/7hOD//uWn33cO74OF1\n10n33hvacJtNnCg1NIQ/bqUkNdcFvT1ib3LdNhs3lhoWqlxLS4usxAuxdt6vq62tLXnMalfpC0C1\n77I0d5reLGm7CscSmrULF8qXLOnxy9S6dKmaevnXoW1qa6UQv6D25pvK5XKhFElS4ReqtVXW1CTN\nm9fr5WfccYfOOemkrt9saVFLS4tyLS3afuZMrf7Od0qMNpjOSWJrcm1t0nvv9Wn7u5PJ5dTU1KQl\nIXz2q19+WUvuu0+S1L9/fw0bNqzkMYs1NTVp06ZNoY4pSYMOOkjbDR4c+rgVlMhc9/bb6/Xii83K\nZHo+qWfp0lY99VSwP8gtW7athgyx0PJSOfQ2L3T2r//6on75y71CjKh0pW5TGPr3d91ww8Ktfp+C\nuO++1fqnf1oiScpmR+mJJ1oV4u08NWdORh/9aIuOOiq8guT//m+A5s3bVlJV/5Exkbmuaf1KrV/6\njGpqa3qcr3XdUjW9E2zfrt+6JcqYIv+960nQvLD9kplaPXrL/bKZM2fqOxXaX5Py+2A1Te9KH/Sw\nD7bxnZ7frxBbv15vPr+g5HFWL3tZS+bn9+tqams0dMhQ1fYLr5zbtGmTmpua5SFfoG/A0E9oh51G\nhzpmO3Ov3NUEC3/BWyFpqrv/d9H0KyV93d1HFE3jMocAuuTusf4zJLkOQBjIdQDSoJRcV9Ejs+6+\n0sxWSep81/URkl7vNG+sEzgAdIdcByANyHUAohbFBaAekfSpTtMOKEwHgKQg1wFIA3IdgMhEUcxe\nIelIMxsrSWZ2jKSdJV0fQSwAUC7kOgBpQK4DEJlKXwBK7v6cmZ0k6b/NbKPyBfWR7v5upWMBgHIh\n1wFIA3IdgChV9AJQAAAAAACEIYrTjAEAAAAAKAnFLAAAAACg6lDMAgAAAACqTmyKWTMbYWb3m9nC\ngPOPMbNlZja302NiuWMNqrfbVFhmrJnNMbPHzewZMzu5nDH2hpldYGbPmtlTZvZrM9tpK/PH6jMy\ns8lm9rSZPWZmfzSz/bcy/2Azu7WwzLNmdoWZ1VQq3iD6sE0vdfF5nFWpeLfGzPoX/p1bzGx0gPlj\n/xl1Rq7bvAy5rkzIdeS6OCDXbV6GXFcm5DpynSTJ3SN/SPq8pGck/U7SGwGX2VXSLVHHHvI2DZS0\nRNK0wutRkt6X9PkYbM9Zkv4uabvC66sk/bFaPiNJ+0taK2ls4fWxklZI2rmHZe6RdFvheT9Jf5L0\nw6i3pcRtmht13D3ENkbSk5JulZSTNDrAMrH+jLqIl1zn5Loyx0+uc3Jd1A9y3eZlyHXli59c5+Q6\nd4/NkdkWSYdK+oskC7hM0Pmi0pdtmippgLvfIknu/pakuyR9vxwBBmVmGUkXSrre3TcUJl8l6SAz\n+1xPi5Y9uODOl/Sgu78kSe7+O0nLJX2rq5nNbJykL0r6UWH+FknXSDrbzLarSMRb16ttqgJ1kk6W\ndEuQmavkM+qMXJc3VeS6ciHXxR+5rmtx+j3qCrkuXp8RuS7+KpLrYlHMuvtcd18fdRxh6uM2HS7p\nuU7TnlE+uWwTTmR9so+knQqxSJI8f/+4JZKOiCqoXpqkovgL/qLu4z9cUpO7/61o2jOStpX02fDD\n65PeblOsufvf3P0NBf/Psho+ow7IdZuR68qHXBdz5LrqRK6LHXJdzFUq18WimC3BWDO7r9CH8ICZ\nnRh1QCXaTdI7naYtU/5zGlPxaD60W+FnV7F9bCvLRv4ZmdkQSdtry/iX68Nt62w35bev2LKi9yLV\nx22SpDozm1XoxZhrZhea2YCyBVpesf6MQhb571HIyHVlQK7rgFxXnSL/PQoZua4MyHUdpD7X1ZYt\nnPLbKGmhpLPd/V0z21fSw2Y2yt2vjji2vtpOUnOnac1F70WlrvCzc2yb1HNccfmMuou/Wd3HX6f8\n9nWeXz0sU0l92SZJelnST939WTMbKum3kj4j6Qvhh1h2cf+MwhKX36MwkevKg1z3IXJd9YnL71GY\nyHXlQa77UOpzXdmOzJrZpWaW28qjz1c/c/fl7v6VwmkRcvfnJd2ofA9AWZR7myStl9T5ryntrzco\nZL3YnvbTarqKrdu4oviMutFT/N2dMlTRz6IP+rJNcvevuvuzhefvS7pI0rGF/5CqTSw+I3Jdn5Dr\nyoNcV0CuCx+5rk/IdeVBrisg15X3yOyVkn66lXlWhLzONyRtb2ZD3H1lyGNL5d+mNyTt0mnaCOWv\nALaohHG7E3R7PlEUy5Ki90ZIeqSX6yz3Z7QFd19pZquUj7fYCEmvd7PYG5KGdzG/elimYvq4TV15\no/Bzd0nPhxFbBcXlMyLX9R65rgzIdT0i15WOXNd75LoyINf1KHW5rmzFrLuvVf7y0mVhZl+R9Jq7\nP100eZSk9eX6ZSr3Nkl6WFKDmZl7/prUkg6Q9Cd3bwp7ZUG3x8wWKH/O/qckPV2YNlzSR9VD0ovi\nM+rBI8rHX+wASb/uZv6HJf3YzPYuakQ/QPlTbP5UnhB7rVfbZPmrxP2ju88qmjyq8HNJF4vEXSw+\nI3Jdn5DryodcR64rC3Jdn5DryodcR67L8xjch6j9ISkraWE37z2montbSWpQ/vLmNYXXI5X/K9eP\not6OErapTtJiSVMLr0cp/xe0I2KwHWdK+ps+vB/ZjyQ9US2fkaT9JK3Rh/fuOqbwbzu88PpSSS8o\nfwn99mXulnRr4Xk/SX+UdGnUn0Uftql/4XW98r0VQwqvB0j6X+XvAWZRb0/RdtUr/1frXTtNr7rP\nqIdtJNeR68oVP7mOXBebB7mOXFfG+Ml15Dq5ezwuAGVm/6j8fYQ+ImmYmT0l6WF3/0HRbNtKKr6M\n+WxJ35X0RzPbpHzC+Jny98mKXF+2yd3Xm9nnJd1gZqcov01nu/vDFQy9S+5+nZkNUv7fu1nSW5K+\n1Gm22H5G7v6cmZ0k6b/NbKPy/eJHeqHvQ/kEsG2nxaZKus7MnpZUo/xfjH6gmOjFNrVfEv155f+6\n9/vC/AMlzVP+P1lXxMysn6THJQ2W5JJ+Y2Zvu/txhVmq7jPqjFyXR64rH3KdJHJd5Mh1eeS68iHX\nSSLX5dcTg20FAAAAAKBXqv0+swAAAACAFKKYBQAAAABUHYpZAAAAAEDVoZgFAAAAAFQdilkAAAAA\nQNWhmAUAAAAAVB2KWQAAAABA1aGYBQAAABAaM9vTzBrNLGdmh0YdD5KLYhYAAABAaNz9FXevb38Z\nZSxINopZAAAAAEDVoZgFAAAAsAUzqzWzy8xsgZk9ZmZ/MbPzC+8NNLOfFd57xsx+Z2a7b2W8bpcx\ns/3M7P8KpyaPLky73MzeMbNbCq+3L5y+vNHMzjGz28zsqeJlkC61UQcAAAAAIJYukXS0pM+4+wYz\n20/SnyVdLukmSTtKGu/uOTP7gaSHzGwvd9/UzXg9LfOcmf2rpIXtM7v7+WY2QoVTld19taR6M1so\n6WRJh7n7KjP7jaRcOf4BEG8cmQUAAADQgZltK+lsST919w2S5O7PSbrMzHaT9K+Sfuzu7UXkjyV9\nVNKJ3YwXZBnratFupv/G3VcV4vqSuy/t5SYiAShmAQAAAHT2cUnbSHqteKK7N0jaW/kC87Wi6esk\nLZc0rpvx+rKM1P0FpN7sOXykQWTFrJn9R+H89olRxQAA5UauAwAgsK4K1+7aItvKGQiqQyTFrJmN\nlPRdcaluAAlGrgMAVLHXJDVJ2qN4opmdKan9lN49iqYPlDRc0gvdjPe3AMusLfwcVLTcKPH/KLoR\n1ZHZ6yT9UF2f/w4ASUGuAwBUJXffKGmmpG+aWZ0kmdkhkk5x93mS7pT0HTOrKSzyHeVP/b2z01BW\nGO+NrS3j7islLZF0cGF9YyXtqy3/H+2ujxYpU/Fi1sy+IKlZ0h8qvW4AqBRyHQAgAX4g6QFJ/2dm\njZK+J+n4wnv/JmmxpPlm9oykz0g60t1bzGxPM5ur/BHVmWY2ZWvLFK3z35UveBslnSLpd5KOMrOb\nzCxTmD5c0nlmdmuZthtVwtwrd9S+8FedJyV9XtK2kt6QVO/uj1csCAAoM3IdAABA+VX6yOx0STe4\n+/IKrxcAKolcBwAAUGbdXR0sdIWbLH9a0jmd3+pmfhq9AXTJ3WPbJ0OuAxCWOOc6AIiDSh6ZPUb5\n0+3mFM6hb28Ov8bM5prZnp0XcPfEPBoaGiKPgW1K3zYlbXvcq6LuS3WuS+L3Lmnbk8RtStr2uFdF\nrgOAyFWsmHX3S919f3c/zN0Pk3RC4a1vF6a9UqlYEKFs0dNstru5gKpFrkuubGO2b8uR6wAAKIuo\nbs0jfXjKHafQAEgych0AAEAZRFLMmtn1ku5R/nLdPzWze6OIo5Lq6+ujDiF0fdqmbNHTGB6tSNrn\nlLTtqTZpzHVS8r537duTrc/2aXlyXfklbXsAAMFU9NY8vWFmHtfYAETHzOQJuigKuQ5AV5KW6wCg\nHKI8zRhplC16GsOjFQDQHXpmAQCIF4pZAAAAAEDV4TRjVCUzzrxKqq393ift1DtyXTqQs9BZ2nId\nAJRDbdQBAH1FAZA87PAjychZaEeuA4BwcJoxKitb9JQ+MgBVhJ5ZAADihWIWAAAAAFB16JlFVSr0\nEkUdBkIW5HNNWh8ZuS4dyFkolsZcBwDlwJFZAAAAAEDV4QJQKM1CSSf2Yv6lkj6Sf5pdmlX2I9nu\n5/29pCF9DQwAwpVtzCpbn+39cvTMAgBQFhSzKE2TpGWS7go4/82SvtHF886OltRaWmgAAAAAkoue\nWZTmRUnHF36GabikvxZ+dqHHfqO4dBiF9PX92c9+pm9+85tqaGhQQ0NDOIPGVBr7yMh16dDTd9su\njsfX2Rv4HlZKGnMdAJQDR2aRTFHvk4W0+/HBBx/owgsvzA/JfQmBxIq6kCyloF60aJHGjRunPfbY\nY4v3LrjgAv3Lv/xLKaEBANAtLgCFysoWPaWPbKsuuugiHXLIIVGHAUDcZ7Ynn/rUpzRv3rwtHkEK\n2Uwmo8cff7wCUQIAkoZiFoipBQsW6J577knFjjAAAADQWxSzqKxs0VOKtB6dddZZmj59urbffvte\nLTd//nydfPLJGj9+vPbbbz+NHz9e5557rtauXbt5nsbGRk2YMEEDBgzQtGnTdP311+uQQw7R8OHD\nlclkdO+992r8+PGb37/uuut08MEHa+edd9bxxx+v999/Xw899JCOOOIIjRkzRoceeqhee+21DnEs\nX75c06ZN07hx47T//vtrwoQJOvPMM7Vw4cJQ/n2ASuvLlYwlcl25kOsAABSzQAz98pe/1Lp163Tq\nqaf2etk//OEPyuVyeuaZZ/Tcc8/pT3/6k15//XVNmzZt8zz19fWaN2+eRo4cqYceekg1NTV64okn\n9Oqrr2rHHXfU5z73Oc2fP18jR47Uww8/rJEjR+pPf/qT5s+frzlz5uj444/X3Llz9fDDD+vVV19V\na2urTjvttA5xfPWrX9U777yj559/Xs8++6wefPBBPfTQQ3riiSdK/vcBEC/Lly/X1KlT9ZnPfEZ7\n7rmnjjnmGN13331lXSe5DgAgd4/lIx8aYu/v7j62F/M3FD1taOhuLved3H1592/3+P2Iw1enhBjW\nr1/vu+66qz/55JPu7r5w4UI3M7/44osDLb9s2TJftWpVh2kPPPCAm5mvWLGiw/Rdd93V99prrw7T\nFi9e7LlcbvP7++yzT4f3jzvuOM9kMr5y5crN06666irPZDLe0tKyedrAgQP9tNNO67Dsfffd508/\n/XS3sQf5vS/ME3mOCutBrqseDXMb+rZcQ0OP321lo/8OlBLDkiVL/B/+4R/8iSeecHf3TZs2+fXX\nX++ZTManT5++1eXNzBsbG3u9XnIdDx48ePDgasZAzFx++eU65JBDdOCBB/Zp+R122EE33HCD7r77\nbq1evVo1NTVat26dJOm1117T0KFDO8y/9957d3g9evToDq/Hjh3b4fWQIUM0bNgw7bjjjpunDR06\nVO6u5cuXa9SoUZKkSZMmadasWVq7dq2mTp2qww47TJMnT+7TNgGIr49+9KN66aWXNr/u16+fzjjj\nDM2ZM0eXXHKJTjnlFI0cOVKS1NDQoN/+9rdbjPGNb3xDAwcO3Px61KhR+t///d8e10uuAwBwmjEq\nK1v0lD6yLSxcuFA33nijrrzyyi3ecw92647TTz9d06dP109+8hMtWLBA8+bN08033yxJam5u7jCv\nmWnQoEHdjmVmqqurCzRNktra2jZPmz17tq688krNnz9fRx99tEaMGKHzzz9fmzZtCrQdQNzQM9s7\nBx10kFpbW/XnP/9587SLL754iyseS9KsWbM6TNtaISuR6wAAFLNArDz66KOqq6vTscceqwkTJmjC\nhAk69thjJUk33nijJkyYoC9/+cvdLt/U1KQ77rhDJ510kvbbb7/N04MWwkEFGa9///4699xz9eKL\nL+rZZ5/V5MmTdeWVV2r69OmhxgIgWmvWrFFTU9MW0zOZ/C5GLpcLfZ3kOgCARDGLSssWPU3p0Yqe\nfOMb39CiRYs6HKH4/e9/L0n65je/qXnz5mn27NndLt/a2qpcLrf56EG7t99+O9Q4O4/flRNOOGHz\n8wkTJujWW2/VuHHj9MILL4QaC1Ap3Ge2a2eddZZmzpy5xfRnnnlGmUxGBxxwQOjrJNcBACSKWSD2\n2o8MBDlCMHDgQE2aNEl33nnn5ttHrFixQldffXWXY7Q3z/e07q7e726Z4umzZ8/WXXfdtfn166+/\nrqVLl+rwww/f6nYAqB5mpuuvv75D3+zdd9+tX/7ylzrjjDO06667hr5Och0AQJIs7FNywmJmHtfY\nUORFSccXfoZpuKS/Fn52wcy63zHZ+h/SK6PEr+8HH3yg/fbbT62trXr77bc1ePBg7bDDDjrvvPN0\n+umnd7vce++9p//8z//UnDlz9JGPfERDhgzRQQcdpIaGBu2+++465ZRTdMwxx+jrX/+6XnrpJdXV\n1Wn06NG67LLLdPTRR0uSnn/+eU2dOlUvvvii6urqNG7cOD322GM65JBD9Pe//13r16/XJz7xCd11\n11268cYbdffdd2vp0qUaO3aszjnnHJ1yyimaMWOG7rnnHq1bt06ZTEburmnTpunb3/52t7H3+Ll2\nnCcun3LJyHXp0NN32y6Ox9fZG/r2PfzrX/+qWbNmac6cOTIzrV69WkOHDtVpp53WY65ql8lk1NjY\nqIkTJ/ZqveQ6AADFLEoTx2IWVSuNO3jkunQgZ6FYGnMdAJQDpxmjsrJFTxPeRwYgWeiZBQAgXihm\nAQAAAABVh9OMURpOM0aI0njqHbkuHchZKJbGXAcA5cCRWQAAAABA1aGYRWVli57SRwagitAzCwBA\nvNRGHQAAAGF65I1HdMp9p4Q+7saWjcrWZ0MfFwAA9A09sygNPbMIURr7yMh14bv/5ft1zZ+v0S3/\ndEtoY65tXqsDZx2oNeev6dPy5CwUS2OuA4By4MgsACBxtuu3nUZvPzq08dY0962IBQAA5UPPLCor\nW/SUPjIAVaS5tblPy5HrAAAoD47MomqZcfYVgOpBzgIAIFwUs6isbNHTEo5W0HsGoNIG1A7o03LZ\nbJajswAAlAGnGQMAAAAAqg7FLCorW/SUIxUAqgg9swAAxAvFLAAAAACg6lS0Z9bMJko6W9KOkmok\n7SDpZne/tpJxIELZoqccrUBCkeuSqZSeWQAAEL5KXwDqREnPufulkmRm+0h6zsxed/ffVTgWACgX\nch0AAECZVfo042slXdP+wt0XSFolafcKx4GoZIuecrQCyUWuSyB6ZgEAiJeKHpl19xfbn5tZRtKp\nkpok/aqScQBAOZHrAAAAyi+S+8ya2fclfUvS+5KOdfd3oogDEcgWPeVoBRKOXJcs9MwCABAvkVzN\n2N0vdfddJF0m6TEzOzCKOACgnMh1AAAA5RPprXnc/X8kPS7piijjQAVli55ytAIpQa5LBnpmAQCI\nl0rfmqe/u2/qNPlF5fvJtlC8A1BfX6/6+vqyxQYgnhobG9XY2Bh1GL1CrgPQW9WY6wAgaubulVuZ\n2Qvu/slO026X9El3H99pulcyNvTRi5KOL/wM03BJfy38BIqYmdzdoo6jJ+S6aN3/8v266bmbdP+J\n94c25prmNfrIjz+iNeevCW1MoCfVkOsAIGqVPs14oJn9R/sLM9tf0hRJsyocBwCUE7kOAACgzCpd\nzF4g6Utm9qSZPSHpZ5L+092vq3AciEq26Cl9ZEgucl0C0TMLAEC8VPo+s3dKurOS6wSASiPXAQAA\nlF+kVzNGCmWLnnK0AkAV4T6zAADEC8UsAAAAAKDqUMyisrJFTzlaAaCK0DMLAEC8UMwCAAAAAKoO\nxSwqK1v0lKMVAKoIPbMAAMQLxSwAAAAAoOpQzKKyskVPOVoBoIrQMwsAQLxQzAIAAAAAqg7FLCor\nW/SUoxUAqgg9swAAxAvFLAAAAACg6lDMorKyRU85WgGgitAzCwBAvFDMAgAAAACqTqBi1sxOL3cg\nSIls0VOOVgCoIvTMAgAQL7UB55tuZttKut3d3y9nQAAAAAAAbE3Q04wXSlotabaZ/drMjjEzK2Nc\nSKps0VOOVgCoIvTMAgAQL0GL2ZPc/RZ3nyTpQkmHSvqLmV1mZh8vX3gAAAAAAGwpUDHr7q8VPX9Z\n0u8lvSLpPElPm9mjZnaSmXFBKfQsW/SUoxUAqgg9swAAxEvQC0A9bGa7mNkFZvaqpEclDZF0gqRd\nJJ0oaYykX5YrUAAAAAAA2gU9kvpZSW9KmibpVkkfc/ej3H22uze7+7vu/kNJe5QpTiRFtugpRysA\nVBF6ZgEAiJegVzNeJunrkp5wd+9qBjP7vqQNYQUGAAAAAEB3gh6ZPc3dH++ukJUkd7/U3Q8KKS4k\nVbboKUcrAFQRemYBAIiXoMXsIDOba2bfbZ9gZt8xs2vNrG//uwMAAAAA0EdBi9mzJf1G0vVF026W\n9K6k68IOCgmWLXrK0QoAVYSeWQAA4iVoMWvufq27b+6Jdfe17n6ppLHlCQ0AAAAAgK4FPs24h/cG\nhxEIUiJb9JSjFQCqCD2zAADES9Bi9mUzu8nM9jCzTOGxp5n9XNKL5QwQAAAAAIDOghaz35a0t6SX\nJbUUHi9J2qvwHhBMtugpRysAVBF6ZgEAiJdA95l19+WSDjazwySNK0z+q7vPLVtkAAAAAAB0I1Ax\n265QvHYoYM3sSHf/Q6hRIbmyRU85WgGgitAzCwBAvAQuZs2sTtLHlb/gk7VPlnS5JIpZAAAAAEDF\nBOqZNbOvSlomaZ6kxyQ1Fh5zJe1bptiQRNmipxytAFBF6JkFACBegl4A6geSviJpB0k17p5pf0h6\nomzRAQAAAADQhaCnGS929/u7eW9yWMEgBbJFTzlaAaCK0DMLAEC8BD0y+2DhSsZduTasYAAAAAAA\nCCJoMXuspPvM7FUze9zM5rY/xJFZ9Ea26ClHKwBUEXpmAQCIl6CnGY+RNEMfXsW483sAAAAAAFRM\n0GL2Lne/uKs3zKwlxHiQdNmipxytAFBF6JkFACBeAp1m7O7n9/DeD8MLBwAAAACArQvaMyszm2Zm\nz3XAJD4AABj8SURBVJrZ/MLrH5rZv5UvNCRStugpRysAVBF6ZgEAiJdAxayZfUvSRZLmSMoVJt8m\n6XNm9t2gKzOz48zsd2b2iJk9ZWa/N7NP9jpqAIg58h0AAEB5BT0y+xVJ+7v7dyWtliR3f0XSyerd\n1YxvlXS7ux/u7gdKel7So2Y2vBdjoJpli55ytALJdqvId4lCzywAAPEStJjNufsHnSe6e6uk3vzv\n3ujudxW9niFpmKQjejEGAFQD8h0AAEAZBS1m+5nZ+M4Tzezo3qzM3f+506Smws++/bkb1Sdb9JSj\nFUgw8l3y0DMLAEC8BL01T4OkP5rZ45L2NLPbJY2VtI+kY0tY/4HK7+D9toQxAKAakO8AAABCFPTW\nPH+QdICkdyUtk7SXpL9K2sfdH+nLis3MlL+o1IXuvqIvY6AKZYuecrQCKUG+SwZ6ZgEAiJegR2bl\n7i9Jmhriui+TtNDdZ3Y3Q/EOQH19verr60NcPYBq0NjYqMbGxqjDKFWP+Y5cByAhuQ4AKsrcvbQB\nzG5y917db9bMzpZ0qKQp7p7rZh4vNTZUwIuSji/8DCKrzUdns9ls90cshit/7J/rvqITM5O7W9Rx\nBLW1fEeuC9/9L9+vm567SfefeH9oY65pXqOdfrSTmi/qfd9sj7kO6Ea15ToAiEKgI7Nmdoskl1Sc\nVNtfH9WbFZrZNwrLfMH9/2/v7oMsq8sDj3+ffhtmepohnVGGoICgKfJS4gtosmYjiSjmRSv+ZRKJ\nIVRSm9Vylizr1hJ8wYQVstqakKjRJIWQMkklpiYvy8Y4KAOFjiIvBQZDWJwZUGCGgWE2M8P0+7N/\n3DtD2/TL7Tvn3HvOvd9PVVd3n/s7v/ucOd3PnKfPec7J+Yh4CXB2Zn5pLfNIUtWZ7yRJksrT6mXG\nbwa+QKN4zeZ6pwPnATe3+mYR8YvAb9O4XPm8RhsZrwa2AB7c9YOrF3zpmQr1MPNd77FnVpKkamm1\nmP1sZl65eGFEnAtctob3uwkYBHYsWJbAh9YwhypkdnaW+Zl59jy0p9B5z5k/h5gPBlp+epRUOea7\nHpKZZCYPPfRQofMODg5yxhlnMDw8XOi8kiT1g5aK2aUK2ebyByPiNa2+WWaOtDpWNZKwcePGloaO\nTYxx6IpDAExMTHDFFVcsPWUmga1Cqi/zXe+Zmp1qOdcttFKuO3z48ImGJUlS32q1Z/aMJRaPAa8D\nTi00IkmSJEmSVtHqZcZ7lln+KPCbxYSifnDsrCyw7JkKSaqikcH2Trab6yRJKkerxeydwNt57m7G\nCRzKzAOlRCVJkiRJ0gpavbvOuzLzkczc0/x4xEJW7RibGDv+9cTERBcjkaS1mZ6bbms9c50kSeVo\ntZj93VYGRUTLj+mRJEmSJKldrV5m/LqI+DI87/ayuWjZeYVEpZ5lz6ykurJnVpKkamm1mP1z4B3A\n/wa+Q6OIPQO4CPgccKQ57qyC45MkSZIk6Xlavcx4HDg/M9+ZmVdl5vsy8500Hs1zSmZenZlX0yhs\npWXZMyupruyZlSSpWlotZs/MzF2LF2bmbuDcBd+/r6jAJEmSJElaTqvF7GkRcf7ihRFxAfADxYak\nXmbPrKS6smdWkqRqabVn9jpgZ0R8FdjdXHYO8OPAfyojMEmSJEmSltNSMZuZfxIR/wK8h8YdixP4\nV+C9mfm1EuNTjxmbGDt+dnZiYsIzFpJqY3JuklsevWXN623702287dfftvScRycZP22czcObTzQ8\nSZL6TqtnZsnMncDOEmORJKmShgaGeNHoi7jxWzeued1d+3fx79/69yVf+/oTX+cVL30Fm8csZiVJ\nWquWi9mI+D7gEmBTZl4TERcB92fmk6VFp55jz6ykOtowvIHtb93O6Ojo2ld+8/IvXfQ3F7UflCRJ\nfa6lG0BFxHnAt4H3A7/SXHwOcEdEvLak2CRJkiRJWlKrdzP+KHBZZr4QeBwgMz8NvAm4pqTY1IN8\nzqykurr+/uvbWs9cJ0lSOVotZocy8+8WL8zMPcBwoRFJkiRJkrSKVovZUyLieQ/Yi4hx4NRiQ1Iv\ns2dWUl1tffnWttYz10mSVI5WbwD1BeCWiPgj4OSIeANwLvBu4PNlBSdJkiRJ0lJaPTP7fuA24Abg\nlcB24CM0CtkPlhOaepE9s5Lqyp5ZSZKqpaUzs5k5C7w/Ij4MvLS5+OHMPFpaZJIkSZIkLaOlYjYi\n5oF7MvN84JvlhqReZs+spLqyZ1aSpGpp9TLj+5qFrCRJkiRJXddqMft/l7qbMUBEXFdgPOpx9sxK\nqit7ZiVJqpa13M34HyLiL4DvAHPN5QFcDPyPEmKTJEmSJGlJyxazEbEemM/MKeBPm4vftMTQLCMw\n9SZ7ZiXVlT2zkiRVy0qXGd8KXNb8emdmDiz1Adx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SpiralSpotterSpArcFiReAnalysisClean.ipynb