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bamford/SpiralSpotterClassifyAnalysisClean.ipynb

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Spiral Spotter Analysis
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SpiralSpotterClassifyAnalysisClean.ipynb
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SpiralSpotterSpArcFiReAnalysisClean.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### SpArcFiRe workflow\n\nFirst session: Spiral1 subject set\n\nBetween sessions: Fixed bug in SpArcFire workflow: if arm 8 was classified as ‘extension’, then stuck in loop (may have affected reduction of first session?). Copied workflow and renamed old workflow SpArcFire1.\n\nSecond session: Spiral2 subject set (although at first had enabled Spiral2Classify - so watch out for some oddities)\n\nFor output and plotting, classifications from session2 are added those from session1.\nPlots created in session had low numbers due to bug (now fixed) where fractions were stored as integers.\n\n### Outputs\n\n* spiral_spotter_sparcfire_data.fits - the collated classifications of the arcs\n* spiral_spotter_sparcfire_consensus.fits - a (poor) attempt at a consensus classification of each arc and counts of each class\n* spiral_spotter_sparcfire_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",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"collapsed": true,
"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 0x10d109b50>"
},
"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'], w.raw[u'version'])",
"execution_count": 5,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "(u'SpArcFiRe1', 813, u'26.80')\n(u'Classify', 3659, u'1.1')\n(u'SpArcFiReShort', 0, u'1.1')\n(u'Classify1', 2628, u'32.34')\n(u'SpArcFiRe', 551, u'1.1')\n"
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": true
},
"cell_type": "code",
"source": "classifications = []",
"execution_count": 6,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "for w in project.links.workflows:\n if w.display_name == u'SpArcFiRe1':\n sparcfire_workflow = w",
"execution_count": 7,
"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=sparcfire_workflow.id,\n last_id=last_seen_id):\n classifications.append(c)",
"execution_count": 8,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "len(classifications)",
"execution_count": 9,
"outputs": [
{
"data": {
"text/plain": "825"
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "np.unique([c.raw['metadata']['workflow_version'] for c in classifications], return_counts=True)",
"execution_count": 10,
"outputs": [
{
"data": {
"text/plain": "(array([u'13.64', u'14.64', u'14.69', u'19.79', u'19.80', u'9.54'], \n dtype='<U5'), array([ 1, 6, 4, 6, 807, 1]))"
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": true
},
"cell_type": "code",
"source": "classifications = []",
"execution_count": 11,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "for w in project.links.workflows:\n if w.display_name == u'SpArcFiRe1':\n sparcfire_workflow = w\nworkflow_version = u'19.80' # not latest due to fixes between sessions",
"execution_count": 12,
"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=sparcfire_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": 13,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "len(classifications)",
"execution_count": 14,
"outputs": [
{
"data": {
"text/plain": "807"
},
"execution_count": 14,
"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'SpArcFiRe':\n sparcfire_workflow = w\nworkflow_version = sparcfire_workflow.raw[u'version']",
"execution_count": 15,
"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=sparcfire_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": 16,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "len(classifications)",
"execution_count": 17,
"outputs": [
{
"data": {
"text/plain": "1358"
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "questions = ['arc{}'.format(i) for i in range(1, 9)]\nanswers = ['good', 'poor', 'weak', 'extension', 'junk', 'missing']",
"execution_count": 18,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "classifications[0].raw",
"execution_count": 19,
"outputs": [
{
"data": {
"text/plain": "{u'annotations': [{u'task': u'T0', u'value': 5}],\n u'created_at': u'2017-04-12T06:30:52.705Z',\n u'href': u'/classifications/47722663',\n u'id': u'47722663',\n u'links': {u'project': u'4256',\n u'subjects': [u'8738959'],\n u'user': u'1610219',\n u'workflow': u'3826'},\n u'metadata': {u'finished_at': u'2017-04-12T06:30:52.441Z',\n u'live_project': False,\n u'session': u'9a8500f9fc012fb5873cb4c392ac5f313c7059468c91f86556295f4792612ce9',\n u'started_at': u'2017-04-12T06:30:40.505Z',\n u'subject_dimensions': [{u'clientHeight': 424,\n u'clientWidth': 424,\n u'naturalHeight': 424,\n u'naturalWidth': 424},\n {u'clientHeight': 424,\n u'clientWidth': 424,\n u'naturalHeight': 424,\n u'naturalWidth': 424},\n {u'clientHeight': 424,\n u'clientWidth': 424,\n u'naturalHeight': 424,\n u'naturalWidth': 424}],\n u'user_agent': u'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36',\n u'user_group_ids': [],\n u'user_language': u'en',\n u'utc_offset': u'-3600',\n u'viewport': {u'height': 703, u'width': 1276},\n u'workflow_version': u'19.80'}}"
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "questions",
"execution_count": 20,
"outputs": [
{
"data": {
"text/plain": "['arc1', 'arc2', 'arc3', 'arc4', 'arc5', 'arc6', 'arc7', 'arc8']"
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "row_data = []\nfor c in classifications:\n row = OrderedDict()\n row['subject_id'] = c.raw['links']['subjects'][0]\n for key in questions:\n row[key] = 5 # missing\n for a in c.raw['annotations']:\n i = int(a['task'][1:])\n if i < 8:\n key = questions[i]\n row[key] = a['value']\n row_data.append(row)",
"execution_count": 21,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "indata = Table(row_data, names=['subject_id'] + questions)",
"execution_count": 22,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "indata",
"execution_count": 23,
"outputs": [
{
"data": {
"text/html": "&lt;Table length=1358&gt;\n<table id=\"table4569996240\">\n<thead><tr><th>subject_id</th><th>arc1</th><th>arc2</th><th>arc3</th><th>arc4</th><th>arc5</th><th>arc6</th><th>arc7</th><th>arc8</th></tr></thead>\n<thead><tr><th>unicode7</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n<tr><td>8738959</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td></tr>\n<tr><td>8739024</td><td>1</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td></tr>\n<tr><td>8738823</td><td>1</td><td>1</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td></tr>\n<tr><td>8738856</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td></tr>\n<tr><td>8739272</td><td>4</td><td>0</td><td>0</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td></tr>\n<tr><td>8738842</td><td>0</td><td>0</td><td>0</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td></tr>\n<tr><td>8738712</td><td>0</td><td>0</td><td>0</td><td>3</td><td>5</td><td>5</td><td>5</td><td>5</td></tr>\n<tr><td>8738829</td><td>0</td><td>0</td><td>4</td><td>4</td><td>5</td><td>5</td><td>5</td><td>5</td></tr>\n<tr><td>8738689</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td></tr>\n<tr><td>8739144</td><td>4</td><td>2</td><td>2</td><td>4</td><td>5</td><td>5</td><td>5</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></tr>\n<tr><td>8740059</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td></tr>\n<tr><td>8739821</td><td>0</td><td>0</td><td>0</td><td>3</td><td>0</td><td>4</td><td>5</td><td>5</td></tr>\n<tr><td>8739832</td><td>0</td><td>0</td><td>2</td><td>3</td><td>4</td><td>3</td><td>1</td><td>5</td></tr>\n<tr><td>8739656</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td></tr>\n<tr><td>8739536</td><td>0</td><td>3</td><td>3</td><td>0</td><td>3</td><td>4</td><td>2</td><td>5</td></tr>\n<tr><td>8739670</td><td>0</td><td>0</td><td>2</td><td>0</td><td>3</td><td>2</td><td>4</td><td>3</td></tr>\n<tr><td>8740095</td><td>0</td><td>0</td><td>4</td><td>3</td><td>2</td><td>2</td><td>2</td><td>5</td></tr>\n<tr><td>8740036</td><td>0</td><td>0</td><td>3</td><td>4</td><td>3</td><td>5</td><td>5</td><td>5</td></tr>\n<tr><td>8739680</td><td>0</td><td>3</td><td>0</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td></tr>\n<tr><td>8739943</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td></tr>\n</table>",
"text/plain": "<Table length=1358>\nsubject_id arc1 arc2 arc3 arc4 arc5 arc6 arc7 arc8\n unicode7 int64 int64 int64 int64 int64 int64 int64 int64\n---------- ----- ----- ----- ----- ----- ----- ----- -----\n 8738959 5 5 5 5 5 5 5 5\n 8739024 1 5 5 5 5 5 5 5\n 8738823 1 1 5 5 5 5 5 5\n 8738856 5 5 5 5 5 5 5 5\n 8739272 4 0 0 5 5 5 5 5\n 8738842 0 0 0 5 5 5 5 5\n 8738712 0 0 0 3 5 5 5 5\n 8738829 0 0 4 4 5 5 5 5\n 8738689 5 5 5 5 5 5 5 5\n 8739144 4 2 2 4 5 5 5 5\n ... ... ... ... ... ... ... ... ...\n 8740059 0 0 0 0 1 1 1 1\n 8739821 0 0 0 3 0 4 5 5\n 8739832 0 0 2 3 4 3 1 5\n 8739656 5 5 5 5 5 5 5 5\n 8739536 0 3 3 0 3 4 2 5\n 8739670 0 0 2 0 3 2 4 3\n 8740095 0 0 4 3 2 2 2 5\n 8740036 0 0 3 4 3 5 5 5\n 8739680 0 3 0 5 5 5 5 5\n 8739943 5 5 5 5 5 5 5 5"
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"collapsed": false,
"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']).astype(np.int)\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'\n if name not in outdata.columns:\n outdata[name] = total\n else:\n assert((outdata[name] == total).all())\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'\n fracname = '{}_frac'.format(name)\n data[fracname] = data[name] / data[totalname].astype(np.float)\n data.remove_column(name)\n return data",
"execution_count": 24,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "data = collate_classifications(indata, questions, [answers]* 9)",
"execution_count": 25,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "data = calculate_fractions(data, questions, [answers] * 9)",
"execution_count": 26,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "data.write('spiral_spotter_sparcfire_data.fits')",
"execution_count": 27,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "data",
"execution_count": 28,
"outputs": [
{
"data": {
"text/html": "&lt;Table length=468&gt;\n<table id=\"table4489133008\">\n<thead><tr><th>subject_id</th><th>total</th><th>arc1_good_frac</th><th>arc1_poor_frac</th><th>arc1_weak_frac</th><th>arc1_extension_frac</th><th>arc1_junk_frac</th><th>arc1_missing_frac</th><th>arc2_good_frac</th><th>arc2_poor_frac</th><th>arc2_weak_frac</th><th>arc2_extension_frac</th><th>arc2_junk_frac</th><th>arc2_missing_frac</th><th>arc3_good_frac</th><th>arc3_poor_frac</th><th>arc3_weak_frac</th><th>arc3_extension_frac</th><th>arc3_junk_frac</th><th>arc3_missing_frac</th><th>arc4_good_frac</th><th>arc4_poor_frac</th><th>arc4_weak_frac</th><th>arc4_extension_frac</th><th>arc4_junk_frac</th><th>arc4_missing_frac</th><th>arc5_good_frac</th><th>arc5_poor_frac</th><th>arc5_weak_frac</th><th>arc5_extension_frac</th><th>arc5_junk_frac</th><th>arc5_missing_frac</th><th>arc6_good_frac</th><th>arc6_poor_frac</th><th>arc6_weak_frac</th><th>arc6_extension_frac</th><th>arc6_junk_frac</th><th>arc6_missing_frac</th><th>arc7_good_frac</th><th>arc7_poor_frac</th><th>arc7_weak_frac</th><th>arc7_extension_frac</th><th>arc7_junk_frac</th><th>arc7_missing_frac</th><th>arc8_good_frac</th><th>arc8_poor_frac</th><th>arc8_weak_frac</th><th>arc8_extension_frac</th><th>arc8_junk_frac</th><th>arc8_missing_frac</th></tr></thead>\n<thead><tr><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n<tr><td>8738666</td><td>4</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.5</td><td>0.25</td><td>0.25</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.25</td><td>0.0</td><td>0.75</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.25</td><td>0.0</td><td>0.75</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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>8738671</td><td>2</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><td>0.0</td><td>0.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.5</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><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>8738673</td><td>1</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><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>8738676</td><td>4</td><td>0.75</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.25</td><td>0.5</td><td>0.0</td><td>0.25</td><td>0.0</td><td>0.0</td><td>0.25</td><td>0.25</td><td>0.0</td><td>0.0</td><td>0.25</td><td>0.0</td><td>0.5</td><td>0.25</td><td>0.0</td><td>0.25</td><td>0.0</td><td>0.0</td><td>0.5</td><td>0.25</td><td>0.0</td><td>0.25</td><td>0.0</td><td>0.0</td><td>0.5</td><td>0.25</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.75</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.25</td><td>0.75</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.25</td><td>0.75</td></tr>\n<tr><td>8738679</td><td>2</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><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>8738681</td><td>1</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><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>8738682</td><td>1</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><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>8738685</td><td>5</td><td>0.8</td><td>0.0</td><td>0.2</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.8</td><td>0.0</td><td>0.2</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.4</td><td>0.0</td><td>0.4</td><td>0.0</td><td>0.0</td><td>0.2</td><td>0.2</td><td>0.0</td><td>0.0</td><td>0.2</td><td>0.2</td><td>0.4</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.4</td><td>0.2</td><td>0.4</td><td>0.0</td><td>0.0</td><td>0.2</td><td>0.0</td><td>0.4</td><td>0.4</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.6</td><td>0.0</td><td>0.4</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>8738687</td><td>3</td><td>0.0</td><td>0.333333333333</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.666666666667</td><td>0.0</td><td>0.333333333333</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.666666666667</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.333333333333</td><td>0.666666666667</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.333333333333</td><td>0.666666666667</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.333333333333</td><td>0.666666666667</td><td>0.0</td><td>0.0</td><td>0.333333333333</td><td>0.0</td><td>0.0</td><td>0.666666666667</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><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>8738689</td><td>6</td><td>0.0</td><td>0.166666666667</td><td>0.166666666667</td><td>0.0</td><td>0.0</td><td>0.666666666667</td><td>0.0</td><td>0.166666666667</td><td>0.0</td><td>0.0</td><td>0.166666666667</td><td>0.666666666667</td><td>0.0</td><td>0.0</td><td>0.166666666667</td><td>0.0</td><td>0.0</td><td>0.833333333333</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.166666666667</td><td>0.0</td><td>0.833333333333</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.166666666667</td><td>0.0</td><td>0.833333333333</td><td>0.0</td><td>0.0</td><td>0.166666666667</td><td>0.0</td><td>0.0</td><td>0.833333333333</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.166666666667</td><td>0.0</td><td>0.833333333333</td><td>0.0</td><td>0.166666666667</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.833333333333</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><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><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><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n<tr><td>8741783</td><td>1</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><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>8741818</td><td>1</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><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>8741868</td><td>1</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><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>8741885</td><td>1</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><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>8741901</td><td>1</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><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>8741964</td><td>1</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><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>8742061</td><td>1</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><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>8742075</td><td>1</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><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>8742149</td><td>1</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><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>8742225</td><td>1</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</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><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</table>",
"text/plain": "<Table length=468>\nsubject_id total arc1_good_frac ... arc8_junk_frac arc8_missing_frac\n int64 int64 float64 ... float64 float64 \n---------- ----- -------------- ... -------------- -----------------\n 8738666 4 1.0 ... 0.0 1.0\n 8738671 2 0.0 ... 0.0 1.0\n 8738673 1 0.0 ... 0.0 1.0\n 8738676 4 0.75 ... 0.25 0.75\n 8738679 2 0.0 ... 0.0 1.0\n 8738681 1 0.0 ... 0.0 1.0\n 8738682 1 0.0 ... 0.0 1.0\n 8738685 5 0.8 ... 0.0 1.0\n 8738687 3 0.0 ... 0.0 1.0\n 8738689 6 0.0 ... 0.0 0.833333333333\n ... ... ... ... ... ...\n 8741783 1 0.0 ... 0.0 1.0\n 8741818 1 0.0 ... 0.0 1.0\n 8741868 1 0.0 ... 0.0 1.0\n 8741885 1 0.0 ... 0.0 1.0\n 8741901 1 0.0 ... 0.0 1.0\n 8741964 1 0.0 ... 0.0 1.0\n 8742061 1 0.0 ... 0.0 1.0\n 8742075 1 0.0 ... 0.0 1.0\n 8742149 1 0.0 ... 0.0 1.0\n 8742225 1 0.0 ... 0.0 1.0"
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "plt.hist(data['total'], range=(-0.5, 10.5), bins=11)\nplt.xlabel('total number of classifications')",
"execution_count": 29,
"outputs": [
{
"data": {
"text/plain": "<matplotlib.text.Text at 0x100535150>"
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x10e1b4c90>"
},
"metadata": {},
"output_type": "display_data"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "def calculate_consensus(data, questions, 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 classification for each arc\n for q in questions:\n outdata['{}_decision'.format(q)] = np.zeros(len(data), np.int8) - 1\n for i, a in enumerate(answers):\n majority = data['{}_{}_frac'.format(q, a)] > 0.5\n outdata['{}_decision'.format(q)][majority] = i\n\n # count good arcs per galaxy\n count = np.zeros((len(answers) - 2, len(data)), np.int)\n for q in questions:\n for i, a in enumerate(answers[:-2]):\n count[i] += outdata['{}_decision'.format(q)] == i\n for i, a in enumerate(answers[:-2]):\n outdata['n_arc_{}'.format(a)] = count[i]\n return outdata",
"execution_count": 30,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "consensus_data = calculate_consensus(data, questions, answers)\nconsensus_data.write('spiral_spotter_sparcfire_consensus.fits')",
"execution_count": 31,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "consensus_data",
"execution_count": 32,
"outputs": [
{
"data": {
"text/html": "&lt;Table length=468&gt;\n<table id=\"table4586149904\">\n<thead><tr><th>subject_id</th><th>arc1_decision</th><th>arc2_decision</th><th>arc3_decision</th><th>arc4_decision</th><th>arc5_decision</th><th>arc6_decision</th><th>arc7_decision</th><th>arc8_decision</th><th>n_arc_good</th><th>n_arc_poor</th><th>n_arc_weak</th><th>n_arc_extension</th></tr></thead>\n<thead><tr><th>int64</th><th>int8</th><th>int8</th><th>int8</th><th>int8</th><th>int8</th><th>int8</th><th>int8</th><th>int8</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n<tr><td>8738666</td><td>0</td><td>0</td><td>-1</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>2</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8738671</td><td>-1</td><td>-1</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8738673</td><td>4</td><td>4</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8738676</td><td>0</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>5</td><td>5</td><td>5</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8738679</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8738681</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8738682</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8738685</td><td>0</td><td>0</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>3</td><td>5</td><td>2</td><td>0</td><td>0</td><td>1</td></tr>\n<tr><td>8738687</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8738689</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</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></tr>\n<tr><td>8741783</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8741818</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8741868</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8741885</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8741901</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8741964</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8742061</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8742075</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8742149</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8742225</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n</table>",
"text/plain": "<Table length=468>\nsubject_id arc1_decision arc2_decision ... n_arc_poor n_arc_weak n_arc_extension\n int64 int8 int8 ... int64 int64 int64 \n---------- ------------- ------------- ... ---------- ---------- ---------------\n 8738666 0 0 ... 0 0 0\n 8738671 -1 -1 ... 0 0 0\n 8738673 4 4 ... 0 0 0\n 8738676 0 -1 ... 0 0 0\n 8738679 5 5 ... 0 0 0\n 8738681 5 5 ... 0 0 0\n 8738682 5 5 ... 0 0 0\n 8738685 0 0 ... 0 0 1\n 8738687 5 5 ... 0 0 0\n 8738689 5 5 ... 0 0 0\n ... ... ... ... ... ... ...\n 8741783 5 5 ... 0 0 0\n 8741818 5 5 ... 0 0 0\n 8741868 5 5 ... 0 0 0\n 8741885 5 5 ... 0 0 0\n 8741901 5 5 ... 0 0 0\n 8741964 5 5 ... 0 0 0\n 8742061 5 5 ... 0 0 0\n 8742075 5 5 ... 0 0 0\n 8742149 5 5 ... 0 0 0\n 8742225 5 5 ... 0 0 0"
},
"execution_count": 32,
"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'SpArcFiRe1':\n sparcfire_workflow_a = w\n if w.display_name == u'SpArcFiRe':\n sparcfire_workflow_b = w",
"execution_count": 33,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "subject_sets = set(sparcfire_workflow_a.links.subject_sets + sparcfire_workflow_b.links.subject_sets)",
"execution_count": 34,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "subject_table_columns = OrderedDict(subject_id=[])\nfor subject_set in subject_sets:\n for subject in subject_set.subjects():\n subject_table_columns[u'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": 35,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "subject_table_columns.keys()",
"execution_count": 36,
"outputs": [
{
"data": {
"text/plain": "['subject_id',\n u'dr8objid',\n u'LOGMSTAR_BALDRY06',\n u'REDSHIFT',\n u'CUR',\n u'arc_filename',\n u'gz2_filename',\n u'NARMS',\n u'dr7objid',\n u'REDNESS_BALDRY06',\n u'PETROMAG_MR',\n u'UNBARRED',\n u'transformed_filename']"
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "subject_table_names = (u'subject_id', u'dr7objid', u'dr8objid', u'REDSHIFT', u'PETROMAG_MR', u'CUR', u'LOGMSTAR_BALDRY06', u'REDNESS_BALDRY06', u'NARMS', u'UNBARRED', u'arc_filename', u'transformed_filename', u'gz2_filename')\nsubject_table_dtype = ('int', 'int64', 'int64', 'float', 'float', 'float', 'float', 'float', 'int8', 'bool', 'string', 'string', 'string')",
"execution_count": 37,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "subject_table = Table([subject_table_columns.get(n) for n in subject_table_names], names=subject_table_names, dtype=subject_table_dtype)",
"execution_count": 38,
"outputs": []
},
{
"metadata": {
"collapsed": true,
"trusted": true
},
"cell_type": "code",
"source": "subject_table.write('spiral_spotter_sparcfire_subjects.fits')",
"execution_count": 39,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "subject_table",
"execution_count": 40,
"outputs": [
{
"data": {
"text/html": "&lt;Table length=500&gt;\n<table id=\"table4622965392\">\n<thead><tr><th>subject_id</th><th>dr7objid</th><th>dr8objid</th><th>REDSHIFT</th><th>PETROMAG_MR</th><th>CUR</th><th>LOGMSTAR_BALDRY06</th><th>REDNESS_BALDRY06</th><th>NARMS</th><th>UNBARRED</th><th>arc_filename</th><th>transformed_filename</th><th>gz2_filename</th></tr></thead>\n<thead><tr><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>int8</th><th>bool</th><th>str28</th><th>str23</th><th>str27</th></tr></thead>\n<tr><td>8740105</td><td>588017112366252055</td><td>1237661358614446146</td><td>0.0337615</td><td>-20.793823</td><td>2.3911686</td><td>10.435781</td><td>0.13368583</td><td>1</td><td>True</td><td>1237661358614446146+arcs.png</td><td>1237661358614446146.png</td><td>1237661358614446146_gz2.png</td></tr>\n<tr><td>8740103</td><td>588017703479541773</td><td>1237661949727735904</td><td>0.037738</td><td>-21.01261</td><td>2.0827732</td><td>10.468281</td><td>-0.18852067</td><td>4</td><td>True</td><td>1237661949727735904+arcs.png</td><td>1237661949727735904.png</td><td>1237661949727735904_gz2.png</td></tr>\n<tr><td>8740101</td><td>587742012761440413</td><td>1237667733986345070</td><td>0.0479483</td><td>-20.861237</td><td>1.7276382</td><td>10.210649</td><td>-0.43032336</td><td>4</td><td>True</td><td>1237667733986345070+arcs.png</td><td>1237667733986345070.png</td><td>1237667733986345070_gz2.png</td></tr>\n<tr><td>8740098</td><td>587736914606031100</td><td>1237662635830935814</td><td>0.0534687</td><td>-20.468973</td><td>1.835413</td><td>10.113575</td><td>-0.27976632</td><td>3</td><td>True</td><td>1237662635830935814+arcs.png</td><td>1237662635830935814.png</td><td>1237662635830935814_gz2.png</td></tr>\n<tr><td>8740095</td><td>587741815711989787</td><td>1237667536936894657</td><td>0.0405101</td><td>-21.133358</td><td>1.6598072</td><td>10.280678</td><td>-0.52943516</td><td>2</td><td>True</td><td>1237667536936894657+arcs.png</td><td>1237667536936894657.png</td><td>1237667536936894657_gz2.png</td></tr>\n<tr><td>8740094</td><td>587739646201495658</td><td>1237665367426400354</td><td>0.0522286</td><td>-20.820257</td><td>2.19425</td><td>10.411082</td><td>-0.05259013</td><td>1</td><td>True</td><td>1237665367426400354+arcs.png</td><td>1237665367426400354.png</td><td>1237665367426400354_gz2.png</td></tr>\n<tr><td>8740091</td><td>587729777979752577</td><td>1237655499204657255</td><td>0.0540473</td><td>-20.732141</td><td>1.5562973</td><td>10.062539</td><td>-0.5369804</td><td>1</td><td>True</td><td>1237655499204657255+arcs.png</td><td>1237655499204657255.png</td><td>1237655499204657255_gz2.png</td></tr>\n<tr><td>8740089</td><td>587737826746302844</td><td>1237663547971207612</td><td>0.0421434</td><td>-21.378235</td><td>1.8047352</td><td>10.459953</td><td>-0.4630425</td><td>1</td><td>True</td><td>1237663547971207612+arcs.png</td><td>1237663547971207612.png</td><td>1237663547971207612_gz2.png</td></tr>\n<tr><td>8740087</td><td>587731869626466431</td><td>1237657590851371141</td><td>0.0515251</td><td>-21.233131</td><td>1.5746632</td><td>10.272995</td><td>-0.611146</td><td>1</td><td>True</td><td>1237657590851371141+arcs.png</td><td>1237657590851371141.png</td><td>1237657590851371141_gz2.png</td></tr>\n<tr><td>8740084</td><td>587728880331653454</td><td>1237654601556558026</td><td>0.0545778</td><td>-20.593534</td><td>1.710804</td><td>10.09313</td><td>-0.39553928</td><td>1</td><td>True</td><td>1237654601556558026+arcs.png</td><td>1237654601556558026.png</td><td>1237654601556558026_gz2.png</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></tr>\n<tr><td>8738687</td><td>587742627986997432</td><td>1237668349211902107</td><td>0.0526503</td><td>-20.453255</td><td>1.9403095</td><td>10.165767</td><td>-0.19773436</td><td>1</td><td>True</td><td>1237668349211902107+arcs.png</td><td>1237668349211902107.png</td><td>1237668349211902107_gz2.png</td></tr>\n<tr><td>8738685</td><td>587727943489945860</td><td>1237653664714850592</td><td>0.0514696</td><td>-21.245523</td><td>1.7615585</td><td>10.382954</td><td>-0.47302866</td><td>2</td><td>True</td><td>1237653664714850592+arcs.png</td><td>1237653664714850592.png</td><td>1237653664714850592_gz2.png</td></tr>\n<tr><td>8738682</td><td>587742013807198300</td><td>1237667735032103006</td><td>0.0309005</td><td>-20.973211</td><td>1.8553371</td><td>10.327075</td><td>-0.35458732</td><td>2</td><td>True</td><td>1237667735032103006+arcs.png</td><td>1237667735032103006.png</td><td>1237667735032103006_gz2.png</td></tr>\n<tr><td>8738681</td><td>587735697523081270</td><td>1237661418747985973</td><td>0.035372</td><td>-20.469849</td><td>1.66819</td><td>10.019936</td><td>-0.40725255</td><td>1</td><td>True</td><td>1237661418747985973+arcs.png</td><td>1237661418747985973.png</td><td>1237661418747985973_gz2.png</td></tr>\n<tr><td>8738679</td><td>587742062128529415</td><td>1237667783353434119</td><td>0.0440799</td><td>-21.364363</td><td>1.3107681</td><td>10.178134</td><td>-0.83274555</td><td>1</td><td>True</td><td>1237667783353434119+arcs.png</td><td>1237667783353434119.png</td><td>1237667783353434119_gz2.png</td></tr>\n<tr><td>8738676</td><td>588010877692412107</td><td>1237655123940606166</td><td>0.0381976</td><td>-21.023554</td><td>2.081499</td><td>10.47119</td><td>-0.19101954</td><td>4</td><td>True</td><td>1237655123940606166+arcs.png</td><td>1237655123940606166.png</td><td>1237655123940606166_gz2.png</td></tr>\n<tr><td>8738673</td><td>587736478129193204</td><td>1237662199354097895</td><td>0.0548665</td><td>-20.875439</td><td>1.6982117</td><td>10.199307</td><td>-0.4547012</td><td>2</td><td>True</td><td>1237662199354097895+arcs.png</td><td>1237662199354097895.png</td><td>1237662199354097895_gz2.png</td></tr>\n<tr><td>8738671</td><td>587735236883317016</td><td>1237660958108221730</td><td>0.0413652</td><td>-21.059275</td><td>1.9228668</td><td>10.398433</td><td>-0.3184793</td><td>1</td><td>True</td><td>1237660958108221730+arcs.png</td><td>1237660958108221730.png</td><td>1237660958108221730_gz2.png</td></tr>\n<tr><td>8738669</td><td>587732772120166516</td><td>1237658493345071231</td><td>0.0335551</td><td>-20.989128</td><td>1.7747154</td><td>10.287678</td><td>-0.4176538</td><td>4</td><td>True</td><td>1237658493345071231+arcs.png</td><td>1237658493345071231.png</td><td>1237658493345071231_gz2.png</td></tr>\n<tr><td>8738666</td><td>587741708326076464</td><td>1237667429550981158</td><td>0.0317634</td><td>-21.06437</td><td>1.6926174</td><td>10.272118</td><td>-0.49279952</td><td>1</td><td>True</td><td>1237667429550981158+arcs.png</td><td>1237667429550981158.png</td><td>1237667429550981158_gz2.png</td></tr>\n</table>",
"text/plain": "<Table length=500>\nsubject_id dr7objid ... gz2_filename \n int64 int64 ... str27 \n---------- ------------------ ... ---------------------------\n 8740105 588017112366252055 ... 1237661358614446146_gz2.png\n 8740103 588017703479541773 ... 1237661949727735904_gz2.png\n 8740101 587742012761440413 ... 1237667733986345070_gz2.png\n 8740098 587736914606031100 ... 1237662635830935814_gz2.png\n 8740095 587741815711989787 ... 1237667536936894657_gz2.png\n 8740094 587739646201495658 ... 1237665367426400354_gz2.png\n 8740091 587729777979752577 ... 1237655499204657255_gz2.png\n 8740089 587737826746302844 ... 1237663547971207612_gz2.png\n 8740087 587731869626466431 ... 1237657590851371141_gz2.png\n 8740084 587728880331653454 ... 1237654601556558026_gz2.png\n ... ... ... ...\n 8738687 587742627986997432 ... 1237668349211902107_gz2.png\n 8738685 587727943489945860 ... 1237653664714850592_gz2.png\n 8738682 587742013807198300 ... 1237667735032103006_gz2.png\n 8738681 587735697523081270 ... 1237661418747985973_gz2.png\n 8738679 587742062128529415 ... 1237667783353434119_gz2.png\n 8738676 588010877692412107 ... 1237655123940606166_gz2.png\n 8738673 587736478129193204 ... 1237662199354097895_gz2.png\n 8738671 587735236883317016 ... 1237660958108221730_gz2.png\n 8738669 587732772120166516 ... 1237658493345071231_gz2.png\n 8738666 587741708326076464 ... 1237667429550981158_gz2.png"
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "data = join(subject_table, consensus_data, keys='subject_id', join_type='left')",
"execution_count": 41,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "data",
"execution_count": 42,
"outputs": [
{
"data": {
"text/html": "&lt;Table masked=True length=500&gt;\n<table id=\"table4624715920\">\n<thead><tr><th>subject_id</th><th>dr7objid</th><th>dr8objid</th><th>REDSHIFT</th><th>PETROMAG_MR</th><th>CUR</th><th>LOGMSTAR_BALDRY06</th><th>REDNESS_BALDRY06</th><th>NARMS</th><th>UNBARRED</th><th>arc_filename</th><th>transformed_filename</th><th>gz2_filename</th><th>arc1_decision</th><th>arc2_decision</th><th>arc3_decision</th><th>arc4_decision</th><th>arc5_decision</th><th>arc6_decision</th><th>arc7_decision</th><th>arc8_decision</th><th>n_arc_good</th><th>n_arc_poor</th><th>n_arc_weak</th><th>n_arc_extension</th></tr></thead>\n<thead><tr><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>int8</th><th>bool</th><th>str28</th><th>str23</th><th>str27</th><th>int8</th><th>int8</th><th>int8</th><th>int8</th><th>int8</th><th>int8</th><th>int8</th><th>int8</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n<tr><td>8738666</td><td>587741708326076464</td><td>1237667429550981158</td><td>0.0317634</td><td>-21.06437</td><td>1.6926174</td><td>10.272118</td><td>-0.49279952</td><td>1</td><td>True</td><td>1237667429550981158+arcs.png</td><td>1237667429550981158.png</td><td>1237667429550981158_gz2.png</td><td>0</td><td>0</td><td>-1</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>2</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8738669</td><td>587732772120166516</td><td>1237658493345071231</td><td>0.0335551</td><td>-20.989128</td><td>1.7747154</td><td>10.287678</td><td>-0.4176538</td><td>4</td><td>True</td><td>1237658493345071231+arcs.png</td><td>1237658493345071231.png</td><td>1237658493345071231_gz2.png</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>8738671</td><td>587735236883317016</td><td>1237660958108221730</td><td>0.0413652</td><td>-21.059275</td><td>1.9228668</td><td>10.398433</td><td>-0.3184793</td><td>1</td><td>True</td><td>1237660958108221730+arcs.png</td><td>1237660958108221730.png</td><td>1237660958108221730_gz2.png</td><td>-1</td><td>-1</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8738673</td><td>587736478129193204</td><td>1237662199354097895</td><td>0.0548665</td><td>-20.875439</td><td>1.6982117</td><td>10.199307</td><td>-0.4547012</td><td>2</td><td>True</td><td>1237662199354097895+arcs.png</td><td>1237662199354097895.png</td><td>1237662199354097895_gz2.png</td><td>4</td><td>4</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8738676</td><td>588010877692412107</td><td>1237655123940606166</td><td>0.0381976</td><td>-21.023554</td><td>2.081499</td><td>10.47119</td><td>-0.19101954</td><td>4</td><td>True</td><td>1237655123940606166+arcs.png</td><td>1237655123940606166.png</td><td>1237655123940606166_gz2.png</td><td>0</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>5</td><td>5</td><td>5</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8738679</td><td>587742062128529415</td><td>1237667783353434119</td><td>0.0440799</td><td>-21.364363</td><td>1.3107681</td><td>10.178134</td><td>-0.83274555</td><td>1</td><td>True</td><td>1237667783353434119+arcs.png</td><td>1237667783353434119.png</td><td>1237667783353434119_gz2.png</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8738681</td><td>587735697523081270</td><td>1237661418747985973</td><td>0.035372</td><td>-20.469849</td><td>1.66819</td><td>10.019936</td><td>-0.40725255</td><td>1</td><td>True</td><td>1237661418747985973+arcs.png</td><td>1237661418747985973.png</td><td>1237661418747985973_gz2.png</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8738682</td><td>587742013807198300</td><td>1237667735032103006</td><td>0.0309005</td><td>-20.973211</td><td>1.8553371</td><td>10.327075</td><td>-0.35458732</td><td>2</td><td>True</td><td>1237667735032103006+arcs.png</td><td>1237667735032103006.png</td><td>1237667735032103006_gz2.png</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8738685</td><td>587727943489945860</td><td>1237653664714850592</td><td>0.0514696</td><td>-21.245523</td><td>1.7615585</td><td>10.382954</td><td>-0.47302866</td><td>2</td><td>True</td><td>1237653664714850592+arcs.png</td><td>1237653664714850592.png</td><td>1237653664714850592_gz2.png</td><td>0</td><td>0</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>3</td><td>5</td><td>2</td><td>0</td><td>0</td><td>1</td></tr>\n<tr><td>8738687</td><td>587742627986997432</td><td>1237668349211902107</td><td>0.0526503</td><td>-20.453255</td><td>1.9403095</td><td>10.165767</td><td>-0.19773436</td><td>1</td><td>True</td><td>1237668349211902107+arcs.png</td><td>1237668349211902107.png</td><td>1237668349211902107_gz2.png</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</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><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>8740084</td><td>587728880331653454</td><td>1237654601556558026</td><td>0.0545778</td><td>-20.593534</td><td>1.710804</td><td>10.09313</td><td>-0.39553928</td><td>1</td><td>True</td><td>1237654601556558026+arcs.png</td><td>1237654601556558026.png</td><td>1237654601556558026_gz2.png</td><td>-1</td><td>-1</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8740087</td><td>587731869626466431</td><td>1237657590851371141</td><td>0.0515251</td><td>-21.233131</td><td>1.5746632</td><td>10.272995</td><td>-0.611146</td><td>1</td><td>True</td><td>1237657590851371141+arcs.png</td><td>1237657590851371141.png</td><td>1237657590851371141_gz2.png</td><td>0</td><td>-1</td><td>4</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8740089</td><td>587737826746302844</td><td>1237663547971207612</td><td>0.0421434</td><td>-21.378235</td><td>1.8047352</td><td>10.459953</td><td>-0.4630425</td><td>1</td><td>True</td><td>1237663547971207612+arcs.png</td><td>1237663547971207612.png</td><td>1237663547971207612_gz2.png</td><td>1</td><td>4</td><td>0</td><td>-1</td><td>5</td><td>5</td><td>5</td><td>5</td><td>1</td><td>1</td><td>0</td><td>0</td></tr>\n<tr><td>8740091</td><td>587729777979752577</td><td>1237655499204657255</td><td>0.0540473</td><td>-20.732141</td><td>1.5562973</td><td>10.062539</td><td>-0.5369804</td><td>1</td><td>True</td><td>1237655499204657255+arcs.png</td><td>1237655499204657255.png</td><td>1237655499204657255_gz2.png</td><td>0</td><td>3</td><td>2</td><td>0</td><td>0</td><td>4</td><td>2</td><td>3</td><td>3</td><td>0</td><td>2</td><td>2</td></tr>\n<tr><td>8740094</td><td>587739646201495658</td><td>1237665367426400354</td><td>0.0522286</td><td>-20.820257</td><td>2.19425</td><td>10.411082</td><td>-0.05259013</td><td>1</td><td>True</td><td>1237665367426400354+arcs.png</td><td>1237665367426400354.png</td><td>1237665367426400354_gz2.png</td><td>-1</td><td>-1</td><td>-1</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8740095</td><td>587741815711989787</td><td>1237667536936894657</td><td>0.0405101</td><td>-21.133358</td><td>1.6598072</td><td>10.280678</td><td>-0.52943516</td><td>2</td><td>True</td><td>1237667536936894657+arcs.png</td><td>1237667536936894657.png</td><td>1237667536936894657_gz2.png</td><td>0</td><td>0</td><td>4</td><td>3</td><td>2</td><td>-1</td><td>4</td><td>5</td><td>2</td><td>0</td><td>1</td><td>1</td></tr>\n<tr><td>8740098</td><td>587736914606031100</td><td>1237662635830935814</td><td>0.0534687</td><td>-20.468973</td><td>1.835413</td><td>10.113575</td><td>-0.27976632</td><td>3</td><td>True</td><td>1237662635830935814+arcs.png</td><td>1237662635830935814.png</td><td>1237662635830935814_gz2.png</td><td>2</td><td>2</td><td>3</td><td>4</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>2</td><td>1</td></tr>\n<tr><td>8740101</td><td>587742012761440413</td><td>1237667733986345070</td><td>0.0479483</td><td>-20.861237</td><td>1.7276382</td><td>10.210649</td><td>-0.43032336</td><td>4</td><td>True</td><td>1237667733986345070+arcs.png</td><td>1237667733986345070.png</td><td>1237667733986345070_gz2.png</td><td>-1</td><td>4</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8740103</td><td>588017703479541773</td><td>1237661949727735904</td><td>0.037738</td><td>-21.01261</td><td>2.0827732</td><td>10.468281</td><td>-0.18852067</td><td>4</td><td>True</td><td>1237661949727735904+arcs.png</td><td>1237661949727735904.png</td><td>1237661949727735904_gz2.png</td><td>0</td><td>0</td><td>0</td><td>-1</td><td>5</td><td>5</td><td>5</td><td>5</td><td>3</td><td>0</td><td>0</td><td>0</td></tr>\n<tr><td>8740105</td><td>588017112366252055</td><td>1237661358614446146</td><td>0.0337615</td><td>-20.793823</td><td>2.3911686</td><td>10.435781</td><td>0.13368583</td><td>1</td><td>True</td><td>1237661358614446146+arcs.png</td><td>1237661358614446146.png</td><td>1237661358614446146_gz2.png</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n</table>",
"text/plain": "<Table masked=True length=500>\nsubject_id dr7objid dr8objid ... n_arc_weak n_arc_extension\n int64 int64 int64 ... int64 int64 \n---------- ------------------ ------------------- ... ---------- ---------------\n 8738666 587741708326076464 1237667429550981158 ... 0 0\n 8738669 587732772120166516 1237658493345071231 ... -- --\n 8738671 587735236883317016 1237660958108221730 ... 0 0\n 8738673 587736478129193204 1237662199354097895 ... 0 0\n 8738676 588010877692412107 1237655123940606166 ... 0 0\n 8738679 587742062128529415 1237667783353434119 ... 0 0\n 8738681 587735697523081270 1237661418747985973 ... 0 0\n 8738682 587742013807198300 1237667735032103006 ... 0 0\n 8738685 587727943489945860 1237653664714850592 ... 0 1\n 8738687 587742627986997432 1237668349211902107 ... 0 0\n ... ... ... ... ... ...\n 8740084 587728880331653454 1237654601556558026 ... 0 0\n 8740087 587731869626466431 1237657590851371141 ... 0 0\n 8740089 587737826746302844 1237663547971207612 ... 0 0\n 8740091 587729777979752577 1237655499204657255 ... 2 2\n 8740094 587739646201495658 1237665367426400354 ... 0 0\n 8740095 587741815711989787 1237667536936894657 ... 1 1\n 8740098 587736914606031100 1237662635830935814 ... 2 1\n 8740101 587742012761440413 1237667733986345070 ... 0 0\n 8740103 588017703479541773 1237661949727735904 ... 0 0\n 8740105 588017112366252055 1237661358614446146 ... 0 0"
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "# not sure how useful/correct this plot actually is...\nfactor = 10\nplt.plot([0, 5], [0, 5], 'k')\nfor n in np.unique(data['NARMS']):\n for m in np.unique(data['n_arc_good'][~data['n_arc_good'].mask]):\n count = ((data['NARMS'] == n) & (data['n_arc_good'] == m)).sum()\n plt.scatter(n, m, s=sqrt(count)*factor)\nplt.axis((0, 5, 0, 5))\nplt.xlabel('n_arms GZ2')\nplt.ylabel('n_arc_good')\nplt.axes().set_aspect('equal')",
"execution_count": 43,
"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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iirPo65sDwMSJZ/OWt7y54KmqY9q0aeywwwRWrLiivvUNEVewww49TJs2reDp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\n/wevoCB8mKWOSgAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x1138cdfd0>"
},
"metadata": {},
"output_type": "display_data"
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": true
},
"cell_type": "code",
"source": "arm_labels = ('1 arm', '2 arms', '3 arms', '4 arms', '5+ arms')\ncolors = ('red', 'blue', 'orange', 'magenta', 'green', 'redorange', 'purple', 'cyan')",
"execution_count": 44,
"outputs": []
},
{
"metadata": {
"collapsed": true,
"trusted": true
},
"cell_type": "code",
"source": "#narms = 'NARMS'\nnarms = 'n_arc_good'",
"execution_count": 45,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "plotdata = data[~data[narms].mask]",
"execution_count": 46,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "def plot_histo_by_arm(field, label, data, narms, bins=10, normed=True, ymax=5):\n fig, axarr = plt.subplots(2, 3)\n for m in range(5):\n ax = axarr.flat[m]\n selection = data[narms] == m\n if selection.sum() > 1:\n _, bins, _ = ax.hist(data[field], bins=bins,\n histtype='stepfilled', alpha=0.1, color='k',\n normed=normed)\n ax.vlines(np.median(data[field]), 0, 10, color='k', linestyle=':')\n _, bins, _ = ax.hist(data[field][selection], bins=bins,\n histtype='step', color=colors[m], label=arm_labels[m],\n normed=normed)\n ax.vlines(np.median(data[field][data[narms].filled(-1) == m]), 0, 10, color=colors[m], linestyle=':')\n ax.legend()\n if m in [2, 3, 4]:\n ax.set_xlabel(label)\n if m in [0, 3]:\n ax.set_ylabel('frequency');\n ax.set_ylim(0, ymax)\n ax.locator_params(nbins=5)\n axarr.flat[-1].axis('off');",
"execution_count": 47,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "plot_histo_by_arm('CUR', '$u-r$ colour', plotdata, narms, normed=True, ymax=2)",
"execution_count": 48,
"outputs": [
{
"data": {
"image/png": 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9/bxFB3RADTCAqKMGGEAUUQOMIMnoa2wzmyhpjaSrJJ2bbD5Q0lNm9qk8xQYA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\no6kXa4C3VtXG3oTTHdfFDcGQ1wAfdthhHHjggUs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"text/plain": "<matplotlib.figure.Figure at 0x113a79e90>"
},
"metadata": {},
"output_type": "display_data"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "plot_histo_by_arm('LOGMSTAR_BALDRY06', 'log$_{10}$(stellar mass)', plotdata, narms, normed=True, ymax=4)",
"execution_count": 49,
"outputs": [
{
"data": {
"image/png": 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YQ+piXmdNB/EIZwDJRA8ugLSiBxdR6fYxQe7+pqQ3zexad3+i0DJmdm0UgaGy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\nTkSuQmCC\n",
"text/plain": "<matplotlib.figure.Figure at 0x113a533d0>"
},
"metadata": {},
"output_type": "display_data"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "plot_histo_by_arm('REDNESS_BALDRY06', 'colour', plotdata, narms, normed=True, ymax=3.0)",
"execution_count": 50,
"outputs": [
{
"data": {
"image/png": 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o0aHNCfQQua7KdLfObE1NjdauXRvqa65e3V8tLftJYmkNRIZcl2K9WWc2k8lo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\n10DPLAAAAIC+oZgFAAAAACQOPbNIHHp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"text/plain": "<matplotlib.figure.Figure at 0x10e0bb250>"
},
"metadata": {},
"output_type": "display_data"
}
]
}
],
"metadata": {
"hide_input": false,
"kernelspec": {
"name": "python2",
"display_name": "Python 2",
"language": "python"
},
"latex_envs": {
"eqNumInitial": 1,
"eqLabelWithNumbers": true,
"current_citInitial": 1,
"cite_by": "apalike",
"bibliofile": "biblio.bib",
"LaTeX_envs_menu_present": true,
"labels_anchors": false,
"latex_user_defs": false,
"user_envs_cfg": false,
"report_style_numbering": false,
"autocomplete": true,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
}
},
"gist": {
"id": "",
"data": {
"description": "SpiralSpotterSpArcFiReAnalysisClean",
"public": true
}
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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