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crawles/gist:ba6183666bbbff57221e

Created May 5, 2014 17:14
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S-wave
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gistfile1.txt
{
"metadata": {
"name": "Results ConNet S-wave"
},
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"input": "import sys\nsys.path.append('/Users/chris/GoogleDrive/s-wave/current')\nimport obspy\nimport io_utils\nimport swave as sw\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_pdf import PdfPages\nimport data_struc as data\nimport data_prep\nimport classifier\nimport metrics_spec\nimport plotting as pp\nimport copy\nimport wadati\nimport sliding_window as slide\nimport spec_utils\nimport spectrogram_cr1\n\n# params\ntesting = True\np_manual,s_manual = 'sac.t1','sac.t2'\np_auto,s_auto = None,'sac.t6'\nafter_p,end = .5,8\nsample_rate = 100.0\n\n# data\nobs_set_dir = \"/Users/chris/GoogleDrive/s-wave/current/data/parkfield/reloc/2002_reloc_sub\"#/te2_qte6\"\ntrain_set_dir = \"/Users/chris/GoogleDrive/s-wave/current/data/parkfield/2001_no_sact1t2/2001202004000\"#/te2_qte6\"\n#train_set_dir = None\n\n# read obs_set,train_set\nobs_set = io_utils.read_sac_dir(obs_set_dir,readZ = False) #no Z data read in\n\nif train_set_dir: #user supplied training set\n train_set = io_utils.read_sac_dir(train_set_dir,readZ = False)[0:10] #no Z data read in\nelse: #use default trainset\n train_set = data_prep.read_default_train()\n\n# prep data\nobs_set,train_set = data_prep.prep(obs_set,Train_set = train_set,\n Testing = testing, P_manual = p_manual,\n S_manual = s_manual, After_p = after_p, \n End = end, Sample_rate = sample_rate)\n\nobs_set_obj = data.Dataset(obs_set)\n\n# make pos and neg set\nS = data.Split(obs_set)\n#S.build_S_pos_neg('sac.t2',(-.33,.33),(1.17,1.83),train_set)\nS.build_S_pos_neg('sac.t2',(-.33,.33),(1.17,1.83),train_set)\n\n# classify\ntest_set = obs_set_obj\ngamma,n,beg,end = .01,40,0,10\ntest = classifier.Test(obs_set_obj,S.pos_set,S.neg_set,gamma)\nR_list = test.test(n)\nobs_set_obj.add_R_list(R_list)\n\n# make pick\nm = metrics.Metrics(beg,end)\nm.initialize_dataset(test_set)\nm.add_max_R_pick_around_predicted('S_pick',1.25)\nm.residual_array('S_pick','sac.t2')\n\nw_man_obs_set = wadati.wadati(m.obs_set,'sac.t1','sac.t2',plot = False)\n\n# make pick\nm = metrics.Metrics(beg,end)\nm.initialize_dataset(w_man_obs_set)\nm.add_max_R_pick_around_predicted('S_pick',1.25)\nm.residual_array('S_pick','sac.t2')\n\nw_app_obs_set = wadati.wadati(m.obs_set,'sac.t1','S_pick',plot = False)\n\nm = metrics.Metrics(beg,end)\nm.initialize_dataset(w_app_obs_set)\nm.add_max_R_pick_around_predicted('S_pick',1.25)\nm.residual_array('S_pick','sac.t2')\n\n",
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"text": " AHAB.HHN\n2002023205801 CRAK.HHN\n2002023205801 AHAB.HHN\n2002023205801 CRAK.HHN\nTrain set = 5 picks"
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{
"ename": "NameError",
"evalue": "name 'metrics' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-9a7d6e32fbb9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;31m# make pick\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMetrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 63\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitialize_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_set\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_max_R_pick_around_predicted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'S_pick'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'metrics' is not defined"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": "/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/oldnumeric/__init__.py:11: ModuleDeprecationWarning: The oldnumeric module will be dropped in Numpy 1.9\n warnings.warn(_msg, ModuleDeprecationWarning)\n"
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": "import metrics",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": "# make pick\nm = metrics.Metrics(beg,end)\nm.initialize_dataset(test_set)\nm.add_max_R_pick_around_predicted('S_pick',1.25)\nm.residual_array('S_pick','sac.t2')\n\nw_man_obs_set = wadati.wadati(m.obs_set,'sac.t1','sac.t2',plot = False)\n\n# make pick\nm = metrics.Metrics(beg,end)\nm.initialize_dataset(w_man_obs_set)\nm.add_max_R_pick_around_predicted('S_pick',1.25)\nm.residual_array('S_pick','sac.t2')\n\nw_app_obs_set = wadati.wadati(m.obs_set,'sac.t1','S_pick',plot = False)\n\nm = metrics.Metrics(beg,end)\nm.initialize_dataset(w_app_obs_set)\nm.add_max_R_pick_around_predicted('S_pick',1.25)\nm.residual_array('S_pick','sac.t2')\n",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "\n\n\n"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\n\n\n\n\n"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\n\n"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\n\n\n"
},
{
"output_type": "stream",
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"text": "\n\n\n\n"
},
{
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},
{
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"text": "/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/statsmodels/regression/quantile_regression.py:183: UserWarning: Convergence cycle detected\n warnings.warn(\"Convergence cycle detected\")\n/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/_methods.py:77: RuntimeWarning: Degrees of freedom <= 0 for slice\n warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n"
},
{
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"stream": "stdout",
"text": "-----------\n('2002023205801', 'WAS NOT INCLUDED')\n-----------\n\n\n\n"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\n\n\n\n"
},
{
"output_type": "stream",
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"text": "\n\n\n\n"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\n\n\n\n\n"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\n\n"
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": "m.percen_within(.2)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": "0.9590163934426229"
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": "obs_cp = copy.deepcopy(m.obs_set)\nspec_obs_full,spec_obs_cut = [],[]\ntr_t = []\nfor i,trs in enumerate(sw.pairwise(obs_cp)):\n tr,tr1 = trs[0],trs[1]\n spec_utils.add_max_spec_amp(tr)\n spec_utils.add_max_spec_amp(tr1)\n\n s_pick = tr.stats.sac.t2 - tr.stats.sac.b\n if .7 < s_pick < 2.3 and tr.stats.S_pick:\n spec_obs_full.append(copy.deepcopy(tr))\n spec_obs_full.append(copy.deepcopy(tr1))\n\n sw.trim_after_start_time(tr,0,3)\n sw.trim_after_start_time(tr1,0,3)\n\n spec_obs_cut.append(tr)\n spec_obs_cut.append(tr1)\n tr_t.append(s_pick)\n",
"language": "python",
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{
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"input": "m = metrics.Metrics(beg,end)\nm.initialize_dataset(spec_obs_cut)\nm.add_max_R_pick_around_predicted('S_pick',1.25)\nm.residual_array('S_pick','sac.t2')",
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "could not broadcast input array from shape (685) into shape (83)",
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"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-b5ee3c3432c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMetrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitialize_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspec_obs_cut\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_max_R_pick_around_predicted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'S_pick'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresidual_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'S_pick'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'sac.t2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/chris/GoogleDrive/s-wave/current/metrics.pyc\u001b[0m in \u001b[0;36minitialize_dataset\u001b[0;34m(self, obs_set)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minitialize_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mobs_set\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobs_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobs_set\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mR_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_R_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobs_set\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_max_R_pick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'maxR'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/chris/GoogleDrive/s-wave/current/metrics.pyc\u001b[0m in \u001b[0;36mbuild_R_list\u001b[0;34m(self, obs_set)\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[0mtr1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 128\u001b[0m \u001b[0mRR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msta_R\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtr0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtr1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 129\u001b[0;31m \u001b[0mR_tr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_R_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtr0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mRR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 130\u001b[0m \u001b[0mR_trs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mR_tr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 131\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mR_trs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/chris/GoogleDrive/s-wave/current/metrics.pyc\u001b[0m in \u001b[0;36mbuild_R_trace\u001b[0;34m(self, tr, R)\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[0mR_norm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_R_norm\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtr_cp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[0mR_norm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mR\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m \u001b[0mR_tr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_R_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mR_norm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0;31m#trim R trace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/chris/GoogleDrive/s-wave/current/swave.pyc\u001b[0m in \u001b[0;36mcreate_R_trace\u001b[0;34m(tr, R)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;31m#trim off beg and end of R according to length of window size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0mR_tr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mR_tr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0mR_tr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwindow_size\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mwindow_size\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mR\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mR_tr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: could not broadcast input array from shape (685) into shape (83)"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": "m = metrics.Metrics(beg,end)\nm.initialize_dataset(spec_obs_full)\nm.add_max_R_pick_around_predicted('S_pick',1.25)\nm.residual_array('S_pick','sac.t2')\n",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": "m.percen_within(.2)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": "0.9429280397022333"
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": "m = metrics.Metrics(beg,end)\nm.initialize_dataset(spec_obs_full[400:])\nm.add_max_R_pick_around_predicted('S_pick',1.25)\nm.residual_array('S_pick','sac.t2')\n",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": "m.percen_within(.2)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": "0.9458128078817734"
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": "m.percen_within(.1)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": "0.7536945812807881"
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": "output_f ='/Users/chris/GoogleDrive/cs760_final_project/DeepLearningTutorials-master/code/outputs/testoutputs16fromsundaynight/testOutput2.txt'\nfrom numpy import genfromtxt\nmy_data = genfromtxt(output_f, delimiter=',',skip_header = 6)\n\n###\n\nR_list = my_data\nspec_obs_cut_obj = data.Dataset(copy.deepcopy(spec_obs_cut[400:]))\nspec_obs_cut_obj.add_R_list(R_list)\n\nm11 = metrics_spec.Metrics(0,10)\nm11.initialize_dataset(spec_obs_cut_obj)\nm11.add_max_R_pick_around_predicted('S_pick',1.25)\nm11.residual_array('S_pick','sac.t2')\n",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": "m11.percen_within(.2)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": "0.9710144927536232"
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": "output_f ='/Users/chris/GoogleDrive/cs760_final_project/DeepLearningTutorials-master/code/outputs/testoutputs16fromsundaynight/testOutput2.txt'\nfrom numpy import genfromtxt\nmy_data = genfromtxt(output_f, delimiter=',',skip_header = 6)\n\n###\n\nR_list = my_data\nspec_obs_cut_obj = data.Dataset(copy.deepcopy(spec_obs_cut[400:]))\nspec_obs_cut_obj.add_R_list(R_list)\n\nm11 = metrics_spec.Metrics(0,10)\nm11.initialize_dataset(spec_obs_cut_obj)\nm11.add_max_R_pick_around_predicted('S_pick',1.25)\nm11.residual_array('S_pick','sac.t2')\n",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": "m11.percen_within(.2)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": "0.9408866995073891"
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": "m11.percen_within(.1)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": "0.7881773399014779"
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": "output_f ='/Users/chris/GoogleDrive/cs760_final_project/DeepLearningTutorials-master/code/outputs/testoutputs16fromsundaynight/testOutput2.txt'\nfrom numpy import genfromtxt\nmy_data = genfromtxt(output_f, delimiter=',',skip_header = 6)\n\n###\n\nR_list = my_data\nspec_obs_cut_obj = data.Dataset(copy.deepcopy(spec_obs_cut[400:]))\nspec_obs_cut_obj.add_R_list(R_list)\n\nm11 = metrics_spec.Metrics(0,10)\nm11.initialize_dataset(spec_obs_cut_obj)\nm11.add_max_R_pick_around_predicted('S_pick',1.25)\nm11.residual_array('S_pick','sac.t2',thresh = 0)\n",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": "m11.percen_within(.2)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": "0.9408866995073891"
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": "m11.residual_array('S_pick','sac.t2',thresh = .25)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": "m11.percen_within(.2)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": "0.968944099378882"
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": "len(m.resid)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": "203"
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": "len(m11.resid)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": "161"
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": "m11.residual_array('S_pick','sac.t2',thresh = .75)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": "len(m11.resid)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": "107"
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": "m11.percen_within(.2)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": "0.9813084112149533"
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": "def gen_metric(obs_set,thresh):\n spec_obs_cut_obj = data.Dataset(copy.deepcopy(obs_set))\n spec_obs_cut_obj.add_R_list(R_list)\n m11 = metrics_spec.Metrics(0,10)\n m11.initialize_dataset(spec_obs_cut_obj)\n m11.add_max_R_pick_around_predicted('S_pick',1.25)\n m11.residual_array('S_pick','sac.t2',thresh = 0)\n",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": "def gen_metric(obs_set,thresh):\n spec_obs_cut_obj = data.Dataset(copy.deepcopy(obs_set))\n spec_obs_cut_obj.add_R_list(R_list)\n m11 = metrics_spec.Metrics(0,10)\n m11.initialize_dataset(spec_obs_cut_obj)\n m11.add_max_R_pick_around_predicted('S_pick',1.25)\n m11.residual_array('S_pick','sac.t2',thresh = 0)\n return m11\n",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": "m11 = 1",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": "m11 = gen_metric(spec_obs_cut[400:],thresh = 0)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": "m11.percen_within(.2)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 32,
"text": "0.9408866995073891"
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": "!CLS",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "/bin/sh: CLS: command not found\r\n"
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": "output_f ='/Users/chris/GoogleDrive/cs760_final_project/DeepLearningTutorials-master/code/outputs/testoutputs16fromsundaynight/testOutput'",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": "for i in xrange(6):\n suffix = str(i) + \".txt\"\n trial_results_file = output_f + suffix\n print trial_results_file",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "/Users/chris/GoogleDrive/cs760_final_project/DeepLearningTutorials-master/code/outputs/testoutputs16fromsundaynight/testOutput0.txt\n/Users/chris/GoogleDrive/cs760_final_project/DeepLearningTutorials-master/code/outputs/testoutputs16fromsundaynight/testOutput1.txt\n/Users/chris/GoogleDrive/cs760_final_project/DeepLearningTutorials-master/code/outputs/testoutputs16fromsundaynight/testOutput2.txt\n/Users/chris/GoogleDrive/cs760_final_project/DeepLearningTutorials-master/code/outputs/testoutputs16fromsundaynight/testOutput3.txt\n/Users/chris/GoogleDrive/cs760_final_project/DeepLearningTutorials-master/code/outputs/testoutputs16fromsundaynight/testOutput4.txt\n/Users/chris/GoogleDrive/cs760_final_project/DeepLearningTutorials-master/code/outputs/testoutputs16fromsundaynight/testOutput5.txt\n"
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": "%matplotlib inline",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": "import matplotlib.pyplot as plt",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": "for i in xrange(6):\n suffix = str(i+1) + \".txt\"\n trial_results_file = output_f + suffix\n R_list = genfromtxt(trial_results_file, delimiter=',',skip_header = 6)\n \n m11 = gen_metric(spec_obs_cut[400:],R_list,thresh = 0)\n print \"Percen with .2, all : \", m11.percen_within(.2)\n print \"Percen with .1, all : \", m11.percen_within(.1)\n m11.plot_hist(15)\n \n m11 = gen_metric(spec_obs_cut[400:],R_list,thresh = .5)\n print \"Percen with .2, confidence > .5 : \", m11.percen_within(.2)\n print \"Percen with .1, confidence > .5 : \", m11.percen_within(.1)\n m11.plot_hist(15)",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen with .2, all : 0.92118226601\nPercen with .1, all : 0.773399014778\nPercen with .2, confidence > .5 : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.92118226601\nPercen with .1, confidence > .5 : 0.773399014778\nPercen with .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.940886699507\nPercen with .1, all : 0.788177339901\nPercen with .2, confidence > .5 : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.940886699507\nPercen with .1, confidence > .5 : 0.788177339901\nPercen with .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.92118226601\nPercen with .1, all : 0.738916256158\nPercen with .2, confidence > .5 : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.92118226601\nPercen with .1, confidence > .5 : 0.738916256158\nPercen with .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.0935960591133\nPercen with .1, all : 0.0443349753695\nPercen with .2, confidence > .5 : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.0935960591133\nPercen with .1, confidence > .5 : 0.0443349753695\nPercen with .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.916256157635\nPercen with .1, all : 0.743842364532\nPercen with .2, confidence > .5 : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.916256157635\nPercen with .1, confidence > .5 : 0.743842364532\nPercen with .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.0689655172414\nPercen with .1, all : 0.00985221674877\nPercen with .2, confidence > .5 : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.0689655172414\nPercen with .1, confidence > .5 : 0.00985221674877\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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pnwcffFATJ05UKBTSzp07c1bbWQPVGIvFdPHFFyscDiscDuupp57KeY3Lly9XSUmJrr76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\nvAHAQYQ3ADjo/wFzmSAEr4p1ygAAAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x112f17190>"
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": "def gen_metric(obs_set,R_list,thresh):\n spec_obs_cut_obj = data.Dataset(copy.deepcopy(obs_set))\n spec_obs_cut_obj.add_R_list(R_list)\n m11 = metrics_spec.Metrics(0,10)\n m11.initialize_dataset(spec_obs_cut_obj)\n m11.add_max_R_pick_around_predicted('S_pick',1.25)\n m11.residual_array('S_pick','sac.t2',thresh)\n return m11\n",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 47
},
{
"cell_type": "code",
"collapsed": false,
"input": "for i in xrange(6):\n suffix = str(i+1) + \".txt\"\n trial_results_file = output_f + suffix\n R_list = genfromtxt(trial_results_file, delimiter=',',skip_header = 6)\n \n m11 = gen_metric(spec_obs_cut[400:],R_list,thresh = 0)\n print \"Percen with .2, all : \", m11.percen_within(.2)\n print \"Percen with .1, all : \", m11.percen_within(.1)\n m11.plot_hist(15)\n plt.show()\n \n m11 = gen_metric(spec_obs_cut[400:],R_list,thresh = .5)\n print \"Percen with .2, confidence > .5 : \", m11.percen_within(.2)\n print \"Percen with .1, confidence > .5 : \", m11.percen_within(.1)\n m11.plot_hist(15)",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen with .2, all : 0.92118226601\nPercen with .1, all : 0.773399014778\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x1121f4390>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen with .2, confidence > .5 : 0.977611940299\nPercen with .1, confidence > .5 : 0.858208955224\nPercen with .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.940886699507\nPercen with .1, all : 0.788177339901\n"
},
{
"metadata": {},
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"text": "<matplotlib.figure.Figure at 0x113bb5810>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen with .2, confidence > .5 : 0.971014492754\nPercen with .1, confidence > .5 : 0.855072463768\nPercen with .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.92118226601\nPercen with .1, all : 0.738916256158\n"
},
{
"metadata": {},
"output_type": "display_data",
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"text": "<matplotlib.figure.Figure at 0x113627050>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen with .2, confidence > .5 : 0.983739837398\nPercen with .1, confidence > .5 : 0.829268292683\nPercen with .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.0935960591133\nPercen with .1, all : 0.0443349753695\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x112f4e090>"
},
{
"ename": "ZeroDivisionError",
"evalue": "float division by zero",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-48-a598c924076d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mm11\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen_metric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspec_obs_cut\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m400\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mR_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mthresh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0;34m\"Percen with .2, confidence > .5 : \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm11\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpercen_within\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m\"Percen with .1, confidence > .5 : \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm11\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpercen_within\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mm11\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_hist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/chris/GoogleDrive/cs760_final_project/project/metrics_spec.py\u001b[0m in \u001b[0;36mpercen_within\u001b[0;34m(self, t)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0mcount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcount\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen with .2, confidence > .5 : "
}
],
"prompt_number": 48
},
{
"cell_type": "code",
"collapsed": false,
"input": "for i in xrange(6):\n print \"textOuput\" + str(i+1)\n suffix = str(i+1) + \".txt\"\n trial_results_file = output_f + suffix\n R_list = genfromtxt(trial_results_file, delimiter=',',skip_header = 6)\n \n plt.close()\n m11 = gen_metric(spec_obs_cut[400:],R_list,thresh = 0)\n print \"Percen with .2, all : \", m11.percen_within(.2)\n print \"Percen with .1, all : \", m11.percen_within(.1)\n m11.plot_hist(15)\n plt.show()\n \n try:\n plt.close()\n m11 = gen_metric(spec_obs_cut[400:],R_list,thresh = .5)\n print \"Percen with .2, confidence > .5 : \", m11.percen_within(.2)\n print \"Percen with .1, confidence > .5 : \", m11.percen_within(.1) \n m11.plot_hist(8)\n plt.show()\n except:\n pass\n",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": " textOuput1\nPercen with .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.92118226601\nPercen with .1, all : 0.773399014778\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x113b98d10>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen with .2, confidence > .5 : 0.977611940299\nPercen with .1, confidence > .5 : 0.858208955224\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x112149090>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "textOuput2\nPercen with .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.940886699507\nPercen with .1, all : 0.788177339901\n"
},
{
"metadata": {},
"output_type": "display_data",
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"text": "<matplotlib.figure.Figure at 0x113c67f10>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen with .2, confidence > .5 : 0.971014492754\nPercen with .1, confidence > .5 : 0.855072463768\n"
},
{
"metadata": {},
"output_type": "display_data",
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"text": "<matplotlib.figure.Figure at 0x1121cb2d0>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "textOuput3\nPercen with .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.92118226601\nPercen with .1, all : 0.738916256158\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x113b77a50>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen with .2, confidence > .5 : 0.983739837398\nPercen with .1, confidence > .5 : 0.829268292683\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x113c66fd0>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "textOuput4\nPercen with .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.0935960591133\nPercen with .1, all : 0.0443349753695\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x1131f1050>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen with .2, confidence > .5 : textOuput5\nPercen with .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.916256157635\nPercen with .1, all : 0.743842364532\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x113c43e50>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen with .2, confidence > .5 : 0.976744186047\nPercen with .1, confidence > .5 : 0.837209302326\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x112217b10>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "textOuput6\nPercen with .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.0689655172414\nPercen with .1, all : 0.00985221674877\n"
},
{
"metadata": {},
"output_type": "display_data",
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"text": "<matplotlib.figure.Figure at 0x1122890d0>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen with .2, confidence > .5 : \n"
}
],
"prompt_number": 51
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": "RESULTS"
},
{
"cell_type": "code",
"collapsed": false,
"input": "for i in xrange(6):\n print \"textOuput\" + str(i+1)\n suffix = str(i+1) + \".txt\"\n trial_results_file = output_f + suffix\n R_list = genfromtxt(trial_results_file, delimiter=',',skip_header = 6)\n \n plt.close()\n m11 = gen_metric(spec_obs_cut[400:],R_list,thresh = 0)\n print \"Percen within .2, all : \", m11.percen_within(.2)\n print \"Percen within .1, all : \", m11.percen_within(.1)\n m11.plot_hist(15)\n plt.show()\n \n try:\n plt.close()\n m11 = gen_metric(spec_obs_cut[400:],R_list,thresh = .5)\n print \"Percen within .2, confidence > .5 : \", m11.percen_within(.2)\n print \"Percen within .1, confidence > .5 : \", m11.percen_within(.1) \n m11.plot_hist(8)\n plt.show()\n except:\n print \"NONE\"",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "textOuput1\nPercen within .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.92118226601\nPercen within .1, all : 0.773399014778\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x113c2c750>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen within .2, confidence > .5 : 0.977611940299\nPercen within .1, confidence > .5 : 0.858208955224\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x1145f0b10>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "textOuput2\nPercen within .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.940886699507\nPercen within .1, all : 0.788177339901\n"
},
{
"metadata": {},
"output_type": "display_data",
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"text": "<matplotlib.figure.Figure at 0x1145ce090>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen within .2, confidence > .5 : 0.971014492754\nPercen within .1, confidence > .5 : 0.855072463768\n"
},
{
"metadata": {},
"output_type": "display_data",
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"text": "<matplotlib.figure.Figure at 0x113b764d0>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "textOuput3\nPercen within .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.92118226601\nPercen within .1, all : 0.738916256158\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x1145f10d0>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen within .2, confidence > .5 : 0.983739837398\nPercen within .1, confidence > .5 : 0.829268292683\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x112121390>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "textOuput4\nPercen within .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.0935960591133\nPercen within .1, all : 0.0443349753695\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x112f700d0>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen within .2, confidence > .5 : NONE\ntextOuput5\nPercen within .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.916256157635\nPercen within .1, all : 0.743842364532\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x113baca50>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen within .2, confidence > .5 : 0.976744186047\nPercen within .1, confidence > .5 : 0.837209302326\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x10c956650>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "textOuput6\nPercen within .2, all : "
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.0689655172414\nPercen within .1, all : 0.00985221674877\n"
},
{
"metadata": {},
"output_type": "display_data",
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"text": "<matplotlib.figure.Figure at 0x112278090>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Percen within .2, confidence > .5 : NONE\n"
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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