S-wave

gistfile1.txt

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  "name": "Results ConNet S-wave"
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     "cell_type": "code",
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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",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import metrics",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "m.percen_within(.2)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "len(spec_obs_cut)\nlen(spec_obs_full)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "len(spec_obs_cut)\n",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "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": []
    },
    {
     "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": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "m.percen_within(.2)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "m.percen_within(.2)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "m.percen_within(.1)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "m11.percen_within(.2)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "m11.percen_within(.2)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "m11.percen_within(.1)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "m11.percen_within(.2)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "m11.residual_array('S_pick','sac.t2',thresh = .25)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "m11.percen_within(.2)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "len(m.resid)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "len(m11.resid)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "m11.residual_array('S_pick','sac.t2',thresh = .75)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "len(m11.resid)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "m11.percen_within(.2)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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": []
    },
    {
     "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": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "m11 = 1",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "m11 = gen_metric(spec_obs_cut[400:],thresh = 0)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "m11.percen_within(.2)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "!CLS",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "output_f ='/Users/chris/GoogleDrive/cs760_final_project/DeepLearningTutorials-master/code/outputs/testoutputs16fromsundaynight/testOutput'",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%matplotlib inline",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import matplotlib.pyplot as plt",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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": []
    },
    {
     "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": []
    },
    {
     "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": []
    },
    {
     "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": []
    },
    {
     "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 0x11225b090>"
      },
      {
       "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 0x1121dbb10>"
      },
      {
       "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 0x113625050>"
      },
      {
       "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",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x1121a4990>"
      },
      {
       "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 0x1145bf110>"
      },
      {
       "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 0x112140b10>"
      },
      {
       "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 0x11321e050>"
      },
      {
       "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 0x11211df90>"
      },
      {
       "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 0x113c7c110>"
      },
      {
       "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 0x112268050>"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Percen within .2, confidence > .5 :  NONE\n"
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}