pyrocko-notebooks/Waveform_Inversion-Double-Couple.ipynb

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    "# Pyrocko Notebook\n",
    "## Double Couple Waveform Inversion (The 2009 Aquila Earthquake)\n",
    "\n",
    "In this Jupyter-notebook we look at teleseismic waveforms of the 2009 Aquila Earthquake and setup `pyrocko.gf` forward modelling to invert for the double couple mechanism of the event. We will use `pyrocko` to handle the seismic data and execute the forward modelling based on pre-calculated Green's function stores, `scipy` delivers the optimisation algorithms.\n",
    "Besides this Notebook you will also need to download the utils_nb.py file from this repository.\n",
    "\n",
    "_Authors:_\n",
    "Andreas Steinberg, Marius Isken\n",
    "\n",
    "-Nov. 2017"
   ]
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  {
   "cell_type": "code",
   "execution_count": 21,
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   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import time\n",
    "import os\n",
    "import scipy\n",
    "import numpy as num\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.plotly as py\n",
    "from collections import OrderedDict\n",
    "\n",
    "import utils_nb\n",
    "\n",
    "from pyrocko import gf, trace\n",
    "from pyrocko import moment_tensor as mtm\n",
    "from pyrocko.gf import ws, LocalEngine, Target, DCSource\n",
    "from pyrocko import util, pile, model, config, trace, io, pile, catalog\n",
    "\n",
    "km = 1000."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimisation Parameters\n",
    "Setup of the optimisation parameters, as well as boundaries for the source parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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    "component = 'Z'\n",
    "f_low = 0.05  # Hz, for a lowpass filter\n",
    "taper = trace.CosFader(xfade=2.0)  # Cosine taper, 2s fade\n",
    "\n",
    "phase = 'P' # Phase to fit\n",
    "tmin_fit = 15. # [s] to fit before synthetic phase onset (from GFStore)\n",
    "tmax_fit = 35. # [s] ... after\n",
    "\n",
    "bounds = OrderedDict([\n",
    "    ('north_shift', (-20.*km, 20.*km)),\n",
    "    ('east_shift', (-20.*km, 20.*km)),\n",
    "    ('depth', (3.*km, 8.*km)),\n",
    "    ('magnitude', (6.2, 6.4)),\n",
    "    ('strike', (100., 140.)),\n",
    "    ('dip', (40., 60.)),\n",
    "    ('rake', (-100, -150.)),\n",
    "    ('timeshift', (-20., 20.)),\n",
    "    ])"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Waveforms\n",
    "We download the instrument-corrected seismic waveforms and use a `pyrocko.pile` to manage the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "name": "stderr",
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     "text": [
      "selecting files... done. 58 files selected.\n",
      "Looking at files [-----------------------------------------] 100% Time: 0:00:00\n",
      "Scanning files [-------------------------------------------] 100% Time: 0:00:00\n",
      "Cannot read file '/home/marius/Development/pyrocko-notebooks/data/aquila_realdata/stations_short.txt': No SEED data detected (file: /home/marius/Development/pyrocko-notebooks/data/aquila_realdata/stations_short.txt)\n",
      "The following file caused problems and will be ignored:\n",
      "/home/marius/Development/pyrocko-notebooks/data/aquila_realdata/stations_short.txt\n"
     ]
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   "source": [
    "# Download the instrument-corrected 2009 Aquila Earthquake data\n",
    "data_path = utils_nb.download_dir('aquila_realdata/')\n",
    "data = pile.make_pile([data_path])\n",
    "traces = data.all()  # retrieves the raw waveform data as a 2D `numpy.array`.\n",
    "\n",
    "for tr in traces:\n",
    "    tr.lowpass(4, f_low)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize Forward Modelling Engine (Seismosizer)\n",
    "We download the precalculated Green's function database (`Store`) *global_2s_25km* from http://kinherd.org"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_id = 'global_2s_25km'\n",
    "if not os.path.exists(store_id):\n",
    "    ws.download_gf_store(site='kinherd', store_id=store_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we fire up the `engine` to forward model synthetic seismograms on our _global_2s_25km_ GF database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = gf.LocalEngine(store_superdirs=['.']) # The Path to where the gf_store(s)\n",
    "store = engine.get_store(store_id)  # Load the store."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get GlobalCMT Start Model\n",
    "We use the GlobalCMT catalog to search for the 2009 Aquila Earthquake and initalize a source for the starting model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmin = util.str_to_time('2009-04-06 00:00:00')  # beginning time of query\n",
    "tmax = util.str_to_time('2009-04-06 05:59:59')  # ending time of query\n",
    "event = catalog.GlobalCMT().get_events(\n",
    "    time_range=(tmin, tmax),\n",
    "    magmin=6.)[0]\n",
    "\n",
    "base_source = gf.MTSource.from_pyrocko_event(event)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Station and _Target_ Setup\n",
    "We use the term _Target_ for a single component of a single station."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "stations_list = model.load_stations('data/aquila_realdata/stations_short.txt')\n",
    "for s in stations_list:\n",
    "    s.set_channels_by_name(*component.split())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we define the `Target` - where to calculate the synthetic seismogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets=[]\n",
    "for s in stations_list:\n",
    "    target = Target(\n",
    "            lat=s.lat,\n",
    "            lon=s.lon,\n",
    "            store_id=store_id,   # The gf-store to be used for this target,\n",
    "            interpolation='multilinear',  # Interpolation method between GFStore nodes\n",
    "            quantity='displacement',\n",
    "            codes=s.nsl() + ('BH' + component,))\n",
    "    targets.append(target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objective Function\n",
    "Now the objective function that will be called by `scipy.optimize`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "source = gf.DCSource(\n",
    "    lat=event.lat,\n",
    "    lon=event.lon)\n",
    "\n",
    "def update_source(params):\n",
    "    s = source\n",
    "    s.north_shift = float(params[0])\n",
    "    s.east_shift = float(params[1])\n",
    "    s.depth = float(params[2])\n",
    "    s.magnitude = float(params[3])\n",
    "    s.strike = float(params[4])\n",
    "    s.dip = float(params[5])\n",
    "    s.rake = float(params[6])\n",
    "    s.time = float(event.time - params[7])\n",
    "    return source\n",
    "\n",
    "def process_trace(trace, tmin, tmax, lowpass=False, inplace=True):\n",
    "    if lowpass:\n",
    "        trace.lowpass(4, f_low)\n",
    "    trace = trace.chop(tmin, tmax, inplace=inplace)\n",
    "    trace.taper(taper)\n",
    "    return trace\n",
    "\n",
    "iiter = 0\n",
    "\n",
    "def trace_fit(params, line=None):\n",
    "    global iiter\n",
    "    update_source(params)\n",
    "\n",
    "    # Forward model synthetic seismograms\n",
    "    response = engine.process(source, targets)\n",
    "    syn_traces = response.pyrocko_traces()\n",
    "\n",
    "    misfits = 0.\n",
    "    norms = 0.\n",
    "\n",
    "    for obs, syn, target in zip(traces, syn_traces, targets):\n",
    "        syn_phs = store.t(phase, base_source, target)\n",
    "        \n",
    "        tmin = base_source.time + syn_phs - tmin_fit  # start before theor. arrival\n",
    "        tmax = base_source.time + syn_phs + tmax_fit  # end after theor. arrival\n",
    "        \n",
    "        syn = process_trace(syn, tmin, tmax)\n",
    "        obs = process_trace(obs, tmin, tmax, lowpass=False, inplace=True)\n",
    "\n",
    "        misfits += num.sqrt(num.sum((obs.ydata - syn.ydata)**2))\n",
    "        norms += num.sqrt(num.sum(obs.ydata**2))\n",
    "    \n",
    "    misfit = num.sqrt(misfits**2 / norms**2)\n",
    "    \n",
    "    iiter += 1\n",
    "\n",
    "    if line:\n",
    "        data = {\n",
    "            'y': [misfit],\n",
    "            'x': [iiter],\n",
    "        }\n",
    "        line.data_source.stream(data)\n",
    "\n",
    "    return misfit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running the optimization and plotting of the Convergence\n",
    "For plotting we use bokeh (which you might need to install: `pip3 install bokeh`)  \n",
    "Jupyter Lab also requires the Bokeh extension: `jupyter labextension install jupyterlab_bokeh`  \n",
    "More info at https://bokeh.pydata.org/en/latest/docs/user_guide/notebook.html"
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     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "from bokeh.io import push_notebook, show, output_notebook\n",
    "from bokeh.plotting import figure\n",
    "output_notebook()\n",
    "\n",
    "f = figure(title='SciPy Optimisation Progress',\n",
    "           x_axis_label='# Iteration',\n",
    "           y_axis_label='Misfit',\n",
    "           plot_width=800,\n",
    "           plot_height=300)\n",
    "plot = f.scatter([], [])\n",
    "show(f, notebook_handle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimisation with SciPy\n",
    "We will use `scipy.optimize.differential_evolution` to find a best fitting model. The method is stochastic in nature (does not use gradient methods) to find the minimium, and can search large areas of candidate space, but often requires larger numbers of function evaluations than conventional gradient based techniques. The scipy solver can easily be exchanged for a method of your favor. If you just want a quick demonstration, you can change the number of maxiter in the solve function to something lower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed: 236.7 s\n",
      "Best model:\n",
      " - Misfit 1.124329\n",
      "--- !pf.DCSource\n",
      "lat: 42.29\n",
      "lon: 13.35\n",
      "north_shift: 14979.312574228847\n",
      "east_shift: 1556.6646508341364\n",
      "depth: 3000.685138774966\n",
      "time: 2009-04-06 01:32:29.191294\n",
      "stf_mode: post\n",
      "magnitude: 6.200159644093804\n",
      "strike: 103.63201050142605\n",
      "dip: 59.783215252520336\n",
      "rake: -148.26831683964517\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def solve():\n",
    "    t = time.time()\n",
    "\n",
    "    result = scipy.optimize.differential_evolution(\n",
    "        trace_fit,\n",
    "        args=[plot],\n",
    "        bounds=tuple(bounds.values()),\n",
    "        maxiter=15000,\n",
    "        tol=0.01,\n",
    "        callback=lambda a, convergence: push_notebook())\n",
    "\n",
    "    source = update_source(result.x)\n",
    "    source.regularize()\n",
    "\n",
    "    print(\"Time elapsed: %.1f s\" % (time.time() - t))\n",
    "    print(\"Best model:\\n - Misfit %f\" % trace_fit(result.x))\n",
    "    print(source)\n",
    "    return result, source\n",
    "\n",
    "\n",
    "# Start the optimisation\n",
    "result, best_source = solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the Results\n",
    "Now we plot the synthetic waveforms produced by our the best model vs. the observed traces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_traces(result):\n",
    "    nstations = len(stations_list)\n",
    "    response = engine.process(source, targets)\n",
    "    syn_traces = response.pyrocko_traces()\n",
    "\n",
    "    fig, axes = plt.subplots(nstations, squeeze=True, sharex=True)\n",
    "    fig.subplots_adjust(hspace=0)\n",
    "    plt.setp([ax.get_xticklabels() for ax in axes[:-1]], visible=False)\n",
    "\n",
    "    for istation, (obs, syn, target) in enumerate(zip(traces, syn_traces, targets)):\n",
    "        ax = axes[istation]\n",
    "        tp = store.t(phase, base_source, target)\n",
    "        tp_onset = base_source.time + tp\n",
    "        tmin = tp_onset - tmin_fit\n",
    "        tmax = tp_onset + tmax_fit\n",
    "        \n",
    "        syn = process_trace(syn, tmin, tmax)\n",
    "        obs = process_trace(obs, tmin, tmax, lowpass=False, inplace=False)\n",
    "        \n",
    "        s1 = ax.plot(obs.get_xdata(), obs.ydata, color='b')\n",
    "        s2 = ax.plot(syn.get_xdata(), syn.ydata, color='r')\n",
    "        s3 = ax.plot([tp_onset, tp_onset], [tr.ydata.min(), tr.ydata.max()], 'k-', lw=2)\n",
    "\n",
    "        ax.text(-.2, 0.5, stations_list[istation].station,\n",
    "                transform=ax.transAxes)\n",
    "        ax.set_yticklabels([], visible=False)\n",
    "\n",
    "    axes[-1].set_xlabel('Time [s]')\n",
    "    plt.suptitle('Waveform fits for %s-Phase and component %s' % (phase, component))\n",
    "    plt.legend(\n",
    "        (s1[0], s2[0], s3[0]),\n",
    "        ('Data', 'Synthetic','%s Phase-onset' % phase),\n",
    "        loc='upper center',\n",
    "        bbox_to_anchor=(0.5, -2.),\n",
    "        fancybox=True, shadow=True, ncol=5)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_snuffler(result, source):\n",
    "    engine = gf.get_engine()\n",
    "    response = engine.process(source, targets)\n",
    "    syn_traces = response.pyrocko_traces()\n",
    "    obs_traces = []\n",
    "    \n",
    "    for obs, syn, target in zip(traces, syn_traces, targets):\n",
    "        tp = store.t('P', base_source, target)\n",
    "        tmin = base_source.time + tp - tmin_fit\n",
    "        tmax = base_source.time + tp + tmax_fit\n",
    "\n",
    "        syn = process_trace(syn, tmin, tmax)\n",
    "        obs = process_trace(obs, tmin, tmax, lowpass=False, inplace=False)\n",
    "\n",
    "        obs_traces.append(obs)\n",
    "\n",
    "    trace.snuffle(obs_traces + syn_traces, stations=stations_list, events= [event])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we plot the station distribution with folium (which you might need to install)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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                var html_9c5e799ff2c4417c876fc71a83f37f99 = $(`<div id="html_9c5e799ff2c4417c876fc71a83f37f99" style="width: 100.0%; height: 100.0%;"><b>SFJD</b></h4></div>`)[0];
                popup_b5cb6e237a6e47d4b7191acef5a76c6c.setContent(html_9c5e799ff2c4417c876fc71a83f37f99);
            

            marker_7e31576beb494f2194a78cf0be8a1a5e.bindPopup(popup_b5cb6e237a6e47d4b7191acef5a76c6c)
            ;

            
        
    
        var marker_6219ce20858949fd8c2adbd8abec754f = L.marker(
            [40.6358, -77.8876],
            {
                icon: new L.Icon.Default(),
                }
            ).addTo(map_50674592c75e4fac96d0103c4cb4f295);
        
    
            var popup_e54ddaf651a24c8fb032e7f713b04056 = L.popup({maxWidth: '100%'
            
            });

            
                var html_3bc3956632b94083ab93ddee37f05ab5 = $(`<div id="html_3bc3956632b94083ab93ddee37f05ab5" style="width: 100.0%; height: 100.0%;"><b>SSPA</b></h4></div>`)[0];
                popup_e54ddaf651a24c8fb032e7f713b04056.setContent(html_3bc3956632b94083ab93ddee37f05ab5);
            

            marker_6219ce20858949fd8c2adbd8abec754f.bindPopup(popup_e54ddaf651a24c8fb032e7f713b04056)
            ;

            
        
    
        var marker_178c684ddf504ceba72222296472e971 = L.marker(
            [20.2263, -88.2763],
            {
                icon: new L.Icon.Default(),
                }
            ).addTo(map_50674592c75e4fac96d0103c4cb4f295);
        
    
            var popup_895954eb7a8f423b9b5d52090ce61b8b = L.popup({maxWidth: '100%'
            
            });

            
                var html_edb46e9f7e964f4086d1326cc4f569de = $(`<div id="html_edb46e9f7e964f4086d1326cc4f569de" style="width: 100.0%; height: 100.0%;"><b>TEIG</b></h4></div>`)[0];
                popup_895954eb7a8f423b9b5d52090ce61b8b.setContent(html_edb46e9f7e964f4086d1326cc4f569de);
            

            marker_178c684ddf504ceba72222296472e971.bindPopup(popup_895954eb7a8f423b9b5d52090ce61b8b)
            ;

            
        
    
        var marker_9b21d491d61c4d76942e2dbef27707d3 = L.marker(
            [-19.2022, 17.5838],
            {
                icon: new L.Icon.Default(),
                }
            ).addTo(map_50674592c75e4fac96d0103c4cb4f295);
        
    
            var popup_946712d8ed404aa89929dedc3000089e = L.popup({maxWidth: '100%'
            
            });

            
                var html_fa533bed7cad48abaefe5e784f2de4af = $(`<div id="html_fa533bed7cad48abaefe5e784f2de4af" style="width: 100.0%; height: 100.0%;"><b>TSUM</b></h4></div>`)[0];
                popup_946712d8ed404aa89929dedc3000089e.setContent(html_fa533bed7cad48abaefe5e784f2de4af);
            

            marker_9b21d491d61c4d76942e2dbef27707d3.bindPopup(popup_946712d8ed404aa89929dedc3000089e)
            ;

            
        
    
        var marker_3399bf2b496e4ad49108689dc2e43776 = L.marker(
            [47.8651, 107.0532],
            {
                icon: new L.Icon.Default(),
                }
            ).addTo(map_50674592c75e4fac96d0103c4cb4f295);
        
    
            var popup_4bf7bc6eee5b47978dd147ca92c0f2c5 = L.popup({maxWidth: '100%'
            
            });

            
                var html_8aea11d360be4e6c81eddfa83d2f98fc = $(`<div id="html_8aea11d360be4e6c81eddfa83d2f98fc" style="width: 100.0%; height: 100.0%;"><b>ULN</b></h4></div>`)[0];
                popup_4bf7bc6eee5b47978dd147ca92c0f2c5.setContent(html_8aea11d360be4e6c81eddfa83d2f98fc);
            

            marker_3399bf2b496e4ad49108689dc2e43776.bindPopup(popup_4bf7bc6eee5b47978dd147ca92c0f2c5)
            ;

            
        
    
        var marker_490460a4abc14ba7b4e027460689b83a = L.marker(
            [38.2289, -86.2939],
            {
                icon: new L.Icon.Default(),
                }
            ).addTo(map_50674592c75e4fac96d0103c4cb4f295);
        
    
            var popup_d0b6334416c54370b5ee2829d8ebe027 = L.popup({maxWidth: '100%'
            
            });

            
                var html_72774889d96d4d8ea1eaa847c481c1d1 = $(`<div id="html_72774889d96d4d8ea1eaa847c481c1d1" style="width: 100.0%; height: 100.0%;"><b>WCI</b></h4></div>`)[0];
                popup_d0b6334416c54370b5ee2829d8ebe027.setContent(html_72774889d96d4d8ea1eaa847c481c1d1);
            

            marker_490460a4abc14ba7b4e027460689b83a.bindPopup(popup_d0b6334416c54370b5ee2829d8ebe027)
            ;

            
        
    
        var marker_2d04ef9636714e1a887921de29ffd477 = L.marker(
            [62.031, 129.6805],
            {
                icon: new L.Icon.Default(),
                }
            ).addTo(map_50674592c75e4fac96d0103c4cb4f295);
        
    
            var popup_7586c4dd03124c23bee2ebefa2358556 = L.popup({maxWidth: '100%'
            
            });

            
                var html_78fea6b2e3b4418585aae69e80ebdf7f = $(`<div id="html_78fea6b2e3b4418585aae69e80ebdf7f" style="width: 100.0%; height: 100.0%;"><b>YAK</b></h4></div>`)[0];
                popup_7586c4dd03124c23bee2ebefa2358556.setContent(html_78fea6b2e3b4418585aae69e80ebdf7f);
            

            marker_2d04ef9636714e1a887921de29ffd477.bindPopup(popup_7586c4dd03124c23bee2ebefa2358556)
            ;

            
        
    
        var marker_23d22333d3ab4056989dd46003ca3a99 = L.marker(
            [44.1188, 142.593],
            {
                icon: new L.Icon.Default(),
                }
            ).addTo(map_50674592c75e4fac96d0103c4cb4f295);
        
    
            var popup_49b5518654e84f29b57644c3ddcdb42c = L.popup({maxWidth: '100%'
            
            });

            
                var html_1f5f631c26c8405c80cd485c1325b5ca = $(`<div id="html_1f5f631c26c8405c80cd485c1325b5ca" style="width: 100.0%; height: 100.0%;"><b>ASAJ</b></h4></div>`)[0];
                popup_49b5518654e84f29b57644c3ddcdb42c.setContent(html_1f5f631c26c8405c80cd485c1325b5ca);
            

            marker_23d22333d3ab4056989dd46003ca3a99.bindPopup(popup_49b5518654e84f29b57644c3ddcdb42c)
            ;

            
        
    
        var marker_ab06c0a50b4a48b1ae4ae45d76f2fa6a = L.marker(
            [50.4348, 58.0164],
            {
                icon: new L.Icon.Default(),
                }
            ).addTo(map_50674592c75e4fac96d0103c4cb4f295);
        
    
            var popup_1395ee48ddfe4464b3d2aa327b449be9 = L.popup({maxWidth: '100%'
            
            });

            
                var html_00cc139779f94660b88d8d309f023897 = $(`<div id="html_00cc139779f94660b88d8d309f023897" style="width: 100.0%; height: 100.0%;"><b>AKTO</b></h4></div>`)[0];
                popup_1395ee48ddfe4464b3d2aa327b449be9.setContent(html_00cc139779f94660b88d8d309f023897);
            

            marker_ab06c0a50b4a48b1ae4ae45d76f2fa6a.bindPopup(popup_1395ee48ddfe4464b3d2aa327b449be9)
            ;

            
        
    
        var marker_702988a68ab74ed9ad06bf10500e9c29 = L.marker(
            [1.3608, 103.7729],
            {
                icon: new L.Icon.Default(),
                }
            ).addTo(map_50674592c75e4fac96d0103c4cb4f295);
        
    
            var popup_6d1555f806b846eaa3191d923d2404b8 = L.popup({maxWidth: '100%'
            
            });

            
                var html_32e48653d46f485ab45d60c16625548f = $(`<div id="html_32e48653d46f485ab45d60c16625548f" style="width: 100.0%; height: 100.0%;"><b>BTDF</b></h4></div>`)[0];
                popup_6d1555f806b846eaa3191d923d2404b8.setContent(html_32e48653d46f485ab45d60c16625548f);
            

            marker_702988a68ab74ed9ad06bf10500e9c29.bindPopup(popup_6d1555f806b846eaa3191d923d2404b8)
            ;

            
        
    
        var marker_e6ced629f457488780808e5b73fe60ad = L.marker(
            [70.9866, -8.5057],
            {
                icon: new L.Icon.Default(),
                }
            ).addTo(map_50674592c75e4fac96d0103c4cb4f295);
        
    
            var popup_4c45f40378c3449aa164015911471855 = L.popup({maxWidth: '100%'
            
            });

            
                var html_a6624f9e4bde4743b56a87ec6bf54dbf = $(`<div id="html_a6624f9e4bde4743b56a87ec6bf54dbf" style="width: 100.0%; height: 100.0%;"><b>JMIC</b></h4></div>`)[0];
                popup_4c45f40378c3449aa164015911471855.setContent(html_a6624f9e4bde4743b56a87ec6bf54dbf);
            

            marker_e6ced629f457488780808e5b73fe60ad.bindPopup(popup_4c45f40378c3449aa164015911471855)
            ;

            
        
    
        var marker_fb0e38c72849468387f02fa653fca586 = L.marker(
            [47.9462, -91.4953],
            {
                icon: new L.Icon.Default(),
                }
            ).addTo(map_50674592c75e4fac96d0103c4cb4f295);
        
    
            var popup_94a12108cf424b0286055134b0404d17 = L.popup({maxWidth: '100%'
            
            });

            
                var html_9a38f98a1d41419b90ed64f91b2271a5 = $(`<div id="html_9a38f98a1d41419b90ed64f91b2271a5" style="width: 100.0%; height: 100.0%;"><b>EYMN</b></h4></div>`)[0];
                popup_94a12108cf424b0286055134b0404d17.setContent(html_9a38f98a1d41419b90ed64f91b2271a5);
            

            marker_fb0e38c72849468387f02fa653fca586.bindPopup(popup_94a12108cf424b0286055134b0404d17)
            ;

            
        
    
        var marker_87bc84da50c14a5a8f780c155d43eb91 = L.marker(
            [14.48724, 49.03779],
            {
                icon: new L.Icon.Default(),
                }
            ).addTo(map_50674592c75e4fac96d0103c4cb4f295);
        
    
            var popup_25175230892e4bfcb951eb5d01cb403d = L.popup({maxWidth: '100%'
            
            });

            
                var html_62ec9530524247bf9017e48fa70cb86f = $(`<div id="html_62ec9530524247bf9017e48fa70cb86f" style="width: 100.0%; height: 100.0%;"><b>MUKA</b></h4></div>`)[0];
                popup_25175230892e4bfcb951eb5d01cb403d.setContent(html_62ec9530524247bf9017e48fa70cb86f);
            

            marker_87bc84da50c14a5a8f780c155d43eb91.bindPopup(popup_25175230892e4bfcb951eb5d01cb403d)
            ;

            
        
    
        var marker_5f9ac382da5b4272848a010d8c35b2e2 = L.marker(
            [13.635, 38.9811],
            {
                icon: new L.Icon.Default(),
                }
            ).addTo(map_50674592c75e4fac96d0103c4cb4f295);
        
    
            var popup_db66b7576753463ca8a1365eabac84df = L.popup({maxWidth: '100%'
            
            });

            
                var html_991b222a0dd541c592a7dbbd08edf17f = $(`<div id="html_991b222a0dd541c592a7dbbd08edf17f" style="width: 100.0%; height: 100.0%;"><b>ADYE</b></h4></div>`)[0];
                popup_db66b7576753463ca8a1365eabac84df.setContent(html_991b222a0dd541c592a7dbbd08edf17f);
            

            marker_5f9ac382da5b4272848a010d8c35b2e2.bindPopup(popup_db66b7576753463ca8a1365eabac84df)
            ;

            
        
    
                var lat_lng_popup_8e635f8bf8dc4112bdb83421337a7bfb = L.popup();
                function latLngPop(e) {
                    lat_lng_popup_8e635f8bf8dc4112bdb83421337a7bfb
                        .setLatLng(e.latlng)
                        .setContent("Latitude: " + e.latlng.lat.toFixed(4) +
                                    "<br>Longitude: " + e.latlng.lng.toFixed(4))
                        .openOn(map_50674592c75e4fac96d0103c4cb4f295);
                    }
                map_50674592c75e4fac96d0103c4cb4f295.on('click', latLngPop);
            
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f1bacc27fd0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_stations():\n",
    "    import folium\n",
    "    fmap = folium.Map(\n",
    "        location=[best_source.lat, best_source.lon],\n",
    "        tiles='Stamen Terrain',\n",
    "        zoom_start=3)\n",
    "    folium.Marker([best_source.lat, best_source.lon],\n",
    "        popup=('2009 Aquila Earthquake'),\n",
    "        icon=folium.Icon(color='red', icon='info-sign')).add_to(fmap)\n",
    "                  \n",
    "    for s in stations_list:\n",
    "        folium.Marker([s.lat, s.lon],\n",
    "                      popup='<b>%s</b></h4>' % s.station).add_to(fmap)\n",
    "    fmap.add_child(folium.LatLngPopup())\n",
    "    return fmap\n",
    "    \n",
    "plot_stations()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 57 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_traces(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively we can plot using snuffler, which will open in a seperate window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_snuffler(result, best_source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}